John M Klein
US Geological Survey
Chuck Spooner
US Environmental Protection Agncy
Tim Kubiak
US Fish and Wildlife Service
Wayne Poppe
TN Valley Authority
Andrew Robertson
DOC/National Oceanic and Atomospheric
Administration
William Walker
National Park Service
Warren Harper
USDA. Forest Service
Maude Bullock
Naval Operations
Fred Banach
CT Dept of Envir Protection
Fred Van Alstyne
NY Dept of Envir Conservation
Rodney S DeHan
fi Geological Survey
Mike Talbot
Wl Dept of Natural Resources
Ion Craig
OK Dept of Environmental Quality
Wayne Hood
AZ Dept of Environmental Quality
Jessica Landman
Natural Resources Defense Council
Tony Wagner
Chemical Manufacturers Association
Lewis Britt
National Cattlemen's Beef Association
Robert Ward
CO State University
Norman LeBlanc
HRSO
David Denig-Chakroff
Madison Water Utility
David Rexing
Southern NV Water System
Albert Gray
Water Environment Federation
Dave Pollison
DE River Basin Com
Linda Green
Watershed Watch, Univ of Rl
Jim Fletcher
Morongo Band of Cahuilla Indians
Haiq Kasabach
NJ State Geological Survey
Jim Cox
National Assoc of State Cons Districts
Ellen McCarron
FL Dept of Environmental Prot
ludith Henderson
Community Youth Council for Leadership
and Education
•
Proceedings of the NWQMC National Monitoring Conference
National Water-Quality Monitoring Council
Monitoring:
Critical Foundations To
Protect Our Waters
In association with
U.S. Environmental Protection Agency
U.S. Geological Survey
National Oceanic and Atmospheric Administration
U.S. Department of Agriculture
July 7 - 9,1998 - Reno, Nevada
-------
842R980O7
The National Water-Quality Monitoring Council
Proceedings of the NWQMC National Conference
Monitoring: Critical Foundations to
Protect Our Waters
in association with
U.S. Environmental Protection Agency
U.S. Geological Survey
National Oceanographic and Atmospheric Administration
U.S. Department of Agriculture
July 7-9, 1998
Reno, Nevada
-------
Foreword
These Proceedings contain the presentations, discussions, and resulting recommendations
from among nearly 400 participants representing monitoring interests from federal, state, tribal,
local, academic, and private organizations. The conference from which they were taken was, in
part, made possible by financial support provided by the U.S. Environmental Protection Agency,
the U.S. Geological Survey, the National Oceanographic and Atmospheric Administration, and
the U.S. Department of Agriculture.
Should additional information on any of the papers in Section El be desired, it would be most
efficient to contact the individual authors directly. Other information regarding NWQMC
authority, Council membership, additional copies of the Proceedings, the discussion or
recommendations contained in these Proceedings, and future Council activities should be
directed to Ms. Sarah Lehmann (U.S. EPA, 202-260-7021, e-mail: lehmann.sarah@epamail.
epa.gov) or Ms. Toni Johnson (U.S. Geological Survey, 703-648-6810, e-mail: tjohnson@usgs.
gov).
Appropriate Citation: U.S. Environmental Protection Agency. 1998.
Proceedings of the NWQMC National Conference Monitoring: Critical
Foundations to Protect Our Waters. U.S. Environmental Protection Agency,
Washington, DC. 663 pages plus appendixes.
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Table of Contents
Section Page
Foreword iii
Acknowledgments xi
Executive Summary ES-1
I Introduction 1-1
n Recommendations to Council IT-1
m Conference Papers TTT-1
Track A-^\Monitoring Design Strategies J ni-3
A Locally Designed Watershed Monitoring Program IH-5
John Cavese, Beth Siebert
Design of Stream Sampling Networks and a GIS Method for Assessing
Spatial Bias JH-15
Alison C. Simcox
Designing a Comprehensive, Integrated Water Resources Monitoring
Program for Florida JJQ-27
Kevin Summers, Rick Copeland, Tom Singleton, Sam Upchurch,
Anthony Janicki
Coordinating Site-Specific NPDES Monitoring to Achieve
Regional Monitoring in Southern California TTT-41
Janet Y. Hashimoto, Stephen B. Weisberg
Warm Season Algal Populations in Four Long Island Sound
Harbors JH-49
Steven Yergeau
Performance-Based Quality Assurance—The NOAA National
Status and Trends Program Experience .... ID-63
A.Y. Cantillo, G.G. Lauenstein
Trend Detection in Land Use and Water Quality Data for the
Herrings Marsh Run Watershed . . . IJJ-75
J.M. Rice, J. Spooner, M.G. Cook, K.C. Stone, S.W Coffey,
F.J. Humenik, P.O. Hunt
Alternatives for Evaluating Water Quality and BMP Effectiveness
at the Watershed Scale UI-85
George Ice, Ray Whittemore
Water Quality Monitoring in a Developing Coastal Region: Fear
and Loathing in Calabash, North Carolina IH-97
Janice E. Nearhoof, Lawrence B. Cahoon
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Table of Contents (continued)
Section Pa§e
Spatial and Temporal Trace Level Monitoring Study of South San
Francisco Bay HI-105
Daniel Watson, Lisa Weetman, Donald Arnold, Charles Allen,
Kenneth Lee, James Powars, Joe Theisen, Hannah Truong,
Robert Wandro
Water Quality Assessment Program in the Indian River Lagoon,
Florida: II. Redesigning a Monitoring Network IJJ-121
Gilbert C. Sigua, Joel S. Steward, Janice D. Miller,
Wendy A. Tweedale
Improving Indicator Selection for Regional Stormwater
Monitoring JH-133
Brock B. Bernstein, Michael Drennan
Storm Water Metals—Issues and Historical Trends, Lawrence
Livermore National Laboratory ID-137
Erich R. Brandstetter
Monitoring and Assessing the Environmental and Health Risks of
Separate Sanitary Sewer Overflows (SSOs) JH-147
Sarah J. Meyland, Melinda Lalor, Robert Pitt
A Comprehensive Approach to Urban Stormwater Impact
Assessment JH-153
Betsy Johnson, Kimberly Yandora, Scott Bryant
Identifying the Potential for Nitrate Contamination of Streams in
Agricultural Areas of the United States JH-163
David K. Mueller, Jeffrey D. Stoner
Key Water Quality Monitoring Questions: Designing Monitoring
and Assessment Systems to Meet Multiple Objectives TTT-175
James E. Harrison
Gaining Public Support for Urban Water Quality Management via
Monitoring IJJ-189
Adrienne Greve, Robert C. Ward
Using 618O and dD to Quantify Ground-Water/Surface-Water
Interactions in Karst Systems of Florida JH-195
Brian G. Katz
Water Quality Monitoring for Integrated Wastewater and
Stormwater Management JH-209
Lawrence B. Cahoon, Janice E. Nearhoof
Monitoring the Beneficial Impacts of CSO Control Implementation JH-221
Carol L. Hufnagel, Vyto P. Kaunelis
VI
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Table of Contents (continued)
Section Page
History, Goals and Redesign of New Jersey's Ambient Ground
Water Quality Network ffl-235
Michael E. Serfes
A Reconnaissance for New, Low-Application Rate Herbicides in
Surface and Ground Water in the Midwestern United States, 1998 HI-245
William A. Battaglin, Edward T. Furlong, Mark Burkhardt,
C. John Peter
Track B-fMethodology and Information Sharing < JH-257
A Comparison of Water-Quality Sample Collection Methods Used
by the U.S. Geological Survey and the Wisconsin Department of
Natural Resources IH-259
Phil A. Kammerer, Jr., Herbert S. Garn, Paul W. Rasmussen,
Joseph R. Ball
Quantification ofDioxin Concentrations in the Ohio River Using
High Volume Water Sampling JH-271
Samuel A. Dinkins, Jason P. Heath
An Alternative Regression Method for Constituent Loads from
Streams UI-281
Ping Wang, J^ewis C. Linker
Determining Comparability of Bioassessment Methods and Their
Results JU-293
Jerome Diamond, James Stribling, Chris Yoder
Performance Based Methods System JH-305
Ann B. Strong
High-Re solution Water-Column Profiles of Chlorophyll
Fluorescence in Payatte Lake, Idaho JU-309
Paul F Woods
Comparison of Temporal Trends in Ambient and Compliance
Trace Element and PCB Data in Pool 2 of the Mississippi River,
1985-95 m-317
Jesse Anderson, Jim Perry
Tailoring of Data Quality Objectives to Specific Monitoring
Questions JH-329
Revital Katznelson
Quality Assurance/Quality Control Plan for Agricultural Nonpoint
Source Pollution Monitoring Research JH-341
Tamim Younos, Saied Mostaghimi, Carol Newell, Phillip McClellan
Calabazas Creek Pilot Sediment Sampling Study JH-351
Terence D. Cooke, David D. Drury
Vll
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Table of Contents (continued)
Section Pa8e
Binational Water Quality Monitoring Activities Along the Arizona-
Sonora Border Region IH-363
Mario Castaneda
Enhancements of Nonpoint-Source Monitoring Programs to Assess
Volatile Organic Compounds in the Nation's Ground Water m-371
Wayne W. Lapham, Michael J. Moran, John S. Zogorski
Middle Gila River Watershed Water Quantity, Water Quality, and
Biological/Habitat Assessment Study, Phoenix, Arizona JU-383
Mike Gritzuk, Paul Kinshella, Robert Hollander, Andrew Richardson,
Frank Turek, Juliet Johnson
Environmental Monitoring Program to Support the Rouge River
National Wet Weather Demonstration Project JH-389
Louis C. Regenmorter, Vyto P. Kaunelis
Translation of Water Quality to Usabilities for the Catawba River
Basin JH-399
Carl W. Chen, Joel Herr, Laura Ziemelis, Robert A. Goldstein,
Larry Olmsted
Track C—-Indicators and Reference Conditions TTT-409
Biological Criteria Development for the Ohio River, USA IJJ-411
E.B. Emery, A.H. Vicory, Jr.
Rapid Bioassessment ofBenthic Macroinvertebrates Illustrates
Water Quality in Small Order Urban Streams in a North Carolina
Piedmont City JH-419
Kimberly Yandora
Bioassessments in Arizona: What is Different about Biomonitoring
in Southwestern Streams? JH-429
Patti Spindler
Development of a Benthic Index ofBiotic Integrity for Maryland
Streams JH-435
James B. Stribling, Jeffrey S. White, Benjamin K. Jessup,
Daniel Boward, Martin Kurd
The Influence of Land Use and Stream Morphology on Urban
Stream Water Quality JH-453
Judith A. Gerlach Okay
Development of Biocriteria for Wetlands in Montana JU-463
Randall S. Apfelbeck
Indicators of Reservoir Ecological Condition EH-477
Don L. Dycus
via
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Table of Contents (continued)
Section Page
Evaluation of Wet Weather Pollution Sources on Large Rivers
Utilizing Biological Communities HI-487
Geoffrey M. Edwards, Peter A. Tennant, John T. Lyons
Evaluation Monitoring as an Alternative to Conventional Water
Quality Monitoring for Water Quality Characterization/
Management HI-499
Anne Jones-Lee, G. Fred Lee
The Index of Watershed Indicators—An Evolving National Tool JH-513
Chuck Spooner, Sarah Lehmann
An Analysis of Long-Term Water Quality Trends in Virginia JH-519
Carl E. Zipper, Golde I. Holtzman, Patrick Darken, Pamela Thomas,
Jason Gildea, Leonard Shabman
Loads and Yields of Suspended Sediment and Nutrients for
Selected Watersheds in the Lake Tahoe Basin, California and
Nevada JH-525
Timothy G. Rowe
Track D—Linking Monitoring to Environmental Management
and Decision Making . I IJJ-537
Use of a Numerical Rating Model to Determine the Vulnerability
of Community Water-Supply Wells in New Jersey to Contamination
by Pesticides JH-539
Eric F. Vowinkel
The Puget Sound Ambient Monitoring Program—Case Study of
Coordinated Regional/State Monitoring EH-547
Scott Redman
Integrating Ambient and Compliance Monitoring in the Kennebec
River Basin, Maine JH-555
Keith Robinson, David Courtemanch
Evaluation of Nutrient Loads and Sources in the Ohio River Basin JU-567
Deborah M. Olszowka, Jason P. Heath, Peter A. Tennant
Institutional Challenges in Monitoring—Stream Gaging as an
Example IH-573
Emery T. Cleaves
Integrating Upland and In-Channel Monitoring Results to Improve
Ecosystem Condition at Heavenly Ski Resort JH-577
Sherry Hazelhurst
Monitoring Ground Water Quality IJJ-585
A. Roger Anzzolin, Mary Siedlecki
IX
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Table of Contents (continued)
Section Page
Developing a Multi-Agency 305(b) Monitoring Program for the
Coastal Waters of Alabama IH-595
Kevin Summers, John Carlton, Steve Heath
Important Concepts and Elements of an Adequate State Watershed
Monitoring and Assessment Program IH-615
Chris O. Yoder
Linking Water Quality Data for Source Water Protection
Assessments IH-629
Steven P. Roy
IV Conference Wrap-up/Summary of Open Discussion IV-1
Appendixes
A Agenda A-l
B List of Participants B-l
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Acknowledgments
The National Water Quality Monitoring Council (Council) is indebted to Elizabeth Fellows
(U.S. EPA) and Nancy Lopez (USGS) for their vision, leadership, energy, and generous
contribution to the creation of the Intergovernmental Task Force on Monitoring (ITFM) and its
subsequent evolution into the Council. The Council is also thankful to Ms. Fellows for being the
first to recognize the need for a conference to focus national attention on the issue of monitoring.
Chuck Spooner (U.S. EPA) and John Klein (USGS) are currently serving as co-chairs of the
Council. The Council offers its gratitude to those who formed the conference planning com-
mittee. This committee, chaired by Rodney DeHan (Florida Department of Environmental
Protection), designed the conference format, reviewed all of the abstracts submitted and, along
with others who served as speakers, moderators, facilitators, track coordinators, and report-back
chairs, worked diligently to ensure the success of the conference. The Council is also grateful to
all of the environmental monitoring professionals who felt the topic of technical and pro-
grammatic coordination was important enough to submit papers and make presentations and
prepare posters for this conference. Last but not least, heartfelt thanks go to the staffs of Tetra
Tech and the Ground Water Protection Council (GWPC) whose dedication, creativity, and hard
work provided critical technical and administrative support to the conference. The names of the
numerous individuals contributing to the success of the conference are listed below (in
alphabetical order):
Fred Banach, Connecticut DEP; Session moderator
Herb Brass, U.S. EPA; Session moderator; moderator and chair of discussion panel
Jeff Bryant, GWPC; Conference publicity, registration, and on-site logistics
Chi Ho Sham, Tetra Tech, Inc.; Session facilitator
Emery Cleaves, Maryland Geological Survey; Session moderator
Greg Colianni, U.S. EPA; Planning Committee member
Geoff Dates, River Watch Network, Vermont; Session moderator
Tom Davenport, U.S. EPA; Session moderator
Rodney DeHan, Florida DEP, General Conference Chair, session moderator
Dave Denig-Chakroff; Session moderator, report back chair
Jerry Diamond, Tetra Tech, Inc.; Session facilitator
Don Dycus, Tennessee Valley Authority; Planning Committee member, session moderator
Elizabeth Fellows, U.S. EPA; Planning Committee member
Linda Green, University of Rhode Island; Planning Committee member, session moderator
Ben Grunewald, GWPC; Conference publicity, registration, and on-site logistics
Joe Hall, U.S. EPA; Planning Committee member
Jon Harcum, Tetra Tech, Inc.; Session facilitator
Michalann Harthill, USGS; Planning Committee member, session moderator, and report
back chair
Dennis Helsel, USGS; Session moderator
Elizabeth Herron, University of Rhode Island; Session moderator
Wayne Hood, Arizona DEQ; Planning Committee member, facilitator
Paul Jehn, GWPC; Session facilitator
Charles Job, U.S. EPA; Planning Committee member
XI
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Carol Keppy, CTIC; Session moderator
Joanne Kurklin, USGS, Executive Secretary of Planning Committee
Sue Laufer, Tetra Tech, Inc.; Session facilitator
Sarah Lehmann, U.S. EPA; Planning Committee member
Nancy Lopez, USGS; Session moderator, report back chair
Abby Markowitz, Tetra Tech, Inc.; Track Coordinator
Ellen McCarron, Florida DEP; Session moderator
Mike O'Neill, Utah State University/USDA; Session moderator
Paul Orlando, NOAA; Session moderator
Mike Paque, GWPC; Session facilitator
Amanda Richardson, Tetra Tech, Inc.; Track Coordinator
Andrew Robertson, NOAA; Planning Committee member
Steve Roy, Tetra Tech, Inc.; Session facilitator
Tom Sanders, Colorado State University; Session moderator
Lynn Singleton, State of Washington DOE; Session moderator
Dan Smith, USDA/NRCS; Planning Committee member, report back chair
Chuck Spooner, U.S. EPA; Session moderator
Sam Stribling, Tetra Tech, Inc.; Session facilitator
Fred Van Alstyne, New York DEC; Session moderator
Chris Victoria, Tetra Tech, Inc.; Session facilitator
Tony Wagner, Chemical Manufacturers' Association; session moderator
Robert Ward, Colorado State University; Planning Committee member; session moderator
Chris Yoder, Ohio EPA/U.S. EPA; Session moderator
Xll
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Executive Summary
The National Water-Quality Monitoring Council (NWQMC) was established as the successor
to the Intergovernmental Task Force on Monitoring Water Quality (ITFM) in 1997 and is jointly
chaired by the U.S. Environmental Protection Agency and the U.S. Geological Survey. It is
charged with enhancing collaboration and coordination of water resource quality monitoring
activities at the national, state, tribal, and local levels, as well as similar activities involving
business and industry, academia, agriculture, and environmental groups. As part of these efforts,
the Council presented the conference Monitoring: Critical Foundations to Protect Our Waters,
with the specific goal of providing a forum for interaction among these groups, including the
exchange of ideas, the presentation of reports on successful monitoring and collaborative efforts
and indicator development activities, and the enhancement of communication and public
involvement in monitoring. Through its efforts, the Council wants to highlight the importance of
and support monitoring that provides knowledge of ecosystem quality, processes, and
sustainability. It wants to emphasize the need for scientifically based indicators, designs,
methods, and data management systems to allow meaningful communication to environmental
policy and management decision makers.
There were nearly 400 conference participants, and approximately 100 oral and poster
presentations were offered in 30 "presentation and discussion" workshops. The workshops were
organized into four broad tracks—Monitoring Design Strategies, Methodology and Information
Sharing, Indicators and Reference Conditions, and Linking Monitoring to Environmental
Management and Decision Making. The objective of each workshop, following the presentations,
was to develop a set of recommendations that would be forwarded to the NWQMC. The
recommendations represent the direct input of the broadly based environmental monitoring
community to short- and long-term strategies of the Council, and they will be incorporated into
the Council's work plan.
Several overriding issues surfaced throughout the discussions, including the necessity of
defining data quality objectives prior to monitoring project or program design. There was
discussion on the need to endorse and support development of regionally calibrated reference
conditions using biological, physical, and chemical indicators. It was suggested that the Council
take a leadership role in standardizing the performance-based methods system for increasing the
monitoring community's ability to share data and information. Associated with that, and also
frequently discussed, was the proposition that the Council develop technical and programmatic
guidance documents for network design, sampling methods, data analysis and interpretation of
results, program development, and training. One of the most pervasive issues occurring
throughout all tracks was the need for increased public education and outreach on environmental
concerns, with greater involvement of volunteer monitoring groups.
Following are selected recommendations developed by the conference participants. In the
proceedings document the recommendations are presented, in full, to emphasize priorities of
conference participants. Below, however, in an effort to minimize redundancy, only those
recommendations are presented that best capture the principal areas of concern.
ES-1
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Technical Guidance
• Develop technical guidance on determining appropriate and sufficient levels of quality
assurance/quality control (QA/QC).
• Facilitate and sponsor development of guidance for monitoring network design, including
consideration of geographic scale, site selection, and current and historic land use/land
cover.
• Develop guidance on producing QA/QC plans, including data quality objectives (DQOs)
and specific QC activities.
• Sponsor/fund development of technical guidance documents for the monitoring
community.
Monitoring Program Design
• Develop standard designs for data collection protocols, database structure, and
metadata/data reporting.
• Facilitate and sponsor establishment of a systematic and standardized approach for
developing regionally relevant indicators with special emphasis on valid physical,
chemical, and biological endpoints and criteria.
• Promote resource-based, integrated monitoring approaches that go beyond strictly
meeting narrow programmatic objectives.
• Begin the discussion to support integration of total maximum daily load (TMDL) work
with other state water quality monitoring needs.
Methods
Support the concept of performance-based methods systems (PBMS) and define the
approach for field and laboratory activities.
Assess laboratory and field methodologies to ensure comparability between methods that
are intended to measure identical environmental characteristics.
Outreach
Provide mechanism, or determine other opportunities, for funding the establishment and
support of state- or regional-level water resource quality monitoring councils.
Take a leadership role in developing, adopting, and serving as a clearinghouse for
monitoring guidance, including sample collection, sample and data analysis, and data
reporting.
ES-2
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• Develop and disseminate outreach and educational materials to organizations and
managers about PBMS, data quality, and QA/QC issues.
• Sponsor and coordinate a national monitoring conference every 12 to 18 months,
maintaining interactive features.
• Become a clearinghouse for standards, methods, databases, models, and analytical
frameworks and promote scientific and technical credibility of information.
• Improve awareness of the Council's functions and goals.
• Promote long-term volunteer monitoring.
ES-3
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Section I
Introduction
In 1992, the Intergovernmental Task Force on Monitoring Water Quality (ITFM) was
established under the United States Geological Survey (USGS) authority to review national water
quality monitoring activities and to develop an integrated national monitoring strategy. The Task
Force was chaired by the United States Environmental Protection Agency (EPA) with the USGS
serving as vice-chair and executive secretary. In 1995, ITFM produced its final report, The
Strategy to Improve Water-Quality Monitoring in the United States. The National Water Quality
Monitoring Council (NWQMC) was formed in October 1997 as the successor to the ITFM with
expanded membership and a permanent mandate to implement the strategy. The strategy contains
principal recommendations on many issues including: institutional collaboration, monitoring
framework, data-collection methods, environmental indicators, data management, and
assessment and reporting approaches.
The Council is a nationwide partnership of water monitoring and information management
authorities from federal and state agencies, tribes, municipalities, business and industry,
academia, agriculture, environmental groups, and others with expertise in environmental
monitoring. It is charged with coordinating and providing guidance for implementation of the
voluntary, integrated, nationwide monitoring strategy developed and recommended by the ITFM.
Currently, the Council supports the work of several workgroups including: the Methods and Data
Comparability Board, Source Water Assessment Task Group, Information Resources Task
Group, Monitoring Design Task Group, and the Ground Water Focus Group.
The strategy is designed to stimulate and support the monitoring improvements necessary to
achieve better water quality information required by federal, state, tribal, and local decision
makers, private organizations, and the public. The Council will focus on the quality of surface
water and groundwater, including estuaries and near coastal waters, associated aquatic
communities and habitats, wetlands, sediments, and air.
In the past, environmental programs focused on single-media, command-and-control
approaches to pollution prevention. Today, changing priorities are leading federal, state, tribal,
and local agencies and organizations toward adopting a broader, more integrated approach to risk
reduction and pollution prevention. Monitoring requirements have shifted with these changing
programmatic priorities. Therefore, it is important that the Council address, in a coordinated
manner, how, what, when, and where agencies and organizations monitor. The Council's
challenge in this area is to emphasize:
• Simultaneous modernization and integration of agency information systems and use of
Internet to communicate data more widely
• Use of biological condition as the principal indicator of the state of ecological integrity,
along with associated physical, chemical, and hydrologic measures as indicators of
environmental stress
1-1
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• Improved understanding of the relationships among ecological health, human health, and
economic conditions
• Identification of research needs and supporting research on all topics associated with the
evaluation of the health of aquatic systems and prediction of how such systems respond to
stress
• Improved access to ancillary data such as current and historic land use conditions and
atmospheric deposition of organic and inorganic contaminants
• Design and implementation of nonpoint source evaluation and control programs
• Monitoring of groundwater, wetlands, and coastal systems, their ecological interfaces,
and evaluating their role as critical components of multidimensional watersheds.
To meet these new challenges, the Council is currently developing a long-term workplan that
will affirm and implement many of the recommendations from The Strategy to Improve Water-
Quality Monitoring in the United States, while adding its own perspectives and priorities
(including many of the recommendations made by participants during the first national
monitoring conference).
Purpose and Structure of the First National Monitoring Conference
To involve a variety of monitoring professionals in this work and to address the questions
being posed to the scientific and stakeholder communities, the Council sponsored a national
monitoring conference in July 1998. The National Council and the conference planning
committee organized a conference that would be innovative and interactive in both content and
structure. The conference was designed to combine opportunities for participants to share ideas,
experiences, expertise, and proposals for new procedures with a formal process in which
participants, in various topic areas, collectively develop recommendations for the Council.
The title of the conference, Monitoring: Critical Foundations to Protect Our Waters,
reflected the desire to use this meeting as an opportunity for building the foundation of
communication, collaboration, inclusiveness, and positive action. The meeting was designed to
provide participants with some of the new tools, larger networks, increased knowledge, and
renewed motivation needed to protect our waters. Specifically, the goals of the conference were
to:
• Spotlight the importance of monitoring ecosystems and ecological infrastructure
sustainability
• Spotlight the need for scientifically based monitoring to refine and support water
management policy and practices
• Provide a forum for communication and collaboration
1-2
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• Encourage the sharing of successful monitoring designs, protocols, methods, and data
management systems
• Encourage public participation and awareness of monitoring information.
In addition to selecting presentations representing a wide variety of innovative projects,
potential collaborative programs, and technical knowledge throughout the country, the Council
wanted to use this conference as a forum for meaningful interaction and input from conference
participants. The Council was interested in creating a formal conference structure for promoting
thoughtful discussion and the exchange of ideas and to take advantage of the breadth and depth
of participants' expertise.
The conference was organized into 90-minute workshops (six concurrent sessions, each
containing five workshops), grouped under four thematic tracks. The tracks were developed
based on the abstract topics submitted to the conference planning committee. Several topics were
explored in more than one workshop; for example, there were three separate workshops on
nonpoint source monitoring, each with different speakers. The tracks and workshops topics were:
• Track A—Monitoring Design Strategies
- Monitoring Design
- Monitoring Coastal Systems
- Nonpoint Source Monitoring
- Monitoring Wetlands
- Monitoring Urban Stormwater and Sewer Discharges
- Multidimensional Watershed Monitoring
• Track B—Methodology and Information Sharing
- Data Comparability and Collection Methods
- QA/QC for Monitoring Programs
- Tools for Communicating Monitoring Results
• Track C—Indicators and Reference Conditions
- Biological Indicators and Reference Condition Development
- Wetlands Indicators
- Watershed Indicators
• Track D—Linking Monitoring to Environmental Management and Decision Making
- Vulnerability Assessment
- Section 305(b)
- Monitoring for TMDLs
- Source Water Issues, Both Surface and Ground Water
- Successful Program Collaboration.
1-3
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Each workshop had a moderator, a facilitator, and two to four speakers giving 10- to 12-
minute presentations. The presentations took place during the first half of each workshop. The
second half of the workshop was devoted to a facilitated discussion. The purpose of each
discussion was to identify one to three recommendations for the Council on the workshop's
topic. After the workshops were over, facilitators, moderators, and others involved with the
tracks worked through a process to refine and consolidate the recommendations based on the
thematic tracks. By the end of the process, a coherent set of recommendations and discussion
topics was developed for each track. A representative of each track gave his/her 20-minute report
back, summarizing the issues and recommendations developed over the course of the conference.
At the end of the conference there was an hour-long open-microphone session to allow for
additional comments, discussion of the Track Reports, and input on the conference as a whole.
Organization of This Document
This document is divided into four sections:
• Section I—Introduction to the Council and the Conference
• Section II—Recommendations to the National Council
• Section HI—Papers Presented at the Conference
• Section IV—Conference Wrap-up/Summary of Open Discussion.
Although the workshop papers were presented first at the conference, the Track Report
(Section IT) comes first in this document to highlight the participatory and collaborative nature of
the conference. These reports have become a critical tool for the Council in developing and
implementing priorities and they are as much a product of the conference as are the individual
papers. Section HI showcases some of the papers that were presented at the conference. Section
IV summarizes comments made during the final open discussion session of the conference,
where participants were invited to bring up any issues, comments, or questions that remained.
Further information on the Council, Council membership, and its workgroups and activities
may be obtained from:
U.S. Environmental Protection Agency U.S. Geological Survey
Ms. Sarah Lehmann Ms. Toni Johnson
401 M Street, SW U.S. Geological Survey
U.S. EPA/OW Water Resources Division
Mail Stop 4503F MS 440 National Center
Washington, DC 20640 12201 Sunrise Valley Drive
Phone: (202) 260-7021 Reston, VA 20192
Fax: (202) 260-1977 Phone: (703) 648-6810
E-rnail: lehmann.sarah@epamail.epa.gov Fax: (703) 648-5644
E-mail: tjohnson@usgs.gov
1-4
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Section II
Recommendations to Council
This section is organized according to the four broad areas, or thematic tracks, around which
the overall conference was structured: monitoring design strategies, methodology and informa-
tion sharing, indicators and reference conditions, and linking monitoring to environmental
management and decision making. The 30 individual sessions each consisted of one to four
presentations followed by a facilitated discussion. Recommendations to the Council came out of
these discussions and are the result of direct input from the conference participants. The recom-
mendations are intended as direction to the Council and reflect a recognized need by the con-
ference participants for improvements in water monitoring. Just as important are the discussions
that led to the recommendations, which can provide additional insight into implementation
strategies. Overlap of topics and discussion exists among the tracks. For example, information-
sharing mechanisms (part of Track B) are critical for linking monitoring results to environmental
management decision making (Track D). Tracks were intended to enhance integration of topics,
while at the same time channeling discussion to produce more substantial and focused
recommendations.
Track A—Monitoring Design Strategies
Background and Issues
Monitoring program design is recognized by the Council as one of the most critical areas of
the discipline of environmental monitoring. It is the foundation of monitoring to sufficiently and
accurately address programmatic concerns and scientific monitoring questions. It is often thought
of as the distribution of sampling site locations and the number of samples necessary to obtain a
certain level of statistical certainty in answering questions. However, design is predicated on
development of data quality objectives (DQOs) and includes:
• Questions to be addressed
• Indicators to be used
Sampling network designs needed to
answer questions at multiple geographic
and temporal scales
Appropriate sampling and analytical
methods for different categories of
pollution, such as nonpoint sources and
urban stormwater and sewer discharges
Appropriate sampling and analytical
methods for monitoring different types of
water resources, such as groundwater,
marine, coastal, and wetlands systems.
The Data Quality Objective (DQO) process
is a systematic planning tool based on the
scientific method for establishing criteria for
data quality and for developing data
collection designs. DQOs themselves are
qualitative and quantitative statements
developed by data users to specify the
quality and quantity of data needed to
support specific decisions; they are
statements about the level of uncertainty that
a decision maker is willing to accept in data
used to support a particular decision.
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A portion of the discussion included focus on the integration of biological, physical, and
chemical indicators into a multidimensional, ecological process-oriented model of watersheds
and its importance for comprehensive understanding of watershed function and sustainability.
Throughout the "monitoring-design-strategy" discussions, several themes arose as common
issues that led to development of specific recommendations, including: outreach and education;
methods and protocols comparability; producing or sponsoring guidance on field sampling,
laboratory analysis, and indicators; evaluation of existing work on monitoring watersheds;
clarification of terms, such as multidimensional monitoring of systems, and monitoring multi-
dimensional systems; and, finally, funding support.
Concerns and Topics
Presentations in this track focused on general topics and specific issues related to nonpoint
source monitoring, monitoring in coastal and wetlands systems, monitoring urban storm water
and sewer discharges, monitoring program design, and approaches to monitoring multidimen-
sional watersheds. Outreach and education represented one of the biggest areas of concern raised
during these discussion sessions. Participants suggested involving public agencies, citizen
groups, and private corporate interests in defining questions to be addressed by monitoring.
Articulating those issues leads to development of the monitoring program-management goals,
and design of the appropriate technical objectives for data collection. The technical objectives of
a monitoring program provide the basis for determining sampling site network design, data
collection and analysis methods, necessary levels of quality assurance/quality control, and the
sufficiency of data for answering management objectives. Public and private involvement in this
process, especially during the initial period of question and goal development, will help ensure
that past monitoring investments are considered and that monitoring results will be useful to
decision makers and to public interests. Public input to this process allows monitoring personnel
to develop the technical components of a program, thereby making it more responsive to public
and policy needs as well as meeting the science-based dataquality objectives.
Several mechanisms for outreach and education were identified by participants. It was felt
that the Internet should be used as a principal outlet for Council operational and technical
information: web page(s) should link to agencies, public groups, or other pertinent interests, as
well as provide access to documents, monitoring data, and results. Participants strongly felt that
annual forums, such as this conference, should serve for the exchange of information on "success
stories" and inter-regional coordination, as well as advertising the existence of the Council's
workgroups, committees, and new-member recruiting efforts. There should be focused effort on
increasing the involvement and use of citizens' monitoring information. Part of a coordinated
public outreach initiative could be developing guidance documents for defining monitoring needs
and the technical objectives. The Council should also ensure that there are regional coordinators
who have regular communication with their constituent states, tribes, river basin commissions,
and other public and private groups through Internet home pages, newsletters, forums, or other
media.
There was substantial discussion on technical issues associated with comparing methods,
applying protocols, selecting the appropriate indicators, and ensuring that technical
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environmental indicators are tied to programmatic goals, including regulatory requirements (such
as Water Quality Standards).
Detailed technical guidance is required for indicator selection; the design of monitoring
programs for urban storm water, coastal, and broad-scale nonpoint sources; and coordination and
consistency among multiple programs. Participants pointed out that there are numerous high-
quality monitoring programs in coastal areas. However, coordination and communication among
those programs is lacking, particularly among state, tribal, and federal levels. There needs to be
increased emphasis on bringing together multidisciplinary teams, including technical (statistical,
science, and engineering) and nontechnical (public citizens groups, private industry, and
economics), to define monitoring goals and objectives.
There is a need to develop and test models for predicting changes in the health of watersheds
using multidimensional models and other interpretive approaches. Such research should include
definition of the data requirements of such models. Programmatic structure and statutory
requirements, such as for total maximum daily loads (TMDLs), the Safe Drinking Water Act's
Source Water Protection initiatives, and the Clean Water Act's biannual water quality inventory
(Section 305[b]), need to be used to address or frame issues on multidimensional watershed
monitoring.
One issue that surfaced repeatedly was that of the role the Council should play in identifying
or securing funding and other resources for monitoring. It was recommended that the Council
investigate a process for funding the establishment and operation of state-level or regional
councils, as well as pursuing corporate sponsorship for education and outreach efforts.
Recommendations—Monitoring Design Strategies
1. Outreach and Education
• Develop a strong Internet presence (Council home page) that will provide a
mechanism for information sharing, data accessibility, and an avenue for initiating
and enhancing collaboration
• Ensure annual conferences for information exchange, trading of success stories, that
would include training workshops and field trips to innovative projects
• Publicize "Lessons Learned"
• Pursue corporate sponsorship to increase and improve outreach capabilities
• Sponsor/fund development of technical guidance documents for the monitoring
community
• Sponsor/fund program interpretation for the monitoring community, for example, an
annotated version of the Clean Water Act
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2. Comparable Methods and Protocols
• Develop technical guidance on determining appropriate and sufficient levels of
quality assurance/quality control (QA/QC)
• Determine minimum components and measurement parameters necessary for
assessing watershed integrity
« Advise the Methods and Data Comparability Board (MDCB) in its effort to develop
analytical methods for nonpoint source monitoring
• Develop standard designs for data collection protocols, database structure, and
metadata/data reporting
3. Sampling Methods and Indicators
• Establish a forum or workgroup to facilitate development of meaningful and useful
indicators
• Produce a document that will guide users through decisions on comparability of
sampling and analysis methods (catalogue of method performance characteristics)
4. Multidimensional Watershed Monitoring (MDW)
» Provide incentives toward developing an integrative approach to MDW, using input
from broad perspectives and areas of expertise
• Implement multidimensional watershed monitoring not only to satisfy needs for
natural resource management, but also to help monitoring entities meet programmatic
and statutory requirements (such as TMDLs, Source Water Protection, and the
National Water Quality Inventory [NWQI])
5. Funding Issues
• Provide mechanism, or determine other opportunities, for funding the establishment
and support of state- or regional-level water resource quality monitoring councils
• Develop relationship with potential corporate sponsors of education and outreach
initiatives.
Track B—Methodology and Information Sharing
Background and Issues
Sessions under this track focused on sampling and analysis methods and mechanisms for
sharing information. Numerous issues are associated with field sampling methods and laboratory
analysis and interpretation. There is also a broadly recognized need to increase the use of field
and laboratory methods with documented performance characteristics and to initiate projects and
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studies from which the results can be compared
or combined. This task is one of the principal
goals of the performance-based methods system
(PBMS) advocated by the Council's MDCB.
The Performance-Based Methods System
(PBMS) is defined as a system that permits
the use of any appropriate and accepted
A, f., ,, A ,. . , sampling and analytical technology that
Many of the methods/issues are captured . . , .. ..... , . \ ... . .
J r demonstrates the ability to meet established
performance criteria and complies with
specified data quality objectives.
under the heading of QA/QC. The statistical
uncertainty of a method (needed for statements
of precision and accuracy) is closely related to
the performance characteristics of that method
(required documentation for a PBMS). For example, as the uncertainty factors of data produced
by a certain method are better understood, there are consequences to the potential design of a
monitoring program using that method. This fact is true especially regarding the:
• Number and location of sampling sites
• Frequency and timing of samples
• Appropriate sample and data analysis
• Results interpretation
• New data combined with data from other sources.
Concerns and Topics
Presentations and discussions in this track were grouped around the themes of data com-
parability and collection methods, QA/QC for monitoring programs, and tools for communi-
cating monitoring results. The critical nature of defining DQOs for a program or method was
recognized. There is no standard approach among agencies for DQO development. However,
sessions within this track included discussions of formulating study or project questions,
selecting measurement parameters, determining spatial and temporal sampling design, and
defining acceptable rates of sampling and measurement error (statistical uncertainty), all of which
are steps in the DQO process.
Other necessary steps are determining the temporal and geographic limits to which the study
or project results can be applied and objectively defining decision rules for implementation of
management or remedial actions. There was substantial discussion around the concept of
performance-based methods comparison—how widely recognized they are, and what is meant by
the term "methods performance characteristics." The information required for PBMS is, in part,
very similar to a portion of the information required for development of DQOs. The precision
and accuracy of a method are part of the uncertainty measurements necessary for DQO
development. They are also two of the performance characteristics that must be compared with
the precision and accuracy of another method to help determine level of comparability.
Many participants felt that the PBMS concept needs to be supported by management and that
the approach needs to be defined for both field and laboratory activities. In addition, management
should fund projects to define the performance characteristics of a program's field and laboratory
methods and compare the characteristics to those of other methods. The Council should define
methods for reporting/documenting method performance characteristics, data quality, and
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associated metadata. There should be increased effort at coordinating the PBMS concept under
development by the Council's Methods Board with that of the U.S. EPA's Environmental
Monitoring Management Council. Collaboration between these two methods groups should
produce:
• A specific framework comparing the performance characteristics of two or more methods
• Guidance on which and how many characteristics are necessary for decisions on
comparability.
Participants felt that the Council needs to develop a PBMS Implementation Plan that details
the mechanics of performing the comparisons, defines who (specifically) is responsible for
documenting method performance characteristics, defines program certification requirements to
be completed, and specifies who will pay for the work. The Council should take a leadership role
in PBMS development to review and recommend a variety of reference methods that could be
used for the basis of a national PBMS program. The Council (especially, the Methods Board)
should become involved in the National Environmental Laboratory Accreditation Program
(NELAP) on field and laboratory data quality issues and be involved in any kind of monitoring
guidance. In addition to developing consensus on a formalized DQO process, the Council needs
to increase its emphasis on QA/QC by developing a standardized approach to establishing a
quality assurance framework and quality control activities and producing a format for writing QA
Program and Project Plans. Some believed that a useful product of the Council and Methods
Board would be a document highlighting "success stories" in data comparability studies and
collaboration efforts. There was a very specific suggestion that the Council (or the Methods
Board) should form a workgroup to address the issue of how to best use nondetect data in the
analysis and interpretation of analytical, environmental chemistry.
As in Track A, there was substantial discussion on the need for public outreach and education
initiatives by the Council. There were suggestions that the Council should view itself, in part,
serving as a conduit for environmental monitoring information, through such venues as
professional conferences, newsletters, and the Internet. The Council needs to establish a process
for increasing training and education: evaluate and respond to federal and state agency and tribal
needs; encourage data sharing, collection, and subsequent use of metadata; educate organizations
and managers about data-quality issues. The Council can encourage use of private and public
media (newspapers, magazines, radio, and TV), develop ways to publicize monitoring results
(some methods for local, small-scale monitoring; others for broader-scale state or national
efforts), and make media releases a routine process. Monitoring information could be used for
educational spots on public TV or for demonstrations and presentations to schools. One
interesting suggestion was that an approach needs to be developed that would provide
understandable monitoring information to be provided to state legislatures. Part of the public
outreach approach that was put forward was to encourage more local participation in the process
of monitoring coordination and collaboration. Ways to accomplish this include connecting with
more citizens' monitoring organizations, promoting/supporting state or regional monitoring
councils, and developing mechanisms for establishing private/nonprofit partnerships.
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Another suggestion was to increase the accessibility of data and monitoring results through
Internet links, with mapped data (in geographic information systems) readily available to the
media and the public. However, with greater ease of access will come increased potential for
misuse of data and results. Some responsibility for understanding data limitations and uncertainty
must fall on the user; but the data owner or supplier must ensure that sufficient metadata and
other qualifiers are, in essence, "attached" to the primary data, including careful and specific
definition of the appropriate uses of the data.
Recommendations—Methodology and Information Sharing
1. Data Comparability and Collection Methods
• Support the concept of PBMSs and define the approach for field and laboratory
activities
• Take a leadership role in developing, adopting, and serving as a clearinghouse for
monitoring guidance, including sample collection, sample and data analysis, and data
reporting
• Coordinate with NELAP
• Encourage and coordinate pilot studies that demonstrate resolution of data
comparability issues
• Promote initiation of regional, state, and tribal monitoring councils, including
citizens' monitoring interests
• Develop and disseminate outreach and educational materials to organizations and
managers about PBMS, data quality, and QA/QC issues
• Provide specific documentation of metadata requirements and reporting formats
2. Quality Assurance/Quality Control for Monitoring Programs
• Assess laboratory and field methodologies to ensure comparability between methods
that are intended to measure identical environmental characteristics
• Develop guidance on producing QA/QC plans, including DQOs and specific QC
activities
• Promote and provide adequate education and training to ensure implementation of
QA/QC programs and procedures
3. Tools for Communicating Monitoring Results
• Develop routine educational and reporting activities that will reach the public, media,
managers, decision makers, and elected officials
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• Sponsor and coordinate a national monitoring conference every 12 to 18 months,
maintaining interactive features
• Use Geographical Information System (GIS), the Internet, and other electronic tools
for reporting data at multiple geographic scales
• Develop mechanisms to enhance access to monitoring data
• Ensure use of appropriate data qualifiers to prevent misuse of data and monitoring
results.
Track C—Indicators and Reference Conditions
Background and Issues
An indicator is a measurable feature of the aquatic environment that provides evidence of
ecological quality. Monitoring results on ecological indicators must be transformed from
technically complex data into a form that is understandable to natural resource managers and
policy decision makers. This task can be accomplished by using reference conditions. Reference
conditions are numeric expressions of the physical, chemical, and biological characteristics that
would be expected to occur at site with or without only minimal impairment. Indicators that use
reference conditions as baseline or background for determining impairment were the foci of
several presentations in this track, including the process for developing the indicators and the
associated impairment decision thresholds, applying those indicators in broad scale and long-
term monitoring and for short-term assessments. Indicators are intended to provide the technical
description of environmental conditions that reflect ecological realism, rather than measurements
of single chemicals using high precision methods that will likely have little relationship to
ecological degradation. Also, there were discussions of indicators for wetlands systems and the
U.S. EPA's Index of Watershed Indicators. In the five sessions in this track, it was felt that
additional biological monitoring needs to be encouraged through guidance documents. All
discussed the need for increased financial support for selecting reference sites and developing
regionally calibrated reference conditions. In addition, participants felt that public outreach and
education on indicators (and the work of the Council, in general) should be a priority.
Concerns and Topics
Developing measures (reference conditions) of ecological quality in a geographic and
temporal framework allows indicators to be tailored to the range of environmental conditions that
would be expected for a particular region. To accomplish regional calibration of indicators (i.e.,
developing reference conditions for a specific ecoregion and type of habitat), it is necessary to
have a database of measurements from both a series of minimally impaired reference sites and
sites that have experienced various levels of known physical or chemical degradation or
hydrologic alteration. It is also important to have substantial experience working with aquatic
communities in the region. Measurement and analysis by experienced professionals of
environmental parameters (biological, chemical, or physical) from this range of sites and impacts
provides the foundation for indicator selection and reference condition calibration.
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Participants expressed concern that not enough financial and staff support is being provided
to states, tribes, and other monitoring groups to properly develop reference conditions, including
reference site selection, field sampling, and calibration of indicators. They suggested that a
standardized approach needs to be endorsed by the Council, perhaps a re-endorsement of the
multimetric approach, which in itself acknowledges the importance of biological assemblages
and trophic pathways. There should also be specific effort to maintain flexibility for individual
state programs, in particular, how biological or ecological indicators are used to define
designated uses. The use of chemical indicators should serve to document chemical speciation,
solubility, mobility, bioavailability, and the effects of these processes on the toxicity of mixtures.
There was substantial discussion on the applicability of the Index of Biological Integrity (IBI),
and how it is used to determine the effects of physical and chemical degradation on overall
biological condition.
A recurring issue in this track was that the Council should fund development of a specific
guidance document for monitoring network design that includes geographic scale, site selection,
and land use/land cover considerations. It was also felt that active support of the Council for the
development of indices of physical habitat integrity is critically needed.
There is currently a guidance document being written by the U.S. EPA for developing IBIs.
Participants felt strongly that the Council should endorse and advertise the existence of the effort,
and, potentially, help expedite its completion. Related to this were strong feelings that the
Council should help advance scientific verification
of the wetlands IB I procedures by supporting pilot
studies for study design, field data collection, and
data analysis. There needs to be increased
collaboration among national and state programs, .,...,,. ,
0 data as indicators of environmental condi-
tion. It incorporates aspects of biogeogra-
phy, population, trophic, behavioral, and
pollution-tolerance information. The index is
typically used with data from fish, benthic
macroinvertebrate, and sometimes
periphyton sampling.
such as the U.S. Army Corps of Engineers' coastal
wetlands restoration program. The Council should
support development of, and provide funding for,
regional and state workshops on biological
monitoring in wetlands and workshops should be
used to develop regional and public partnerships,
including the establishment of state-level councils.
The Index of Biological Integrity is a
broadly based approach for applying
biological-community- or assemblage-level
As in Track B, participants felt that the Council should actively promote the strengthening of
QA/QC requirements for increased program documentation. This effort would include
appropriate metadata, general and specific program characteristics, specific methods, and
data/quality control limits, etc., to make datasets more usable to a broader audience.
Participants discussed various policy statements that should be made and formalized by the
Council. They recommended that the Council aggressively support sustained, long-term
ecological monitoring that transcends the changing of political terms. A workgroup should be
established to develop guidance for a system of designated uses within a consistent framework
among states; however, the details of specific use definition can and should be determined by the
states themselves. It was suggested that the Council could provide policy support for assessment
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activities such as methods, index, and reference condition development. This support could be
accomplished by providing a "letter of endorsement" to individuals or organizations that are
applying for grants or contract funding—the letter could be submitted as documented support for
their proposal. Another mechanism would be to ensure sufficient staff or financial support to
accomplish tasks and program and project management. An idea was also voiced that the Council
should develop a mechanism allowing indicator development and monitoring programs to
acquire funds from regulatory enforcement actions.
Public outreach initiatives were cited as an overarching need. There was widespread
confusion regarding the relationship of the Council to the ITFM, its precursor. A suggestion was
made to distribute all ITFM documents to all conference participants (including nonattending
registrants). The Council needs to work with academia to develop water resource quality
monitoring and management curricula. Outreach to educational institutions should include land
grant universities, USDA extension sites, and continuing education programs. Public outreach
efforts should also include news releases, peer-reviewed journal publications, marketing booths
at other conferences, brochures, and Internet web sites, etc. The Council should develop user-
friendly outreach tools that communicate the accomplishments of the Council, its Methods
Board, and the past accomplishments of the ITFM.
Recommendations—Indicators and Reference Conditions
1. Biological Indicators and Reference Conditions
• Facilitate and sponsor establishment of a systematic and standardized approach for
developing regionally relevant indicators with special emphasis on valid physical,
chemical, and biological endpoints and criteria
• Facilitate and sponsor development of guidance for determining reference conditions
and the selection of regional reference sites addressing both spatial and temporal
issues
2. Watershed Indicators
• Develop an approach for establishing state and regional councils that would recognize
watershed, and estuarine, and coastal zones, where applicable
• Facilitate and sponsor development of guidance for monitoring network design,
including consideration of geographic scale, site selection, and current and historic
land use/land cover
3. Wetlands Indicators
• Fund state-level workshops to increase awareness and enhance development of
partnership efforts (e.g., state councils)
• Endorse and help expedite completion of the wetlands IBI document and fund data
collection for pilot projects verifying wetlands IBIs
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4. Overall Recommendations
• Develop compendium or database of methods for identifying or determining
indicators and reference conditions
• Encourage hierarchical organization of data and information among data gatherers,
and transfer monitoring results in a format useful to natural-resource-use managers for
adaptive management of lands.
Track D—Linking Monitoring to Environmental Management and Decision Making
Background and Issues
The overall design and organization of a monitoring program directly affects how results are
communicated to management and policy decision makers. If a monitoring project is directed by
a partnership among several organizations, each partner will own communication needs. If the
data and information are being accessed by multiple monitoring groups in the partnership,
capabilities to equally access the results need to be considered at the outset of the project.
This track included presentations and discussions related to the Clean Water Act programs,
the NWQI (Section 305[b]) and TMDLs; Section 303[d]. Source protection issues under The
Safe Drinking Water Act Amendments of 1996 were the topic of one session. Another workshop
focused on the vulnerability of groundwater aquifers to contamination from pesticides and
nitrates and the use of models to predict the potential or risk of groundwater contamination.
Three sessions on successful program collaboration highlighted the continuing interest in
partnering to maximize use of limited resources: working together to solve problems, building a
better understanding of both water issues, and managing water resource.
Concerns and Topics
Most of the discussion in the sessions of Track D focused on one of three areas:
• Linkages among monitoring organizations, resource managers, and decision makers;
between potential partners and collaborators; and between the goals of volunteer and
professional organizations
• Communications for outreach and technology transfer: informing decision makers,
educating elected officials, fostering a monitoring constituency, and publicizing the
Council's role in monitoring
• Technical nonpartisan leadership to support a national discussion on cross-program,
cross-agency monitoring issues, to set standards for good science and high-quality data
and information, to sponsor the development of state-of-the-art technical guidance to
address the monitoring community needs, and to offer a forum to integrate monitoring
activities that would serve broader management goals in addition to meeting program
requirements.
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Overall, participants in these sessions felt that weaknesses in existing datasets and the
insufficiency of high quality monitoring information contributed to a continued lack of
understanding of ecosystem vulnerability. These weaknesses have been the result of poorly or
inappropriately designed monitoring programs or networks, imprecise and incomparable
methodologies, and lack of commitment for long-term monitoring. One need identified was for
the Council to focus monitoring design activities to make them more influential in future
management decisions.
The Section 305(b) reporting process was seen as being insufficient to support well-informed
management decisions and ineffective for prioritizing management actions. In addition, there was
consensus that, in general, there is no broad-based constituency to support water quality
monitoring in the United States. Lack of effective outreach to explain monitoring-and the use of
monitoring results-to a broader segment of society was seen as a key roadblock in preventing the
development of such a constituency.
Two approaches for correcting this are to promote the integration of volunteer monitoring
with other types of monitoring activities and establishing the Council as a clearinghouse for
organizing and disseminating information and data. In the TMDL session, participants looked to
the Council to facilitate technical discussion and communications as well as to take on a role to
evaluate the efficacy of the TMDL program as a water resource management approach as a
whole.
The Council was asked to press for states to adhere to monitoring guidelines based on
recommendations of the ITFM, including supporting the development of reference conditions,
increased use of biological indicators, and improved collaboration among multiple programs.
Participants also said there needs to be greater emphasis placed on appropriate formatting of
monitoring data and assessments results so they can be meaningfully transferred to decision
makers in Section 305(b), TMDLs, vulnerability assessments, source water assessments,
stormwater protection, and other programs. The Council could serve as the coordinator among
federal, state, and tribal monitoring entities to turn the Section 305(b) process into a useful
management tool.
There was discussion on the widely recognized need for ensuring that monitoring programs
are specifically designed to address stated monitoring goals. In part, this means promoting
resource-based monitoring rather than strictly programmatic monitoring. For example, the
definition of "water quality" should really refer to "water resource quality," so that it communi-
cates not only water purity, but also the place of water in a properly functioning ecological
system and its relationship to all aspects of watershed function. Resource-based monitoring and
other goals-based monitoring may also be at risk where monitoring requirements to support the
TMDL requirements that result from lawsuit settlements may completely take over state budgets
for water-quality monitoring.
The Council should encourage the use of best available technologies for source water pro-
tection, advance the local database concept, and produce a summary document on current
approaches for delineation of surface water and groundwater. The Council needs to develop a list
of minimum data elements for source water assessments. For TMDLs, the council was asked to
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support technology transfer on TMDL listing requirements and TMDL development, to demon-
strate need for and support long-term monitoring, and to encourage the development of technical
capabilities to scientifically address processes and impacts of air deposition, groundwater, clean
sediment, and flow.
There is a substantial need for the monitoring community to have access to information on
successful monitoring partnerships and programs and technical guidance and information. Many
participants felt that the Council would be an appropriate organization to serve as a clearing-
house—a "library" for monitoring databases, methods, geographic and analytical frameworks,
and models. There needs to be a conduit for demonstrating management decisions that have been
influenced by monitoring data and results. The Council could foster the collection of accurate
locational information as part of the database library. It could also serve to actively solicit
descriptions of successful partnerships and collaborative models and thereby enhance oppor-
tunities for collaboration, particularly between professional and volunteer monitoring groups.
The Council was asked to seek mechanisms to increase involvement of the public in
decisions using monitoring data and other information. This step would include undertaking
aggressive development of a public outreach program designed to increase the visibility of
monitoring data as the source of useful decision-making information, and educating the public on
the need for monitoring. Each Council member needs to see themselves as an ambassador for the
Council, presenting its mission, goals, and vision at conferences and seminars. The Council must
promote and advocate long-term volunteer monitoring, along with the supporting efforts to
increase credibility of volunteer monitoring. There is substantial need for public outreach
activities, such as topical workshops, informational brochures and newsletters, informational web
sites, and continued national conferences (12- to 18-month intervals).
Recommendations—Linking Monitoring to Environmental Management and Decision
Making
1. Linkage to Management Decision Makers
• Increase the visibility of monitoring as the source of information useful for
environmental management and decision making
• Actively foster collaboration between professional and volunteer monitoring activities
(regional or statewide councils, university partnerships, etc.)
0 Press monitoring groups to adhere to monitoring guidelines recommended by the
ITFM
• Promote resource-based, integrated monitoring approaches that go beyond only
meeting narrow programmatic objectives
• Begin the discussion to support integration of TMDL work with other state water-
quality-monitoring needs
• Specifically address development of appropriate approaches for formatting of
monitoring information to enhance its use by decision makers
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2. Clearinghouse for Monitoring Information
• Provide access to information on successful monitoring partnerships and programs
• Become a clearinghouse for standards, methods, databases, models, and analytical
frameworks and promote scientific and technical credibility for information
• Sponsor the discussion of issues and the development of technical guidance on air
deposition, groundwater influence, clean sediments, and flow within the context of
TDML development
3. Public Outreach
• Council members should be ambassadors for their organization and more effectively
communicate its goals, vision, and mission
• Improve awareness of the Council's functions and goals
• Increase involvement of public in decisions using monitoring data and other
information
• Promote long-term volunteer monitoring
• Develop and release regular informational brochures
• Help inform and educate elected officials on complexity of technical issues related to
TMDLs
4. Provide External Expert Review
• Review the potentially very costly monitoring requirements that have resulted from
settlements of the TMDL lawsuits and the impact on state monitoring programs
• Evaluate efficacy of the TMDL approach as a management strategy
• Develop and present a series of topical workshops on programmatic and technical
subjects.
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Section III
Conference Papers
This section includes 60 of the papers that were presented at the National Monitoring
Conference in July 1998. Several other papers were presented but are not included herein
because complete copies were unavailable at press time. Papers appear in consecutive order
within the tracks to which they were assigned and presented.
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Track A—Monitoring Design Strategies
Monitoring Design
Monitoring Coastal Systems
Nonpoint Source Monitoring
Monitoring Wetlands
Monitoring Urban Stormwater and Sewer Discharges
Multidimensional Watershed Monitoring
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A Locally Designed Watershed Monitoring Program
John Cavese, Senior Environmental Specialist
BP Chemicals, 1900 Fort Amanda Road, Lima, OH 45804
Beth Seibert, Coordinator
Ottawa River Coalition, 219 West Northern Avenue, Lima, OH 45801
Introduction
Organized in 1993, the Ottawa River Coalition (ORC) is a non-profit organization representing 43
member organizations working together to understand and protect water quality in the local watershed.
The Ottawa River drains a modest, yet diverse, 372 square mile watershed situated in the Lake Erie Basin.
Ohio EPA has identified the Ottawa River in Lima, Allen County, Ohio, as one of their most impaired
streams. Low dissolved oxygen levels, particularly during the hot and dry summer months, are a
particular concern. Past studies of the river focused on the stream as it flows through the City of Lima
because of industrial and municipal point source discharges. One of the ORC's first efforts was the
compilation and evaluation of all known studies on the river system. These previous studies were often
short in duration and conducted over a limited portion of the stream.
A long-term monitoring program was designed by the ORC for the purpose of identifying water
quality trends in the watershed. It began in August 1995 and involves chemical analysis of nine tributaries
as well as the main channel. Comparisons are being made on the changing water quality of the river
system as it proceeds through the basin. The program is dependent on weekly site visits by citizen
volunteers and is currently receiving partial funding from a 319 grant. The ORC is in the process of
interpreting the 3 years of data collected as well as determining future program modifications.
The Ottawa River Coalition
Organized in 1993 as a result of increasing attention to water quality in the local river system, the
ORC initially involved 19 organizations, agencies, institutions and businesses. Today the ORC represents
the collaborative efforts of 43 member organizations all working together to understand and protect water
quality in the local watershed. Membership currently includes the following:
Ada Village Council
Allen County Combined Health Department
Allen Co. Emergency Management Agency
Allen County Farm Bureau
Allen Soil & Water Conservation District
Alloway Environmental Testing Service
Bath Township Trustees, Allen County
BP Chemicals Inc.
Cargill, Inc.
City of Lima, Parks, Rec., and Forestry
Ford Motor Company, Lima Engine Plant
Fowler and Hadding
Johnny Appleseed Metro Park District
Lima-Allen Co. Neighborhoods In Partnership
Lima Lincoln Mercury
Metokote Corp.
Akzo Nobel Chemicals, Inc.
Allen County Commissioners
Allen County Engineer
Allen County Sanitary Engineer
Allied Environmental Services
Bassett Associates
Bluffton College
BP Oil - Lima Refinery
City of Lima, Utilities Department
Columbus Grove Village Council
Fort Shawnee Village Council
Hardin Soil & Water Conservation District
Kalida Village Council
Lima-Allen Co. Regional Planning Commission
Maumee Watershed Conservancy District
Mid Bus, Inc.
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Monroe Township Trustees, Allen County Monsanto Life Sciences
Natural Resources Conservation Service ODNR Division of Wildlife
Ohio State Univ. Extension, Allen County PCS Nitrogen Ohio L.P.
Putnam Soil & Water Conservation District Sugar Creek Township Trustees, Allen County
The Ohio State University at Lima Tri-Moraine Audubon Society
Waste Management
In its first four years, the ORC focused on establishing the organization, building working
relationships, and obtaining additional financial support. In 1997 the mission, goals and objectives were
revisited to reflect a more evolved and aware organization. The mission of the ORC is to promote the
wise management of the Ottawa River and its watershed as a valuable community resource. The goals and
objectives identified to accomplish that mission are:
1) Generate public awareness and educate the public of the benefits of improving water quality of
the Ottawa River.
• Clarify the benefits of improving water quality in the river system.
• Promote stewardship of the natural resources in the watershed.
• Educate the public on nonpoint source pollution.
• Increase name recognition in the community.
« Educate public on state of water quality in the watershed and challenges of water quality
improvement.
• Emphasize demonstration projects for improved water quality.
• Promote a better understanding by the public of drainage in the watershed.
2) Work collectively to improve water quality in the Ottawa River.
• Promote effective natural resource management practices •within the 'watershed.
• Improve chemical and biological -water quality within the watershed.
3) Continue to study and monitor the quality of the river.
• Provide for a wide-base monitoring program to compile data that focuses on the entire
watershed.
• Stay informed of EPA regulations governing the use and status of the Ottawa River.
4) Continue to seek an adequate financial base to maintain operations of the ORC as a non-profit
organization.
• Secure adequate financial resources from membership, grants, retail activities, and future
initiatives.
• Utilize available funds wisely in support of the ORC mission and goals.
5) Provide a forum for stakeholders representing varying viewpoints and uses of the watershed.
• Continue membership promotion.
• Expand the ORC base of potential influence.
• Provide for a broad-base of public input to address issues and concerns affecting the
watershed.
The ORC operates through eight working committees. The Executive Committee provides guidance
and direction for the organization. The Monitoring Committee directs the activities of the volunteer
assisted strategic monitoring program and evaluates the resulting data. The Watershed Committee
addresses drainage issues, agricultural land use, and has been steering inventory efforts. The Community
Relations Committee coordinates information and education efforts. The Wetlands Committee demon-
strates the use of wetland gardens for on-site home septic waste treatment. The NPDES Committee
provides a collaborative approach to understanding the combined impact from OEPA permitted dis-
charging facilities. The Historical Review Committee reviews and archives river survey reports and
III-6
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historical data. The Flow Augmentation Committee is presently considering the concept of increased flow
for improved dissolved oxygen levels.
The ORC currently has two full time staff: a Coordinator and a Program Assistant. Both positions are
funded through grants. Office space and other support are being provided by Allen Soil and Water
Conservation District and the USDA Natural Resources Conservation Service (NRCS). Over 30
volunteers provide support to the ORC programs, with the greatest support to the monitoring program.
Volunteers are managed under the USDA NRCS Earth Team Program which assists in tracking the hours
contributed as well as providing tort liability protection. The ORC has also hosted college internships,
paid and unpaid.
Ninety-five percent of the ORC's revenue is grant funded. In 1995 the ORC was awarded $298,696
from the Ohio EPA through a Section 319 Grant. In 1996 ORC received $40,000 in support of natural
resource conservation planning and assistance from the USDA Natural Resources Conservation Service .
In 1997 a total of $3,500 was received from ODNR Division of Soil and Water Conservation in support
of nonpoint source pollution prevention projects. Only three percent of the ORC's annual revenue is
derived from membership dues, while the remaining two percent is the result of retail activities. Annually
the ORC benefits from over $60,000 of in-kind contributions from its members and other community
supporters.
The Ottawa River Watershed
The Ottawa River Watershed encompasses 372 square miles including portions of five Ohio counties.
(Reference Figure 1, Lake Erie Basin Map) The Ottawa River is a headwaters tributary to the Maumee
River of northwestern Ohio. The entire Maumee River Watershed drains into the western basin of Lake
Erie, the southernmost of the Great Lakes.
The Ottawa River Watershed lies upon a low relief glacial till area of Ohio (Reference Figure 2,
Ottawa River Watershed Map). The elevation drop over 60 stream miles is slightly over 200 feet (less
than 4 feet per mile). The soils are generally high in clay content, leading to increased sediment runoff
and accelerated erosion. NRCS has designated 25,000 acres of cropland within the watershed as being
highly erodible.
The Ottawa River Watershed is populated with 100,000 residents, 40,000 being located within the
city limits of Lima, Ohio. Lima serves as a hub for a thirteen county area, taking commuters into
consideration, the population in the watershed easily doubles on any given day. Besides the City of Lima,
the watershed extends to fifteen other smaller communities. As a resource, the Ottawa is utilized for
public, industrial, and agricultural water supply.
Located within the historic Great Black Swamp, aggressive drainage practices to make the land
productive and habitable have led to dramatic changes in the hydrologic conditions within the watershed
over the past 100 years. Current land use within the Ottawa River Watershed is approximately 80%
agricultural, 5% woodland, and 15% urban. The predominantly agricultural area is realizing the constant
pressure of urban development, especially in the areas immediately surrounding the City of Lima.
While efforts, such as conservation tillage, have been adopted within the agricultural community to
reduce soil erosion the urbanized areas presently practice very little in the way of runoff and sediment
control. Allen County is in the process of developing Stormwater Management and Sediment Control
Regulations that should positively impact water quality in the river basin.
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Water Quality
Previous studies of the Ottawa River have identified various point sources of pollution. Historically
the regulated community has been targeted for water quality improvements. Ohio EPA's 1996 study of
the river concluded that point source impact has declined despite industrial growth. The ORC has
concluded that nonpoint pollution sources have not been investigated thoroughly or properly addressed.
Many of the contributing pollutant loads are nonpoint in nature. Through watershed inventory and stream
monitoring efforts the ORC is trying to understand the nonpoint source impacts.
The Ottawa River and the majority of its tributary miles have been designated as warm water habitat
by the Ohio EPA, although portions still do not meet this criteria. A recent study commissioned by the
Ohio EPA (URS / Limno-Tech, 1993) indicated low dissolved oxygen and excess sedimentation to be
major problems for the stream system. Other studies performed on the Ottawa River from 1956 to present
have indicated nonpoint source pollution to be a contributing component of the water quality problem in
the Ottawa River. The ORC has created an expansive library containing all of the historical studies of the
watershed.
Based upon all available information, the ORC has concluded that the issue of nonpoint source
pollution must be addressed and thoroughly investigated. The nonpoint source water quality concerns
within the Ottawa River Watershed can be categorized into five major areas: 1) urban nonpoint and point
source runoff, 2) agricultural runoff and its associated sediment, nutrient and pesticide loads, 3)
stormwater runoff from residential areas, 4) physical condition of the river and river banks, and 5) current
land use within the immediate riparian corridor of the river and its tributaries.
ORC Monitoring Efforts
The ORC Monitoring Committee is responsible for the development of the strategic monitoring
program and interpretation of resulting data. The committee is composed of members of the ORC
representing academia, industry, municipality, regulatory, environmental, and community business
leaders. Based on gaps in available data, the monitoring committee proposed a strategic watershed
monitoring program. The strategy being to monitor each of the nine major tributaries and a corresponding
upstream point in the main channel. These efforts are designed to identify water quality trends and
contributions of the nine different tributaries throughout the watershed.
Monitoring is conducted on a weekly basis from March through September (Reference Figure 3, Field
Data Sheet). Parameters were chosen based on known point and nonpoint issues. Field measurements
include temperature, pH and dissolved oxygen. Laboratory analysis is conducted on monthly composite
samples for the following nutrients: nitrate/nitrite, ammonia-N, and phosphorus. Total suspended solids
analyzed from grab samples collected twice a month. Due to the high cost of metals analysis, composite
samples are collected every other month from March through September (four times per year). Laboratory
analysis is done through Alloway Environmental Testing Services Inc., a local EPA approved facility, and
is costing the ORC approximately $6,500 annually.
The monitoring program is dependent on a minimum of 11 volunteers to cover a total of 20
monitoring sites. (Reference Figure 4 Monitoring Sites) The committee pursued volunteer assistance as a
way to involve the general public. Volunteers have come from various walks of life including students
(middle school to college), landowners, agency personnel and other professionals, retired persons, etc..
The ORC spends approximately $2,100 per year to equip the volunteers for field analysis and sample
collection. The ORC staff trains the volunteers and provides them with backup assistance.
While the positive side of utilizing volunteers far outweighs the negative, there are some limitations
that have resulted. For example, there is no way to regulate the monitoring times or days, which adds
difficulty to interpreting a parameter such as dissolved oxygen that is impacted by a diurnal cycle. Also,
III-8
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very few volunteers remain with the program past a year due to the weekly time commitment, so there is
always the challenge for the staff to keep all sites manned and data coming in.
Monitoring Results
To date the ORC has accumulated over 2.5 years of data which is managed electronically through an
MS-Excel spreadsheet program. Periodic metals analysis has been conducted for Copper, Cadmium, Lead
and Mercury. Copper and Cadmium were recently dropped from the monitoring program due to all
samples registering below method detection limits. Lead and Mercury are frequently registering above
method detection levels but well below water quality limits.
Since the monitoring effort was begun in 1995, the data collected has shown a minimal number of
exceedences of the State of Ohio water quality standards for nutrients. While no correlation's have been
made to date on nutrient loading data for the Ottawa River, the ORC continues to closely evaluate the
data and compare it to rainfall volume and intensities.
Based on results from a synoptic survey conducted in September 1995, the decision was made to add
periodic coliform bacteria testing to the monitoring program (two times per year). Further coliform
bacteria testing continues in order to evaluate potential impacts from urban combined sewer overflows
and rural septic systems.
Dissolved oxygen levels taken during volunteer sampling have not shown wide variation. Since most
of the sampling is being performed during daytime hours we have not been able to reproduce the known
diurnal dissolved oxygen swings which have been previously reported (URS/Limno-Tech, 1993). There-
fore the ORC has considered 24 hour mechanical sampling. At this point in time the ORC is searching for
funds in order to obtain the necessary instrumentation.
Summary
The monitoring program is periodically reviewed by the monitoring committee. Based upon data
results and trends necessary changes are made continually to improve the program's effectiveness. A
result of the monitoring program has been the identification of individual tributaries which are con-
tributing a considerable pollutant load to the river system. In addition, the citizen volunteer base is able to
assist in communicating with the individual communities on just how valuable a resource we have.
Irregardless of the present and historical water quality issues encountered in the Lima, Allen County
area, the contribution of the Ottawa River to the Auglaize River / Lake Erie watershed continues to
remain of high quality. The ORC has been well accepted by the community, business and government
officials. Over the last five years overall perception of the Ottawa River as a community resource has
been greatly enhanced by the many efforts of the ORC.
The strength of the ORC monitoring efforts have been in the fact that the entire program has been
locally designed and managed. While other watershed organizations may desire to draw upon existing
monitoring programs they should focus upon the specific needs and issues of their local watershed area.
Acknowledgments
Recognition must be given to the Ohio EPA for the foresight to assist in watershed management
programs by providing Section 319 grant funds. Thanks to all the member organizations and volunteers
who have graciously donated their time and efforts to the Ottawa River Coalition. Also to Alloway
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Environmental Testing without whom the logistics and cost of sample analysis would have simply been
overwhelming.
References
Study of the Ottawa River; Biological and Chemical Studies for the Lima Refinery,
Standard Oil Company. Academy of Natural Sciences of Philadelphia, Department of
Limnology, 1956, Allen County, Ohio.
Ottawa-Auglaize River Survey. Academy of Natural Sciences of Philadelphia, Department of
Limnology, 1960, Biological Studies conducted for the SOHIO Chemical Company
Water Quality Study of the Ottawa River, Allen and Putnam Counties, Ohio. Ohio EPA,
1979, G.L. Martin, TJ. Balduf, D.O. Mclntyre, and J.P. Abrams.
Ottawa River Dilution Study. US EPA, Region V. 1981, Eastern District Office, Environmental
Services Division
Report on the Biological Condition and Habitat Assessment of the Ottawa River. EA
Engineering, Science, and Technology, Inc., 1989, Prepared for BP America, Inc.
A Summary of the 1989 Biological and Water Quality Survey of the Ottawa River, Allen
and Putnam Counties, Ohio and Changes Since 1985, Ohio EPA, 1990, R.A Sanders and
J.T Freda
Ohio Water Resource Inventory, Ohio EPA, 1992, Executive Summary.
Biological and Water Quality Study of the Ottawa River, Hog Creek, Little Hog Creek, and
Pike Run (Hardin, Allen, and Putnam Counties, Ohio), Ohio EPA, 1992, OEPA Technical
Report EAS/1992-9-7
Final Report of Ottawa River Study (Dissolved Oxygen Study), URS Consultants, Inc. and
Limno-Tech, Inc., 1993, Prepared for the Ohio EPA
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" to promote the
wise management and use
of the Ottawa River and its
watershed as a valuable
community resource."
- Ottawa River Coalition Mission
Figure 1. Ottawa River watershed.
m-ii
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Watershed Facts
372 square mile area
Population 100,230
River system outlets Into the Auglalza River
northwest of Kallda In Putnam County
Map Legend
Q
Fort Jennl
Scott's Crossg
Figure 2. Ottawa River watershed map.
Ill-12
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Field Data
Log Sheet
OTTAWA RIVER COALITION
Volunteer Monitoring Program
Month:_
Collected By:
Location
|= Little Hog Creek
= Lost Creek
= Little Ottawa River
|= Honey Run
|= Sugar Creek
1= Plum Creek
= Mouth of the Ottawa River System
8
9
10
11
12
A |= Tributary Sample
B 1= Ottawa River Sample
= Grass Creek
= North St./Lima
= Railroad/Lima
= Dug Run
= Pike Run
Dates
Times
Sample Containers
Field SI
Field S1 52 S3
Field S1 S3
Field- S1 S2 S3
Current Field Conditions
Temp.
PH
D.O.
Comments / Prior Week Conditions
(weather / in-stream)
(In-Field Measurements)
Temperature
Dissolved Oxygen
PH
F
F
F
Jan
2x
2x
2x
Feb
2x
2x
2x
Mar
4x
4x
4x
Apr
4x
4x
4x
May
4x
4x
4x
Jun
4x
4x
4x
Jul
4x
4x
4x
Aug
4x
4x
4x
Sep
4x
4x
4x
Oct
4x
4x
4x
Nov
4x
4x
4x
Dec
2x
2x
2x
(Nutrients / Indicators)
Nitrate/Nitrite
NH3-N
Phosphorus
Total Suspended Solids (TSS)
S1
S1
S1
S2
4x
4x
4x
4x
4x
4x
2x
4x
4x
4x
2x
4x
4x
4x
2x
4x
4x
4x
2x
4x
4x
4x
2x
4X
4x
4x
2x
4x
4x
4x
2x
4x
4x
4x
2x
(Metals)
Lead
Mercury
Cadmium
Copper
S3
S3
S3
S3
3X
3X
3X
3X
3X
3X
3x
3x
3x
3x
3x
3X
3x
3x
3x
3x
Updated:
Orc_dala.xls 6/9/1998
Figure 3. Field data sheet.
-------
lurnbuu Grove Putnam Co
Corner k \
s
... (oCairo
Hun\ \ N S\' s-
Figure 4. Ottawa River watershed monitoring sites.
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Design of Stream Sampling Networks and
a GIS Method for Assessing Spatial Bias
Alison C. Simcox, Ph.D.
Tufts University, Department of Civil and Environmental Engineering
Medford,MA02155
Abstract
Bias in the design of stream-sampling networks is a major cause of inaccurate characterization of
water quality at state and national levels. This paper distinguishes the three types of bias that commonly
occur in water-quality assessments, and places emphasis on 'design bias' associated with location of
sampling in watersheds. An environmental index is described that can be used to recognize and reduce
design bias, and to provide a consistent means for comparing the overall water quality of watersheds
within a region. The index provides a means to differentiate the component parts of a watershed, its
subsheds, in terms of two sets of features: natural landscape features and anthropogenic features
('stressors'). Together, these features largely determine the variability of the quantity and quality of water
discharged from watersheds. Index values increase with landscape complexity or anthropogenic stress or
both. Spatial bias is reduced by ensuring that each subshed is sampled in proportion to its expected
influence on basinwide water quality. Use of the index is demonstrated in a watershed in southern New
Hampshire.
Introduction
Currently, the principal means by which the U.S. Environmental Protection Agency (EPA), Congress,
and the public evaluate the quality of water in each state is through information given in reports required
under Section 305(b) of the Clean Water Act. These reports provide water-quality information for each of
the 305(b) 'target populations' within each state: streams, lakes, groundwater, coastal waters, and
wetlands. Data for 305(b) reports are obtained from a large number of sampling sites, the locations of
which commonly are biased to meet a variety of state objectives, such as compliance monitoring. As a
consequence, quantitative and qualitative statements concerning the overall water quality of a 305(b)
target population for a state and, ultimately, for the nation may be misleading.
The primary goals of the study (Simcox, 1998) briefly described in this paper were (1) to identify
sources of bias in stream-sampling networks in watersheds, (2) to develop methods to identify and reduce
bias in network design, and (3) to develop measures to compare the overall water quality of watersheds in
a state or specified region.
Classification of Bias
Sources of bias were identified by reviewing 305(b) reports from eight states: Alabama, Georgia,
Maine, Michigan, New Hampshire, Oregon, Washington, and Wisconsin. Based on this review, bias was
divided into three types: design, analytical, and statistical (Table 1). As shown in this table, errors in
measured values of water-quality variables (e.g., chemical concentration values) are placed into one of
two general categories: 'random variance' or 'bias'. Random variance refers to (1) deviations due to
natural variation inherent in a sampled population (also known as 'sampling error') and to (2) deviations
that result from use of imprecise field or laboratory instruments or measurement procedures.
If true population values are over- or under-estimated in a consistent manner, error is classified as
'bias'. 'Design bias' refers to bias associated with sampling design, which prescribes the location and
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frequency of sampling. When design bias is present, the sampled and target populations will not match; as
a result, samples will not be representative of the intended target population. 'Analytical bias' refers to
bias associated with field or laboratory equipment or protocols. When analytical bias is present, the
sampled and target populations may or may not match. If these populations do not match (i.e., design bias
is present), analytical bias will add further error. If sampled and target populations are designed to match,
analytical bias will be the main source of bias preceding statistical data analysis.
Statistical bias is fundamentally different from design and analytical bias in that statistical analysis
generally proceeds from the assumption that all samples consist of random, independent, identically
distributed measurements from a common underlying population. There is no concern about whether or
not that population is, in fact, the intended (target) population. With statistical bias, the concern is with
the statistical estimator of a population parameter, such as sample mean, median, variance, or a quantile,
rather than with the similarity between sampled and target populations or with the correctness of sampling
and analytical protocols.
Sources of Design Bias
Sources of design, analytical, and statistical bias in water-quality assessments are discussed in Simcox
(1998) and NCASI, Inc. (in preparation). This paper focuses on design bias, especially bias associated
with the location of stations in watershed sampling networks.
Design bias can be attributed to three main factors: spatial design (i.e., location of sampling),
temporal design (i.e., sampling frequency/times), and scale effects. Bias due to spatial design of a
sampling network is common in 305(b) programs because states generally do not attempt to obtain
observations representative of an entire 305(b) target population, such as all of a state's streams or lakes.
Rather, they use a 'targeted monitoring' approach in which sampling sites are selected according to the
purpose of monitoring, such as compliance monitoring or assessment of water quality in an area of special
ecological value. If only these data are used to make inferences about the entire 305(b) population, such
as a stream population of a watershed, two consequences are likely: (1) an estimate of average population
values (e.g., mean or median concentration values of water-quality constituents) may be biased if
characteristics of the observed part of the population differ from those of the unobserved part, and (2) an
estimate of variance of the population average may be incorrect.
Spatial bias may also result if data are spatially correlated. This can occur when observations are
taken upstream and downstream on the same waterbody so that the upstream sample forms part of the
downstream sample. Although spatial correlation of data between stations in many watershed stream-
monitoring networks is minor because of the dispersed nature of sampling locations, consequences of
correlation can be serious. Many statistical tests assume that data are independent so that these tests
cannot be used or have less power when observations in a dataset are correlated. If correlation is present,
hypothesis tests used to compare groups of data, such as data from two waterbodies or from two time
periods, and trend tests may lead to the conclusion that differences and trends exist when they, in fact, do
not. Moreover, the width of a confidence interval about the mean (e.g., mean constituent concentration
value) will be larger for correlated than for uncorrelated data because a correlated dataset provides less
information about the mean than does an independent dataset of the same size. Similarly, if variance of
the mean is estimated for a correlated dataset using an estimator that assumes independent data, variance
may be seriously underestimated so that the sample mean appears more precise than it actually is (e.g.,
Helsel and Hirsch, 1995; Haan, 1977).
State water-quality assessments may also contain bias due to the frequency or time of sampling (i.e.,
temporal design) at individual stations. Temporal-design bias may be caused by sampling only during
specified flow conditions (e.g., low or high flow) or times of the year (e.g., once each season), or even by
sampling at random times. In addition, as with spatial data, if temporal data from individual stations are
correlated, use of statistical tests can result in significant bias. For estimates of mean annual water quality,
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sampling only during low or high flows will tend to emphasize constituents that generally occur in higher
concentrations during those periods and de-emphasize those that occur in lower concentrations. The
concentration of many water-quality constituents is correlated with streamflow and reflects the uneven
distribution of storm events throughout the year. Because of this, distributing sampling throughout the
year will not ensure that estimates of annual mean values are unbiased. As noted by Helsel (1995),
random or arbitrary selection of sampling times may also result in bias by over-emphasizing commonly
occurring lower streamflows. Helsel (1995) recommended that sampling be done more frequently during
periods of greater expected variability in the concentration of water-quality variables of interest. Because
expected variability may differ considerably among a suite of water-quality variables, some researchers
suggest that those requiring more or less frequent monitoring than other variables be treated separately
(e.g., Ward, R.C. et al., 1990). For example, many variables associated with biomonitoring such as
pesticide residues can be monitored with less frequency or at different times than variables associated
with water chemistry.
A less obvious source of design bias in water-quality assessments is bias associated with scale.
Mixing data from various spatial scales can lead to invalid conclusions because environmental processes
commonly operate differently at different scales. For example, the water-quality effects of a particular
land use in a large watershed with a large number of land uses are unlikely to be the same as those in a
small watershed with a small number of land uses. Another example is given by Helsel (1995), who noted
that simple random sampling over a broad region tends to result in greater representation of smaller
watersheds and, therefore, a greater emphasis on the water-quality effects of land uses in those
watersheds.
Rationale for Developing Environmental Indices
A method was developed to help water-resource managers identify bias in sampling design and to
prioritize watershed areas for sampling when the sampling objective is to assess overall water quality of a
watershed. The method also provides a means for aggregating water-quality data from subsheds into
basin wide water-quality measures; when the method is repeated for a group of similarly scaled
watersheds, consistent and comparable water-quality measures are produced.
The method uses environmental indices, tools that are increasingly being used for management
decision-making. The indices are developed on simple zero to one scales and provide a means to
differentiate the component parts of a watershed, its subsheds, in terms of two sets of features: natural
landscape features and anthropogenic features ('stressors')- Together, these features largely determine the
quantity and quality of water discharged from each subshed throughout the year and, thus, the relative
influence of each subshed on overall quantity and quality of water discharged from the watershed.
Subsheds with greater influence have higher index values, indicating greater natural landscape complexity
or anthropogenic stress or both, than subsheds with lower index values. Spatial bias is reduced by
ensuring that each subshed is sampled in proportion to its expected influence on basin wide water quality.
The intent was to develop indices that can be easily understood by water-resource managers.
Assessment of water quality in streams is a multidimensional problem because water quality at any
specified stream location is the product of the interaction between many environmental factors, including
soil type, vegetation, biological activity, climate, precipitation, topography, land use, effluent discharge,
channel size, basin size, and other factors. To produce useful indices, it was necessary to reduce the
dimensionality of the problem by selecting a subset of environmental factors that commonly cause water
quantity to vary and water quality to be degraded. Reducing dimensionality allows data requirements to
be kept to a manageable level. It also avoids overwhelming decision-makers with dozens of indicators,
many of which are likely to be correlated and, therefore, contain redundant information.
The indices also were developed to be compatible with a particular approach to water-quality
assessment. This approach is a watershed-based approach advocated by the EPA (U.S. Environmental
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Protection Agency, 1997) for the 305(b) program in which 'waterbodies' are defined as subshed areas
(11-digit or 14-digit Hydrologic Unit Code (HUC) watersheds) comprising a larger watershed. If subshed
areas are at the 11-digit HUC scale, the larger watershed is an 8-digit HUC watershed; if subshed areas at
the 14-digit HUC scale, the larger watershed is an 11-digit HUC watershed. The 8-digit HUC watershed
is the more likely choice for the larger watershed area since these are the units used by most states for
water-resource planning.
Development of Environmental Indices
The Contoocook River watershed in southern New Hampshire (Figure 1), a tributary watershed of the
Merrimack River watershed, is used to demonstrate development and use of environmental indices.
Spatial data at a scale of 1:24,000 for this watershed were linked and analyzed using GIS software
(ARCVIEW™) with the spatial analyst extension to support raster modeling.
The Contoocook River watershed is an 8-digit HUC watershed comprised of six 11-digit HUC
subsheds, which are referred to by the last three numbers of their hydrologic-unit code (Figure 1). The
watershed covers about 764 square miles and elevations range from over 3,000 feet in the southwest to
almost 300 feet in the northeast. The geology is typical of the region, with crystalline bedrock overlain by
Pleistocene-aged glacial deposits, which form productive aquifers along river valleys. Like other
watersheds in the region, most land area is forested and rural, with agriculture, industry, and population
concentrated along river valleys.
An environmental index, composed of a 'landscape subindex' and a 'stress subindex', was developed
for each of the six subsheds of the Contoocook River watershed. As shown in Figure 2, each subindex
was derived by combining several measures of watershed features and adjusting the value of the subindex
so that it ranged from zero to one. Although three measures were used for each subindex in this case
study, the method is flexible and the number of measures could be increased or decreased.
Landscape Subindex
The main assumptions used in developing the landscape subindex are that, in the absence of
anthropogenic activity, subsheds with similar landscape features behave similarly hydrologically (as
indicated by streamflow hydrographs) and have similar water quality at similar stream discharge (as
indicated by constituents that show a strong correlation with stream discharge). For the case study
watershed, which is underlain by relatively unreactive crystalline rocks, it was also assumed that geology
is not the dominant control on stream chemistry. This assumption will not be true in some watersheds;
however, the procedure for developing indices is flexible and could be modified to include geology.
The first step was to simplify digital landscape data. Using a GIS, land cover was generalized into
two categories, forested and open; topographic slope was divided into two slope categories, flatter and
steeper; and elevation data were divided into four equal-sized quarters (elevation quartiles). The number
of categories is not fixed and could be modified in other watersheds. The slope categories indicated the
relative steepness of land area through the watershed. Another measure of slope, average stream-channel
slope, was considered, but had insufficient power to discern physiographic differences between subsheds.
The procedure for defining elevation categories was chosen because it is a statistically robust procedure
that is not overly influenced by extreme elevation values.
Simple GIS functions were used to calculate the percentage of area within each land cover,
topographic slope, and elevation category for each of the six subsheds. These percentages were entered in
tables, such as those shown below for subshed 010:
III-18
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Slope Forest Cover (% area) Elevation Percent
Forested Open Quartile Area
Flatter 10 8
Steeper 66 16
s = 27.54
x-Smin) = 0.63
The relationship between topographic slope and land cover was not as useful for characterizing
subsheds in this watershed as it might be in other watersheds. The slope information, however, was useful
for producing a measure of landscape complexity based on the assumption that complexity increases with
the proportion of steeper to flatter areas. First, the standard deviation of entries in each of the six tables
containing the slope variable was calculated. These values were normalized onto a scale of zero to one
using the following operation: (s-smin)/(simx-smin), where s is the standard deviation of all entries in a table
for a given subshed, Smi,, is the lowest s for the set of six tables, and s^* is the highest s for the same set.
To meet the complexity assumption, a slope value for dividing flatter from steeper areas was selected so
that the proportion of flatter to steeper areas was less than one for each subshed. This is necessary because
the procedure measures variability in regard to magnitude, but not position, of entries in a table.
For the elevation table, landscape complexity was assumed to increase as the proportion of area
within each elevation quartile becomes more similar. To meet this assumption, the normalized elevation
measure for each subshed was calculated using the compliment of the equation given above, (smax-s)/(smax-
Smin), so that higher values of the measure represent an increase, rather than a decrease, in landscape
complexity.
In addition to normalized measures of topographic slope and elevation, the landscape subindex also
included a normalized measure of subshed size. This measure was simply derived by rescaling the
measurement units used to describe subshed area (square miles), so that they fell within a range of zero to
one. The three normalized measures were combined using equal weighting to yield a landscape subindex
for each subshed. Results were found to be insensitive to unequal weights and equal weighting was
deemed reasonable.
Stress Subindex
Three stress factors, population stress, point-source stress, and nonpoint-sources stress were
developed to assess the influence of human activities within the Contoocook River watershed. Population
stress has previously been used by the U.S. Geological Survey (USGS) (e.g., Meade, 1995) and is defined
as the ratio of human population upstream from a stream location to mean annual streamflow at that
location. The inverse of this stress factor can be thought of as 'per capita annual streamflow' , or the
amount of water in the stream that is theoretically available on an annual basis to each person in a
subshed. This stress factor gives only a rough indication of potential impact of population on water
quality because, although water quality tends to decline in heavily populated areas, it may also decline in
areas of agricultural, silvicultural, industrial, or mining development that have sparse human populations.
Mean annual streamflow of each subshed of the Contoocook River watershed was estimated by
transferring streamflow information from USGS gaging stations to the outlet of each subshed using a
drainage-area-ratio method described in Hirsch (1979). Population in each subshed was estimated from
1990 U.S. Census Bureau data. Values of population stress were normalized by rescaling values to range
III-19
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from zero to one. Not unexpectedly, the three subsheds containing the main stem Contoocook River had
the highest population stress.
Point-source stress is the ratio of mean annual discharge from industrial and municipal point sources
in a subshed to the mean annual streamflow for that subshed. This measures the proportion of annual
streamflow in a subshed that potentially contains pollutants from reported point sources. Mean annual
streamflow for each subshed was previously calculated, and mean annual discharges from facilities with
National Pollution Discharge Elimination System (NPDES) permits were obtained from the EPA's Permit
Compliance System (PCS) database. Normalized values for point-source stress were derived by rescaling
values to range from zero to one. Like population stress, the highest values of point-source stress were for
subsheds containing the main stem Contoocook. The lowest point-stress value, however, corresponded to
the subshed containing the lowermost reaches of the main stem Contoocook.
Nonpoint-source stress is simply the percentage of subshed area that has agricultural or silvicultural
land use or is heavily settled, land characteristics that are commonly identified as significant contributors
to nonpoint-source pollution. Definition of a 'settled' area will vary depending on the overall population
density of a region. For the Contoocook River watershed, a 'settled' area was defined as one that has a
population density of 1000 or more people per square mile. Areas of silvicultural development were not
available for the Contoocook River watershed so could not be included in calculations of nonpoint-source
stress.
Point-source and nonpoint-source stress were combined using equal weighting to yield a stress index
for each subshed. Population stress was excluded from the calculation because the nonpoint-source stress
measure included information about population distribution. Calculation of population stress was still
worthwhile, however, as an indicator of the potential impact of population on water quality. In addition,
the GIS datalayer based on U.S. Census Bureau data was the best source of information for estimating
extent of 'settled' areas for calculation of nonpoint-source stress.
Environmental Index
Landscape and stress subindices were combined to produce an overall environmental index for each
subshed. As sensitivity to weights was relatively low and as there was no compelling reason to weight
subindices unequally, equal weighting was used.
Uses of the environmental indices are described below. It should be noted that the separate
components, the landscape subindices and the stress subindices, also are useful. These components
provide indicators of expected natural water-quality variability (landscape subindices) and of
vulnerability to surface-water pollution (stress subindices) throughout a watershed. As described below,
stress subindices were particularly useful for revealing the presence of spatial bias in an existing sampling
network in the Contoocook River watershed. For some watersheds, it may only be necessary to produce
landscape subindices or stress subindices. Use of the landscape subindex alone is equivalent to assigning
little weight to anthropogenic activity. Such an approach might be appropriate for design of sampling in a
watershed that is largely comprised of wilderness areas. Similarly, consideration of the stress subindex
alone gives little weight to the impact of the natural landscape on water quality. This approach may be
justified for watersheds that are largely comprised of urbanized, industrialized, silvicultural, mining, or
agricultural areas.
111-20
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Application of Environmental Indices
Identification of Spatial Bias
The environmental indices were used to assess spatial design bias in a sampling network used by the
New Hampshire Department of Environmental Services (New Hampshire DES) (Figure 1). The spatial
pattern of stations has evolved over a number of years, with many stations being sampled only
occasionally and for various objectives. In general, the focus of sampling has been along the main stem
Contoocook in subshed 010 and near locations of NPDES facilities. Environmental-index values were
used to indicate spatial-design bias that might occur if water-quality data from all stations shown in
Figure 1 were used to obtain a basinwide measure of water quality. Table 2 compares the existing
distribution of stations among subsheds to a distribution that results from assigning stations to subsheds in
proportion to subshed weights (i.e., index values).
Even though subshed 010 has the highest index value and, therefore, a high sampling priority, there is
clearly a higher proportion of stations in this subshed than are needed to obtain a basinwide water-quality
measure. The number of stations is, in fact, close (13 versus 12 stations) to the number that results from a
proportional allocation using point-source stress alone.
Sampling Design and Measurement of Basinwide Water-Quality
Sampling-design decisions include determining where to locate sampling stations and where to
increase sampling frequency. As shown in the previous section, index values can be used as subshed
weighting factors to prioritize subsheds for sampling. The first sampling station is assigned to the subshed
with the highest index (i.e., weight); additional stations are assigned to subsheds in proportion to subshed
weights. As well as indicating where to locate stations as they become available, the index also indicates
where the most benefit can be gained by increasing sampling frequency. For example, sampling
frequency commonly is uniform throughout the sampling network and is performed throughout the year,
at least on a quarterly basis. If sampling frequency can be done more often, say monthly, at some stations
in the network, these stations can be selected according to subshed weight.
An alternative to using index values to assign weights to subsheds is to use the index values to assign
weights to water-quality data from each subshed. In this case, data are aggregated by multiplying water-
quality results representative of each subshed by the weight for the associated subshed and summing over
all subsheds. This takes the following mathematical form:
X=
K
where,
X is a basinwide measure of water-quality for variable x,
Xi is the mean annual value of the water-quality variable x in subshed i,
w. is the weight assigned to a water-quality variable in subshed i, and
N
K = 2^1 wi is me summation of weights for subsheds 1 to N, the total number of subsheds in the
1=1
watershed.
111-21
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Index values also can be used to produce consistent basinwide measures of water quality for a number
of watersheds in a region. To produce these measures, a watershed scale that includes all watersheds of
interest must be identified. These watersheds can then be viewed as subsheds of a regional watershed. For
example, the Contoocook River watershed is one of 17 eight-digit HUC watersheds that comprise the
Merrimack River watershed. By applying methods described herein, consistent measures of basinwide
water quality could be produced for all subsheds, except those subject to tidal influence.
Location of Stations In Subsheds
The emphasis of the bias-assessment method described above is on determining the relative influence
of each subshed area on basinwide water quality rather than on specifying precise sampling locations.
Precise locations can be chosen when the target population is comprised of stream reaches or miles, but
cannot be chosen when the target population is comprised of subshed areas. [Random and nonrandom
approaches to sampling design - targeted/judgmental, distributed (systematic), probability, and stratified -
are described in Simcox (1998) and NCASI, Inc. (in preparation)].
Two approaches to assessing the adequacy of existing station locations or to selecting sampling
locations are suggested. A 'professional judgment' can be made whether existing locations are likely to
provide adequate sampling intensity in a subshed (as indicated by its environmental index) and to produce
water samples representative of that subshed. Alternatively, the bias-assessment method can be used to
narrow the choice of sampling location by defining subsheds at a higher spatial resolution (e.g., for the
Contoocook River watershed, define subsheds at the 14-digit HUC scale rather than at the 11-digit HUC
scale) and producing indices for these smaller subsheds. The new indices can be used in two ways. The
number of subsheds throughout the original area of interest is now likely to be large enough so that
statistical procedures can be applied. In this case, subsheds with similar index values can be grouped.
Subsheds in each group can be selected randomly for sampling and results extrapolated to unsampled
subsheds. Alternatively, the new indices can be used to differentially weight subsheds within the larger
subsheds (e.g., for the Contoocook River watershed, the 11-digit HUC subsheds). Weights can then be
used as previously described to prioritize subsheds for sampling and to derive overall water-quality
measures for each larger subshed.
Conclusions
A review of state water-quality assessment confirms that design bias associated with the location of
sampling is a significant cause for concern about the accuracy of basinwide water-quality assessments.
This bias is commonly caused by a failure to identify sampling objectives and to link these to appropriate
sampling-design methods.
The environmental indices developed in this study are useful for identifying bias in the design of
stream-sampling networks in watersheds and for providing consistent basinwide measures of water
quality. A common problem in developing environmental indices is aggregating subindices measured in
different units. This problem is overcome by normalizing the values of subindices onto a common zero-
to-one scale. Although these values are only valid for comparing the subsheds that occur within a
specified larger watershed, this larger watershed can be defined at a wide range of spatial scales.
Development and use of the environmental indices were successfully demonstrated in the
Contoocook River watershed in New Hampshire. Index values indicated the relative sampling intensity
appropriate for each subshed, and revealed that an existing sampling network is likely to over-represent
point sources of pollution if used to assess basinwide water quality. Spatial bias was reduced by using
index values as weights and reapportioning stations throughout the watershed. When applied to chemical
and physical sampling data, these weights are effective for producing basinwide measures of water
quality.
111-22
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References Cited
Haan, C.T. 1977. Statistical Methods in Hydrology. The Iowa State University Press.
Helsel, D. R. 1995. Design of a Relational Water Quality Assessment Program in Proceedings of the 1995
American Statistical Association Annual Meeting, Orlando, Florida.
Helsel, D.R. and Hirsch, R.M. 1995. Statistical Methods in Water Resources. Elsevier Science Publishers.
Hirsch, R.M. 1979. An Evaluation of Some Record Reconstruction Techniques. Water Resources-
Research, 15(6):1781-1790.
Meade, R.H. 1995. Contaminants in the Mississippi River, 1987-92. U.S. Geological Survey Circular
1133.
NCASI, Inc. In preparation. Design of Stream Sampling Networks and a GIS Method for Assessing
Spatial Bias. National Council of the Paper Industry for Air and Stream Improvement, Inc.,
Department of Civil Engineering, Tufts University, Medford, MA.
Simcox, A. C. 1998. Design of Stream Sampling Networks and a GIS Method for Assessing Spatial Bias.
Tufts University, Medford, MA. Ph.D. Thesis.
U.S. Environmental Protection Agency. 1997. Guidelines for Preparation of the Five-Year State Water
Quality Assessments (305(b) Reports) and Annual Electronic Updates. Office of Water, Washington,
D.C. EPA 841-S-97-002.
Ward, R.C. et al. 1990. Design of Water Quality Monitoring Systems. Van Nostrand Reinhold.
111-23
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Figure 1. Location of subsheds and sampling stations in the Contoocook River watershed
(New Hampshire DES, 1997).
111-24
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Measure (for each subshed)
Evaluation s*
of Landscape b. Elevation Standard Deviation of Matrix
Features • —
Subshed Size Area —
Population Population/ _
Evaluation of
Anthropogenic ^ Point Sources Point-Source Discharge/
Features \ Mean Annual Discharge
Nonpoint % Area Agriculture,
Sources Silviculture, Settled
Evaluation of ^^r Landscape Subindex ~
Landscape and
Anthropogenic ^^ Stress Subindex
Features
Normalized Measure
1
1
. 1
1
. b. 1
i
0
j
0
1
1 | 0
Landscape subindex
^ (1
^ ji
1
1
1
. 1
1 1
--I \
0
--I
0
1
~* \ \ "o
Stress Subindex
1 —
1
1
1
0
1
— ^ l ,
1 | 0
Environmental Index
Figure 2. Procedure for developing environmental indices.
111-25
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Table 1. Types of Variance and Bias in Water-Quality Assessments
Random Variance
Random Sampling
Variance
Deviations from
true value of a pop.
unit caused by random
selection process
(e.g., cone, of DO in 2
samples from same
stream reach taken at
same time differ due
to natural variation
between samples)
(Precision)
Random Measure-
ment Variance
Deviations from
true value of a
pop. unit caused
by uncertainties
inherent in making
measurements
(e.g., cone, of DO in 2
samples from same
stream reach taken at
same time differ due
to imperfection of
analytical device)
B
Design Bias*
Consistent over-
or under-estim-
ation of true values
in pop. units due to
sample design
(e.g., mean pH of
stream reach
estimated from
measurements
taken just below
industrial
discharge)
las
Analytical
Bias
Consistent over-
or under-
estimation of true
values in pop. units
due to error in field
or lab devices or
protocols
(e.g., mercury
concentration 2
ppm too high for
each pop. unit due
to miscalibration
of field device)
Statistical Bias
Discrepancy
between the
expected value of
an estimator and
the pop. parameter
being estimated
(e.g., average
turbidity value in
stream reach over-
estimated by
nonrobust
estimator (e.g.,
arithmetic mean)
*Both spatial and temporal bias
Table 2. Distribution of Sampling Stations in the Contoocook River Watershed
(New Hampshire DES, 1997)
Subshed
010
020
030
040
050
060
Total
Index
0.75
0.32
0.62
0.49
0.47
0.25
No. Stations
(Figure 1)
13
1
4
3
2
1
24
Proportion
of total
0.54
0.04
0.17
0.13
0.08
0.04
1.00
No. Stations
(based on index)
6
3
5
4
4
2
24
Proportion
of total
0.25
0.13
0.21
0.17
0.17
0.08
1.00
111-26
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Designing a Comprehensive, Integrated Water Resources
Monitoring Program for Florida
Kevin Summers
U.S. Environmental Protection Agency
Gulf Ecology Division, Gulf Breeze, FL
Rick Copeland, Tom Singleton
Florida Department of Environmental Protection, Tallahassee, FL
Sam Upchurch
Environmental Resource Management, Inc., Tampa, FL
Anthony Janicki
PBS&J Engineering, Tampa, FL
Abstract
In late 1996, Florida DEP initiated an effort to design a multi-tiered monitoring and assessment
program that integrated the monitoring of multiple natural resources (e.g., streams, groundwater, lakes,
estuaries) with the execution of multiple programs (e.g., 305(b) reporting, TMDL establishment,
ecosystem management, permitting, bio-criteria). The program is being designed in a manner that
maximally provides information to other important state needs such as basin-wide assessments,
development of TMDLs, and the provision of information for permitting. The design, at present, consists
of three monitoring tiers focused on three spatial levels of data collection and resource assessment. Tier 1
(Status and Trends) will establish the condition of all aquatic resources in the state by broad geographic
divisions (i.e., USGS Accounting Units, Water Management Districts, and Florida DEP Districts) using a
probabilistic design to report 305(b) results. Tier 2 (Basin Assessments) will examine individual basins
to establish environmental condition and to set TMDLs by water body using a variety of statistical
designs and incorporating significant levels of "found" data. Tier 3 focuses on local conditions within a
single water body to provide the information necessary to examine issues associated with re-permitting.
All tiers will focus on the utilization of biological and ecological data compared to the previous reliance
on physical and chemical variables. Tier 1 is scheduled to be initiated in 1999. The process by which a
multi-faceted, multi-objective comprehensive monitoring plan for Florida waters is being developed and
implemented is described.
Introduction
In 1996, the Florida Department of Environmental Protection initiated an effort to re-design their
environmental monitoring programs in order to create a multi-resource comprehensive, integrated
monitoring program (IWRM—Integrated Water Resources Monitoring) that would fulfill many of the
department's needs. These needs included 305(b) reporting, TMDL establishment, ecosystem
management needs, permitting, and the development and testing of biocriteria. The design of a
comprehensive, integrated approach has resulted in the adoption of a three-tiered monitoring framework
that has an integrated sampling design and provides information for many of the department's issues
outlined above.
m-27
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Monitoring Framework
The three tiered monitoring framework (Figure 1) focuses on three spatial levels of data collection
and resource assessment. Tier 1 (Status and Trends) establishes the condition of all aquatic resources in
the state by broad geographic divisions (i.e., USGS Hydrologic Code Units, Water Management
Districts, and Florida DEP Districts). This scale is important for the assessment of the condition of the
State's resources (e..g., to address 305(b) issues). Tier 2 (Basin Assessments) examines individual basins
to establish their specific environmental condition and to set basin-level TMDLs (Total Maximum Daily
Loads) by water body using a variety of statistical designs and incorporating significant levels of "found"
data (historical information collected under a variety of designs and approaches). Tier 3 focuses on local
conditions within a single water to provide the information necessary to examine issues associated with
permitting.
To aid in the transition from earlier monitoring programs in FDEP to the proposed approach, a four-
level of monitoring has been adopted that continues the collection of environmental data from a subset of
historic locations at relatively short time scales (i.e., monthly). This collection level, termed the
Temporal Variation Network, will ease the early interpretations (i.e., first 6-10 years) of the Status and
Trends Tier particularly with regard to annual trend and seasonal variation.
The design of the Status and Trends Tier of IWRM will, for the first time, permit FDEP to answer
many water-resource related questions with an unbiased, rigorous data set. The collection of information
has been designed to place statistically sound confidence limits on the survey results. This design is, in
part, dictated by the questions it must address.
Formulation of the questions to be addressed by IWRM Network and the Status and Trends Tier was
initiated at a meeting of over 50 representatives from throughout FDEP in November 1996. A list of over
200 issues and desired outcomes of a comprehensive, state-wide monitoring plan was formulated by this
group. These assessment questions ranged from site-specific or issue-based questions to broad questions
related to water quality of the state as a whole. This list of assessment question was condensed into a set
of 28 topics, from which the final assessment questions were drawn. These questions comprise the
"roadmap" by which the success of the Status and Trends Tier will be determined.
The Status and Trends Tier monitoring design is structured to address these questions at two
different scales: (1) the state as a whole and (2) large drainage basins, or drainage basin complexes
within the state. The state has been subdivided into 20 of these large drainage basins (Figure 2) which are
termed reporting units. The questions that the Status and Trends Tier is designed to address, therefore,
relate to the status of water quality on a regional and state-wide basis. They do not address smaller
drainage basins, ecosystem management areas, counties, or localities. These smaller areas are addressed
by other monitoring tiers within the IWRM Network.
Addressing the assessment questions is a three-step process. First, monitoring must be accomplished
following standardized protocols for data acquisition. Second, the larger "parent" population (e.g., State
of Florida, Water Management District, etc.) From which the sample data were collected must be
characterized in order to statistically describe the magnitude and variability of the distributions of
indicators used to evaluate the water resource. Finally, the distributions are used to draw inferences about
the overall status of the resource in question.
Natural Resources To Be Monitored
The Status Network of the IWRM Program is designed to ultimately monitor and report on all waters
of the state of Florida. In order to systematically sample the many different occurrences of water, they
have been subdivided into "resources". Each resource constitutes a readily identifiable occurrence of
ffl-28
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water of interest for the purposes of management. The resources that will be monitored as part of the
Status Network include
• Ground water,
• Lakes,
• Rivers, streams and canals
• Estuaries and near-shore, marine waters, and
• Wetlands.
Scale of a water body has an effect on sampling strategy and, in many cases, management of these
resources. As a result, some of the resources have been subdivided to facilitate sampling and resource
evaluation. The resources and their subdivisions are discussed in the following subsection.
Ground water, as a resource, includes those portions of Florida aquifers that have the potential for
supplying potable water or affecting the quality of currently potable water. Florida has three aquifer
systems (Southeastern Geological Society, 1986), all of which will be sampled. These aquifers include
the Surficial aquifer system (SAS), Intermediate aquifer system (IAS), and Floridan aquifer system
(FAS). The ground-water resource is subdivided into two target populations for the purposes of sampling
and resource characterization. These subdivisions are unconfined aquifer and confined aquifer.
Typically, the SAS, which is unconfined and near the land surface, can be readily affected by human
activities. Because of this vulnerability to contamination, the SAS will be randomly sampled where
present. In areas where the SAS is not present and either the LAS or FAS is unconfined, these aquifers
will be sampled as part of the unconfined-aquifer target population. The confined-aquifer target
population includes confined portions of either the LAS and FAS, depending on which is most heavily
utilized as a source of public-water supply. The rationale for sampling a confined-aquifer target
population is that pumpage for municipal supply typically involves high volumes of water which may
induce lateral or upward movement of saline-water intrusion. Since the effects of salt-water intrusion
take many years to reverse and the resulting degradation of water quality may result in significant and
costly changes in water-supply systems, the Department feels that the confined LAS and FAS should be
monitored as part of the Status Network. The design for sampling groundwater resources is described
below. Networks to be used include the Ambient Program's Background Network and wells located at
facilities that have been permitted by the Department. The Background Network was created to monitor
background ground-water quality throughout the state, so wells were not placed in areas known, or
strongly suspected, to have contaminated ground water. The areas that were excluded included coastal
areas where salt-water intrusion was suspected and many heavily industrialized areas. Agricultural,
residential, and local, isolated industrialized areas were not avoided.
Lakes have also been subdivided into two groups: (1) small lakes, which are 10 hectares or less in
size, and (2) large lakes, which are over 10 hectares in area. This differentiation on the basis of area is
intended to accommodate differing sampling strategies and methods. Small lakes will be randomly
sampled from a list frame, while large lakes will be randomly selected for sampling from a grid. The
details of this sampling plan are given in Section IV.
Only perennial rivers, streams, and canals will be sampled. These have been subdivided into two
categories based on stream order (Horton, 1945). Wadeable streams are perennial streams of orders 1-4.
Non-wadeable streams and canals include higher order streams (order >4) that are expected to require
different sampling strategies than the smaller streams. Canals predominate in many areas of the state
where former streams and rivers have been modified to enhance drainage. Because they require similar
sampling strategies and represent master drainage systems, they are included in the non-wadeable stream
category.
ffl-29
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Florida's estuaries and nearshore marine environments will be sampled as part of the Status Network.
Estuaries are defined as coastal water bodies that are bounded upstream by head of tide and downstream
by articulation with the Gulf of Mexico or the Atlantic Ocean. Nearshore waters include all coastal
waters surrounding the shoreline of Florida to a point three miles from the shoreline. Sampling of
estuarine and nearshore waters will be timed to coincide with the Status and Trends Tier's rotation
through reporting units (to the extent possible) so that the terrestrial and marine/estuarine sampling will
be synchronous. The specific aspects of the estuarine sampling design will not be described in this
manuscript.
There is a great need in Florida to include wetlands in the IWRM Program. The health of wetlands,
including areas, hydrologic regimes, water quality, and biological integrity are changing from year to
year. While physical and chemical criteria for wetlands exist, the Department has not adopted methods
for biological assessment of wetlands. Resources do not exist to develop these criteria and include
wetlands in the monitoring program at this time. Consequently, it is premature to include wetlands in the
Status Network. Recognizing the need for this type of monitoring, however, the IWRM has included
wetlands as a resource to be monitored.
Indicators To Be Measured in the Ambient Tier
The candidate lists of indicators to be measured as part of the IWRM Ambient Tier are presented
below by water resource in Tables 1-3. These lists are considered candidate since final choice of
indicators will depend in great part not only on applicability and utility of the data generated but also the
feasibility. It should be recognized that meeting the published holding times ortho-phosphate, total
coliform, and fecal coliform has routinely been a problem in past monitoring efforts and that such
problems will likely continue into Tier I. However, a number of reviewers of the IWRM design have
supported including these indicators since there is some utility in the information provided by these
indicators, despite the short-coming of not meeting the holding times. The habitat assessments to be
conducted will be based on those protocols accepted as part of the FDEP BioRecon procedures.
The Temporal Variability (TV) Network will include wadable and non-wadable streams as well as
small and large lakes. The indicator lists for these resources will be identical to those shown above for
the Ambient Tier, with two exceptions. First, some indicators such as the habitat assessment will not be
repeated monthly, but likely will be limited to two time periods within the year. Secondly, discharge
and/or stage data from those sites where discharge and/or stage is being monitored by USGS or the Water
Management Districts will also be included.
Design of the Status and Trends Tier
The assessment question workshop resulted in three primary approaches: (1) Site-Specific, (2) Basin,
and (3) State. The site-specific approach requires a delineation of the hypotheses to be tested by the
monitoring activity (e.g., a comparison of selected areas receiving anthropogenic impacts to reference or
unaffected sites). The basin and state approaches, if applicable to all waters, require the probabilistic
approach. These approaches require that the boundaries of the monitored population (e.g., waters of the
State, waters of a District, waters of a reporting area) be determined, acceptable uncertainty criteria are
ascertained, and the appropriate design and reporting strata be determined.
The spatial and temporal aspects of a monitoring design are derived from the assessment questions
and the variation associated with the selected indicators. The state-level assessment questions (and some
basin-level questions) tended to call for monitoring results that apply to "all" Florida waters. A
probabilistic design is required to meet this need although thousands of probabilistic options are
available. The site-specific group's questions called for results that would differentiate among selected
m-so
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sites or test working hypotheses. As a result, a set of judgmental sites would be required to address each
hypothesis. Because both forms of questions were posed then a multi-tier design should be incorporated
to include aspects of these approaches.
Uncertainty criteria must be defined and agreed upon to select a monitoring design that has the
appropriate power to address the assessment questions. For example, one assessment criteria might be
that all status or "health" assessments have 95% confidence intervals of ±10% such that an assessment of
estuarine sediments with contaminant concentrations greater than criteria A would be X%±10% (e.g.,
35±10% of all Florida estuarine sediments). This type of uncertainty pertains to probabilistic statements.
Site-specific assessments also would require uncertainty criteria at primarily the level of discrimination
often referred to as a p-level. For example, the uncertainty level for discrimination between affected and
reference sites might be a 95% chance of discerning a difference if a difference exists between the sites.
Appropriate design strata can include many approaches. Basically, a rule of thumb is that if you wish
to answer an assessment question with regard to a strata with the desired level of certainty then that strata
should be designed into the overall monitoring plan. However, if the strata simply represents a
geographic unit that you want information on (e.g., by habitat unit, use type, etc.) but do not care whether
the design certainty level is met, then the strata should not be incorporated into the monitoring design.
In general, the use of strata within a sampling design enhances the power to detect differences
because it optimizes the design based on the natural variability characteristics of what is being measured.
However, in broad scale monitoring designs where many indicators are being utilized, what is optimal for
one indicator is often not optimal for another. In addition, to design a monitoring plan based on strata that
represents the entire resource (i.e., "all" Alabama coastal waters) requires that the physical distribution of
the selected strata be known and the variability of the indicators in question be known. Often this is not
the case. While much information is known concerning potential strata in Alabama waters, rarely can a
known distribution be determined for all strata variables without preliminary sampling.
Several options were discussed by the working group with regard to stratification for both state-wide
and Water Management District-wide monitoring. Final strata included: (l)Base geography (i.e., the
State of Florida), (2) Water Management District (WMD) boundaries, and (3) Four reporting area within
each WMD comprised of a single or multiple hydrologic units (HUCs).
These strata represent reasonable approaches to developing a spatial sampling design. The key to
selecting the appropriate strata is a determination of the needs of our monitoring program, the availability
of data on the distribution of the strata, the availability of data on the spatial variability of indicators of
interest within the strata, and the ramifications of multiple strata on sampling size (i.e., reduces sampling
size for site-specific monitoring and increases sampling size for ecosystem-wide sampling). Because the
selected strata represent a graduated subdivision of the base stratum (State of Florida), the design needs
only to be determined for the reporting units of the WMDs. Figure 2 shows the reporting units for each
WMD.
The actual placement of sites and the total number of sites is also based on the assessment questions.
Since many of these questions require assessments for "all" Alabama coastal waters then an element of
the sampling design must be extrapolable and thus probabilistic in nature. This does not necessarily mean
that the sites are randomly placed, although that type of placement is one possibility. Probabilistic simply
infers that the sites are representative and not biased. If the sites can be placed judgmentally (i.e., based
on experience and knowledge) so that they are representative of selected strata (e.g., habitats, use zones),
then the requirement for a probabilistic nature for the design will be met. The specific protocol for the
selection of sample sites for each resource type (e.g., small lakes, wadeable streams, etc.) can be
somewhat different. Specific protocols by resource type are listed below under the heading, Sample
Selection Protocols.
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Designing the temporal aspects of the sampling plan also relate directly to the initial set of
assessment questions. If the desire of the planning group is to fully characterize short-term status and
trends, then monthly sampling are required. For these types of questions, the Temporal Variation
Network (TV Network) was created from a subset of SWAMP sites. If the question lends itself to longer-
term assessment of status and trends, then seasonal or annual samples are required. The issue with the
choice of a temporal framework is not that the values of the indicators change all the time, but rather,
what is the time scale of interest. If the desire of the planning groups is to "understand" coastal
phenomena then shorter-time scale sampling is appropriate that includes monthly and seasonal variation.
However, if the desire of the planning group is to ascertain changes in overall status and long-term
trends, then annual or semi-annual time scales tend to be more useful.
Many of the proposed indicators exhibit large intra-annual variability (i.e., they are seasonal)(Oviatt
and Nixon 1973, Jefferies and Terceiro 1985, Grassle et al. 1985, Holland et al. 1987). Generally,
monitoring programs do not have the monetary resources to characterize this variability or to assess
status in all seasons for "all" resources (i.e., all Alabama coastal waters). Therefore, sampling has often
been limited to a confined portion of the year (i.e., an index period) when indicators are expected to show
the greatest response to anthropogenic and climatic stress. The annual sampling sites for the Ambient
Network utilize an index period for 4-8 weeks for sampling for each resource type. For example, most
coastal ecosystems in the Northern hemisphere, mid-summer (July-August) is the period when ecological
responses to pollution exposure are likely to be most severe. During this period, dissolved oxygen
concentrations are most likely to approach stressful, low values (USEPA 1984, Officer et al. 1984, Oviatt
1981). Moreover, the cycling and adverse effects of sediment contaminant exposure are generally
greatest at the low dilution flows and high temperatures that occur in mid-summer (Connell and Miller
1984, Sprague 1985, Mayer et al. 1989). The index periods for each resource type are shown in Table 4.
The assessment questions raised by the majority of the workshop participants suggest the a
generalized probabilistic design for the Ambient Tier with nested designs supplementing the remaining
Basin and Site Tiers. The overall design must include both ecosystem-wide annual elements based on
reporting strata and collected over a five-year period and site-specific monthly elements to characterize
intra-annual or seasonal trends. In addition, the design must permit an estimate of the condition of
Florida's resources each year with an enhanced estimate every five years. The designs for six of the eight
resource types are described below. The two coastal designs will be determined during the next 30 day
period and will be compatible with the designs for the remaining resources.
Groundwater (Confined and Unconfined)
The protocol for site selection for groundwater water for two strata—confined and unconfined—is
based on available information relating to established wells. The 8 step protocol is listed below.
(1) Entire State was subdivided into 4-township blocks representing 12x12 miles.
(2) The land use for the State was overlaid on this grid and the proportion of urban/industrial versus
non-urban/non-industrial use was determined as a percentage of entire grid space (e.g., 15%
urban and 85% non-urban).
(3) A random number between 0 and 1 was selected for each grid square and compared to the
numbering series for land use (in above example 0.00-0.15 was urban and 0.16 to 1.0 was non-
urban). This activity was completed three times for each grid square (e.g., Pass 1 random
selection was .11, Class = Urban; Pass 2 random selection was .58, Class = Non-Urban; Pass 3
random selection was .64, Class = Non-urban).
ffl-32
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(4) The background and VISA well locations were overlaid on the GIS map of land-use (urban vs.
non-urban) and grid pattern and created two map based on groundwater design stratum (i.e.,
confined and unconfined, if unknown that well was treated as both types).
(5) Florida state map was reduced to 15 reporting units as designated by WMDs (i.e., cookie-cut
reporting units from state map).
(6) Each reporting unit is comprised of several grid squares. Depending upon the number of grid
squares per reporting unit, the number of pass to select 30 sampling sites is determined. For
example if 15 grid squares comprise a reporting unit then two passes are completed (i.e., two
wells from each grid square). No reporting areas contained more than 30 grid squares or less than
10 grid squares. If the number of grid squares was not divisible into 30 then the final pass
involved random selection of remaining grids. For example if 11 grid squares existing in a
reporting unit then two passes would select two wells from each grid and 8 of the 11 grid squares
would be selected for a third pass.
(7) For each grid square, the selected land use was noted. If the land use for urban/industrial, then a
random location in the grid square (latitude/longitude) was noted and provided to FLDEP who
will review permit files to located the permitted well closest to the random location. If the non-
urban use is selected, a random well location from those available in the grid square is selected.
(8) Thirty wells for each design stratum are selected in this manner.
As an example, using the Year 1 selection for Northwest Florida WMD (NorthwestC) for unconfined
wells, 5 of the 30 "wells" selected were urban and are represented by a latitude/longitude that will be
coupled with a permitted well, 17 of the 30 wells are known confined aquifer wells, and 8 of the 30 wells
are unknown "confined aquifer" wells. Ten alternate wells have been provided in case a well is
unsampleable, no permitted well exists, or an unknown well really represents the opposite stratum (in this
case is really "unconfined).
Streams (Wadeable and Non-Wadeable)
The protocol for site selection for stream surface waters for two strata—wadeable (Orders 1-4) and
Non-Wadeable (Orders >4)—is based on available DLGs (Digital Line Graphs) for Streams provided by
USGS and from the RiverReach3 File provided by EPA.
(1) All streams were identified for the State of Florida and segments were identified with regard to
stream order. All ephemeral streams were deleted from base population.
(2) All stream segment were subdivided into meter-long segments with associated latitude-longitude
coordinates for the segment.
(3) Segments associated with each reporting unit within the Water Management Districts were
determined and a list frame for each stratum within each reporting unit was developed.
(4) Thirty random samples for each stratum were selected and the appropriate segments were located
on Reporting Unit maps.
Twenty additional random samples were selected for each stratum to be used for potentially
unsampleable segments (as replacements).
IH-33
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Small Lakes (<10 hectares)
The protocol for site selection for small lake surface waters—<10 hectares in surface area—is based
on available DLGs (Digital Line Graphs) for Surface Waters provided by USGS and from the
RiverReachS File provided by EPA.
(1) All lakes <10 hectares in surface area were identified for the State of Florida.
(2) All small lakes were associated latitude-longitude coordinates for the epicenter of the lake.
(3) Small lakes associated with each reporting unit within the Water Management Districts were
determined and a list frame for each reporting unit was developed.
(4) Thirty random samples (30 lakes) were selected for each Reporting Unit and the appropriate
small lakes were located on Reporting Unit maps.
Twenty additional random samples (small lakes) were selected for each Reporting Unit to be used for
potentially unsampleable lakes (as replacements).
Lake Lakes (>10 hectares)
The protocol for site selection for large lake surface waters—>10 hectares in surface area—is based
on available DLGs (Digital Line Graphs) for Surface Waters provided by USGS and from the
RiverReach3 File provided by EPA.
(1) All lakes >10 hectares in surface area were identified for the State of Florida.
(2) Large lakes associated with each reporting unit within the Water Management Districts were
determined and a triangular grid resulting in hexagonal spatial units was overlaid on the reporting
area such that approximately 30 hexagon contained large lakes or portions of large lakes.
(3) A random location was identified in each hexagon based upon an angular momentum program.
The number of "hits" (intersections of random points and large lake surface area) was
determined. If this intersection resulted in 30 locations for a Reporting Unit, then these 30 sites
become the sampling sites for that reporting Unit. If the intersection is greater than or less than
29-31 sites, the process in repeated with varying distances between the triangular grid centers
until 29-31 sampling sites for each Reporting Unit are determined.
(4) The "thirty" random samples (29-31 latitude-longitude coordinates in large lakes) for each
Reporting Unit were located on Reporting Unit maps.
An additional random sample was selected for hexagonal space and coupled to the original sampling
site for each Reporting Unit to be used for potentially unsampleable locations (as replacements). As this
design is spatially dependent, only the coupled alternative site can be used in the event of a unsampleable
location.
Five-Year Sampling Cycle
The overall state design provides for collection for all Reporting Units comprising the Florida within
a five-year period (1999-2003, 2004-2008, etc.). One Reporting Unit from each Water Management
District is selected randomly to be sampled twice in the five-year period—resulting in five Reporting
Units for each WMD (three units once and one unit twice). One of the "five" Reporting Units from each
Water Management District is selected randomly for each sampling year. The only constraint on the
random selection is that the same Reporting Unit cannot be sampled two years in a row. The distribution
m-34
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of Reporting Units throughout the first five-year cycle in shown in Table 5. The total number of samples
by resource type of each Water Management District over the five year cycle is shown in Table 6.
Inclusion Probabilities
The inclusion probability for each sample site has been determined and equal within each resource
type x reporting unit combination. For example, for wadeable stream segments, the inclusion probability
for each segment is determined as the product of 1/30 x 30/# segments in Reporting Unit. Thus, the
integrity of the inclusion probabilities throughout the sampling in order to combine condition estimates:
(1) for each reporting unit to create an estimate for each WMD, and (2) for each WMD to create an
estimate for the State.
Temporal Variation Network (TV Network)
The ecosystem-wide surveys are supplemented by intensive surveys conducted monthly at 80
locations throughout Florida and represent all resource types. The purpose of these sites is to fully
characterize the hydrograph at the location. This characterization will include all seasonal and short-term
variation that could make interpretation of population trends difficult in early years. This difficulty is
often due to changes in indicators that are related to climatic shifts and variation in seasonal lengths. In
general, these issues are easily ascertained in population-level data after 6-10 years. In the interim, the
temporal variation network will provide additional information on short-term variability for each
resource.
Conclusions
Florida's IWRM Network represents an early adaptation by a state of probabislistic approaches to
collect the information necessary to meeting its 305(b) requirements. In addition, IWRM will use its
sampling networks to address other important issues to Florida including basin assessments, the
determination of total maximum daily loads, the allocation of those loads, and long-term permitting
issues. IWRM is presently expanding its resource base to include estuarine resources and expects that
this monitoring activity will initiate through the Florida Marine Research Institute in 1999. In 1998, the
Gulf of Mexico program initiated an effort to help the Gulf States adopt a core, comprehensive,
integrated coastal monitoring approach that includes probabilistic sampling as one of its elements. Early
discussions by this group have praised Florida's efforts and described their program as a framework upon
which a Gulf-wide consistent surface-water monitoring program could be based.
In 1996, EPA's 305(b) Working Group described probabilistic sampling approaches as an acceptable
approach for collecting environmental condition data and reporting 305(b) results. At present, four states
are adapting their overall water resources monitoring programs to utilize probabilistic surveys as an
element of their overall programs.
Literature Cited
Connell, D.W. and G.J. Miller. 1984. Chemistry and Ecotoxicology of Pollution. New York: John Wiley
and Sons.
Grassle, J.F., J.P. Grassle, L.S. Brown Leger, R.F. Petrecca and N.J. Copely. 1985. Subtidal
macrobenthos of Narragansett Bay. Field and mesocosm studies of the effects of eutrophication and
organic input on benthic populations, pp. 421-434. In: J.S. Gray and M.E. Christiansen (eds.). Marine
HI-35
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Biology of Polar Regions and Effects of Stress on Marine Organisms. New York: John Wiley and
Sons.
Holland, A.F., A.T. Shaughnessy, and M.H. Hiegel. 1987. Long-term variation in mesohaline
Chesapeake Bay macrobenthos: Spatial and temporal patterns. Estuaries 10:227-245.
Horton, R.E. 1945, Erosional development of streams and their drainage basins: Hydrophysical approach
to quantitative morphology, Bulletin, Geological Society of America 56:275-370
Jefferies, H.P. and M. Terceiro. 1985. Cycle of changing abundances in the fishes of Narragansett Bay
area. Mar. Ecol. Prog. Ser. 25: 239-244.
Mayer, F.L., L.L. Marking, L.E. Pedigo and J.A. Brecken. 1989. Physiochemical factors affecting
toxicity: pH, salinity, and temperature, Part 1. Literature review. U.S. Environmental Protection
Agency, Office of Research and Development, Gulf Breeze Environmental Research Laboratory.
Officer, C.B., R.B. Biggs, J.L. Taft, L.E. Cronin, M.A. Tyler, and W.R. Boynton. 1984. Chesapeake Bay
anoxia: Origin, development, and significance. Science 223: 22-27'.
Oviatt, C.A. 1981. Some aspects of water quality in and pollution sources to the Providence River.
Report for U.S. Environmental Protection Agency, Region I, September 1979-September 1980.
Oviatt, C.A. and S.W. Nixon. 1973. The demersal fish of Narragansett Bay: An analysis of community
structure, distribution, and abundance. Est. Coast. Mar. Sci. 1: 361-378.
Southeastern Geological Society,. 1986. Ad hoc Committee on Florida Hydrostratigraphic Unit
Definition. Hydrogeological Units of Florida. Florida Geological Survey Special Publication No. 28.
Sprague, J.B. 1985. Factors that modify toxicity. pp. 124-163. In: G.M. Rand and S.R. Petrocelli (eds.).
Fundamentals of Aquatic Toxicology: Methods and Applications. New York: Hemisphere
Publication Corp.
Taylor, J.K. 1978. Importance of inter-calibration in marine analysis. Thai. Jugo. 14:221.
USEPA. 1984. Chesapeake Bay: A Framework for Action. Prepared for the U.S. Congress by the U.S.
Environmental Protection Agency, Region III, Philadelphia, PA.
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Overview of Tiered Monitoring
Temporal Variability
Public
Information
Tier II
Basin
Assessment
Public
Hearings
Tier III
Regulatory
Historical
Data
Figure 1. Conceptual framework for the IWRM program.
m-37
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DEP Sampling Strategy
By Year
Water Management District Region Legend
M B2 B3 'ID4 I i5 LH1 &4Z]2&4:lJ3&5 '<
Water Management DiatrictJYr . 1 | Yr 2 (j^f^^
Northwest i C
Suwannee 1 3
Same Johns C
Southwest i D
South Florida ! 3
A , 3
A 1 D
D ' 3
A 1 C
C A
*' «l*-~-s|
c ID
c ; D
c ! A
A 1 3
D A
93.5-041
Figure 2. Reporting units for the IWRM program.
ffl-38
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Table 1. Indicators for Groundwater Sampling
GROUND WATER
FIELD
Temperature
pH
Specific Conductivity
Dissolved Oxygen
Water Level
Time/Date
Land Use
LABORATORY
Na
K
Ca
Mg
Cl
F
S04
N03+N02
NH3
TKN
Ortho-P
TP
TOC
Color
Turbidity
TSS
Alkalinity
TDS
Total Coliform
Fecal Coliform
Table 2. Indicators for Sampling Streams and Canals
STREAMS— WAD ABLE AND NON-WAD ABLE
FIELD
Temperature
pH
Specific Conductivity (Salinity if tidal)
Dissolved Oxygen
Velocity, Stage
Time/Date
Habitat Assessment from
Biorecon Protocols
LABORATORY
Na
K
Ca
Mg
Cl
F
S04
N03+NO2
NH3
TKN
Ortho-P
TP
TOC
Color
Turbidity
TSS
Alkalinity
TDS
Total Coliform
Fecal Coliform
Chlorophyll
(non-wadable only)
Table 3. Indicators for Sampling Lakes
LAKES— SMALL (<10 ha) AND LARGE (>10 ha)
FIELD
Temperature
PH
Specific Conductivity (Salinity if tidal)
Dissolved Oxygen
Secchi Disc Depth
Time/Date
Lake Level
Habitat Assessment (provisional)
LABORATORY
Na
K
Ca
Mg
Cl
F
S04
N03+NO2
NH3
TKN
Ortho-P
TP
TOC
Color
Turbidity
TSS
Alkalinity
TDS
Total Coliform
Fecal Coliform
Chlorophyll
m-39
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Table 4. Index Periods for the Integrated Water Resources Monitoring Program by Water
Management District and Resource Type
Water Management Small
District Lakes
Northwest
Suvvanee
St. Johns
Southwest
South
A-M
A-M
M-M
A-S
A-S
Large
Lakes
M-J
M-J
J-A
J-S
J-S
Wadeable Sampling Months Non- Confined
Streams Wadeable Streams GW
J-A
M-J
M-A
J-J
J-J
S-O
J-A
A-O
M-J
M-J
F-M
F-M
F-M
F-M
F-M
Unconfined
GW
S-O
S-O
S-O
M-A
M-A
Table 5. Sampling Distribution for the First 5-Year Cycle of the
Integrated Water Resources Monitoring Program
Water Managemen
District
Northwest
Suwanee
St. Johns
Southwest
South
t
1999
A
B
A
A
A
2000
B
A
B
C
D
Sampling Year
2001
C
D
D
D
C
2002
D
C
B
B
A
2003
C
D
C
A
B
Table 6. Number of Samples by Resource Type and Water Management
District Over 5-Year Cycle
Water Management Small
District Lakes
Northwest
Suwanee
St. Johns
Southwest
South
Totals
150
150
150
150
150
750
Large
Lakes
150
150
150
150
150
750
Wadeable Resource Type Non-
Streams Wadeable Streams
150
150
150
150
150
750
150
150
150
150
150
750
Confined
GW
150
150
150
150
150
750
Unconfined
GW
150
150
150
150
150
750
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Coordinating Site-Specific NPDES Monitoring to Achieve Regional
Monitoring in Southern California
Janet Y. Hashimoto, Chief, Monitoring and Assessment Office
U.S. Environmental Protection Agency, Region IX
Stephen B. Weisberg, Executive Director
Southern California Coastal Water Research Project
Abstract
Regional-scale monitoring provides environmental decision-makers with important information for
developing long-term, comprehensive management strategies. However, sustained funding for such
efforts through large government programs has proven difficult. An alternative or supplemental approach
for achieving regional monitoring is to integrate ambient monitoring efforts required under National
Pollutant Discharge Elimination System (NPDES) discharge permits. This approach was successfully
implemented in the Southern California Bight coastal area during the summer of 1994, with the
cooperation of twelve organizations leading to a $4 million program that sampled biological, chemical
and oceanographic parameters at 261 sites from Point Conception to the Mexican border. Key factors
that led to success of the southern California regional monitoring program include: 1) cooperation
between regulator and discharger communities, based on a shared need for regional-scale data; 2) a
commitment from regulators to exchange a portion of routine discharger monitoring requirements, for
participation in regional monitoring, providing cost-neutrality for dischargers who participated; 3) a
participatory management structure in which the program was jointly developed by regulators and
dischargers and 4) the presence of a neutral local, scientific organization to serve as a facilitator.
The survey produced the first integrated snapshot of near coastal marine conditions in the Bight,
allowing managers to assess relative risk among sources and types of environmental stress. It also
produced a series of scientific assessment tools, such as a bioassessment index, generated only when
regional-scale data are available. The most significant long-term benefit of the effort was the improved
dialogue between regulators and dischargers on monitoring methods and results, which led to jointly
agreed upon standardized methods and performance criteria for future monitoring in the region. The
success of the 1994 regional monitoring project has formed the infrastructure for an expanded regional
monitoring effort in summer 1998, which will include more participating organizations, more habitats
sampled and more indicators measured.
Introduction
Monitoring is an essential part of environmental management. It provides decision-makers with
information about which problems require management action and which are the highest priority
problems. It also provides a feedback mechanism for assessing whether management actions have been
effective in reducing or eliminating the problem.
Most monitoring is focussed on site-specific assessments of areas suspected to be of concern, but
increasingly regional monitoring (monitoring of entire watersheds or groups of watersheds or monitoring
of biogeographic regions) is being recognized as an important component of the management information
system (NRC 1990a). Regional monitoring provides information about relative risk from different
pollutant sources (e.g., wastewater discharges, storm water runoff). More importantly, regional-scale
monitoring yields a larger perspective about cumulative impacts to ecosystem health, which is the bigger
picture perspective environmental managers and the general public are seeking.
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While managers have increasingly recognized the value of regional monitoring, funding sources to
implement and sustain it have not been easy to identify. Unlike local monitoring in which the decisions
are immediate and the parties potentially affected are readily identifiable, regional monitoring is used for
longer term strategic planning without a clear "responsible party." Implementing regional efforts through
large, centrally-funded state/federal programs has proven difficult. The U.S. Environmental Protection
Agency (EPA) Environmental Monitoring and Assessment Program (EMAP) attempted to design and
implement a national-level monitoring program to assess the Nation's environmental resources by
building a series of regional monitoring programs throughout the country. This showed promise but
proved too costly and succumbed to changing agency leadership. NOAA Status and Trends maintains a
national program for monitoring marine waters, but the sample density is too low to make estimates of
condition within an individual region, and budget reductions are eroding the program's sampling
frequency. State programs are even more subject to fluctuating funding and political pressures, and local
governments are usually too small to finance or carry out large-scale regional monitoring programs.
An alternative approach for achieving regional monitoring is to integrate the monitoring efforts of
discharge permittees regulated under the National Pollutant Discharge Elimination System (NPDES)
program. Most NPDES discharge permits contain requirements to monitor ambient receiving waters.
Individually, these programs are small and localized, typically directed towards assessing environmental
effects at the end of a discharge pipe. Cumulatively, the monitoring effort can be substantial. In the
southern California near shore marine environment, cost estimates for NPDES-permit mandated
monitoring exceed $10 million annually, three times the value of federal and state government
monitoring efforts combined (NRC 1990b).
Pooling of numerous NPDES programs to achieve a regional monitoring framework presents several
challenges. Most NPDES monitoring is spatially limited and permittees must be flexible and receptive to
spatial expansion of their programs beyond the borders of their outfall influence in order to achieve
regional coverage. The indicators measured, the techniques by which they are measured and the quality
assurance used in generating the data often differ among programs. Data management systems typically
differ among programs, making data-sharing difficult. While these challenges exist, overcoming them has
a potentially large reward.
One location in which these challenges have been overcome and NPDES programs are being used to
form the backbone of a regional monitoring network is the Southern California Bight (SCB). Here we
describe the program being employed in that area and discuss some of the factors that led to its success.
The Southern California Bight Pilot Project
The Southern California Bight Pilot Project (SCBPP) was a cooperative effort among twelve public
agencies (Table 1) in which routine ambient compliance monitoring was redirected towards cooperative
regional monitoring during the summer of 1994. Coordination of local compliance monitoring yielded a
$4 million program that sampled 261 sites, using standardized methods, along the continental shelf
between Point Conception and the United States/Mexico border (Figure 1). Measurements taken included
water quality, sediment contamination, sediment toxicity, benthic infaunal condition, fish assemblages,
fish tissue contaminant concentrations and the presence of marine debris.
Four primary factors led to the success of the SCBPP: 1) cooperation between the regulator and the
discharger communities, 2) a commitment to cost-neutrality for the dischargers who participate, 3) a
participatory management structure and 4) the presence of a neutral local, scientific organization to serve
as a facilitator. First, cooperation of the regulator and discharger communities was forged through their
mutual participation in the Southern California Coastal Water Research Project (SCCWRP) Authority.
SCCWRP is a non-profit, local, marine research agency that is jointly administered by the four largest
NPDES wastewater dischargers in the SCB and the five NPDES regulating agencies that oversee those
111-42
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dischargers. The need for regional monitoring was mutually agreed to in discussions before the
SCCWRP Commission. The SCCWRP Commission, composed of senior management representatives
from each participating agency, became a formal organizational body to receive, review and respond to
the results of the monitoring plans. A major strength of the SCCWRP Commission in this role is that the
recipients of that information have the authority to implement management actions in response to the
project results.
Second, in order for NPDES dischargers to actively participate in the monitoring effort, they needed
to be relieved of some of the burden of conducting their existing monitoring requirements under their
permits. It would have been a significant financial and resource burden for them to conduct both permit-
required ambient monitoring and regional monitoring. Therefore, NPDES dischargers were provided the
opportunity to exchange a portion of their routine NPDES permit-required monitoring effort for
participation in the regional monitoring program. The regulatory agencies' intent in allowing this
flexibility was to allow participation at minimal incremental cost (i.e., cost-neutrality). The dischargers
were asked to sample a larger spatial area and increase the number of stations sampled, which was
accomplished in three ways. The first was to focus all sampling effort in a single season (summer),
allowing reallocation of effort from other seasons towards the program. The second was to reduce
replication and use that effort to expand the number of sites. The third was to eliminate stations from
routine monitoring efforts that have historically provided little information. Each of these changes in the
ambient monitoring programs was publicly noticed, heard at public hearings (with no public objection),
and approved by the regulatory agencies.
While the goal of this exchange was to retain the same level of permit-required effort for
participating dischargers, most participants indicated that there was an increased level of effort for
regional monitoring. The additional effort was an increased investment of staff time to develop
standardized methods and participate in interlaboratory calibration exercises to document that
standardization had been achieved. All participants, however, felt that the increased knowledge and staff
education gained through participation offset the extra cost of the time invested.
The third factor contributing to the project's success was a participatory management structure. Each
organization had the opportunity for influencing project direction through participation in three levels of
project management. The first was the Steering Committee, which was responsible for formulating the
monitoring objectives (the questions to be answered by the study) and the sampling design to achieve
those objectives. The Steering Committee was supported by a series of Technical Committees, each
representing a specialized field of interest (e.g., benthic infauna, fish, sediment toxicity). The members of
the technical committees were bench scientists who conducted the day-to-day work in their specialized
field. They prepared the detailed plans for all the monitoring elements (including methods manuals, QA
plans and database structure), conducted intercalibration exercises and provided the technical input into
the monitoring plans. Both the Steering Committee and the Technical Committees reported to the
SCCWRP Commission, bridging the gap between the scientific, technical staff and management. The
committee members' collective scientific ideas and plans were brought before their management
representatives in the SCCWRP Commission for discussion at the senior management level. This
structure facilitated management decision-making based on strong technical input and recommendations.
The final outcome was a regional monitoring program developed through consensus and input by
participants at all management levels.
Lastly, SCCWRP staff were available to serve as coordinators for the project. Other participating
organizations could not undertake many project activities, such as statistical design, database
management and report preparation, within available resources. SCCWRP's staff provided the technical
expertise and manpower to conduct such tasks, when necessary. Since SCCWRP is jointly administered
by regulators and dischargers, their staff provided non-partisan credibility in project development and
interpretation of results.
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Benefits of the Project
Implementing the SCBPP had four primary benefits, two of which were generic to regional
monitoring and two of which were specific to the cooperative mechanism used to achieve the program.
The first benefit was to provide a much-needed assessment of the overall condition of the SCB. Most
previous monitoring in the SCB was compliance-based and focused on assessing conditions around a
select set of discharge outfalls; cumulatively, this monitoring covered less than 2% of the area in the
Bight. Redirecting that monitoring provided information about regional status (e.g., the percent of area in
the entire SCB that was subject to contaminant influence), spatial patterns of human influence, relative
risk of contaminants among various habitat types and relative risk among types of stressors (e.g., which
chemicals are most prevalent in SCB sediments). These types of assessments are necessary for regional
planning and cannot be obtained when sampling is locally focused.
A second benefit of the SCBPP was a series of technical tools that could only be developed with
regional data sets. For example, the project produced iron-normalization curves for the SCB, allowing
distinction between natural and anthropogenic contributions of metals in sediments (Schiff and Weisberg
1998); developing such curves requires considerable data from sites far from human influence, which
were not available when monitoring was focused on assessing conditions near points of human discharge.
Similarly, the SCBPP data led to the development of the Benthic Response Index (BRI), a first attempt at
marine biocriteria for Southern California (Bergen et al. 1998), which also requires considerable data
from a variety of habitats.
A third benefit, which resulted from participation by the multiple organizations, was the development
of a series of methods manuals containing standardized field, laboratory and data management
approaches that increased comparability of data among participants, even after the SCBPP was
completed. The methods manuals were necessary to overcome differences in collection and processing
techniques among programs and were accompanied by a series of intercalibration exercises to ensure data
comparability. These manuals were produced by gleaning the most effective techniques from the
procedures used by each participant. Intercalibration exercises provided the opportunity for cross-training
and methodological improvement by all participants. In several cases, particularly for chemical
measurements, it was not possible to agree on a uniform set of methods because of differences in
instrumentation among participants. In these cases, performance-based criteria were established, with the
intercalibration exercises serving as a means for ensuring adherence to the criteria.
For data management, the approach to comparability focused on standardized data transfer protocols.
These protocols detailed the information to be submitted with each sample collection or processing
element, the units and allowable values for each parameter and the order in which that information was to
be submitted. Use of standardized data transfer protocols allowed each participating organization to
retain their existing data management system, yet output the data in a manner that allowed sharing among
organizations.
A fourth benefit of the project was an improved dialogue between regulators and dischargers on the
goals of monitoring, the methods used to achieve it and the ways in which monitoring data should be
interpreted. When these kinds of issues are discussed in the context of regulators asking dischargers how
to cooperatively build a regional program, the discussions take on a more positive tone than when
dischargers initiate the discussion with disagreement about methods required in the permit for their
facility. One concern during the dialogue may be agreeing to employ the least precise or least expensive
approach being used amongst the dischargers. However, we had the opposite experience. Many of the
methods required in existing permits were outdated (e.g., requiring PCB aroclors rather than PCB
congeners) and the open discussion that ensued from the cooperative project provided a forum for
dischargers suggesting improved methods. By focusing first on which questions should be answered by a
IH-44
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regional monitoring program, we were able to identify the methods that were appropriate to addressing
those questions.
The Next Steps
Based on the success of the cooperative regional-scale monitoring begun during the SCBPP, the
regulatory agencies are again willing to provide permit monitoring flexibility to more dischargers to
conduct a similar effort in 1998. Whereas the offer for participation in the SCBPP was limited to the
largest dischargers who were members of SCCWRP, the offer for participation in the 1998 Southern
California Bight Regional Monitoring Project (Bight '98) was made to all NPDES permitted dischargers.
Fifty organizations, including all of the participants from the 1994 SCBPP, have agreed to participate
(Table 2).
The inclusion of new participants provides several benefits. The additional resources brought by new
participants expands the number of habitats and indicators that can be sampled. Sampling for Bight '98
will include all of the areas sampled in 1994, plus a new focus on near shore habitats (bays, harbors and
beaches) and offshore islands. More than 400 sites will be sampled for all of the parameters that were
measured in 1994. The program will also add a shoreline microbiology component in which bacterial
indicators will be measured to assess beach quality. While local health agencies are not under the
jurisdiction of NPDES permitting agencies, the opportunity to redirect the large amounts of permit-
required bacterial monitoring into a unified framework led to their participation in the project.
While we originally focused on NPDES permittees, expansion of the program led to participation by
other new types of groups. One is researchers associated with universities or federal laboratories.
Whereas addition of new dischargers to the program added resources for sampling more sites, addition of
researchers added the expertise for adding more types of measurements. Most dischargers have field
sampling and routine analytical capabilities, but do not have research capability. In contrast, most
researchers have the abilities to add new types of measurements, but do not have the resources to sample
at many sites or to gather the more routine chemical and biological parameters which may be an
important part of interpreting the newer measures. Partnership between these groups leads to a more cost-
effective program for everyone.
The second new type of participants is volunteer monitoring organizations, which currently are
focusing primarily on the shoreline microbiology portion of the study. For the volunteers, the
collaborative nature of the program provides the unique opportunity for technical interaction and
integration with their professional counterparts, including participation in the intercalibration exercises
and database development activities. For Bight '98, volunteer efforts will contribute more data, thereby
improving the precision of our estimates. Connection with the volunteer programs will also provide a
ready outlet for the project's reports and a public education vehicle.
Conclusion
The approach of integrating NPDES discharger monitoring into a regional monitoring design, as
implemented in Southern California, will not necessarily work in all areas of the country. Its success
requires a cooperative relationship between regulators and dischargers, which does not exist everywhere.
It also requires a high density of NPDES permitted facilities to achieve an acceptable sample density.
Fortunately, the areas of the country that are most in need of regional monitoring, those where
cumulative impacts are likely to be important management considerations, are generally areas where
numerous permitted facilities are located. Therefore, perhaps a similar approach, as the one we
successfully implemented in the Southern California Bight, could be attempted in other areas and result
in similar successes.
ffl-45
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References
Bergen M., S.B. Weisberg, D. Cadien, A. Dalkey, D. Montagne, R.W. Smith, J.K. Stull, and R.G.
Velarde. 1998. Southern California Bight 1994 Pilot Project: IV. Benthic Infauna. Southern
California Coastal Water Research Project. Westminster, CA.
National Research Council. 1990a. Managing Troubled Waters: The Role of Marine Environmental
Monitoring. National Academy Press. Washington, DC 125p.
National Research Council. 1990b. Monitoring Southern California's Coastal Waters. National Academy
Press. Washington, DC 154p.
Schiff, K. and S.B. Weisberg. 1998. Iron As a Reference Element for Determining Trace Metal
Enrichment in California Coastal Shelf Sediments. Marine Environmental Research (in press).
ffl-46
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Station locations for the Southern California Bight Pilot Project
(coastal shelf at 10-200 m depth).
Inset shows Los Angeles area wasterwater outfalls.
Figure 1. Southern California Bight Pilot Project study area.
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Table 1. Participants in the Southern California Bight Pilot Project 1994
California State Water Resources Control Board
City of Los Angeles, Bureau of Sanitation
City of San Diego, Department of Water Utilities
County Sanitation Districts of Los Angeles County
County Sanitation Districts of Orange County
Los Angeles Regional Water Quality Control Board
San Diego Regional Water Quality Control Board
Santa Ana Regional Water Quality Control Board
Santa Monica Bay Restoration Project
Southern California Coastal Water Research Project
U.S. Environmental Protection Agency, Office of
Research and Development
U.S. Environmental Protection Agency, Region IX
Table 2. Participants in the Southern California Bight Regional Monitoring Program 1998
Algalita Marine Research Foundation
Aliso Water Management Authority
Aquatic Bioassay and Consulting Laboratories
Center for Environmental Cooperation (CEC)
Central Coast Regional Water Quality Control Board
Channel Islands National Marine Sanctuary
Chevron USA Products Company
City of Long Beach
City of Los Angeles Environmental Monitoring
Division
City of Los Angeles Stormwater Division
City of Oceanside
City of Oxnard
City of San Diego
City of Santa Barbara
City of Ventura
Columbia Analytical Services
Encina Wastewater Authority
Goleta Sanitation District
Granite Canyon Marine Pollution Studies Lab
Instituto de Investigacione, Oceanologicas (UABC)
Los Angeles Department of Water and Power
Los Angeles County Dept. of Beaches and Harbors
Los Angeles County Dept. of Health Services
Los Angeles Regional Water Quality Control Board
Los Angeles County Sanitation Districts
Marine Corps Base - Camp Pendleton
National Fisheries Institute of Mexico (SEMARNAP)
Orange County Environmental Health Division
Orange County Public Facilities and Resources
Orange County Sanitation District
San Diego County Dept. of Environmental Health
San Diego Interagency Water Quality Panel
San Diego Regional Water Quality Control Board
San Elijo Joint Powers Authority
Santa Ana Regional Water Quality Control Board
Santa Barbara County Health Service
Santa Monica Bay Restoration Project
Secretaria de Marina (Mexican Navy)
Southeast Regional Reclamation Authority
Southern California Coastal Water Research Project
Southern California Edison
Southern California Marine Institute
State Water Resources Control Board
Surfrider Foundation
University of California, Santa Barbara
University of Southern California, Wrigley Institute
for Environmental Studies (WIES)
U.S. EPA Region IX
U.S. EPA Office of Research and Development
U.S. Geological Survey
U.S. Navy, Space and Naval Warfare Systems Center,
San Diego
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Warm Season Algal Populations in Four Long Island Sound Harbors
Steven Yergeau, Director of Research
Save the Sound, Inc., 185 Magee Avenue, Stamford, CT 06902
Abstract
Near-shore water quality gathered in Save the Sound's water quality monitoring program are
analyzed in relation to seasonal algal blooms and the extent and duration of hypoxic events. Dissolved
oxygen, chlorophyll a, and phytoplankton taxa were determined weekly between May and October, 1997
at 5 or 6 stations in each harbor.
Results from the 1997 testing season in four harbors (Echo Bay in New Rochelle, NY; Milton Harbor
in Rye, NY; Cos Cob Harbor in Greenwich, CT; Stamford Harbor in Stamford, CT) are compared to see
the influences of industrial and residential impacts on the phytoplankton populations. Correlations
between chlorophyll a, as a measure of algal biomass, and dissolved oxygen are investigated in relation
to different stations and the different harbors. Phytoplankton population structure is also examined for
the degree of similarity to naturally balanced assemblages to determine the effects of environmental
change on the populations.
Introduction
There is a complex system of rivers, bays, wetlands, and beaches that bind and nourish Long Island
Sound. This system thrives on a delicate balance of vital elements, and this balance is threatened by the
pressures of human activities. The vitality of Long Island Sound is an integral part of the economy and
ecology of the region (Altobello, 1992). Many people live near the Sound and enjoy fishing, boating,
swimming, and birding. In fact, ten percent of the United States' population lives within 50 miles of
Long Island Sound (Long Island Sound Study, 1994). The Sound is one of the most productive estuaries
in the nation, supporting a diverse assemblage of marine life. It is estimated that the Sound contributes
approximately $5.5 billion annually to the regional economy, with recreational and commercial fishing
alone contributing over $1 billion per year (Altobello, 1992).
Long Island Sound undergoes seasonal hypoxic events where levels of dissolved oxygen drop below
3.0 milligrams per liter (mg/1), usually at the height of summer (Yergeau and Ayala, 1998). Oxygen
enters the water from the churning action of the tides and wind, and from photosynthesis of marine
plants. In the marine environment, nitrogen generally acts as the limiting nutrient for algal growth. As
with many estuaries, there is an overabundance of nitrogen in Long Island Sound. Nitrogen enters the
Sound through many sources such as sewage treatment plants, leaking septic systems, storm-water
runoff, and acid rain all of which lead to algal blooms (American Oceans Campaign, 1996). When the
algae die and sink to the bottom, oxygen is consumed during their decomposition by naturally occurring
bacteria (Long Island Sound Study, 1994). This increased demand for oxygen is in addition to the normal
oxygen use from the metabolic activities of animals and plants. Hypoxia (oxygen levels below 3.0 mg/1)
can be most severe during the summer when stratification prevents highly oxygenated surface water from
mixing with the poorly oxygenated bottom water. Severe hypoxic events have occurred in the Sound that
have resulted in large fin fish and shellfish kills (Long Island Sound Study, 1994; Brosnan and Stubin,
1992; Miller et al., 1992; and Poucher et al., 1992). This period of low dissolved oxygen in the Sound has
been identified by the Long Island Sound Study as the highest priority upon which New York,
Connecticut, and the U.S. Environmental Protection Agency are focusing their efforts and resources
(Long Island Sound Study, 1994).
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The states of Connecticut and New York have set criteria or water quality standards for water
resources depending on a water body's class and/or designated use(s). Dissolved oxygen should not be
less than 5.0 mg/1 at any time for: protecting marine fish, shellfish, and wildlife habitat; harvesting
shellfish for transfer to approved areas for purification prior to human consumption; primary contact
(swimming) and secondary contact (navigation); and recreation (Class SB Waters) (CT DEP, 1992; NYS
DEC, 1991). Dissolved oxygen should not be less than 6.0 mg/1 (Connecticut water quality standard) or
5.0 mg/1 (New York water quality standard) at any time for shellfish harvesting for direct human
consumption (Class SA Waters) (CT DEP, 1992; NYS DEC, 1991).
To determine the extent of hypoxic events in the coastal areas of Long Island Sound, water quality
monitoring efforts are underway using chemical indicators. The development of a biological indicator for
monitoring estuarine water quality to supplement dissolved oxygen concentration measurements can aid
in the expansion of monitoring efforts in large coastal bodies of water, such as Long Island Sound. The
measure being investigated by Save the Sound, a diversity index based on phytoplankton presence or
absence (from now on referred to as the phytoplankton diversity index, or PDI) has been developed and
assessed (Yergeau, Lang, and Teeters, 1997). The PDI is incorporated into Save the Sound's water
quality monitoring program to supplement data on dissolved oxygen, pH, Secchi disk depth, temperature,
salinity, and chlorophyll a concentration, and to help analyze and report on water quality data collected
on a weekly basis from harbors in Long Island Sound. Phytoplankton were chosen as the indicators of
water quality since microalgae have short generation times and respond quickly to changing water quality
conditions (Yergeau, Lang, and Teeters, 1997). The purpose of Save the Sound's water quality
monitoring program is to collect baseline data to be used in determining status and trends in embayments
not included in larger research programs and to provide information on remediation of those areas. The
results are used to supplement regional monitoring efforts, to provide data for further scientific research,
to educate citizens about Long Island Sound pollution, and to advocate for better land and water use
practices and improved pollution control.
Materials and Methods
Sampling Locations
Two Connecticut and two New York harbors were tested weekly from May 17 to October 11, 1997.
Surface (0.5 meter (m) below water surface) and bottom (total water depth minus 0.5 m) measurements
included dissolved oxygen, salinity, temperature, and Secchi disk depth. Photosynthetic pigment
chlorophyll a was sampled weekly at the surface (1.0 m below water surface) and analyzed as a measure
of algal biomass. Algal diversity was sampled bi-weekly at the surface (1.0 m below water surface). In all
harbors, measurements were taken from boats in the morning starting at approximately 7:00 am and
running to approximately 9:00 am.
Echo Bay: New Rochelle, NY (Figure 1)
Echo Bay is a wide harbor with industrial development surrounding the waterfront. On the western
bank are residential areas and Hudson Park, a multiple use outdoor facility. On the northern shore is the
sewage treatment plant that treats waste from different towns within Westchester County with the
majority coming from New Rochelle. In the center of Echo Bay is Five Islands Park, a complex with
areas for boating, fishing, and swimming.
In the northeast section is the Mill Pond fed by the Premium River. This area is filled with tidal flats
and marsh areas beneficial to a variety of wildlife. The Mill Pond is separated from Echo Bay by a dam
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that creates a waterfall into the harbor. In the northwest reaches, Echo Bay is fed by the Stephenson
Brook, a culverted stream that runs underneath the city.
Milton Harbor: Rye, NY (Figure 2)
Milton Harbor is a narrow harbor with primarily residential, marina, and mooring areas. The dredged
channel has approximately 2.0 m of water between the head of navigation and Milton Point and averages
2.5 m deep beyond Milton Point. There is a large tidal flat area on the northeastern quadrant. The harbor
is fed by Blind Brook, which originates at the Westchester Airport. The Marshlands Conservancy and a
golf course are on the western bank of the harbor. Hen Island is a residential island accessible by boat
only.
Cos Cob Harbor: Greenwich, CT (Figure 3)
Cos Cob Harbor is an extension of the Mianus River. The harbor is divided into the inner and outer areas
by the Metro North Railroad Bridge. The inner harbor has mudflats on the east bank which occupy more
than half the width of the harbor. On the west side, there are several marinas running the entire length of
the harbor just to the south the Mianus River Dam. In the outer portion, there are large homes and
Riverside Yacht Club on the east side of the harbor. The west side consists of mostly undeveloped land
and the remains of the Cos Cob Power Plant.
Stamford Harbor: Stamford, CT (Figure 4)
Stamford Harbor is primarily industrial in its surrounding land use, however there are also residential
and mooring areas dotting its shores. The harbor is divided into east and west branches, forming a Y'.
The Woodland Cemetery and Kosciusko Park peninsula divides the two branches. Both the east and west
branches have small tidal flat areas along the shores of the peninsula. On the western bank of the west
branch is Southfield Park, a public beach adjacent to the Hoffman fuel dock.
The east branch of the harbor is separated and protected from the mouth of Long Island Sound by a
hurricane barrier. Along the entire western bank of the east branch are tidal mudflats. A condominium
complex is located just behind a series of boat slips and docks. To the north is the Stamford sewage
treatment plant and its freshwater outlet at the dead end of the East Branch.
Water Quality Measurements
In Echo Bay and Milton Harbor, dissolved oxygen (measured in mg/1) and temperature (recorded as
degrees Celsius (°C)) were measured using a Yellow Springs Instrument (YSI) model 58 Dissolved
Oxygen meter with digital display, stirring unit, and 5700 field probe. Dissolved oxygen readings were
not adjusted for salinity in the field, but were corrected using calculations in a computer database
(Yergeau, 1997). Salinity (reported as parts per thousand (ppt)) was measured using a YSI model 33
Salinity-Conductivity-Temperature (SCT) meter with analog display. Salinity measurements were
compensated for changes in temperature manually by direct dial (Yergeau, 1997).
The volunteers air calibrated the dissolved oxygen meter and red-lined the SCT meter before they
began each testing session. Each day the dissolved oxygen probe was also checked for air bubbles and
the membrane was changed, if necessary. The salinity and dissolved oxygen probes were attached to a
platform and readings were taken at the surface (probes at 0.5 m below the water's surface), at one meter
intervals, and at the bottom (probes 0.5 m above the bottom).
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In Cos Cob Harbor and Stamford Harbor, a Hydrolab H20 Multiprobe was used to measure dissolved
oxygen, salinity, temperature, and pH. The volunteers air calibrated the probes before they began each
testing session. Each day the dissolved oxygen probe was also checked for air bubbles and other
membrane problems. The instrument automatically adjusts dissolved oxygen readings for salinity and
temperature, and automatically adjusts salinity readings for temperature. No additional calculations were
used to correct these values (Yergeau, 1997).
Water clarity was measured by using a Secchi disk. Secchi disk depth was determined by taking the
average of the water depth that the disk disappears from sight and the depth at which it reappears into
view (Yergeau, 1997). Two volunteers perform this measurement and these two averages are then
averaged for the final reading at that sampling site.
Water samples for chlorophyll a analysis were collected using a Van Dorn sampler. Water samples
were taken at 1.0 m below the water surface. The mixed water sample was filtered on the boat using a
manifold and hand pump. The volume of water filtered was determined by comparing the color on the
filter to a color chart after a dark green or dark brown color was reached on the filter paper. The filter
apparatus was rinsed three times with distilled water after each use. The filter was placed in a foil packet,
labeled, and stored on ice until it was transferred to the laboratory freezer. Any samples held longer than
three weeks in the laboratory were noted in the sample log book as such, since there may be possible
degradation of the chlorophyll in those samples (Greenberg et al., 1992).
Chlorophyll a extraction and analysis was performed at Save the Sound's water quality laboratory by
a member of the research staff or by trained technicians following Standard Methods' protocols
(Greenberg et al., 1992). Pigments were extracted after grinding the filter with a Teflon pestle in a 55.0
milliliter (ml) grinding tube with a 90% aqueous acetone solution. The samples were clarified in a
centrifuge for 20 minutes, then analyzed using a UV/VTS spectrometer with a 2.0 nanometer (nm) band
width. A band width of 2.0 nm is necessary since chlorophyll has a narrow absorption peak and a
larger-sized band width would underestimate the chlorophyll a concentration (Greenberg et al., 1992).
The following exception to Standard Methods was performed: after being clarified, the samples were
resuspended and centrifuged two more times to insure 99.1% retrieval of chlorophyll a (Yergeau, 1997).
Chlorophyll concentrations were corrected for pheophytin a, so that chlorophyll a values were not
overestimated (Greenberg et al, 1992). The correlation between dissolved oxygen and chlorophyll a was
calculated using Lotus 1-2-3 version 5 and the correlation coefficient (r) was compared to critical values
to determine statistical significance at the 1% level (Rohlf and Sokal, 1981).
Water samples for phytoplankton identification were collected using a Van Dorn sampler. Water
samples were taken at the surface (1.0 m below the surface of the water). The mixed water sample was
poured into a 500 ml opaque brown bottle containing 15.0 ml of Lugol's solution to preserve the sample.
The 500 ml samples are measured in a graduated cylinder and filtered to concentrate the phytoplankton
and to facilitate identification. An amount of filtered water equal to l/100th of the original sample size
was used to wash the sample off the filter (for a lOOx concentrated sample). Three (3) slide views from
this lOOx concentrated sample are then observed, with phytoplankton identified, and indication of
presence or absence noted. Samples were analyzed within three weeks time to ensure there was no
degradation of the sample and to coincide with the chlorophyll a analysis (Yergeau, 1997).
The PDI was determined using the calculations given in Yergeau, Lang, and Teeters, 1997. The
resulting number, based simply on the presence or absence of the taxa within three (3) slides, falls
between 0 and 20, with an increase in diversity as PDI increases.
NOTE: All volunteers and laboratory technicians are trained thoroughly by Save the Sound's
research staff, with particular attention given to consistency of data collection. Volunteers must complete
a six hour training course (classroom and field work) before they can participate in the program
(Yergeau, 1997).
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Results
Echo Bay: New Rochelle, NY (Table 1 & Figure 5)
The water quality in Echo Bay was fair during the testing season. Four out of the five stations
experienced violations of the water quality standard (5.0 rng/1 dissolved oxygen) at least once. Station 5
stayed above the water quality standard for that same period.
Dissolved oxygen levels were the lowest at the stations closest to Stephenson Brook. Station 1, at the
Stephenson Brook outfall, had the worst water quality compared to the other stations in the harbor. The
dissolved oxygen dropped in mid-July (7/12) and rebounded in July and August, but dropped below the
standard in the middle of August (8/17) and did not recover until mid-September (9/14).
Station 2, located at the northern end of Hudson Park, had poor water quality compared to other
stations in the harbor with dissolved oxygen levels violating the water quality standard at the beginning
of August (8/2) and remaining below the standard for the rest of the month.
Station 5 had the best water quality in the harbor. The water quality remained above the standard for
the entire testing season at this site.
Surface levels of chlorophyll a were similar at stations 2, 3, 4, and 5. Levels of chlorophyll at these
stations were relatively low, with most of the values between 10.0 and 15.0 jag/1, indicating some
development of small algal blooms. Chlorophyll a concentrations ranged from 1.1 ug/1 to 26.4 jag/1. A
large algal bloom was detected at station 4 when chlorophyll concentration was 26.4 (ig/1. The average
chlorophyll value was 11.4 jag/1. Chlorophyll a and dissolved oxygen were positively correlated with
statistical significance at the 1% level (r= 0.24). The PDI averaged 12.15 for the season.
Milton Harbor: Rye, NY (Table 1 & Figure 5)
The water quality in Milton Harbor was poor during the testing season. Dissolved oxygen was below
the water quality standard (5.0 mg/1 dissolved oxygen) at every station at least once during the season.
Dissolved oxygen levels were the lowest at the stations closest to Blind Brook. Stations 1 and 2 had
the poorest water quality; violations of the water quality standard occurred most often at these sites. The
lowest level observed in this harbor (2.9 mg/1) occurred at station 1, the most inward site, on July 12.
Stations 5 and 6, located towards the deeper portions of the harbor, had the highest dissolved oxygen
levels. Oxygen levels were above or equal to the water quality standard throughout most of the testing
season.
Surface levels of chlorophyll were similar in stations 1, 2, 3, and 4. Chlorophyll a values at these
stations were moderate with most of the values between 10.0 and 20.0 jag/1, indicating some development
of algal blooms. Chlorophyll a concentrations ranged from a high of 23.8 (Jg/1 to a low of 0.2 (jg/1. The
average chlorophyll value was 10.4 jag/1 for 1997. Chlorophyll a and dissolved oxygen were negatively
correlated with statistical significance at the 1 % level (r= -0.59). PDI averaged 11.87 for the season.
Cos Cob Harbor: Greenwich, CT (Table 1 & Figure 5)
Overall, the water quality in Cos Cob Harbor was poor during the testing season. Dissolved oxygen
levels were below the water quality standard (5.0 mg/1 dissolved oxygen) at every station at least once
during the season.
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The stations closest to the Mianus River had the worst water quality. The duration that oxygen levels
were below the water quality standard was longer and hypoxia (<3.0 mg/1 dissolved oxygen) occurred
more frequently at these sites. Station 1, located just south of the Mianus River Dam and Route 1, had the
worst water quality compared to other stations in the harbor. At station 1, dissolved oxygen levels were
below the water quality standard in the bottom water for most of the last half of the testing season
(8/2-10/11) and went hypoxic twice: in mid-July (7/19) and early August (8/2).
At station 6, located at the mouth of the harbor, bottom water oxygen levels were below the water
quality standard only four times: twice in late July (7/12); once at the end of August (8/24); and again in
September (9/20).
Surface chlorophyll was similar at stations 2, 3, 4, 5, and 6 in Cos Cob Harbor between. Station 1,
however, had the lowest chlorophyll levels for most of the season but experienced the largest bloom in
the harbor. Chlorophyll a concentration on June 7 was measured at 41.9 ug/1, indicating a very large
bloom. The levels ranged from 0.9 ug/1 to 41.9 ug/1. At the other stations in Cos Cob Harbor, chlorophyll
a values were low with most of the values between 5.0 and 10.0 ug/1 for most of the season. The overall
average chlorophyll value was 7.0 ug/1. Chlorophyll a and dissolved oxygen were negatively correlated
with statistical significance at the 1% level (r= -0.06). PDI averaged 11.86 for the season.
Stamford Harbor: Stamford, CT (Table 1 & Figure 5)
The water quality of Stamford Harbor was rated as poor during the season. Dissolved oxygen was
below the water quality standard (5.0 mg/1 dissolved oxygen) at every station at least once in the testing
season.
The stations furthest from the mouth of the harbor had the poorest water quality. At stations 1 and 5,
in the east and west branches, respectively, the oxygen levels were below or very close to the water
quality standard for most of the season (7/2-9/27) and were hypoxic (<3.0 mg/1 dissolved oxygen) one
time at station 1 and five times at station 5 from August to September. Station 5, located in the West
Branch, had the worst water quality with oxygen levels that were above the water standard only three
times during the last half of the testing season (7/26-10/11).
At most of the stations (stations 1, 2, 3, and 4), chlorophyll a levels were similar throughout the
monitoring season. Chlorophyll a values at these stations were low, with most of the values between 5.0
and 13.0 ug/1. Station 3, however, experienced the largest level of chlorophyll detected during the season.
On August 23, the level of chlorophyll a was measured at 43.7 ug/1 in the surface water, indicating a very
large algal bloom. The overall average chlorophyll value was 7.8 ug/1. Chlorophyll a and dissolved
oxygen were negatively correlated with statistical significance at the 1% level (r= -0.23). PDI averaged
12.05 for the season.
Discussion
These preliminary results show that there is impairment of the harbors studied. They also show that
other factors not currently analyzed are influencing dissolved oxygen concentrations and algal bloom
formation in areas of Long Island Sound.
In theory, the water quality in the more developed harbors should be worse than the water quality in
the less developed harbors. Stamford Harbor and Milton Harbor both followed this logic with their water
quality results. Echo Bay and Cos Cob Harbor, however, did not fit with this logic. Echo Bay had the
highest rated water quality during this study and Cos Cob Harbor had water quality rated as poor.
m-54
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The shape of Echo Bay is having an influence on the flushing in and out of nutrients as well as the
blooms formed by those nutrients. The widened mouth of Echo Bay allows for a large amount of tidal
exchange. Cos Cob Harbor however has a narrow harbor mouth which experiences less tidal exchange.
Cos Cob Harbor also has mostly residential development surrounding its shores. These areas are
currently not sewered. Old, failing septic systems may be responsible for the low average dissolved
oxygen readings during the course of this study.
It is seen that the rivers and creeks that drain into the harbors had a large influence on their water
quality. Those stations closest to these tributaries generally had the lowest dissolved oxygen levels. This
is indicative of nonpoint pollution influencing the harbors in this study. Further study of the nutrient
inputs, particularly nitrogen, into these harbors would clarify this situation. Human activities, such as
discharges from sewage treatment plants and nonpoint source runoff, are responsible for 56% of the total
annual nitrogen load in the Sound (Long Island Sound Study, 1994).
A major source of nonpoint pollution is stormwater runoff which carries contaminants, including
nutrients, metals, oils, and pesticides. In more developed areas, impervious paving materials prevent
rainwater absorption by the soil, thereby increasing the amount of contaminants carried in the runoff. An
analysis of rainfall data from 1997 is currently being undertaken to determine the atmospheric
contribution to the hypoxia/algae dynamics in these harbors.
The effects of hypoxia on the living marine resources in the Sound depend upon the extent, duration,
and intensity of the hypoxic period. It is likely that an increase in the sources and occurrences of coastal
pollution, due to human activity, will result in more intense hypoxic events which severely stress and kill
commercially and recreationally important fish and shellfish. The areas of concern identified in this study
will be watched closely as they will be more severely impacted by poor water quality conditions in the
future. Land use practices must be improved around these areas of concern to minimize nonpoint and
point source pollution and their potential impact on marine life.
Acknowledgments
I would like to thank Iliana Ayala, Research Assistant at Save the Sound, and Ann Lang and Robert
Teeters, for their hard work during all phases of this study.
Literature Cited
Altobello, M.A. 1992. The Economic Importance of Long Island Sound's Water Dependent Activities.
Report to U.S. Environmental Protection Agency Region 1, pp. 1-41.
American Oceans Campaign. 1996. Estuaries on the Edge: The Vital Link Between Land and Sea.
American Oceans Campaign, Washington, DC. 297 p.
Brosnan, T.M. and A.I. Stubin. 1992. Spatial and Temporal Trends of Dissolved Oxygen in the East
Creek and Western Long Island Sound. In: Proceedings from the Long Island Sound Research
Conference. Southern Connecticut State University, New Haven, CT. pp. 169-175.
Connecticut Department of Environmental Protection (CT DEP). 1992. Connecticut Water Quality
Standards. Bureau of Water Management, Hartford, CT. pp. 1-68.
Greenberg, A.E., L.S. Clesceri, and A.D. Eaton (eds.). 1992. Standard Methods for the Examination of
Water and Wastewater (18th ed.). American Public Health Association, Washington, DC. Part
10000; pp. 17-24.
Long Island Sound Study (LISS). 1994. Comprehensive and Conservation Management Plan. Long
Island Sound Office of the U.S. Environmental Protection Agency, Stamford, CT. pp. 1-168.
m-55
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New York State Department of Environmental Conservation (NYS DEC). 1991. Water Quality
Regulations: Surface Water and Groundwater Classifications and Standards, Albany, NY. pp. 1-64.
Poucher, S.L., L.L. Coiro, and D.C. Miller. 1992. Development of Dissolved Oxygen Criteria for Long
Island Sound: The Subacute Effects. In: Proceedings from the Long Island Sound Research
Conference. Southern Connecticut State University, New Haven, CT. p. 209.
Rohlf, FJ. and R.R. Sokal. 1981. Statistical Tables. W.H. Freeman and Company, New York, NY. pp.
166-168.
Yergeau, S. 1997. Quality Assurance Project Plan for Save the Sound's Adopt-a-Harbor Program. Save
the Sound, Inc., Stamford, CT. 84 p.
Yergeau, S., A. Lang, and R. Teeters. 1997. Assessment of Phytoplankton Diversity as an Indicator of
Water Quality. In: Proceedings of the 22nd Annual Conference of the National Association of
Environmental Professionals. NAEP, Orlando, FL. pp. 1062-1068.
Yergeau, S. and I. Ayala. 1998. 1997 Long Island Sound Water Quality Report: Eleven Harbors and
Coves in the Western and Central Sound. Save the Sound, Inc., Stamford, CT. 80 pages.
EI-56
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Route 1
Stephenson
Brook
New Rochelle
Municipal
Marina
Premium Point
Pine Island
Station 1: (Stephenson Brook Outfall) Located at the culvert
where Stephenson Brook empties into Echo Bay.
This station is accessible only by land.
Station 2: (Red Nun 10) Located off the northern end of
Hudson Park and west of a small mudflat island.
New Rochelle Municipal Marina is to the west
Station 3: (Red Nun 6) Located off the southern end of
Hudson Park and Echo Island.
Station 4: (Flashing Green Can 3BR) Most seaward station
located east of Davenport Neck at the mouth of
Echo Bay and west of Premium Point
Station 5: (Mill Pond Dam) Located at the waterfall created
by the Mill Pond dam. Volunteers tie up to the pier
closest to the dam.
Figure 1. Echo Bay (New Rochelle, NY) water quality monitoring stations 1-5.
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Playland
Parkway
Sewage Disposal
Facility • } Blind Brook
a Milton Point
Station 1: (Red Nun 14) Northern-most station south of the Blind
Brook outlet and two marinas (Shongut Marina and Rye
Municipal Boat Basin).
Station 2: (Red Nun 12) Located south of station 1, within a large
tidal flat
Station 3: (Red Nun 10) Located south of station 2, within the
southern portion of a tidal flat.
Station 4: (Red Nun 8) Located south of station 3, just east of Maries
Neck.
Station 5: (Red Nun 6) Located south of station 4, just west of
Milton Point and east of Hen Island.
Station 6: (Green Can 3) Most seaward station, located south of
station 5, just west of Scotch Caps.
Figure 2. Milton Harbor (Rye, NY) water quality monitoring stations 1-6.
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Mian us River ,—Dam
Route
Rai
Station 1: (Mianus River Dam) Northern-most station which
is just south of the Mianus River Dam and Route 1
(a-ta., Post Road). There is usually a considerable
amount of freshwater that pours over the dam.
Station 2: (Lobster Float) This station is located at a lobster
float owned by a local lobsterman, Anthony
Coviello, and is south of the 1-95 bridge on the east
side of the channel.
Station 3: (Town Docks) This station is off of the main
channel where Mill River enters the harbor just
south of Greenwich Town Docks.
Station 4: (Green Can 13) This buoy is across from the
remains of the Cos Cob Power Plant
Station 5: (Green Can 11) This buoy lies off of Riverside
Yacht Club and is in the widest part of the harbor.
Three Greenwich Audubon Society osprey nesting
platforms are located on the western side of the
harbor.
Station 6: (Green Can 7) This station is the furthest out into
the Sound located at the mouth of the harbor. This
station has a soft bottom and experiences strong
Figure 3. Cos Cob Harbor (Greenwich, CT) water quality monitoring sampling stations 1-6.
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Stamford Water Pollution
Control Facility
Station 1: (East Branch/Save the Sound) Located in the east
branch south of the Stamford sewage treatment plant
outlet.
Station 2: (East Branch/Green Can 1) Located just outside the
hurricane barrier of the east branch. Local people
fish this area.
Station 3: (Green Can 11) Located beyond Jack Island, this is
the roost seaward station.
Station 4: (West Branch) Located between Hoffman Oil Dock
and Southfield Park.
StationS: (West Branch) A riverine site near Herberts
Landing fuel dock and Genovese Cement Company.
Figure 4. Stamford Harbor (Stamford, CT) water quality monitoring sampling stations 1-5.
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Echo Bay Milton Cos Cob Stamford
PDI
DO
H Chlor. a
[ Secchi
Figure 5. Water quality measurements by harbor (seasonal averages).
Table 1. Water Quality Measurements by Harbor (Seasonal Averages)
Harbor
Echo Bay
Milton Harbor
Cos Cob Harbor
Stamford Harbor
DO
9.56 mg/1
7.51 mg/1
6.36 mg/1
6.59 mg/1
Secchi
1.53m
0.93m
1.17m
1.18m
Chlor. a
11.54 ng/1
10.20 (ig/1
7.00 ng/1
' 7.80 ug/1
PDI
12.15
11.87
11.86
12.05
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Performance-Based Quality Assurance—
The NOAA National Status and Trends Program Experience
A.Y. Cantillo and G.G. Lauenstein
Quality Assurance Project and Mussel Watch Project Managers
National Oceanic and Atmospheric Administration, National Status and Trends Program
N/ORCA21, 1305 East West Highway, Silver Spring, MD 20910
Abstract
The NOAA National Status and Trends (NS&T) Program determines the current status of, and changes
over time in the environmental health of US estuarine and coastal waters. Concentrations of organic and
inorganic contaminants are determined in bivalves, bottom-dwelling fish and sediments. The quality of the
analytical data generated by the NS&T Program is overseen by the performance-based Quality Assurance
Project. This Project has been in operation since 1985and is designed to document sampling protocols,
analytical procedures and laboratory performance, and to reduce intralaboratory and interlaboratory
variation. To document laboratory expertise, the QA Project requires that all NS&T laboratories participate
in a continuing series of intercomparison exercises utilizing a variety of materials. The organic analytical
intercomparison exercises are coordinated by the National Institute of Standards and Technology, and the
inorganic exercises by the National Research Council of Canada. The QA Project will facilitate comparisons
among different monitoring programs with similar QA activities and thus extend the temporal and spatial
scale of such programs.
Introduction
The NOAA National Status and Trends (NS&T) Program determines the current status of, and changes
over time in the environmental health of US estuarine and coastal waters. Concentrations of organic and
inorganic contaminants are determined in bivalves, bottom-dwelling fish and sediments. Two projects of the
NS&T Program are the major producers of data: the National Benthic Surveillance Project and the Mussel
Watch Project.
The National Benthic Surveillance Project collected and analyzed benthic fish and sediments from sites
around the coastal and estuarine United States, including Alaska, during the years 1984-1992. This effort
was performed primarily by NOAA's National Marine Fisheries Service. The Mussel Watch Project collects
and analyzes bivalve mollusks and associated sediments from around the United States, including the Great
Lakes, Alaska, Hawaii, and Puerto Rico. The Mussel Watch Project began in 1986. This effort is admin-
istered by NOAA, with collection and analyses being primarily performed under contract. The NS&T core
analytes include 24 polycyclic aromatic hydrocarbons, 20 polychlorinated biphenyl congeners, DDT and its
metabolites, 9 other chlorinated pesticides, organotins, 5 major elements, and 12 trace elements. Sampling
sites are described in Lauenstein et al (1997). Results of the NS&T Mussel Watch Project can be found in
Cantillo and Lauenstein (this volume).
Quality Assurance Project
The quality of the analytical data generated by the NS&T Program is overseen by the performance-
based Quality Assurance (QA) Project. The QA Project, in operation since 1985, assures that despite
differences in the analytical methodologies used, data are comparable between all participating laboratories.
The QA Project is designed to document sampling protocols, analytical procedures and laboratory
performance, and to reduce intralaboratory and interlaboratory variation.
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Sampling
Since contaminant concentrations vary as a function of where samples are collected, it is essential to
occupy the same site year after year in order to determine environmental trends. With the new Global
Positioning System (GPS) technology a site can be re-occupied to within a few meters. All sites are be
plotted on charts and/or maps that are helpful for reoccupying sites in relation to prominent land features.
Photographic documentation of each site, and directions as to how to reach the site, are also important. Since
field samples must be collected in a consistent manner all individuals regardless of their level of scientific
expertise are trained in field sampling protocols.
Analytical Methods
The NS&T QA Project is performance based and does not prescribe specific analytical methods but
encourages the use of state-of-the-art procedures. This allows the use of new or improved analytical
methodology or instrumentation without compromising the quality of the data sets. It also encourages
laboratories to use the most cost-effective methodology while generating data of documented quality. The
methods used by the various laboratories contributing to the NS&T monitoring effort have been documented
by Lauenstein and Cantillo (1993 and 1997).
Intel-comparison Exercises
To document laboratory expertise, the QA Project requires that all NS&T laboratories participate in a
continuing series of intercomparison exercises utilizing a variety of materials. The organic analytical
intercomparison exercises are coordinated by the National Institute of Standards and Technology (NIST),
and the inorganic exercises by the National Research Council of Canada (NRC). The QA Project facilitates
comparisons among different monitoring programs with similar QA activities and thus extends the temporal
and spatial scale of such programs.
The materials used for the intercomparison exercises include samples with unknown (to the partici-
pants) contaminant concentrations, and Standard Reference Materials (SRMs) and/or Certified Reference
Materials (CRMs). The type and matrix of the exercise materials change yearly and have increased in
complexity over time. Typical results of the intercomparison exercises are discussed below.
Two sediment and two tissue materials were used for the 1993 intercomparison exercise for trace metals.
National Research Council Canada BCSS-1 and NIST SRM 1566a were the known materials, and NRC
prepared Sediment T, a freeze-dried Mississippi Delta sediment, and Tissue S, a freeze-dried mussel tissue
homogenate collected by the International Atomic Energy Agency (IAEA) in the Mediterranean off the coast
of France, as the unknowns. Typical results of intercalibration exercises for the NS&T cooperating
laboratories are shown in Figure 1.
Results of analyses of SRMs and/or CRMs are not compiled and evaluated as part of the trace organic
intercomparison exercises. Rather, unknown materials are prepared by NIST for each exercise. SRMs and
CRMs are analyzed as part of the analytical sample string in which the unknown materials are analyzed.
These results are part of the control chart information described above. Some of the materials used for trace
organic exercises, however, are cuts of the material used to prepare NIST SRMs or are candidate SRMs in
the certification process and so are, in effect, unknowns.
As part of the 1993 trace organic exercise, a fish homogenate of carp collected in Saginaw Bay was
prepared by NRC and provided to NIST. This material was analyzed for all NS&T analytes except
polycyclic aromatic hydrocarbons, since these compounds are found in very low concentrations in fish
tissue. Typical PCB results are shown in Figure 2. Most of the PCB results fall within the range defined as
111-64
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±35% of the consensus value plus or minus one standard deviation. In this example a positive bias is shown
in the data reported by laboratory A.
Reference Materials, Blanks, Duplicates, and Matrix Spikes
The analysis of reference materials, such as the NRC CRMs and NIST SRMs, and of control materials
generated for use by NS&T contract laboratories as part of the sample stream, is required.
To identify suitable reference materials for use by the NS&T Program and following the recommenda-
tion of the Intergovernmental Oceanographic Commission/United Nations Environment Programme/IAEA
Group of Experts of Standards and Reference Materials (GESREM), NOAA began in 1986 a compilation of
standard and reference materials for use in marine science (Cantillo and Calder, 1990; Cantillo, 1993). In
response to the needs of the NS&T Program, NOAA contributed funds to the production of eight NIST
Standard Reference Materials and seven internal standard solutions (Table 1). The SRMs are based on
natural matrices and are prepared at two concentration levels. The calibration solutions are for each of the
three chemical classes of analytes quantified by the NS&T Program. The latter are used to facilitate the
preparation of multipoint calibration curves. The internal standard solutions were prepared at the request of
the NS&T contract laboratories and are currently available for purchase from NIST. These SRMs, CRMs,
and control materials have been, and continue to be, used by NS&T contract laboratories to maintain
analytical control.
A minimum of 8% of the organic analytical sample string consists of blanks, reference or control
materials, duplicates, and spike matrix samples. The use of control materials does not entirely replace the
use of duplicates and spiked matrix samples. A minimum of 2% of the standard inorganic sample string
consists of calibration materials and reference or control materials. Analytical data from all control materials
and all matrix reference materials are reported to the NS&T Program office and these data are stored as part
of the NS&T data archive.
Method Detection Limits
When the program began, data at or near the detection limit were to be reported following procedures
defined by Keith et al. (1983) who defined the limit of detection (LOD) as the lowest concentration level
that can be determined to be statistically different from a blank. The standard deviation (used to determine
the LOD) was defined by replicate measures of the difference between the lowest concentration of analyte
instrumentally detectable and a blank value. Any measured value below the LOD were considered to be not
detected. The limit of quantitation (LOQ) was defined as lOx the standard deviation of the blank.
In 1990, the NS&T Program replaced LODs and LOQs with Method Detection Limits (MDLs). These
values are not based on the variability of blanks but rather on the standard deviation of the signal from
replicate analysis of real matrix samples containing, in principle, low levels of the analyte (CFR, 1990). The
MDL is "x" times the standard deviation, where "x" is defined by the Student's t-distribution to cover 99%
of the distribution of possible values (for 7 analyses, x = 3.5). MDLs were developed from a minimum of 7
replicate analyses. Actual detection limits from the program are provided in Lauenstein and Cantillo, 1993
and 1997.
Precision and Accuracy
Acceptable limits of precision for organic control materials are ±30% on average for all analytes, and
±35% for individual analytes. These limits apply to those materials where the concentrations of the
compounds of interest are at least 10 times greater than the MDLs. The application of these guidelines in
111-65
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determining the acceptability of the results of the analysis of a sample is a matter of professional judgement
on the part of the analyst, especially in cases where the analyte level(s) are near the limit of detection.
Control Charts
The results of the routine analysis of reference materials, other control materials, and blanks are
reported annually to the NS&T Program office and are used to prepare control charts. Examples of control
charts can be found in Cantillo (in preparation). Some of the congeners quantified as part of the NS&T
Program coelute when using commonly available gas chromatography columns for analyte separation. A
discussion of this topic can be found in Lauenstein and Cantillo (1993) and Schantz et al. (1993).
Sample Re-analyses
Once data have been received for a given year of monitoring, data are compared to analyte concentra-
tions for that same site from earlier years. Sample homogenates are reextracted and reanalyzed for sites
where unexpected increases or decreases of contaminant concentrations were noted. These reanalyses are
performed by an independent laboratory. Approximately 15 samples are reanalyzed annually for trace
elements and organic contaminants.
Determinations of Ag concentrations in mussels and oyster tissue digested with nitric acid were low
relative to those measured in undigested tissue analyzed by ultrasonic graphite furnace atomic adsorption
spectroscopy (Daskalakis et al., 1997). Good results were obtained, however, using a mixture of
hydrochloric and nitric acids for digestion. Archived NOAA Mussel watch samples collected in 1986-1993
and originally analyzed following HNO3 or HNO3-HC1O4 digestion were reanalyzed with HNOs-HCl
digestion in 1995. Results suggest that only 44% of the redetermined Ag concentrations were within 20% of
the original values. Most of the re-analyses yielded higher concentrations, and one tenth of them were more
than 100% higher. As a result the new data yield information that indicated there were decreasing Ag trends
along the northeast coast of the US.
Potential Contract Laboratory Testing
Laboratories competing for a Mussel Watch Project contract were required to submit results of their
analysis of a test sample (Lauenstein and Cantillo, 1997). During the 1989 selection process, laboratories
that appeared to qualify on the basis of their written proposals were provided a gravimetrically prepared
solution with "unknown" quantities of an undefined number of organic chemicals. Li 1994, competing
laboratories were once again tested but this time using matrix materials for the quantification of both trace
elements and organic chemicals. Three laboratory groups participated in the exercises. For the 1989
gravimetrically prepared solutions, all participating laboratories were able to identify the chemicals and in
all but two cases were able to report concentrations to within ±25% of the known values. In 1994, all
laboratories were within the acceptance criteria for the quantification of trace elements and chlorinated
chemicals in homogenate samples, though two laboratories were outside of the acceptable range for one of
the four PAHs used to evaluate laboratory performance.
Retrospect
Performance Improvement
It has been shown that the performance of laboratories improves with time, as the result of experience
gained through participation in intercomparison exercises (Cantillo, 1995; Willie and Berman 1996; Willie,
1997). This improvement can be demonstrated through the continued analysis of a material, such as a CRM,
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SRM or a control material with known analyte concentrations. The NOAA intercomparison exercises for
trace metals for 1991 through 1993 used BCSS-1 as part of the exercise materials. These exercises are open
to all laboratories, not just those participating in the NS&T Program. Typical results reported by a laboratory
joining the exercise program in 1991 are shown in Figure 3. The accuracy of the Cr, Zn and Se
determinations improved with time, as did the precision of the Se analysis.
As part of the evaluation of results of the intercomparison exercises for major and trace elements, NRC
assigns a performance evaluation criteria based on the number of times results reported by a laboratory fall
within acceptable criteria. The percentage of laboratories achieving superior or good performances has
increased since 1991 from 46% to 83% (Figure 4). Superior-rated laboratories submitted results for most
analytes within the 95% confidence intervals; good-rated laboratories submitted many results within the
accepted range with a minimum number of outliers (Willie, 1997).
No CRMs or SRMs are analyzed specifically as part of the trace organic intercomparison exercises, so
an evaluation similar to the one done for the trace metal exercises using changes in CRM and SRM results
over time is not possible.
A measure of improvement of laboratory performance can be made, however, by comparing the
performance of a laboratory joining the exercises for the first time and that of a laboratory that has
participated for several years. Laboratories newly joining the exercises usually have larger percent errors
than the veteran laboratories. Within a year or two, however, the performance of the new laboratories
typically improves and equals those of the veteran laboratories.
Data Quality Assurance
The QA Program should include the monitoring data. The contractor laboratories that perform the
analyses provide a first level evaluation of the quality of the data based on the limits of acceptability in the
NS&T contract. Results of analytical sample strings that fail to pass the QA guidelines are rejected and the
samples re-analyzed. Rejected results are not forwarded to the NS&T Program office.
Once the data reach the NS&T Program office, QA evaluation of the data is done by the NS&T
Program manager as the new data are manipulated and merged with the existing multi-year data set. This
process has resulted in the detection of some discrepancies.
Data Filters
Filters to detect high analyte values and large changes in a given site from one year to another should be
used on new data before it becomes part of the NS&T database. Abnormally high values submitted to the
Program office often revealed differences in sampling location.
In one instance, one oyster sample of a set of triplicates obtained at the same site, was composited from
oysters growing on a creosote-covered decking pole. This sample had unusually high values for PAHs and
the results were discarded. The contractor had documented via photography the location of each station in
the site as required by the contract.
In several instances, data were reported to the NS&T Program office in units different than those of
previous submissions. This type of discrepancy is easily detected since the differences are often in multiples
of 1000 (i.e., ng/g instead of ng/g).
Data Quality Control Parameters
A system should be in place to link the data QA parameters associated with an analytical sample string,
such as blanks, duplicates, matrix spikes, and reference materials results, to the corresponding samples
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analyzed as part of that analytical sample string. The information is currently found in the contractor
laboratory data submission reports. Efforts are underway to store the data quality parameters in electronic
form so it can be related to the corresponding sample data.
Long-Term Data Archival
The NS&T Program is a long-term monitoring effort. As time passes, there is a natural turn-over of
personnel involved in the Program. As this happens, corporate memory about the Program decreases, and
detailed documentation becomes very important. A coordinated effort is in place to archive the NS&T
electronic data and critical documentation in more than one geographical location so that in case of loss of
material from one location, duplicates can be found elsewhere. Although there is an effort to reduce the
amount of paper documentation, it should be remembered that electronic media are ephemeral and are easily
corrupted. Paper documentation, as well as electronic versions, should be encouraged for critical
information.
Critical documentation should consist of, at minimum, details of sample location and sampling times,
detailed description of analytical methods, the data, data QA parameters, results of the performance by the
contract laboratories in the intercomparison exercises, and any changes/errors found and corrected.
The current status of NS&T Program critical documentation is as follows:
• Two sampling site description documents have been published (Lauenstein et al. 1993 and 1997).
« Two sampling and analytical methods documents have been published (Lauenstein and Cantillo,
1993 and 1998).
• Several documents listing the NS&T data have been published (NOAA 1989 and 1991).
• The NS&T data is available in diskette form from the NS&T Program office and can be
downloaded from the NS&T Internet page.
• One document of data QA parameters is under review (Cantillo, in preparation) and several others
are in preparation.
• A series of documents have been published describing the results of the NS&T intercomparison
exercises for major and trace elements [Valette-Silver, 1992; Cantillo, 1995; Willie and Berman,
1996, and other technical memoranda by the same authors; and Willie, 1997].
• Several documents have been published describing the results of the NS&T intercomparison
exercises for trace organic analysis (Cantillo and Parris, 1993; Cantillo, 1995; Parris et al., 1998).
Other documents describing the trace organic results are planned.
Conclusions
Quality assurance is an essential part of environmental monitoring programs, especially those that are
not constrained by specified analytical procedures. The performance based QA project described in this
paper allows for the introduction of new instrumentation and analytical techniques that may result in
improved data quality or savings in time and resources. Analytical precision and accuracy of new
laboratories joining an existing monitoring program can be quantified and improved, and the performance of
veteran laboratories can be monitored and corrected if necessary. CRMs and SRMs provide the benchmark
necessary to document laboratory performance. If data are biased during some portion of the monitoring
program, the elucidation of real trends could be missed or alternately false trends could be indicated. Quality
assurance should be considered in laboratory selection, sample collection, contaminant quantification, and
review of analytical results.
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References
Cantillo A.Y. 1993. Standard and reference materials for marine science. IOC Manuals and Guides 25.
UNESCO, Nairobi, Kenya. 577 pp.
Cantillo A.Y. 1995. Quality Assurance Project intercomparison exercise results 1991 1993. NOAA Tech.
Memo. 79, NOAA/NOS/ORCA, Silver Spring, MD. 219 pp.
Cantillo A.Y., and G.G. Lauenstein. This volume. NOAA's Mussel Watch Project: 11 years of coastal
monitoring for chemical contaminants.
Cantillo A.Y., and J. Calder. 1990. Reference materials for marine science. Fresenius Z. Anal. Chem.,
338:380-2.
Cantillo A.Y., and R.M. Parris. 1993. Quality Assurance Project trace organic intercomparison exercise
results 1986 - 1990. NOAA Tech. Memo. NOS ORCA 69, NOAA/NOS/ORCA, Silver Spring, MD
179pp.
Cantillo, A.Y. (in preparation) National Status and Trends Program quality assurance/control parameters.
NOAA Tech. Memo. NOS ORCA, NOAA/NOS/ORCA, Silver Spring, MD.
Cantillo, A.Y. 1995. Standard and reference materials for environmental science (Part 1 and Part 2). NOAA
Tech. Memo. 94. NOAA/NOS/ORCA, Silver Spring, MD. 752 pp.
Code of Federal Regulations (1990) 40 CFR, Ch. 1, Part 136, Appendix B.
Daskalakis, K.D., T.P. O'Connor, and E.A. Crecelius. 1997. Evaluation of digestion procedures for
determining silver is mussels and oysters. Environ. Sci. Technol. 31:2303-2306.
Keith, L. H., W. Crummett, J. Deegan, Jr., R. A. Libby, J. K. Taylor, and F Wentler (1983) Principles of
environmental analysis. Anal. Chem., 55:2210-18.
Lauenstein, G.G., and A.Y. Cantillo (eds.). 1993. Sampling and Analytical Methods of the NOAA National
Status and Trends Program National Benthic Surveillance and Mussel Watch Projects 1984-1992: Vol. I
IV. Tech. Memo. NOS ORCA 71. NOAA/NOS/ORCA, Silver Spring, MD.
Lauenstein, G.G. and A.Y. Cantillo. 1997. Analytical evaluation of laboratories wishing to perform
environmental characterization studies. Environ. Toxicol. and Chem. 16(7): 1345-1350.
Lauenstein G.G. and A.Y. Cantillo (eds.). 1998. Sampling and analytical methods of the National Status
and Trends Program Mussel Watch Project: 1993-1996 update. NOAA Tech. Memo. NOS ORCA
130, 233 pp.
Lauenstein, G.G., A.Y. Cantillo, S. Kokkinakis, S. Frew, H.J. Jobling and R.R. Fay. 1997. Mussel Watch
Project site descriptions through 1997. NOAA Tech. Memo. ORCA 112. NOAA/NOS/ORCA, Silver
Spring, MD. 354 pp.
Lauenstein, G.G., M. Harmon, and B.P. Gottholm. 1993. National Status and Trends Program: Monitoring
site descriptions for the first five years of Mussel Watch and National Benthic Surveillance Projects.
NOAA Tech. Memo. NOS ORCA 70. NOAA/NOS/ORCA, Rockville, MD. 360 pp.
NOAA. 1989. A summary of data on tissue contamination from the first three years (1986-1988) of the
Mussel Watch Project. NOAA Tech. Memo. NOS OMA 49. 22 pp. plus appendices.
NOAA. 1991. Second summary of data on chemical contamination in sediments from the National Status
and Trends Program. NOAA Tech. Memo. NOS OMA 59. 29 pp. plus appendices.
Parris, R.M., M.M. Schantz, and S.A. Wise. 1998. NIST/NOAA NS&T/EPA EMAP Intercomparison
Exercises Program for organic contaminants in the marine environment: Description and results of 1997
organic intercomparison exercises. NOAA Tech. Memo. ORCA 133. NOAA/NOS/ORCA, Silver
Spring, M. 56 pp. plus appendicies.
Schantz, M.M., R.M. Parris, J. Kurz, K. Ballschmiter, and S.A. Wise. 1993. Comparison of methods for the
gas-chromatographic determination of PCB congeners and chlorinated pesticides in marine reference
materials. Fresenius Z. Anal. Chem. 346:766-78.
Valette-Silver N. 1992) Elemental analyses in marine sediment and biological tissues. NOAA Tech. Memo.
NOAA/NOS/ORCA 66, Rockville, MD. 39 pp. plus appendices.
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Willie, S., and S. Berman. 1996. NOAA National Status and Trends Program tenth round intercomparison
exercise results for trace metals in marine sediments and biological tissues. NOAA Tech. Memo. ORCA
93. NOAA/NOS/ORCA, Silver Spring, MD. 52 pp. plus appendices.
Willie, S. 1997. NOAA National Status and Trends Program eleventh round intercomparison exercise
results for trace metals in marine sediments and biological tissues. NOAA Tech. Memo. ORCA 120.
NOAA/NOS/ORCA, Silver Spring, MD. 51 pp plus appendices.
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111-71
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Dashed lines are ± uncertainty.) (ug/g dry wt.)
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with performances rated in the superior and good category for the analysis of tissues (Willie, 1997).
fable 1. NOAA Has Partially Funded the Production of Eight NIST SRMs and Seven Internal
Standard Solutions
The SRMs aK t^o^iStaral matrix materials and calibration soludons at two concentration levels of the three
chemical classes of analytes.
SRM 1491 Aromauc HydroearbonS in rlexane/Toluene
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1493 Chlorinated Biphenyl Congeners in .'i,2s4-TrimeUiy Ipentane
1941 Organics in Marine Sediment
SRM 1974 Drganics in Mussel Tissue (Mytilusedulis)
SRM 2260 Aromatic Hydrocarbons in Toluene
SRM 2261 Chlorinated Pesticides in Hexane
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ni-73
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111-74
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Trend Detection in Land Use and Water Quality
Data for the Herrings Marsh Run Watershed
J.M. Rice, Extension Specialist
Department of Biological and Agricultural Engineering
North Carolina State University, Raleigh, NC
J. Spooner, Extension Specialist
Water Quality Group in the Department of Biological and Agricultural Engineering
North Carolina State University, Raleigh, NC
M.G. Cook, Professor Emeritus
Department of Soil Science
North Carolina State University, Raleigh, NC
K.C. Stone, Agricultural Engineer
USDA Agricultural Research Service, Coastal Plains Soil
Water and Plant Research Center, Florence, SC
S.W. Coffey, Environmental Specialist
Division of Soil and Water Conservation
Department of Environment and Natural Resources, Raleigh, NC
FJ. Humenik, Coordinator of Waste Management Programs
College of Agriculture and Life Sciences
North Carolina State University, Raleigh, NC
P.G. Hunt, Soil Scientist and Research Leader
USDA Agricultural Research Service
Coastal Plains Soil, Water, and Plant Research Center, Florence, SC
Abstract
Agricultural non-point source pollution has been of concern, particularly where intensive operations
exist near environmentally sensitive waters. In 1989 the United States Department of Agriculture (USDA)
addressed these concerns by funding eight Water Quality Demonstration Projects with the goal of
increased voluntary adoption of agricultural best management practices (BMPs). The Herrings Marsh Run
Demonstration Project in Duplin County, North Carolina has been able to document water quality
improvements as a result of widespread BMP implementation. By combining the resources and expertise
of various federal, state and local agencies and a receptive agricultural community, BMPs have been
installed throughout the watershed to address several aspects of farm management and rural land use.
Accurate land use data, that could be linked to the subwatershed water quality monitoring stations,
proved to the most difficult information to collect. Current government databases typically do not contain
detailed records. Farmers, and in some cases contract growers, consider some information to be
proprietary. Standard government records were supplemented with farm surveys and drive-by field
observations to document land use changes.
Water quality monitoring and USGS gaging stations were installed on streams in subbasins within the
watershed to help characterize the overall water quality of the drainage area. In addition, by utilizing
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subbasins it was possible to compare and contrast changing land use patterns on the watershed. Water
monitoring data from the watershed outlet have confirmed an improvement in water quality as evidenced
by a 50% decrease in the nitrate-nitrogen (NO3-N) concentration.
Introduction
Even though significant progress has been made in the development and implementation of
agricultural best management practices (BMPs), there has been little progress at documenting the water
quality impacts of their implementation on a watershed scale. A United States Department of Agriculture
(USDA) Water Quality Demonstration Project was initiated in 1990 in the Herrings Marsh Run (HMR)
Watershed, located in the Northeast Cape Fear River Basin in Duplin County, North Carolina. This
project was one of the original eight Demonstration Projects funded by USDA as part of the 1989
Presidential Water Quality Initiative. These projects were conceived as cooperative efforts by the
Cooperative Extension Service, the Natural Resources Conservation Service, and the Farm Services
Agency. The overall goal of the water quality projects was to promote wide spread, voluntary adoption of
BMPs to protect and improve water quality. For the North Carolina project, the USDA-Agricultural
Research Service was added to the cooperative effort to provide water quality monitoring capability so
that the water quality impacts of the BMPs could be assessed.
Duplin County has the highest agricultural income of any county in North Carolina and also ranks as
one of the highest poultry and swine producing counties in the United States (NC Agricultural Statistics).
The county is typical of the southeastern Coastal Plain of the United States with sandy soils and relatively
shallow water tables. The lateral flow of shallow ground water is the primary source of base stream flow
in the region.
Grab samples and observations made before the project was initiated indicated higher than expected
ammonia and nitrate levels in certain sections of the watershed. Therefore, nitrogen was targeted as the
primary pollutant for reduction.
The total area of the Herrings Marsh Run Watershed is 5050 acres. Agricultural management
practices on the watershed are typical of the southeastern Coastal Plain and include approximately 2700
acres of cropland, 1750 acres of woodlands and 525 acres of farmsteads, poultry facilities, and swine
facilities. The remainder of the land area is in road right-of-way and ponds and lakes. The major
agricultural crops on the watershed include tobacco (324 acres), corn (1026 acres), soybean (650 acres),
wheat (300 acres) and vegetables (400 acres). The predominant soil series in the watershed is Autryville
fine sand; secondary soil series are Norfolk loamy sand, Marvyn-Gritney soil complex and Blanton sand.
Methods
Land Management and BMP Implementation Tracking
BMPs targeted for implementation included many traditional practices such as conservation tillage
and other erosion control measures as well as animal waste storage structures. There was also an
emphasis on implementing new and innovative practices such as turkey mortality composting and nutrient
management and waste utilization, both of which were based on soil type and realistic crop yields. In
addition to these new management practices, there were also landscape modifications, i.e., a riparian area
restoration and an in-stream wetland created by a beaver dam.
Many approaches were employed to monitor changing land management and treatment practices. The
land use information for those individuals who received technical or financial assistance was available
from the standard USDA records which are linked to farm and field numbers. Unfortunately, in many
cases, these records are not in electronic format, that could easily be geographically referenced to a
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watershed or hydrologic unit. This required manual sorting of data and digitizing of maps to enable the
information to be incorporated into a geographic information system (GIS). The GIS was used to facilitate
tracking the changing land use patterns on a watershed basis and relating any changes to changes in water
quality. To supplement these existing databases, on-farm surveys were conducted with area producers.
This required a time commitment from the producers to meet with a project technician and fill out a rather
detailed survey instrument. Finally, semi-annual windshield crop surveys were conducted. This consisted
of touring the watershed and noting on a map the crops in each field and the presence of new structural
practices. In the absence of better information, regarding crop inputs, average rates of fertilizer and
pesticide application and standard management practices were assumed.
Water Quality Monitoring
Surface water sampling stations were established in August 1990, at three locations within the
watershed (Figure 1). Station 1, Red Hill, was located at the stream outlet from the watershed. Station 2
was located along a tributary downstream from intensive swine and poultry operations. Station 3 was
located along the main channel flowing through woodlands. The woodlands located above Station 3 have
more substantial riparian buffers compared to those buffers on the streams above Station 2. Station 3 was
chosen to represent background conditions due to the large riparian buffers and relatively small areas of
agricultural production. Station 4 was installed in August 1991, to provide additional information about
the eastern portion of the watershed. The U.S. Geological Survey in Raleigh, NC, installed gaging
stations at the initial three sampling stations in April 1991, and the final installation was completed at
Station 4 in August 1991. At the gaging stations, stage height is measured at 15-minute intervals using
automated water level recorders and the flow was calculated from the stage-discharge curves developed
for each stream reach.
Automated water samplers installed in 1990 at each of the sampling stations were programmed to
collect daily, time-based daily composite samples. The automated samplers were reprogrammed in
October 1993, to collect 2-day composite samples comprised of 24 sub-samples taken at 120-minute
intervals. Beginning in November 1994, 3.5-day composite samples were collected. Each composite
sample consisted of 42 sub-samples collected at 120-minute intervals. Later, in March 1997, the samplers
were programmed to collect a 7-day composite sample consisting of 42 sub-samples taken at 240-minute
intervals. Diluted sulfuric acid was placed in the sampler bottles prior to sample collection to reduce
nutrient degradation. The acidified samples were collected each week for nutrient analysis. The sample
collection has been continual from October 1990 to the present time. All water quality data was converted
to weekly averages to provide a consistent data set.
The North Carolina Department of Environment and Natural Resource, Division of Water Quality,
investigated the macroinvertebrate biotic index, taxa richness, habitat variables and site conditions at the
watershed outlet and a nearby-unrelated watershed to produce a bioclassification for the watershed. The
bioclassification rating scale is: Good, Good-Fair, Fair, Fair-Poor, and Poor. The sites were evaluated
annually to assess changes.
A network of monitoring wells was established on a grid system throughout the watershed to assess
the general condition of shallow ground water. These wells were sampled monthly and analyzed for
nutrients and selected pesticides known to be used in the area. A residential well screening program was
also initiated to increase awareness of water quality issues among watershed residents and to assess the
status of drinking water quality.
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Results/Discussion
Land Management and BMP Implementation
Most producers in the watershed were willing to consider at least a singular BMP or a system of
BMPs. The use of traditional soil conservation and erosion control practices such as conservation tillage,
field borders, and grassed waterways has increased significantly. Relatively new practices were also
employed to reduce nitrogen sources and transport. These practices included nutrient management, waste
utilization, riparian area restoration, and in-stream wetlands.
Project personnel have developed nutrient management plans for nearly 2,500 acres (over 90% of the
cropland in the watershed). The amount of cropland being managed under of nutrient management plan
has increased significantly since the inception of the project (Figure 4). Producers have certified that plans
covering half of the acreage (1360 acres) are being followed. The status of the remaining plans is
uncertain due a lack of owner certification of adherence to their plan. It is hoped that at least some parts
of the plans are being followed. Several animal waste utilization plans were also developed following the
same principles of nutrient application; i.e., soil testing, matching crop nutrient needs to soil test results,
crediting of nutrients from animal waste and timing of nutrient application to meet the requirements of the
plant. The increase in the acres associated with waste utilization is a result of larger land application areas
on existing farms and more new swine facilities which began operating during the past seven years.
Theses new facilities have resulted in a doubling of the swine population in the watershed during the time
span of the project (Figure 5). The increase in the swine population was most noticeable in Subwatershed
3 which previously had relatively little swine production.
Surface Water
While the linkage between land use and improved water quality is difficult to establish with the
available data, the cumulative effects of BMP implementation and changing landscape features have
resulted in a decrease in the NO3-N concentration and mass flux leaving the watershed outlet (Figures 2
and 3).
The reduction of NO3-N concentration at Station 2 appears to be a direct result of a beaver dam
impoundment since the reduction corresponds with first signs of beaver construction activity (Figure 6).
While there are seasonal fluctuations, there has been a decrease in the mean NO3-N concentration at
Station 2.
The mean nitrate-N concentration at Station 3 has remained relatively unchanged even though the
subbasin monitored by this station has experienced the most significant increase in swine population. This
consistent water quality may reflect the adherence to approved practices and the presence of significant
riparian buffers in the subbasin. Due to the relative short amount of time since animals have been
introduced at the new swine facilities, the water quality results are inconclusive at this point.
The bioclassification of the watershed improved from Fair to Good-Fair following the 1995
evaluation (Lenant and Eaton, 1995). It is uncertain if this change was a response to BMP implementation
or the result of annual weather variations. The severe damage caused by Hurricanes Bertha and Fran
during 1996 caused the bioclassification of the watershed to decline from the Good-Fair rating to a Poor
rating following the 1996 evaluation. The watershed was not re-evaluated during 1997 due to a lack of
resources in the state agency doing the evaluation.
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Ground Water
The ground water NO3-N concentration was below the safe drinking water standard of 10 parts per
million (ppm) on 78 percent of the farms which had monitoring wells installed (Hunt et al., 1995). Only
five farms had wells with NO3-N concentrations exceeding the drinking water standard. One of these
farms, a swine farm, was then selected for more intensive study of Bmp implementation and water quality
impacts. The new management practices and increased land area for waste application have resulted in a
decrease in the average ground water NO3-N concentration at this site (Gale et al., 1996). When pesticides
were detected in monitoring wells, the concentrations were usually lower than the maximum contaminant
level (MCL) set by the U.S. Environmental Protection Agency (US-EPA). The incidence of higher
concentrations appear to be associated with the mixing of chemicals and their loading in chemical
application equipment.
Residential drinking water wells tended to have a higher incidence of both high (>10 ppm) NO3-N
and pesticide detections. The residential wells tended to be older wells of poor construction relative to the
monitoring well. Of the wells less than 100 feet deep, 27 percent had NO3-N concentration above 10 ppm
while none of the wells greater than 100 feet exceeded that level. Pesticides were detected in 34 percent
of the wells with two wells exceeding the MCL. These two wells were shallow (<30 feet deep) and had
historically been used to mix pesticides. Both of the wells were replaced with new, properly constructed
wells greater than one hundred feet deep.
Conclusions
Although it has not been possible to directly link individual BMPs with improvements in water
quality, on the watershed basis, the cumulative impacts of BMPs and landscape modifications have
resulted in a decrease in nitrate concentration and mass loading at the watershed.
The importance of landowner cooperation and commitment in a successful watershed project cannot
be over emphasized. Producers were most willing to adopt practices requiring the least risk or which
could be phased into their operations. Nutrient management based on soil type and yield potential was
one such practice that producers often initiated on a small acreage the first year, then expanded the use in
subsequent years as they gained confidence in the results. In some cases, such as animal waste manage-
ment, producers adopted practices they hope will preclude additional regulations or will enable them to
remain in compliance with changing requirements.
Data indicate that, in general, current pesticide BMPs used by local producers are successful in
maintaining ground water quality. The more frequent detection of nitrates and pesticides in residential
wells as opposed to monitoring wells indicates that improper well construction and use contribute to the
risk of contamination.
Lessons Learned
To document water quality improvements from BMP implementation on a watershed basis, it is
preferable to:
• Have a commitment up-front from producers to maintain detailed records.
• Design land treatment tracking and water quality monitoring schemes that can be matched on
hydrologic and temporal bases.
• Select a small watershed with the aim of reducing the time needed to detect changes in water
quality.
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« Isolate natural changes in a watershed, either gradual (beaver ponds) or catastrophic (hurricanes),
which can mask improvements in water quality brought about by BMP implementation.
• Employ multiple approaches to monitoring in order to provide the best chance of obtaining
meaningful results.
• Collect baseline water quality data to document the magnitude of change.
• Utilize a paired watershed design in which two similar watersheds are monitored for
approximately 2 years to determine their relationship to each other (calibration period) and then
BMPs are implemented in one of the watersheds (treatment watershed). A change in the
relationship between the treatment and the control watershed indicates the water quality impact
for the BMPs.
References
Gale, J.A., Osmond, D.L., Line, D.E., Spooner, J., Coffey, S.W., Humenik, F.J., Broome, S.W., Hunt,
P.O., Stone, K.C., Robillard, P.D. 1996. Understanding the Role of Agricultural Landscape Feature
Function and Position in Achieving Environmental Endpoints. Final Project Report to US-EPA.
North Carolina State University, Raleigh, NC.
Hunt, P.O., Stone, K.C., Humenik, F.J., Rice, J.M., Impact of Animal Waste on Water Quality in an
Eastern Coastal Plain Watershed, Animal Waste and the Land-Water Interface, edited by Kenneth
Steele, Lewis Publishers, 1995.
Lenant, D., Eaton, L. 1995, Preliminary investigation of the effects of Hurricane Fran on coastal area
streams, Memorandum to Ken Eagleson. Department of Environment and Natural Resources,
Division of Water Quality, Raleigh, NC.
1996 North Carolina Agricultural Statistics, North Carolina Department of Agriculture, 1996.
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Figure 1. Subwatersheds and stream monitoring stations within the Herrings Marsh Run Watershed.
3.5-
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Figure 2. Nitrogen reduction at the watershed outlet, nitrate-nitrogen concentration.
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2500
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1/1/90 1/2/91 1/3/92 1/3/93 1/4/94 1/5/95 1/6/96 1/6/97 1/7/98
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Figure 3. Nitrate-nitrogen reduction at the watershed outlet, mass flux.
W aste U tilization
Nutrient Management
91 92 93 94 95
Project Years
96
97
Figure 4. Nutrient management and waste utilization plan implementation in the
Herrings Marsh Run Watershed.
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111-84
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Alternatives for Evaluating Water Quality and BMP Effectiveness
at the Watershed Scale
Dr. George Ice, Principal Scientist
Dr. Ray Whittemore, Principal Research Engineer
National Council of the Paper Industry for
Air and Stream Improvement
Abstract
Water resource agencies have identified the need to develop watershed-scale assessments to evaluate
progress in meeting the goals of the Clean Water Act. EPA's Watershed Initiative exemplifies this new
focus. The forestry community has been a leader in developing these watershed evaluation techniques.
Three distinct approaches are discussed and examples are provided. These include large watershed-scale
monitoring, watershed-scale adaptive management assessments, and watershed modeling/monitoring
combinations.
Large watershed monitoring differs from site-specific or small watershed monitoring in the critical
treatment of transport and fate monitoring. Often, these studies involve measurement of tributaries and
multiple reach response.
Adaptive management approaches are designed to learn from ongoing management. One well-
accepted adaptive management approach, Watershed Analysis, is a structured procedure for examining
watershed conditions, landscape and management hazards, and aquatic resources at risk. Another adaptive
management approach for watersheds is the Source Search Method. This can involve a synoptic survey to
identify "hot spots" associated with specific management and site condition combinations.
One of the most appealing approaches is the development of realistic models. Models can be used to
test different alternatives and are not confounded by the weather or watershed variability associated with
even well-paired adjacent basins. The development of calibration and validation data sets is critical to
making models effective. Some examples of watershed-scale models used in assessing water quality
response include DHSVM, BOISED, and BASINS2.
These examples demonstrate that modeling and monitoring should be coordinated to efficiently assess
BMPs at the watershed scale.
Introduction
The Federal Clean Water Act and development of state nonpoint source control programs for
silviculture necessitated evaluation of control practices. While early assessments focused on site-specific
impacts and local response, more comprehensive assessments are now being required to assess the
effectiveness of controls at a watershed scale and cumulatively with other activities. This paper provides a
guide to approaches useful for evaluating the effectiveness of forest practices at the watershed scale.
Evolution of Forest Watershed Protection in the United States
Protection of watershed and water quality on forest lands predates passage of the Federal Water
Pollution Control Act Amendments of 1972 (PL 92-500), commonly referred to as the Clean Water Act.1
The Federal Water Pollution Control Act Amendments of 1972 entirely replaced previous legislation including the Federal Water Pollution
Control Act of 1956. It has been periodically amended and is referred to as the Federal Water Pollution Control Act as Amended by the Clean
Water Act of 1977 (Bureau of National Affairs, Inc. Environmental Reporter 1993).
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Observations concerning impacts to streams from forest management activities go back to the birth of
forestry in the United States (Schenck 1955). The origins of the National Forest system are tied directly to
watershed concerns. Steen (1991), writing about the early legislation that established the National Forests,
concluded that: "the primary driving force behind forest reserve legislation at that early time was the
protection and enhancement of water supplies, including flood protection."
Forest watershed studies began early in the century (Bates and Henry 1928) and have continued and
intensified at a number of well-known sites in this country (e.g., Coweeta Hydrologic Laboratory,
Hubbard Brook Experimental Forest, H.J. Andrews Experimental Forest, and Caspar Creek Watershed).
However, passage of the 1972 Act greatly accelerated the development of state water quality protection
programs for forestry and the need to assess the effectiveness of control practices. First, the Act
recognized the need for area-wide protection programs. Under Section 208, states were required to
develop area-wide (watershed or regional) water quality management plans. Second, the Act recognized
two classes of pollutants: point sources, which are "end-of-pipe" sources usually associated with
industrial facilities or municipal waste treatment plants; and nonpoint sources, which are diffuse sources,
often generated by hydrologic events, that are difficult to distinguish from background loads.
One step in developing area-wide water quality protection programs was to "...identify, if
appropriate, agricultural and silvicultural nonpoint sources of pollution..." and to develop ".. .procedures
and methods (including land-use requirements) to control, to the extent feasible, such sources" (Senate
Committee on Environmental and Public Works 1978).
Originally, this area-wide planning focused on locations identified as having water quality problems.
This approach was successfully challenged in court by the Natural Resource Defense Council (NRDC vs.
Train). As a result, all state lands (not just designated areas) were subject to planning under Section 208.
In 1975, as required by NRDC vs. Train, EPA developed revised regulations to implement Section
208. Rey (1980) reported that the new regulations established the concept of Best Management Practices
(BMPs) as the appropriate tool for nonpoint source control. EPA defined a BMP as:
"a practice or combination of practices, that are determined by a state, or designated area-
wide planning agency, after problem assessment, examination of alternative practices,
and appropriate public participation, to be the most effective, practicable (including
technological, economic and institution considerations) means of preventing or reducing
the amount of pollution generated by nonpoint sources to a level compatible with water
quality goals."
The concept of "better land-management practices" for forest watersheds goes back at least 50 years
(Craddock and Hursh 1949). However, the EPA regulations for the 208 programs solidified BMPs as the
nonpoint source control tool of choice. O'Laughlin (1996) stated that:
"BMPs include structural and nonstructural measures, operational and maintenance
procedures, and distribution and scheduling of activities. These are all aimed to minimize
soil erosion and stream sedimentation, and together comprise a system of interacting
measures, rather than a single practice, for application on a site-specific basis to reflect
site-specific conditions."
Operationally, BMPs are useful for forest nonpoint sources because they are often designed to prevent
adverse impacts before they occur, and they provide a measure of certainty about the operator's
responsibility to protect water quality. Also, it is generally easier to assess BMP implementation than it is
to measure water quality standard compliance in dynamic forest stream systems. However, this means
that state and federal agencies must connect BMPs with the goals of ".. .reducing the amount of pollution
generated by nonpoint sources to a level compatible with water quality goals" (Rey 1980).
Initial assessments of BMP effectiveness involved research plots, field evaluations, small paired
watershed studies, and application of agricultural models like the USLE to forest conditions. These all
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provide valuable information, but they are also limited. Most were designed to provide only a local
assessment of impacts, often to an individual operation. The assumption was that if impacts could be
minimized at the site, they would be diluted downstream. However, concerns about cumulative effects
caused forest managers to explore alternative assessment approaches.
Evolution of Watershed-Scale Assessments
Cumulative watershed effects (CWE) for forestry are defined as ".. .changes to the environment
caused by the interaction of natural ecosystem processes with the effects of two or more forest practices."
Early assumptions about downstream dilution, transport of impacts, and stream response were challenged.
Channel classification became an important factor in interpreting the potential for response to upstream
management impacts. Federal agencies began analyzing for cumulative watershed effects from forestry
under the National Environmental Policy Act of 1969 (NEPA) and requirements of the Clean Water Act
(Coats and Miller 1981). Most of the early assessment methods used additive models that accumulated
material loads to streams or indirect indexes of cumulative effects such as the equivalent roaded area
method. In some National Forests, management limits were set, based on the percent of watershed
harvested or roaded, to avoid cumulative effects. Often these limits were imposed without regard to site
conditions, practices, or BMPs applied to control impacts. These types of limits continue today in
proposals such as the Columbia Basin Intermountain Plan. With maturation of CWE assessments,
watershed specialists began to recognize both the dynamic nature of watersheds and streams, and the
potential for and need to address operational fall-down of BMPs (Callaham and DeVries 1987). This led
to development of BMPs such as "diversion proof road designs" (Hagans and Weaver 1987) and "debris
torrent-resistant road crossings" designed to minimize impacts to watersheds during extreme events.
Coincident with the rise in interest in CWE assessments, watershed management received increasing
attention by the early 1990s. This attention was highlighted by the overwhelming interest in the
Watershed '93 Conference and the growth of watershed-related organizations like the Watershed
Management Council and the American Institute of Hydrology. In recent years, watershed-scale
assessments were further stimulated by legal requirements to develop total maximum daily load limits
(TMDLs) and the growth of GIS technology and landscape ecology methods capable of addressing
spatially complex watershed problems.
Alternatives for Evaluating BMP Effectiveness at the Basin Level
Forest nonpoint source control programs are increasingly being asked to assess the effectiveness of
BMPs at a watershed scale for multiple activities over long time periods to demonstrate that watershed,
water quality, and landscape ecology goals can be met.
The Washington State Timber/Fish/Wildlife (TFW) Water Quality Monitoring Steering Committee
report (1997) identifies long-term watershed trends as a key component to assessing BMP effectiveness.
Basin-wide cumulative response to BMPs remains one of several "potential" issues for the forestry
community in its efforts to validate the effectiveness of BMPs. But larger scales for both spatial and
temporal assessments create problems for those attempting to validate BMPs. While this is a difficult
task, there are three general approaches: adaptive management approaches, large watershed monitoring,
and models.
Small watershed studies, upstream/downstream monitoring of management activities, and direct on-
slope measures of BMP effectiveness provide for much more control of conditions and opportunities for
replication of study sites. So why is it desirable to have a large watershed-scale component for monitoring
effectiveness? There are at least five reasons. A watershed-scale assessment allows for an integrated or
cumulative measure of BMP and program effectiveness. It allows BMP effectiveness to be placed in the
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context of realistic variations in water quality throughout a watershed (see "source search" discussion in
Adaptive Management Approaches section) and over time; it allows for assessment of the
conservative/non-conservative nature of water quality parameters; it connects upslope hazards with
downslope aquatic resource risks; and it allows for assessment of unanticipated consequences that might
not be identified at the site scale. We can show that overall water quality has improved for the nation
since the 1972 Federal Water Pollution Control Act Amendments were enacted. We can document that
silvicultural BMPs have the capability to protect water quality. But can we document that overall water
quality is benefiting from forest NFS control programs?
One other important reason for looking at effectiveness on a watershed scale is new information about
what scales are most sensitive to management. While the smallest headwater streams can have the
greatest changes because impacts are not diluted and load-to-flow ratios are the greatest, these systems are
also the most variable naturally. Often, headwater systems are exposed to dramatic changes in flow and
water quality even without management impacts and organisms are adapted to these fluctuations. In large
streams, the non-conservative nature of pollutants causes upstream impacts to be muted as the system
moves toward equilibrium with its environment. Sensitive reaches, such as unconfmed channels or
deposition zones, may occur at key locations in the watershed, and risks to these sites need to be linked
with upslope hazards.
Adaptive Management Approaches
Adaptive management approaches are designed to utilize ongoing management as a test from which
to learn. Probably the two most useful examples of adaptive management monitoring at the watershed
scale are source searches and watershed analysis.
Source search or synoptic survey assessments involve a snapshot of water quality throughout a basin
at one time. While there are potential problems associated with this type of infrequent monitoring view
(i.e., potential differences in hydrologic conditions in the watershed or unrepresentative conditions for
estimating pollutant loads), this can be a powerful tool. An example is the Mokelumne River Watershed
in the Sierra Nevada Mountains of California, where accelerated forest management activities in the
watershed were blamed for eutrophication of downstream reservoirs. A synoptic survey found that 5% of
the watershed (below the portion of the watershed experiencing forest management) was contributing
60% of the nitrate load (Dahlgren 1996). This was found to be a result of geologic sources. Annual
variations in eutrophication were found to correlate better to basin water yield and lake residence time
(well-known factors affecting eutrophication).
Watershed Analysis (WA) is ".. .a structured approach to develop a forest practice plan for a
[Watershed Administrative Unit] based on a biological and physical inventory" (Washington Forest
Practices Board 1993). Information about watershed hazards from forest management and public
resources at risk are used to develop watershed-specific prescriptions (Figure 1). For example, there may
be evidence of past erosion in the watershed associated with roads constructed on erosive soils near
streams without proper control of runoff. Sediment transported downstream from these sediment sources
may have resulted in the filling of pools and degradation of habitat quality for fish. These types of
observations result in "conditioning" of BMPs to prohibit or place management limits on roads
constructed in the landtypes and conditions found to cause problems. WA has some very structured
elements, such as:
• minimum education and training requirements for WA team members
• standard assessment methodologies
• module synthesis by the expert teams
• management and public involvement
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• rewards for participation
• situational syntax and hypothesis development
• monitoring of performance and use of triggers for reassessment
Watershed Analysis provides useful stand-alone watershed scale information by developing
hypotheses that connect upslope hazards with downstream risks through key watershed processes. It has
stimulated development of basin-scale assessment tools. It has also allowed sharing of experiences
between watershed assessments.
Large Watershed Monitoring
How does a large watershed monitoring study differ from a small basin study? Obviously the scale of
the watershed differs, but the questions to be addressed and the monitoring design also differ to some
extent. Small basin studies usually measure the integrated response to management and may provide
some on-slope or upstream monitoring to support the assessments about impacts. Large watershed studies
are usually designed to measure tributary and multiple reach response to assess the conservative/non-
conservative nature of pollutant/flow transport. This can allow an interpretation of the relative hazards in
the watershed (erosive soils), risks (i.e., deposition zones), and connections. One of the first examples of
this type of large watershed study was Caspar Creek in California, where multiple subbasins and mid-
reach sections were monitored (Cafferata 1984). Another example is the Mica Creek watershed study in
Idaho (McGreer et al. 1995). For Mica Creek, watersheds are nested to allow assessment of impacts as
they are routed downstream. Watershed 3 (613 acres) in the headwaters of the West Fork of Mica Creek
serves as a control to adjacent Watersheds 1 (354 acre) and 2 (440 acres). Watershed 5 on Mica Creek
(1,655 acres) serves as a control for the watershed formed below 1, 2, and 3 at Station 4 (1,457). Station 6
downstream on Mica Creek (3,616 acres) is a control for Station 7 (3,031) on the West Fork of Mica
Creek (Figure 2).
The Alabama Demonstration Watershed Project provides another example of a large basin monitoring
effort. This effort involves a screening of 2,500 to 12,000 acre (4 to 18 mile ) subbasins within the
Sepulga River Basin of southern Alabama (Lockaby et al. 1996). The total Perdido-Escambia River
Basin, including the Sepulga Basin, covers over 5,000 square miles. Comparisons are being made
between land-use activities, BMP implementation, and water quality for the subbasins. More detailed
monitoring of subbasins, including source search for pollution-generating sites, will occur as contrasting
land-management history/water quality subbasins are identified.
One other kind of large watershed scale monitoring approach takes advantage of remote sensing
opportunities to collect information where site-specific information is not useful or where technology
provides advantages for basin-wide assessments. Aerial reconnaissance methods are used to assess forest
management impacts that are often widely distributed but observable from the air. For example,
landslides are traditionally measured at the watershed or even regional level using aerial photos or aerial
reconnaissance. Recent work by the Oregon Department of Forestry is providing some information about
the detection bias associated with aerial photo and aerial reconnaissance methods compared with on-the-
ground measurements (Robison 1996). NCASI is working with researchers at Oregon State University to
develop a low cost, rapid response video method for recording landslides (Rosenfeld et al. 1996). Thermal
remote sensing has also been used to track stream temperatures quickly throughout a basin (Torgersen
1996).
NASA has recently released a Research Announcement to "...solicit proposals for the establishment
of Regional Earth Science Application Centers (RES ACs) designed to apply remote sensing and attending
technologies to well-defined problems and issues of regional significance." Real-time use of remote
sensing information by water resource managers is one of the sector-specific problems identified. The
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application of large-scale remote sensing information to water quantity and quality assessments could
present a major advance in watershed assessment.
Models
One of the major problems with developing large paired basin comparisons is that even small
adjacent watersheds can experience dramatically different weather patterns. One of the approaches that is
most appealing to foresters is the development of realistic models that can simulate response to forest
management practices at both the site and watershed scale. This approach allows forester managers to
predict the impact of their actions on water quality and develop management solutions, rather than just
documenting impacts after the fact. Unfortunately, much work has gone into developing various
watershed models, while little work has gone into validating these models, calibrating them to different
watershed conditions, and making them user-friendly. One example is the effort to apply the USLE to
forest watershed assessments (James and Hewlett 1992). Conversion of GIS data was found to create
major modeling errors and overestimates of erosion and sedimentation.
NCASI has been working with the University of Washington and Battelle Northwest Laboratories to
validate the Distributed Hydrologic-S oil-Vegetation Model (DHSVM) against watershed data collected
by companies for different conditions. "DHSVM accounts explicitly for the effect of topography and the
spatial distribution of land surface processes at the scale of currently available Digital Elevation Models
(30-90 m)" (Wigmosta 1996). Features include spatially distributed, digital elevation model grid-based
approach; automated model setup using the GIS ARCINFO; explicit, spatially-distributed representation
of road networks; spatially distributed vegetation and soils properties; topographic control on absorbed
short-wave radiation, precipitation, and downslope water movement; a two-layer soil rooting zone model;
a spatially distributed two-canopy evapotranspiration model; simplified topographically-driven surface
and subsurface flow routing; GIS post-processing of model outputs; and channel flow routing (Figure 3).
Testing of DHSVM is occurring on gaged industry watersheds, including the Little Naches in central
Washington, the Deschutes in western Washington, Mica Creek in northern Idaho, and Carnation Creek
on Vancouver Island (Figure 4). This model has been converted from requiring a mainframe computer to
a PC-based Windows NT environment. A users manual is planned for development in 1998.
A very useful application of a well-calibrated empirical model is provide by Megahan et al. (1992).
Megahan et al. used BOISED, a version of the Forest Service R1-R4 sediment yield prediction model
(WATSED), to provide a retrospective assessment of sediment yield for a tributary of the South Fork of
the Salmon River. WATSED is based on locally derived empirical streamflow and sediment yield data,
and uses stand properties and landscape units defined in terms of landform, lithology, and soil character-
istics. Onsite surface and mass erosion estimates are adjusted for slope delivery based on topographic
conditions, and downstream sediment delivery is adjusted on the basis of a watershed sediment delivery
ratio. The model is sensitive to alternative forest cutting and soil disturbance activities, including
silvicultural practices, alternative road construction practices, and wildfire. Megahan et al. estimated that
abusive jammer logging and associated road construction boosted sediment yields from about 450 Mg of
sediment to over 1300 Mg. With present day BMPs, the authors estimate sediment increases could have
been reduced by 45 to 95% (Figure 5).
An emerging tool for modeling is BASINS2 (Whittemore and Ice 1998). BASINS2 is a comprehen-
sive EPA software package recently released by the Office of Water. It is designed to enable water quality
analysts and watershed managers to perform studies using geographic information system (ArcView),
watershed landuse and water quality monitoring data, and state-of-the-art environmental assessment tools.
BASINS2 provides information for any of the 2,150 watersheds in the conterminous United States. It
incorporates models such as the Nonpoint Source Model (HSPF version 11), TOXIROUTE, and
QUAL2E. B ASINS2 has much promise, but its coarse spatial scale and treatment of land-use activities do
not support assessments of alternative forest management activities at this time (Figure 6).
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Monitoring/Model Integration
Clearly, modeling and monitoring are inseparable (EPA 1997). Even detailed process models require
calibration and validation studies to ensure that they are performing acceptably. In the future, we may
begin to develop hybrid monitoring and modeling systems which incorporate real-time remote sensing
data to parameterize models and provide for rapid calibration to existing conditions. These models can
then be used to test alternative management scenarios. In the interim we must continue to use the results
of past experience along with careful translations of those experiences to the site- and watershed-specific
conditions encountered.
Literature Cited
Bates, C.G., and Henry, A.J. 1928. Forest and streamflow at Wagon Wheel Gap, Colorado—Final Report.
Suppl. 30. Monthly Weather Rev.
Cafferata, P. 1984. The North Fork of Caspar Creek: a cooperative venture between CDF and USFS.
Jackson Demonstration State Forest Newsletter.
Callaham, R.Z., and DeVries, J.J. [Eds.]. 1987. Proceedings of the California Watershed Management
Conference. Wildland Resource Center. University of California: Berkeley, CA. Report 11.
Coats, R.N., and Miller, T.O. 1981. Cumulative silvicultural impacts on watershed: a hydrologic and
regulatory dilemma. Environment Management 5(2): 147-160.
Craddock, G.W., and Hursh, C.R. 1949. Watersheds and how to care for them. In Trees: yearbook of
agriculture 1949. USDA, Washington, DC. p. 603-609.
EPA. 1997. Models 2000 Workshop, December 15-16. Athens, GA.
Dahlgren, R.A. 1996. The use of a source-search study to address concerns about eutrophication of lakes
in the Mokelumne River basin. In Proceedings of the 1995 NCASI West Coast Regional Meeting.
Special Report No. 96-04. National Council of the Paper Industry for Air and Stream Improvement,
Inc.:Research Triangle Park, NC. p. D17-D19.
Hagans, O.K., and Weaver, W.E. 1987. Magnitude, cause and basin response to fluvial erosion, Redwood
Creek basin, northern California. In Erosion and Sedimentation in the Pacific Rim. Beschta, R.L.,
Blinn, T., Grant, G.E., Ice, G.G., and Swanson, F.J. [Eds.]. IAHS Publication No. 165. International
Assoc. of Scientific Hydrology: Wallingford, Oxfordshire, p. 419-428.
Ice, G.G., Olszewski, R., Glass, D., Sugden, B., Cundy, T., McGurk, B., Whittemore, R., and Megahan,
W. 1997. Development and evaluation of watershed models for integrated assessment of point and
nonpoint source effects on water quality. In Proc. International Emerging Technologies Conference.
Pulp and Paper, Orlando, FL.
Ice, G.G., and Holloway, J.M. 1996. Modeling nitrogen and phosphorus runoff from managed forest
watersheds: do current watershed analysis and TMDL approaches work? Abstract to Nitrogen
Cycling in Forested Catchments Conference. AGU, Sunriver, OR.
James, D.E., and Hewitt, m, M.J. 1992. To save a river. Geo Info Systems 2(10):III36-49.
Lockaby, G., Teeter, L., Flynn, K., MacKenzie, M., and Feminella, J. 1996. Relationship between
watershed characteristics and non-point source pollution: cumulative impacts. Draft Study Plan.
Auburn Univ. School of Forestry: Auburn, AL.
McGreer, D.J., Cundy, T.W., and Gravelle, J.A. 1995. Mica Creek cumulative watershed effects study. In
Watershed Management: Planning for the 21s' Century.[Ward, T.J., Ed.] ASCE: New York, NY. p.
300-309.
Megahan, W.F., Potyondy, J.P., and Seyedbagheri, K.A. 1992. Best management practices and
cumulative effects from sedimentation in the South Fork Salmon River: an Idaho case study. In
Watershed Management: Balancing Sustainability and Environmental Change. Naiman, R.J. [Ed.].
Springer-Verlag: New York, NY. p. 401-414.
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O'Laughlin, J. 1996. Idaho water quality policy for nonpoint source pollution: a manual for
decision-makers. Report No. 14. University of Idaho: Moscow, ID.
Perkins, W.A., Wigmosta, M.S., and Nijssen, B. 1997. Development and testing of a road and stream
drainage network simulation within a distributed hydrologic model. Pacific Northwest National
Laboratories, Battelle, NW: Richland, WA.
Rey, M.E. 1980. The effects of the Clean Water Act on forest practices. In Proceedings of Symposium on
Forestry and Water Quality in the 80s. Water Pollution Control Federation: Washington, DC. p. 11-
30.
Robison, G. 1996. Flood effects overview. Forest Log. p. 18.
Rosenfeld, C.L., Gaston, G.G., and Pearson, M.L. 1996. Integrated flood response in the Pacific
Northwest. EOM (November 1996 reprint).
Schenck, C.A. 1955. The Biltmore story. Minnesota Historical Society: St. Paul, MN.
Senate Committee on Environment and Public Works. 1978. The Clean Water Act showing changes made
by the 1977 amendments. Series No. 95-12. US Gov. Printing Office: Washington, DC.
Steen, H.K. 1991. The Beginning of the National Forest System. FS-488. USDA Forest Service.
Torgersen, C.E. 1996. Multiscale assessment of thermal patterns and the distribution of Chinook salmon
in the John Day River Basin, Oregon. M.S. Thesis. Oregon State Univ.: Corvallis, OR.
TFW Monitoring Steering Committee. 1997. Workshop Review Draft-TFW Effectiveness
Monitoring/Evaluation Program Description and Project Plan.
Washington Forest Practices Board. 1993. Board Manual: Standard Methodology for Conducting
Watershed Analysis. Version 2.0, Washington DNR, Olympia, WA.
Whittemore, R., and Ice, G.G. 1998. Watershed management in the forest products industry:
implementation of watershed assessment methods. Paper presented at Watershed Management:
Moving from Theory to Implementation,. Denver, Colorado. Water Environment Federation.
Wigmosta, M.S. 1996. A process-based GIS modeling system for watershed analysis. In Proceedings of
the 1995 NCASI West Coast Regional Meeting: Special Report 96-14. National Council of the Paper
Industry for Air and Stream Improvement, Inc.: Research Triangle Park: NC. p. D38-D48.
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Identified
Hazards
Resource
Assessment
Watershed Processes
Public Resources
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• resources
. \
Figure 1. Feedback in Washington watershed analysis is designed to connect watershed hazards and risks,
develop prescriptions to address impacts, and provide effectiveness monitoring.
Scale 1:45,500
Figure 2. Locations and design of Mica Creek Watershed Study in northern Idaho, showing
controls at different scales.
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stream
roads
Figure 3. DHSVM model utilizes CIS data to simulate spatially explicit hydrologic response and route
discharge through road and stream reaches.
Observed and simulated discharge 1993 -1995.
Observed
Simulated
10/01/93
01/01/94
04/01/94
07/01/84
10/01/94
1
01/01/95
04/01/95
Figure 4. A comparison between observed discharge for The Little Naches River in central Washington and
simulated discharge using DHSVM (from Wetherbee and Lettenmaier in press).
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1400
1200
1000
OB
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® 600
400
200
First logging
Wildfire
Hist. Record -•- Impr. Design
helicopter — Undisturbed
Max. Mitigation
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995
Years
Figure 5. Megahan et al. (1992) used WATSED to demonstrate how BMPs have reduced sediment
yield from historic loads in Dollar Creek, ID.
Figure 6. BASINS2 combines easy access to data, integrated watershed models,
and a PC-compatible Windows environment.
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Water Quality Monitoring in a Developing Coastal Region:
Fear and Loathing in Calabash, North Carolina
Janice E. Nearhoof, Research Associate
Department of Biological Sciences
University of North Carolina at Wilmington, Wilmington, NC 28403
Phone: (910) 962-7338; Fax: (910) 962-4066; E-mail: nearhoofj@uncwil.edu
Lawrence B. Cahoon, Professor
Department of Biological Sciences
University of North Carolina at Wilmington, Wilmington, NC 28403
Abstract
Public responses to a water quality monitoring project in south Brunswick County, a rapidly developing
region of coastal North Carolina, illustrate the challenges even such a relatively benign effort can face, aside
from issues of access and basic safety. This project is intended to provide baseline water quality data for a 55-
square-mile region that will be served by a combined wastewater and stormwater management system, the
first of its kind in coastal North Carolina. Data are intended to yield a statistical description of existing water
quality, help identify problem areas that require enforcement or other remedial actions, and evaluate the
effectiveness of the regional system as its programs are implemented.
Local responses to the water quality monitoring program provide valuable lessons for others initiating
monitoring projects in such areas. Problems included misperceptions about the nature and purpose of water
quality monitoring, sometimes resulting in hostility; opposition to monitoring activity because it was thought
to be associated with development; fear that certain water pollution sources might be identified; difficulties
with regulatory and enforcement agencies; concerns about publicity for findings of "poor" water quality; and,
in some cases, determined opposition to the regional wastewater and stormwater management concept.
Positive experiences included improving public support for and awareness of good water quality, direct
responses to citizen concerns about specific locations and problems, development of working relationships
with regulatory and enforcement authorities, and effective cleanup actions. Monitoring programs of this kind
can benefit from effective publicity and public information from the outset of the effort, strong support from
sponsors, responsiveness to the public, demonstration of the reliability of data, and achievement of visible
results.
Introduction
The influx of tourists and permanent residents is swelling the population centers of North Carolina's
coastal counties, carrying with it many of the problems associated with land disturbance, waste water
treatment and increased stormwater runoff. The 1990 U.S. Census forecasts a 12.3 percent increase in the
state's population, from 6,632,449 to 7,444,960 by the year 2000. Thirty-two percent of North Carolina
households can be found in the coastal region with an 18 percent increase expected by the year 2000.
The coastal plain of North Carolina is characterized by gentle slopes, low elevations, sandy soils and
a shallow water table. The area covered by wetlands is substantial; it is estimated that 95% of the
wetlands in North Carolina occur in the coastal area (Wilson 1962). Open fresh water covers
approximately 1,560 km2, regularly flooded salt marshes 236 km2, with sounds and bays covering 9,160
km2 (Kuenzler et al 1977).
This physiography poses an intriguing challenge for wastewater treatment. An assessment of septic
systems in coastal North Carolina found many in violation of the regional drain line to water table
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separation (Duda and Cromartie 1982). These conditions result in incomplete nitrification and diminished
treatment of fecal pathogens. Septic tank pollution of ground water has occurred in Emerald Isle,
Morehead City and Bogue Banks in Carteret County (Kuenzler et al 1977).
Brunswick County is located on the southeast coastal plain directly on the North Carolina/South
Carolina border (Figure 1). Because of its water orientation Brunswick County's year-round population
has grown from 35,777 in 1980 to 50,985 in 1990 (Hayes and Associates 1995). The most intense growth
has centered around Shallotte Township's coastal zone (Dewberry and Davis 1995), which includes areas
along the intracoastal waterway, lake front lots and Sunset Beach. Horry County, South Carolina, lies
immediately across the state line and includes the popular water front resort of Myrtle Beach. Horry
County has seen significant population growth in the past 20 years, increasing from 69,992 in 1970 to
144,050 in 1990.
Average precipitation in Brunswick County is 52 inches per year, the maximum amount of water
available for ground-water recharge (Heath 1997). Depending on the underlying soil, recharge rates in
Brunswick County range from 4 to 12 inches per year, the latter being one of the highest rates in the state.
The areas of recharge are susceptible to contamination from stormwater runoff. Surface soil in the area
consists of a 1,200-foot-thick bed of sand, silt, clay, seashells, limestone and sandstone. Sinkholes, caused
by water high in dissolved carbon dioxide percolating down into and dissolving the underlying shell and
limestone layers, are common in the area (Heath 1997).
The aquifers underlying Brunswick County are current and future sources of potable water. Most of
the soils in south Brunswick County are classified as severe for septic tank absorption due to wetness,
flooding, ponding, high water table and poor filtration qualities (Hayes and Associates 1995). Septic tank
drain fields and land spreading of municipal and industrial waste may already be adversely affecting the
quality of water in the surficial aquifer (Heath 1997). Water in the deepest aquifers in Brunswick County,
i.e., those with depths 800 to 1200 feet below the surface, contain water about half as salty as sea water.
South Brunswick County includes old time residents, recent retirees, small family farms, apartment
complexes, beach front homes, golf communities and businesses. Approximately 12,000 tourists visit the
area each day during the summer months (NCDEH 1997). Most have been drawn there for the recrea-
tional opportunities afforded by the area's proximity to the ocean and to enjoy the well manicured golf
courses. Fishing, swimming, boating and sailing are also common activities, but shellfishing has been
closed for some time due to average fecal coliform counts above the state's limit of 14 colony forming
units (cfu)/100ml.
Sewage treatment is provided by a few small package plants in Oyster Bay, Ocean Isle, Bricklanding
Plantation and Carolina Shores. Most domestic wastewater treatment in the 201 Planning Area is
accomplished with onsite septic tanks that may reach densities as high as 8 per acre. The restaurants in
downtown Calabash have septic tanks that must be pumped daily or weekly during the summer months
(NCDEH 1997).
Stormwater management is not consistent throughout South Brunswick County. The town of Sunset
Beach adopted a Stormwater Management Ordinance on August 7, 1995 that is stricter than the current
state policy in that it requires existing property owners to comply with the new regulations regardless of
previous practices. Sunset Beach also has an ordinance officer who actively seeks compliance from local
developers and homeowners. Other areas of the 201 Planning Area are not monitored as closely and many
construction locations lack visible siltation control measures. Stormwater management consists of man-
made ditches, small retention ponds in some housing complexes and diversion of runoff into existing
streams and the intracoastal waterway. In rural areas farmland and building sites are ditched along their
perimeters with heavy equipment to remove water. Natural water courses have been piped and covered
over in the upper drainage basin of the Shallotte River to facilitate golf course construction. Some
sections of the intracoastal waterway, small streams and ponds have riparian buffer zones, but vegetation
along two coastal lakes has recently been removed. Buffer zones, necessary to trap runoff to prevent
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sediment and nutrients from entering waterways, can be found on some waterfront properties. The county
maintains greenboxes for refuse collection in some locations although their placement on the banks of
major water courses needs to be questioned. During storm events much of the household garbage,
bathroom trash and construction debris is washed into the upper reaches of the Caw Caw River. One
refuse station was cleaned up after photographs of the site were shown at a public meeting. A second site
is still waiting for cleanup by the county. Bags of household garbage have been petition for a Contested
Case Hearing, Docket 95-0828, Minnie Kelly Hunt et al., on July 20, 1995. On December 14, 1995,
Administrative Law Judge Thomas R. West issued a written Recommended Decision to the NCDEHNR
directing that an Environmental Impact Statement (EIS) be prepared in conjunction with the proposed
SBWSA project. SBWSA volunteered without further litigation to prepare an EIS and to incorporate
wastewater treatment and a stormwater management plan into one regional management scheme.
The Water Quality Assessment portion of the EIS document requires a comprehensive look at
existing water quality by implementing an aggressive and thorough water quality monitoring program. On
October 15, 1996, SBWSA contracted with the University of North Carolina at Wilmington, Department
of Biological Sciences to perform water quality monitoring. These data would be used to establish
baseline water quality, identify problems and to measure improvements in water quality as regional
management practices are initiated.
Monitoring Program
The SBWSA 201 Planning Area is in the coastal plain of the Lumber River Basin and includes the
subbasins of the upper Caw Caw River, the upper basin of the Shallotte River, the entire subbasin of
Calabash Creek, as well as a coastal drainage subbasin situated directly behind Sunset Beach. The
Shallotte River system consists of 11 streams with a total area of 87 square miles, Calabash Creek
consists of 6 streams with a combined area of 19 square miles and the upper area of the Caw Caw consists
of drainage from the Shingletree Swamp, the Caw Caw Swamp and the Persimmon Swamp (Dewberry
and Davis 1995). The lower portion of Calabash Creek is influenced by tidal action, but the sections
above the dams at Sunset Lake (upper northern branch) and Medcalf Lake (upper eastern branch) are
freshwater. Drainages in developed areas are usually man-made ditches, as are many of the road side
drainages. Some natural flows have been diverted or piped underground to accommodate golf course and
road construction. Most of the fresh surface water in the Project Area is classified C for secondary
recreation, fishing, and aquatic life propagation and survival. Tidally influenced waters are designated SA
for shellfishing, primary recreation and fishing, as well as aquatic life propagation and survival.
Approximately 78 percent of the area is forested.
Thirty-six locations are being monitored for fecal coliforms, chlorophyll a, total suspended solids,
turbidity, pH, dissolved oxygen, salinity, total phosphate, total nitrogen and temperature. Monitoring
locations were chosen after the examination of aerial photographs and topographic maps and on site visits
to determine drainage patterns. Legal access and the safety of field personnel where also taken into
consideration. Monitoring locations represent drainage from residential areas, rural farmlands, business
districts, golf courses, the intracoastal waterway, construction areas and natural undisturbed areas.
Twenty-eight sites are fresh water including streams, ditches and golf course ponds. Tidally influenced
sites include the lower Calabash Creek drainage, Spartina marshes and navigable portions of the
Intracoastal Waterway. Each of the sites is sampled every three weeks. All laboratory analyses follow
Environmental Protection Agency (EPA) guidelines for standards and quality control.
Monitoring by UNC-Wilmington began in October 1996 and is planned to continue at least until late
in 1999. Monitoring by the SBWSA will begin upon completion of their onsite laboratory and will
continue indefinitely as part of the regional environmental management plan.
Funding for the monitoring project, construction of the treatment facility and the stormwater
management program is being provided by loans, stormwater assessment fees and sewage system hook-
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up charges. The towns of Calabash and Sunset Beach have agreed to loan the authority $200,000 each and
Brunswick County has loaned $25,000. A 37 million dollar bond package is also planned in the near
future. Stormwater management fees have been assessed from calculations of impact derived from aerial
photography, tax maps and GIS information, which, when combined, give an accurate estimate of
impervious surface for each parcel of land. Seventeen of the golf courses in the area not initially billed for
stormwater runoff because of the volume of information necessary for proper assessment, have received
statements for approximately $10,500 each. A proposal has recently been approved to cut the stormwater
fees for undeveloped land from 25 cents per acre to 7 cents. Existing homes will pay $375.00 for sewage
system hook plus varying line installation fees, while future users will pay $1,760.00 plus connection
costs.
Fear and Loathing
We were prepared to accept and answer concerns from some organized groups, but the initial public
response to the water quality monitoring project of suspicion and fear was unexpected. During the first
few public meetings many of the residents expressed a distrust of anyone associated with local or state
government. The fact that the research team was from a state university was apparently enough to make
many residents uneasy. Many held misconceptions as to the nature of water quality monitoring and how
the data were to be used. Some residents did not want their neighborhoods to be associated with "bad
water" while others refused to pay for something they didn't understand the need for. Some of these fears
were quenched during the first few meetings of the Water Quality Board, but others have surfaced and
have become acute problems for the monitoring team.
Harassment of field personnel began even before the first water sample was collected and analyzed.
During a photographic and site assessment field trip along a state right of way, one employee was
approached in a hostile manner by a unidentified individual. Intimidation of this sort continued with a
phone call from a local businessman who did not want us sampling in his area. Verbal abuse has been
directed at government employees in general and we were warned by a state agency of an antigovernment
militia group in the area. A few individuals have attempted to take water sample containers away from
our field technicians on more than one occasion. Contact with local law enforcement has helped keep
such matters from escalating, but the incidents have not stopped completely.
Many of the residents did not understand why they received a stormwater management assessment
fee, even though fliers with appropriate information had been included along with their usual water bills
for several months. One gentleman felt he was being billed "without representation" and declared
"You'all'r a bunch of communists and are gonna burn in hell." Some of the townspeople of Calabash, an
area that has centralized sewer, have protested against the project because they fear it will somehow
compete with "their" privately owned company. This is far from the truth, as the SBWSA is not intended
to compete for existing customers but to provide service to the many new residents. Data have revealed
water quality problems in specific areas and some locally elected officials have responded by attacking
the professional credentials of the research team. As a university laboratory involved in water quality
monitoring, we are not required to hold the state certification needed by a self-monitoring industrial
laboratory doing National Pollution Discharge Elimination System (NPDES) permit compliance work.
One individual has used this situation to declare on numerous occasions at public meetings and in the
newspaper that our data are not valid and should be ignored. We have also been accused of using
improper scientific technique, of being unqualified and incompetent and dishonest in our data reporting.
One individual, unhappy with our findings, contacted the university chancellor and demanded our
termination.
High fecal coliform counts, condoms and tampon applicators at one monitoring location indicated a
possible source of human sewage contamination. One local official blamed the high fecal coliforms on
"fish and turtles" and "warm and cold blooded mammals." Squirrels were cited for placing feminine
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hygiene articles into the creek that flows past a country club. Ducks were maligned for defecating to
excess a nearby pond. One individual suggested rainwater carrying feces from whales and dolphins as the
source of the fecal bacteria. In reality (a novel concept for some) septage haulers were not exercising
proper care when pumping off their trucks into a collection station up stream of the monitoring location.
The package plant using the same stream for effluent discharge has also had a problem with its chlorina-
tion and disinfection system, so improperly treated sewage has been discharged into the stream. On
occasion heavy rains have resulted in high fecal coliform counts in other nearby monitoring locations,
suggesting possible stormwater infiltration of sewage pipes and overflows. The septage haulers' dumping
privileges have been revoked, the sewage treatment plant is installing a UV disinfection system and
several repairs to pipes and lift stations have been undertaken. Refuse has been eradicated and fecal
coliform counts have diminished in some monitoring locations as a result.
The state's regional enforcement agency was notified of the excessively high fecal coliform counts
and the solid debris found in the discharge ditch. Their initial response was one of skepticism; but after
photographs, video tape and eyewitnesses came forward, a joint sampling scheme between our research
team and the agency was adopted. Our results concur closely with theirs and the initial difficulties have
been replaced with a positive professional relationship.
Many residents fear a regional wastewater system will encourage and promote uncontrolled
development in the area. Many of the undeveloped parcels in the Planning Area are not suitable for septic
tank installation. Hook-up to a sewage system would permit construction on some of these lots but not all.
Several developers are constructing individual package plants on site to handle sewage treatment for their
customers. Having many individual plants will put a strain on the already overloaded state inspection
teams and maintenance of several smaller plants will prove a burden to future residents. More people
continue to move into the area and development continues in spite of these difficulties.
Lush, well-manicured golf courses are a significant economic feature of the area, and more are under
construction. The application of fertilizers that contain a surfactant, which eases the task of mechanical
spraying, has sometimes resulted in a layer of white foam in some ditches and streams. Nutrient loading
has caused many of the fresh water lakes, ponds and canals to have excessive weed and algal growth.
Plant respiration at night and decomposing vegetation on the bottom drop oxygen levels in the water
column, stressing aquatic life. Better management practices and perhaps a greater understanding of the
problems associated with fertilizer runoff by greens managers will result in a decrease of nutrient
overloading.
A controversy exists over the traditional practice of ditching an area with heavy equipment to control
stormwater runoff. This method removes the water from the site, but it does not control the quality of the
water. This misconception has proven to be one of the more frustrating aspects of the project. This will be
a difficult concept to change, due to the unyielding attitudes of some community leaders. Many areas
suffer from heavy sedimentation, erosion of top soil, and nutrient runoff, again causing unwanted weed
and algal growth.
Conclusion
Current trends in South Brunswick County indicate a pattern of growth and future development,
consequently the need for wastewater treatment and stormwater management will increase. The Project Area
is expected to have a 172 percent increase in the residential population within the next 20 years (URS
Greiner, 1997). This growth must be accompanied by an acceptance and understanding of the need for
improved services. We feel that a strong public relations campaign at the onset of the project would have
alleviated many of the predicaments we have experienced.
The project will include the establishment of green corridors to protect feeding and nesting areas for
wildlife, such as the endangered wood stork, Mycteria americana. Riparian buffer zones are planned to
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control sedimentation and nutrient runoff. The 120-acre Carolina bay, drained several years ago on the
treatment plant building site, is slated for restoration and scientific study. All of these positive aspects are
the results of a collaborative effort between government, engineering and scientific data collection.
According to predicted trends, people will continue to move into the 201 Planning Area and
development will certainly strive to meet the demand for housing, support services and recreational
facilities. Economic and sociopolitical ideals need to adjust to permit the areas infrastructure to grow and
develop to keep up with future need. A regional sewage treatment and stormwater management plan will
facilitate long-term water quality solutions for South Brunswick County.
Acknowledgements
SBWSA generously provided funding for this project. Thanks to Eric Cullum, Bryant Sykes, T. Chris
Collura and Kevin Rowland for their efforts in the field and laboratory. Special thanks to the squirrels of
Calabash for their support and kind contributions.
Literature Cited
Dewberry and Davis Engineers. 1995. Brunswick County coastal Lumber River basin stormwater
management study. DD No. 95051. 80 pp.
Duda, A. M. and K. D. Cromarie. 1982. Coastal pollution from septic tank drainfields. Journal of the
Environmental Engineering Division, Proceedings of the American Society of Civil Engineers.
108:1265-1279.
Hayes, M. H. 1995. Town of Calabash 1994 land use plan. N.C. Doc. No. 32440066 North Carolina
Coastal Management Program. 29 pp.
Heath, R. C., 1997. Aquifer-sensitivity map of Brunswick County, North Carolina. Prepared for:
Brunswick County Planning Dept. 22 pp.
Kuenzler, E. J., P.J. Mulholland, L. A. Ruley and R. P. Sniffen. 1977. Water quality of North Carolina
coastal plains streams and effects of channelization. Project No. B-084-NC UNC-WRRI-77-127.
160 pp.
North Carolina Division of Environmental Health, Shellfish Sanitation Section. Report of sanitary survey
Calabash area, area A-l. 1997.
URS Greiner, 1997. South Brunswick Water and Sewer Authority proposed regional water treatment
facility environmental impact statement. DWQ project no. CS370706-01
Wilson, K. A. 1962. North Carolina Wetlands: Their distribution and management. North Carolina Wild.
Res. Comm., Raleigh. 169 pp.
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'Brunswick
County
Project location
Figure 1. South Brunswick County, NC and the 201 planning
area.
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Spatial and Temporal Trace Level
Monitoring Study of South San Francisco Bay
Daniel Watson, Lisa Weetman, Donald Arnold, Charles Allen, Kenneth Lee, James Powars,
Joe Thiesen, Hannah Truong, and Robert Wandro
Environmental Services Department, City of San Jose
4245 Zanker Road, San Jose, CA 95134
Phone: (408) 945-3739; Fax: (408) 934-0476
Abstract
In February of 1997 an ambitious sampling program was initiated by the City of San Jose to
characterize water quality parameters at twelve sites in San Francisco Bay south of the Dumbarton
Bridge. The purpose of this study was to describe spatial and temporal variability in an enclosed water
body for trend analysis and modeling. The study design incorporated the use of "clean techniques" to
monitor trace metals, microbiological testing, and general water quality measurements. Sampling was
conducted biweekly. Ambient water concentrations in the extreme South Bay of metals tested decreased
on a gradient northward toward the Central Bay. Total mercury values were highest in Coyote and
Guadalupe Creeks, possibly originating from abandoned cinnabar mines. Microbiological samples were
highest in these two creek systems; both areas harbor bird rookeries. Levels of most total metals measured
correlated well with Total Suspended Solids, and a to lesser degree, with Total and Dissolved Organic
Carbon. High total metals correlated well with wind and storm events. Dissolved metals concentrations
showed little seasonal variability and a decreasing spatial trend from sources of in-put, toward the central
portion of the San Francisco Bay, which is influenced by oceanic water.
Introduction
Over 100 years ago a chemist named Forchhammer postulated that it is not the quantity of elements
that rivers pour into the sea which determines the chemical elements in seawater. He theorized that
concentrations are inversely proportional to the facility with which the elements hi seawater are made
insoluble by general chemical actions in the sea (Strumm and Morgan 1996). Estuaries are mixing zones
where chemicals come to equilibrium between their soluble and insoluble forms. As such, it is important
to study the interaction of chemicals in bays and estuaries.
The State of California's Regional Basin Plan for San Francisco Bay (SFRWQCB 1995) recognized
that the South San Francisco Bay is a "unique water-quality limited, hydrodynamic and biological
environment that merits continued special attention" and that "site-specific objective are absolutely
necessary in this area." The State listed South San Francisco Bay as an impaired water body on its 1996
303(d) and TMDL Priority List, primarily due to high total metal concentrations. Two studies performed
in the South Bay (S.R. Hansen and Associates 1992; CH2M Hill et al. 1991) attempted to address the
issue of toxicity from copper and/or nickel in Bay waters and establish Water Effect Ratios (WERs) for
these two metals. A later study (City of San Jose 1997) measured ambient concentrations of Cu and Ni in
South San Francisco Bay waters and performed biweekly copper WER tests on the blue mussel, Mytilus
edulis, from January 1996 through March 1997.
Total nickel and copper concentrations frequently exceed water quality objectives during the dry
season (May to October) when flows from Publicly Owned Treatment Works (POTWs) dominate in-put
to the South Bay (SFEI1993, 1994, 1995,1996; and City of San Jose 1997). This fact has lead some
people to conclude that the sources of these metals in the extreme South Bay are mainly from POTWs.
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Dissolved Cu and Ni, however, did not show large concentration peaks or seasonal fluctuations (City of
San Jose 1997). Most of the Cu and Ni discharged from San Jose/Santa Clara Water Pollution Control
Plant (SJ/SC WPCP) are dissolved, 96 and 98%, respectively.
The City of San Jose initiated a monitoring study to elucidate spatial and temporal metal loading in
the area south of the Dumbarton Bridge (Figure 1). A team from the City of San Jose's Environmental
Services Department designed a monitoring study to characterize the deep channels and mudflats and to
detect non-point source stream concentrations. Sampling began in February 1997. Results will be used for
model validation as the Regional Board prepares a Total Maximum Daily Load (TMDL) study for the
South Bay.
Methods
To facilitate comparisons with data produced by the Regional Monitoring Plan (RMP), the San Jose
team adopted techniques employed by the RMP. Samples for trace metal analysis were collected
employing EPA Method 1669. The current San Jose study coordinated three sampling events in 1997 to
coincide with RMP cruises in the South Bay. In addition, the RMP cruises took samples in the South Bay
to be processed by the City of San Jose laboratory as an additional inter-comparative check. Although the
data from the 1997 RMP is currently not available, results of the comparison will be published in the
1997 Annual RMP Report.
Ten stations (SB01-SB10) were selected in the extreme South Bay (south of Dumbarton Bridge) to
represent both channel and mudflat locations. Previous studies had sampled channel stations, but
overlooked mudflat sites. Two other stations, Standish Dam (SB 11) and Alviso Marina (SB 12), were
added to monitor water flowing into the Bay from Coyote and Guadalupe Creeks, respectively. These two
stations were sampled from the shore. Sampling frequency was biweekly in order to assess temporal
change over seasons. Sampling times were varied to better assess tidal influence on metal concentrations.
The City's previous study (City of San Jose, 1997) sampled South Bay stations only at high tide.
Certain station locations were selected to promote comparisons with previous studies. Four of the ten
South Bay stations in this study were at the same location to RMP sampling sites. Two stations, also,
corresponded to locations in the City's WER study (City of San Jose, 1997). Station SB01 in the current
study (located 0.85 nautical miles north of the Dumbarton Bridge) corresponds to B A30 of the RMP sites
and DBN in the City's previous study. Station SB02 corresponds to BA20 for the RMP. Station SB03
corresponds to BA10 of the RMP and CC in the City's previous investigation. Station SB04 is equivalent
to the RMP station C-3-O. It should be noted that SB04 is the station closest to the convergence of
Coyote Creek and Artesian Slough, which can be highly influenced by water discharged from the SJ/SC
WPCP. Also, station SB05 of the present study corresponds to the Local Effect Monitoring (LEM) site
for the cities of Sunnyvale and San Jose. The US Geological Survey in Menlo Park has monitored
sediment and tissue (of the clam Macoma balthica) pollutant levels at this LEM site since 1993.
Sampling was performed aboard a seventeen foot Boston Whaler. Water was collected at a depth of
approximately one meter using Teflon and C-Flex® tubing with a peristaltic pump. A deep-cycle marine
battery powered the peristaltic pump, which was encased with a voltage converter in a water resistant
polyethylene housing.
Total and dissolved trace metals were collected using "clean techniques" in triplicate 500 ml HOPE
bottles (3X HNO3 rinsed; 3X NannoPure water rinsed) for Cu, Ni, and Se. Single samples were collected
in 1L Teflon (PTFE) bottles for total mercury. Dissolved metal samples were filtered in situ by drawing
water through 5.0|0. and 0.45(i filters (Micron Separation, Inc.) connected in series. Filters were used
repeatedly with ample flushing between stations and were replaced when flow diminished. Other water
quality parameters measured were Total Suspended Solids (TSS), Total Dissolved Solids (TDS), Total
Organic Carbon (TOC), Dissolved Organic Carbon (DOC), salinity (S%), and conductivity.
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Microbiological samples were collected in two-liter sterile polyethylene jugs for total coliform, fecal
coliform, and enterococcus. Samples were maintained at 4°C in ice chests and returned to the laboratory
within four hours of sampling.
Trace Metal Analysis
Samples were preserved at the laboratory using ultra pure nitric acid (pH below 2). Copper and nickel
were analyzed using a modification of EPA Method 1640. This technique involves precipitation of Cu &
Ni by cobalt-pyrrolindinedithiocarbamate followed by filtration and detection by Inductively Coupled
Plasma - Mass Spectroscopy (ICP-MS). Selenium was analyzed using a combination of EPA Method
270.2 and 270.3. Selenium hydride was generated and concentrated on an iridium coated graphite tube.
The concentrated selenium was then vaporized on the tube and detected by atomic absorption
spectroscopy. Method detection level for selenium was 0.002 u,g/L. Mercury was sampled in one-liter
Teflon bottles which were acid cleaned and filled with 0.1% HC1 until time of sampling. Bottles were
filled completely with the sample to minimize headspace and preserved in 0.1% HNO3. Mercury was
analyzed using EPA Method 245.2 modified for gold amalgamation concentration on a Perkin-Elmer
FIMS system. The method detection level for mercury was 0.0007(ig/L.
Samples for Cu, Ni, and Se were sent to a commercial laboratory in Washington when the City's
laboratory was unable to handle the backlog (June to December, 1997). This commercial laboratory used
similar methods. Results were verified with standard reference materials, spikes, and duplicates.
Water Quality Constituents
Total Organic Carbon (TOC) and Dissolved Organic Carbon (DOC) were analyzed on a Shimadzu
5000 Carbon Analyzer using Standard Methods, 18th edition, Method 5310B and ASTM D-2579-93.
Total Suspended Solids (TSS) were analyzed using Standard Methods, 18th edition, Method 2540D. Total
Dissolved Solids (TDS) were analyzed using Standard Methods, 18th edition, Method 2540C.
Conductivity was analyzed using Standard Methods, 18th edition, Method 2510B. Salinity was measured
with a Reichert temperature compensated refractometer to the nearest 0.5 part per thousand (S°/0o)-
Microbiological Analysis
Microbiological assessment was done in conjunction with the San Jose/Santa Clara WPCP chlorine
reduction study. Baseline data existed for bacteria in the South Bay from a Receiving Water Monitoring
survey performed as a requirement of the City of San Jose's NPDES permit in the late 1980's and early
1990's. Several years had elapsed, however, since that sampling ended in 1994.
Total coliform bacteria were assessed using Standard Methods, 18th edition, Method 9221C. Fecal
coliforms were quantified using Standard Methods, 18th edition, Method 922IE. Enterococcus
microorganisms were assayed by Standard Methods, 18th edition, Method 9230B. All units are expressed
in Most Probable Number (MPN)/100 ml.
Quality Assurance/Quality Control
Blanks. To confirm absence of background contamination at parts per billion levels, numerous blanks
were required. Blanks were used to assess contamination caused by sample bottles, NannoPure water,
day-to-day field equipment, field sampling, and filtration. Trace metal blanks were analyzed without the
concentration step of cobalt precipitation. NannoPure water was also sampled for blanks of TOC/DOC
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and TSS/TDS. Bacteriological equipment and field blanks were also taken for each sampling event to
characterize biological contaminants that might be retained and passed to the next sample.
Standard Reference Material. With each sampling event a standard reference material (SRM) was
submitted as a blind sample. The standard reference material, CASS-3, (Canadian Research Council,
Coastal Seawater) was preferred because of the laboratory's need to analyze low levels of metals in site
water.
Spikes. Spiking of certain samples was performed as a quality assurance check when samples were
sent to a commercial laboratory. This step was to verify consistency with the City of San Jose
Environmental Services Department laboratory results and percent recovery.
Replicate Sampling. Both total metals and dissolved metals were taken in triplicate at each station in
order to have complete confidence that values detected were not anomalous. One station (SB 10) was
selected to perform duplicate sampling for TSS/TDS, TOC/DOC, and mercury, which were otherwise
represented by one sample at the other eleven stations.
Inter-Study Comparisons. Coordinated with previous and on-going sampling studies discussed
above were part of this monitoring Quality Assurance Plan.
Results
Mean total copper concentrations (Figure 2) was highest in Coyote (SB 11) and Guadalupe Creeks
(SB 12) upstream from the South Bay sites. These two stations are located on the right side of the graphs
(light gray bars). The series of six stations on the left hand of the graphs (dark bars) represent channel
stations. Stations Sb07-SB10 (four light gray bars on the right side of the graphs) represent mudflat
stations around the periphery of the South Bay. Stations SB04 and SB05 (in the lower section of Coyote
Creek) both had mean total copper concentrations approaching the creeks of nearly 11 Hg/L. Mean total
copper in the San Jose/Santa Clara effluent (center dark bar) was 4.3 Hg/L. Non-point source in-put from
the creeks and re-suspension appear to be the main origin of total copper in lower Coyote Creek, showing
a progressive decrease in concentration from south to north in the study site. Standard error bars for the
creek stations were larger because of the variability in seasonal flows and sediment loads.
Mean dissolved copper concentrations remained fairly consistent (between 2.3-2.9 |J.g/L) from the
creeks to the station north of the Dumbarton Bridge (SB01). By comparison, the mean dissolved copper
concentration in the SJ/SC WPCP final effluent was 3.9 (ig/L. Most (nearly 92%) of the copper in the
final effluent is in the dissolved phase compared to South Bay sites which had dissolved-to-total ratios of
51% or less. No significant elevation in mean dissolved copper is evident in Coyote Creek downstream of
the confluence with Artesian Slough. In fact, there was a decrease in the dissolved-to-total ratios at
stations immediately downstream from the SJ/SC WPCP (stations SB04 and SB05). This decrease in
ratios may be due primarily to higher total copper concentrations associated with particles transported
down Coyote Creek.
Since the South Bay is shallow with a large tidal prism of approximately 1.8 billion cubic feet
(CH2M HELL. 1990), water column metals concentrations are greatly affected by re-suspension of bottom
sediments (RMA. 1998). The models used in the RMA report employed RMP copper data to project
values in the San Francisco Bay at both higher-high tide and at lower-low tide. The RMA report
concluded that: "There may be large short-term variations in the concentration of total copper through the
deposition and re-suspension processes. Observed variation in the concentration of dissolved copper are
much less dramatic." Concentrations of copper observed in the current study and the City's 1996-97 WER
study verify these conclusions and should add greater precision to a water quality model of the South Bay,
given the quantity of data collected. Further analysis of tidal differences with the City's present study data
will be useful in TMDL development.
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Similar concentrations of total and dissolved copper were obtained in the City's WER study (City of
San Jose 1996/97) and the mean RMP data from years 1993 to 1996 (Figures 3 & 4.) Also, concentrations
of the metal in the SJ/SC WPCP final effluent are shown for comparison. Most values are in very close
agreement between the three studies. Symbols on the concentration bars indicate comparable stations
between the studies. Mean dissolved copper agrees well when comparing stations, with a range of
concentrations in the South Bay of 2.3 to 3.6 |ig/L. Wide variability in the standard error bars of the two
creek stations, SB 11 and SB 12, is likely due to the sporadic nature of stream flow, sediment load, pH
fluctuations, and non-point sources of metals.
Mean total nickel was also highest in the creeks, decreasing at channel stations progressing northward
in the South Bay (Figure 5). Concentrations of total nickel average approximately 31 and 28 (J-g/L in
Coyote and Guadalupe Creeks, respectively. Most levels of total nickel in the South Bay ranged from 8 to
11 (J-g/L, with stations SB04 and SB05 showing the transition from non-point source input. The Water
Quality Criterion for nickel in marine water is 8.3 |ig/L. By contrast, mean total nickel in the final effluent
was approximately 7.4 fig/L. This concentration was below any other mean total Ni value in the South
Bay.
Mean dissolved nickel showed a very similar pattern to mean dissolved copper. Concentrations in
Coyote Creek at Standish Dam (SB 1 1) averaged 5.01 (J-g/L, and decreased further downstream. Stations
SB04, SB05, and SB03 had mean dissolved nickel concentrations of 4.65, 3.65, and 3.43p.g/L,
respectively. In contrast, the mean dissolved nickel in the SJ/SC WPCP final effluent was 7.09 |J.g/L.
Dissolved nickel constitutes 96% of the nickel discharged from the treatment plant. Some of the lowest
percent dissolved-to-total nickel ratios were also observed just downstream of the confluence with
Artesian Slough, as were observed for dissolved-to-total copper.
Inter-study comparisons of total and dissolved nickel with the City's WER study and the mean RMP
1993 to 1996 data are presented in Figures 6 & 7. Also, concentrations of nickel in the SJ/SC WPCP final
effluent are shown for comparison to the receiving water. Both mean dissolved and total nickel show
similar relationships as did the mean dissolved and total copper. The station at the mouth of Coyote Creek
(SB03, BA10, CC) showed agreement within a few tenths of a part per billion between the three studies
for both dissolved and total nickel. Some differences between the studies may be explained by differences
in tides. The City of San Jose's WER study collected water only at high tide, whereas the current City
study specifically collected samples from all tidal cycles. The RMP collects water samples from various
tidal cycles. Standard error bars were large for total nickel at the two creek stations. Standard errors bars
were proportionally less for dissolved nickel than for dissolved copper. Most mean dissolved nickel
concentrations in the South Bay ranged from 2.4 to 5.0 |0.g/L. The RMP data showed a mean dissolved
value of 7.4 |J.g/L Ni at station C-3-0, closest to Artesian Slough. This higher value may reflect dissolved
nickel coming from the SJ/SC WPCP if most of the RMP samples were collected on ebbing or low tides.
All of the mean dissolved nickel values for South Bay stations were below the Water Quality Criterion for
nickel in marine water of 8.3 |ig/L despite many mean total nickel concentrations exceeding that level.
Despite the fact a large proportion of nickel and copper discharged by the San Jose/Santa Clara Water
Pollution Control Plant is predominantly dissolved metal, the observed impact to dissolved metals in the
receiving water was not obvious. A dilution study performed in 1990 (CH2M HILL. 1990) concluded under
worst case scenarios (dry season with little dilution of the effluent, neap tides, and Delta outflow at a
minimum) that the dilution at SB04 (RMP site C-3-0) was 10.6. By the time the water reaches Calaveras
Point (site SB03 and RMP site BA10) and northward, the dilution study concluded there would be dilution of
Mean total selenium concentrations most dramatically demonstrated the influence of non-point source
pollution (Figure 8). The mean total selenium concentration in Guadalupe Creek was 2.74 |ig/L and
1.21u.g/L in Coyote Creek at Standish Dam. Mean total selenium concentrations decreased northward in
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the South Bay with most values between 0.3 and 0.5 (ig/L. Mean total selenium in the SJ/SC WPCP final
effluent was 0.56 |ig/L.
There was little difference in the dissolved and total selenium concentrations; thus the percent
dissolved-to-total ratios reflect this fact. Mean percent dissolved selenium at most stations was over 80%.
The percent dissolved selenium downstream of the confluence of Artesian Slough and Coyote Creek was
similar to the percent dissolved selenium at Standish Dam (SB 11). Dissolved selenium was not measured
in the final effluent of the treatment plant.
An inter-study comparison of mean total mercury between the present study and mean RMP 1993 to
1996 data shows very good agreement and indicates that the sources of mercury may be upstream of the
two creek stations (Figure 9). There are a number of abandoned cinnabar mines in the watershed of the
two creeks draining into the South Bay. Stations SB04 and SB05 are among the highest of the South Bay
stations with mean total mercury concentrations over 0.05 ng/L. Mean total mercury concentrations at
other South Bay stations do not exceed the Water Quality Criterion of 0.025 ^ig/L.
Other water quality parameters (S%, TSS, and TOC) are shown in Figure 10. Mean salinity values
were around 2% at the creek stations. Salinity increased along channel stations into the estuary, reaching a
mean salinity of 22.2% at station SB01, north of Dumbarton Bridge. These measurements were taken at
approximately one meter and do not characterize the tidal wedge of saline water that may be present
along the bottom of the creeks and the South Bay at high tide. Total Suspended Solids were highest at
stations SB03 and SB05, as were Total Organic Carbon values. These stations are geographically located
in an area of the South Bay and creek system that provides an area of deposition for fine sediments. Silt
and clay brought in by run-off and re-suspended particles transported off mudflats by the strong
northwesterly winds in late Spring and Summer likely deposit at the south end of the Bay.
Mean concentrations of fecal coliform bacteria at the 12 study sites and in the SJ/SC WPCP final
effluent are presented in Figure 11. Similar to metals, the source of bacteria was shown to be in the creeks
draining into the South Bay. A mean fecal coliform value of 1300 MPN/100 ml was observed at Standish
Dam (SB11) and just under 1000 MPN/100 ml observed on Guadalupe Creek (SB 12). Concentrations of
fecal bacteria decreased progressively northward into the Bay. Standard error bars were high at the creek
stations and two stations in lower Coyote Creek (SB04 & SB05), likely reflecting the seasonal habits of
birds and mammals that frequent these waterways. Levels of fecal bacteria and standard error bars were
less pronounced in the South Bay away from sources of in-put, perhaps reflecting poor survival in a saline
environment.
Since more than half of the South Bay consists of tidal mudflats and a large tidal prism cycles over
shallow shoals, water quality is likely to vary depending on the tidal influences. Future statistical analyses
will compare trace pollutant concentrations and water quality parameters at different tide cycles.
Conclusions
The current study serves as a valuable link between point source pollutant monitoring, watershed
monitoring, and the Regional Monitoring Program. Increased spatial and temporal frequency of sampling
in this study will likely provide greater confidence in modeling trace pollutant concentrations and loading
in the South San Francisco Bay. Mean total metal concentrations were higher with greater variability than
dissolved mean metal concentrations. Mean total and dissolved metals (copper, nickel, and selenium)
decreased progressively from the two creek sites northward into the estuary. Percent dissolved-to-total
copper and nickel ratios showed a noticeable decrease at the two stations in Coyote Creek downstream of
the confluence with Artesian Slough. Concentrations of mean total and mean dissolved copper and nickel
downstream of the SJ/SC WPCP were not significantly higher than metal concentrations in the ambient
(up-stream) receiving water.
III-110
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Total copper and nickel concentration frequently exceeded marine Water Quality Criteria values in
the South Bay, whereas the mean dissolved levels of these two metals did not exceed the Criteria. Mean
total mercury concentrations were highest at the two creeks and diminished to 0.025 |J.g/L (the marine
Water Quality Criterion) or less in the South Bay. Inter-study comparisons of mean total and dissolved
metals show good agreement of data with two previous studies. The current study links most pollutant
concentrations, including fecal bacteria and mercury, in the South San Francisco Bay to the two major
creeks, Guadalupe and Coyote Creeks, draining the watershed.
References
CH2M HILL, Larry Walker & Associates, Kinnetic Labs, Inc. 1991. Site-specific Water Quality Objectives
for South San Francisco Bay. Prepared for the City of San Jose Department of Water Pollution Control.
CH2M HILL. 1990. DRAFT: South Bay Dye Study (Provision E5D). Prepared for the City of San Jose
Department of Water Pollution Control.
City of San Jose. 1997. Development of a Site-specific Water Quality Criterion for Copper in South San
Francisco Bay. Prepared by the Environmental Services Department, City of San Jose, CA.
RMA. 1998. Administrative Draft: Impacts of the BADA Discharges on Copper Levels in the San
Francisco Bay. Prepared for the Bay Area Dischargers Association. Larry Walker & Associates, Inc.
and Resource Management Associates, Inc.
S. R. Hansen & Associates. 1992. Development of site-specific criteria for copper for San Francisco Bay.
Prepared for the San Francisco Regional Water Quality Control Board, Oakland, CA.
SFEI. 1993. Regional Monitoring Report for Trace Substances; 1993 Annual Report. Prepared by the San
Francisco Estuary Institute.
SFEI. 1994. Regional Monitoring Report for Trace Substances; 1994 Annual Report. Prepared by the San
Francisco Estuary Institute.
SFEI. 1995. Regional Monitoring Report for Trace Substances; 1995 Annual Report. Prepared by the San
Francisco Estuary Institute.
SFEI. 1996. Regional Monitoring Report for Trace Substances; 1996 Annual Report. Prepared by the San
Francisco Estuary Institute.
Standard Methods for the Analysis of Water and Wastewater, 18th edition. 1994. APHA/AWWA/WPCF.
Strumm, W. and Morgan, J.J. 1996. Aquatic Chemistry, 3rd edition. John Wiley and Sons, Inc. pp. 1022.
Ill-Ill
-------
Palo Alto wdcP
/''• ~ r
•r«J /
QBrlestbn
/Slough
WPCP Sunnyvale (
122.'1"W
122.0~U
SBS SUES
REFERENCE LOCATIONS
*SB01 Channel Marker #14
*SB02 Channel Marker #16
*SB03 Channel Marker #18
*SB04 CC Railroad Bridge
*SB05 LEM site in Coyote Creek
*SB06 Between Channel Markers #17 & 18
SB07 Mouth of Mowry Slough
SB08 Mouth of Newark Slough
SB09 Mouth of Mayfield Slough
SB 10 Mouth of Charleston Slough
*SB11 Standish Dam in CC
*SB12 Alviso Yacht Club Dock
LONGITUDE
122.08.60W
122.05.04W
122.03.01W
121.58.64W
122.01.48W
122.04.30W
122.03.27W
122.05.41W
122.07.08W
122.05.99W
121.55.29W
121.58.45W
LATITUDE
37.30.48N
37.29.59N
37.27.27N
37.27.59N
37.27.84N
37.28.52N
37.29.54N
37.29.92N
37.27.06N
37.28.19N
37.27. ION
37.25.34N
RMP SITES
BA30
BA20
BA10
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.
.
•
«
•
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BW15
Figure 1. South San Francisco Bay site map showing 11 of the 12 stations sampled in the South Bay Study
(SBS). Site SB 11 located at Standish Dam in Coyote Creek is not within the range of the map presented. Refer to the
above table for references to analogous sites from the Regional Monitoring Program. Note: * indicates sites for
potential sediment monitoring.
m-112
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"}
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MEAN DISS. Cu
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MEAN TOTAL Cu
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- - -
-------
MEAN DISSOLVED COPPER (ug/L) +/- S.E.
O I-*
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SB01
SB02
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SB03
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MEAN DISS. Ni
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MEAN TOTAL Ni
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-------
MEAN DISSOLVED NICKEL (ug/L) +/- S.E.
SB01
SB02
SB06
SB03
SB05
SB04
WPCP FE
SB07
SB08
SB09
SB10
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I
63
O
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MEAN TOTAL NICKEL (ug/L) +/- S.E
SB01
SB02
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SB03
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SB08
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SB11
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RMP BA20
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•a
M£AN% DISS.Se
(og/L W- SE)
MEAN DISS. Se
(•g/L +/- SE)
MEAN TOTAL Se
(ug/L +/- SE)
-------
Figure 9. Inter-study comparison — total mercury.
MEAN TOTAL MERCURY (ug/L) +/- S.E
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-------
MEAN TOC (mg/L) +/- S.E.
MEAN TSS (mg/L) +/- S.E.
MEAN SALINITY (ppt) +/- S.E.
-------
to
o
Figure 11. All sampling events — fecal coliforms.
MEAN FECAL COLIFORMS (mg/L) +/- S.E.
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-------
Water Quality Assessment Program in the Indian River Lagoon, Florida:
II. Redesigning a Monitoring Network
Gilbert C. Sigua, Joel S. Steward, Janice D. Miller, and Wendy A. Tweedale
Environmental Sciences Division, St. Johns River Water Management District (SJRWMD)
P.O. Box 1429, Palatka, FL 32178
Abstract
The protection and maintenance of good water quality related to the re-establishment of a healthy
"seagrass ecosystem" in the Indian River Lagoon (IRL) can best be accomplished through re-examination
and refinement of the water quality monitoring network (WQMN) to meet current needs. Modifications to
the IRL-WQMN were intended to focus its mission toward a) providing answers to specific questions
related to the long-term management of seagrass and the water quality of its environment, b) increasing
the statistical power of the data collected, c) increasing the effectiveness of staff and laboratory resources,
and d) collecting complementary data for the calibration of the Pollution Load Reduction Model. A
selective reduction in the number of sampling stations was proposed to eliminate statistically unnecessary
sampling, resulting in a more efficient sampling effort in the IRL system. The stations that are retained
will continue to provide information on the long-term spatial and temporal trends of water quality and
help discern the covariant or causal link between seagrass coverage (distribution/density) and water
quality. The proposed modifications also included the following changes: 1) selection of existing
sampling stations based on their proximity to seagrass measurement transects; 2) an increase in the
sampling frequency; 3) inclusion of near-bottom nutrient samples; 4) measurement of the organic
fractions of total suspended solids; and 5) centralization of laboratory analyses to reduce potential
analytical errors. By streamlining the WQMN as proposed, staff and laboratory resources will be used
more effectively and place less budgetary demand on the participating agencies. It will be a more cost-
effective and efficient monitoring tool to measure the water quality of the seagrass environment.
Introduction
The IRL-WQMN was established in 1988 as a coordinated multi-agency project spanning the entire
length (~ 248 km) of the IRL system (Figure 1). Water quality monitoring in the IRL system consisted of
sampling at regular intervals (monthly) for a suite of parameters agreed upon by the different participating
agencies. The active participants of the network are the St. Johns River Water Management District
(SJRWMD), South Florida Water Management District (SFWMD), Volusia County, Brevard County,
Indian River County, and NASA-Dynamac. These agencies collectively managed a total of 150 stations
(nearly one station per Lagoon 1.6 km). The IRL-WQMN had the task to generate information on the
physical and chemical conditions of the IRL and to infer the Lagoon's well-being or biological integrity.
The IRL-WQMN is an invaluable management tool (Steward et al 1994), with a mission to:
• characterize the URL over the long term - assess the status and trends in estuarine water chemistry
in relation to primary producers as indicators of biological integrity, especially seagrasses, the
key macrophytes;
• identify problem areas (via indicators of biological integrity de-stabilization, i.e., some trend
toward phytoplankton dominance over macrophytes);
• measure the effectiveness of management objectives and actions intended to remediate the
problem areas;
• provide current information to re-direct or re-focus management plans; and
III-121
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• provide accountability to the public by relating progress toward restoration and protection of the
IRL.
This five-point mission of the IRL-WQMN is facilitated by close coordination of the participating
WQMN agencies to ensure proper, quality-assured operation of the network, and the periodic evaluation
and modification of the network as necessary to meet current resource assessment needs.
Indian River Lagoon System: An Overview
The IRL, from Ponce DeLeon Inlet to Jupiter Inlet, is a biogeographic transition zone, rich in habitats
and species, and with the highest species diversity of any estuary in North America. This system is
comprised of three interconnected estuarine lagoons, the Mosquito Lagoon (ML), the Banana River
Lagoon (BRL), and the Indian River Lagoon (IRLB-Indian River Lagoon-Brevard County; IRLIR-Indian
River Lagoon-Indian River County). The Lagoon system receives inputs of salt water from the ocean
through inlets and freshwater from direct precipitation, groundwater seepage, surface runoff, as well as
discharges from creeks and streams (non point sources) and wastewater treatment plants (point sources).
Generally, little flushing action exists at the northern end of the estuary as tidal influence in that area is
small and overwhelmed by wind. In areas close to the inlets, tidal elevations and currents are more
pronounced, and, thus flushing is improved.
Mosquito Lagoon. Mosquito Lagoon is a large, shallow estuarine system along the east central coast
of Florida in Volusia and Brevard counties (Figure 1). The northern end of the lagoon connects to the
Atlantic Ocean through Ponce DeLeon Inlet near New Smyrna Beach. The 57-km long watershed of ML,
is bounded on the east by the barrier island and on the east, west, and south by dune ridges (Higman
1994).
Banana River Lagoon. Banana River Lagoon is located in Brevard County. The 52-km long
watershed of the BRL lies east of Merritt Island and west of the barrier islands. These barrier islands are
composed of relict beach ridges formed by the action of wind and ocean waves (Brown et al 1962). The
western watershed boundary is the Kennedy Parkway until the parkway turns west, at which point the
boundary follows a dune ridge south (Steward and VanArman 1987). The prominent physical feature of
this drainage area is Cape Canaveral, which is located on the barrier island. South of the Cape is
Canaveral Barge Canal, a navigational channel which connects the IRLB and the BRL with Port
Canaveral and the Atlantic Ocean.
Indian River Lagoon. The Indian River Lagoon is about 648 sq. km in area. It has four connections
to the Atlantic Ocean, namely: Sebastian Inlet, Fort Pierce Inlet, St. Lucie Inlet, and Jupiter Inlet via Kobe
Sound. Circulation and flushing in the IRLB and IRLIR are greatly influenced by freshwater inflows,
inlets, and winds. Principal freshwater sources for the IRLB and IRLIR are natural streams, direct land
runoff, and a number of wastewater treatment plants. The major streams are located south of Merritt
Island and include Eau Gallie River, Crane Creek, Turkey Creek, Sebastian River, and St. Lucie River.
Modifications to the Existing IRL-WQMN
Specific resource management questions were developed to re-focus the WQMN within the context
of its mission and enable conversion of data into meaningful information regarding the interrelationship
of water quality, light, and seagrass requirements. The WQMN participant agencies have offered the
following set of questions whose answers should be attempted by the network:
• Generally, what constitutes good or poor water quality?
• What is the Lagoon-wide concentration gradient of the primary water quality constituents or
variables that control water column light attenuation (K) and primary productivity?
Ill-122
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• Where is water quality considered poor (and where is it good)? What distinctions in water quality
can be discerned between or among segments of the IRL (e.g., is there a statistical difference in
water quality characteristics between the north and south segments of Banana River Lagoon)?
• In any specific segment of the IRL, is water quality declining or improving? If water quality is
improving, is K showing a concomitant decrease? Have areas of demonstrated improvement been
subjected to management actions (e.g., implementation of pollutant load reduction goals via
stormwater retrofits or other pollutant controls)?
• Is there a quantitative or statistical relationship between poor water quality areas, a certain high
level of K, and poor seagrass coverage areas (and is the converse apparent for good areas)?
• Are there seasonal or other temporal patterns in water quality, lagoon-wide or per segment?
• Can the WQMN complement other programmatic work, especially the development of the
Pollutant Load Reduction Model? For example, does the current network account for the major
"light attenuators"? What spatial and temporal density of sampling stations and events are
adequate for the purpose of PLR Model calibration?
These questions provided guidance in the redesign of the WQMN with respect to what variables need
to be measured, spatial and temporal sampling specifications, and analytical procedures.
Regular data analysis and presentation of monitoring results should be as routine as the monitoring
itself. They are important as a means to evaluate the WQMN's performance—its ability to answer
resource management questions and provide accountability to the public. Knowing the type of
information to be presented determines the methods of data analysis and presentation. Green (1979)
emphasized the importance of developing testable hypotheses during the design phase of environmental
studies. The development of testable hypotheses and the selection of statistical methods are the first steps
in evaluating the expected performance of the WQMN.
Redesign of the IRL-WQMN was proposed in 1995 and was adopted in 1996 for implementation by
the different participating agencies. The modifications to the WQMN will be in effect for up to three
years before re-evaluation. Modifications to the IRL-WQMN are the following:
• a substantial decrease in the number of fixed sampling stations in the lagoon proper in Volusia,
Brevard, and Indian River counties (23 lagoon stations and all 12 SJRWMD tributary stations
retained, 50 lagoon stations deleted); reduction in the number of stations will eventually result in
substantial reduction in cost;
• the selection of existing sampling stations based on their proximity to seagrass measurement
transects managed by the SJRWMD;
• an increase in the sampling frequency at each station to three times per station during a tidal cycle
(-12 hr) each month (dropping back to 1 time per station per month after three years);
• the inclusion of near-bottom nutrient samples, to be collected at each station at least once during a
tidal cycle each month for 2-3 years to help calibrate the PLR Model;
• the measurement of the organic and inorganic fractions of total suspended solids; and
• a centralization of laboratory analyses for proper quality control and quality assurance of
analytical results.
Sampling Methods And Redesign Protocols
The coordination of a multi-agency monitoring network in a consistent manner demands standardized
methods, procedures, and equipment (Sigua et al 1996). This standardization is necessary to produce
III-123
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reliable and comparable data. This section identifies and describes sampling methods that were adopted
by the IRL-WQMN participating agencies.
Number and Locations of Sampling Stations. The SJRWMD proposed a selective reduction (from
150 to 23 stations) in the number of sampling stations to eliminate statistically unnecessary sampling,
resulting in a more efficient sampling effort in the IRL system (Table 1). The stations that were retained
continue to provide information on the long-term spatial and temporal trends of the water quality and help
discern the covariant or causal link between seagrass coverage (distribution/density) and water quality.
The decision to retain or delete stations was initiated with a sequence of statistical analyses of water
quality data that served to group neighboring stations representing a Lagoon segment of relatively
homogeneous water quality. Then, within each segment, one or two representative stations were selected
based on proximity to seagrass monitoring transects and to prominent watershed impacts (e.g., wastewater
treatment plants, significant tributary or stormwater inflows, inlets, etc.). The accepted number of
segments per reach is supported by the results of multivariate analyses The variables selected for the
analyses were salinity, DO, turbidity, chlorophyll a, TP, and TKN, with a 1988 - 1991 period of data
record. For each lagoon reach, principal component analysis of these data identified the principal
variable(s) responsible for inter-segment variability. In ML, the principal variable was turbidity; in the
BRL and IRL-B, it was salinity; and in the IRL-IR, it was both turbidity and salinity (SAS 1988).
The grouping of "like" stations or a segmenting of each of the lagoon reaches, based on principal
component analysis, was confirmed by cluster and kriging analyses and univariate analyses. Sampling
stations (Figure 1) were selected within each segment based on their proximity to existing seagrass
transects. Station selection was also based on major or representative land-based activities and resource
features within the IRL Basin (e.g., wastewater treatment plants, inlets, and tributary inflows from
significant urban watersheds and relatively undeveloped watersheds).
As an aid to the foregoing discussion, an schematic (Figure 3) depicts the sequence of statistical tests
used to determine segmentation (the number and placement of lagoon segments). First, the IRL Basin was
treated as four separate reaches: Mosquito Lagoon (ML), Banana River Lagoon (BRL), Indian River
Lagoon - Brevard County (IRLB), and Indian River Lagoon - Indian River County (IRLIR). Water
quality variables that can affect seagrasses, and for which there are sufficient data, were selected to
delineate reach segments. The water quality data for each reach were assumed to be normally distributed,
and have independence of observation and homogeneity of variance over the period of record. Second,
each reach was initially divided into segments based on a visual discrimination of water quality
differences between station groupings aided by spatial and temporal data distribution plots. Then,
multivariate analyses, including proc manova and principal component analysis, served to accept or reject
the segmentation hypotheses, as stated below. Once the segments of each reach were delineated, one or
more stations were chosen to represent each segment. Most stations were chosen based on their proximity
to existing seagrass transects.
1) Mosquito Lagoon: (Ho - null hypothesis; Ha - alternative hypothesis)
Ho: Total segments = 0
Ha: Total segments * 0 (at least 3 segments based on visual discrimination)
Result: Reject Ho; Mosquito Lagoon has 4 segments (based on turbidity)
2) Banana River Lagoon:
Ho: Total segments = 0
Ha: Total segments * 0 (at least 3 segments based on visual discrimination)
Result: Reject Ho; Banana River Lagoon has 3 segments (based on salinity)
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3) IRL-B (Brevard County)
Ho: Total segments = 0
Ha: Total segments ^ 0 (up to 8 segments based on visual discrimination)
Result: Reject Ho; Indian River Lagoon (IRL-B) has 7 segments (based on salinity)
4) IRL-IR (Indian River County)
Ho: Total segments = 0
Ha: Total segments * 0 (at least 3 segments based on visual discrimination)
Result: Reject Ho; Indian River Lagoon (IRL-IR) has 3 segments (based on turbidity and
salinity)
General Sampling. Three near-surface samples per station will be collected during a tidal phase each
month to capture any tidal or seasonal trends in the lagoon. Near-bottom nutrient samples will be collected at
each station at least once during a tidal cycle (-12 hr) each month. Photosynthetically active radiation (PAR)
measurements for each station will be done after 10:00 a.m. at least once a month. Sampling will be done on
the Tuesday following the second Monday of each month, or on the day(s) agreed to by the WQMN group,
or as soon as possible after that day if inclement weather prevents sampling. Water samples will be shipped
within the appropriate holding time to a centralized laboratory for chemical analyses. Each county will
continue to sample and measure for the different parameters (meteorological and physical) listed in Table 2.
Monitoring schemes and measurements for these physical characteristics should involve in situ methods. The
list of water column chemical properties shown in Tables 3 were the major consideration for the
centralization of laboratory analyses in the IRL.
Near-Surface and Near-Bottom Sampling. It is recommended that at least one of the three samplings
(Circuits 1, 2, or 3 as shown in Figure 2) during the tidal cycle run include a near-bottom water sample.
The near-surface sample can be taken using a water grab sampler (Van Dorn). Prior to and at the time of
sampling from the near-surface or near-bottom depth, it is very important that there is minimal sediment
disturbance. The station should be approached slowly, ideally with boat engine cut-off as the boat is
coasting into position, and then the anchor gently lowered. The sample should be taken as far away from
any points of possible disturbance (anchor, engine, other sampling activities, etc.). The recommended
depth at which the near-bottom sample would be taken using a "bilge pump" or peristaltic pump is 0.3 m
above the bottom. The IRL database (1988-1991) reveals that the depth of the water column at most of
the retained stations is usually more than 1.25 m. If the mean water column depth is less than 1 m, no
near-bottom sample will be taken; only the near-surface sample.
Evaluation of WQMN Performance
Evaluation of the redesigned IRL-WQMN will be performed after three years of implementation. The
questions that will be addressed in the evaluation are:
1. Can the modified sampling design meet the requirements of a long-term, ambient water quality
monitoring program focused on the seagrass environment?
2. In the short term, can the modified WQMN meet the calibration needs of the PLR Model? Can
the WQMN, in conjunction with other seagrass diagnostic projects and the PLR Model, establish
better water quality targets for the IRL, and accurately measure IRL conditions relative to those
targets?
3. How can the redesigned WQMN be further modified to ensure that the objectives or questions of
the monitoring program are sufficiently addressed?
The cost of the IRL-WQMN within the SJRWMD is substantial. In order to derive the most benefit
from the network, it is essential to periodically (at least every three years) evaluate its expected
performance. This performance information will provide the basis for determining the feasibility of
III-125
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proposed sampling strategies and optimizing the overall monitoring effort. Initially, this evaluation will
take place following a first year of sampling based on the modified network.
References
Brown, D. W., W. E. Kenner, J. W. Crooks, and J. B. Foster. 1962. Water resources of Brevard County,
Florida. 104 p.
Higman, J. 1994. Mosquito Lagoon water quality status: spatial and temporal patterns. SJRWMD. 123 p.
SAS Institute, Inc. 1988. SAS/STAT User's Guide. Release 6.03. SAS Institute, Gary, NC. USA. 494 p.
Sigua, G. C., W. A. Tweedale, J. D. Miller, and J. S. Steward. 1996. Inter-agency implementation of
modified water quality monitoring program for the Indian River Lagoon: Methods and QA/QC
Issues. Technical Memorandum # 19. St. Johns River Water Management District, Palatka, Fl. 36 p.
Steward, J. S., R. Virnstein, D. Haunert, and F. Lund. 1994. Surface water improvement plan for Indian
River Lagoon. 119 p.
Steward, J. S. and J. A. VanArman. 1987. Indian River Lagoon Joint Reconnaissance Report. Contract
Report Nos. (CM-137 and CM-138). Florida Office of Coastal Management. Department of
Environmental Regulation. 367 p.
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IKL-WQMN Sites
I Hydro logic Basins
/\/ County Boundary
Figure 1. Indian River Lagoon showing the old water quality monitoring stations.
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Volusia County
Begin
\
Field Blank
Surf Physical
Surf Water
Surf Physical
Surf Water
Surf Physical
Surf Water
PAR
Surf Physical
Surf Water
N-B Physical
N-B Water
PAR
Surf Physical
Surf Water
N-B Physical
N-B Water
PAR
\
Surf Physical
Surf Water
N-B Physical
N-B Water
Surf Physical
Surf Water
Surf Physical
Surf Water
Surf Physical
Surf Water
Figure 2. Sampling strategy for Volusia County—example.
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IRL Water Quality Data
7K7V, TP, A/Ox, Salinity, TSS, Turbidity, Chlorophyll a, Phaso-pigments, Color, etc.
Inspection of Data Homogeneity
Plots of Spatial
Distribution
Plots of Temporal
Distribution
Multivariate/Multiparametric Data Analyses
ProcMANOVA
Principal Component Analyses - Isolation of groups of'parameters which
account for most of the variability in IRL water quality characteristics.
Yes
No
Cluster Analyses - Place objects into groups or clusters; i.e., grouping of several
stations and providing economy in the number of parameters which vary independently
Kriging Analyses - Spatial representation of water quality parameters showing values
from unsampledplaces having minimum variances (Block/Punctual)
Univariate Analyses
ProcANOVA, GLM, Means, etc.
Interpretation/Conclusion
Figure 3. Flowchart of statistical tests for the segmentation of the Indian River Lagoon (ML-Mosquito
Lagoon; BRL-Banana River Lagoon; IRLB-Indian River Lagoon-Brevard County; IRLIR-Indian River
Lagoon-Indian River County).
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Table 1. Sampling Stations Being Monitored per Modified IRL-WQMN
in the Indian River Lagoon
Sampling Station
V02
Vll
V17
ML02
102
107
110
113
116
118
121
123
127
B02
B04
B06
B09
IRJ01
IRJ04
IRJ05
IRJ07
IRJ10
IRJ12
IRL Segment
Mosquito Lagoon
Mosquito Lagoon
Mosquito Lagoon
Mosquito Lagoon
Indian River Lagoon
Indian River Lagoon
Indian River Lagoon
Indian River Lagoon
Indian River Lagoon
Indian River Lagoon
Indian River Lagoon
Indian River Lagoon
Indian River Lagoon
Banana River Lagoon
Banana River Lagoon
Banana River Lagoon
Banana River Lagoon
Indian River Lagoon
Indian River Lagoon
Indian River Lagoon
Indian River Lagoon
Indian River Lagoon
Indian River Lagoon
Sampling Agency
Volusia County
Volusia County
Volusia County
NASA-Dynamac
SJRWMD
SJRWMD
SJRWMD
SJRWMD
SJRWMD
Brevard County
Brevard County
Brevard County
Brevard County
NASA-Dynamac
Brevard County
Brevard County
Brevard County
Indian River County
Indian River County
Indian River County
Indian River County
Indian River County
Indian River County
Table 2. Water Column Physical Parameters for the Indian River Lagoon Water
Quality Monitoring Network
Physical Parameters
Unit
Water Temperature
pH
Dissolved Oxygen
Conductivity
Salinity
Secchi
Depth of Collection
Depth of Sample Site
Air Temperature
Wind Direction
Wind Velocity
Cloud Cover
degrees Celsius
pH units
mg/L
H mhos/cm
parts per thousand
meters
meters
meters
degrees Celsius
degrees
miles per hour
percent
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Table 3. Water Column Chemical Parameters for the Indian River Lagoon Water
Quality Monitoring Network
Chemical Parameters
Unit
Color
Turbidity
Total Organic Suspended Solids
Total Inorganic Suspended Solids
Chlorophyll a
Chlorophyll b
Chlorophyll c
Pheopigments
Chlorophyll a corrected
Chlorophyll a / Pheopigment Ratio
Total Organic Carbon as C
Total Kjeldahl Nitrogen as N
Nitrate + Nitrite as N
Dissolved Kjeldahl Nitrogen as N
Total Phosphorus as P
Total Orthophosphorus as P
Dissolved Phosphorus as as P
Silica as SiO2-D
PCU
NTU
mg/L
mg/L
Mg/L
Mg/L
Mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
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Improving Indicator Selection for Regional Stormwater Monitoring
Brock B. Bernstein
308 Raymond St., Ojai, CA 93023
Phone: (805) 646-3849
Michael Drennan
MDA, Inc., 17712 Miranda St., Encino, CA 91316
Introduction
Stormwater management and monitoring programs continue to undergo rapid evolution. In a
relatively short period of time monitoring programs monitoring program they have progressed from an
initial focus on characterizing the nature and extent of Stormwater problems to comprehensive efforts to
identify sources of Stormwater pollution, determine the effectiveness of control measures, and track a
myriad of management activities. In that same time period management programs have moved from BMP
implementation based strictly on best professional judgment (some have characterized as a shotgun
approach), to regionally integrated watershed management efforts based much more solidly on sound
science. This accelerated evolution has resulted in constant pressure to develop and implement improved
indicators and study designs that can provide the information needed by these developing management
initiatives. We suggest that a continuation of the historical emphasis on demonstrating compliance as the
primary goal of monitoring and management will be counterproductive in a number of ways. We suggest
that as this evolution continues, Stormwater managers will need to continually consider the proper
balance of monitoring dollars allocated to traditional regulatory (or compliance) monitoring and
nonregulatory performance monitoring.
Instead, we suggest that Stormwater monitoring programs indicator selection be broadened to include
measures indicators of success with stated environmental goals as well as of indicators of compliance
with regulatory objectives. Success is a performance issue that measures protection and enhancement of
environmental value. Compliance is a legal issue that reduces liability. Both are important. Including
measures indicators of success in monitoring programs requires shifts in both methods and mindsets,
with attendant shifts in the potential risks and rewards of monitoring and management actions. We
demonstrate that the increased rewards typically outweigh any increased risks from broadening indicator
selection.
A Brief History
Despite a recognition that contaminated Stormwater runoff was an important urban pollution problem
(e.g., Weibel et al. 1964), it received little regulatory and management attention throughout the 1960s and
1970s (Barton 1978). Attention instead focused on large and readily identifiable point sources such as
municipal wastewater treatment plants and industrial discharges. The relative ease of identifying,
treating, and monitoring point sources made this a logical management choice during the early years of
intensive water pollution control efforts.
In recent years, significant improvements in point source discharges and a greater understanding of
the nature and potential impacts of Stormwater runoff (e.g., Hunter et al. 1979, Eganhouse and Kaplan
1981, Cole et al. 1984, Hoffman et al. 1983) have combined to raise the scientific and public profile of
this issue. Thus, despite the problems attendant upon dealing with pollution from diffuse arrays of
difficult to identify sources, the Water Quality Act of 1987 amended §402(p) of the Clean Water Act to
require NPDES permits for Stormwater discharges. Rather than identifying definite discharge limits for
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specific pollutants, stormwater permits require instead that pollutants be reduced to "the maximum extent
practicable" (MEP). While this is difficult (some might say impossible) to define, the principal
motivation for stormwater management and monitoring remained the need to demonstrate compliance
with permit conditions. As a result, the vast majority of parameters measured in typical stormwater
monitoring programs are either: 1) the concentrations and loadings of pollutants, or 2) the level of effort
applied to various management actions (e.g. miles of street swept, or number of catch basins cleaned). As
we argue below, this approach does fulfill one of the essential roles of compliance, protecting against
traditional forms of liability resulting from exceeding discharge standards in NPDES permit conditions. It
does not, however, demonstrate that beneficial uses are necessarily being protected nor that essential
societal values are being enhanced.
Challenges at the Regional Scale
While the goals of the Clean Water Act (CWA) are straightforward ("fishable, swimmable waters",
and "protection and restoration of the physical, chemical and biological qualities of the nation's
waters")prohibit discharges to stormwater and reduce stormwater contamination to the maximum extent
practicable), achieving them in practice is challenging and complex. Even the more direct stormwater
objectives (such as prohibiting discharges to stormwater and reducing stormwater pollution to the MEP)
Stormwater runoff must necessarily be viewed at watershed scales. As in municipal wastewater systems,
stormwater collects through a network of ever larger channels until it finally reaches a discharge point
(i.e., outfall or creek/river mouth). Unlike wastewater systems, however, natural channels can support
beneficial uses along their entire length. Thus, monitoring must not only account for a range of diffuse
and poorly defined sources, it must also address potential impacts throughout the entire network of
channels rather than around a single terminal discharge point.
In recognition of these and other ways in which stormwater systems differ from traditional large
point sources, many municipalities have grouped themselves into larger stormwater agencies that
encompass entire watersheds or sub-watersheds. This approach to organizing stormwater programs helps
to coordinate similar activities (e.g., loadings estimation, trend monitoring) within a watershed. It leaves
unresolved, however, the issue of coordinating the full range of agencies and activities that potentially
affect both pollutant loads and beneficial uses within watersheds. Beneficial uses are often affected, for
example, by channel construction and maintenance activities, land use patterns and changes in these,
wildlife management practices, and regional air pollution control strategies, to name but a few. Recently
increased attention to this broader array of impacts has put increased pressure on stormwater
management programs to consider a wider set of activities than simply pollution control and monitoring
(Environmental Statutes 1987, NRC 1990, U.S. EPA 1991).
Such developments complicate the search for reliable and information-rich indicators upon which to
base monitoring programs. In regional contexts in general, and for stormwater programs in particular,
indicator selection is beset by difficult challenges. Ecology has only recently begun to address processes
at the landscape and regional scale, with the result that the development of reliable indicators of pattern
and process at these scales is still in its early stages. In addition, larger ecological units such as water-
sheds are often strongly influenced by intermittent but intense events such as extremes of rainfall or
temperature. Most monitoring and research datasets are not long enough to provide adequate insight into
how watersheds respond to such events, which in turn hampers our ability to select robust indicators that
will be useful over the long term.
At a more pragmatic level, historic data that can help establish reference conditions is often difficult
to find and, once found, hard to integrate at the needed spatial scale. A recent National Academy of
Sciences study (NRC 1995) documented a wide variety of incompatibilities that can impede attempts to
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create regionally consistent background datasets. Obtaining the agreement among relevant agencies
needed to address these and other similar problems can be frustrating and time-consuming.
Redefining Risk and Defining What Counts
Overcoming these sorts of scientific, technical, and administrative barriers is crucial and we do not
intend to minimize their importance in the least. However, focusing only on these constraints can cause
us to overlook another set of equally important concerns. We thus believe monitoring design efforts
could benefit from stepping back and viewing indicator selection in a larger context.
As mentioned above, most indicators selected for routine monitoring programs reflect
straightforward regulatory compliance, i.e., "Are levels of constituent X decreasing?" or "Are streets
being swept?" The assumption underlying this approach is that achieving this kind of compliance ensures
that broader goals related to habitat and water quality protection and restoration are being met. That is, if
pollutant levels decline, then water and habitat quality will necessarily improve, with attendant
improvement in a wide range of beneficial uses. This sort of monitoring, and the approach to compliance
it supports, certainly reduces the risk that programs will be accused of not meeting their permit
requirements. Used properly, this kind of monitoring can also provide useful feedback about whether
management programs are in fact reducing levels of pollutants.
While this focus on formal compliance reduces one kind of program risk or liability, it increases
another and potentially more far-reaching kind. As mentioned above, beneficial uses can be degraded by
a wide range of insults and activities. Some of these are unrelated to pollution but are directly related to
the way stormwater is managed. By focusing monitoring narrowly on traditional compliance measures,
we can lose sight of the core societal values that motivate environmental protection and regulation in the
first place, making compliance instead an end in itself. This is problematic because environmental
programs of all kinds throughout the U.S. are facing increasing pressure to show attention to and tangible
improvements in these core values. These programs' constituencies are therefore more and more likely to
hold them accountable to a much broader definition of performance. An overly narrow focus on formal
compliance runs the risk of breaking the connection to these core values with a resulting loss of support
for current environmental management programs.
We therefore suggest that indicator selection be broadened to include measures of success as well as
of compliance. These measures of success should be more closely tied to the actual beneficial uses of the
receiving waters the regulations are intended to protect, rather than to the quality of a particular discharge
to those waters. For example, if a creek has been designated to support specific beneficial uses such as
cold water aquatic life, recreation, or drinking water supply, indicators such as the presence of trout, the
availability of stream habitat, or the ability of the water to support wading and swimming can be defined.
For example, indices of biologic integrity (IBIs) have been used to assess biological resources by
integrating information about the elements of biological systems and the processes that generate and
maintain them. The IBI evaluates the full spectrum of human disturbances (water quality, habitat
structure, energy source, flow regime, and biotic interactions) in an ecological rather than a laboratory-
based chemical toxicity context (Karr 1998).
As an example of this approach, the Rouge River National Wet Weather Demonstration Project in
Michigan has identified a series of indicators for public use which convey clear, succinct information to
non-technical users about the overall health of the aquatic resource. These indicators include public uses
(fishing, wading/swimming, canoeing/boating, aesthetics) and river conditions (dissolved oxygen, river
flow, bacteria, aquatic life [based on an IBI], and stream habitat [also based on a field method which
evaluates several factors]). Each of these indicators was rated as good, fair, or poor and plotted on multi-
color maps for the public's use.
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While there may be those who criticize this approach as too indirect, and not relevant to the narrow
interests of compliance with stormwater regulations, the public is demanding that the resources spent on
environmental protection be correlated with actual results. It is no longer acceptable to merely measure
compliance with permit conditions and assume the goals of the CWA are satisfied. As a crude example,
stormwater dischargers can currently demonstrate they are in compliance with their permit merely by
documenting the amount of debris removed from their catch basins. Nothing requires them to
demonstrate that the amount of debris removed is actually significant relative to the threat that debris
poses to the receiving waters. While a discharger may be satisfied with mere compliance with permit
conditions, the public only cares if the actual goals of the CWA are being reached.
Success is a performance issue that measures protection and enhancement of environmental value.
Compliance is a legal issue that reduces liability. Both are important. However, including measures of
success in monitoring programs will require shifts in both methods and mindsets. It demands a
willingness to hold programs accountable to performance standards that are often harder to define and/or
measure. It means asking agencies and their staff to look beyond their existing expertise with pollutants
and engineering-oriented management practices to issues of habitat, ecology, land use, and broader
public policy. Together these will result in changes to the existing calculus for estimating the potential
risks and rewards of monitoring and management actions.
Programs that demonstrate a good-faith effort to address the concerns that matter most to their
constituencies by protecting core environmental values build up a reservoir of good will that can provide
an effective buffer against accusations that strict compliance standards are not being met. More
importantly, they protect against the even more risky charge that programs are out of touch with what
really matters and therefore not worth continuing.
References
Barton, K. 1978. The other water pollution. Environment 20(5): 12-20.
Cole, R. H. et al. 1997. Preliminary findings of the priority pollutant monitoring project of the
Nationwide Urban Runoff Program. J. Water Poll. Cont. Fed. 56(7) 898-908.
Eganhouse, R. P. and I. R. Kaplan. 1981. Extractable organic matter in urban stormwater runoff. 1.
Transport dynamics and mass emission rates. Env. Sci. Tech. 15(3): 315-326.
Environmental Statutes, National Estuary Program. 1987. Clean Water Act, Section 320. United States
Code 1330.
Hoffman, E. J. et al. 1983. Annual input of petroleum hydrocarbons to the coastal environment via urban
runoff. Can. J. Fish. Aquat. Sci. 40(Supplement 2): 41-53.
Hunter, J. V. et al. 1979. Contribution of urban runoff to hydrocarbon pollution. J. Water Poll. Cont. Fed.
51(8): 2129-2138.
Karr, James R. 1998. Going Beyond Water Quality to Protect Fish and Aquatic Ecosystems. Proceedings
of the American Fisheries Society, 126th Annual Meeting.
Murray, James E. 1997. Rouge River National Wet Weather Demonstration Project, Implementing an
Urban Watershed Approach, Wayne County Department of Environment.
National Research Council (NRC). 1990. Managing troubled Waters. Washington, D.C.: National
Academy Press.
National Research Council (NRC). 1995. Finding the Forest in the Trees: The Challenge of Combining
Diverse Environmental Data. Washington, D.C.: National Academy Press.
U.S. EPA. 1991. The Watershed Protection Approach: An Overview. EPA/503/9-92/001. Washington,
D.C.: Office of Water.
Weibel, S. R. et al. 1964. Urban runoff as a factor in stream pollution. J. Water Poll. Cont. Fed. 36(7):
914-924.
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Storm Water Metals—Issues and Historical Trends,
Lawrence Livermore National Laboratory
Erich R. Brandstetter, Environmental Scientist
Water Guidance and Monitoring Group, Lawrence Livermore National Laboratory
P.O. Box 808, L-627, Livermore, CA 94551
Phone: (925) 424-4961; Fax: (925) 422-2748; E-mail: brandstetterl ©llnl.gov
Abstract
Lawrence Livermore National Laboratory (LLNL) is operated by the University of California under
contract with the U.S. Department of Energy (DOE) under Waste Discharge Requirements (WDR) 95-
174, allowing storm water discharges associated with industrial activities. Permit requirements include:
• Building drain tracking
• Non-storm water discharge tracking
• Dry and wet season observations
• Runoff sampling and analysis
• Development and application of a Storm Water Pollution Prevention Plan which includes detailed
Best Management Practices (BMPs)
There are no chemical-specific limits for LLNL storm water effluent; the only limit applies to an annual
fish toxicity test.
LLNL has over 10 years of storm water metals data. Recent data seem to indicate that concentrations
of some metals are increasing in LLNL storm water effluent. If these trends can be attributed to LLNL, it
may lead to increased and costly changes to infrastructure and BMPs. However, the trend may be due to
changes in testing laboratories, and/or changes in procedures used at the laboratories (specifically, there
may have been a shift from procedures which recover dissolved metals to procedures which recover total
metal concentrations). It is also not clear how much of the trend is due to sediment loads, natural
concentrations, or off-site contributions. During the 1997/1998 season, sampling at site influent and
effluent locations included analysis for total suspended solids and analysis for metals in both filtered and
unfiltered storm water. This study presents the preliminary conclusions of the 1997/1998 sampling. The
comparison of filtered/unfiltered metals results, and comparison of these results with total suspended
solids indicated that the source of increasing metals was naturally-occurring sediments being transported
in storm water.
Introduction
LLNL serves as a national resource of scientific, technical, and engineering capabilities. The
Laboratory's mission focuses on nuclear weapons and national security, and over the years has been
broadened to include areas such as strategic defense, energy, the environment, biomedicine, technology
transfer, the economy, and education. The Laboratory carries out this mission in compliance with local,
state, and federal environmental regulatory requirements. It does so with the support of the Environmental
Protection Department, which is responsible for environmental monitoring and analysis, hazardous waste
management, environmental restoration, and assisting Laboratory organizations in ensuring compliance
with environmental laws and regulations.
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LLNL comprises two sites. The focus of this paper is the Livermore main site, which occupies an area
of 3.28 square kilometers on the eastern edge of Livermore, California, approximately 80 kilometers
southeast of San Francisco. Immediately to the south of the Livermore site is Sandia National
Laboratories, operated by Lockheed-Martin under DOE contract. There are also some low-density
residential areas and agricultural areas to the south of LLNL. Business parks are located to the southwest
and north of the site, and a 200-hectare parcel of open space to the northeast has been rezoned to allow
development of light industry. A small amount of low-density residential development lies to the east of
the site, and agricultural land extends to the foothills that define the eastern margin of the Livermore
Valley. Flow patterns at the site are such that storm water at sampling locations includes runoff
components from these other sources, including agricultural land, industrial facilities, parking lots, and
landscaped areas.
LLNL first monitored storm water runoff at the Livermore site in 1975. The original monitoring
network, designed to detect pesticides, was expanded in 1990 to cover new locations and additional water
quality parameters (including metals). Additional changes in 1993 complied with the National Pollutant
Discharge Elimination System (NPDES) General Industrial Activities Storm Water Permit. It is important
to show that LLNL is not contributing to storm water metals concentrations, in order to verify that
provisions of the existing, BMP-based storm water permit are protective of storm water quality.
Methods
During the 1997/1998 wet season, source investigations were conducted to determine how much of
the metals are present in the liquid (dissolved) and how much in sediments (suspended) being transported,
for example, during high flow events. The study was also designed to evaluate how much of the loading
in each fraction (dissolved and suspended) originates off site, and how much is contributed by on-site
sources, and to relate concentrations of constituents in storm water from a particular storm and location to
the concentration of total suspended solids from the same storm and location. To accomplish these goals,
samples for applicable constituents were collected in duplicate. One sample was analyzed for total
concentration (i.e., dissolved and suspended) of the constituents of interest. The second sample was
passed through a 0.45 |xm filter in order to evaluate the dissolved component. Although particles smaller
than 0.45 (Xm (i.e., not dissolved) will of course pass through this filter, this removes the majority of the
sediments, and is therefore adequate for evaluation of the dissolved fraction of the storm water. LLNL's
contracts with the analytical laboratories are designed around collections or "suites" of analyses. Metals
are included in two suites identified by "GENMIN" (general minerals) and "NPDESMETALS" (low
reporting level drinking water metals under NPDES). Because there is some overlap in the metals
specified in these two suites, some metals were subjected to duplicative analyses. In such cases, all
analyses were utilized. Samples were also analyzed for concentration of total suspended solids (TSS).
Sample collection was by "grab sampling." In this method, technicians were dispatched to the field
during rain events. When flow was observed, samples were collected by the technicians in one-liter
bottles. Sample bottles, pre-labeled for the various analyses, were then shipped to off-site laboratories for
analysis.
There are two main storm water flow pathways through the Livermore site (Figure 1). The majority of
the site drains into Arroyo Las Positas. Arroyo Las Positas effluent is monitored at location WPDC, and
influent to Arroyo Las Positas is monitored at locations ALPE, ALPO, and GRNE. Arroyo Seco crosses
the southwest corner of the site. Influent and effluent monitoring locations for Arroyo Seco are ASS2 and
ASW, respectively. During the 1997/1998 wet season, runoff samples from four storms were collected at
these locations. For one sample set, the NPDESMETALS analyses reported as total metals were filtered.
Because of this error by the analytical laboratory, this portion of the data was eliminated from the study.
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Because there are no numerical criteria that limit concentrations of specific constituents in storm
water effluent, various criteria were used to evaluate LLNL storm water quality. In the federal multisector
permit, the Environmental Protection Agency (EPA) established benchmark values for 41 parameters but
stressed that these concentrations were not intended to be interpreted as effluent limitations. Rather, they
are levels that the EPA has used to determine if storm water discharged from any given facility merits
further monitoring. Maximum Contaminant Levels (MCLs) for drinking water and Ambient Water
Quality Criteria (AWQC) protective of fresh water organisms, developed by California and the federal
government were also used as comparisons with LLNL storm water analytical results. However, these
criteria are defined for other purposes, and are therefore not directly applicable to storm water effluent.
Nevertheless, use of a broad range of criteria can help to evaluate LLNL's storm water management
program and to allow LLNL to ensure high quality in its storm water effluent. Of greatest concern are
constituents that exceeded comparison criteria at effluent points, but for which the influent concentrations
were less than the corresponding effluent concentrations (indicating a possible on-site source). Each year,
constituents identified by this screening process are subjected to detailed analysis, generally including
evaluation of all historical data. It was this process that identified the apparent increasing trend in storm
water metals. A review of data for the past five years identified four metals selected by this screening in
multiple years, and with greater frequency of effluent values higher than influent values in recent years.
The metals are chromium, copper, iron and zinc. These metals were therefore selected for detailed
analysis in this study.
Data were evaluated in a variety of ways. First, dissolved concentrations were compared to total
concentrations for each metal. Next, concentrations (both dissolved and total) were compared to total
suspended solids concentrations. Finally, the dissolved and total concentrations were compared against
the historical record. This last comparison was done on a location-specific basis, so that the apparent
increases in influent and effluent concentrations for both pathways across the site could be evaluated for
the relative contribution of total metals and dissolved metals.
Results
For every metal at every location, median total concentrations were greater than median dissolved
concentrations (Table 1), generally by a factor of three or more. Dissolved concentrations of chromium
and iron were almost all below their respective detection limits (0.001 and 0.05 mg/L, respectively).
Median total iron concentrations ranged as high as 100 times the median dissolved concentrations. This
provides the first clear indication that it is total metals that result in concentrations above the comparison
criteria.
Plots of concentration and TSS (Figure 2) provide further evidence that suspended solids are at the
root of the high concentrations. While there is a lot of variability in the data, all four metals show a clear
relationship, with total concentrations increasing as TSS increases. As expected, dissolved concentrations
do not increase with increasing TSS. For zinc, on the contrary, some of the highest dissolved
concentrations occurred at low TSS levels.
Some of the observed variability may be due to between-sample variability. Separate sample bottles
are submitted for each analysis (i.e. GENMIN filtered, GENMIN unfiltered, NPDESMETALS filtered,
NPDESMETALS unfiltered, and TSS). The actual TSS level in the bottle submitted for GENMIN
unfiltered analysis, for example, will not be identical to the TSS level in the bottle submitted for TSS
analysis. Routine procedures exist at LLNL to collect duplicate samples at a frequency of approximately 1
out of every 10 samples. Duplicate samples are collected at the same location as the routine samples,
immediately following collection of the routine sample. To evaluate the impact of between-sample
variability, TSS in routine and duplicate samples from 1993 to present (25 data pairs) were compared.
The average difference between duplicate and routine samples was 33%, and the difference ranged as
high as 117%. Thus, between-sample variability may account for much of the variability observed, both
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in the relationship between total metal concentrations and TSS discussed above, and between dissolved
and total metals concentrations (below).
Dissolved concentrations seem to be somewhat positively correlated to total concentrations for zinc,
but again there is a lot of variability (Figure 3). No relationship is apparent for chromium, copper and iron
(the two rows of points at dissolved copper concentrations of 0.01 and 0.001 mg/L are the result of
differing reporting limits for the GENMIN and NPDESMETALS analyses, respectively). This provides
strong evidence that the high concentrations observed in the historical record are due to metals bound up
in the sediments, transported by turbulent storm flows, and not available for dissolution into the liquid
phase.
The total and dissolved concentrations are compared to the historical record in Figure 4. In these
historical trend figures, all available data for the influent and effluent locations of the two runoff
pathways through the Livermore site have been aggregated. Also, data have been aggregated on a wet
season basis—that is, October of one year through May of the next—rather than on a calendar year basis.
Thus, data labeled 96/97 represent October 1996 through May 1997. For the 1997/1998 season, this was
done separately for the total concentrations and dissolved concentrations. Because it is not certain a priori
whether the historical data represent total concentrations, dissolved concentrations, or some combination,
the lines connecting historical data points were not extended to the 1997/1998 data points. Also shown on
each plot are the comparison criteria available for each constituent. To simplify interpretation of these
complex plots, note that squares and diamonds were used to represent the Arroyo Seco pathway; and
circles and triangles for the Arroyo Las Positas pathway. Also, solid shapes represent effluent, while open
shapes represent influent.
The historical trends were compared to the historical trend in TSS concentrations (Figure 5). In the
Arroyo Seco pathway, TSS at both influent and effluent locations increased up to the 1996/1997 season,
and then decreased in the 1997/1998 season. In the Arroyo Las Positas pathway, TSS levels were
relatively low during the most recent two years, and alternated between high and low values during the
preceding 3 years. These trends were compared qualitatively to the trends for each of the metals, as
summarized in Table 2. In general, metal concentrations corresponded to TSS concentrations, with only a
few cases in which there was little or no correspondence. For example, the 1993/1994 high TSS in Arroyo
Las Positas is generally not reflected in the metals data. This indicates that the historical record starting in
1994/1995 is primarily based on total concentrations, but that there may be some inconsistencies.
For the 1997/1998 data, chromium, copper and iron, median dissolved concentrations for both
pathways at both influent and effluent locations were at or very near the reporting limits. Similarly,
median dissolved zinc concentrations were generally much lower than median total zinc concentrations.
Total concentrations are more consistent with the historical record, and generally correspond with the
1997/1998 TSS values, once again indicating that the historical trend is primarily due to total metals
concentrations.
Discussion
An important lesson learned in this study is that it is essential that procedures at analytical
laboratories be closely monitored in order to ensure that the correct data are being produced. Even during
the course of this study, which required close, personal contact with laboratory personnel, not all analyses
were done as specified. In the future, metals such as iron, for which there is a clear and consistent
difference between dissolved and total concentrations, will be used to determine if the laboratory has used
filtration on the appropriate analyses. Iron is a major component in aluminosilicate minerals that make up
a major portion of sediment load.
The available data indicate that the apparent trend in increasing metals concentrations is due to a shift
from analyses that recover the dissolved portion (i.e. filtered), to analyses that recover total metals. This is
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compounded by the observed increase in TSS in Arroyo Seco, due to the metals associated with the
naturally-occurring sediments. There is a clear positive correlation between TSS and total metals
concentrations, although there is notable variability. This variability could be reduced by homogenizing a
large storm water sample volume and then separating it into the bottles submitted for the various analyses.
Plans are underway to collect 1998/1999 samples in this manner. There is no relationship between
dissolved concentrations and TSS. Only in the case of zinc was a relationship observed between dissolved
concentrations and total concentrations. Where effluent metal concentrations were higher than influent
concentrations, the data indicate that this is due to higher TSS levels.
While it is not completely clear how much of the historical record should be attributed to total metals
analyses, it is clear that high metal concentrations are due to the suspended solids transported in the storm
water. Because these high metal concentrations can be attributed to naturally-occurring sediments, costly
changes to infrastructure and additional BMPs are not necessary. If reduction of storm water metal
concentrations is needed, the appropriate response would be erosion control measures.
Ill-141
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Patterson Pass Road
Storm water
sampling locations
—' Drainage channel
LLNL perimeter
Scale: Meters
200 400
Figure 1. Livermore site storm water sampling locations.
ffl-142
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Chromium
0.12 -
g 0.1 T
= J 0.08 ^
E CJ 0.06 T
l£0.04^
0 0
0 0.02 -O&O
n flM-^ v ^
0
500 1000 1500
TSS (mg/L)
Iron
« |
:=! 60 i D D D
c i
3 40 +
|g°Ua
0 £
D
*0 1000 1500
TSS (mg/L)
Copper
li °-15 T
£ 01} D
» 0.05 i D n
£ .«S8w. u AJ,
0 500 1000 1500
TSS (mg/L)
Zinc
3- 0.5 T Q
l%2:.£o g
1 °-1 i.9an ° x
0 500 1000 1500
TSS (mg/L)
| +Dissolved-GENMIN DTotaMSENMIN XDissolved-NPDESMETALOTotal-NPDESMETAL
i
Figure 2. Relationship between metals concentrations and TSS.
Chromium
•o
"
0.004 - +
0.003 '
i 0.002
« — 0.001
0 0
0.05 0.1
Total (mg/L)
0.15
•o ^
J ~
i E
5
Copper
0.01 -I
0.008
0.006
0.004
0.002
-Wff + +
J
•f£
*«• + +
0 0.05 0.1 0.15
Total (mg/L)
Iron
Zinc
0.15 i
•D
?5 0.1
S £ 0-05
±
^* _i_
J$ti*4t+ + +
0 0.2 0.4 0.6
Total (mg/L)
Figure 3. Relationship between dissolved and total metal concentrations.
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Chromium
0.06 T
5 0.05
£ 0.04
Copper
90/91 91/92 92/93 93/94 94/95
Year
95/96
96/97
97/98
X
O
A
-Arroyo Seco influent
-Arroyo Las Positas influent
Arroyo Seco influent dissolved
Arroyo Las Positas influent dissolved
Arroyo Seco influent total
Arroyo Las Positas influent total
-MCL
Benchmark
-Arroyo Seco effluent
-Arroyo Las Positas effluent
Arroyo Seco effluent dissolved
Arroyo Las Positas effluent dissolved
Arroyo Seco effluent total
Arroyo Las Positas effluent total
AWQC
Figure 4. Annual median metal concentrations in LLNL storm water.
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Iron
8
7 -
75> 6-
E
~~ 5 +
J4
CB
1 3 "
O
C '
O '
o
1 -•
O1
92/93
93/94
94/95
95/96
Year
96/97
0.3 T
^ 0.25 -
ra
5. 0.2
c
S 0.15
CB
**
8 o-1
c
5
0.05 •
Zinc
0
90/91
92/93 93/94 94/95 95/96 96/97
Year
97/98
X
O
A
-Arroyo Seco influent
-Arroyo Las Positas influent
Arroyo Seco influent dissolved
Arroyo Las Positas influent dissolved
Arroyo Seco influent total
Arroyo Las Positas influent total
-MCL
Benchmark
X
-Arroyo Seco effluent
-Arroyo Las Positas effluent
Arroyo Seco effluent dissolved
Arroyo Las Positas effluent dissolved
Arroyo Seco effluent total
Arroyo Las Positas effluent total
AWQC
Figure 4 (continued).
III-145
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92/93 93/94 94/95 95/96 96/97 97/98
Year
- -n- - Arroyo Seco Influent
- -o- - Arroyo Las Positas Influent -
Benchmark
-Arroyo Seco Effluent
-Arroyo Las Positas Effluent
Figure 5. Annual median TSS concentrations in LLNL storm water.
Table 1. Median Influent and Effluent Concentrations, 1997/1998 Wet Season
TSS
Chromium
Copper
Iron
Zinc
Dissolved
Total
Dissolved
Total
Dissolved
Total
Dissolved
Total
Arroyo Seco
Influent
55
0.001
0.0047
0.0048
0.0175
0.05
1.45
0.0505
0.0785
Effluent
306
0.001
0.018
0.0038
0.023
0.05
6.1
0.0325
0.185
Arroyo Las Positas
Influent
139
0.001
0.011
0.0044
0.015
0.05
5.2
0.02
0.061
Effluent
121
0.0014
0.016
0.0062
0.017
0.05
3.3
0.059
0.205
Table 2. Qualitative Comparison of Agreement Between Historical
TSS and Metals Concentrations
Chromium
Copper
Iron
Zinc
Arroyo Seco
Influent
medium
low
high
high
Effluent
high
high
high
medium
Arroyo Las Positas
Influent
high
high
low
low
Effluent
high
low
high
high
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Monitoring and Assessing the Environmental and Health Risks of
Separate Sanitary Sewer Overflows (SSOs)
Sarah J. Meyland, MS, JD, Associate Professor
Department of Environmental Technology
New York Institute of Technology
Phone: (516) 686-7765; Fax: (516) 686-7919
Dr. Melinda Lalor and Dr. Robert Pitt
Department of Civil and Environmental Engineering
The University of Alabama at Birmingham
Abstract
Targeted water quality monitoring helps develop a better understanding of ambient conditions and
trends and helps guide regulatory responses to issues of public health protection and water resource uses.
An EPA-sponsored project is studying separate sanitary sewer overflows (SSOs) to determine the types
of changes in stream quality that occur during and after an overflow event. The discharges from SSOs
can contain nutrient-rich sanitary wastewater, pathogens, toxic and hazardous chemicals and heavy
metals. Many SSO discharges occur during wet weather events and their contribution to water quality has
not been extensively documented. It may be difficult to distinguish water quality changes caused by
SSOs using traditional parameters of water quality (such as DO, TSS, BOD5, etc.) from the many other
contributions, both point and nonpoint, that streams and larger drainage areas receive. However,
pathogen loads and toxicants from SSOs may be very important in urban watershed quality. This is
especially true when SSOs drain into recreational contact waters, environmentally sensitive areas,
drinking water sources or economically important waters such as shell fishing beds. Sewers can be a
significant source of disease-producing protozoa such as Cryptosporidium and Giardia, which may
remain viable for extended periods in streams and the stream beds.
A multiyear project examining the environmental and ecological impacts and public health risk
associated with SSOs will complete phase one in mid-1998. This paper will review the goals of this US-
EPA-funded study, look at main issues of the investigation, describe how the study and its results will be
presented using a GIS-based system to increase its user-friendliness and application to many interested
stakeholders.
Introduction
It has long been understood that sewage collection, treatment and safe disposal is an essential
community service that protects public health and environmental quality, especially water resources. The
Federal Clean Water Act (1972) established the regulatory basis for setting treated sewage effluent
standards and regulating sewage discharges. The Clean Water Act also required that sewage treatment
plant operators receive NPDES permits (National Pollutant Discharge Elimination System program) that
authorize the effluent discharge and within which the discharge quality criteria are defined.
In the early-1990s, the U.S. Environmental Protection Agency turned its attention to other sewage-
related discharges, such as Combined Sewer Overflows (CSOs) and Separate Sanitary Sewer Overflows
(SSOs). In 1995, following on the heels of a control policy on CSOs (Federal Register, April 19, 1994)1,
EPA sought stakeholder-advise on policy considerations for controlling and eliminating SSOs. Whereas
CSOs fall within various exceptions allowed for discharges at the sewage treatment plant ("upsets" and
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"by-pass") and thus can be covered by the discharge permit, SSOs occur within the sewage collection
system itself. These discharges are generally related to collection system failures that are without permit
authorizations and are thus illegal. It is estimated that there were nearly 20,000 municipal separate
sanitary sewer systems in the United States as of 1996 serving 147 million people. By comparison, there
are only approximately 1,100 combined sewer systems serving about 43 million people in the U.S.
As part of the policy-making process, EPA funded a research project (CX-824848)2 in 1996 to help
define environmental impacts and health-related risks that could be attributed to SSOs. This report is
based on the Phase One work related to the project. It has been a collaborative effort between universities
in New York (New York Institute of Technology) and Alabama (University of Alabama at Birmingham)
and a New York environmental organization (Citizens Environmental Research Institute).
Sanitary Sewer Overflows
Sewage in typical municipal domestic waste collection systems carries a variety of constituents that
can alter local environmental conditions as well as spread disease-causing agents such as bacteria, viruses
and protozoa. Sewage may include untreated human and animal wastes, household chemicals, industrial
chemicals, pesticides, oxygen-demanding pollutants, suspended solids, nutrients, toxicants, floatable
matter, radioactive materials and pathogens. Separate sewage collection systems are not intended to
convey a significant level of stormwater and are expected to deliver the sewage in tact to the sewage
treatment plant for processing prior to discharge. However, as many municipalities know, a number of
things can go wrong along the route of the sewer lines. Sewer lines can leak, break, or become clogged
with grease and debris; pump stations can fail, or the lines can simply be overwhelmed with sewage
normal flow or by sewage combined with extraneous water from storms or other inflow, thus exceeding
the conveyance capacity of the pipes. When the sewer lines are overwhelmed or blocked, the sewage
backs up and overflows at the point of least resistence, which could be a building basement, a manhole
cover, pump station or break in the line. The overflowing raw sewage from the collection system is
known as a sewer system overflow. The overflow may be in an isolated location where it goes undetected
for a long period or it may occur in a spot where it offers the potential for considerable human contact
such as a street or residential basement or drinking water supply.
The unpredictable and random nature of SSOs, in part, makes them very hard to monitor and study.
Unlike a CSO, which often occurs at a pre-designed location in the system, an SSO can happen almost
anywhere along the sewer route. Many more SSOs occur during wet weather conditions than dry and the
quality of the discharge can be very different between wet and dry overflows as well as over the period of
a wet weather event. Historically, most SSOs have gone unreported or under-reported. Some of the most
egregious overflow conditions have resulted in EPA enforcement actions, costing local communities
millions of dollars to redesign, rebuild and/or tighten their collection systems. Perhaps more importantly,
SSO have been specifically implicated in several locations where public health was directly affected. For
example, in 1990 in Cabool, Missouri, SSOs leaked into nearby water lines, contaminating the drinking
water with a pathogenic strain of E. coli. Four people died and about 250 were sickened. (EPA, Sanitary
Sewer Overflows, 1996)3
A point that adds further importance to the concern over SSOs is the mounting evidence that sewage
discharges in general contribute to a growing list of environmental warning signals such as the re-
emergence of certain diseases, nutrient loading to the world's water resources, hazardous algal blooms
(HABs) such as Pfiesteria, human illness from water-borne exposure, endocrine-disrupting chemical
exposures and changes in bio-diversity and ecosystem health.
Regulatory agencies and water resource managers have noted that surface water quality tends to
decline following a storm event. This understanding is limited, however, by the use of a limited set of
water quality characteristics (pH, BOD5, DO, TTS and Coliform counts) in regulatory programs that do
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not fully portray the levels and types of contaminants that are added to the nation's waters and
sedimentary materials. Policy makers now seek to identify more clearly the contribution that each of
many components adds to the overall water quality picture, especially at the local level. The purpose of
this SSO research project was to provide guidance in assessing the health risk and environmental impacts
related to SSO events.
SSO Research Project
The SSO research project had multiple aspects that were all intended to culminate in the
development of a protocol or evaluative framework that sewage system operators and the interested
public could use to gauge the potential impacts and risks of SSO events. Its tasks included:
1. Characterize typical sewage composition at an SSO
2. Locate SSO sites and monitor them before, during and after SSO events
3. Based upon direct observations, apply one or more computer models that predict fate and
transport of sewage constituents under various SSO event scenarios
4. Complete a health risk assessment of the constituents released during SSOs and describe the
environmental impacts
5. Develop a methodology for assessing a range of SSO events and their environmental and health
risk impacts
6. Develop a Geographic Information System (GIS) that will track the project and apply the SSO
assessment methodology for the broader public audience that would use the assessment
framework.
SSO Discharges in a Typical River System: The Cahaba River
The area of Birmingham, Alabama (including Jefferson County, AL), has a history of SSO problems
and impacts. At least two river systems (the Cahaba and the Black Warrior) run through the county. The
Cahaba River in particular has been the subject of intense scrutiny and enforcement activity by the U.S.
EPA and local citizens organizations. Researchers at the University of Alabama at Birmingham carried
out a substantial portion of the project, building upon extensive work on storm water impacts to urban
watersheds including the Cahaba River.
The Cahaba River runs for 190 miles, from the hills northeast of Birmingham to the Alabama River
to the south, near Selma, Alabama. It is typical of many rivers across the U.S. that drain substantial land
areas and which change in character and use from one portion of their watershed to another. The Cahaba
River, for instance, drains 1,870 square miles in eight (8) counties. Nearly 800,000 people live within its
watershed.
In the Birmingham area, the average daily discharge into the Cahaba River is 26 million gallons of
sewage effluent from 24 municipal and 16 private sewage treatment plants. More than 100 industries also
have over 176 outfall points into the Cahaba River or its tributaries. An average of 40 million gallons per
day of treated sewage is released to the river in total. The river also receives high volumes of poorly or
untreated sewage as well. From January through March of 1995, for instance, Jefferson County released
271 million gallons of wastewater that did not receive at least secondary level treatment. During the
summer, parts of the river flow near Birmingham are nearly 100 treated sewage. (Bolton, The
Birmingham News, May 5, 1996)4
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Equally important, the river is a drinking water source and a recreational area. An average of 57
million gallons of water per day is withdrawn from the system for drinking water, supplying about 0.5
million people.
The Study Issues and Monitoring Locations:
Phase One of the SSO Study was designed to identify the main environmental issues, undertake
preliminary field monitoring and equipment testing, and provide a literature review of research relevant
to SSO discharges. During this phase, the project addresses the key physical and chemical processes
affecting the fate and transport of toxicants and pathogens released by SSOs. The processes identified as
most important were:
• Dilution and transport of toxicants and pathogens in the water column
• Deposition of settables
• Resuspension or scour of sellable conslituenls
• Chemical exchange or dilution between the water column and sediment pore water
To test the impact that these processes have on discharges from SSOs, two urban streams were
identified for study within the Birmingham area: 5-Mile Creek, a tribulary to the Cahaba River in
northern Birmingham, and a creek in Homewood, a suburb in southern Birmingham, which empties into
Shades Creek and on inlo ihe Cahaba River. Each slream has an SSO discharge point
The 5-Mile Creek site covers a 3-mile reach with 10 sampling points and has a continuous SSO
discharge, which runs overland for 300 feet before reaching the creek. The overland flow was also
studied. The Homewood study site covers a 2.5-mile reach with 10 sampling points. For each waterbody,
upslream, midstream and downstream locations for sampling were established.
In-stream water quality parameters were also monitored. A review of the literature was conducted
lhal characterized the constiluents commonly present in municipal sewage. The following list was chosen
for use in the impact studies (wet and dry weather conditions) of SSO discharges.
• Turbidity
• Conductivity
• pH
• Total Chlorine
• Fluoride
• Phosphorus
• Nitrate
• Ammonia
• Potassium
• Detergents
• E. coli
• Enterococci
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In addition to the water quality parameters, specific pathogens were of interest:
• Protozoa: Giardia lamblia and Cryptosporidium parvum
• Bacteria: Pseudomonas Aeruginosa, Fecal Streptococcus, Fecal/Total Coliform
These pathogens have been implicated in health risks from exposure via consumption and recreational
waters exposure such as bathing beaches.
A series of tests were performed by the Alabama researchers to investigate the presence and fate of
the pathogens. In-Situ Pathogen Die-Off studies using standard methods were performed where protected
containers of sample pathogens were placed in the stream and then sampled 6 times over a 21-day period.
Most of the pathogens showed continued viability in the streams over the length of the study time.
Modification of the current methods for the identification of Giardia and Cryptosporidium was also
undertaken.
Another study was conducted that examined the length of time needed by bacteria to acclimate to
additional materials present in sewage discharges under stream conditions and to measure the
photosynthesis and respiration (P/R) rates for various mixtures of sewage and stream water. Previous
studies had shown that traditional BOD5 results may be inaccurate due to the need of bacteria to
acclimate to the new mixtures of sewage and receiving waters.
YSI6000 Continuously Monitoring Water Quality Sondes were deployed in the study area at 5-Mile
Creek (2 upstream and 2 downstream from the SSO discharge) and the following water quality
parameters were monitored:
• Dissolved Oxygen
• Oxidation Reduction Potential (ORP)
• Conductivity
• Turbidity
• Depth
• Temperature
• pH
The equipment was tested in unattended conditions for 2-week periods where it collected data at 15
minute intervals. This information was used in the P/R rate studies as well as storm even tracking. The
equipment operated well. The obvious value of the continuous monitoring is that it develops a more
comprehensive picture of water quality under many weather conditions.
Pollutants from SSOs enter the water column of the stream and also invade the water held in the
interstitial spaces of the sedimentary material on the stream bed. Of the five processes that affect
pollutant exchange between in-stream water and the interstitial water, those that promote turbulent
mixing are the most important. Sediment size is a controlling feature. Nearly 60 sediment samples were
tested to characterize the material and search for pollutants. Among the chemical compounds detected
were sulfur compounds, phenols, phthalate esters and petroleum compounds, PAHs and steroids.
Computer Modeling:
The extensive field and laboratory work is essential to the application of computer modeling of
environmental and health risk. The results of the field monitoring will be used to calibrate the predictive
model or models that are selected as most suitable for the project. Over 40 predictive models were
ffl-151
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reviewed by NYIT for use in the project. From the initial screening 12 models were selected for a
detailed analysis of model parameters, assumptions, default values and sensitivity.
During Phase Two of the Project, the model/s will be calibrated and predictive evaluations run under
a range of SSO event conditions.
GIS Application
While the field and laboratory work was undertaken in Alabama, a GIS application was created at
NYIT that will be used to track the results of the project and provide a generic tool for applying the
methodology and Phase Two results of the project. The US-EPA BASINS program in conjunction with
Arc View 2.1 was used as the foundation of the GIS. To create a base map, USGS digital quad maps
(1:24,000) with geo-referenced features locating the study sites were obtained commercially. Data bases
from BASINS were selected to demonstrate how localities could use some initially available
environmental monitoring data to supplement original data collection. The level of detail available in the
BASINS data may not be fine enough for some areas of the country.
References
1. CSO Control Policy, Federal Register, April 19, 1994 (59 Federal Register 18688).
2. U.S. EPA Cooperative Agreement CX-824848-01-1. 1996. Developing and Testing a Methodology
to Assess the Health Risks and Environmental Impacts from Sanitary Sewer Overflows. Citizens
Environmental Research Institute, Farmingdale, N.Y.
3. U.S. Environmental Protection Agency. 1996. Sanitary Sewer Overflows: What are they, and how do
we reduce them? Office of Wastewater Management, Washington, D.C. EPA-832-K-96-001.
4. Mike Bolton, The Cahaba: A River in Crisis, The Birmingham News, May 5, 1996, 7A.
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A Comprehensive Approach to Urban Stormwater Impact Assessment
Betsy Johnson, Kimberly Yandora, and Scott Bryant, P.E.
Greensboro Storm Water Services
401 Patton Avenue, Greensboro, NC 27406
Abstract
Stormwater monitoring has been conducted in Greensboro, North Carolina, since April 1994 as
required by the City's National Pollutant Discharge Elimination System (NPDES) municipal Stormwater
permit. Runoff sampling is conducted quarterly at storm sewer outfall pipes from seven sites, which
generally represent the City's land uses. Over thirty water quality parameters are tested in grab and
composite storm event samples. As part of the permit requirement, results are evaluated and pollutant
loading estimates are calculated to determine pollutant specific annual and seasonal loads for each site
and the entire city. To complement and expand upon this data, City staff also monitored ambient stream
water quality under baseflow conditions, evaluated wet weather runoff for acute toxicity, and collected
benthic macroinvertebrates for biological assessment in order to characterize Stormwater impacts.
The results of the Stormwater runoff sampling and load estimates are sufficient to characterize the
discharges of varying land uses in the City. However, these data do not provide relevant information on
the impact to the receiving waters. Since there are currently no end-of-pipe regulatory limits on
Stormwater nor wet weather criteria for streams, a tiered approach to impact assessment is needed.
Although the runoff data show significant pollutant loads, the baseflow stream sampling and toxicity
testing results indicated no major problems. The benthic data, however, show a stressed aquatic
community. These studies point to a need for further monitoring including wet weather stream sampling
and sediment sampling and/or instream toxicity testing. The data collected to date indicate that the City's
urban stream system is stressed due to Stormwater runoff quantity and quality impacts.
Introduction
Stormwater monitoring has been conducted in Greensboro, North Carolina, since April 1994 as
required by the NPDES municipal Stormwater permit conditions. The only Stormwater monitoring
explicitly required by the NPDES permits is the storm event sampling. Municipalities larger than 100,000
persons (NPDES Phase I Stormwater regulations) sample runoff from five to ten representative drainage
areas to characterize runoff for their locale. However, these data do not provide relevant information on
the impact to the receiving waters. In order to characterize impacts to the receiving waters, a more
comprehensive study and approach is needed. In Greensboro, the Storm Water Services Division is
examining many facets of its stream and aquatic environment to monitor and quantify the impacts of
Stormwater runoff so that a watershed-based management program can be developed to mitigate those
impacts.
Methods
In the first five years of its NPDES Stormwater permit, Greensboro's staff developed a monitoring
program both to meet the permit requirements and to make initial assessments of Stormwater impacts to
the City's stream resources. The staff sampled storm event runoff, stream quality, and the biological
community, assessed habitat, and collected samples for toxicity tests. This approach has shown that
Stormwater impacts are diverse and a range of control measures within the watershed must be used for
urban water quality improvement.
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Storm Event Runoff Monitoring
Storm event sampling characterizes the quality of stormwater runoff from urban nonpoint
sources feeding the municipal storm sewer system during wet weather. In general, the quality and
quantity of stormwater runoff depends on the land use type and impervious surface area. In
developed urban areas such as Greensboro, there is significant impervious area in which water
will not infiltrate and which causes runoff to increase over predevelopment flows. Industrial,
commercial, and residential activities bring pollutants into contact with stormwater runoff, which
are then carried into receiving streams and lakes. Wet weather sampling enables the City to
determine pollutant concentrations and loadings from urban nonpoint sources and provides data
to assist in the development and location of effective best management practices for minimizing
urban pollutant discharges to area receiving waters.
Wet weather monitoring in Greensboro currently focuses on characterization of discharges from various
land use types. Staff performs quarterly monitoring of representative storms (0.1 to 0.8 inches of rainfall
within 3 hours) to measure the quality of runoff from 7 sites (see Table 1). Each site represents a different
land use type. These data provide a baseline for comparison with special study areas and provide data for
estimates of city-wide pollutant concentrations and loads.
Each wet weather site is sampled by a team of two monitoring technicians who are on 24-hour call for
storm events. Samples are taken manually from each storm sewer pipe outfall and delivered to a contract
lab for analysis. Two types of samples are taken during a storm event: first a grab sample to measure the
"first flush" of stormwater runoff and then a time-weighted composite sample is taken over a three-hour
period to measure average runoff quality. Field parameters including water temperature, pH, conductivity,
and turbidity are measured in addition to site specific rainfall and flow depths. Samples are analyzed for
an extensive list of parameters per the NPDES requirements (see Table 2).
Ambient Stream Monitoring
To augment the storm event runoff monitoring and to establish a general baseline for determining
impacts to the receiving streams, an ambient instream monitoring program was developed. Since July
1996, staff has monitored seven stream sites on a monthly basis (see Table 3). (North and South Buffalo
Creeks and their tributaries represent the major stream systems in the most urbanized areas of
Greensboro.) Similar to the storm event sampling process, samples are collected by a team of two
monitoring technicians and delivered to a contract lab for analysis. Field parameters including flow level,
water temperature, pH, conductivity, and turbidity are also measured. Conventional parameters are
sampled monthly while metals are added on a quarterly basis (see Table 4). Samples are taken on the
second Tuesday of each month, generally under baseflow conditions.
This stream monitoring provides water quality data and information on the health of the stream during
dry weather. The data will provide a reference point for determining stormwater impact under wet
weather conditions.
Toxicity Testing
To estimate the toxic impact of stormwater runoff on the receiving stream, toxicity testing of runoff
was conducted. Due to the sporadic nature of stormwater runoff events, the State of North Carolina
recommended acute toxicity testing. During the winter quarter of 1997 (January - March 1997) grab
samples were collected during the first flush of storm events at each of the seven land use characterization
outfalls. Samples were delivered to a contract lab where a 48-hour test was run on each sample using
fathead minnows (Pimephales promelas). Sample dilutions (0, 12.5, 25, 50, 75, and 100 percent) were
fabricated and mortality of minnows measured over a 48-hour period.
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Biological Monitoring - Benthic Macroinvertebrates
During the summer of 1997, staff sampled the aquatic benthic invertebrate communities at 31 stream
sites across Greensboro to further assess the impacts from urban stormwater. Sites were selected to
complement wet weather sampling sites, ambient stream sampling sites, and to provide a comprehensive
coverage of the city including streams draining to the city's drinking water supply reservoir. Samples
were taken using a kicknet in riffle area habitats. Subsamples of 100 organisms were taken from each
sample and sent to a contractor for taxonomic identification.
Habitat Assessment
Along with the biological assessment of the city's streams, habitat assessments were also conducted.
Urban streams typically become degraded once contributory drainage impervious area exceeds approxi-
mately ten percent (Schueler, 1995). In addition, prior to 1994 and implementation of the municipal
NPDES stormwater permit, the City dredged some local streams as part of its routine drainage mainte-
nance. These factors have contributed to degrade the aquatic habitat and impact the biological commu-
nity. With an improved understanding of the local impacts of past stream channel maintenance practices,
the City no longer performs routine dredging of streams.
Two assessment methods have been used to evaluate stream channel conditions and habitat. The first
was a qualitative survey of a subsample of city streams to assess stream channel stability, vegetation, and
maintenance practices. A quantitative survey was done in conjunction with the biological monitoring
study using the format provided by EPA's Rapid Bioassessment Protocol. This assessment created a
numerical score for localized sites including substrate characterization, channel sinuosity, channel
alteration, streambank vegetation and stability, and riparian vegetation.
Results and Discussion
Urban Stormwater Runoff
The characterization of receiving streams as impaired varies depending on the amount of impervious
area and land uses in the drainage basin. Sites with little impervious area and restricted land uses,
undeveloped or low-density residential sites have few problems. Age and quality of the drainage system
and maintenance of the land uses appear to play a role in runoff quality. Sites with large quantities of
pavement and roof area generate significantly more runoff and more pollutants. On these sites, there is
greater area for pollutants to accumulate between rain events.
Pollutants of concern identified in the City of Greensboro's sampling program include:
• suspended and dissolved solids,
• oxygen consuming wastes,
• bacteria,
• nutrients, and
• heavy metals.
A significant finding of the runoff sampling has been that stormwater contains pollutants similar to
wastewater discharges to streams and in some cases with concentrations that are higher. Also, the findings
from Greensboro's storm event sampling program are generally comparable to the national NURP study.
Elevated levels of suspended and dissolved solids and turbidity are found in nearly all samples except
the undeveloped site (Country Park). The TSS data average about 200 mg/1 during the first flush of runoff
III-155
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but drop to below 80 mg/1 in the composite samples. For comparison, wastewater discharges must meet a
limit of 30 mg/1, but in Greensboro are usually less than 10 mg/1 due to stringent treatment requirements.
Turbidity frequently exceeds the state standard of 50 NTU in the first flush of stormwater runoff.
Fecal coliform and fecal streptococcus have been present at all sampling locations. Exceedances of
the state standard for fecal coliform (200 colonies/100 ml) have occurred at all sites. The only site that is
occasionally within the standard is the undeveloped site. Most fecal data is far above the required levels
for wastewater discharges (200 colonies /100 ml). Some sites have exhibited exceptionally high values,
which may indicate leaks from the sanitary sewer lines to the storm sewer system.
Although the dissolved oxygen (DO) of runoff itself does not appear to be of concern, the BOD and
COD levels are generally much higher in urban runoff than domestic wastewater discharges. This is
especially true locally in Greensboro, where due to the low flow streams and high temperatures, waste-
water discharges are required to meet very stringent BOD limits of less than 5-10 mg/1. Though
wastewater discharges are continuous and stormwater discharges are sporadic, the BOD in urban
stormwater runoff is exerted over a longer period of time than in many other wastewaters (Field and Pitt,
1990). The long-term BOD of some storm runoff may be much higher than that of domestic wastewater;
and sediments may store BOD which become resuspended and move the area of DO deficit further
downstream.
Greensboro is located at the headwaters of the Cape Fear River Basin in an area that has been
designated by the State as Nutrient Sensitive Waters (NSW). This stream classification requires waste-
water discharges to meet a phosphorus limit of 2 mg/1 to protect Jordan Lake, a major regional water
supply and flood control reservoir, from more frequent algal blooms. The runoff data indicates that
nutrient loading from urban sources is less severe than from wastewater discharges. However, the nutrient
levels (phosphorus and nitrogen) from the Athena (90% impervious) first flush runoff are greater than or
equal to the required wastewater discharge limits. The nutrient loading levels are approximately 50%
lower in the composite sampling. A more important consideration may be the total nutrient loading from
nonpoint sources as compared to the point sources.
Metals have been detected at all sites. Higher concentrations are present at sites with greater amounts
of impervious area and with more industrial land uses. Copper, lead, and zinc exceed the state standards
(7 |^g/l, 25 u.g/1, and 50 |4.g/l respectively) at all sites except the low density residential and undeveloped
sites. Other studies (e.g., Moran, 1998) indicate that significant sources of these metals, particularly
copper, include automotive wear and tear (including automotive brake pads) and roof runoff.
Annual Pollutant Loading Estimates
Annual pollutant loading estimates have been calculated using the "Simple Method" (MWCOG,
1987), where: pollutant load (Ibs/yr) = [ runoff (acre-ft/yr) * event mean concentration of pollutant (mg/1)
* conversion factor ]. An effective annual rainfall of 38.3 inches per year (based on the average for
Greensboro) was used to estimate runoff for each sampling site. At the end of the sampling year, the
actual annual rainfall is converted to effective annual rainfall and the load estimates are revised
accordingly. The estimates attached as Table 5 are annual pollutant loading averages based on analysis of
local storm event sampling results between June 1995 and June 1997. Table 5 also provides the loadings
based on pollutant event mean concentrations from the EPA NURP study for comparison to the local
findings. The estimates are comparable, but the local pollutant concentrations and loadings are generally
lower than findings and estimates based on the national NURP study.
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Ambient Stream Monitoring
Monitoring results to date indicate that during dry weather the water quality of the city streams is only
slightly impaired. There are no significant instream problems during dry weather except in areas with dry
weather discharges. One site is located in an industrial corridor which has both stormwater runoff
problems and dry weather discharges. Fecal coliform levels consistently exceed state standards. A master
plan is currently under development for the largest of the industrial sites, which will address containment
and treatment of stormwater runoff and dry weather discharges.
Water quality is consistently uniform during baseflow conditions. The most noticeable changes
during or immediately following wet weather is the sediment load from upstream construction areas.
Turbidity levels exceed the state standard when there is soil loss from construction sites. There are also
significant differences between sediment loads in North and South Buffalo Creeks. South Buffalo carries
a much higher load, which can be largely attributed to increased construction activities in the South
Buffalo Creek watershed.
The instream baseflow water quality data contrasts with wet weather data, which indicates that
significant levels of pollutants are entering the streams via stormwater runoff. In July 1998, baseflow
monitoring will be reduced to quarterly and wet weather monitoring will be conducted twice during the
year. Stream monitoring will also be added at six United States Geological Survey (USGS) flow-gaged
stream sites in order to correlate water quality and flow information for future watershed modeling and
master planning efforts.
Acute Toxicity Tests
The results of the acute toxicity tests indicated that the first flush samples were not toxic according to
the standards of the test. No acute toxicity (48-hour, fathead minnow) was found at any site. The LC50
was greater than 100% at all sites. The LC50 is a measure of the strength of a sample in which 50% of the
population is found to die after a 48-hour exposure (Standard Methods, 1994). For the samples obtained
in the city, even full strength or the 100% dilution did not cause a 50% mortality. Very little mortality was
seen at the storm sites. Merritt (75% impervious, commercial site) had limited mortality of 35% at the full
strength sample. The cause of this mortality is unclear. The water quality data for Merritt were generally
better than the other sampled sites. However, conductivity was higher and dissolved oxygen was lowest
of all the samples. This is consistent with a study in Kentucky that found that mortality in the bioassays of
stormwater runoff was most affected by low DO concentrations in the runoff (Marsh, 1993).
The tests indicate that the first flushes of stormwater runoff were not acutely toxic. However, reviews
of the data show exceedances of water quality standards for copper, lead and zinc as well as detectable
levels of other metals. Therefore it is likely that stormwater may have a chronic impact. Acute toxicity
testing was selected because storm events are short-term events. Yet, these events occur frequently with
an average of four significant events per month. Some of the effects of stormwater discharges are
associated with organic and toxic pollutant accumulations over a long time and are not associated with
individual runoff events (Field and Pitt, 1990). The true impact on the stream then can become a chronic
impact. Chronic toxicity testing could determine whether the stormwater runoff itself has a toxic impact.
Other studies suggest that the toxic impact from stormwater runoff is manifested in the interface
between the sediment and water column. Pollutants bound to suspended particles in the water accumulate
in the bottom sediment and are readily available to aquatic organisms or may be resuspended during
storms. Sampling by Wisconsin DNR found that petroleum byproducts and heavy metals were present in
bottom sediment (Masterson, 1994). Another researcher, Dr. G.J. Pesecreda of North Carolina, has
conducted instream toxicity tests using the Stonefly (Pteronarcys dorsatd) to determine their response to
urban runoff and wastewater in comparison to a control site. He found that mortality in sites receiving
urban runoff experienced greater mortality than an instream reference site. In addition, there was no
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significant difference in mortality to the test organisms exposed above and below a wastewater plant
when both were exposed to urban stormwater runoff (Pesecreda, 1997). Sediment sampling and sediment
toxicity tests may be needed to evaluate the relationships of pollutant transport and storage in sediments
to water quality impairment as measured by the biological community.
Biological Community
Results from benthic macroinvertebrate samples indicated that biotic communities are relatively
tolerant to periodic storm events and are more severely impacted by degradation in habitat and continuous
water quality problems. Thirteen of the 31 sites sampled rated "good-fair" using the North Carolina Biotic
Index (NCBI). This "good-fair" rating was supported by similar ratings from taxa richness and EPT
abundance values. Only two sites had "poor" biotic communities. These two sites were located in the
North Buffalo Creek basin and had no Ephemeroptera, Tricoptera, and Plecoptera (EPT) and high
percentages of Chironomids. Both of these sites receive runoff from old industrial and commercial areas
and also had degraded habitat.
Overall, South Buffalo Creek indicated less impaired biotic communities and water quality than
North Buffalo. An in-stream site (Big Tree) actually indicated "excellent" NCBI and had a high habitat
score. Even the highly industrialized Gillespie site had "fair" NCBI rating and 15 EPT species. However,
water quality and stream degradation was observed as the South Buffalo Creek traversed downstream
through the city.
As expected, water quality and biotic communities in the water supply watersheds were less impaired
than North and South Buffalo Creeks due to better habitat and less urban runoff. In general, the water
supply watershed areas are less developed than the North and South Buffalo basins. However, Bryan
Park, which was selected as a reference site, received only a "good-fair" biotic rating but had a high
habitat score. Although little urban development is present in the Bryan Park drainage area, agricultural
activities in the area are believed to have contributed to the stream degradation.
Macroinvertebrate samples are useful to determine short-term changes in habitat and water quality
changes. They would be useful to determine the effects of stream restoration or changes in riparian
buffers as well as the effects of new construction or point source discharges. Habitat, water chemistry and
biotic communities interact to form the aquatic ecosystem; therefore, there is a need to study and identify
all three to manage aquatic resources properly.
Aquatic Habitat
The assessment of stream channels to evaluate maintenance practices and channel stability indicated
that the past City maintenance practices in combination with high velocity and increased volume of storm
flows resulted in stream channels that are unstable and highly erodible. The historical practice of
maintaining stream channels as a drainage network included routine dredging to remove sandbars and
widen channels, and routine mowing up to and including the stream banks using a boom mower. These
practices have disturbed habitat within the stream and removed most vegetative cover. In 1994, the
routine dredging ceased. While some mowing has continued, the City continues to evaluate its vegetative
maintenance practices to develop an optimum balance between the environment and other public
concerns related to stream systems. Wildlife studies conducted by the Audubon Society in Greensboro
compared stretches of the same stream, North Buffalo Creek, with and without vegetative cover. Their
findings indicate that there is a more diverse songbird community where there is more cover. This study
also found that radio-tagged turtles would not enter areas without vegetative cover (Audubon, 1998). As
noted, the City continues to evaluate its maintenance practices to provide a better balance between flood
routing, aquatic habitat, and related stream system issues.
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Habitat assessment was conducted at 31 locations throughout the city. All locations indicated some
impact from human development. Some sites, especially in parks and the water supply watershed, had
very good riparian buffer zones, channel flow and pool variability but lacked streambank stabilization and
had high sediment deposition. Conversely, other sites no longer have meanders, canopy cover, or
epifaunal substrate cover but have stabilized banks and low sediment deposition. However, all sites were
able to support aquatic communities. Schueler states that stream degradation occurs at 10-20%
impervious (Schueler, 1995). However, stream alteration takes place any time the watershed is disturbed.
Habitat should always be evaluated when assessing water quality. Future monitoring is planned to
evaluate restoration of riparian vegetative zones along stream reaches that had previously been mowed
and/or dredged.
Conclusions
The monitoring of stormwater runoff required by the NPDES permits is not sufficient to determine
the overall impact of stormwater on the City's receiving waters. And, without this determination, the City
cannot develop a complete strategy for improving water quality in the city streams. A more
comprehensive approach to urban stormwater monitoring and determining the impacts of urban
stormwater runoff upon receiving waters includes:
• a storm event outfall monitoring program (as required by the NPDES permit),
• an ambient stream monitoring program,
• wet weather acute toxicity testing at storm sewer outfalls,
• a biological monitoring and habitat assessment program,
• an instream wet weather monitoring program,
• sediment sampling, and
• chronic toxicity testing.
While meeting its NPDES requirements, the City of Greensboro has begun implementation of a
comprehensive program for water quality monitoring and stormwater impact assessment. In 1998, a
network of USGS stream gages will be added to allow continuous tracking of rainfall, streamflow, and
provide early warning of potential flooding. The city's monitoring staff will sample the streams at the
USGS sites at least six times per year during variable flow regimes in order to develop a database for
model calibration.
A watershed-based stormwater runoff and stream model will be developed during 1998 and 1999 to
predict both water quality and quantity impacts including a determination of the impacts of future land
use changes. With this tool, the City can develop watershed-based strategies for reducing the impacts of
future urban development, as well as mitigating impacts of existing development and other factors within
the watershed. Comprehensive and proactive watershed management, including improved water quality,
is the goal of the City of Greensboro's stormwater program.
References
Audubon Society, T. Gilbert Pearson, February 1998, StreamGreen Streamlife Study.
Field, R. and R.E. Pitt, 1990, "Urban Storm-Induced Discharge Impacts," Water Environment &
Technology 2(8): 64-67.
Marsh, J.M. Assessment of Nonpoint Source Pollution in Stormwater Runoff in Louisville, Kentucky,
USA. Bulletin of Environmental Contamination and Toxicology.
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Masterson, J. P., and Bannerman, R.T. 1994. Impacts of Stormwater Runoff on Urban Streams in Milwaukee
County, Wisconsin. National Symposium on Water Quality, AWRA.
Moran, Kelly D., 1998, "Copper, Brake Pads, & Water Quality: Can a National Voluntary Partnership
Improve Water Quality?", Proceedings of the Water Environment Federation Specialty Conference,
Watershed Management: Moving From Theory to Implementation, Denver, CO.
Pesecreda, GJ. 1997. Response of the Stonefly Pteronarcys dorsata in Enclosures from an Urban North
Carolina Stream, Bulletin of Environmental Contamination and Toxicology.
Schueler, T.R., December 1995, Environmental Land Planning Series: Site Planning for Urban Stream
Protection, Center for Watershed Protection.
Standard Methods for the Examination of Water and Wastewater, 1992.
Metropolitan Washington Council of Governments (MWCOG), July 1987, Schueler, T.R., Controlling
Urban Runoff: A Practical Manual for Planning and Designing Urban BMPs.
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Table 1. Wet Weather Monitoring Sites in the City of Greensboro
Site
Athena Court
Country Park
Husbands St.
Merritt Dr.
Randleman Rd.
Union St.
Willoughby Blvd.
Drainage Area
21 acres
20 acres
13 acres
23 acres
26 acres
33 acres
1 3 acres
Land Use
Commercial - Heavy
Open
Industrial
Commercial - Light
Residential - High
Mixed
Residential - Low
% Impervious
90
2
74
75
50
75
20
Table 2. Sampled Parameters
Ammonia Nitrogen (NH3)
BOD, 5-day (BODS)
Chemical Oxygen Demand (COD)
Cyanide, Total *
Fecal Coliform, MF *
Fecal Streptococcus - Tube *
Nitrate + Nitrite, Nitrogen
Phosphorus, Total Dissolved (TDP)
Phosphorus, Total (TP)
Solids, Total Dissolved (TDS)
Solids, Total Suspended (TSS)
Total Kjeldahl Nitrogen (TKN)
Chlorides
Antimony *
Arsenic *
Beryllium *
Cadmium
Chromium
Copper, Total
Lead
Mercury, Total *
Nickel
Selenium*
Silver *
Thallium *
Zinc, Total
EPA 624 *
EPA 625 *
EPA 608 *
* These parameters apply only to the grab sample. Other parameters apply to both grab and composite samples.
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Table 3. Stream Monitoring Sites
Site Location and Stream Order
South Buffalo Creek at Big Tree Way
(2nd order stream)
South Buffalo Creek at Hillsdale Park
(3rd order stream)
Tributary to Mile Run Creek at Gillespie Golf Course
(2nd order stream)
South Buffalo Creek at McConnell Creek
(4th order stream)
North Buffalo Creek at City Arboretum
(3rd order stream)
North Buffalo Creek at Lake Daniel Park
(4th order stream)
Tributary to Richland Creek at Battleground Park
(1st order stream)
Land Use
High density residential
Residential
Industrial
Agricultural, low density residential
Residential
Residential
Undeveloped
Table 4. Sampled Parameters in Ambient Stream Monitoring Program
Monthly Parameters
Dissolved Oxygen
Temperature
PH
Turbidity
Conductivity
Chlorides
BODS
COD
Ammonia Nitrogen
Nitrate/Nitrite
TKN
TP
TOP
TSS and TDS
Fecal Coliform
Fecal Streptococcus
Quarterly Parameters
Aluminum
Arsenic
Cadmium
Chromium
Copper
Iron
Lead
Mercury
Silver
Zinc
Nickel
Table 5. Comparison of Annual Pollutant Loadings Based on NURP and Local Sampling Data
Parameter
TSS
TDS
TP
TOP
TN
BOD5
COD
Cadmium
Copper
Lead
Zinc
Estimated annual loadings based on
NURP data Obs/yr)
39,933,876
13,366,988
83,544
25,063
551,388
2,005,048
15,706,211
167
8,354
39,767
58,982
Estimated annual loadings based on
local sampling data (Ibs / yr)
8,531,186
15,744,167
46,172
37,111
118,112
2,874,528
10,391,131
72
3,382
3,372
23,391
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Identifying the Potential for Nitrate Contamination of Streams
in Agricultural Areas of the United States
David K. Mueller, Hydrologist
U.S. Geological Survey
Phone: (303) 236-2101, x235; Fax: (303) 236-4912; E-mail: mueller@usgs.gov
Jeffrey D. Stoner, Hydrologist
U.S. Geological Survey
Abstract
Agricultural practices have been linked to water contamination by nutrients, including nitrate, in the
United States. High concentrations of nitrate can be a health hazard in drinking water and can cause
eutrophic conditions to develop in estuaries. Nitrate discharging from the Mississippi River has con-
tributed to the depletion of oxygen in a large area of the Gulf of Mexico. Water-quality data collected
during 1993-95 for the U.S. Geological Survey's National Water-Quality Assessment (NAWQA)
Program can be used to determine areas in the United States that might be susceptible to nitrate con-
tamination. Analysis of data collected prior to 1990 identified significantly higher nitrate concentrations
in water from agricultural areas compared to urban areas or undeveloped areas. For this paper, data
describing nitrate concentrations in samples collected from streams under consistent protocols were
compiled for 72 sites downstream from agricultural basins in NAWQA study areas. Mean annual
concentrations at these sites, along with computed annual nitrate yields and loads, were compared relative
to geographic factors, including land use, soil characteristics, and nitrogen inputs in the upstream basins.
The most significantly correlated factors were used in multivariate statistical models to help identify the
potential for nitrate contamination associated with various environmental settings within the United
States. The best model results were for relations between nitrate yield or load and measures of streamflow
and upstream nitrogen sources. These results indicate a potential for using spatial data to estimate nitrate
contamination in streams over broad areas of the Nation.
Introduction
According to the U.S. Environmental Protection Agency's 1994 evaluation of the state of the Nation's
waters, 37 percent of the assessed rivers and streams were impaired by excessive nutrient levels (USEPA,
1994). An important negative effect of excessive nutrient concentrations is accelerated eutrophication of
streams and receiving waters. Although the growth rate in most fresh-water ecosystems is limited by
phosphorus, nitrogen is usually more important in salt water. In recent years, the Mississippi River has
discharged as much as one million megagrams (Mg) of dissolved nitrate-nitrogen annually into the Gulf of
Mexico (Goolsby and Battaglin 1995). Nitrate and other nutrients are suspected of being responsible for a
large zone of hypoxia (seasonally low dissolved oxygen concentrations) along the Louisiana-Texas coast
(Justic et al 1993). Agriculture, specifically the use of nitrogen and phosphorus fertilizer, is a suspected cause
of the Gulf of Mexico zone of hypoxia (Rabalais et al 1996).
A portion of the nitrate in the Nation's streams and ground water comes from distributed nonpoint
agricultural activities, such as the application of inorganic fertilizer and animal manure. Nitrogen not used by
plants or returned to the atmosphere is readily converted to nitrate in the soil. Nitrate is soluble and persistent
in water and therefore can be leached to the water table or delivered to nearby streams. Environmental factors
other than land use, such as available water, soil drainage, water residence time, and available carbon, can
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have important effects on the presence and concentration of nitrate in streams and ground water (Mueller et al
1995; Nolan etal 1997).
Knowledge of the quality of the Nation's streams and aquifers is important because of the implications to
human and aquatic health and because of the significant costs associated with decisions involving land and
water management, conservation, and regulation. In 1991, the U.S. Congress appropriated funds for the U.S.
Geological Survey (USGS) to begin the National Water-Quality Assessment (NAWQA) Program to help
meet the continuing need for sound, scientific information on the spatial extent of the water-quality problems.
Objectives of the NAWQA Program include understanding how these problems are changing with time, and
what effects human actions and natural factors have on water quality conditions. Understanding regional
patterns and environmental factors affecting nitrate concentrations in streams and shallow ground water is
essential for effectively developing programs to manage and protect these water resources.
This paper describes methods used to characterize nitrate, in terms of concentration, load, and yield, at
stream sites sampled for the NAWQA Program. The distribution of nitrate at these sites is compared to
measures of agricultural land use upstream. This analysis uses data collected by consistent methods during
1993-95 within the 20 NAWQA study units shown in figure 1. A study unit is a major hydrologic system of
the United States in which the NAWQA studies are focused (Gilliom et al 1995).
Methods
Each NAWQA study-unit investigation team screened and reviewed available data on nutrients,
pesticides, and associated environmental data for streams and ground water. These analyses were used to
develop an environmental framework for selecting sampling locations to answer questions about agricultural
and urban land-use effects on water quality. A study-unit stream-monitoring network typically included 10 to
12 sites representing watersheds of various scales. About half of the watersheds are small, typically 50-500
square kilometers (km2), and have relatively homogeneous land use. The remaining sites are located at outlets
of large complex watersheds, which commonly contain multiple land uses and include a substantial
percentage of study-unit area (10-100 percent). More specific information about study-unit sampling design
is provided by Gilliom et al (1995).
Consistent methods for the collection and handling of water-quality and ancillary data are critical for
national assessments where trends are analyzed in space and over time. Rigorous protocols were established
for stream sampling procedures (Shelton 1994). Samples were collected using a depth-integrating sampler at
multiple vertical locations in the stream cross section. Nutrient samples were chilled and maintained at 4
degrees C until analyzed at the laboratory. Samples for analysis of dissolved constituents were filtered in the
field, within two hours of collection, through a nitrocellulose filter or a polyether-sulfone medium with a pore
size of 0.45 micrometers. All analyses were performed at the USGS National Water-Quality Laboratory
according to methods described by Fishman (1993). Determinations were made for a suite of nutrients that
included dissolved nitrate plus nitrite as nitrogen, hereinafter referred to as nitrate, at a detection limit of 0.05
milligrams per liter (mg/L).
Sampling at stream sites in the 20 NAWQA study units generally started by March 1993 and continued
through the 1994 and 1995 water years (October 1993 through September 1995). Routine sampling included
field blanks and replicates to assess measurement bias and variability (Mueller et al 1997a).
Spatial geographic data for the drainage basins upstream from sampling sites were compiled from a
variety of sources. These data included land use and land cover classified by Anderson et al (1976) and soil-
series characteristics reported by the U.S. Soil Conservation Service (1993). Estimated nitrogen inputs
included commercial fertilizer application (Battaglin and Goolsby 1995), manure application (Smith et al
1997), and deposition of atmospheric nitrogen (National Atmospheric Deposition Program 1997). Only the
atmospheric-deposition data were available for the entire period of NAWQA sampling (1993-95). Fertilizer
data were available through 1994, so the period 1992-94 was used to obtain an average that was likely to
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have been applied in the NAWQA basins prior to and during the time of sampling. This was justifiable
because fertilizer application rates have varied little over the past decade. Manure application data are based
on the Census of Agriculture, which is made on five-year intervals. The latest available data were for 1992.
Site Selection
Samples were collected at about a monthly frequency from 227 sites in the 20 NAWQA study units.
Three of these sites were affected by substantial upstream diversions; therefore, drainage areas could not be
defined. Two sites in the San Joaquin Valley of California were on sloughs draining the same area and were
considered a single site in subsequent analyses. Thus, ancillary data could be determined for 223 drainage
basins. Of these, 92 were classified as small to moderately sized agricultural basins, according to the
following criteria:
1. More than 25 percent of the area is cropland or more than 50 percent is cropland plus pasture,
2. The site is not significantly affected by urban sources, and
3. The drainage basin is less than 20,000 km2.
Additional criteria were applied to select sites suitable for computing annual nitrate loads. These criteria
were:
4. Availability of daily streamflow data for at least one of water years 1994 and 1995.
5. Significance of a statistical model relating measured nitrate concentrations to streamflow at the
site.
Streamflow records were adequate at 88 of the 92 sites downstream from agricultural basins, but 16 sites
were excluded because of problems identified in fitting the statistical model.
The resultant data set included 72 agricultural sites, distributed among the 20 NAWQA study units, as
shown in figure 1. This distribution of sites was affected by the intensity of agriculture in the study units and
the local interest in agriculture-related surface-water quality issues. Sites were concentrated in the Mid-
Atlantic area, the upper Midwest, and the Northwest. No sites were selected from study units in Nevada or
the Rio Grande valley; however, intensive agriculture is limited in these areas.
Estimation of Daily Nitrate Concentrations
The basic sampling frequency at NAWQA stream sites was monthly, with several additional high flow
samples collected to characterize that part of the hydrograph where concentrations might be more variable. A
subset of sites was selected for more intensive sampling during time periods when concentrations of certain
constituents, primarily pesticides, were expected to be high. Intensive sampling frequencies varied from
biweekly to as often as every other day.
Two difficulties arise from this variability of sampling frequency:
• Summary statistics, such as mean concentration, used to characterize a site might be biased
because of variations in sampling frequency during the period of record, and
• Comparisons among sites might be biased because of different sampling frequencies and numbers
of samples at different sites.
These difficulties might be avoided by using flow-weighted mean concentrations, but only if sampling was
distributed evenly over the hydrograph. Intensive sampling during high flow or low flow, such as was
common at some NAWQA sites, might overemphasize one end of the hydrograph and yield a biased result.
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Another method for decreasing bias in site characterization and comparison is to estimate a concentration
value for each day of a common period of record, such as a particular water year, and compute a flow-
weighted mean of these estimates. There is a long history of statistical models that have been proposed to
make such estimates. The model selected for the present analysis is a modification of a multivariate
regression equation used by Cohn et al (1992) to estimate nutrient transport to Chesapeake Bay. This model
relates nitrate concentration (C) to streamflow (Q) and time (T), measured in years:
ln(C) = BO + Bl ln(Q) + B2 sin(2nT) + B3 cos(27iT) + B4 T (1)
where ln() denotes the natural logarithm. The sin and cos terms are included to consider seasonally that is
independent of streamflow; the remaining time term corresponds to long-term trends. Retransformation of the
estimated ln(C) values produces a bias that is corrected in the present analysis by using a method proposed by
Ferguson (1986):
CDV = exp[B0 + BI ln(Q) + B2 sin(27tT) + B3 cos(2rcT) + B4 T] exp[//2] (2)
where CDv is the nitrate concentration estimated for particular daily values of Q and T, and j is the estimated
variance of the residuals from regression of sample data at a particular site.
The regression model (equation 1) was fit for each site that met the first 4 criteria listed above. All
measured nitrate concentrations in NAWQA samples from a site were used to fit the model. Models for each
site were evaluated by assessing the overall significance and analyzing the distribution of residuals. This
constituted the fifth site selection criteria. Sites were retained for subsequent analysis only if the model fit was
significant (p<0.10) and the distribution of residuals was reasonably normal and homoscedastic.
Results
Adequate models were obtained for 72 sampling sites downstream from agricultural areas. For each of
these sites, flow-weighted mean nitrate concentration was computed from the daily streamflow and estimated
daily concentration values. The period used for this computation was water years 1994 and 1995. If daily
values were not available for a complete water year, that year was not used in the computation. The
distribution of these flow-weighted means of daily concentration estimates is shown in figure 2 in relation to
the means and flow-weighted means computed from the sample data from the 72 agricultural stream sites.
The sample means are generally lower than the flow-weighted means for estimated daily concentrations,
probably because high concentrations are not adequately sampled at many sites. The flow-weighted sample
mean might be the most appropriate summary statistic for individual sites, but the variability among sites is
smaller than for the other statistics, and differences among sites might be underestimated. The flow-weighted
mean of daily estimates preserves the variability among sites without the low bias for individual sites.
Therefore, these mean values from nitrate concentrations estimated by the regression models for each site
seem to provide the best representation of nitrate at the site and also the best data for comparison among sites.
Another advantage of using daily estimates of nitrate concentration is that they can be combined with
daily streamflow and converted to mass loads (also referred to as flux, discharge, or export). The daily loads
can then be summed to provide an estimate of annual load. The annual estimates are generally more accurate
than the daily values because positive and negative errors in the daily values tend to cancel out in the annual
summation. For the 72 NAWQA sites downstream from agricultural areas, mean annual nitrate loads were
computed using the estimated daily nitrate concentrations for complete water years 1994 and 1995. The ratio
of these loads to the upstream drainage area for each site was used to estimate mean annual nitrate yield, in
mass per unit area.
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Correlation of Nitrate to Upstream Basin Characteristics
Initially, nitrate at the 72 stream sites was compared to selected geographic characteristics of the
upstream basins using bivariate correlation. Nitrate at the sites was represented by:
1. Flow-weighted mean concentration (of sample data or daily-value estimates), in milligrams per
liter (mg L"1),
2. Mean annual load, in megagrams (Mg), and
3. Mean annual yield, in megagrams per square kilometer (Mg km"2).
The selected basin characteristics were:
1. Contributing drainage area of the basin, in km2,
2. Mean streamflow during water years 1993-95, computed from the daily-values data, in cubic
meters per second (m3 s"1),
3. Mean annual runoff, computed from mean streamflow and basin area, in centimeters per square
kilometer (cm km"2),
4. Mean annual nitrogen (N) input, computed as the sum of applied commercial fertilizer, applied
manure, and atmospheric deposition, in Mg,
5. Mean N input rate, in Mg km"2,
6. Cropland area, in percent of the total basin,
7. Population density, in number of people km"2, and
8. Soil drainage category.
These characteristics represent some of the expected influences on nitrate mass and movement in a basin. The
influence of the first 6 characteristics is obvious; all are related to basin size, water availability, or agricultural
sources of nitrate. In addition, runoff provides a regional gradient because the amount of water per unit area
that flows from a basin is large in the humid East and small in the arid West. Population density is an
indication of nitrate sources from wastewater treatment plants or septic systems. These sources are expected
to have less influence than agricultural sources of nitrate in rural areas, but are included in the analysis for
completeness. Soil drainage has been shown to influence nitrate concentrations in ground water (Nolan et al
1997) and in streams during the late spring and early summer (Mueller et al 1997b). In this analysis, drainage
in a mapped soil polygon was numerically categorized based on soil hydrologic group. Poorly drained soils
(group D) were assigned a value of 1 and well-drained soils (group A) were assigned a value of 4. The soil
drainage number for each basin was the area-weighted mean for all soil polygons in the basin.
Results of the correlation analysis are listed in table 1. Two coefficients are shown for each bivariate
correlation: the Pearson coefficient indicates the degree of linear correlation and the Spearman coefficient
indicates the degree of monotonic, but not necessarily linear, correlation. The highest correlation coefficients
(at least 0.4) are shown in bold. The strongest correlations were between mean annual load and either mean
annual N input or mean streamflow. This is certainly reasonable because the outflow load is a function of the
available nitrate mass and the available transport medium (water). Both types of flow-weighted mean
concentration were most strongly correlated to the N input rate and somewhat less strongly correlated to
cropland area. Both of these basin characteristics indicate intensity of agriculture. Nitrate yield was
moderately correlated to several basin characteristics, including runoff, N input rate, and population density.
These three characteristics, as well as nitrate yield, are evaluated on a per area basis, so it seems logical that
they would be correlated. N input rate and population density represent the primary nitrate sources in the
basin, runoff is the transport medium, and nitrate yield is the downstream result.
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The moderately strong negative correlation between nitrate yield and N input is unexpected, but might be
numerically related to the nearly equivalent correlation between yield and basin size. Agricultural intensity is
generally low in large basins compared to what it can be in small basins. Thus, even though the nitrate load
downstream from a large basin might be high, the per-unit-area yield can be relatively low.
Soil drainage was not significantly correlated with any measure of nitrate at these NAWQA stream-
sampling sites. However, all the measures of nitrate were on an annual basis. If nitrate had been evaluated on
a seasonal basis, there might have been stronger correlations with soil drainage. For example, high nitrate
concentrations during spring and summer might result primarily from storm events, which would be more
likely to occur in areas conducive to surface runoff. Such conditions are common in agricultural areas with
poorly drained soils, where tile drains or pipes are installed to enhance drainage. Conversely, high nitrate
concentrations during periods of baseflow might be more likely in areas where nitrate concentrations are high
in ground water. This condition is common in agricultural areas with well-drained soils on reasonably flat
slopes.
Prediction of Nitrate from Basin Characteristics
Based on the correlation results, several multiple regression models were evaluated to determine the
potential for predicting nitrate concentration, load, or yield using characteristics of the upstream drainage
basin. Initially, the characteristics that were most strongly correlated with each measure of nitrate were
included in the model. In some cases, characteristics were excluded to maintain independence of the
explanatory variables. For example, basin area was excluded from the nitrate-load model because it was
cross-correlated with streamflow, which was considered a better explanatory variable. After the initial
calibration, explanatory variables that did not contribute significantly to a model's fit were removed from that
model.
Separate models of nitrate concentration were calibrated using the flow-weighted sample mean and the
flow-weighted mean of daily estimates. N input rate and cropland area were significant explanatory variables
in both models, and they fit the nitrate data about equally well. Results for the model fit using flow-weighted
means of daily estimates are shown in figure 3. The 1:1 line represents a perfect fit of model estimates to
'observed' data. The standard error of the detransformed estimates is 2.1 mg/L, or 76 percent of the observed
mean. This model tends to overestimate for sites with low mean nitrate concentration and underestimate for
sites with high concentrations. This pattern is even more pronounced for the model fit using flow-weighted
sample means.
The model of annual nitrate load (figure 4) included streamflow and mean N input as explanatory
variables. The standard error is 0.56 Mg, or 80 percent of the mean 'observed' load. This error is similar to
the concentration model, but the load model appears to fit the observed data better than the concentration
model. However, the fit of the load model is strongly controlled by a few sites that had large loads. The four
sites with the largest loads were all in Midwestern study units, so regional differences affecting sites with
lower loads might not be apparent.
The model of annual nitrate yield (figure 5) appears to fit about as well as the load model. The standard
error of the detransformed estimates is 0.71 Mg km"2, or 75 percent of the mean 'observed' yield. It also has a
cluster of highly influential sites with high yields, but these are located in several regions of the Nation.
Therefore, this model might be more appropriate than the load model for national-scale application. The
explanatory variables in the yield model are runoff and N input rate. Population density did not contribute
significantly to the fit when these other variables were in the model.
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Discussion
Analysis of the nitrate and geographic data from streams draining agricultural basins suggests the
following conclusions:
• NAWQA data can be used to estimate daily nitrate concentrations using periodic measurements
of nitrate concentration and daily measurements of streamflow. A regression model including
seasonality and time trend seems appropriate for many sites in a variety of locations around the
Nation.
• Estimates from these models can be used to compute flow-weighted mean nitrate concentrations,
nitrate loads, and nitrate yields. Correlations between these measures of nitrate and the
geographic characteristics of upstream drainage basins are reasonably strong and can be
explained logically.
• Development of regression models for predicting nitrate in streams as a function of upstream
basin characteristics seems promising. The models presented in this paper were calibrated using
data from only 72 sites in 18 of the 20 NAWQA study units. Model fit was adequate, particularly
for prediction of nitrate yield from runoff and N input rate, but errors for some individual
predictions were large.
These results are an initial attempt at synthesizing the NAWQA nutrient data on a national scale, and
represent only a preliminary analysis. Using the daily estimates of nitrate concentration, it is possible to
determine not only annual, but also seasonal nitrate loads and yields. Analysis of seasonal data might be
useful in the assessment of management practices to limit nitrate impacts. When data are available from the
second set of study units, expected by the spring of 1999, it might be possible to refine the models. In
particular, data from additional study units will provide a better representation of the Nation and might be
more adequate for determining regional differences in nitrate outflow in response to basin characteristics.
Future efforts of the NAWQA nutrient synthesis project will be to expand this analysis to include other
nitrogen and phosphorus species. Results are not expected to be as good for these other species.
Concentrations are generally lower than for nitrate and many measurements are less than detection, both of
which can cause problems in fitting regression models. The analysis might also be used to evaluate nitrate at
nonagricultural sites or at sites downstream from lower-intensity agriculture. This might be useful in
determining nitrate concentrations that could be expected in the absence of major human influences.
References
Anderson, S.K., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A land use and land cover classification
system for use with remote sensor data. U.S. Geological Survey Professional Paper 964, 28 pp.
Battaglin, W.A., and D.A. Goolsby. 1995. Spatial data in geographic information system format on
agricultural chemical use, land use, and cropping practices in the United States. U.S. Geological
Survey Water-Resources Investigations Report 94-4176, 87 pp.
Cohn, T.A., D.L. Caulder, E.J. Gilroy, L.D. Zynjuk, and R.M. Summers. 1992. The validity of a simple
statistical model for estimating fluvial constituent loads: An empirical study involving nutrient loads
entering Chesapeake Bay. Water Resources Research 28(9): 2353-2363.
Ferguson, R.I. 1986. River loads underestimated by rating curves. Water Resources Research 22(1): 74-
76.
Fishman, M.J. 1993. Methods of analysis by the U.S. Geological Survey National Water Quality
Laboratory—Determination of inorganic and organic constituents in water and fluvial sediments. U.S.
Geological Survey Open-File Report 93-125, 217 pp.
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Gilliom, R.J., W.M. Alley, and M.E. Gurtz. 1995. Design of the National Water-Quality Assessment
Program: Occurrence and distribution of water-quality conditions. U.S. Geological Survey Circular
1112,33pp.
Goolsby, D.A., and W.A. Battaglin. 1995. Effects of episodic events on the transport of nutrients to the
Gulf of Mexico, in Proceedings of First Gulf of Mexico Hypoxia Management Conference, December
5-6, Kenner, LA, p. 8.
Justic, D., N.N. Rabalais, R.E. Turner, and W.J. Wiseman. 1993. Seasonal coupling between riverborne
nutrients, net productivity and hypoxia. Marine Pollution Bulletin 26(4): 184-189.
Mueller, D.K., P.A. Hamilton, D.R. Helsel, KJ. Hitt, and B.C. Ruddy. 1995. Nutrients in ground water
and surface water of the United States—An analysis of data through 1992. U.S. Geological Survey
Water-Resources Investigations Report 95-4031, 74 pp.
Mueller, D.K., J.D. Martin, and T.J. Lopes. 1997a. Quality-control design for surface-water sampling in
the National Water-Quality Assessment Program. U.S. Geological Survey Open-File Report 97-223,
17pp.
Mueller, O.K., B.C. Ruddy, and W.A. Battaglin. 1997b. Logistic model of nitrate in stream of the upper-
Midwestern United States. Journal of Environmental Quality 26(5): 1223-1230.
National Atmospheric Deposition Program. 1997. National atmospheric deposition program (NRSP-
3)/National trends network, 1997. NADP/NTN Program Office, Illinois State Water Survey,
Champaign, IL.
Nolan, B.T., B.C. Ruddy, K.J. Hitt, and D.R. Helsel. 1997. Risk of nitrate in ground waters of the United
States—A national perspective. Environmental Science and Technology 31(8):2229-2236.
Rabalais, N.N., R.E. Turner, D. Justic, Q. Dortch, W.J. Wiseman, and B.K.S. Gupta. 1996. Nutrient
changes in the Mississippi River and system responses on the adjacent continental shelf. Estuaries
19(2b): 386-407.
Shelton, L.R. 1994. Field guide for collecting and processing stream-water samples for the National
Water-Quality Assessment Program. U.S. Geological Survey Open-File Report 94-455, 42 pp.
Smith, R.A., G.E. Schwarz, and R.B. Alexander. 1997. Regional interpretation of water-quality
monitoring data. Water Resources Research 33(12):2781-2798.
U.S. Environmental Protection Agency. 1994. The quality of our Nation's water: 1992. U.S.
Environmental Protection Agency Office of Water Report 841-S-94-002, 43 pp.
U.S. Soil Conservation Service. 1993. State soil geographic database (STATSGO)—Data users guide.
Miscellaneous Publication Number 1492, 88 pp.
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Figure 1. Distribution of stream sampling sites downstream from agricultural basins
in 20 NA WQA study units.
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i
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25th percehtiie
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Figure 2. Di
Flow-weighted
sample mean
Flow-weighted
mean of doily
estimates
in the NAWAQ study units.
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Flow—weighted mean nitrate from daily estimates
in milligrams per liter
Figure 3. Flow-weighted mean nitrate concentrations for 72 NAWQA stream sites
predicted from regression on upstream nitrogen input rate and cropland area.
123456
Mean annual nitrate load
in megagrams
Figure 4. Mean annual nitrate loads for 72 NAWQA stream sites predicted from regression
on streamflow and upstream nitrogen input.
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Mean annual nitrate yield
in megagrams per square kilometer
Figure 5. Mean annual nitrate yields for 72 NAWQA stream sites predicted from
regression on upstream runoff and nitrogen input rate.
Table 1. Linear (Pearson) and Monotonic (Spearman) Correlations between Nitrate
At 72 NAWQA Stream Sties and Characteristics of the Upstream Drainage Basins
[Coefficients greater than or equal to 0.4 are shown in bold type.]
Characteristic of the
upstream drainage basin
Basin area
Mean streamflow
Mean annual runoff
Mean annual nitrogen input
Mean nitrogen input rate
Cropland area
Population density
Soil drainage
Linear and (monotonic) correlation with nitrate in stream
Flow-weighted
sample mean
-0.25 (-0.39)
-0.27 (-0.36)
0.30 (0.19)
-0.13 (-0.26)
0.60 (0.54)
0.30 (0.40)
0.07 (0.23)
0.06 (0.09)
Flow-weighted
mean of daily
estimates
-0.24 (-0.39)
-0.28 (-0.35)
0.26 (0.18)
-0.13 (-0.25)
0.57 (0.54)
0.42 (0.44)
0.05 (0.19)
-0.01 (0.05)
Mean annual
load
0.63 (0.59)
0.73 (0.77)
0.05 (0.19)
0.78 (0.64)
0.01 (-0.02)
0.06 (0.17)
0.36 (0.24)
0.09 (-0.14)
Mean annual
yield
-0.28 (-0.51)
-0.20 (-0.15)
0.62 (0.73)
-0.20 (-0.41)
0.46 (0.46)
0.20 (0.17)
0.13 (0.51)
-0.01 (-0.01)
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Key Water Quality Monitoring Questions:
Designing Monitoring and Assessment Systems to Meet Multiple Objectives
James E. (Jim) Harrison, Environmental Scientist
U.S. EPA Region 4, Atlanta, GA
E-mail: harrison.jim@epamail.epa.gov
Key Monitoring and Assessment Questions
Monitoring water resources means many things to different people and entities. Federal and state
agencies have varying missions, legislative mandates, organizational cultures and stakeholder concerns.
Political and organizational boundaries, and other real or imagined obstacles to cooperation, often tend to
keep everyone's monitoring systems separate and disjointed. Still, regulatory agencies and others who
monitor water quality often share common key questions (see ITFM 1994 and SAMAB 1996b)
including:
• What is the desired/reference condition (standards and criteria)?
• Where (and what) are our problems (screening)?
• How do we fix (or prevent) them? and,
• Are we making progress over time (evaluation) at multiple spatial scales: large, medium and
small areas? [Natural resources agencies should emphasize evaluation of environmental results to
meet the spirit and intent of the Government Performance and Results Act (GPRA).]
Other important questions and variations of those listed above can also tend to dominate agencies'
agendas, monitoring approaches, and, sometimes, monitoring resources. Some of these include:
• Are environmental laws being complied with? Compliance with environmental regulations and
statutes through compliance inspections, sampling, self monitoring and audits will always be
important to insure, for example, that end-of-pipe limits are met and that required management
practices are in place.
• Are emergencies and spills being responded to quickly and effectively? Crises and accidents will
always happen but should be minimized through effective prevention approaches and education.
• How do stresses and combinations of stresses affect aquatic ecosystem condition? Basic and
applied scientific inquiry into the mechanisms and processes of environmental, social and
economic factors affecting water resource integrity such as the national water and watersheds
research program (see NSF and EPA 1998) and the U.S. Geological Survey's National Water
Quality Assessment Program (see Gilliom and others 1995) will always be crucial to eventual
success in restoring and maintaining water quality.
Answering each of these questions may require different monitoring network designs. Managing the
nation's waters to restore and maintain aquatic ecosystem integrity requires reliable answers to all of
these questions. Thus, a systems view (Senge 1990) is needed to simultaneously address multiple
objectives, and to successfully answer all of our key questions for the least cost. Now, let's consider each
of the four key questions in turn.
What Is the Desired/Reference Condition (Standards and Criteria)?
Continuing development of protective standards and criteria for water resource integrity that go
beyond existing chemical and toxicity based approaches will be essential to protect and restore waters
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affected by a wide variety of stresses caused by humans. Knowing what we want for the desired
condition of waters will require increasing attention to biological condition measures and biocriteria for
fish, benthic and other communities (Davis and Simon 1995), measuring and understanding habitat
attributes influenced by both riparian and watershed factors such as clean sediment, and channel
type/stability (Rosgen 1994), and development of protective criteria for nutrients and their effects on
biological systems (USEPA 1998).
All of these new types of criteria will require integration of workable spatial frameworks to define
where the criteria apply and to use them effectively. Vast differences in aquatic communities, soils,
geology, vegetation and land use (for example) across the nation necessitate criteria tailored to different
regions with similarity of environmental characteristics. Consistent multi-agency and multi-national
approaches for defining ecological regions (see Commission for Environmental Cooperation 1997) are
becoming available. Likewise, watershed boundaries can now be quickly and economically delineated for
any point using digital elevation models (OEM's). Thus, both ecological regions and watersheds
(Omernik and Bailey 1997) will be essential spatial stratifications for applying our new criteria to real-
world situations. Integration of landscape data (Naiman 1996), reference site data from relatively
unimpacted reference areas (Hughes 1995) and impacted site in-stream data will be essential to better
define attainable conditions for aquatic life. Science-based indicators of aquatic ecosystem conditions
and stresses will evolve and improve over time as our knowledge and understanding of aquatic systems
increases.
Figure 1 shows the locations of over 200 stream reference sites established by the eight Southeastern
states of EPA Region 4 and the ecoregions that they represent. Note that the ecoregional boundaries cross
state lines and administrative divides such as the EPA regions. Long term data gathering from the
reference areas will be crucial to document natural ranges of the best expected biological, habitat,
channel and substrate conditions and the variability of important stresses such as nutrients and
sedimentation.
Figure 2 illustrates the ongoing rebalancing of monitoring program efforts. Two decades ago most
monitoring centered on chemical variables. Toxicity testing was added to the mix during the 1980's.
Biological, habitat (Karr 1993), channel morphology and hydrologic measures (Poff and others 1997) are
now being actively developed and added to the water quality toolbox.
Where (and What) Are Our Problems?
Traditionally, finding water quality problems has usually relied on citizen complaints and on targeted
monitoring networks that have often been biased toward known problem areas such as point sources.
Screening to identify problem areas and significant stresses in complete and systematic ways remains a
continuing need, especially for dispersed nonpoint source issues. Since in-stream/on-the-ground
monitoring must be done within resource constraints, systematic screening strategies are needed to
identify potential problem areas and to prioritize more intensive targeted monitoring to confirm problems
and then solve them. The clear need for more comprehensive screening is underscored by numerous
citizen suits targeting Total Maximum Daily Loads (TMDL's). Many of these suits emphasize
shortcomings in identification of impaired waters by existing monitoring efforts.
One promising option for this type of systematic screening utilizes extrapolation of existing data by
building and calibrating empirical or other models relating landscape and in-stream factors (Zucker and
White 1996). Such an approach relies greatly on new but readily available satellite (or aerial photo)
based landscape data, in addition to traditional in-stream monitoring information. This approach can
provide estimates of in-stream condition with known confidence, assuming the calibration data comes
from a statistical sample, where little or no in-stream information is available. One distinct advantage of
a landscape modeling approach is that we will soon have consistent landscape classification information
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for the entire country through the Multi-Resolution Landscape Characterization (MRLC) consortium
(Vogelmann and others 1998a). Integration of multi-resolution remote sensed data (satellite and air photo
coverages) will be critical to systematic screening at multiple scales. Coarser resolution satellite data
might be most effective for characterizing large areas such as ecoregions or river basins; while finer
resolution data from air photo interpretation might be most accurate and cost effective for smaller areas
such as watersheds or subwatersheds. Predictions of likely problem areas using extrapolation based on
calibrated relationships between landscape and in-stream factors can be confirmed with more intensive
in-situ sampling appropriate to the scale of the resource being studied.
The Southern Appalachian Assessment (SAMAB 1996a) provides some examples of potential
landscape factors that might be used to construct empirical relationships linking landscape and in-stream
factors. Figure 3 maps intensive human land use within ecoregions and hydrologic areas for the Southern
Appalachians. Similarly, Figure 4 shows the percentage of riparian forest for the same area. The lower
Nolichucky River drainage in Eastern Tennessee shows high potential stress for both of these factors.
Looking more closely at the actual land use pattern in this area (Figure 5) and further analyses of smaller
drainages can show where stresses are concentrated. Examination of specific stress factors such as
riparian forest cover might also guide targeting of potential restoration actions: note the stream side
zones dominated by agricultural crops and by urban land uses. A wide variety of potential landscape
indicators of catchment health (Jones and others 1996) are worthy of consideration. More discussion on
approaches and considerations for building models linking landscape and in-stream factors follows in the
"building and extrapolating landscape/in-stream relationships" section below.
How Do We Fix (and Prevent) Them?
Detailed on-the-ground data, process modeling and higher resolution information are often required
to prescribe and implement solutions to site-specific water quality problems. Point source restoration
opportunities have traditionally been guided by site specific waste load allocation (WLA) studies and
modeling of dissolved oxygen, nutrients and toxics. Nonpoint sources are addressed primarily by
voluntary implementation of best management practices (BMP's). New tools are becoming available
through Environmental Protection Agency (EPA) and state efforts to develop total maximum daily loads
(TMDL's) for impaired waters. Some of these include the "BASINS" modeling package (Lanlou and
others 1996) which uses the ArcView geographic information system (GIS) software as input to the
HSPF (Hydrologic Simulation Package Fortran) hydrologic and water quality model; and air photo
interpretation techniques (Malone and Bower 1998), such as the Tennessee Valley Authority's (TVA)
nonpoint source atlas process. Detailed attributes identified from low or medium altitude photographs aid
evaluation of causes and sources of nonpoint source pollution, and can thus help prioritize and guide
voluntary restoration activities.
The eventual success of water quality restoration and maintenance efforts will likely rest more and
more on voluntary citizen actions focusing on local, multi-stakeholder watershed protection approaches
(Montgomery and others 1995) in addition to regulatory and permitting actions. Local community action
should involve monitoring of water quality (see Firehock and West 1995 for example) as well as direct
restoration and management actions. Best management practices (BMP's) implementation and other
approaches such as stream channel restoration will all benefit from spatial data integration, clear priority
setting (USEPA 1996) and careful evaluation of the effectiveness (at small to large scales) of manage-
ment actions. Increasing local responsibility for environmental results such as local evaluation and
enforcement of construction and stormwater runoff BMP's are one trend in this direction. Community
knowledge of the interrelationships between landscape activities and water resources (Arnold 1996) and
a clear positive vision of the value of healthy waters can help to insure that growing human populations
and infrastructure do not destroy our collective water resource heritage. Making explicit choices about
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development practices and land use patterns to protect water resources will be crucial to our future
quality of life. All communities should take advantage of the best available scientific input and
recommendations (see Nichols and others 1997 for example) to make these decisions.
Are We Making Progress over Time at Multiple Spatial Scales?
Evaluation of environmental results (Committee on Government Affairs 1993) may be the bottom
line, but thus far, our monitoring and assessment systems can not yet defensibly answer this question for
large areas. Statistical samples examining specific types of waters in a geographic area are proving their
utility for evaluating results for large areas over time. Examples from the Eastern US include the
Savannah River Basin Regional Monitoring and Assessment Program (REMAP) sites (Figure 6)
(Raschke and Howard 1996), the South Florida Ecosystem Assessment (Stober 1996) and the Mid-
Atlantic Highlands Assessment of EPA Region 3. These studies, and many other similar examples from
around the country, provide unbiased estimates of the distribution of aquatic ecosystem condition with
known confidence. EPA's guidance to states on preparing biennial water quality reports to Congress
(USEPA 1997) explicitly encourages both probability based sampling approaches to describe water
quality status for large areas, and the testing and potential use of modeling approaches for better
screening of potential problem areas and targeting of more intensive monitoring.
Spatial and temporal aggregation of comparable monitoring results of known quality from multiple
agencies and groups can also, in principle, multiply the utility and effectiveness of everyone's monitoring
programs and projects. Aggregation can often be problematic, however, since most monitoring site
networks have not been designed to allow easy aggregation. Usually, sampling sites are biased to known
or suspected problem areas. Additionally, deciding what area a sampling point or points represents can be
difficult. Representativeness of sample points must be known to defensibly extrapolate to larger areas.
Constructive synergies between evaluation and screening approaches are possible by using data from
statistical surveys as input to models relating landscape and in-stream indicators. This allows the data
from statistical networks to do double duty as the model building and calibration data that feed
landscape/in-stream models as a promising screening tool.
Building and Extrapolating Landscape/In-Stream Relationships
Building Empirical Models Relating Landscape and In-Stream Factors
Linking landscape ecology with in-stream indicators of biological, habitat and chemical quality is an
active area of research and development. Numerous landscape pattern and structure metrics have been
proposed (Riitters and others 1995) and many are being actively evaluated for their value in predicting
water resource conditions (O'Neil and others 1997). Suites of potential indicators of watershed condition
are already being produced for large areas of the United States. One extensive example is a recent
landscape atlas of the Mid-Atlantic region—EPA Region 3 (Jones and others 1997). Models covering
limited areas and one or more in-stream factors have also been developed. A decade ago, before
widespread availability of GIS capabilities and techniques, Steedman pioneered this type of modeling by
relating watershed and riparian forest and urban land cover to in-stream fish community condition—the
Index of Biological Integrity or IBI (Steedman 1988). Significant relationships have also been shown
between watershed and riparian agricultural uses, and in-stream chemistry (conductivity and
nutrients)(Hunsaker and Levine 1995).
Considerable recent work elucidates a number of interesting aspects of landscape/in-stream
relationships. Roth and others considered riparian and watershed factors at multiple spatial scales. They
found that whole watershed measures, including those for the riparian zone, had higher predictive
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capability. Their results also suggest that presence of wetlands may be important for some regions where
they naturally occur (Roth and others 1996). Work by Richards and others emphasizes that natural or
anthropogenic landscape features can potentially dominate in-stream end points. Watershed soils,
geology, land form and other natural characteristics should be considered along with human induced land
uses (Richards and others 1996). Some studies are beginning to focus on large areas. Wang and others
study of the effect of land use on Wisconsin-streams suggests levels of agricultural influence which may
begin to dominate in-stream biological and habitat integrity. Their work also suggests factors that may
mitigate agricultural influences; some natural, such as stream gradient, and some human influenced, such
as intact riparian vegetation (Wang and others 1997).
Numerous investigators are considering the influence of urban and suburban development on water
resource integrity. Schueler has reviewed available studies relating impervious cover to in-stream biology
and habitat. He suggests that watershed impervious area of greater than 10% usually results in significant
impact to aquatic systems and that imperviousness greater than 25% usually results in severe impacts.
Hydrologic changes to in-stream flows due to impervious pavements, buildings, etc. are considered to be
the instigating factor for in-stream habitat deterioration such as eroding stream banks that result in
significant decreases in biological integrity (Schueler 1994). May and others' study of urbanization
effects on Puget Sound Lowland Ecoregion small streams found that riparian buffer integrity, availability
of large woody debris (an important habitat indicator), and biological integrity (benthic and fish) was
strongly related to watershed urbanization as measured by total impervious area. They were also able to
establish significant interrelationships between watershed urbanization, riparian corridor integrity, and
in-stream biological end points (May and others 1997). Maxted and Shaver found significant nonlinear
relationships between impervious cover and biological community integrity measures for streams in
Delaware. Their results also raise questions about the effectiveness of storm water retention or detention
systems versus infiltration designs. Study sites with retention/detention storm water BMP's did not
appear to improve biological conditions (Maxted and Shaver 1996). Since effective impervious area can
be influenced by the pattern, density and drainage connection of impervious surfaces to surface water,
and by soil compaction, for example, effective imperviousness may be a more accurate indicator of
potential hydrologic changes (Alley and Veenhuis 1983) and ensuing aquatic stresses.
In addition to the work just mentioned, numerous scientific teams continue to investigate
landscape—water relationships. Some of this ongoing work is summarized in, Proceedings: 1998 Water
and watersheds program review, a progress report on research jointly funded by the National Science
Foundation and EPA (NSF and EPA 1998).
The fundamental assumption for building empirical models relating landscape and in-stream factors
is that in-stream condition (biology, chemistry, habitat) can be predicted as a function of watershed
landscape patterns and practices (forest, agriculture, urban, population, etc.). Mathematically:
in-stream condition = f(landscape factors).
Relationships of this sort will likely differ by ecological region since the sensitivity, resilience,
recovery potential and stresses affecting ecological areas with similar environmental characteristics will
be different depending on the distribution of soils, geology, vegetation, land form, land use and other
factors that are important in defining ecological regions.
Four key challenges are important for further development and use of landscape/in-stream models:
1. Developing specific relationships for particular ecological regions (or subregions if needed)
2. Constructing models that work well for landscapes dominated by urban, agriculture or forest land
uses, rather than models targeted to a narrow portion of the landscape
3. Using the models to extrapolate results to areas without in-stream data (We must go beyond just
developing scientifically interesting models with known properties/confidence), and,
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4. Using extrapolated information to make real world decisions.
Landscape/in-stream relationships might be constructed using existing data sets (usually based on
targeted (biased) networks of monitoring sites) or by developing new data sets based on statistical
samples of appropriately stratified landscapes. Caution should be exercised with existing data sets,
especially where autocorrelation could result from multiple data points being influenced by the same or
similar upstream watersheds. Statistical samples have the added advantages of reduced bias and known
confidence for the distribution of measured end points.
The following hypothetical example illustrates landscape factors that might be important for a
particular model/ecological region:
fish IBI = a (watershed forest)
+ b (watershed crop)
+ c (riparian wetlands)
+ d (riparian natural)
+ e (effective impervious area and/or road density)
+ f (road crossing density)
+ g (impounded stream length)
+ h (population density)
+ other factors (mine density, etc.)
Extrapolation
Extrapolation of landscape/in-stream models is possible since relatively up-to-date land use/land
cover data is becoming available everywhere in the coterminous US (Vogelmann and others 1998b).
Relationships developed between landscape and in-stream data allow extrapolation to all similar areas
without in-stream data. This results in an empirically based census of likely in-stream quality based on
the calibrated landscape/in-stream model. Appropriate uses for these estimates of the distribution of
likely in-stream condition include: screening for potential problem areas, targeting of additional
monitoring to confirm problems, prioritization of TMDL/restoration efforts, and evaluation of resource
condition for large areas. Evaluation constitutes an important potential use since aggregation of a census
of the condition of all waters should be inherently easier than aggregation based on biased sampling
networks that cover only a small fraction of streams. For example, water condition for a region could be
based on aggregating known condition from sites with in-stream sampling with the model based
inferences estimated for the remaining areas lacking in-stream data.
Conclusions
Figure 7 illustrates an additional paradigm for thinking about how monitoring resources can be
distributed to answer our key monitoring questions. Dividing the monitoring resource pie by key
questions should be just as important as balancing effort among techniques (chemical, biological, habitat,
etc.) and resource types (streams, lakes, estuaries, wetlands). This vision emphasizes that more effort and
integration may be required for reference condition, screening and evaluation monitoring. The slices are
not static; their sizes can and should change over time as techniques and capabilities evolve.
Limited public and private resources for water quality monitoring, protection and restoration also
compel true collaboration within and between government agencies and private entities concerned with
the integrity of the nation's water resources. As all agencies move toward environmental results based
management (USEPA 1996) we must continuously make, expand and institutionalize opportunities to
develop and share both quality assured geographic data and analysis tools with all our partners: states,
other federal agencies, local and regional governments, businesses, non-governmental organizations
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(NGO's) and citizens. Data integration using GIS techniques is critical since this will allow extrapolation
based on landscape/in-stream relationships to meet our needs for comprehensive, systematic screening
for potential problems. It will also foster use of all available data, both in-stream and remote sensed
landscape data, to understand and solve water quality problems. To realize this vision we must make data
and analyses easily shared and used by all potential partners. To make this a reality, every organization
engaged in monitoring water resource quality should commit to making data, analysis tools and results
readily available via the Internet. Working together to understand the condition of our water resources,
watersheds, and ecological areas with common potentials and pressures, can allow easier integration and
extrapolation of available and new data to answer the key, essential monitoring questions common to
every agency, entity and scale of concern:
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macroinvertebrates. Journal of the North American Benthological Society. 14:197-202.
Gilliom, R.J., Alley, W.M., and Gurtz, M.E. 1995. Design of the national water-quality assessment
program: occurrence and distribution of water-quality conditions. U.S. Geological Survey Circular
1112. Sacramento, California.
Hughes, R.M. 1995. Defining acceptable biological status by comparing with reference conditions. In
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resource planning and decision making. Boca Raton, FL: Lewis Publishers, pp. 31-47.
Hunsaker, Carolyn T.; Levine, Daniel A. 1995. Hierarchical approaches to the study of water quality in
rivers. BioScience. 45:193-203.
Intergovernmental Task Force on Monitoring Water Quality (ITFM). 1994. Water quality monitoring in
the United States: 1993 report of the intergovernmental task force on monitoring water quality.
Interagency advisory committee on water data. Washington, DC.
Jones, K.B., Riitters, K.H., Wickham, J.D., Tankersley, R.D. Jr., O'Neill, R.V Chaloud, D.J., Smith,
E.R., and Neale, A.C. 1997. An ecological assessment of the United States mid-Atlantic region: a
landscape atlas. EPA/600/R-97/130. U.S. Environmental Protection Agency. Office of Research and
Development. Washington DC.
Jones, B., Walker, J., Riitters, K.H., Wickham, J.D., and Nicoll, C. 1996. Indicators of Landscape
Integrity, Chapter 12: in Indicators of Catchment Health: a technical perspective. CSIRO Publishing.
Victoria, Australia.
Karr, James R. 1993. Defining and assessing ecological integrity: beyond water quality. Environmental
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Lahlou, M., Shoemaker, L., Paquette, M., Bo, J., Choudhury, S., Elmer, R., and Xia, F. 1996. Better
assessment science integrating point and nonpoint sources: BASINS version 1.0 user manual. EPA-
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Malone, D., and Bower, S. 1998. Personal communication. Tennessee Valley Authority. Chattanooga
TN. April 2, 1998.
May, C.W., Horner, R.R., Karr, J.R., Mar B.W., Welch, E.B. 1997. Effects of urbanization on small
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Wang, L., Lyons, J., Kanehl, PI, and Gatti, R. 1997. Influences of watershed land use on habitat quality
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Baltimore, Maryland, pp. 571-574.
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Figure 1. Ecoregions and stream reference sites for EPA Region 4 states.
Habitat
Chemistry
Toxlcity/biomarkers
Channel Morphology
Biology
Landscape
Hydrology
Figure 2. Dividing the monitoring pie by technique.
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Intensive Human Influence (Percent)
0-10
10-25
25-40
40-60
H60-100
A/ Statelin.shp
~ Boundary.shp
Figure 3. Intensive human land use.
Riparian Forest (Percent)
•|0-40
40-60
60-75
[ "175-90
[ j 90 -100
A/ Statelin.shp
|~| Boundary.shp
Figure 4. Riparian forest
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00
Water
Forest
Agriculture
Urban
Figure 5. Nolichucky River land use/land cover.
-------
Figure 6. Savannah River Basin REMAP sites.
Screening
Compliance/crises
Reference Condition
Evaluation
Fix Problems
Figure 7. Dividing the monitoring pie by key questions.
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Gaining Public Support for Urban Water Quality Management via Monitoring
Adrienne Greve, Graduate Research Assistant
Robert C. Ward, Professor
Chemical and Bioresource Engineering Department
Colorado State University, Fort Collins, CO 80523
Water management, both in terms of quantity and quality, is a global as well as national concern. Public
access to environmental data has increasingly become a component of water management policy. This is due
in part to the rise of sustainable development ideology. Sustainable development, however, demands that
information is not only available, but is utilized in an effort to increase public understanding and involve-
ment. It recognizes that public education is critical to long term change. Today, it is generally accepted that
public participation makes for better water management (Long et al. 1996). This is especially true in areas
with increasing population densities, our cities.
Threats to the quantity and quality of water are being accentuated by urbanization. Even in more
developed countries where the population growth rate has begun to level, the proportion of the population in
urban areas has risen from 55 to 70 percent of the total population (Heinke 1997). Cities are frequently
economic centers. Wages are higher and the purchasing power of the population is greater. This results in
faster rates of resource consumption and waste production. Urban areas, alone, are inherently ecologically
unsustainable as pointed out by Rees and Wackernagel (1996), however, urban areas are a global as well as
national reality. In the United States, as well as other developed countries, the challenge is to minimize the
impact of urban development in an effort to approach regional sustainability. Gaining public understanding
and involvement in water issues is key to achieving the changes necessary to minimize the human 'footprint'
on the water resources of a region.
This paper presents an approach that aims to involve and inform an urban population in an effort to
reduce the impact urban areas have upon the water resources of a region. An information system, building
upon existing water quality monitoring efforts, is used to create a simple before and after picture through
which to view the water quality impacts of an urban community. This system carefully builds upon the
goals of sustainable development. It would place responsibility on the community as a whole to take care
of its water resources by raising awareness, as well as use a system of feedback that allows the system to
evolve with people's needs. In order to change individual use habits and increase public support for water
conservation and wastewater treatment, community members need an understanding of impacts on both a
local and larger regional scale. This aim is stated in Agenda 21, a document written by the UN after the
1992 Earth Summit in Rio de Janeiro, as the "sensitization of the public to the issue of protecting water
quality within the urban environment." In viewing water impact, the proposed monitoring information
system explains how much water quality changes as a result of water use in a city's water/wastewater
system. This information can then be viewed in a larger context by creating a systematic comparison
between the impacts of each city in a region. The data gathered by the monitoring system is used to
construct an index based on regional rank. Such a ranking system will put the data in a regional context
and introduce competition to water conservation and wastewater treatment efforts.
Water Impact
Typically the term "water impact" is used to describe a change in water quality. Within this system, water
quantity is included as one of eight variables that describe the impact of a community on water resources.
Quantity is included as a water quality variable because this monitoring information system looks at the
impacts of user actions on the beneficial uses of water such as recreation, consumption, waste assimilation,
and ecosystem maintenance. Each of these uses requires both quantity and quality. It is also important to
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realize that in addition to requiring water of good quality and large quantity, urban areas pose a threat to both
these dimensions of water management.
Monitoring System
The monitoring network itself is very simple. An urban area for the interests of this system is loosely
defined as one with sewer service to private homes and drinking water provided by a municipal utility.
Water entering each drinking water treatment facility is sampled prior to treatment and the water exiting
each sewage treatment plant is sampled after treatment has been completed. This type of monitoring
system creates a before and after picture of the community's affect on water in terms of overall water
quality.
Because monitoring already takes place at each treatment plant sample site, it would be cost effective
to choose contaminants already being sampled by one or both of the treatment facilities. Many of the
contaminants monitored by treatment plants are toxins and specific chemicals. These are important for
maintaining a healthy water supply for human consumption, however they are not necessarily good
indicators of general water quality. Water quality as defined by this monitoring system, requires the
establishment of a set of basic constituents that are indicators of general water health.
The U.S. Environmental Protection Agency (1997) report on national water quality included a set of four
conventional pollutants within an index of watershed indicators. The pollutants include ammonia,
phosphorus, pH, and dissolved oxygen (DO). In a report by the National Water Quality Assessment
(NAWQA) program (Zogorski et al, 1990) on the use of waste water information, eight water quality
constituents were identified as both useful and commonly reported. These eight add carbonaceous
biochemical oxygen demand (CBOD), biochemical oxygen demand (BOD), chlorine residual, suspended
solids (SS), and fecal coliform to the EPA's ammonia, phosphorus, and pH. Dunette (1979) suggests a water
quality index based on DO, fecal coliform, pH, total solids, ammonia, and BOD. Using these groupings as a
guideline, seven conventional pollutants have been chosen in addition to flow data. These contaminants are
BOD, DO, fecal coliform, SS, ammonia, chlorine, and phosphorus.
Data Analysis
Impact is constructed as a difference between incoming and outgoing quality. It is critical that the data is
in a form that allows for the comparison of cities with differing populations. Changes in concentration are
inherently a comparable measure as it allows for comparisons between cities of various sizes by being a
measure of amount per unit volume. The change in quantity will be divided by population in order to
compare per capita impact. Eventually this value could be replaced by an average per capita use based on
individual water meters. The value that is actually reported to the public is a rank that is based on a
comparison to all other communities in the region. This rank acts as a simple water quality index. It is not an
absolute index as it is based on a comparison with other cities.
Construction of the Water Quality Index
• Typical annual concentrations for each contaminant will be determined based on the median
value from periodic sampling over one year. The monitoring network will give periodic pollutant
concentrations for water entering the drinking water treatment facility and the water exiting the
sewage plant. If there is more than one plant in either situation, the average of the concentrations
for each sampling period will be found, weighted based on flow. The difference between the
median concentrations will be used as the impact of the city for a given contaminant. This
procedure will be used for all variables save water quantity. The water quantity median difference
will be divided by population in order to gain a per capita impact.
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• Water quality data sets often have attributes that prove difficult to handle with classical data
analysis methods. This system of analysis is based primarily on nonparametric procedures and
thus avoids many of the problems faced by classical techniques. For example, the use of the
median to estimate the typical value for a given variable is not affected by the presence of
outliers, non-normality, or missing values. The variables to be measured are conventional water
constituents and are unlikely to sink to concentrations so low that the results are reported as "non-
detect". Last, the comparative nature of the index construction removes influence by seasonal
variation as all areas in a given region should have similar cycles.
• Cities will be ranked based on the annual median difference between incoming and outgoing
water for each variable measured.
Not all variables should be ranked using the same methodology.
DO is viewed favorably when its value is high. The difference should be taken as incoming less
the outgoing concentration. The values are then ranked from smallest to largest with the smaller
value being most desirable.
O,-O0 = DOD
where: Oj = median oxygen concentration of influent to city
O0 = median oxygen concentration of effluent from city
DOD = dissolved oxygen difference between inflow and outflow
Thus, cities that provide more oxygen to their effluent than was in their raw water, generate
negative values. By choosing the smaller values as desirable, negative numbers will be ranked
highest.
Quantity is a factor in water conservation efforts. Its value is viewed favorably when the
incoming versus outgoing per capita value is small. Thus, on a per capita basis, water quantity
will be ranked from smallest to largest, with the smallest value being most desirable.
Q, -Qo .
population
where: Qi = median quantity of water flowing into a city's water treatment plant
Qo = median quantity of water discharged from the wastewater treatment plant
QD = per capita difference between water quantity into the water treatment plant and
that leaving the wastewater treatment plant
Cities that lose/consume little water, receive higher rankings. QD could be negative in situations
where there is high infiltration or leaky sewers. In order to not falsely reward cities that simply have
leaky systems, the lowest value permitted will be zero. Any negative values will be treated as zero.
All cities that have value of zero will receive the same rank.
BOD, fecal coliform, SS, nitrogen, phosphorus, and chlorine: For each of these contaminants,
the smaller the value the better quality the water. The difference in median value should be taken
as incoming concentration less the outgoing concentration and ranked from largest to smallest,
with the largest value being most desirable.
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€,-€„= CD
where: d = median contaminant concentration of influent to a city
C0 = median contaminant concentration of effluent from a city
CD = contaminant difference between inflow and outflow concentration
Because low values of C0 are desirable, cities with positive values of CD will be ranked highest.
A negative value of CD signifies a negative impact on the river or receiving water.
• The ranks of each measured variable will be averaged for each urban community. The variables
can be weighted if there is a desire to place more emphasis on one or more of the variables. For
example, water conservation could be weighted more heavily in order to be on a more equal
footing with water quality. The cities will then be ranked once again based on the average of the
eight variables. This final rank is the index number.
Reporting Methods
The monitoring information system aims to inform, educate, and involve a general public in water
resource stewardship. The rank that a city achieves needs to be treated as a common starting point where
careful explanation will lead to a greater understanding of the problems facing urban areas and what can be
done to solve them. The various mediums utilized by the media provide a venue from which many people
gather scientific information (Miller 1986). A common problem facing the media is time and space required
to provide water quality information (Long et al. 1996). This is likely due to the need to explain many of the
terms associated with water quality data. Specific water quality variables are not part of the general
knowledge base of the public. However, the names of other cities within a given region are references with
which a majority of the population can identify. The data analysis protocol reports a single number that is a
rank based on how a community compares to others in the region. This is a clear and concise introduction to
the water quality information. Using a newspaper article as an example, seeing a headline such as "Fort
Collins drops behind Denver in per capita water impact, 15th overall in Colorado" will be clear to nearly all
community members. A newspaper article offers both an appropriate multilevel format and a good example
of a well-circulated, highly visible vehicle with which large portions of the public can be reached. Following
such a headline should appear a brief summary accompanied by clear, eye-pleasing graphics. These graphics
should remain consistent so each time the report is published the presentation is more familiar, and
information more quickly conveyed.
Following the summary and headline, more detailed information could be included. In this section each
pollutant could be explained along with its impacts on water quality. Ideally, links between pollutant levels
and water use behaviors could be drawn. These links can be drawn to a large variety of behaviors from direct
water use, to purchasing more efficient appliances, to public pressure for budget allocations for sewage plant
improvements. The links, however, are not a part of the information/monitoring system being proposed
herein. The goal is to simply engage the public in discussions about possible links by reporting comparisons
of cities in a region.
A final section in an article can point those readers who would like more information, or have feedback,
to appropriate avenues. Such information should include access to the raw data, data analysis protocol
procedures, public meetings, and other related information. There should also be a section where feedback
can be requested and advisory committees formed. Feedback should play an increasingly important part of
system operation
In addition to media involvement, other avenues of communication should also be used in order access as
large a portion of the public as possible. Such measures may include web pages, inserts to accompany water
bills, open meetings, newsletters, and outreach programs in school. In each instance the top/down type of
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approach utilized in the example of the newspaper article will be useful. Beginning with a concept that
everyone is likely to understand, such as the index rank, is effective regardless of the type of communication.
Implementation Considerations
This approach to monitoring and reporting, in order to be successful, must be cooperatively implemented
over an entire region. Implementation requires that sampling technique, analysis procedure, and data storage
is standardized for all participating communities in order for accurate comparisons. It is important to realize
that a system such as the one described here cannot be implemented and simply left. It is assumed that this
system, if maintained, over a long time period will result in the public having an increased awareness, and
greater amount of water information incorporated into their general knowledge base. The reporting, data
analysis, and all other components of the system must evolve with the public's growing informational needs,
through a series of feedback systems that allow public involvement. Stronger ties between specific water uses
and water quality changes must be established.
This system, if implemented, is very visible. Public pressure is often an instigator of change. This
pressure will likely result in improvements in sampling technique and analysis in order to improve accuracy.
Improving public knowledge and creating interregional competition should also increase demand and support
for new technologies that offer greater water efficiency and pollutant removal. This system could eventually
also incorporate urban non-point source pollution comparisons, thus creating an assessment of total impact by
a city's population.
The suggestions presented here carefully build upon existing knowledge about monitoring as well as
upon the evolving concepts of sustainability. Political use of the information places added burdens upon those
designing and implementing the system to be as scientifically sound as possible. Already the public shapes
water policy at the ballot box (Long et al. 1996). Increased informational visibility is in hopes that a better
informed public makes better decisions, however such publicity places added pressure on the system. If such
a program is successful, particularly in gaining media coverage, the topic of water quality has the potential to
become an even more important political issue. This implies that in addition to being scientifically sound, the
results need to be clearly and carefully explained.
It must also be kept in mind that this system ranks cities based on their impact on water, not the
subsequent impact on the environment. A large metropolitan area may have the same rank as that of a much
smaller town, but may have a much more dramatic impact on the downstream environment because its
effluent comprises a large percentage of the stream flow.
The ranks from this system will, to a large extent, reflect the difference between the incoming water and
the permit regulations placed on a treatment plant. These regulations relate directly to the upstream water
quality and the stream classification. This means that the ranking scheme will favor those cities whose
effluent standards most closely resemble the upstream conditions. While this does not necessarily pose a
problem, it is something that must be kept in mind and carefully explained.
Conclusions
Gaining public attention and support for ongoing improvements in water management are difficult when
the general public does not readily understand water data. This paper presents a way of gaining interest and
support using urban impact comparisons rather than actual water quality measurements. The approach is not
unlike the SARA 313 Toxic Release Inventory comparisons being reported for industries.
The proposed system carefully integrates water quality and water quantity management across a city
while engaging the public. Through careful implementation by city water managers, such an information
system can be a means to gain valuable public support to lighten the "footprint" cities place on the water
environment.
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municipal wastewater information for use in a national water-quality assessment: A case study of the
upper Elinois River Basin in Illinois, Indiana, and Wisconsin. U.S. Geological Survey, Open-File
Report 90-375.
World Wide Web Pages
Agenda 21 http://www.igc.apc.org/habitat/agenda21/
EPA Homepage http://www.epa.gov/
EPA Strategic Plan Draft http://www.epa.gov/ocfopage/
Sustainable Development Indicators (SDI Group) http://venus.hq.nasa.gov/iwgsdi/1997SDI.html
President's Council on Sustainable Development http://www.whitehouse.gov/PCSD
Rio Declaration http://www.igc.apc.org/habitat/agenda21/rio-dec.html/
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Using 818O and 5D to Quantify Ground-Water/ Surface-Water Interactions
in Karst Systems of Florida
Brian G. Katz, Research Hydrologist
U.S. Geological Survey
227 N. Bronough St., Suite 3015, Tallahassee, FL 32301
Abstract
Stable isotopes of oxygen and hydrogen are naturally occurring tracers that can provide quantitative
information about surface-water/ground-water interactions. 518O and 8D were used to determine the
amount of surface water mixing with ground water in three different karst systems of northern Florida. In
one area, water from a sinkhole lake (Lake Barco) with an enriched isotopic signature moved downward
and laterally beneath the lake and mixed with ground water. Fractions of lake water that mixed with
ground water ranged from 0.23 to 0.65 and were related to the proximity of the sampling site to the lake
and the presence of buried solution features that facilitate the connection between the lake and the Upper
Floridan aquifer. In a second area, a recharge pulse from the Little River (a sinking stream) produced an
enriched isotopic signature in water samples from some wells downgradient from the river capture zone
and from a series of sinks that received Little River water during high-flow conditions. Fractions of Little
River water that mixed with ground water and water in sinks ranged from 0.13 to 0.96 and were related to
the proximity to the river capture zone and the degree of connectivity between the sinks and zones in the
Upper Floridan aquifer. In a third area, water from wetlands and shallow sinkhole lakes with an enriched
isotopic signature has mixed with deep ground water from the Upper Floridan aquifer in an area of
northwestern Leon County. Fractions of evaporated surface water that mixed with ground water ranged
from 0.10 to 0.34 and are related to heavy pumping of municipal supply wells and leakage from sinkhole
lakes.
Introduction
Fundamental to understanding the factors controlling ground-water quality in karst systems is an
accurate delineation of flow patterns and quantification of hydrochemical interactions between ground
water and surface water. As a result of dissolution of the carbonate rocks that comprise the Upper
Floridan aquifer in northern Florida, numerous karst features (sinkholes, swallow holes, solution lakes)
have developed, resulting in water from the land surface moving directly into the aquifer. Very little is
known about the quality of water that moves downward through karst features and naturally recharges the
Upper Floridan aquifer, the principal source of water supply in this area. Natural recharge of water from
sinking streams to this aquifer can result in water-quality contamination, such as high concentrations of
iron, hydrogen sulfide, and organic material, and undesirable bacteria, protozoa, and fungi (Krause 1979;
McConnell and Hacke 1993). Concentrated recharge of water through sinkholes and other solution
features provides little opportunity for attenuation of contaminants prior to entering the aquifer system.
This paper focuses on the use of 818O and SD to quantify interactions between ground water and
surface water. Due to the enrichment of 818O and 5D in surface water that undergoes evaporation, the
resulting isotopic signature is different than that of ground water and provides an ideal conservative tracer
for evaluating the extent of mixing of surface water and ground water (Gonfiantini 1986). The signature
of recharge from sinking streams in karst aquifers has been traced over distances of several kilometers
using these stable isotopes (Greene 1997). Differences between the composition of the water isotopes
(818O and 8D) in rainfall, ground water, stream runoff to a sinkhole, and lake water are used to quantify
mixing of ground water and surface water. Results are presented for three study areas that represent
III-195
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various types of interactions between ground water and surface water: (1) leakage of water from a
seepage lake, (2) a recharge pulse from a sinking stream during high-flow conditions, and (3) recharge
from wetland areas and solution lakes. Other chemical and isotopic tracers were used in these studies;
however 518O and 8D were most effective in quantifying mixing between ground water and surface water
(Katz et al 1995a, 1997). Results from this study provide a framework for a better understanding of the
hydrochemical interactions between ground water and surface water in karst systems and for evaluating
the susceptibility of ground water to surficial contamination.
Study Areas
A brief description of each study area is provided in this paper. More detailed information on the
hydrogeology and physiography of each study area can be found in the references listed in each section.
The climate of the three study areas is humid subtropical, with 30-year (1961-90) mean annual rainfall
ranging from 131 and 134 cm at Lake Barco and Little River, respectively, to 167 cm at Leon County
(Owenby and Ezell 1992).
Lake Barco
Lake Barco, located in north-central Florida, is an acidic seepage lake typical of other seepage lakes
along ridges, uplands, and highlands in Florida that have no surface-water inflow or outflow. The Lake
Barco catchment is in the Central Lake physiographic district (Brooks 1981), which is characterized by
the formation of active sinkholes. Lake Barco is considered to be a cover-collapse sinkhole, which is
typical of many of the lakes in the Central Lake District (Sinclair and Stewart 1985; Arrington and
Lindquist 1987). Ground-penetrating radar (GPR) and seismic reflection surveys provided direct evidence
of karst features and downwarping of beds within the surficial deposits in the Lake Barco basin (Sacks et
al 1992).
The hydrogeologic framework consists of the Ocala Limestone (Upper Eocene age) unconformably
overlain by the Hawthorn Group (Miocene age), which consists of a variable mixture of sand, gravel,
clay, phosphate, and carbonate sediments (Scott 1988). The intermediate confining unit within the
Hawthorn Group is probably breached in places (Sacks et al 1992). Undifferentiated surficial deposits
(Holocene to Pliocene age) lie above the Hawthorn Group and consist of poor to well sorted sands with
variable amounts of silt and clay. The location of the 1PNB well nest (upgradient from Lake Barco), the
2PNB well nest (downgradient from the lake), and other sampling sites are shown in figure 1. Samples of
ground water, lake water, and rainfall were collected during May 1991 through September 1992.
Little River Sinks
The Little River, which drains a watershed of 88 km2, is a second order ephemeral stream in eastern
Suwannee County (fig. 2). The Little River is typical of streams that originate in the Northern Highlands
physiographic subdivision that disappear underground as they approach the toe of the Cody Scarp, which
separates the Northern Highlands from the karst plain of the Gulf Coastal Lowlands. Direct localized
recharge of river water is concentrated in sinkholes along the Cody Scarp and typically receives little
filtration as it enters the Upper Floridan aquifer (Katz and Catches 1996). After a period of sustained
rainfall the Little River flows into its first capture point, Mud Sink, which is typically laden with sediment
from the stream channel. After its acceptance capacity is exceeded, water from Mud Sink overflows into a
channel that leads into another sinkhole, named Stick Sink (fig. 2), approximately 120 m downstream.
The channel continues downstream from Stick Sink, leading into another group of small sinkholes. These
sinkholes contain dry caves that apparently captured flow from the Little River prior to the opening and
enlargement of Mud and Stick Sinks.
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Land use in the watershed is mainly agricultural where pasture is the principal use, as well as some
row crops, poultry, and dairy farms. The locations of wells sampled, and sampling sites for Mud Sink,
Stick Sink, and Little River are shown in figure 2. Water samples were collected during low-flow
conditions (November-December 1995) and high-flow conditions (April 1996).
Leon County Study Area
In Leon County, Florida, more than 3,300 karst features have been identified based on areal
photography, topographic maps, and satellite imagery (Benoit et al 1992). These karst features include
sinkholes, closed depressions, swallow holes, springs, open basins suspected of originating from solution
processes, and large lake basins with known sinkholes. Study sites in Leon County are located in the
Northern Highlands (Brooks 1981), which contains large solution (sinkhole) lakes, such as Lake Jackson,
(fig. 3) and smaller lakes.
In addition to recharge to the Upper Floridan aquifer through solution lakes, there are numerous
locations in the northern part of the study area where stormwater runoff from small drainage basins flows
directly into sinkholes and recharges the Upper Floridan aquifer (Benoit et al 1992). One of the largest of
these systems is the FG sinkhole (fig. 3), which receives surface water from a drainage area of about
1,010 ha and flows directly into the sinkhole, approximately 24 m deep, 20 m wide, and 60 m long. About
0.1 to 0.2 m3/s of water typically enters the aquifer from this sinkhole during periods of normal rainfall
(Katz et al 1997). The location of sampling sites, including municipal wells (96-130 m in depth below
land surface) and FG sink, and potentiometric-surface contours for the Upper Floridan aquifer are shown
in figure 3. Water samples were collected from wells and FG sink during November 1994 to May 1994.
Methods
In the three study areas, ground-water samples were collected from monitoring wells by using a
positive displacement dual piston pump with a 0.95-cm Teflon discharge line at pumping rates of
approximately 0.06 L/s (Katz et al 1995a, 1997). Wells were purged for at least three casing volumes with
the pump intake positioned approximately 1 to 2 m above the screened interval. The pump was
subsequently lowered into the top of the screened interval or open hole interval for sampling. Water
samples from municipal supply wells that tap deep parts of the Upper Floridan aquifer in Leon County
(denoted CW-xx and LVW-1, fig. 3) were collected prior to any treatment using in-line turbine pumps
(pumping rates ranged from approximately 30 to 160 L/s). Surface-water samples (Lake Barco, Lake
Bradford, FG sink, Mud and Stick Sinks) were collected at a depth of approximately 0.5 to 1 m below the
water surface, using a peristaltic pump with silicone rubber and tygon tubing. Samples of rain water were
collected using a wet/dry atmospheric deposition collector at or near each study area.
Stable isotopes of oxygen and hydrogen in water samples were analyzed at the U.S. Geological
Survey Isotope Fractionation Laboratory in Reston, Virginia, using techniques described by Coplen et al.
(1991 1994). Standard 8 (delta) notation (Gonfiantini 1981) is used for the stable isotopes, as defined by:
8 (per mil) = [(Rsample/Rs.andard) - 1 ] X 1,000 (1)
where R = 18O/I6O and D/H for 818O and 8D, respectively. Oxygen- and hydrogen-isotope results are
reported in per mil relative to VSMOW (Vienna Standard Mean Ocean Water) and are normalized on
scales such that the oxygen and hydrogen isotopic values of SLAP (Standard Light Antarctic
Precipitation) are -55.5 per mil and -428 per mil, respectively (Coplen 1994). The 2a precision of 818O
and 8D results is 0.2 and 1.5 per mil, respectively.
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where Ym, Ygw, and Ysw denote the concentrations of 518O and 5D in the mixture, ground-water, and
A two-component mixing model was used to estimate the fraction of surface water that mixes with
ground water. For a two-component mixture, the fraction of surface water (fsw) in the mixture is defined
as:
fsw = v *m ~ Ig
trations of 518
surface-water end members, respectively.
Differences in the isotopic composition of surface water, rainfall, and ground water result in relatively
high precision for detecting the mixing proportion of surface water in ground water. The sensitivity (S) of
the method for detecting the proportion of lake water or stream water that mixes with ground water can be
determined by the following expression (Payne 1983):
S =+/-X/Y; (3)
where +/-X is the variability (10) of the isotopic composition of the lake or stream, in per mil; and Y is
the difference between the isotopic composition of surface water and ground water, in per mil.
Results and Discussion
Differences in the content of D and I8O in ground water, rainfall, and surface water were used to
determine mixing processes in the ground-water flow system in the three study areas. D and 18O data
typically are plotted on a diagram showing 8D versus 818O relative to VSMOW. Mean annual values
of 8D and 818O in precipitation collected at many locations around the world plot along a line with a slope
of 8 and intercept of +10 (8D=8818O+10), commonly referred to as the global meteoric water line (MWL)
(Craig 1961). The variability in isotopic composition of rainfall from one site to another is a function of
several factors, including storm-track origin, rainfall amount and intensity, atmospheric temperature, and
the number of evaporation and condensation cycles (Dansgaard 1964). The stable-isotopic composition of
waters relative to the MWL reveals important information on ground-water recharge patterns, the origin
of waters in hydrologic systems, and mixing of ground water and surface water. For example, rainwater in
the three study areas is slightly depleted in 8D and 8I8O relative to the isotopic composition of seawater
(8D and 818O = 0.0 per mil), because rain originates as evaporation from the Gulf of Mexico and most
likely has been through only one evaporation-condensation cycle.
Interactions Between Lake Barco and Ground Water
The ground-water samples upgradient from Lake Barco (1PNB well nest) clearly show a meteoric
origin, as their isotopic composition is similar to that of rainfall (3 month composite samples collected
during April 1991 through September 1992; Katz et al 1995a). The similarity in isotopic composition
between rainfall and upgradient ground water indicates a rapid recharge rate with evapotranspiration
processes not affecting the stable isotopic composition of water. Water samples from two in-lake wells,
MLW-2 and MLW-6 (fig. 1) have isotopic compositions similar to meteoric water and plot along the
MWL (fig. 4), indicating that ground-water inflow occurs at these sites. These wells were originally
installed beneath the bottom of Lake Barco (Sacks et al 1992); however, during 1989-90 lake volume had
decreased by 45%, and when water samples were collected in 1992 these wells were outside of the lake
perimeter.
The isotopic compositions of ground water downgradient from Lake Barco (MLW-4 and 2PNB well
nest) have enriched values relative to meteoric water and plot along a mixing line described by the
expression, 8D= 4.6818O - 1.3 (r2 = 0.987). The mixing line connects the isotopic composition of the
surface-water and ground-water end members — evaporated Lake Barco water and ground water
upgradient from the lake (fig. 4). The isotopic shift in the composition of ground water downgradient
III-198
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from Lake Barco provides evidence for lateral flow from the lake toward the downgradient sites, despite
the strong downward head gradient at the nested wells at the 2PNB site (Sacks et al 1992). The isotopic
composition of ground water downgradient from Lake Barco was nearly identical for samples collected
during the three separate occasions, May 1991, November 1991, and August-September 1992, (Katz et al
1995a) and, for the sake of clarity, the isotopic compositions are shown in figure 4 for the most recent
samples.
The relative position of the sites downgradient from Lake Barco along the mixing line indicates the
relative proportion of lakewater outflow (leakage) that has mixed with meteoric water (ground water
upgradient from the lake). Isotope mass-balance calculations (eqn. 2) using 518O indicates that the largest
fraction of lake water leakage (0.65) occurred in water samples from well MLW-4 (located directly
beneath the lake) and 2PNB-FL (0.64), which is hydraulically connected to the lake as a result of the
breached confining unit (table 1). Progressively smaller fractions of lake-water leakage (0.56 - 0.23) have
mixed with ground water from 2PNB-60 to 2PNB-20, respectively (table 1). Distinct differences in the
isotopic composition of lake water and ground water provide a high precision estimate for the limit of
detection of lake water in ground water (+/-0.043) using eqn. (3) and data for both 5I8O and 8D. The high
degree of precision also provided constraints when using mixing ratios of lake-water leakage and
meteoric (recharge) water to model the chemical evolution of ground water downgradient from Lake
Barco (Katz et al 1995b).
Interactions Between the Sinking Little River and Ground Water
Samples of ground water collected during low-flow conditions had similar 818O and 5D values as
were observed in rainfall (monthly composites collected during June 1995 through May 1996). The
isotopic composition of these samples plot along the global meteoric water line (Craig 1961), indicating
that they are probably little affected by evaporation (fig. 5). In contrast, during high-flow conditions,
the 818O and 8D composition of water from Little River had a more enriched isotopic composition than
that of rainfall and most samples of ground water, indicating that evaporation has occurred (table 1). Also
during high-flow conditions, water samples from four sites had higher 8I8O and 8D values than samples
collected during low-flow conditions: JOW-3D, JOW-5S, Stick Sink and Mud Sink (fig. 5). The isotopic
composition of water from these four sites and the Little River plot along a mixing line described by the
expression SD=5.3818O+0.31 (r2=0.982), which connects the isotopic composition of the end members,
Little River, and ground water collected during low-flow conditions. The enriched isotopic signature in
water samples from the four sites indicates that river water is mixing with ground water in the following
proportions (table 1): Mud Sink (0.85-0.96), Stick Sink (0.44-0.50), JOW-3D (0.59-0.66), and JOW-5S
(0.13-0.16).
Interactions between river water and ground water in the study area can be highly variable and are
dependent upon the distance from the capture zone for the Little River and the degree of interconnection
between the aquifer and the Little River sinks. The aquifer is typical of many carbonate aquifer systems
that contain both conduit networks and diffuse flow, where conduits and fractures have an important
influence on the interactions between river water and ground water.
Interactions Between Surface Water and Deep Ground Water, Leon County
818O and 8D values were similar for samples of rainfall (monthly composited samples) collected
during January through August 1995, samples of water from seven municipal supply wells, and surface
runoff flowing into FG sink (all plot along the global meteoric water line). A most surprising finding was
that water from five deep municipal supply wells located in the northwestern part of the study area (CW-
19, CW-23, CW-15, CW-26, and LVW-1) was enriched in 18O and D (fig. 6). This isotopic enrichment
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indicates that surface water which has undergone evaporation, is mixing with ground water in deep parts
of the Upper Floridan aquifer. The isotopic composition of water from these five sites plot along a mixing
line described by the expression SD=4.9818O-0.78 (r2=0.996), connecting the isotopic composition of an
evaporated surface water (approximated by the average isotopic composition of water from Lake Barco)
and deep ground water that plots along the global meteoric water line (fig. 6). Using 518O and 8D, the
following proportions of surface water that mixed with ground water ranged from 0.10 (LVW-1) to 0.34
(CW-26) (table 1). Mixing of surface water and ground water is supported by estimates of ground-water
age. Recharge to ground water has occurred within the last 40 years, based on tritium analyses of ground
water (Katz et al 1997).
Mixing of surface water with ground water at depths greater than 60 m is probably facilitated by
water moving downward through sinkholes into the Upper Floridan aquifer and possibly by heavy
pumping in certain areas of the aquifer. Contributing areas for selected water- supply wells were
delineated by tracking particles backward toward areas of recharge by using a calibrated three-
dimensional flow model (Davis 1996). A large, saturated wetland area and shallow sinkhole lakes lie
within contributing areas for wells in the northwestern part of the study area. Water from deep wells in
the Upper Floridan aquifer upgradient from these sources of enriched surface water had 818O and 8D
values that plot along the global meteoric water line (Sprinkle 1989), indicating that little or no
evaporation occurred during recharge to the Upper Floridan aquifer north and upgradient of the study
area.
Summary and Conclusions
Oxygen and hydrogen stable isotopes are highly effective naturally occurring tracers of mixing of
different water sources because oxygen and hydrogen constitute and move with water molecules. Surface
water, which is preferentially enriched in 818O and 8D, has an isotopic signature that provides conserva-
tive tracers for evaluating the extent of mixing of river water and ground water. Stable isotope measure-
ments can provide very useful quantitative information about recharge patterns and interactions between
ground water and surface water.
The use of 8 O and SD in quantifying mixing between surface water and ground water was
demonstrated for three different hydrologic systems in the karst terrain of northern Florida. Water from a
sinkhole lake (Lake Barco) with an enriched isotopic signature is moving downward and laterally beneath
the lake and mixing with downgradient ground water. Fractions of lake water that mixed with ground
water ranged from 0.23 to 0.65 and were related to the proximity of the site to the lake bottom and the
presence of solution features that facilitate the connection between the lake and the Upper Floridan
aquifer. A recharge pulse from the Little River, a sinking stream, produced an enriched isotopic signature
in water from wells downgradient from the river capture zone and from a series of sinks that receive Little
River water during high-flow conditions. Proportions of Little River water that mixed with ground water
and water in sinks ranged from 0.13 to 0.96 and were related to the proximity to the river capture zone
and the degree of connectivity between the sinks and zones in the Upper Floridan aquifer. Water from
wetlands and shallow sinkhole lakes has mixed with deep ground water in an area of northwestern Leon
County. Proportions of evaporated surface water that mixed with ground water ranged from 0.10 to 0.34.
Recharge of enriched surface water into the Upper Floridan aquifer might result from heavy pumping of
municipal supply wells.
Mixing of surface water and ground water from the Upper Floridan aquifer indicates that the aquifer
is highly susceptible to contamination from activities at the land surface, particularly where karst features
are present. The vulnerability of the aquifer in all three study areas to contamination has been
demonstrated by ground water that receives recharge of relatively recent origin (within the last 40 years
based on measurements of tritium; Katz et al 1995a, 1997) and the presence of perchloroethylene in deep
ground water in the Upper Floridan aquifer in the Leon County area.
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The collection and analysis of water samples for stable isotopes of water should be included as part of
monitoring programs where interactions between ground water and surface water are likely to exist. The
combination of isotopic and chemical data provides a powerful tool for identifying and quantifying the
processes controlling ground-water quality. A better understanding of hydrochemical interactions
between surface water and ground water, and of the processes controlling the chemical composition of
ground water, will assist regulators in making more informed environmental decisions for protecting the
valuable water resources of the Upper Floridan aquifer.
Acknowledgments
The studies described in this paper were funded jointly by the U.S. Geological Survey and the Florida
Department of Environmental Protection. The author gratefully acknowledges L.A. Sacks and I.E.
Landmeyer for their review comments and suggestions that significantly improved earlier versions of this
paper.
References
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and W.L. Wilson, eds., Proceedings of second multidisciplinary conference on sinkholes and the
environmental impacts of karst. Florida Sinkhole Research Institute, Orlando:31-39.
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development in Leon County, Florida, for the delineation of wellhead protection areas. Northwest
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Katz, E.G., and Catches, J.S. 1996, The Little River basin study area- hydrochemical interactions
between ground water and surface water: Southeastern Geological Society, Tallahassee, FL,
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EXPLANATION
^| WETLAND
• WATER-TABLE WELL
,PNBO WELL NEST
O IN-LAKE WELL
A
METERS
45-|
30-
15-
SEA
LEVEL
-15-
1PNB
WELL
NEST
EXPLANATION
•MLW-4 GROUND-WATER
SAMPLING SITE
— "^ DIRECTION OF
GROUND-WATER
FLOW
ORGANIC-RICH
SEDIMENTS
2PNB
WELL
NEST
A'
METERS
r45
-30
Surficial aquifer
MLW-6 '2PNB-20
Intermediate
confining unit •
(Hawthorn Group)
Surficial aquifer
Intermediate
confining unjj
1PNB-FL*
Upper Floridan aquifer
(Ocala Limestone)
•2PNB-60
•2PNB-80
-^
'2PNB-FL
Upper Floridan aquifer
(Ocala Limestone)
-15
SEA
VERTICAL SCALE GREATLY EXAGGERATEDQ
\-
200 METERS
•-15
0
500 FEET
Figure 1. Map of Lake Barco study area showing location of sampling sites for ground water and lake water.
III-203
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83°00'
30'15'
82°49'
EXPLANATION
Little River
study area
Cody Scarp
(approximate
location)
Little
River
Springs
5° GROUND-WATER
SAMPLING SITE
MAP
NUMBER
1
2
3
4
5
6
7
JOW-1
JOW-2
JOW-3S.-3D
JOW-4
JOW-5S.-5D
JOW-6
JOW-7
Rainfall collection site
River sampling site
5 KILOMETERS
I
Figure 2. Map of Little River study area showing location of sampling sites for
ground water, river water and rainfall.
III-204
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84°15'
Leon County
study area
EXPLANATION
3CT30'
30'25' -
12 POTENTIOMETRIC CONTOUR—
Altitude, In meters, at which water
would have stood in tightly cased
wells. Datum is sea level
CW-23 GROUND-WATER
• SAMPLING SITE
Figure 3. Map of Leon County study area showing location of sampling sites for
ground water and surface water.
O
o:
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ou
20
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20
30
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~-_a RAINFALL (4/91-9/92)
: * GROUND WATER 4
- UPGRADIENT ^
FROM LAKE $,*
E BARCO JS&fv
— ^^ •*
; $••' ^BARCO ;
9- •'' /CC -
£'' xx ^"^^Mixing line :
x^M'rW-4 5D=4.65 8Q-1.3 I
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X.-2PNB-60 :
'x 2PNB-80 :
2PNB-40 ~
: A^T 2PNB-20 :
: /' i
—
~
i , , , , i , , , , •
-10 -505
DELTA OXYGEN-18, PER MIL, VSMOW
10
Figure 4. Deuterium and oxygen-18 content of rainfall, ground water, and lake water from
the Lake Barco study area compared to the global meteoric water line.
III-205
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CO
on
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-5
10
15
20
25
30
35
dri
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o Monthly rainfall samples
- (6/95-6/96)
WATER SAMPLES
' COLLECTED DURING:
H * Low-flow conditions
• High-flow conditions . 10
=! 0
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RAINFALL (1995)
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UPPER FLORIDA
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_i — i — i • i . i .
-10 -8 -6 -4 -2 0 2 4 6 8 10
DELTA OXYGEN 18, PER MIL, VSMOW
Figure 6. Deuterium and oxygen-18 content of ground water, river water, and rainfall from
the Leon County study area compared to the global meteoric water line.
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Table 1. Mixing Proportions of Surface Water and Ground Water in
Three Study Areas of Northern Florida
Concentrations of 5I8O and §D are in per mil; Fsw denotes fraction of surface
water mixing with ground water, using equation (2); ESW denotes evaporated
surface water (see text).
Site name
5180
5D
(5180)
(6D)
Lake Barco study area (August-September 1992)
MLW-4
2PNB-20
2PNB-40
2PNB-60
2PNB-80
2PNB-FL
Lake Barco
Mud Sink
Stick Sink
JOW-3D
JOW-5S
Little River
0.95
-2.25
-1.00
0.25
-0.45
0.85
2.50
-11.0
-5.55
-2.50
-2.00
2.00
3.60 16.5
0.65
0.23
0.39
0.56
0.46
0.64
1.0
Little River sinks study area (April 1996)
-1.67
-2.75
-2.15
-3.17
-1.6
-9.2
-14.9
-10.9
-15.6
-7.7
0.96
0.44
0.66
0.13
1.0
Leon County study area (February-March 1995)
0.62
0.25
0.40
0.48
0.49
0.60
1.0
0.85
0.50
0.59
0.16
1.0
CW-15
CW-19
CW-26
CS-23
LVW-1
ESW
-1.99
-1.11
-0.77
-1.89
-2.43
4.20
-9.47
-6.60
-3.78
-11.2
-12.7
19.8
0.17
0.28
0.33
0.18
0.11
1.0
0.19
0.26
0.34
0.14
0.10
1.0
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III-208
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Water Quality Monitoring for Integrated Wastewater and Stormwater Management
Lawrence B. Gaboon, Professor
Department of Biological Sciences
University of North Carolina at Wilmington, Wilmington, NC 28403
Phone: (910) 962-3706; Fax: (910) 962-4066; E-mail: cahoon@uncwil.edu
Janice E. Nearhoof, Research Associate
Department of Biological Sciences
University of North Carolina at Wilmington, Wilmington, NC 28403
Abstract
Water quality monitoring in a 55-square mile coastal region in southeast North Carolina is providing
information about water quality and pollution sources prior to construction of a regional wastewater treatment
system with an integral Stormwater management program. Monitoring data provide a statistical basis for
evaluating changes in water quality as the regional system is constructed and as development in this rapidly
growing area proceeds. The effectiveness of pollution control approaches, including Stormwater management
measures, elimination of septic tanks for waste treatment, and more innovative measures such as land
application of treated wastes will be evaluated by use of the data base and the statistical description it
provides of each monitoring location and drainage unit within the region.
Fecal coliform bacteria, total nitrogen, total phosphorus, chlorophyll a, total suspended solids, turbidity,
dissolved oxygen, percent saturation, pH, temperature, and salinity have been measured at 36 monitoring
locations, which cover several distinct drainage units, some of them heavily developed and others relatively
undisturbed. Variation in the characteristics of the locations, including land uses, human densities, and
drainage pattern, results in few significant correlations between water quality parameters over the whole
region that might otherwise be expected. Several parameters display marked seasonality as well. Processes
and events in the immediate vicinity of each monitoring location clearly drive significant excursions from
statistically average conditions, although Stormwater runoff events have yielded a very noisy signal at best.
However, careful data comparisons have already enabled identification of likely sources of water quality
problems. Consequently, predictions of water quality and evaluations of impacts are very location-specific,
but promise to be quite useful as the data base expands and additional information is considered.
Introduction
The South Brunswick Water and Sewer Authority (SBWSA) was created in 1993 as a regional govern-
mental entity by an interlocal agreement among the incorporated towns of Sunset Beach and Calabash and
Brunswick County for the purpose of developing a regional sewage treatment system. Rapid development
along North Carolina's coastline is driving the need for regional wastewater treatment and disposal systems
as conventional septic systems become problematic at higher densities, especially in many coastal soils, and
incorporated coastal municipalities lack the revenue base to build centralized waste treatment systems.
Rapid development raises other environmental and social concerns, however, and opposition to regional
wastewater systems has developed around two arguments. First, centralized sewage treatment permits
development of areas with soil types inappropriate for septic tanks, permitting higher population densities,
which by themselves drive various secondary socioeconomic impacts, such as the need for increased and
improved infrastructure and for revenue sources to support these needs. Second, experience in coastal North
Carolina and elsewhere has demonstrated that centralized wastewater treatment systems, by driving
increasing population density, also have the indirect effect of increasing the area and proportion of hardened
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surfaces, causing increases in stormwater runoff and resulting nonpoint pollutant loadings to surface waters
(Mallinetal., 1998).
Both arguments about negative secondary impacts of centralized wastewater treatment have been
invoked by opponents of the SBWSA regional wastewater treatment plan. Consequently, SBWSA has agreed
to abide by such population density restrictions as the State of North Carolina's Department of Environment
and Natural Resources may include in its permitting process and has incorporated a Stormwater Management
Program in its overall project plan. The Stormwater Management Program includes a water quality
monitoring program, which was begun in 1996 through a contract with the University of North Carolina at
Wilmington (UNCW).
The aims of the water quality monitoring program are to:
1. Quantify baseline water quality conditions before installation of central wastewater and stormwater
treatment systems by SBWSA;
2. Identify the nature and locations of existing water quality problems so that remedial actions can be
targeted on them and so that the most effective policies, practices, and treatment systems can be
adopted;
3. Evaluate progress in the improvement of water quality in the SBWSA 201 Planning Area as
programs and systems are implemented.
This paper describes some of the results of the water quality monitoring program, the uses to which those
results have been put prior to construction of the SBWSA treatment systems, and some of the broader lessons
offered by patterns in the data.
Methods and Materials
Water quality monitoring focuses on surface waters throughout the SBWSA 201 Facilities Planning
Area, a 55 square mile portion of coastal Brunswick County near the North Carolina-South Carolina border
that encompasses estuarine waters (including a portion of the Atlantic IntraCoastal Waterway (AICW), tidal
and freshwater wetlands, a variety of natural and artificial ponds and lakes, and portions of several coastal
river basins (the Caw Caw River and the Shallotte River), as well as the entire Calabash River basin (Fig. 1).
Twenty two monitoring locations were established in October, 1996, nine more were identified and sampled
beginning in February, 1997, and an additional five were added in September, 1997. Each monitoring
location was chosen to represent water quality conditions in a particular water body or in a portion of a
drainage. The criteria for selecting each location were that each had to be representative of its area or
drainage, be safely and legally accessible, and have standing water year round. Sampling at three monitoring
locations has been discontinued after determining that they failed to meet one or more of these criteria.
Owing to the need to have data about stormwater runoff effects on water quality, our sampling program uses
a routine schedule that over time has allowed us to sample during both storm event and non-event periods.
Eleven water quality parameters are measured at each of the monitoring locations, which are visited in
groups of eight or nine once or twice per week so that every monitoring location is sampled about every three
weeks. Dissolved oxygen (mg/liter), percent oxygen saturation, pH, temperature (°C), and salinity (ppt) are
measured in situ simultaneously with a YSI Model 85 meter. Turbidity (NTU) is measured on a single bottle
sample with an HF Scientific DRT-15CE turbidometer. Total suspended solids (mg/liter) are measured on
triplicate water samples by gravimetry (APHA, 1995). Chlorophyll a ((J-g/liter) is measured in triplicate with a
Turner 10-AU fluorometer following Welschmeyer (1994). Fecal coliform bacteria (CFU/100 ml) are
measured by membrane filtration (MFC) using the standard multiple dilution method (APHA, 1995). Total
nitrogen (M-g/liter) and total phosphorus (|ig/liter) are measured on triplicate water samples by the simulta-
neous persulfate digestion method of Valderrama (1981) followed by colorimetric analysis on an Alpkem
Flow Solution 3000 autoanalyzer. All sample collections are logged in field notebooks following standard
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QA/QC procedures and returned to the laboratory using proper chain of custody procedures. Field sampling
personnel use cameras to document sampling procedures, sample collections, and field conditions at the
monitoring locations. All laboratory analysis data are logged immediately in lab notebooks, recorded in
dedicated computer files, and reported to SBWSA by fax or in monthly data reports. Unusual data indicating
problems are reported immediately. Data, methods descriptions, and data interpretations are also posted on a
World Wide Web page created to inform the public about this project: (http://www.uncwil.edu/cmsr/
waterQ).
Results
We initially expected that some of the water quality parameters measured at our monitoring locations
would be correlated or at least show some relationship. For example, we expected that two different measures
of the material suspended in water, turbidity and total suspended solids, would closely correlate. However,
simple plots of the values of these parameters measured simultaneously and pooled for all monitoring
locations revealed no clear relationship (Fig. 2). This lack of a relationship becomes more reasonable if one
considers that the sources and nature of suspended material may vary among locations, especially considering
the variation in the nature of the waters sampled, and that turbidity (a measure of light scattering by
suspended material) and total suspended solids (a measure of the mass of suspended materials) are two
different properties of suspended materials. Consequently, a plot of the log-normalized ratio of turbidity
(NTU) to total suspended solids (mg/liter) by monitoring location reveals that some sites have suspended
materials with significantly different ratios of light scattering to mass than others (Fig. 3). We interpret this
result to mean that the properties of suspended materials vary significantly among monitoring locations, with
important implications for management of sedimentation and turbidity impacts on water quality.
Temporal variation is another factor that drives significant variability in our data set. The most obvious
temporal pattern is seasonal variation in temperature and its effect on the solubility of dissolved oxygen. Our
dissolved oxygen data set shows a very strong seasonal effect (Fig. 4). However, seasonal variation accounts
for only a portion of the variability in dissolved oxygen values. Variability among monitoring locations is
again a significant factor contributing to overall variability.
We expected that storm events would drive a significant portion of pollutant loadings to surface waters in
the 201 Planning Area and that sampling results from event and non-event periods would differ accordingly.
We examined the fecal coliform bacteria data set, owing to the short lifetime of fecal coliform bacteria in the
environment, to look for such storm event effects and again found patterns quite different from those
expected (Fig. 5). There is a tendency for fecal coliform values to be higher during or immediately after (<24
hours) a rain event, but the lowest fecal coliform values for over one third of the monitoring locations are
found during a rain event. A few of the highest fecal coliform values found at some locations occurred during
non-event periods. Some of the highest fecal coliform values, e.g., those at monitoring locations 3, 4, and 5,
although recorded during storm events, were apparently caused by discharges of incompletely chlorinated
sewage from a private sewage treatment system upstream of those locations (Fig. 6). Some of the locations
with higher than average fecal coliform values, e.g., monitoring locations 7, 12, and 27, are downstream of
residential areas served by septic tanks, which may overflow during rainy periods (Fig. 6). For example, Fig.
7 shows fecal coliform data during the period March 25, 1997 through May 25, 1998 for monitoring locations
20 (near a golf course), 27 (a small basin with many septic tanks) and 31 (downstream of a septage disposal
site). Southeastern North Carolina experienced very heavy el Nino rains during January-February, 1998,
which appeared to cause septic tanks to overflow upstream of location 27. Disposal upstream of monitoring
location 31 of septage pumped from failing septic tanks in the 201 Planning Area has apparently caused fecal
coliform contamination of the water at that location independently of storm event runoff (Figs. 6, 7).
Consequently, a variety of human factors appear to confound the expected effects of storm events on
pollutant loadings.
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Golf courses also contribute pollutants to surface waters in the SBWSA 201 Planning Area. Several golf
courses have been under construction while our water quality monitoring program has been underway.
Construction activities at courses upstream of monitoring locations 32,1, and 2 have apparently contributed
to turbidity levels considerably higher than at most other monitoring locations (Figs. 8, 9). Golf courses also
use considerable quantities of fertilizers. Our nutrient data show somewhat elevated total phosphorus values
at monitoring locations 19,20,21, and 22, all near or just downstream of golf courses, although the highest
average total phosphorus values are associated with sewage effluent discharged upstream of locations 4 and 3
(Figs. 6, 8, 10). Nutrient loading also contributes to elevated chlorophyll a levels we observe at monitoring
locations 19, 20, 22, 25, and 35 (Figs. 8, 11). Monitoring locations 14 and 25, which are located on lakes near
a golf course, are heavily loaded with aquatic macrophytes, another indicator of nutrient loading that is not
quantified in our monitoring program. These loadings of pollutants from golf courses are attributable to
stormwater runoff, inadequate runoff controls, and lack of riparian buffers to absorb pollutants.
Discussion
Variability in water quality data from routine monitoring results from several factors, including among-
site differences, seasonally, variable effects of stormwater runoff events, and differences in the nature of
point sources. This variability can obscure patterns that might otherwise be expected. Thus the goal of
establishing "baseline" or "average" conditions in any area requires a substantial data base and, in many
cases, sophisticated data manipulation and statistical analyses.
The inherent variability in water quality data and the multiple sources of that variability make the task of
explaining the data to the public or other users correspondingly more difficult. Such simple questions as, "Is
the water quality at this site good or bad?", or "What is causing the water problems here?", become much
more difficult to answer when one considers a data base large enough to show the true variability associated
with any of the sites or measurements. Nevertheless, it is possible to explain what typical values for a given
parameter at a given location should be when a data set is large enough, and to demonstrate what an unusual
value might indicate. We have had some success in pointing out anomalies in our data that subsequently were
confirmed to have been caused by unusual events in the field, such as waste spills.
Some of the variability in any given parameter can sometimes be better understood if other parameters
are measured simultaneously, as impacts on water quality often affect more than one parameter. However,
even our sampling protocol, which measures eleven parameters simultaneously, leaves some gaps.
Consequently, we have tried to identify or devise other methods of analyzing water quality, such as analysis
of detergents as a signal for human-derived wastewater and analysis of sediment-bound coliform bacteria as
an indicator of longer term fecal loading patterns. One measure for which we would like to find a standard
technique is macrophyte biomass, as very large portions of primary producer biomass in nutrient-loaded
surface waters appear to occur in this form. These thoughts lead to the conclusion that the monitoring and
research communities must continue to discuss information needs.
Acknowledgments
We thank the South Brunswick Water and Sewer Authority for its generous financial support of this
work. We thank Eric Cullum, Bryant Sykes, Chris Collura, Lynn Bullard, and Zhehong Ying for their
contributions to the project. Finally, we thank the duck lovers of Carolina Shores for keeping us on our toes.
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References
APHA. 1995. Standard methods for the examination of water and waste water. American Public Health
Association. Washington, D.C., A.E. Greenberg, ed.
Mallin, M.A., L.B. Cahoon, J.J. Manock, J.F. Merritt, M.H. Posey, R.K. Sizemore, W.D. Webster, and T.D.
Alphin. 1998. A Four Year Environmental Analysis of New Hanover County Tidal Creeks, 1993-1997.
UNCW Center for Marine Science Research Report No. 98-01, Wilmington, N.C.
Valderrama, J.C. 1981. The simultaneous analysis of total nitrogen and phosphorus in natural waters. Mar.
Chem. 10:109-122.
Welschmeyer, N.A. 1994. Fluorometric analysis of chlorophyll a in the presence of chlorophyll b and
phaeopigments. Limnol. Oceanogr. 39: 1985-1993.
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N
Water
Golf Course
Dense Development
Figure 1. Map of the South Brunswick Water and Sewer Authority (SBWSA) 201 Facilities Planning Area,
showing water quality monitoring locations in the Area's major drainage basins (Caw Caw, Calabash, and
Shallotte Rivers, and the estuarine areas associated with the Atlantic IntraCoastal Waterway (AICW) and the
Atlantic Ocean.
150
125 -
100 -
JZ)
i-
u
50 -\
25 -
0 -
• •
Figure 2. Plot of turbidity (NTU)
vs. total suspended solids
(nog/liter) data from 31 water
quality monitoring locations
during the period October, 1996 to
January, 1998.
0 50 100 150 200
Total Susp. Solids, mg/l
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10'
CO
10
10
-1
-2
'sii
hi1"!!
l
10
I T
i
30
15 20 25
Site
Figure 3. Plot of the log-transformed ratio of turbidity (NTU) to total suspended solids (mg/liter) for each of 36
monitoring locations in the SBWSA 201 Planning Area.
cn
E
cz
O
en
i2
5
10 -
5 -
i i i i i i f i i i i i
0 30 60 90120150180210240270300330360
Julian Days
Figure 4. Plot of dissolved oxygen (mg/liter) vs. Julian Days (ordinal days of the calendar) during the period
October, 1996 to May, 1998 for all 36 monitoring locations in the SBWSA 201 Planning Area.
III-215
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10"
105-
J2 1°4H
E
o ioj ^
0 102H
10l
• •
• •
V V
v
•v
Vlt
§
« VO
»V«VV«V
• vv
V • * 9
10 15 20 25
Sample sites
30
Figure 5. Plot of mean fecal
colifonn bacteria concentrations
(CFU/100 mis) for each of 31
monitoring locations during the
period October, 1996 to
November, 1997 during storm
event periods (open triangles) and
non-event periods (Oiled circles).
Note log scale.
N
&-
Water
f-^-y Golf Course
Dense Development
Figure 6. Map of the SBWSA Area showing monitoring locations with remarkable fecal coliform results.
in-216
-------
o
o
0
in
E
o
o
4-000
3600 -
3200 -
2800 -
24-00 -
2000 -
1600 -
1200 -
800 -
400 -
0 -
0 60 120 180 240 300 360 420
Days (1=March 25, 1997)
Figure 7. Plot of mean fecal coliform bacteria concentrations (CFU/100 mis) between March 25,1997 and May
25,1998 for locations 20 (near golf course), 27 (small basin with septic tanks), and 31 (downstream of septage
disposal site). The period between day 300 and day 400 had major el Nino rains.
N
Dense Development
Figure 8. Map of the SBWSA Area showing monitoring locations near golf courses.
m-217
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175 -
150 -
125 -
100 -
50 -
25 -
•I
••
• ••
T ^ ^""7
0 5 10 15 20 25 30 35
Site Number
Figure 9. Plot of turbidity (NTU) for all 36 monitoring locations during the period October, 1996 to May, 1998.
2500
2000 -
CO
2 1500
O
-E
Q.
CO
.0 1000
500 -
0 5 10 15 20 25 30 35
Site Number
Figure 10. Plot of total phosphorus (ng/Iiter) for all 36 monitoring locations during
the period October, 1996 to March, 1998.
III-218
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120 -f
CD
80 -
Q.
O
o
40 -
i ;
>• •
0 5 10 15 20 25 30 35
Site Number
Figure 11. Plot of chlorophyll a (^ig/liter) for all 36 monitoring locations during
the period October, 1996 to May, 1998.
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III-220
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Monitoring the Beneficial Impacts of CSO Control Implementation
Carol L. Hufnagel, P.E., Senior Project Manager
McNamee, Porter and Seeley, 220 Bagley, Suite 710, Detroit, MI 48226
Phone: (313) 964-0790; Fax: (313) 964-6957; E-mail: Hufncaro@mcnamee.com
Vyto P. Kaunelis, P.E., Director of Public Works
Department of the Environment, Wayne County, Michigan
Introduction
The Rouge National Wet Weather Demonstration Project was initiated in 1992 to identify and
implement measures to improve water quality in the Rouge River. The watershed approach included the
construction of 10 CSO retention treatment basins to control a portion of the CSO discharges. An
evaluation of the effectiveness of these facilities will assist in determining the design criteria for future
CSO control projects. The evaluation will help to identify the relative impacts of CSO versus stormwater
discharges, to further facilitate evaluation of various projects on a financial basis. Six CSO facilities are
currently in operation as of April, 1998, and the remainder will be operational in late 1998. This paper is
intended to describe the basin and supporting river monitoring studies and intended outcomes of the
evaluation study.
CSO Project Description
Project Background
The Rouge River watershed is located in southeast Michigan within Wayne, Oakland and
Washtenaw Counties. The City of Detroit and 47 other communities are located wholly or partially
within the watershed. Combined sewer systems are prevalent in much of the tributary area. Prior to
implementation of the Rouge Project there were approximately 59,300 acres of CSO service area, with
157 outfalls. The initial series of basin and separation projects will control or partially control
approximately one half of the service area and 83 of the outfalls.
The Michigan Department of Environmental Quality (MDEQ) established a definition of "adequate
treatment" for CSOs as part of the National Pollutant Discharge Elimination System (NPDES) permitting
program. Under this definition, CSOs must be eliminated through sewer separation or the construction of
basins capable of completely capturing the 1-year/1-hour storm and detaining the 10-year/1-hour storm
for 30 minutes. Among CSO communities, the level of required control was a major issue since the
estimated costs ranged from roughly $1 billion to $3 billion. Believing that a smaller level of control
would meet water quality objectives, the CSO communities contested the resultant proposed NPDES
permits.
Negotiations were conducted by a U.S. District Court-appointed monitor in an attempt to identify a
less costly first round CSO control program and avoid lengthy litigation over the permit requirements.
The negotiations led to a settlement document that was incorporated as a formal modification to the
disputed NPDES permits. The revised permits required each permittee, at selected CSOs, to construct
and evaluate varying sizes of CSO demonstration basins. A two-year time period was allotted to evaluate
the performance of these Phase I CSO control basins. Evaluation findings will establish the level of
control needed for the remaining CSOs. These controls will be implemented as Phase II of the project
prior to 2005.
m-221
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It is recognized that other sources are contributing to water quality problems in the river. Even with
control basins in place for all CSO outfalls, the Rouge River still will not meet all water quality
standards. Pollution sources include stormwater, septic systems, sediment, air deposition and others
which need to be controlled to attain desired uses in the river. Thus, evaluation efforts will also need to
improve the understanding of relative impacts caused by CSO versus other sources and what water
quality can be expected following implementation of CSO control projects.
CSO Control Objectives
Primary CSO objectives of the Rouge Project include control of Phase I CSO outfalls and the
determination of the level of control for remaining outfalls. A listing of these objectives follows:
1. Control or eliminate CSO discharges within the Rouge River watershed.
2. Test the performance of different CSO control technologies, including ten retention-treatment
basins and six sewer separation projects
3. Utilize performance data from Phase I facilities to establish the level of control necessary for
remaining uncontrolled CSOs in the watershed.
4. Identify how CSOs, urban stormwater, illicit discharges, septic systems and other sources can be
managed to most effectively achieve water quality protection goals.
CSO Projects Implemented
The location of CSO projects implemented in Phase I is shown in Figure 1. The Rouge River divides
into four branches, including the Lower, Middle, Upper and Main. Following the completion of Phase I
facilities, all CSOs on the Upper Main and a section of the Upper Rouge will be controlled. Other
segments of the river will be partially controlled.
Design criteria for CSO basin projects are identified in Table 1. A range in sizing criteria was
established as part of the permit negotiations. The range of sizing criteria results in basin sizing from
0.06" to 0.29" (in inches over the tributary area). Facilities also incorporate a variety of additional
features or variations in compartment sizing and sequencing in an effort to improve their effectiveness.
Monitoring Programs
Basin Monitoring Objectives
The primary goal for the basin evaluation study is to identify the level of control required for future
control projects. A series of objectives were developed to address this goal. These objectives are
identified in Table 2, along with an indication of an initial hypothesis to be tested, and the data which
will be collected for addressing the objective.
Basin Monitoring Data Sets
The data collected as part of the basin monitoring efforts are summarized in Table 3.
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River Monitoring
River monitoring is intended to address issues regarding the impact of CSO capture and treated basin
discharge. It will also show the stormwater impact on sections of the river upstream of the CSO areas. As
part of the river monitoring program, weekly monitoring of dissolved oxygen and bacteria in areas where
CSO controls are being implemented is being conducted. During CSO discharge events, and during
events where CSO basins fill but do not discharge, river monitoring will be conducted to provide a
picture of the river response to stormwater-only discharges, and stormwater/treated CSO discharges.
Samples will be collected upstream and downstream of the CSO facilities before, during and after the
events. Additional efforts will include identification of the total residual chlorine (TRC) plume at
instream sites during and following basin discharge.
River monitoring locations are identified in Figure 2. A summary of river sampling and monitoring
activities is provided in Table 4.
Program Implementation
Peer Review Process
As part of the basin evaluation study, a peer review committee assembled by the Water Environment
Research Foundation (WERF) is participating in a review of the project. The peer review committee is to
review the monitoring and study plan for completeness, evaluate its suitability to achieve goals, and to
maximize transferability between this project and other CSO control efforts. The peer review committee
met initially to review the study plan and again in October, 1997 to review the preliminary data set.
Current Status of Monitoring Program
Five CSO basins were placed into operation in 1997 and an additional basin was placed in operation
in 1998. The Inkster, Redford, Dearborn Heights and Acacia Park basins have been collecting data. The
Bloomfield Village and the Birmingham basins have been addressing various start up issues with respect
to accuracy and operation of metering and sampling equipment. Table 5 shows the operational and
evaluation status of these basins.
Routine river monitoring has been conducted since May 1, 1997. Significant wet weather events
occurred on July 2-4 and on September 9-11, 1997. Basin and river monitoring was performed for the
Inkster and Redford CSO facilities.
Implementation Challenges
Data sets are being compiled for the Inkster, Redford, Dearborn Heights and Acacia Park CSO Basins.
The Bloomfield Village and Birmingham basins are operational and are resolving some metering issues.
Sampled Events
Samples have been collected for a number of events since the beginning of the monitoring period.
The total number of events which have been sampled are provided in Table 6.
HI-223
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Data Availability and Discussion
Limited data from the evaluation basin monitoring and river monitoring are now available. These
data are primarily from the Inkster, Redford and Acacia Park facilities. In addition, river monitoring was
conducted during some of the basin overflow events. The river monitoring data from the July and
September events are also available.
Sharp transitory DO drops at the start of wet weather events caused by high initial CBOD
concentrations in uncontrolled CSOs were not observed downstream of the Redford or Inkster basins
discharge points for the July 2, 1997 or the September 10,1997 events. The CBOD concentrations in the
basin overflow are now in the range of 5 to 30 mg/L, instead of up to several hundred mg/L previously,
and overflows are delayed until more instream flow is available for dilution. Figures 3 and 4 indicate that
annual CBOD contributions (Ibs/acre) from the untreated Redford CSOs were nearly triple that of
upstream stormwater runoff, while the treated Redford CSO basin effluent now contributes about half as
much as upstream stormwater runoff.
Gradual sags in DO during wet weather due to the combined effects of low pre-event DO and BOD
contributions from stormwater runoff were observed in the vicinity of the Redford basin for the July 2,
1997 event. At the first monitoring location downstream of the basin the DO dropped below 5 mg/L for
several hours during the event. However, the DO impairment occurred hours after the treated basin
effluent would have passed that monitoring location, so it appears that upstream stormwater inputs were
the primary cause of the DO sag. To be certain, continuous DO data downstream of the basin would be
need to be monitored.
Dry weather DO impairments in CSO controlled areas are expected to improve over a period of
years, but some degree of dry weather impairment is expected to remain. These dry weather impairments
in the CSO impacted areas are primarily caused by high sediment oxygen demand (SOD) and low
reaeration due to naturally flat river bed slopes. The primary contribution to the SOD problem is the
discharge of oxygen-demanding, settleable solids by uncontrolled CSOs; but decaying plant material,
stormwater runoff and other unidentified sources also contribute. Results to date have shown a
significant reduction in the settleable solids discharged.
The dry weather DO impairments upstream of the CSO impacted areas are expected to continue.
However, these impairments are typically less severe than in the CSO impacted areas.
The summer of 1998 will be a primary time frame for evaluating the impacts of CSO basin
discharges on instream water quality. During this period, CSO controls will have been completed on
localized reaches of the river. Extensive dissolved oxygen and water chemistry monitoring will be
performed during the warmer weather months of May through October.
Table 7 summarizes the yearly influent flow and effluent flow frequency, volume and duration for
the Inkster, Redford and Acacia Park basins. The monitoring period for the Dearborn Heights Basin
began in September of 1997; therefore, the table reflects only seven months of data at the Dearborn
Heights Basin. As can be seen in the table where CSO basins are in place most rain events have no
discharge to the river.
Samples collected during the evaluation monitoring provide information regarding the range in
concentrations. These data provide some insight into the concentrations observed at the influent and
effluent of the CSO basins. These data are summarized in Table 8.
Table 9 presents the pollutant load removals at the Inkster, Redford, Dearborn Heights and Acacia
Park basins. CSO basins are effectively removing the majority of pollutants which were previously
reaching the river.
ffl-224
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Bacteria contributions from CSOs to the receiving water have been completely eliminated for many
of the wet weather events, because the CSO basins do not overflow for the many smaller events. When
CSO basins do overflow, public health is protected by the disinfection that occurs in the basins. In 1997
the bacteria levels in the effluent of the Inkster, Redford and Acacia Park Basins have been controlled.
That is, the event geometric mean concentrations have consistently fallen below the Michigan
Department of Environmental Quality permit limits of 400 colony forming units (cfu) per 100 milliliters.
Limit is defined as the numerical value established in the NPDES permit that must be met before there is
a violation. There were no effluent events at the Dearborn Heights basin in 1997.
Conclusions
Data are currently being compiled to identify the level of control required for future facilities.
Included within this data set are event volume, frequency and duration and pollutant concentrations. Data
are also being collected at the facilities to compare the efficiency of varying operational modes, to
evaluate dewatering and decanting capabilities and to evaluate the effectiveness of disinfection and the
presence of residual chlorine.
Routine river monitoring includes continuous DO and temperature measurements and periodic
sampling for bacteria. Monitoring for wet weather events includes DO, temperature, and bacteria as well
as additional parameters. In addition, during overflow events the river is monitored for total residual
chlorine.
Limited data are available through March, 1998. As additional data are collected, they will be
evaluated relative to the basin monitoring objectives.
m-225
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ROUGE RIVER WATERSHED
CSO Drainage Area After Phase I
QAKPAIK J
1 i"~Tl reHN
/gjs3/map/gra/asize/awshed/awcsopla.gra 0&23/98
LEGEND
Watershed boundary AP -Acacia Park retention basin
Major branches BV-Bloomfield Village retention basin
Tributaries „ . . .
Community boundaries B -Birmingham retention basin
"" County boundaries DH-Dearborn Heights retention basin
^| rjso areas separated ^ -Hubbell-Southfield retention basin
»i'i I -Inkster retention basin
CSO areas controlled by basins PF-Parftan^ken retention basin
CSO areas remaining after Phase I R -Redford Township retention basin
CSO retention treatment basins SM-Seven Mile retention basin
RR-River Rouge retention basin
Figure 1. CSO drainage area after Phase I.
m-226
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ROUGE RIVER WATERSHED
Monitoring Locations for CSO Basin Evaluations
OAKPARK T" '"
|^| FERNDALE
LEGEND
Watershed boundary
Major branches
Tributaries
Community boundaries
County boundaries
DT - Dearborn Tunnel
Combined sewer
drainage areas
CSO retention treatment basins
CSO basin monitoring locations
G-Grab
C -Continuous / Automatic (temporary)
P -Continuous / Automatic (permanent)
AP-Acacia Park retention basin
BV-Bloomfield Village retention basin
B —Birmingham retention basin
DH-Dearbom Heights retention basin
HS -Hubbell-Southfield retention basin
I -Inkster retention basin
PF -Puritan-Fenkell retention basin
R -Redford Township retention basin
SM-Seven Mile retention basin
RR-River Rouge retention basin
/gis3/map/gra/asize/awshed/awcseva].gra 02/09/98
Figure 2. Monitoring locations for CSO basin evaluations.
ffl-227
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Q
O
PQ
U
140,000
120,000
100,000
80,000
60,000
40,000
20,000
0
^Upstream
Stoimwater
•CSO Prior to
Control
E CSO After Control
^Upstream
Stormwater
• CSO Prior to
Control
E CSO After Control
Figure 3. Upper Rouge River annual estimate
of CBOD loads, Ibs.
Figure 4. Upper Rouge River annual estimate
of CBOD loads, Ibs/acre.
m-228
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Table 1. Rouge River CSO Detention Projects Summary Data
Basin Name
& Location
Birmingham
Hubbell-
Southfield
Puritan-
Fenkell
Seven Mile
River Rouge
Redford, MI
Inkster, MI
Dearborn
Heights, MI
Acacia Park
Bloomfield
Village
Compartmental
2 compartments
Total volume=5.5 MG
1st basin- 10 MG
2nd basin - 12 MG
Total =22 MG
2 compartments
each = 1 .4 MG
Total = 2.8 MG
2 compartments
each = 1.1 MG
Total = 2.2 MG
2 compartments upper and lower
Total = 2
2 parallel compartments
each 0.9 MG
Total=1.9MG
1st flush compartment - 1.1 MG
2 detention compartments
each = 1 MG
Total = 3.1 MG
3 compartments, each -0.9 MG
Total = 2.7 MG
2 compartments
Total Volume = 4 MG
3 compartments
Total volume = 10MG
Basin Configuration
2 compartments in series with
1 1' tunnel
2 tanks in series with the
capability of running the 1st
basin as 1st flush capture tank
2 tanks operating in parallel
2 tanks operating in parallel
2 compartments in series
lower compartment fills first
then the upper
2 parallel compartments
preceded by a swirl concentrator
1 first flush tank followed by 2
detention tanks operating in
parallel
3 detention tanks in parallel
with the capability of using the
1st tank for a 1st flush capture
2 compartments in series
3 compartments filling series
through different elevation weir
Combined
Drainage
Area
1175
14400
649
463
929
551
833
340
816
2325
Basin Dimensions
140'xl20'x20'
each compartment
900'x240'xl6.5'
Overall Basin
236'x99.5'x8'
each compartment
200'x91.5'x8'
each compartment
lower 135'dia. 46.2' deep
upper 135' dia. 21.8' deep
180'x66'xll.2'
each compartment
186'x60'xll.75'
each detention tank
175'x60'xll.6'
each compartment
160'x80'x20'
each compartment
157.5'xl28.5'x20'
each compartment 1
Design Storm
one year - one
hour storm
Built within site
constraints
one year - one
hour storm
one year - one
hour storm
ten year - one
hour storm
one year - one
hour storm
one year - one
hour storm
ten year - one hour
storm
one year - one
hour storm
one year - one
hour storm
Detention
Time
30
20
30
30
20
20
30
30
30
Inches Over
Drainage
Area
0.17"
0.06"
0.16"
0.18"
0.21"
0.13"
0.14'
0.29"
0.18"
0.16"
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Table 2. Goals and Data Requirements for the CSO Demonstration Projects
Goal
Hypothesis to be Tested
Data Required
Establish sizing criteria/design criteria
for later facilities
Basins smaller than current state criteria can provide pollutant load
reductions necessary to achieve water quality goals. Identification of
"best" basin size will need to be determined relative to total flow
generated, quality of flow, and other influent factors. A range in sizes
may be recommended dependent on flow and load based
characteristics.
Influent/effluent flow (event totals); influent and effluent load
of key parameters; number of events; rainfall data to identify
what triggers event; river loads
2. Quantify changes in loading to the
Rouge (i.e., before and after control
CSO control projects)
Basins as designed/constructed will result in significant annual
reductions in CSO loads to the river.
Separation may result in increased loads to the river in areas where
the collection system previously had good capacity.
Influent load for all wet weather events which result in flow
to basins; effluent loads for all discharge events;
corresponding precipitation data; in separation areas, selective
monitoring of new stormwater outfalls to collect flow volume
and quality data
3. Following implementation of CSO
controls, identify remaining instream
water quality impairment which can be
attributed to CSO discharge.
Temporal dissolved oxygen sags will no longer occur where CSOs are
controlled. Bacterial levels will improve. Long term dissolved oxygen
will still be impaired.
Instream water quality monitoring data at selected locations
(i.e., dissolved oxygen, other key parameters); model results
(calibrated)
4. Identify "better design" methodology.
(i.e., should basins be equipped with
first flush tanks, swirl concentrators,
shunt channels?). Provide recommen-
dation on how to configure a CSO
basin. Identify "treatment" capability of
flow through basin operation.
Improved retention facility design may result in improved load
reduction over storage volume alone, and may provide equivalent
removals to larger facilities.
Basin effectiveness (% removal during flow through) will be
quantifiable.
Compare effluent quality of basins with swirls, first flush
tanks or shunt channels to those without; compare similar
events at basins which can operate in different modes; prepare
evaluation plan
Samples sufficient to determine basin influent and effluent
loadings for a range of storms and operating conditions;
discrete samples
5. Identify distribution of CSO load
entering containment/treatment
structure (pollutograph, etc.).
Load will be more concentrated at the beginning of storm event.
"Small" events (i.e., those completely captured) account for
significant loads.
Collect discrete samples over portions of the influent event
(this could likely be either time or flow paced, but would need
to be evaluated at each facility); samples need to be able to
define the pollutograph shape (load distribution)
6. Identify proportion of CSO flow
captured by collection system / basins.
Flow captured by the collection system will be significant, and may
equal or exceed the amount captured in basins. Total flow captured
will be approximately 80 percent or greater on an annual basis
(except for Hubbell - Southfield).
Assess flows in collection system (at connection to interceptor
or other suitable location) with available monitoring
capabilities (may not be possible at all facilities); monitor
basin flows (continuous record
(continued)
-------
Table 2. Goals and Data Requirements for the CSO Demonstration Projects (continued)
Goal
Hypothesis to be Tested
Data Required
7. Identify ability to add additional CSO
tributary area to existing CSO basins
as an interim/final control measure.
Ties closely with goal #1 regarding basin sizing.
Same data as identified in #1
Develop cost versus benefit
comparisons for constructed facilities.
Per gallon cost of facilities is greater in smaller facilities. The benefit
provided by smaller, as opposed to larger facilities, in terms of load
reduction, number of discharge events or frequency of water quality
criteria exceedences is also less. A "most cost effective" size will
need to assess the relative costs and benefits of various options.
Cost data; data developed and analyzed in other sections
to
Address other issues, e.g.:
a. Decanting. Define quality of
decant water. Define best timing to
discharge decant water, i.e., river
conditions.
b. Dewatering. Identify ability to
dewater facilities.
c. Chlorination/dechlorination.
Define chlorine plume (if any).
Define biological river impacts.
a. Decanting can be performed under some conditions which will not
cause detrimental river impacts.
b. Dewatering can generally be performed prior to the next event.
Dewatering will result in continued high flows in the downstream
system.
c. Chlorine impacts on the stream are minimal or localized. Residual
plume may be quantifiable and measurable downstream of the
basin (during discharge). NAOC1 dosing can be planned prior to
events to minimize residual impacts.
a. Monitor ability to dewater, including WWTP flow
conditions and local flow conditions; hourly sampling of
basin contents for a period of 4-5 hours once influent has
stopped
b. Monitor ability to dewater, including WWTP flow
conditions and local flow conditions
c. Instream sampling at 3 or more locations (such as bridge
crossings) for minimum of four storm events; record of
NaOCl usage, influent chlorine demand and effluent
coliform (minimum four events resulting in discharge)
10. Review volumetric sizing issues, to
determine if the basins were sized
consistent with their design objective,
or rather if they are larger or smaller
than required for the design criteria.
Basins are likely sized conservatively. Volume as provided is
sufficient to provide design detention time required for the design
storm.
compare basin CSO volume versus rainfall
11. Identify impacts of separation versus
retention in terms of annual and event
impacts.
Separation results in increased loads; however, impact of these loads
on the receiving stream is limited. Separation results in no sanitary
bypass if properly implemented, and sanitary flows fall within
predicted post separation flow range.
data (or model study) of pre-/post-flow volumes in newly
separated areas; monitoring of flow and sampling at selected
stormwater discharge locations; flow monitoring of upstream
portions of the collection system
12. Other items.
Basement flooding is not worsened by the basin/separation project.
Basins will successfully remove floatables. Innovative features
contribute to the effectiveness of the facility.
basement flooding complaint records; visual inspection by
operators; oil and grease samples of basin effluent; data
regarding innovative features
-------
Table 3. Basin Monitoring Data Set
Data Set Objective
Number and description of events
Pollutant load quantification
Pollutant concentration variability
Evaluation of varying operational
plans
Evaluation of dewatering,
decanting
Effectiveness of disinfection and
presence of residual chlorine
Data Collected
Volume, duration and frequency of influent and effluent
Determination of influent load for a majority of events, including captured
events; determination of effluent load for all events. Primarily for CBOD,
TSS, NH3, TP. Secondary interest includes metals, alkalinity, hardness,
soluble CBOD and bacteria.
Identification of pollutant concentration variability at influent and effluent for
a minimum of 10 events, with additional events monitored if required. The
parameters sampled at a frequency to determine variability include: CBOD,
TSS, NH3, TP.
Comparison of efficiency of varying operational modes, e.g. first flush versus
flow through, swirl concentrator followed by basin versus basin only.
Identification of duration to dewater (to the treatment plant), quality of decant
(potential discharge of settled basin contents to river).
Effluent and instream monitoring for bacteria and residual chlorine.
Table 4. River Monitoring Data Set
Data Set Objective
River recovery - long term
dissolved oxygen
Public health conditions
Wet weather river response
Total residual chlorine
Data Collected
Routine monitoring of dissolved oxygen levels at instream locations upstream
and downstream of CSO facilities and tributary areas. Continuous recording
with dissolved oxygen/temperature probes. Continuous flow record. Sediment
oxygen demand.
Periodic sampling for bacteria in the vicinity of CSO basins. Sampling
upstream and downstream of the facilities during dry and wet weather.
Water quality measurements upstream and downstream of CSO basins and
outfalls before, during and following events. Some events will be sampled
during basin overflow conditions. Other events which cause flow to reach the
CSO basin facility will be sampled, as these represent events which would
have previously resulted in CSO discharge to the river. Sampling will include
CBOD, BOD, TSS, NH3, TP, dissolved oxygen, temperature and bacteria.
Identification of TRC plume downstream of CSO basins during discharge.
Intent is to identify extent, concentration and duration of TRC impact.
IH-232
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Table 5. Rouge River CSO Detention Projects Operational
and Evaluation Status
Basin Name
Inkster, MI
Redford, MI
Dearborn Heights, MI
Acacia Park, MI
Bloomfield Village, MI
Birmingham, MI
Status
Basin in operation 1/97
Evaluation 6/97
Basin in operation 1/97
Evaluation 6/97
Basin in operation 6/97
Evaluation 9/97
Basin in operation 1/97
Evaluation 8/97
Basin in operation 7/97
Evaluation 1/98
Basin in operation 4/98
Table 6. Basin Monitoring through March 1998
Facility
Inkster
Redford
Dearborn Heights
Acacia Park
Influent events
22
15
4
5
Discharge events
6
5
2
3
Table 7. Rouge River CSO Basin Influent and Effluent Frequency, Volume and Duration
Facility
Inkster1
Redford1
Dearborn Heights2
Acacia Park3
Influent
Frequency,
No./ Year
40
25
9
35
Volume,
MG
130.36
66.13
57.91
68.14
Duration,
HH:MM
335:40
319:55
178:05
327:36
Effluent
Frequency,
No./ Year
7
7
3
3
Volume,
MG
65.22
39.39
41.32
29
Duration,
HH:MM
132:10
161:10
113:30
100:17
Notes: 1 4/97 - 3/98
2 9/97 - 3/98, 12 months of data not yet available
3 5/97 - 4/98
m-233
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Table 8. Quality Concentration Data
1997 - March 1998 Monitoring
Facility
Inkster
Redford
Dearborn
Heights
Acacia
Park
Bloomfield
Village
BOD
Influent
(mg/L)
4-101
9-66
NA
10-119
10-114
Effluent
(mg/L)
2-48
2-21
NA
7-97
6-43
CBOD
Influent
(mg/L)
18-70
17-130
10-45
30-52
NA
Effluent
(mg/L)
2-25
12-33
14-43
6-38
NA
TSS
Influent
(mg/L)
28-373
24-781
38-204
14-183
29-256
Effluent
(mg/L)
47-194
24-158
40-104
20-129
52-143
NH3
Influent
(mg/L)
1.00-
8.80
2.30-
11.70
0.80-
5.50
0.06-
0.90
0.11-
2.11
Effluent
(mg/L)
0.22-
3.65
1.30-
4.79
1.60-
4.04
0.07-
0.19
0.15-
0.32
TP
Influent
(mg/L)
0.58-
1.90
0.85-
3.10
0.54-
1.50
0.25-
0.98
0.45-
1.61
Effluent
(mg/L)
0.47-
1.00
0.63-
1.23
0.58-
1.10
0.23-
0.79
0.49-
0.61
Note: Ranges presented reflect the 10th and 90th percentiles.
Table 9. Pollutant Load Removal
1997 - March 1998
Estimated Percent Load Removal
Basin
Inkster
Redford
Dearborn Heights
Acacia Park
CBOD
75%
64%
49%
94%
TSS
67%
77%
68%
74%
NH3
84%
53%
35%
88%
PHOSJT
63%
55%
53%
74%
Volumetric
Reduction
48%
40%
25%
51%
m-234
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History, Goals and Redesign of New Jersey's Ambient
Ground Water Quality Network
Michael E. Serfes, Hydrogeologist
New Jersey Geological Survey
Abstract
New Jersey is a hydrogeologically diverse, densely populated and highly industrialized state.
Successful management of the State's ground-water and surface-water resources requires an effective
monitoring program. The Ambient Ground Water Quality Network : a cooperative New Jersey Geological
Survey and United States Geological Survey project, was started in 1982. Since that time 364 regional
network wells, currently 22 wells yearly, have been sampled for: field parameters, major ions, nutrients,
trace elements, radioactivity, and volatile organic compounds. Goals for the network (established in 1986)
are to: 1. Determine the chemical concentration ranges of ground-water constituents within and between
rock types, 2. Determine the geochemical reasons for any differences, and 3. Determine long term trends
in ambient quality by resampling using an 8- to 20- year cycle.
The first two goals were accomplished by 1995 mainly by sampling wells unaffected by known point
sources. During 1996 and 1997, a pilot watershed network design guided sampling to address the third
goal. In 1997, it was decided to redesign New Jersey's surface and ground-water quality networks to
address current information needs.
Goals of the new ground-water network are: 1. To assess the water-quality status, 2. To assess water-
quality trends, 3. To evaluate transfer relations, 4. To identify emerging issues. To accommodate these
goals the network will consist of 150 water-table wells, located using a stratified-random site selection
approach. Statewide distribution of the 150 wells will be: 60 in agricultural areas, 60 in urban/suburban
areas, and 30 in undeveloped land use areas.
Introduction
New Jersey is the fifth smallest state in the nation yet is one of the most hydrogeologically diverse
(figure 1). Approximately 8 million people live within New Jersey's 7,836 square miles making it the
most densely populated state in the nation. Highly concentrated urban and industrial centers, shrinking
agricultural and undeveloped areas, expanding suburban areas and protected and unprotected undeveloped
areas generally characterize the State's land use (figure 2). Because of the high population and variable
land uses, the State's streams, lakes, ponds, bays, ocean and ground water are impacted to varying
degrees by point and non-point sources of pollution. To understand and properly manage the quality of
water in the state it is necessary to establish effective monitoring programs. One such program is New
Jersey's Ambient Ground Water Quality Network (AGWQN).
Network Background and History
New Jersey's AGWQN is a cooperative program between the New Jersey Department of
Environmental Protection (NJDEP) and United States Geological Survey (USGS) and is funded under
section 106 of the Federal Clean Water Act, the NJDEP and a USGS fund match. It is part of the
NJDEP/USGS cooperative Water Quality Surveillance Network that originally only involved monitoring
surface-water quality and ground-water levels. The ground-water quality network was started in 1982 in
response to the growing need for adequate ambient ground-water-quality data by regulators. From 1982 to
III-235
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1997, 364 regional network wells have been sampled (Figure 3). A list of the current physical and
chemical parameters analyzed are given in table 1. These include:
1. Field parameters
2. Major ions
3. Nutrients
4. Trace elements
5. Radioactivity
6. Volatile Organic Compounds
The field sampling and analytical procedures and protocols follow the QA/QC guidelines used by the
USGS.
During the first few years an intensive ground-water-quality assessment was conducted in the
Ramapo and Rockaway glacial valleys in northern New Jersey at the same time regional sampling was
conducted in the Coastal Plain of southern New Jersey. In 1986 a lack of data in the northern bedrock
portion of the state was acknowledged and became the focus of subsequent sampling. Finalized goals for
the network were also established during that time. Those goals were to determine:
1. The chemical ranges of ground-water constituents within and between rock types.
2. The geochemical reasons for the differences observed
3. Long term trends in ambient quality by resampling using an 8 to 20 year cycle.
From 1987 to 1995, 210 wells were sampled in northern New Jersey. Of those, 165 were in fractured
bedrock and 45 were in glacial and other unconsolidated surficial materials (Serfes, 1994 & 1998). Data
from the bedrock portion of the Newark Basin can be found at http://www.state.nj.us.dep.njgs.
In meeting the first two goals above, wells were selected so that they would not be influenced by
known point sources of pollution. Wells down gradient from pollution sites were avoided. Thus the
sampling is biased toward natural ground-water quality. Determining the exact recharge area for deep
wells in fractured bedrock and stratified glacial deposits is very complex and beyond the scope of the
network. Therefore, non-point source pollution from various land uses was not considered in the well
selection process. Some samples did however contain nitrate concentrations above background (
-------
4. To identify emerging issues (ex: mercury in ground water).
The committee also agreed on several objectives. These are:
1. To evaluate land use impacts to ground water.
2. To accommodate watershed assessments at the Watershed Management Region level.
3. To fulfill a National Performance Partnership System (NEPPS) commitment to evaluate ambient
ground-water quality at the water table in New Jersey.
New Jersey's watershed management areas and regions are shown in figure 3. The proposed network
addresses the new network goals and objectives, and is designated the Ambient Water Table Quality
Monitoring Network.
Network Design
The quality of ground water at the water table is generally the first and most significantly impacted
portion of the ground-water system. Because the hydraulic connection with the land surface is generally
vertical, land use activities should directly correlate with ground-water quality at the wells. Determining
the exact recharge areas and time of travel for deeper wells is generally much more difficult.
The proposed Ambient Water Table Quality Monitoring Network consists of 150 water-table wells
randomly distributed throughout the state within 3 broad land use stratification's (figure 4). The stratified-
random site selection process was conducted using a method outlined in Scott (1990). The 150 wells will
be in agricultural lands (60), urban/suburban lands (60) and undeveloped lands (30). This network will
also serve as an early warning system for identifying emerging threats to ground water quality.
Preliminary work by the USGS has shown that a random statewide site selection process based on
1986 land use, yields an approximately equal area weighting of sites for the 3 land uses within each of the
State's 5 Watershed Management Regions (figure 4).
Shallow wells will be installed unless suitable ones exist at the random sites selected. Approximately
30 shallow wells will be sampled each year in one of the five Watershed Management Regions.
Therefore, the network will be completely sampled every 5 years.
The current list of physical and chemical parameters analyzed for the network samples will be
reviewed and revised as appropriate. Also, the field criteria for siting wells, such as the acceptable percent
of the target land use, site access concerns, and monitor well construction, need to be determined.
Network Design and Goal Achievement
Goal 1: Status
The status of the water-table ground-water quality in New Jersey will be determined using various
indicators (for example, nitrate concentration). Nonparametric statistical measures such as the median and
other percentiles, will be determined using the indicator concentration data. Confidence intervals for these
percentile values will be determined using the method outlined in Spruill and Candela (1990).
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Goal 2: Trends
One of the 5 Watershed Management Regions will be sampled each year. Therefore, the entire
network, and individual WMRs, will be sampled on a 5 year cycle. After at least two cycles, water quality
trends can start to be developed.
Goal 3: Transfer Relations
The ability to determine a cause and effect for water-quality concerns is very important. By sampling
wells screened just below the water table, a direct hydraulic connection to the overlying land surface can
usually be made. Activities at the land surface can be assessed for causal effects.
Goal 4: Emerging Issues
The quality of ground water at the water table is generally the first and most significantly impacted
portion of the ground-water system. New threats to ground-water quality from non point sources of
pollution should be detectable there first. It is also a good place to start looking for compounds that are
newly identified or are of recent concern in the environment. Examples are the controversial endocrine
disrupting compounds and MTBE.
Acknowledgements
Appreciation is extended to the USEPA for funding, and all persons from the NJDEP and USGS that
were involved in establishing and maintaining, the Ambient Ground Water Quality Network. Special
thanks go to those from NJDEP and USGS currently involved in the redesign process, particularly USGS
staff: Mark Ayers and Eric Vowinkel for their technical advice and Paul Stackelberg for that and
producing the stratified random well-site maps .
References
Scott, J.C., 1990, Computerized Stratified Random Site-Selection Approaches for Design of a Ground-
Water-Quality Sampling Network, U.S. Geological Survey, Water-Resources Investigations Report
90-4101.
Series, M.E., 1994, Natural Ground-Water Quality in Bedrock of the Newark Basin, New Jersey. New
Jersey Geological Survey , Geological Survey Report GSR 35, p. 28.
Serfes, M.E., in press, Ground-Water Quality in Bedrock Aquifers of the Highlands and Valley and Ridge
Physiographic Provinces of New Jersey. New Jersey Geological Survey, Geological Survey Report
GSR.
Spruill,T.B., Candela, L., 1990, Two Approaches to Design of Monitoring Networks. Ground Water .
v. 28, no. 3, pp. 430-442.
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75°
74°
DRAFT
40°
39°
Generalized Aquifer
Map of New Jersey
0 8 16 24ml
modffiedfrom 1:100.000 scde
ago COGEOM^P dota m
nod83 state-plcne-coordnate feet
Figure 1. Generalized aquifer map of New Jersey with aquifer descriptions.
IH-239
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o
Q
Q
Q
EC
LLJ
DESCRIPTION OF AQUIFERS /WD CONFINING UNITS
Peistocene Glacial Sediments - Till and stratified deposits of sand, gravel, and lake-bottom sediments that exceed 50 ft thickness. Inlcudes glacial fans or
deltas, fluvial outwash, ice-contact and morainal deposits. Primary intergranular porosity and permeability. Average yield of high-capactity wells in sands
and gravels approximately 525 gpm. Ground-water quality varies with the sedimentary texture and mineral content Average ground water is fresh, slightly
alkaline, moderately-hard to hard, and of the calcium-bicarbonate type.
Tertiary Sand and Gravel Aquifers near Cape May - Sand and gravel deposits of the Beacon Hill Gravel, Bridgeton Formation, and Cape May Formation.
Water-table aquifers. Primary intergranular porosity and permeability. Average yield of high-capacity wells in Cape May Formation is approximately 220 gpm
Ground-water quality is similar to the unconfined portions of the Kirkwood-Cohansey aquifer system.
Tertiary Kirkwood-Cohansey Aquifer System - Sand, gravel, and clay. Includes water table aquifer, confined Kirkwood aquifers (Atlantic City 800-foot sand
aquifer and Rio Grande water-bearing zone), and confined Cohansey aquifer in Cape May County. Primary intergranular porosity and permeability. Leakage
to confined parts provides water. Average yield of high capacity wells is approximately 390 gpm Average ground water is fresh, acidic, highly corrosive, and
has low dissolved solids. Less corrosive water is common in confined aquifers. High iron and magnanese problems locally. Salinity may be elevated in
confined parts near coastal areas. Sodium chloride type water is common.
Cretaceous Mount Laurel-Wenonah Aquifer - Glauconitic sand overlying micaceous sand. Unconfined in outcrop and confined in the subsurface. Primary
intergranular porosity and permeability. Leakage to confined parts provides water. Average yield of high-capacity wells is approximately 200 gallons per
minute. Average ground water is fresh, moderately hard and alkaline. Iron levels may be elevated. Calcium and magnesium levels decrease with depth while
sodium and potassium levels increase. Calcium-bicarbonate type waters dominate.
Cretaceous Englishtown Aquifer System - Upper and lower sand with localized clay beds. Unconfined in outcrop and confined in the subsurface. Primary
intergranular porosity and permeability. Leakage to confined parts provides. Average yield of high-capacity wells approximately 290 gpm. Average ground
water is fresh, moderately hard and is alkaline. Salinity, sodium, and potassium levels increase with depth. Calcium and magnesium levels decrease with depth.
Locally elevated Iron and manganese levels. Calcium-bicarbonate type waters dominate.
Cretaceous Potomac-Raritan-Magothy Aquifer System - Interbedded sand, gravel, silt, and clay separated into lower, middle and upper aquifers. Unconfined in
outcrop and confined in the subsurface. Primary intergranular porosity and permeability. Leakage to confined parts provides water Average yield of
high-capacity wells 600 gpm. Average ground water is fresh, moderately hard with a near-neutral pH. Salinity increases towards the coastline near Delaware
and Raritan Bays. Elevated iron and manganese are common. Calcium and magnesium levels decrease and sodium and potassium levels increase with depth.
Calcium-bicarbonate type waters dominate.
Confining Units of Cretaceous and Tertiary Age - Composed of silt, clay, and thin layers of sand. Includes the stratigraphic interval from the Shark River Fm.
to the Navisink Fm., the Marshalltown Fm., and the the combined Woodbury, Merchantville, and Cheesquake Fms. The confining units locally contain
important sandy aquifers such as the Red Bank Sand, Tinton Sand, Vincentown Formation, Shark River Formation, and Piney Point aquifer. Ground-water
quality is generally good, but may locally require quality treatment.
Jurassic Basalt - Hard, dense, and highly-fractured igneous rocks. Secondary fracture porosity and permeability. Average yield of high-capacity wells 90 gpm.
Ground-water quality data is sparse but normally fresh, somewhat alkaline, moderately hard, and of the calcium-bicarbonate type.
Jurassic Diabase - Hard and dense igneous rocks. Secondary fracture porosity and permeability. Few high capacity wells. Ground-water quality data is sparse
but normally fresh, somewhat alkaline, moderately hard, and of the calcium-bicarbonate type.
Jurassic-Triassic Brunswick Aquifer System - Sandstone, siltstone, and shale of the Passaic, Towaco, Feltville, and Boonton Formations. Secondary fracture
porosity and permeability Average yield of high capacity wells approximately 200 gpm. Average ground water is fresh, slightly alkaline, non-corrosive and
hard. Calcium-bicarbonate type waters dominate. Subordinate calcium sulfate waters are associated with high total dissolved solids. Inlcudes conglomerate of
Jurassic-Triassic age along the Newark basin border fault system.
Triassic Lockatong Formation - Silty anjillite, mudstone and fine-grained sandstone and siltstone with minor limestone. Secondary fracture porosity and
permeability. Average yield of high capacity wells approximately 50 gpm. Average ground water is fresh, slightly alkaline, noncorrosive and hard. Calcium
bicarbonate type waters dominate.
Triassic Stockton Formation - Arkosic sandstone. Secondary fracture porosity and permeability Average yield of high capacity wells approximately 200 gpm.
Average ground water is fresh, slightly acidic, corrosive and moderaterly hard. Calcium-bicarbonate type waters dominate.
Devonian Formations - Sandstone, shale, conglomerate and limestone in the Valley and Ridge and Green Pond ML Region. Stratigraphic interval from the
Rondout Fm. and Connelly Conglomerate to the Marcellus Shale and Skunnemunk Conglomerate. Secondary fracture porosity and permeability. Few high
capacity wells. Yields best in bottom carbonates. Ground-water quality is very good for most purposes, however, treatment may locally be required for
parameters such as hardness, iron and manganese.
Silurian Formations - Conglomerate, sandstone, siltstone and shale in the Valley and Ridge and Green Pond Mt Region. Stratigraphic interval from the the
Shawangunk Formation and Green Pond Conglomerate to the Roundout and Bershire Valley Limestone. Secondary fracture porosity and permeability. Few
high capacity wells yielding approximately 35 gpm in Bloomsburg Red Beds. Ground-water quality is very good for most purposes but may locally exceed
hardness, iron, and manganese parameters.
Ordovician Martinsburg Formation - Claystone slate, siltstone and sandstone. Secondary fracture porosity and permeability. Water stored and transmitted in
fractures. Average yield of high capacity wells approximately 50 gpm. Average ground water is fresh, slighltly alkaline, noncorrosive, and moderately hard.
Calcium-bicarbonate waters dominate.
Cambrian-Ordovician Carbonates - Dolomite and limestone with minor shale, sandstone, and quartzite. Sequence includes the Jacksonburg Limestone,
Kittatinny Supergroup, and Hardyston Quarzite. Secondary fracture porosity and permeability with solution enhancement of both bedding-plane and
non-bedding fractures. Average yield of high capacity wells approximately 250 gpm. Average ground water is fresh, slighltly alkaline, noncorrosive and hard.
Calcium-magnesium-bicarbonate type waters dominate.
Lower Paleozoic and Precambrian Igneous and Metamorphic Rocks - Gneiss, marble, and granite of the Reading and Trenton Prongs. Secondary fracture
porosity and permeabilitySolution channels occur in marble. Average yield of high capacity wells approximately 85 gpm. Average ground water is fresh,
slighlty acidic, corrosive and moderately hard. Calcium-bicarbonate type waters dominate.
Figure 1 (continued).
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GENERALIZED LANDUSE
IN NEW JERSEY (1986)
| Urban
£3 Agricultural
Undeveloped
Water bodies and wetlands
Watershed Management Regions
Figure 2. Generalized landuse map of New Jersey based on 1986 data.
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O Ground-Water Monitoring Stations
I I Watershed Management Regions
I | Watershed Management Areas
1 Upper Delaware River
2 Wallkill, Pochuck, Papakating
3 Pompton, Pequannock, Wanaque, Ramapo
4 Lower Passaic, Saddle
5 Hackensack River, Pascack
6 Upper Passaic, Whippany, Rockaway
7 Elizabeth, Rahway Rivers, Woodbridge
8 North & South Branch Rarrtan
9 Lower Raritan, South River, Lawrence Brook
10 Millstone River
11 Central Delaware River Tributaries
12 Monmouth Watersheds
13 Bamegat Bay Watersheds
14 Mullica, Wading River
15 Great Egg Harbor, Tuckahoe
15 Cape May Watersheds
17 Maurice, Salem, Cohansey
18 Lower Delaware Tributaries
19RancocasCreek
20 Crosswicks Creek
N
A
10
0 10 20 Miles
Produced By:
NJDEP Water Monitoring Management
James E. Mumman, Administrator
June 1998
Figure 3. Wells sampled in New Jersey's Ambient Ground-Water Monitoring Network.
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GENERALIZED LANDUSE
IN NEW JERSEY (1986)
| Urban
L Agricultural
i Undeveloped
Water bodies and wetlands
Selected Well Sites
• Urban
• Agricultural
A Undeveloped
Watershed Management Regions
Figure 4. Proposed randomly selected well sites for the Ambient Water Table Quality Monitoring Network, 1998.
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Table 1. Water-Quality Parameters Analyzed in New Jersey's
Ambient Ground Water Quality Network 1998
Characteristics
Field Alkalinity (mg/L as CaCO3)
Hardness,(mg/L as CaCO3)
Oxygen, dissolved (mg/L)
pH (standard units)
Major and Minor
Dissolved Constituents (mg/L)
Calcium
Chloride
Fluoride
Magnesium
Nutrients, Dissolved (mg/L)
Nitrogen, NH3, (as N)
Nitrogen, NH3 + Organic, (as N)
Nitrate, [NO2+NO3] - [NO2]
Trace and Minor
Dissolved Constituents (/Jg/L)
Aluminum
Arsenic
Barium
Cadmium
Chromium
Copper
Iron
Organic Constituents
DOC- Dissolved Organic Carbon, (mg/L as C)
VOC- Volatile Organic Compounds
Solids, dissolved (mg/L)
Specific Conductance (uS/cm)
Temperature
Potassium
Silica
Sodium
Sulfate
Nitrogen, NO2, (as N)
Nitrogen, NO2+NO3, (as N)
Phosphorous ortho, (as P)
Lead
Manganese
Mercury
Selenium
Silver
Zinc
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A Reconnaissance for New, Low-Application Rate Herbicides in Surface and Ground
Water in the Midwestern United States, 1998
William A. Battaglin, Hydrologist
USGS, Box 25046, MS 406, Denver, CO 80225, Phone: (303) 236-5950 x202
Edward T. Furlong, Research Chemist
USGS, Box 25046, MS 407, Denver, CO 80225, Phone: (303) 467-8080
Mark Burkhardt, Research Chemist
USGS, Box 25046, MS 407, Denver, CO 80225, Phone: (303) 467-8093
C. John Peter, Research Associate
DuPont Agricultural Products, Barleymill Plaza, PO Box 80015, Wilmington, DE 19880-0015
Phone: (302) 992-2277
Abstract
A new generation of low-application rate herbicides that function by inhibiting the action of a key
plant enzyme are gaining popularity among farmers. Sulfonylurea (SU), sulfonamide (SA), and
imidazolinone (Evil) herbicides are classes of herbicides that function in this manner. These herbicides are
applied either pre- or post-emergence to crops at rates ranging from 0.001 to 0.25 pound active ingredient
per acre. This is much less than the application rate of many other corn and soybean herbicides. SUs, SAs,
and IMIs have very low toxicities to mammals and other animals, but like other herbicides they can cause
problems with nontarget plants even when only small amounts of the originally applied material remain in
the soil or in water. Little is known about the occurrence, fate, or transport of these new herbicides in
surface water or ground water in the United States. The purpose of this reconnaissance is to gain addi-
tional information and understanding about the occurrence of selected SUs, SAs, and IMIs in water
resources of the midwestern United States, the area of highest use for these compounds. This study will
build upon knowledge and information gained in earlier herbicide reconnaissance studies conducted by
the U.S. Geological Survey (USGS) between 1989 and 1997. Approximately 200 samples from small
streams, larger rivers, reservoir outflows, and wells will be collected and analyzed in 1998. The study is
the result of a cooperative research and development agreement between the USGS and E.I. DuPont.
Background
During the last 20 years, a generation of low application rate herbicides has been developed that act
by inhibiting the action of a key plant enzyme, resulting in stopped growth and eventual plant death.
These herbicides are gaining in popularity among farmers. Sulfonylurea (SU), sulfonamide (SA), and
imidazolinone (Evil) herbicides are three classes of compounds that share the same mode of action. These
compounds typically are applied at much lower rates than triazine or acetanilide herbicides. Crops that
can be treated with SUs, SAs, and IMIs include barley, corn, cotton, durum wheat, rice, canola, peanuts,
soybeans, sugar beets, spring wheat, and winter wheat. Some of these compounds are also approved for
use on Conservation Reserve Program acreage (land set aside from crop production generally because
fanning it could result in significant soil loss) and for noncropland weed control. The total corn, soybean,
and wheat acreage on which 9 SUs, 1 SA and 2 IMIs were applied in eleven midwestern States (Iowa,
Illinois, Indiana, Kansas, Kentucky, Minnesota, Missouri, Nebraska, Ohio, South Dakota, and Wisconsin)
from 1990 through 1997 is shown in figure 1 (U.S. Department of Agriculture, 1991-98). In 1997, the
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area treated exceeded 66 million acres. For comparison, atrazine, a triazine herbicide, was used on about
42 million acres of corn in the same 11-State area in 1997.
Although applied over comparable areas, SU, SA, and MI herbicides typically are applied at post-
emergence at extremely low rates (0.001 to 0.25 pounds of active ingredient per acre) These application
rates are much lower than for other commonly used herbicide classes; hence, their overall use amount is
small. For example, in 1996, in the same 11-State midwestern area, an estimated 23,200 tons of atrazine
and 19,360 tons of metolachlor were applied to cropland, while the estimated use of 9 SUs, 1 SA, and 2
IMIs was only 1,150 tons (U.S. Department of Agriculture, 1997).
The soil half-life of SUs, SAs, and Mis generally ranges from 1 to 25 weeks depending on soil pH
and temperature. The water solubility of SUs, SAs, and Mis generally ranges from 6 to 40,000 part per
million. The water solubility of SUs is dependent on water pH, and the water solubility of SAs and Mis
is dependent on temperature and soil moisture content.
Toxicity
SUs, SAs, and Mis act upon a specific plant enzyme (acetolactate synthase) that is not found in
mammals or other animals and they are reported to have very low toxicities in animals (Brown, 1990,
Meister, 1997). Plants demonstrate a wide range in sensitivity to SUs, SAs, and Mis (Peterson et al.,
1994) with over a 10,000 fold difference in observed toxicity levels for some compounds. The EC50
(concentration causing a 50 percent reduction in a chosen plant characteristic for which a toxicity
endpoint exists, for example lab tests measuring biomass development) values for 5 aquatic plants are
shown on figure 2 (U.S. EPA, 1997; Sabater and Carrasco, 1997; Fairchild et al., 1997). The EC50 values
plotted are for green algae (Selenastrum capricornutum), duckweed (Lemna gibba), blue-green algae
(Anabaena flos-aquae), freshwater algae (Scenedesmus costatum), and freshwater diatom (Navicula
pelliculosa). In some cases, EC50 values from more than one test on the same plant species are included.
EC50 values for several herbicides range over 3 orders of magnitude. The EC50 data plotted on figure 2
support the hypothesis that a concentration of 0.1 mg/L in water is the baseline for non-target aquatic
plant toxicity.
Crop toxicities reported as EC25 values (application rates in pounds per acre resulting in a 25%
reduction in a chosen plant characteristic for which a toxicity endpoint exists, for example lab tests of
juvenile plant growth) were used to estimate the herbicide concentrations in soil water that could result in
non-target crop stress (U.S. EPA, 1997). EC25 values ranged from less than 0.00002 to more than 1.0
pound per acre. The soil water concentrations resulting from the EC25 application rate were calculated
assuming that the upper portion of an acre of a typical cropped loam soil has approximately 50 percent
pore space which under ideal growing conditions is occupied half by water and half by air (Buckman and
Brady, 1969). Thus, the top 1 foot of soil would contain 3 inches of water and this water would weigh
679,100 pounds. If we assume that rain/irrigation water distributes the mass of herbicide equal to the
EC25 value to this water and no herbicide degradation, then the herbicide concentration in that soil water
can be estimated as:
Soil water concentration in parts per billion (ppb) = (EC25 (Ibs/acre) / (679,100 (Ibs/acre)) * 109 (1)
For example, using equation (1) the concentration of nicosulfuron in soil water which may be of concern
for juvenile onion plants can be estimated as:
concentration in ppb= (0.0039 (Ibs/acre)/ (679,100 (Ibs/acre)) * 109 = 5.7 ppb.
Estimated soil water concentrations computed using the above equation for 9 crops are shown on figure 3.
Note that 1 ppb is equal to 1 mg/L. These estimates do not account for herbicide degradation. The crops
for which data are plotted are corn, soybeans, sorghum, canola, sugar beets, radish, onion, lettuce, and
cabbage. Data on figure 3 indicate that non-target crop stress is unlikely to occur when soil water
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concentrations of SUs, SAs, and IMIs remain below 0.01 mg/L. Herbicide performance and non-target
crop toxicity have been reported to vary by soil pH, soil organic content, and climate (Blair and Martin,
1988).
Because SUs, SAs, and Mis are active at very low concentrations, they can cause a problem with
plant vigor in some crop rotations even when only 1 percent or less of the originally applied material
remains. Some of these herbicides have demonstrated residual phytotoxicity to rotation crops like corn,
sunflowers, sugar beets, and dry beans for a year or more after application (Anderson and Barrett, 1985;
Anderson and Humburg, 1987). Marrs et al. (1989) noted that a buffer of 5-10 meters is needed for
ground sprayers to minimize SU impacts on non-target native plants. Fletcher et al. (1993) indicated that
spray drift containing SUs at concentrations less than 1 percent of the recommended application rate may
adversely impact fruit tree yields. Felsot et al. (1996) suggested that the appearance of chlorotic spots on
crops in south central Washington is a result of exposure to low levels of SU herbicides in precipitation
and not from direct spray drift. However, Obrigawitch et al. (1998) question the validity of Fletcher's
findings and the results of other studies that based their findings on short-term plant-response
assessments. In an extensive review of existing field data, Obrigawitch et al. (1998) found that a treatment
rate of 0.1 gram of the most active SU ingredient per hectare (0.00009 pound per acre) represents a
"threshold dose" and would be unlikely to reduce the yields of even the most sensitive non-target plants.
This threshold dose represents 1 percent or less of the use rates of SUs. Using the equation above, this
dose would result in a soil water concentration of 0.13 mg/L. The data shown on figure 3 support
Obrigawitch's "threshold dose" hypothesis. The data shown on figures 2 and 3 suggest that there is a
potential for SU, SA, and IMI herbicide to have an adverse effect on aquatic plants or crops if they were
to occur in river water, rainwater, irrigation water, or soil water at concentrations greater than 0.1 mg/L.
Measured and Expected Environmental Concentrations
As of July 1998, detections of SUs, SAs, and IMIs in water collected from environmental settings
have been rare and the few reported detections have been at nanogram per liter concentrations
(Bergstrom, 1990; Michael and Neary, 1993; D'Ascenzo et al., 1998; Steinheimer et al., 1998). However,
several studies indicate that some SUs, SAs, and IMIs herbicides may leach beyond the active root zone
and enter ground water or surface water systems (Anderson and Humburg, 1987; Bergstrom, 1990; Flury
et al., 1995; Veeh et al., 1994). Once in ground water or surface water, some SUs, SAs, and IMIs would
tend to persist as the parent compound while others would tend to hydrolyze (Dinelli et al., 1997; Harvey
et al., 1985). A study by Afyuni et al. (1997) indicated that between 1.1 and 2.3 percent of an applied SU
was lost in runoff during a simulated rainfall event 24 hours after herbicide application.
Because of their low application rates and low overall use amounts, concentrations of SUs, SAs, and
IMIs are expected to be low or nondetectible in most water resources. Environment Canada uses a
calculated Expected Environmental Concentration (ECC) to evaluate the potential hazard of pesticides to
nontarget aquatic organisms. ECC values for selected SUs ranged from 3 to 20 mg/L and equaled 67
mg/L for imazethapyr (Peterson et al., 1994). For comparison, ECC values for selected triazine herbicides
were more than 100 times greater at 2,667 to 2,867 mg/L. These are worst case exposure estimates and
assume overspray of a 15 cm deep water body with the maximum application rate.
One can also assume based upon their chemical characteristics, application rates, and acres treated
that individual SUs, SAs, and IMIs herbicides would be expected to occur in surface or ground water at 1
to 0.1 percent or less of the concentration of common triazine herbicides. The USGS measured
concentrations of 11 common herbicides and 2 herbicide metabolites in samples from 52 Midwestern
rivers during runoff events that occurred soon after herbicide application in 1989, 1990, 1994 and 1995
(Goolsby et al., 1994; Battaglin and Goolsby, 1996). The median concentrations in these samples
represent an estimate of expected environmental concentrations based upon observation. Median atrazine
concentration for the four years of data ranged from 5.5 to 10.9 mg/L; median cyanazine concentrations
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ranged from 1.3 to 2.7 mg/L; and median metolachlor concentrations ranged from 1.7 to 2.5 mg/L.
Maximum concentrations for these three compounds for the 4 years ranged from 10.6 to 108 mg/L. Thus,
one could expect to observe SUs, SAs, and Mis herbicides in Midwestern rivers during post-application
runoff events at concentrations ranging from 0.001 to 0.1 mg/L. Further, one could expect maximum
concentrations of SUs, SAs, and IMIs herbicides to range from 0.01 to 1.0 mg/L.
Objectives and Hypotheses
Currently, little is known about the occurrence, fate, or transport of SUs, SAs, and IMIs in surface
water and ground water in the United States. The overall objective of this project is to determine if and at
what concentrations selected SUs, SAs, and IMIs occur in surface and ground water resources of the
midwestern United States. Specific objectives include:
1. Validate an existing analytical method for selected SUs, SAs, and Mis provided by DuPont.
2. Lower the limit of quantitation of this method.
3. Add other herbicides and herbicide degradation products to the list of analytes.
4. Conduct a reconnaissance to determine the environmental occurrence and distribution of SUs,
SAs, and IMIs herbicides in surface water and ground water in the midwestern United States.
5. Determine the frequency of detections and concentrations of selected other pesticides in
midwestern rivers.
Specific hypotheses to be tested are:
1. SU, SA and MI herbicides will be detected in surface water and ground water in the midwest.
2. The frequency of detections and concentrations of SU, SA, and MI herbicides will be
significantly less than that of other herbicides that are applied in greater total amounts.
3. The frequency of detections and concentrations of SU, SA, and MI herbicides will be greater in
post-emergence runoff samples than in pre-emergence runoff samples.
4. The frequency of detections and concentrations of SU, SA, and MI herbicides will be greater in
streams and reservoirs than in ground water.
5. The frequency of detections and concentrations of SU, SA, and MI herbicides will be greater in
smaller watersheds that are predominantly agricultural than in larger watersheds that have more
diverse land use and land cover.
Plan of Study
This study will involve collecting approximately 200 samples during a 1998 reconnaissance. Samples
will be collected from streams, large rivers, reservoir outflows, and wells. When and where possible
samples will be collected in conjunction with USGS National Stream Quality Accounting Network
(NASQAN) and National Water Quality Assessment (NAWQA) activities, both to reduce the cost of
sample collection and to insure availability of QA/QC and other water-quality data (other herbicides,
insecticides, and nutrients). All 1998 herbicide reconnaissance samples will be sent to the Methods
Research and Development Program personnel at the USGS National Water Quality Lab (NWQL) in
Arvada, Colorado, and analyzed for a minimum of 16 herbicides (table 1) using high performance liquid
chromatography coupled with mass spectrometry. This analytical method will have a quantification limit
of less than 0.1 mg/L for all analytes. Samples will also be analyzed for other pesticides and nutrients by
standard methods.
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Sampling Sites
One hundred sites will be sampled in this study (figure 4). Samples will be collected from 75 surface-
water sites in the Upper Mississippi, Missouri, and Ohio River basins. Fifty-two of the surface water sites
to be sampled have been studied in previous Midcontinent Herbicide Initiative (MHI) investigations
(Thurman et al., 1992; Goolsby et al., 1994). These sites were selected out of the set of 150 sites sampled
in 1989 using a stratified random method (Scribner et al., 1993). The original 150 surface water sites were
selected from the population of all USGS gaging stations in a 10-State area using a stratified random-
sampling procedure to ensure adequate geographic distribution. The number of sites per State was
proportional to corn and soybeans production, and sites were randomly selected by county within the
State. The sites sampled in 1989 were ranked according to the total herbicide concentration in the post-
application sample. These sites were then divided into three equal groups and 26 sites were randomly
selected from the highest concentration group while 13 sites were randomly selected from each of the
other two groups. It is important to note that this sampling strategy is not designed to produce an unbiased
estimate of herbicide occurrence in all midwestern streams. Rather the intent was to target higher risk
areas while still capturing the variability of the entire population. Another reason for sampling the 52 sites
is the considerable historical data at these sites which will enable us to put in context the concentrations
measured in 1998 samples with those measured in 1989, 1990, 1994 and 1995.
Samples also will be collected at selected NASQAN and NAWQA sites and just downstream from
five reservoirs at locations that were sampled in a previous investigation (Coupe et al., 1995; Scribner et
al., 1996). The majority of ground water samples will be collected from a network of wells in Iowa that
are part of the Iowa Ground Water Monitoring (IGWM) program (Detroy et al., 1988; Kolpin et al.,
1997). Wells from this network have been sampled systematically since 1982. Samples will also be
collected from selected wells in the Lower Illinois NAWQA study unit.
Sampling Schedule
Two samples will be collected at each surface-water and reservoir site (one on each of two site visits),
and one sample will be collected at each ground water site. The first surface-water samples will be
collected after pre-emergence herbicides have been applied (usually May or June) and following a
precipitation event that produces a significant increase in streamflow. Ideally, streamflow should be
representative of runoff conditions with flow at or above the 50th percentile (50 percent exceeds values
for the period of record, published in State USGS Water Resources Data reports). These samples will be
referred to as pre-emergence runoff samples. The second surface-water samples will be collected after
post-emergence herbicides have been applied (usually June or July) again following a precipitation event
that produces runoff conditions and streamflows at or above the 50th percentile. These samples will be
referred to as post-emergence runoff samples. The first NASQAN and reservoir samples will be collected
in May or June, 2-3 weeks after the first surface-water samples were collected from nearby sites. The
second NASQAN and reservoir samples will be collected in June or July, 2-3 weeks after the second
surface-water samples were collected from nearby sites. Collection of these stream and reservoir samples
may require special visits to sites. Ground-water samples will be collected in June, July or August.
Sampling Procedure
Samples will be collected using protocols that are identical to those used for the collection of samples
for low levels of other dissolved organic compounds (Shelton, 1994), such as the NWQL schedule 2001
which is a capillary-column gas chromatography/mass spectrometry method for pesticide analysis, or the
schedule 2050 which is a high-pressure liquid-chromatography method for pesticide analysis. The equal-
width-increment sampling method (Edwards and Glysson, 1988) is recommended, but equal-discharge-
III-249
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increment sampling is also acceptable on larger rivers. All equipment will be precleaned with a
Liquinox/tap-water solution, rinsed with tap water, deionized water, and then methanol, and air dried. All
pesticide samples will be filtered through 0.7-mm pore-size baked glass-fiber filters using an aluminum
plate filter holder and a ceramic or stainless steel piston fluid metering pump with all teflon tubing into
precleaned 1-liter or 125-ml amber glass bottles. Samples will be immediately chilled and shipped on ice
from the field to the labs within two days of collection.
Five 125-ml amber glass bottles from each site will be sent to the USGS laboratory in Lawrence,
Kansas for analysis of herbicide compounds. Two 1-liter glass bottles from each site will be sent to the
USGS NWQL in Denver for SU, SA, and MI herbicide analysis. One 1-liter glass bottle and one 125-ml
polyethylene bottle will be sent to the USGS NWQL in Arvada for pesticide (schedule 2001) and nutrient
(schedule 2752) analysis. Field measurements for specific conductance, pH, and temperature will be taken
for all samples and stream discharge will be obtained either by direct measurement, from a rating curve,
or estimated from a nearby gaging station.
Analytical Methods
Methods Research and Development Program personnel at the USGS NWQL will validate and
improve a routine analytical method, provided by DuPont Agricultural Products, for measuring trace
(ng/L) concentrations of selected SU, SA, and IMI herbicides in water samples. The method will use high
performance liquid chromatography (HPLC) coupled with mass spectrometry. The analytical method to
be validated was developed by DuPont as one of five methods developed in association with the
EPA/Industry Multianalyte Methods group. The DuPont method (Rodriguez and Orescan, 1996) uses
electrospray liquid-chromatography/mass spectrometry (LC/MS) to detect 16 (table 1) SUs, SAs, and
IMIs with a reporting limit of 0.1 mg/L for all analytes. Other analytical methods with similar or lower
reporting limits for selected compounds have also been reported (Di Corcia et al., 1997; Nilve et al.,
1994). Improvements to the DuPont method will include (1) switching from external standard
quantitation to internal standard quantitation, (2) increasing the sample size for extraction from 250 ml to
1 liter, (3) testing several new extraction media, and (4) expanding the list of target analytes to include
other SU, SA, and IMI herbicides and herbicide metabolites.
In addition, all samples will also be analyzed for several other classes of pesticides and for nutrients.
Samples will be analyzed for 12 herbicides and 5 or more herbicide metabolites by gas chromatography/
mass spectrometry (GC/MS) using methods described by Thurman et al. (1990), Meyer et al. (1993) and
Aga et al., (1994) by staff at the USGS lab in Lawrence. This method has an analytical reporting limit of
0.05 mg/L for most analytes. Samples will be analyzed for 41 pesticides and pesticide metabolites by
GC/MS with selected-ion monitoring using methods described by Zaugg et al. (1995) by staff at the
USGS NWQL. This method has analytical reporting limits that range from 0.001 to 0.018 mg/L. All
samples will be analyzed for dissolved nitrite, nitrate plus nitrite, ammonia, and orthophosphate by an
automated colorimetric procedure (Fishman and Friedman, 1989) by staff at the USGS NWQL. About
100 samples will be analyzed using both the original DuPont method and the modified DuPont method.
Splits from these samples will also be sent out for confirmatory analysis at a DuPont laboratory.
Results will be presented that summarize the occurrence and distribution of SUs, SAs, and IMIs in
midwestern water resources, and place their occurrence and distribution in relation to those of the other
herbicides and herbicide metabolites measured in this study. If possible, estimates of the use of SUs, SAs,
and Mis or surrogates for those use estimates such as estimates of cropped land, will be used in statistical
models that may help identify watershed conditions that influence SU, SA, and MI concentrations.
III-250
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Quality Control
Quality control (QC) samples will be collected at selected sites to provide information on the
variability and bias of the measured SU, SA, and EVII concentrations. These samples will consist of
concurrent replicates (CR), which are two samples collected as closely as possible in time and space, but
processed, handled, and analyzed separately; laboratory spikes (LS), which are collected like concurrent
replicates, then spiked in the lab with a known quantity of selected target analytes, and analyzed
separately; or field blanks (FB), which are blank solutions that are subject to the same aspects of sample
collection, field processing, preservation, transportation, and laboratory handling as the environmental
samples. Concurrent replicates will be submitted blindly to the USGS NWQL, while laboratory spikes
and field blanks will be identified as such. QC samples will only be collected and processed for the SU,
SA, and Evil analysis. A total of 26 QC samples will be collected.
Data Management
Water quality data will be managed using SAS software. Data will also be made available via the internet
after it has been quality assured. Spatial data such as the location of sampling sites, the extent of drainage
basins, and information about land use within those basins, will be managed using a geographic
information system.
Budget
Where possible, sampling has been planned in conjunction with on-going NAWQA and NASQAN
investigations to minimize costs and maximize information. Local USGS offices will be compensated for
expenses associated with collection, processing, and shipment of samples to appropriate laboratories. The
majority of costs associated with this effort will be covered by funds originating from a Cooperative
Research and Development Agreement (CRADA) between the USGS and DuPont Agricultural Products
(Battaglin et al., 1998).
References
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Brown, H.M. 1990. Mode of action, crop selectivity, and soil relations of the sulfonylurea herbicides.
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Coupe, R.H., D.A. Goolsby, J.L. Iverson, D.J. Markovchick, and S.D. Zaugg. 1995. Pesticide, nutrient,
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Detroy, M.G., P.K.B. Hunt, and M.A. Holub. 1988. Ground-water-quality-monitoring program in Iowa:
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Di Corcia, A., C. Crescenzi, R. Samperi, and L. Scappaticcio. 1997. Trace analysis of sulfonylurea
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chromatography with UV detection, and confirmatory analysis by an electrospray/ mass detector.
Anal. Chem., 69:2819-2826.
Dinelli, G., A. Vicari, A. Bonetti, and P. Catizone. 1997. Hydrolytic dissipation of four sulfonylurea
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Edwards, T.K. and D.G. Glysson. 1988. Field methods for measurement of fluvial sediment. U.S.
Geological Survey OFR 86-531, 118 p.
Fairchild, J.F., D.S. Ruessler, P.S. Haverland, and A.R. Carlson. 1997. Comparative sensitivity of
Selenastrum capricornutum and Lemma minor to sixteen herbicides. Arch. Environ. Contam.
Toxicol., 32:353-357.
Felsot, A.S., M.A. Bhatti, G.I. Mink, and G. Reisenauer. 1996. Biomonitoring with sentinel plants to
assess exposure of nontarget crops to atmospheric deposition of herbicide residues. Environ. Toxicol.
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Fishman, MJ. and L.C. Friedman. 1989. Methods for determination of inorganic substances in water and
fluvial sediments. U.S. Geological Survey Techniques of Water-Resources Investigations, Book 5,
chap. Al, 545 p.
Fletcher, J.S., T.G. Pfleeger, and H.C. Ratsch. 1993. Potential environmental risks associated with the
new sulfonylurea herbicides. Environ. Sci. Technol. 27(10):2250-2252.
Flury, M., J. Leuenberger, B. Studer, and H. Fluhler. 1995. Transport of anions and herbicides in a loamy
and a sandy field soil. Water Resources Research 31(4):823-835.
Goolsby, D.A., L.L. Boyer, and W.A. Battaglin. 1994. Plan of study to determine the effect of changes in
herbicide use on herbicide concentrations in midwestern streams, 1989-94. U.S. Geological Survey
Open-File Report 94-347, 14 p.
Harvey, J., J.J. Dulka, and J J. Anderson. 1985. Properties of sulfometuron methyl affecting its
environmental fate: Aqueous hydrolysis and photolysis, mobility and adsorption on soils, and
bioaccumulation potential. Journal of Agricultural and Food Chemistry 33:590-596.
Kolpin, D.W., S.J. Kalkhoff, D.A. Goolsby, D.A. Sneck-Fahrer, and E.M. Thurman. 1997. Occurrence of
selected herbicides and herbicide degradation products in Iowa's ground water, 1995. Ground Water
35(4):679-688.
Marrs, R.H., C.T. Williams, A.J. Frost, and R.A. Plant. 1989. Assessment of the effects of herbicide spray
drift on a range of plant species of conservation interest. Environ. Pollut., 59:71-86.
Meister, R.T., 1997. Farm Chemicals Handbook '97. Meister Publishing Company, Willoughby, OH
Meyer, M.T., M.S. Mills, and E.M. Thurman. 1993. Automated solid-phase extraction of herbicides from
water for gas chromatographic-mass spectrometric analysis. Journal of Chromatography, 629:55-59.
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Nilve, G., M. Knutsson, and J.A. Jonsson. 1994. Liquid-chromatographic determination of sulfonylurea
herbicides in natural waters after automated sample pretreatment using supported liquid membranes:
J. Chromatogr. A., 688:75-82.
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sulfonylurea herbicides using field approaches. Pestic. Sci., 52:199-217.
Peterson. H.G., C. Boutin, P.A. Martin, K.E. Freemark, N.J. Ruecker, and M.J. Moody. 1994. Aquatic
phyto-toxicity of 23 pesticides applied at expected environmental concentrations. Aquatic Toxicol.
28:275-292.
Rodriguez, M. and D.B. Orescan. 1996. Analytical method for the quantitation and confirmation of
selected sulfonylurea, imidazolinone, and sulfonamide herbicides in surface water using electrospray
LC/MS. DuPont Report No. AMR 4118-96.
Sabater, C., and J.M. Carrasco. 1997. Effects of chlorsulfuron on growth of three freshwater species of
phytoplankton. Bull. Environ. Contam. Toxicol., 58:807-813.
Scribner, E.A., D.A. Goolsby, E.M. Thurman, M.T. Meyer, and W.A. Battaglin. 1996. Concentrations of
selected herbicides, herbicide metabolites, and nutrients in outflow from selected midwestern
reservoirs, April 1992 through September 1993. U.S. Geological Survey Open-File Report 96-393,
128 p.
Scribner, E.A., E.M. Thurman, D.A. Goolsby, M.T. Meyer, M.S. Mills, and M.L. Pomes. 1993.
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water-quality assessment program, U.S. Geological Survey OFR 94-455, 42 p.
Steinheimer, T.R., R.L. Pfeiffer, C.J. Peter, M.J. Duffy, and W.A. Battaglin. 1998. Reconnaissance
survey of sulfonamide, sulfonylurea and imidazolinone herbicides in surface streams and ground
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Thurman, E.M., M.T. Meyer, M.L. Pomes, C.A. Perry, and A.P. Schwab. 1990. Enzyme-Linked
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Veeh, R.H., W.P. Inskeep, F.L. Roe, and A.H. Ferguson. 1994. Transport of chlorsulfuron through soil
columns. J. Environ. Qual., 23:542-549.
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Geological Survey National Water Quality Laboratory—Determination of pesticides in water by C-18
solid-phase extraction and capillary-column gas chromatography/mass spectrometry with selected-ion
monitoring. U.S. Geological Survey Open-File Report 95-181, 49 p.
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70
60
UJ 50
DC
LL 40
o
CO
Z 30
20
10
0
I
I
1
1990 1991 1992 1993 1994 1995 1996 1997
Year
SULFONYLUREAS
••• SULFONAMIDES
IMIDAZOUNONES
Figure 1. Estimated acres of corn, soybeans, or wheat treated with selected sulfonylurea, sulfonamide, and
imidazolinone herbicides, 1990-97, in midwestern States.
Herbicide
. . ,,
«
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"T-
i i in
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A
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0.1 1.0 10.0 100 1,000 10,000 10**5 10"6 10**7
A Freshwater diatom
B Green algae
* Duckweed
° Blue-green algae
* Freshwater algae
EC50 value for aquatic
plants in
Figure 2. EC50 concentration values for 5 aquatic plants for selected sulfonylurea, sulfonamide, and
imidazolinone herbicides.
111-254
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Herbicide
chlorsulfuron
nicosulfuron
triasutfuron
prosulfuron
pnmisulfuron
metsulfuron
imazethapyr
imazapyr
flumetsulam
halosulfuron
bensutfuron
O Com
%£ Soybean
tt Sorghum
A Lettuce
Q Cabbage
Cp Radish
-fN
A A Jl IIA
/ A \ ^fr- *-*/ \
0.01 0.1
1.0 10.0 100 1,000 10,000
Estimated soil water concentration in
/j.g/L after EC25 dosage to soil
Onion
Canola
Sugar beet
Figure 3. Estimated soil water concentrations calculated from EC25 values for 9 crops for selected sulfonylurea,
sulfonamide, and imidazolinone herbicides.
L..
SAMPLING SITES
A MHI stream
* NASQAN river
• Reservoir
* NAWQA river
O Well
500 kilometers
Figure 4. Location of sites proposed for sampling in 1998.
III-255
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Table 1. Common Names, Chemical Class, Trade Names, and Crops Treated for Select
Sulfonylurea, Sulfonamide, and Imidazolinone Herbicides
Common name
bensulfuron methyl
chlorimuron ethyl
chlorsulfuron
flumetsulam
halosulfuron methyl
imazapyr
imazaquin
imazethapyr
metsulfuron methyl
nicosulfuron
primisulfuron methyl
prosulfuron
sulfometuron methyl
thifensulfuron methyl
triasulfuron
triflusulfuron methyl
Class
sulfonylurea
sulfonylurea
sulfonylurea
sulfonamide
sulfonylurea
imidazolinone
imidazolinone
imidazolinone
sulfonylurea
sulfonylurea
sulfonylurea
sulfonylurea
sulfonylurea
sulfonylurea
sulfonylurea
sulfonylurea
Trade names'
Londax
Classic, Canopy, Reliance
Glean, Telar, Finesse
Broadstrike, Preside, Scorpion
Battalion, Manage, Permit
Arsenal, Chopper
Scepter, Detail
Pursuit
Allie, Ally, Escort
Accent
Beacon, Tell
Peak
Oust
Pinnacle, Reliance
Amber, Logran
Upbeet
Crops Treated
rice
soybeans, peanuts
grains, CRP
corn, soybeans
corn, sorghum, turf
noncropland
soybeans
soybeans, corn
grains, pasture, noncrop
corn
corn
corn, sorghum, grains
trees, noncrop, turf
soybeans, grains, corn
grain, fallow
sugarbeets
'Any use of trade, product,
imply endorsement by the
or firm names in this publication is for descriptive purposes only and does not
U.S. Government.
III-256
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Track B—Methodology and Information Sharing
Data Comparability and Collection Methods
Quality Assurance/Quality Control for Monitoring Programs
Tools for Communicating Monitoring Results
m-257
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m-258
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A Comparison of Water-Quality Sample Collection Methods Used by the
U.S. Geological Survey and the Wisconsin Department of Natural Resources
Phil A. Kammerer, Jr., Water Quality Specialist
Herbert S. Garn, Chief, Hydrologic Studies and Data Collection Section
U. S. Geological Survey, Water Resources Division, Wisconsin District
Paul W. Rasmussen, Statistician
Joseph R. Ball, Water Resources Specialist
Wisconsin Department of Natural Resources
Abstract
The U.S. Geological Survey (USGS) and the Wisconsin Department of Natural Resources (WDNR)
monitor the quality of Wisconsin's water resources. Sample collection and processing protocols differ
between agencies, and samples are analyzed by different laboratories. Data comparability is an issue to
data users who may wish to combine or exchange data, but the degree to which differences in protocols
affect results of water-quality monitoring is unknown. The primary objective of this study was to evaluate
differences in results of water-quality monitoring caused by differences in sample-collection methods.
Other data-comparability issues (sample processing and relative laboratory performance) were examined
only to the extent necessary to accomplish the primary objective. Two sample-collection methods, flow-
integrated sampling (USGS) and grab sampling (WDNR), were compared for three streams over different
flow conditions and for one lake. Constituents used for the comparisons were dissolved orthophosphate,
total phosphorus, dissolved chloride, chlorophyll a, and suspended sediment/total suspended solids. Each
stream was sampled four times: twice at base flow and twice at high flow. The lake was sampled two
times. Concurrent samples were collected by each of the two sample-collection methods. The effects of
between-agency differences in sample processing and analytical procedures on results of water-quality
analyses were removed by splitting all samples between laboratories and evaluating sample-collection
methods independently using the results from each laboratory. The split for each laboratory was further
split into triplicate samples to evaluate laboratory precision. Laboratories used in the study were the
USGS National Water Quality Laboratory (NWQL), USGS Iowa District sediment laboratory, and the
Wisconsin State Laboratory of Hygiene (WSLH).
Split plot analysis of variance and paired t-tests were used to test for significant differences (p<0.05)
in constituent concentrations between sampling methods and between laboratories for constituents
analyzed by comparable laboratory methods. Concentrations of total phosphorus and dissolved chloride
did not differ significantly between sampling methods. Concentrations of dissolved orthophosphate were
significantly different among methods, which was not expected, and suspended sediment and total
suspended solids also differed significantly between sampling methods. Concentrations of suspended
sediment and total suspended solids were usually lower in grab samples than in flow-integrated samples.
Chlorophyll a concentrations were significantly different between samplers. Samples collected by WDNR
had higher concentrations of chlorophyll a than those collected by USGS. Differences in concentrations
of dissolved orthophosphate between samples filtered in the field and samples filtered in the laboratory
were not significant. The effect of point of filtration on chlorophyll a, however, was highly significant.
Lab-filtered samples analyzed at WSLH gave higher concentrations of chlorophyll a than field-filtered
samples at all times.
Differences in concentrations of total phosphorus, dissolved orthophosphate, and dissolved chloride
between samples analyzed by the NWQL and samples analyzed by the WSLH were consistent and highly
significant. Differences in concentrations of chlorophyll a were also significant. Mean concentrations
III-259
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from the WSLH were higher than those from NWQL for all constituents. Lab differences in general
appeared to be more significant and important than sample collection methods. Differences in results
between sampling methods and labs may limit the combining of such data for certain purposes, such as
for determining trends.
Introduction
The U.S. Geological Survey (USGS), Water Resources Division and the Wisconsin Department of
Natural Resources (WDNR) both have programs that monitor ambient water quality at fixed sites on
Wisconsin's streams and lakes. Sample collection and processing protocols used by the programs differ,
and samples are analyzed by different laboratories. The degree to which these differences in protocols
affect the results of the monitoring programs is unknown. Data comparability is an issue at both agencies
and to other present and potential data users who may wish to combine or exchange data. This study was
conducted as part of a pilot project in Wisconsin under a larger effort to improve water-quality monitoring
nationwide by the Intergovernmental Task Force on Monitoring (ITFM) Water Quality (ITFM, 1995).
Purpose and Scope of Study
The primary objective of this study was to evaluate between-agency differences in water-quality
monitoring results caused by differences in sample collection methods. A secondary objective was to
evaluate comparability of data obtained from the two laboratories used in the study. Laboratory precision,
laboratory comparability, and sample processing and preservation methods were evaluated only to the
extent necessary to accomplish the primary objective.
A sampling program was designed to test for differences in water-quality data resulting from the two
sampling methods and use of two laboratories. Sample-collection methods were compared at sites on
three rivers and one lake for selected constituents over different flow conditions during the summer of
1993 to summer 1994. The intent of the study was to evaluate sample-collection methods rather than
water-quality conditions at particular sites.
For rivers, monitoring programs and protocols included in the comparison were the USGS' National
Stream Quality Accounting Network (NASQAN) and National Water-Quality Assessment Program
(NAWQA) and the WDNR's Ambient Monitoring Network. Two principal sample-collection methods
were compared: cross-sectionally integrated, flow-weighted ("integrated") sampling and grab sampling.
Both USGS programs collect flow-integrated samples, and the WDNR collects grab samples.
For lakes, monitoring protocols used for the WDNR and the USGS Wisconsin District lake
monitoring programs were compared. Both monitoring programs use similar sampling methods; grab
samples were collected at discrete depths in the lake. Depth profiles of water temperature, pH, specific
conductance, and dissolved oxygen concentration were measured at the time of sample collection.
Study Design
Many factors can contribute to apparent differences in water-quality measurements. In order to
identify differences due specifically to sample-collection methods, other factors must be identified and
either measured or eliminated. USGS and WDNR monitoring programs use different analytical
laboratories and follow different protocols for field processing, preservation, and sample shipment. The
USGS preserves samples, and filters samples for dissolved constituents in the field. For streams, all
samples, except those for suspended-sediment concentration analysis, are shipped to the USGS National
Water Quality Laboratory (NWQL) in Denver, Colorado. Samples for suspended-sediment concentration
analysis are shipped to the USGS Iowa District sediment laboratory in Iowa City, Iowa. For lakes,
III-260
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samples are shipped to either NWQL or the Wisconsin State Laboratory of Hygiene (WSLH) in Madison,
Wisconsin, depending on the lake being sampled. The DNR preserves samples in the field and either
ships or hand-delivers them to WSLH where samples for dissolved constituents are filtered prior to
analysis. Protocols for preservation of nutrient samples also differ between agencies. Analytical methods
are "comparable" between agencies for most constituents measured, but differ for some. Within-
laboratory precision and accuracy could be expected to differ between laboratories.
The effect of between-agency differences in sample processing and analytical procedures on
monitoring results was removed for the purposes of this study by splitting all samples between
laboratories. Samples for each laboratory were processed according to the respective standard protocols
used by each agency for routine samples (Fishman, 1993;Wisconsin State Laboratory of Hygiene, 1993).
The effects of sample-collection method on monitoring results was evaluated independently using the
results from each laboratory. Within-lab analytical precision was evaluated by splitting the samples
submitted to each laboratory into triplicates (see figure 1). Laboratory accuracy was not evaluated in this
study, thus any differences between laboratories reported herein are relative differences rather than
absolute differences from a true value. The effect of the point of sample filtration (field versus laboratory)
was evaluated for selected constituents only for samples analyzed by WSLH.
Selection of Constituents
The constituents evaluated in this study were selected based on their inclusion in agency monitoring
programs and their usefulness as surrogates to represent the behavior of other broader groups of
constituents. The constituents chosen for samples from rivers were total phosphorus, dissolved
orthophosphate, dissolved chloride, and suspended sediment/total suspended solids. The constituents
chosen for samples from the lake were total phosphorus, dissolved orthophosphate, and chlorophyll a. In
rivers, suspended material may be unevenly distributed through the stream cross section. Because of this,
the greatest differences in monitoring results caused by differences in sampling methods were expected
for samples containing large amounts of suspended material. Total phosphorus and suspended
sediment/total suspended solids are surrogates for constituents associated with suspended material in
rivers. Dissolved chloride is a surrogate for conservative dissolved constituents. Dissolved
orthophosphate is of common interest because of its importance as a plant nutrient. For lakes, chlorophyll
a concentration is used as a measure of algal production. The effect of point of filtration on monitoring
results was tested for dissolved orthophosphate and chlorophyll a.
Selection of Sites
Priority in site selection was given to sites that were included in the routine monitoring programs of
the participants and where the water was expected to contain measurable (detectable) concentrations of
the constituents of interest under all sampling conditions. Additional site-selection criteria for rivers were
that they represent a variety of hydrologic settings and that they were capable of producing substantial
amounts of suspended material at high flow.
The three rivers sampled during this study were the Milwaukee River at Milwaukee (USGS station
number 04087000), the Manitowoc River at Manitowoc (USGS station number 04085427), and the Wolf
River at New London (USGS station number 04079000). Lake samples were collected from Little Green
Lake near Markesan, Wisconsin.
Sampling
Each of the rivers was sampled four times: twice under stable low-flow conditions and twice at high
flow. For the purpose of this study, high flow was defined as a condition when surface runoff was
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entering the river, and concentrations of suspended material in the water appeared to be higher than at low
flow. Low-flow samples were collected in August and October, 1993, and high-flow samples were
collected in April-July, 1994.
During each low-flow sampling, field teams representing each of the three monitoring programs
collected a series of three consecutive samples at about the same time using their standard sample-
collection protocols. Each of the resulting nine samples was then split between laboratories and the split
for each lab was further split into triplicate samples (figure 1). All samples were processed according to
the standard protocols for the laboratory that would receive the sample. The concurrent samples were
collected to measure the variability in results between teams caused by differences in sampling methods.
The consecutive samples were collected to measure the variability in results for a team repeating the same
procedure under assumed stable water-quality conditions in the river.
During each high-flow sampling, each of the three field teams collected a single concurrent sample
which was split and processed in the same manner as the low-flow samples. Consecutive samples were
not collected because the assumption of stable water-quality conditions through the sample-collection
period was not valid under high-flow conditions, when water-quality conditions may change rapidly.
Lake samples were collected in August 1993 and April 1994. Each of the two field teams collected
three consecutive concurrent samples from adjacent boats. The concurrent samples were collected to
measure the variability in results between teams using similar sampling methods. The consecutive
samples were collected to measure the combined variability due to a single team repeating the same
procedure and temporal and spatial changes in lake water quality that took place during the sample-
collection period. Sample splitting and processing were done according to the protocols employed for
river samples.
Methods
Sample Collection
Cross-sectionally integrated, flow-weighted (flow-integrated) sampling is used to collect a composite
water sample in a stream cross-section such that the dissolved and suspended material in the sample is in
proportion to water flow in the cross-section. Variation of water quality in a stream cross-section is often
significant and is most likely to occur because of incomplete mixing of upstream tributary inflows, point-
source discharges, or variations in velocity and channel geometry. The flow-integrated sampling
technique employed by USGS in this study is known as the equal width increment/equal transit rate
(EWI) method (Edwards and Glysson, 1988; Ward and Harr, 1990). In this method, an isokinetic
sampling device (a sampler that allows water to enter without changing its velocity relative to the stream)
is lowered and raised at a uniform transit rate through equally-spaced verticals in the stream cross-section.
Samples were collected either by wading with hand-held samplers or from a bridge using a crane-
mounted sampler, depending on river stage and flow conditions. The number of verticals employed
differed between sites and between sampling teams.
Grab sampling involves dipping a sample from one or more points in a stream cross section. Grab
sampling techniques employed by WDNR in this study were consistent at each site, but differed between
sites, and ranged from wading and dipping samples at from one to three points in the cross section to
sampling from a bridge at a single point using a Van Dorn-type sampler.
Lake-water samples were collected by both agencies at a depth of 1.5 feet below the lake surface
using Van Dorn-type samplers.
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Sample Processing
Sample splitting was done using a cone splitter routinely employed by the NAWQA program. Splits
are accomplished by pouring a sample through the splitter, which splits the sample into 10 equal volumes.
Successive splits of water from the initial split were done as needed to fill sample bottles directly or
provide water for field filtration. Sample bottles for suspended sediment/total suspended solids, total
phosphorus, dissolved orthophosphate (WSLH only), dissolved chloride (WSLH only), and chlorophyll a
(WSLH only) analyses were filled directly from the splitter.
Samples for dissolved chloride, dissolved orthophosphate, and chlorophyll analyses by NWQL were
filtered in the field. Extra samples were also filtered in the field and sent to WSLH for chlorophyll a and
dissolved orthophosphate analyses to evaluate the effect of point of filtration.
Chlorophyll a samples for WSLH, and all phosphorus samples, were iced following processing. All
phosphorus samples sent to NWQL were preserved with mercuric chloride. Total phosphorus samples
sent to WSLH were preserved with sulfuric acid, and dissolved orthophosphate samples were not
preserved. Filters from field-filtered chlorophyll a samples for NWQL were stored and shipped frozen on
dry ice.
Laboratory Methods
Analytical methods used by NWQL and WSLH (table 1) for dissolved orthophosphate and dissolved
chloride analyses are considered comparable methods, so analytical results for these constituents can be
compared between labs in addition to comparison of sampling methods. Analytical methods for total
phosphorus used by both laboratories are comparable for lake samples and nominally comparable for
stream samples. Total phosphorus methods used for stream samples differed in the digestion method used
prior to analysis. Analytical methods employed by WSLH for chlorophyll a and total suspended solids
differ from those employed by NWQL for chlorophyll a and by the USGS Iowa sediment lab for
suspended sediment concentration.
Data Analysis
Laboratory data were analyzed using statistical techniques to evaluate differences in central values
and variability of the sampling results, primarily split plot analysis of variance (ANOVA) that reflected
the physical splitting of samples, and paired t-tests. Differences were considered significant at p<0.05.
Tests were evaluated for each of the constituent concentrations among monitoring programs and between
laboratories (where constituents were analyzed by comparable laboratory methods) and the various
interactions. Data from low-flow dates were used for analyses involving consecutive sampling time and
data from the first sampling time on each date for analyses combining low and high flow conditions. The
statistical comparisons included:
1. Comparison of concentrations of each constituent among monitoring programs (low flow, high
flow, and low and high flow combined).
2. Comparison of consecutive samples collected on the same day under low-flow conditions for
stream samples and for lake samples to determine within-program variability.
3. Comparison of dissolved orthophosphate and chlorophyll a concentrations between samples
filtered in the field and samples filtered in the laboratory.
4. Comparison of concentrations of constituents with comparable analytical methods between
laboratories and quantification of within-laboratory variability.
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Results and Discussion
Comparisons Among and Variability Within Monitoring Programs
For the stream samples, no significant differences among monitoring programs were detected for
concentrations of total phosphorus and chloride. For both stream and lake samples, there was a highly
significant difference among samplers for dissolved orthophosphate. Although the analysis indicated
significant differences among samplers, the differences were smaller than the standard deviation among
lab replicates. In further analysis of these differences by lab, differences were significant only for samples
analyzed at NWQL for data from low-flow samples. There were also significant differences among
monitoring programs for concentrations of total suspended solids or suspended sediment. Because of
differences between labs (methods are not comparable), separate analyses were repeated for each lab. For
samples collected at high flow, the means for NAWQA and NASQAN flow-integrated samples were
significantly greater than those from WDNR grab samples. The pattern was similar for both labs.
Although the pattern was similar at low flow (USGS samples had larger means than WDNR samples), the
differences were smaller and not significant.
For lakes, chlorophyll a concentrations were found to be different between samplers. For chlorophyll
a samples analyzed at WSLH there were significant differences among samplers, but for samples
analyzed at NWQL there were no significant differences. Samples collected by WDNR had higher
concentrations of chlorophyll a than those collected by USGS (mean difference of 4.16 mg/L).
There were no significant concentration differences for total phosphorus, orthophosphate and chloride
data among sequential samples collected on the same day during low-flow conditions for any of the
monitoring programs. There was significant variability associated with sequential samples among
monitoring programs for total suspended solids or suspended sediment, but the differences varied among
dates. A summary of the results of statistical analyses for each of the comparisons is provided in table 2.
In a similar study in Kentucky, comparing surface-grab and flow-integrated stream sampling methods
(Martin et al, 1992) for a larger number of constituents, concentrations of suspended sediment and some
sediment-associated constituents such as total phosphorus, total iron, and total manganese were found to
be significantly lower in grab samples than in integrated samples. The magnitude of the differences
generally increased with streamflow. Median percent differences in concentration were about 20-25
percent. Concentrations of most of the dissolved constituents and common physical properties were not
consistently different.
Comparisons Between Samples Filtered in the Field and in the Laboratory
There was no significant difference in concentrations of dissolved orthophosphate in paired samples
that differed only in point of filtration (field versus lab). The effect of point of filtration on chlorophyll a,
however, was highly significant. Lab-filtered samples analyzed at WSLH gave higher concentrations than
field-filtered samples at all times and were relatively less variable. The mean difference between methods
was 3.3 mg/L.
Comparisons Between and Within Laboratories
A highly-significant difference between laboratories was found for total phosphorus (for both stream
and lake samples), dissolved orthophosphate, and dissolved chloride concentrations. Mean concentrations
from the WSLH were higher than those from NWQL for all three constituents in stream samples, but the
magnitude of the differences varied among dates. For lake samples, total phosphorus concentrations from
NWQL were greater than those from WSLH. The mean differences for stream data were approximately
0.025 mg/L for total phosphorus concentrations up to about 0.30 mg/L, 0.005 mg/L for dissolved
III-264
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orthophosphate concentrations up to about 0.14 mg/L, and no consistent difference for dissolved chloride
concentrations up to about 60 mg/L. The mean difference between labs was 0.017mg/L total P on high-
flow dates and 0.037 on low-flow dates.
The analytical techniques employed by WSLH for chlorophyll a and total suspended solids are
different from those employed by NWQL for chlorophyll a and by the USGS Iowa sediment lab for
suspended sediment concentration, so results from the two labs were expected to be different. Because of
the different lab methods used, analytical results for these constituents were used primarily to emphasize
comparisons of factors within labs, and those between labs are discussed briefly. In general, the sediment
value from the USGS lab was larger than the suspended solids value from WSLH, and the difference was
greater at low flow than at high flow. In regard to chlorophyll a, the values from WSLH were larger than
those from NWQL for every sampling time on each date, although there were no consistent differences
among sampling times.
Within-laboratory variation for each constituent was estimated from the variance among the three
replicates sent to each lab for each sample. For stream samples, the variance in concentrations for total
phosphorus and dissolved chloride was significantly greater for NWQL than for WSLH. For lake
samples, total P variability among lab replicates was not significantly different for the two laboratories.
Chlorophyll a sample replicates, however, analyzed at the NWQL were more variable than those analyzed
at WSLH. Lab variability was much greater for suspended sediment concentrations from the USGS Iowa
District sediment laboratory than for total suspended solids concentrations from WSLH.
A study comparing results between the USGS and Illinois Environmental Protection Agency
laboratories (Melching and Coupe, 1995) also found that differences for some constituents were
statistically significant and large enough to concern water-quality planners and engineers. Findings from
this study also implied that lab differences were more important than sample collection differences from
concurrent sampling, and that data from different laboratories should not be mixed when doing statistical
analyses.
Conclusions
Major findings of this study may be summarized as follows:
1. There were no significant differences among monitoring programs for total phosphorus or
dissolved chloride concentrations for sampling conditions encountered in this study.
2. There were significant differences among monitoring programs in dissolved orthophosphate,
chlorophyll a, and both total suspended solids and suspended sediment concentrations. Where
there were differences, concentrations were greater in flow-integrated samples than in grab
samples. Significant differences among monitoring programs for total suspended solids and
suspended sediment was evident primarily from samples collected at high flow. Means for
NAWQA and NASQAN samples differed from those for WDNR samples. Chlorophyll a
concentrations were different between samplers; samples collected by WDNR had higher
concentrations of chlorophyll a than those collected by USGS.
3. There were no significant concentration differences among consecutive samples for total
phosphorus, orthophosphate, or chloride collected on the same day for any of the field teams.
Therefore, sampling results within teams were repeatable.
4. There was no significant difference in dissolved orthophosphate concentrations between samples
filtered in the field and samples filtered in the laboratory. The effect of point of filtration on
chlorophyll a, however, was highly significant. Lab-filtered samples analyzed at WSLH gave
higher concentrations of chlorophyll a than field-filtered samples.
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5. There were highly significant differences between laboratories for concentrations of total
phosphorus, dissolved orthophosphate, and chloride.
6. There generally were consistent differences between total suspended solids and suspended
sediment concentrations for samples split between labs. Suspended sediment concentrations from
the USGS lab were generally higher and more variable than total suspended solids concentrations
from WSLH. In regard to chlorophyll a, the values from WSLH were larger than those from
NWQL.
7. Within-laboratory variability for total phosphorus, dissolved chloride, and suspended sediment
versus suspended solids was significantly greater for USGS labs than for WSLH.
For the flow conditions encountered in this study, sample collection method affects monitoring results
for some constituents. Sample collection method (flow-integrated versus grab sample) does not appear to
affect monitoring results for some dissolved constituents as represented by chloride or for constituents
associated to some degree with suspended material as represented by total phosphorus. Sample collection
method does appear to affect monitoring results for direct measures of suspended material as represented
by total suspended solids concentration and suspended sediment concentration.
For the constituents measured and the range of concentrations encountered in this study, there were
statistically significant differences in monitoring results for samples split between laboratories and
analyzed by comparable analytical methods. Differences include both effects of laboratory performance
and effects of sample processing between the time of collection and receipt by the laboratory. Sample
processing and analytical methods for total suspended solids and suspended sediment concentration
measurements are not comparable and do not yield comparable results.
Lab differences in general appeared to be more significant and important than sample collection
methods. Generally, interaction terms of lab methods with dates or flow conditions were highly
significant components in the analyses, indicating that differences are not consistent from sampling time
to time to allow the application of simple correction factors. Differences in results between sampling
methods and labs may limit the combining of some data for certain purposes, such as for determining
trends.
Literature Cited
Edwards, T.K., and D.G. Glysson. 1988. Field methods for measurement of fluvial sediment. U.S.
Geological Survey Open-File Report 86-531, 118 p.
Intergovernmental Task Force on Monitoring Water Quality. 1995. The strategy for improving water-
quality monitoring in the United States. U.S. Geological Survey, Office of Water Data Coordination,
Reston, Virginia, 25 p.
Fishman, M.J.(ed.). 1993. Methods of analysis by the U.S. Geological Survey National Water Quality
Laboratory— determination of inorganic and organic constituents in water and fluvial sediments. U.S.
Geological Survey Open-File Report 93-125, 217 p.
Martin, G.R., J.L. Smoot, and K.D. White. 1992. A comparison of surface-grab and cross-sectionally
integrated stream water-quality sampling methods. Water Environment Research 64(7):866-876.
Melching, C.S., and R.H. Coupe. 1995. Differences in results of analyses of concurrent and split stream-
water samples collected and analyzed by the U.S. Geological Survey and the Illinois Environmental
Protection Agency, 1985-91. U.S. Geological Survey Water-Resources Investigations Report 94-
4141, 20 p.
Ward, J.C., and C.A. Harr (eds.). 1990. Methods for collection and processing of surface-water and bed-
material samples for physical and chemical analyses. U.S. Geological Survey Open-File Report 90-
140, 71 p.
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Wisconsin State Laboratory of Hygiene. 1993. Manual of analytical methods, inorganic chemistry unit.
Wisconsin State Laboratory of Hygiene, Environmental Sciences Section, Madison, Wis., (variously
paged).
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to
O\
oo
Sampler:
Sample Round:
Lab:
Sample:
USGS
NAWQA
1,2,3
USGS
NASQAN
1,2,3
/\
WDNR
1,2,3
/\
NWQL
WSLH
NWQL
WSLH
NWQL
WSLH
A//\\ A//\\ /\//\\
1c 1a 1b . 1c
1a 1b 1c 1a 1b 1c FF
A
FF-a FF-b FF-c
(Dissolved Phosphorus only)
1a 1b 1c 1a 1b 1c
/\
FF-a FF-b FF-c
(Dissolved Phosphorus only)
A
FF-a FF-b FF-c
(Dissolved Phosphorus only)
Number of Samples per Site:
3 Samplers x 3 Samples x 6 Split Samples = 54
Field Filtered 09
63 Total Samples
LEGEND
- Split sample through cone splitter
1a 1b 1c
FF - Field filtered sample (others are lab filtered)
NAWQA - National Water Quality Assessment Program
NASQAN - National Stream Quality Accounting Network
NWQL - National Water Quality Laboratory
WSLH - Wisconsin State Laboratory of Hygiene
Figure 1. Sample splitting scheme for streams.
-------
Table 1. Summary of Laboratory Methods Used by the USGS National Water Quality Laboratory
(NWQL) and Wisconsin State Laboratory of Hygiene (WSLH) for Selected Constituents
[NA, not applicable.]
Constituent
Chloride, dissolved
Phosphorus, total (as P)
Phosphorus, ortho,
dissolved (as P)
Sediment, suspended
Solids, total suspended
(residue total at 105 °C)
Chlorophyll a
Chlorophyll a
Parameter
code
00940
00665
00671
80154
00530
70953
32210
Method
NWQL
Automated flow injection; Field filtered
Colorimetry/acid-persulfate digestion
Colorimetry/Phosphomolybdate,
automated; Field filtered
Filtration or evaporation/Gravimetric*
Gravimetric
Chromatographic/fluorometric; Field
filtered
NA
WSLH
Same; Lab filtered
Auto analyzer/Persulfate
digestion
Same; Lab filtered
NA
Same
NA
Spectrophotometric (Trichro-
matic); Lab filtered
* USGS Iowa Sediment Laboratory.
Table 2. Summary of Statistical Analyses Used in Wisconsin Water-Quality Comparison Study
[NS = not significant, p>0.05; * = significant, p<0.05; ** = highly significant, p<0.01; - = not tested; NWQL = National
Water Quality Lab, USGS; WSLH = Wisconsin State Lab of Hygiene.]
Comparison
Sampling program
(NAWQA/NASQAN/
WDNR)
Sampling variability
Field vs lab filtering
Labs NWQL/WSLH
Lab variability
NWQL/WSLH
Data used
Streams
Lakes
Streams
(low flow)
Streams +
lakes
Streams
Lakes
Streams
Lakes
Total P
NS
NS
NS
-
**'
WSLH>NWQL
**>
NWQL>WSLH
*'
NWQL>WSLH
NS
Dissolved ortho
P
**
USGS>WDNR
**'
NS
NS
**'
WSLH>NWQL
NS
NS
NS
Chloride
NS
-
NS
-
**'
WSLH>NWQL
-
**'
NWQL>WSLH
-
Suspended
sediment/ total
suspended
solids
*
USGS>WDNR
-
*'
USGS>WSLH
-
USGS>WSLH
-
**
USGS>WSLH
—
Chlorophyll a
'
*
WDNR>USGS
-
**
Lab>field
-
*'
WSLH>NWQL
-
*'
NWQL>WSLH
Variable among dates.
III-269
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III-270
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Quantification of Dioxin Concentrations in the Ohio River
Using High Volume Water Sampling
Samuel A. Dinkins, Environmental Specialist
Jason P. Heath, Water Quality Monitoring and Assessment Programs Manager
Ohio River Valley Water Sanitation Commission
5735 Kellogg Avenue, Cincinnati, OH 45228
Abstract
The Ohio River Valley Water Sanitation Commission (ORSANCO) is currently involved in a sampling
program to quantify concentrations of dioxin in the Ohio River. Water quality standards and typical ambient
water column concentrations are well below the current analytical detection limit of 10 parts per quadrillion
(ppq). High volume water sampling, however, is a new technique which, for the first time, allows for the
direct measurement of in-stream dioxin levels. This is accomplished by drawing a large volume of water first
through glass fiber filters, which separate and collect the suspended solids. The filtered water then passes
through two XAD-2 resin columns that extract the dioxin in the dissolved phase. The filters and columns are
then analyzed separately to quantify dioxin levels in both the particulate and dissolved phases.
ORSANCO has successfully used high volume water sampling to quantify ambient water column
concentrations on the Ohio and Kanawha Rivers. In each sampling event, 1000 liters of water were
filtered over a twelve-hour period. Dissolved dioxin concentrations were slightly above the US EPA
water quality criterion of 0.013 ppq. Concentrations in the particulate phase were consistently more than
an order of magnitude greater than levels detected in the dissolved phase. These results demonstrate that
high volume water sampling is an effective means of attaining detection levels below the water quality
standard for dioxin, and that the sampling method could prove to be extremely valuable in quantifying in-
stream levels of other pollutants.
Introduction
In 1995, the Ohio River Valley Water Sanitation Commission, an interstate water pollution control
agency serving the Ohio River and its member states, developed and initiated the Ohio River Watershed
Pollutant Reduction Program. The program was initiated to address pollutants known to inhibit the
beneficial uses of the Ohio River and its tributaries. The long-term goal of the program is to generate the
information necessary to achieve water quality objectives utilizing a pollutant reduction strategy. Dioxin
was selected as the first pollutant to be addressed under the program due to elevated levels observed in
fish tissue collected from the Ohio River and the Kanawha River in West Virginia. The concentrations of
dioxin found in the fish suggested a possible water quality problem.
The presence of dioxins in the environment has attracted considerable attention in recent years from
the public and scientific community. These compounds are not intentionally produced, but a variety of
industrial and combustion sources have been identified. The toxic nature of dioxins at extremely low
concentrations poses a very difficult problem for scientists and regulators to address. Typical analytical
detection limits for water samples collected using conventional sampling methods ranges from one to ten
parts per quadrillion (ppq). The US EPA, however, established a water quality criterion for the protection
of human health for 2,3,7,8 tetrachlorodibenzo-p-dioxin (the only dioxin congener with a water quality
standard) at 0.013 ppq (EPA 1984). Due to the limitations of the analytical methods, a stream could have
a concentration that is two orders of magnitude greater than the water quality standard and still not be
detected. As a result of the inability to accurately quantify dioxin concentrations in surface waters that
were at levels of concern, an alternative sampling method was necessitated.
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Background Information on Dioxin
The term dioxin refers to a group of 210 different polychlorinated dibenzodioxins and polychlorinated
dibenzofurans. Only 17 of these congeners have dioxin-like toxicity, with the best known and most toxic
congener being 2,3,7,8 tetrachlordibenzodioxin (TCDD). These compounds are unwanted by-products of
various combustion and chemical processes, and have never been intentionally produced with the
exception of small quantities synthesized for scientific research. A variety of sources have been identified
including pulp and paper mills, incinerators, certain chemical manufacturing processes, wastewater
treatment plants, PCB transformer fires, wood treating facilities, automobile exhaust, and possibly forest
fires.
Dioxins have been found to cause a wide array of physiological responses in animals and humans.
Chloracne, a severe form of acne, is the only response in humans clearly attributed to dioxin exposure.
Several epidemiological studies, however, have suggested dioxins may cause cancer (Bertazzi et al 1993),
decreased growth (Guo et al 1994), decreased birth weights (Lucier 1991), delayed developmental
milestones (Rogan et al 1988), and decreased testis size (Egeland et al 1994). Based on the collective
results of various laboratory studies and limited epidemiological studies, EPA classifies 2,3,7,8 TCDD as
a probable human carcinogen (EPA 1996).
EPA (1989) developed a standardized method to evaluate the toxicity of complex mixtures of dioxin-
like compounds. This procedure assigns a Toxicity Equivalency Factor (TEE) to each of the 17 dioxin and
furan congeners with chlorine substitution in the 2,3,7, and 8 positions. Since the 2,3,7,8 TCDD congener
is the most toxic, its TEF is set at one. The TEFs of the other congeners are based on their toxicity relative
to the 2,3,7,8 TCDD congener (see Table 1). To determine the risk posed by a mixture of dioxins and
furans, the concentration of each congener is multiplied by its corresponding TEF. The resulting
concentration is expressed in terms of 2,3,7,8 TCDD equivalents (TEQ). The summation of all the TEQs
give the estimated total concentration in terms of a 2,3,7,8 TCDD equivalent concentration.
The ultimate fate of dioxins in the environment is their accumulation in aquatic sediments. Dioxins
deposited to soil surfaces strongly bind to particulate matter and are either buried in place, resuspended
into air, or are transported to surface waters through erosion. Once in water, dioxins primarily sorb to
suspended solids. These particles can be transported considerable distances downstream before settling to
the bottom (EPA 1987).
High Volume Water Sampling
Basic Concept
Current analytical detection limits for dioxin congeners for water samples collected using conven-
tional sampling techniques (e.g. discrete water sampling with a bailer or Kemmerer) is approximately two
orders of magnitude greater than EPA's water quality standard of 0.013 ppq. Considering that a body of
water could have concentrations of dioxins exceeding water quality standards and yet be undetectable, it
became obvious that a new sampling technique that allowed for concentrating dioxins from large volumes
of water was necessary. The basic concept of concentrating samples is not new to environmental
sampling. This technique has been used for several years to sample pollutant levels in ambient air. The
difficult task in concentrating large volume samples is capturing the pollutants in both the particulate and
dissolved phases without allowing significant break-through of the contaminants. In order to accomplish
this, two different pollutant removal mechanisms must be employed. Pollutants bound to the particulate
phase can be removed via a filtering system that physically removes all particulate matter. Those
pollutants in the dissolved phase, however, must be extracted from the water utilizing a substance that
attracts the pollutants, and subsequently binds to the target analytes.
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Sampling Unit
Axys Environmental Services of Sidney, British Columbia, Canada, designed the sampling unit used
by ORSANCO to collect high volume water samples. Water samples are collected utilizing a positive
displacement pump that draws river water up through an intake line and pushes the water through the
sampling unit. The intake line is made of Teflon, and is set at a depth several feet below the surface of the
water. A fine mesh screen is placed at the end of the intake to prevent very large debris from clogging the
line. The water drawn into the unit, first passes through an inline filter that removes particles greater than
140 um. This is necessary to avoid large particles from damaging the pump head. Considering dioxins
prefer to bind to small particulate matter, the amount of dioxins sorbed to these large particles is believed
to be negligible.
The water then is pushed through a four-inch glass fiber filter (OFF) that effectively removes all
particles greater than one micron. The OFF is seated in a stainless steel canister with a pressure gauge
mounted to the bottom. As the filter begins to clog with particulates, the pressure gradually increases.
Once the pressure reaches 15 pounds per square in (psi), the filter is clogged and a new filter must be
inserted. The high volume water sampling unit is designed with two GFF canisters arranged in parallel
paths. Valves on each side of the canisters permit the water to be directed through one canister at a time.
This feature allows the filters to be changed quickly with minimal disruption of the sampling. To change
the GFF, the operator simply turns the pump off, switches the valves to direct the water through the other
canister containing a GFF, and then immediately resumes pumping. The used filter is then removed from
the filter holder, and is wrapped in foil, labeled, placed in a sample container, and promptly placed on ice.
After passing through the GFF, the filtered water runs through Amberlite XAD-2 resin columns. The
number and size of the resin columns can vary depending on the users needs. The sampling unit used by
ORSANCO was designed to accommodate the use of two 75 gram (g) Teflon columns arranged in
parallel paths. Thus by pushing the water through two columns simultaneously, the time to filter the
desired volume of water is cut in half.
The resin columns dictate the rate at which water can be pushed through the system. Adequate contact
time is necessary to allow for efficient extraction of the dioxins and other organic pollutants from the
water. The XAD-2 resin is a cross-linked polymer of styrene and divinylbenzene. The extremely
hydrophobic nature of the resin attracts other hydrophobic organic compounds such as dioxins. The
dioxins prefer to attach to the resin rather than to remain dissolved in the water. Thus, the resin effectively
extracts hydrophobic organics out of the water phase and into the solid phase (Axys 1991).
The filtered and extracted water exits the resin columns, and passes through a volume totalizer and
flow meter. A digital display unit allows the user to monitor the total volume filtered, as well as, the rate
at which the water is passing through the system. When using two 75 g resin columns, 1.6 liters per
minute (1/min) is the maximum flow rate at which the resin columns can efficiently extract the dioxins
from the water.
Advantages and Disadvantages of Sampling Method
High volume water sampling offers several advantages over the use of conventional sampling
methods. The most significant benefit of the high volume water sampling method is the ability to attain
extremely low detection limits by concentrating pollutants from very large samples. The method currently
provides the only practical means of detecting dioxins at concentrations below EPA's water quality
standard of 0.013 ppq. A very large volume water sample (e.g. 1000 liters) could be collected using
conventional sampling methods and concentrated in a lab to provide comparable detection limits. This
type of sampling, however, would be impractical due to the difficulty and costs associated with the
transport of the sample to the lab. Also the liquid/liquid extraction would require a tremendous amount of
III-273
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solvent, thus creating the possibility of significant solvent blank contamination. The solvent impurities
may interfere with the analysis, and thereby reducing the accuracy of the analytical results (Axys 1991).
Another useful feature of the high volume water sampling method is that the unit can be programmed
to operate on a timer. When sampling in a protected area, such that the sampling equipment could be left
unattended, the control unit can be programmed to turn on and off at designated times. This provides a
means of compositing samples over long periods of time, while minimizing personnel time in the field.
Also, the high volume water sampling unit can be used for monitoring a variety of pollutants. The XAD-2
resin columns effectively remove hydrophobic organics such as dioxins, chlorinated pesticides, and
PCBs. Metals including copper, iron, lead, cobalt, cadmium, nickel, manganese, and zinc can be
monitored using a trace metal resin column (Axys 1991). Other types of resins for other parameters will
be developed in the future.
There are some disadvantages to using the high volume water sampling method. The equipment
needed to collect samples of this nature is rather expensive. The high volume water sampling unit uses
only Teflon and high grade stainless steel for all parts coming in contact with the sample water. The resin
columns, which are reusable, are also either made of Teflon or stainless steel. The columns must also be
repacked with clean resin after each use, which can cost several hundred dollars each time. The initial
equipment setup for an intensive high volume water sampling program can cost well over $20,000. Also,
depending on the sampling locations, field sampling can prove to be resource intensive. When sampling
in protected areas where the equipment can be safely setup and left unattended, sampling cost are
minimized. Sampling a large river from a boat, however, requires constant supervision of the sampling
unit.
Sampling Results
In 1997, ORSANCO conducted high volume water sampling at four locations (see Figure 1). The
three sites located on the Ohio River were sampled three times each, while five sampling events were
conducted at the Kanawha River site. Special emphasis was placed on sampling the Kanawha River due
to the presence of several potential and confirmed sources of dioxin located upstream. Each full round of
sampling entailed sampling one site per day for four consecutive days. One thousand liters of water were
drawn through the sampling unit at each location over a 12-hour period . Sampling was conducted from a
boat that was anchored just outside of the navigational channel. A second boat was used to collect total
suspended solids (TSS) samples throughout the sampling period.
The filters and resin columns were analyzed separately thus providing results for the particulate and
dissolved phases individually. It should be noted that some portion of the dioxins captured by the resin
columns may be attributed to dioxins bound to particles less than one micron in size that pass through the
glass fiber filter. As expected, considering dioxin congeners have a strong affinity for particulate matter,
the concentrations in the particulate phase were considerably higher than that of the dissolved phase.
Three of the fourteen high volume water samples had undetectable levels of 2,3,7,8 TCDD in the
dissolved phase with detection limits below 0.001 ppq. Dissolved concentrations for 2,3,7,8 TCDD
ranged from less than 0.001 ppq to 0.020 ppq. TEQ dioxin concentrations in the dissolved phase ranged
from 0.0073 ppq on the Ohio River to 0.242 ppq on the Kanawha River. Particulate concentrations were
consistently an order of magnitude greater than dissolved for 2,3,7,8 TCDD and TEQ dioxin
concentrations. Total 2,3,7,8 TCDD concentrations (particulate phase plus dissolved phase) exceeded the
water quality standard in 12 of the 14 samples (see Figure 2), while total TEQ concentrations ranged from
0.133 ppq to 0.906 ppq (see Figure 3).
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Current Sampling Program
Having successfully completed high volume water sampling at four locations in 1997, ORSANCO
has expanded its 1998 field sampling plan to include sampling at 14 locations. Each location will be
sampled three times, preferably under different river flow conditions. Upon completion of this sampling
program, concentrations of dioxins in the upper three hundred miles of the Ohio River will be
characterized. Also, contributions of dioxins from potential sources on the Kanawha River will be
quantified. These sources will be bracketed with a sampling location upstream and downstream of the
source.
To reduce the length of the sampling period, larger XAD-2 resin columns will be used for 1998's
sampling activities. These newly designed columns are made of stainless steel, and hold 250 grams of
resin. The larger columns have a maximum flow rate of 2.2 1/min, and will effectively reduce sampling
times from nearly 12 hours using two 75 gram Teflon columns, to approximately eight hours using one
250 gram column.
Conclusion
High volume water sampling has proven to be an effective means of directly measuring in-stream
concentrations of dioxins at levels below EPA's water quality standard of 0.013 ppq. The sampling
method allows for concentrations in both the particulate and the dissolved phases to be measured
independently. There are a variety of applications of the sampling technique including monitoring
drinking water supplies and effluent discharges from industries and municipalities. The development of
this technique provides a method by which sources could be positively identified, and their pollutant
contributions quantified. The versatility of the high volume water sampling method to be used for a
variety of pollutants other than dioxin, including PCBs, chlorinated pesticides, and several metals, makes
it an extremely valuable technique that should continue to be refined and expanded upon.
Literature Cited
Axys Environmental Systems Ltd. 1991. The Chemistry of Infiltrex Columns. Axys Environmental
Systems Ltd., Sidney, British Columbia, Canada.
Bertazzi, P.A., A.C. Pesatori, D. Consinni, M.T. Landi, and C. Zochetti. 1993. Cancer incidence in a
population accidentally exposed to 2,3,7,8-tetrachlorodibenzo-para-dioxin. Epidemiology 4(5):398-
406.
Egeland, G.M., M.H. Sweeney, M.A. Fingerhut, W.E. Halperin, K.K. Willie, and T.M. Schnoor. 1994.
2,3,7,8-tetrachlorodibenzo-p-dioxin's (TCDD) effect on total serum testosterone and gonadotropins in
occupationally exposed men. American Journal of Epidemiology 139:272-281.
Guo, Y.L., CJ. Lin, WJ. Yao, JJ. Ryan, and C.C. Hsu. 1994. Musculoskeletal changes in children
prenatally exposed to polychlorinated biphenyls and related compounds (Yu-Cheng children). Journal
of Toxicology and Environmental Health 41:83-93.
Lucier, G.W. 1991. Humans are sensitive species to some of the biochemical effects of structural analogs
of dioxin. Environmental Toxicology and Chemistry 10:727-735.
Rogan, W.J., B.C. Gladen, K. Hung, S. Koong, L. Shih, J.S. Taylor, Y. Wu, D. Yang, N.B. Ragan, and C.
Hsu. 1988. Congenital poisoning by polychlorinated biphenyls and their contaminants in Taiwan.
Science 241:334-336.
U.S. Environmental Protection Agency. 1984. Ambient water quality criteria for 2,3,7,8-tetrachlor-
dibenzo-p-dioxin. Office of Water Regulations and Standards. EPA 440/5-84-007.
U.S Environmental Protection Agency. 1987. National dioxin study: Report to Congress. Office of Solid
Waste and Emergency Response. EPA/530-SW-87-025.
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U.S. Environmental Protection Agency. 1989. Interim procedures for estimating risks associated with
exposures to mixtures of chlorinated dibenzo-p-dioxins and -dibenzourans (CDDs and CDFs) and
1989 update. Risk Assessment Forum. EPA/625/625/3-89/016.
U.S. Environmental Protection Agency. 1996. Drinking water regulations and health advisories. Office of
Water. EPA 822-R-96-001.
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West Virginia
Raccoon Cr.
Ohio
Huntington
Intake
Sampling Locations
Figure 1. High volume water sampling locations.
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Round 1
Round 2
Round 3
Gallipolis
Kanawha
Apple Grove Huntington
Figure 2. Total 2,3,7,8 TCDD concentrations (pg/L).
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Round 1
Round 2
Round 3
Gallipolis
Kanawha
Apple Grove
Huntington
Figure 3. Total TEQ dioxin concentrations (pg/L).
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Table 1. Toxicity Equivalency Factors for Dioxins
and Furans (EPA 1989).
Congener
2,3,7,8 TCDD
1,2,3,7,8 PeCDD
1,2,3,4,7,8 HxCDD
1,2,3,7,8,9 HxCDD
1,2,3,6,7,8 HxCDD
1,2,3,4,6,7,8 HpCDD
OCDD
2,3,7,8 TCDF
2,3,4,7,8 PeCDF
1,2,3,7,8 PeCDF
1,2,3,4,7,8 HxCDF
1,2,3,7,8,9 HxCDF
1,2,3,6,7,8 HxCDF
2,3,4,6,7,8 HxCDF
1, 2,3,4,6,7,8 HpCDF
1,2,3,4,7,8,9 HpCDF
OCDF
TEF
1
0.5
0.1
0.1
0.1
0.01
0.001
0.1
0.5
0.05
0.1
0.1
0.1
0.1
0.01
001
0.001
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An Alternative Regression Method for Constituent Loads from Streams
Ping Wang, Water Resources Engineer
Maryland DNR
Lewis C. Linker, Modeling Coordinator
USEPA CBPO
Abstract
Three regression models, the 7-parameter Minimum Variance Unbiased Estimator (MVUE), a multi-
variance classical Ordinary Regression Method (ORM), and a newly developed Unbiased Regression
Method (URM), for fluvial loads from the Susquehanna and James Rivers are compared. The ORMs and
URMs are both based on the 7-parameter log-linear regression equation, with some modifications to the
flow and/or time parameters, but they differ in bias corrections. The ORMs apply an unbiased least-
squares regression methodology, yielding unbiased log[load] estimates, but having bias in the load
estimate due to log-to-normal transformation without bias correction. The ORMs generally have less
standard error of estimate (SE) in daily loads than MVUE. The MVUE, having low total bias, is better in
average load estimates in big time steps than ORM. The URMs apply the newly developed bias
corrections, yielding a virtually unbiased load estimate (total bias < 0.005% in the regression period), and
generally having lower SE than MVUE and ORM. For extrapolation to the regression in the extended
period where the observations are not used to derive the regression, the ORMs generally have a lower SE
than MVUE and URM. There is no unique method which is better than the others, nevertheless, URM
and ORM can be alternative approaches, or supplemental tools, to the MVUE or other regression
methods.
Introduction
Regression models have been applied extensively to estimate fluvial transport (Miller 1951, Bradu
and Mundlak 1970, Cohn et al 1989, 1992, Preston et al 1989, Belval et al 1995, Wang et al 1998). A
concentration - flow relationship based log-linear regression model, the Minimum Variance Unbiased
Estimator (Cohn et al 1989, 1992), has been widely used to estimate sediment and nutrient loads to the
Chesapeake Bay, USA (Cohn et al 1989, 1992, Belval et al 1995, Wang et al 1998).
Various regression models for stream loads have been established (Miller 1951, Bradu and Mundlak
1970, Cohn et al 1992, Preston et al 1989, Walling 1977). Most of them are log-linear regression
differing primarily in their regressor terms. The log-linear regressions use logfload] or logfconc] as the
estimator. The logfload] or log[conc] derived from the regression are then transformed from the log space
to the normal space, usually by exponential of the log values directly. There are two types of bias
associated with the log-linear regressions. Type-1 bias is due to the method used to derive the regression
equation. Type-2 bias is due to the transformation of log-to-normal space from the regression results.
Type-1 bias can usually be avoided by choosing an unbiased regression derivation, such as the least-
squares method (under the classical statistical assumptions, the Gauss-Markov theorem (Grewal 1990)),
as used in this paper. Therefore, this paper will only discuss Type-2 bias, the generation of Type-2 bias,
and methods for bias correction. In this discussion the classical regressions without bias correction will
be classified as the ordinary regression.
Bradu and Mundlak (1970) proposed a Minimum Variance Unbiased Estimator (MVUE) which
involved a bias correction. Cohn et al. (1992) developed their MVUE (will be simply called the MVUE
in this paper, because we will not discuss other MVUE regressions than Cohn's MVUE), which had two
improvements over the precedents' log-linear regression for stream loads. One was the establishment of
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additional regressor terms known as the 7-parameter multi-variance log-linear regression. These
additional terms accounted for seasonality and long term load trends. Another improvement was by
introducing a bias correction. Cohn et al (1992) compared the MVUE results with the rating curve
method (RC) and quasi maximum likelihood estimator (QMLE). The MVUE had lower total bias (about
1-6% for Susquehanna, in comparison to 0%- -24% in RC or -6% in QMLE).
Theoretically MVUE is an unbiased estimator, however, it is sensitive to the assumption of normality
in the log space (Thomas, 1988; Duan 1983, Cohn et al 1992). It may be a biased estimator if the
distribution is not lognormal (Gilbert 1987). Although MVUE was successful in reducing biases
compared to previous studies, there are still certain degrees of bias in the computational outputs, which
may be as low as 1-6% for the Susquehanna, or higher in other rivers such as the Choptank, 4-12%, and
the Potomac, 8-26%. The standard deviations had no virtual differences among the three comparing
methods (Cohn et al 1992, Table 5). Therefore, in this study a new approach in bias correction was
developed, which may produce a minimal calculated bias and low standard error of estimate.
The ordinary regression without bias correction using Cohn et al. (1992) 7-parameter regressors has
shown better results than the RC and QMLE (which had fewer regressors). Therefore, this study was
primarily based on the 7-parameter regression equation, which is simply called ORM if no bias collection
is involved (to distinguish from MVUE, or RC or QMLE). The newly developed bias correction method
developed in this project was applied to the ORM outputs, which is called URM (Unbiased Regression
Method). In addition to the use of Cohn's 7 parameter equation, two other equations which were
modified from Cohn's were used. Both were applied with ORM and URM. In order to distinguish these
methods, the three regressions without bias correction are called ORM1, ORM2, and ORM3, and the
corresponding regressions with bias correction are called URM1, URM2, and URM3. Detail equations
can be find in the text.
This paper will present our approaches, discuss the correlation of load with flow and time, and then
present the regression equations setup, regression results, and the comparison among MVUE, ORMs, and
URMs.
Methodology
In accordance to most published regression methods for fluvial loads, the regression methods for load
estimation are based on known daily mean flows and known discrete concentrations (or loads) to predict
daily constituent loads. The observed concentrations are assumed to represent the average values on the
reported dates. All of the regression are based on the classical statistical assumptions.
The regressors were based on the multi-variance regression equation of Cohn et al. (1992), and its
modifications. The estimator-regressors correlation was analyzed with observed data using simple
graphics. For ORM the estimator term was logfload] (log[conc] for MVUE) in a selected period (e.g.,
1986-1990); some regressor terms were modified from MVUE; new unbiased correction methods
(URM), were applied over ORM. We applied the same data with MVUE, ORM and URM. The outputs
were the predicted daily loads in an expanded period (e.g., 1984-1992, with the extended periods of
1984-1985 and 1991-1992) according to known daily flows. In order to evaluate these methods the daily
loads from ORM, URM and MVUE were compared with observed data.
In the derivation of regression equations for ORM and URM, the principle of least-squares method
was applied to produce estimates that were the Best Linear Unbiased Estimates (BLUE) under the
classical statistical assumptions. Type-2 bias will result in the transformation from a log space to a
normal space for ORM. The software, Estimator_94 (kindly provided by the US Geological Survey), was
used for MVUE regression.
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Note: Here, an approach different from Cohn et al. (1992) was utilized in evaluating regression
methods. Cohn et al. applied Thomas' (1988) split-sampling study to select subpopulations of 75 samples
from several hundreds of observations in 9 years (1980-1988). Split-sampling study is a good evaluation
method for a large number of samples in which each randomly selected subpopulation is normally
distributed. However, it is possible that some important data such as storm-flow samples, which occur
less frequently, might not be selected in some subsets of samples. In fact, it was observed that the
standard deviations (SD) in Cohn et al. (1992) were the same for the different methods for a specific
constituent in a specific river. Therefore, in this study using a small number of sub-samples from rather
irregularly varied samples was avoided to evaluate the regression methods which may require sufficient
representative samples. Instead, all available samples from those years which have sufficient
observations for regression were used.
This evaluation of regression results will consider standard error of estimate (SE) based on daily
data, as well as the goodness in estimating yearly loads, in both the regression period (i.e., the years
which observed data were used for regression) and the extended periods (i.e., the years which observed
data were not used for regression, but used to check regression results). Surely, further analysis with
Thomas (1988) split-sampling study may be useful to evaluate how well a regression method could be
applied to various sampling conditions (including those sample sets deviating from the classical
statistical assumptions), however, this is beyond the scope of this study.
Selection of Observed Data
Water quality data of the constituents, dissolved nitrate + nitrite (NO2.3), dissolved phosphate (PO4),
total phosphorus (TP), total nitrogen (TN), total organic nitrogen (OrN), total Kjeldahl nitrogen (TKN),
and sediments (Sed), from the Susquehanna River (Station 1578310), Maryland, and the James River
(Station 2035000), Virginia, USA, in certain periods (which will be detailed in the following paragraphs)
were used for this study. These observed data were from the USEPA STORET database, which in turn
were mainly from the USGS water quality database. The data included concentrations of constituents and
flows (Q) on specific dates (t). Note: some of the data was derived through simple calculations with the
assumptions of TN = OrN+NH4+NO2.3, and TP = OrP+PO4.
The statistical significance of a linear regression depends on the correlation of the estimator with the
regressors and the quality of the observed data (including their representatives to the whole period). The
stations and periods selected for regression were under good sampling management by USGS, which
includes regular sampling (semi-monthly or monthly) in baseflow conditions and additional sampling
during stormflow conditions.
It was observed that the data after the beginning of 1986 for the Susquehanna River are more
representative—it covers regular sampling (2 samples one month) representing base flow conditions, and
more frequent sampling during storm flows (however, 1991 data cover less storm flows). Data for 1986-
1990 were used for regression derivation, and 1984-1985 data and 1991-1992 data together with 1986-
1990 data were used to check the goodness of estimation in the periods 1984-1985, 1991-1992, and the
regression period 1986-1990, respectively. Therefore, in this study all of the observed data in the years
with good sampling design were used for regression, and it was assumed that the observed data were
"true" values.
TN, TP, NO2.3, PO4, OrN, and Sed were selected for the Susquehanna River. About 238 observations
from 1986-1990 (an average of 48 observations per year) were used for each constituent regression.
About 33 observations from 1984-1985 were available for the comparison of model estimation in the
lower extended years, while 42 observations from 1991-1992 were available in the higher extended
years.
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With the same reasoning for the James River, 7/88-6/92 data were selected for regression derivation,
while 7/88-6/92 and 7/92-6/94 data together with 7/86-6/88 data were used to check the goodness of
estimation for the three periods. As TN and OrN data in 7/88-6/92 from the James River were not
sufficient for MVUE to generate reasonable values of load estimate (although ORM could), only TP,
NO2.3, PO4, Sed, and TKN were selected. About 90 observations for Sed or 200 observations for other
constituents in the four years from 7/88 to 6/92 were used for regressions respectively, while about 14
observations were available for the lower-extended-year comparison and about 69 observations were
available for the upper-extended-year comparison.
Correlation Analysis
Correlation analysis are important to setup regression equations. Based on a simple graphical
method, the writer agreed generally with previous studies (Belval et al 1995, Cohn et al 1992). Therefore,
the correlation analysis will only be reviewed briefly.
The previous work (Belval et al 1995, Cohn et al 1992) showed that the correlation of concentrations
with flow or time is not as significant as the correlations of load with flow or load with time. The most
significant correlations are flow with time, load with flow, and load with time. Surely, if load is merely
dependent upon flow, and flow is a function of time, then the explicit expression of seasonal change of
load with time may be the implicit correlation of flow with time. In such a case, time may not be
considered as a regressor when flow is considered as a regressor for load estimate, otherwise a
colinearity would occur which leads to an instability in the estimates and high standard errors. However,
in addition to flow, other mechanisms or factors may cause differential responses of loads with time;
therefore, time is considered as a regressor. This study agreed with the previous work (Preston et al 1989,
Cohn et al 1989, 1992): the load of one constituent can be considered as a function of flow, as well as the
time, and, consequently, the 7-parameter equation of Cohn et al. (1992) was adopted in this simulation.
Regression Derivation for Load Estimates
Setting up Regression Equations
The 7-parameter multi-variance log-linear regression equation (Cohn 1992, Belval 1995) is used:
ln[C] = PO + p,(/n[QJ) + P2(/4QJ)2 + P3sin(2itT) + p4cos(27tT) + P5T + p6T2 + e (I),
where: ln[ ] = natural logarithm function, C = concentration of a constituent (in mg/1), Q = the
instantaneous discharge (in cubic meter), T = time in years, sin = the sine function, cos = the cosine
function, PX = coefficient of the regression model, 71 - 3.14159, e = model errors. Note: Load: L = Q * C
(in kg/day, but ton/day for sediment).
Eq. I for ORM and URM regressions was modified:
1. Using log[load] on the left-hand side of the equation:
ln[L] = P0 + p,(/n[Q]) + p2(/«[QJ)2 + p3sin(2:tT) + p4cos(27iT) + p5T + P6T2 + e (II).
It would be advantageous to use log[load], instead of log[conc], on the left-hand side of the
equation to emphasize the correlation of load with flow (the importance of which will be
discussed later). The load estimates would have virtually no difference with log[load] or
logfconc] in these two specific equations.
2. Adding a term of the reciprocal of /rc[Q] to represent more variable cases of load-flow
correlation:
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ln[L] = p0 + P,(/n[Q]) + p2(/n[Q])2 + P3/(/«[Q]) + p4sin(27iT) + p5cos(27iT) + p6T + P7T2 + e (IE).
3. Using Q, Q2 and \/Q, instead of ln[Q], (/n[Q])2 and \/ln[Q] for the constituents with more flow-
dependency, such as sediment.
ln[L] = P0 + P,Q + P2Q2 +p/Q + p4sin(27iT) + p5cos(2nT) + p6T + p7T2 + e (IV).
Notations for Regression Derivation
The equation set for regression can be denoted with matrix notation. For a k-parameter (bk)
regression with the estimator E (log[conc] for Eq. I or log[load] for Eq. H-IV) and k regressors Xk (such
as /n[Q] and others) from n samples, a set of n equations can be set up:
E = X .......................... (j), where
E, 1, /rc[Q,J, ...other k-1 regressors PO
Ej 1, /n[Q2], ...other k-1 regressors P,
E=.... X= ........ B =
En 1, ln[Qn], ...other k-1 regressors
The equation-set (j) can be used to estimate log[load] or log[conc] (denoted as E*, with respect to the
"true" value EA) for any dates with known Q, with errors which are usually expressed as residuals (RE).
The estimated load would be: L - exp(E) for Eqs. H-IV, or L = exp(E * Q) for Eq. I.
Let's further denote the estimated loads as L*, true loads as LA, and residuals as RL, yielding LA = L*
+RL.
Bias Due to Transformation from Log-Space to Normal-Space
If the least-squares method is employed for a Best Linear Unbiased Estimate (BLUE), the curve of
estimates (E*) has an overall lower difference compared to the corresponding true values (EA) than with
other regression approaches. There would be no discrepancy between the expected value of the estimator
and the population parameter being estimated. From a statistical point of view, with reference to an
estimate of 5.0, the underestimate (with a true value of 5.1) would have the same chance with the same
magnitude to that of overestimate (with a true value of 4.9). The ratio of underestimate to overestimate is:
15.1 - 5 / 4.9 - 5 I = I 0.1 / - 0.11 = 1. Therefore, ORM (a BLUE method) for log(load) estimate is unbiased.
However, when the estimated log values are transformed from log space to normal space, a bias would
occur. The ratio of exponential underestimate to the exponential overestimates is: I exp(5.1) - exp(5.0)l / 1
exp(4.9)-exp(5.0)l = >l.
The underestimated load would be greater than the overestimated load after the transformation for
the equal amount of underestimated and overestimated log[load]. This agrees with Cohn et al. (1992) that
without bias correction load is generally underestimated for the log linear regressions (such as RC or
ORM). This is the reason that MVUE has been a useful estimator applied by many researchers
extensively (Belval et al 1995, Wang et al 1998). The magnitude of bias depends upon the magnitude of
deviation in logfload]. The lower the deviation of log(load) estimate is, the smaller the bias due to the
log-to-normal transformation.
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Regression Used in This Paper
1. MVUE
MVUE uses Eq. I and applies the unbiasing correction, LAMVUE = (L*) §m. where gm is a Bessel
function with the variables of estimated variances (Cohn et al 1989, 1992).
2. ORM
ORMs used Eq. II-IV for the regression derivation, without an unbiasing correction.
A)ORMl:UsingEqn.
B) ORM2: Using Eq. IV for sediments and Eq. HI for other constituents.
C) ORMS: Using Eq. n, but the time in Terms 6 and 5 is a pure decimal time of the year, denoted
as t.
The equations of ORMs are similar to that used for MVUE, with the exception log[load] (instead
of log[conc]) on the left-hand site of the equation. The equation of ORM2 has modified flow
terms; either Eq. HI or Eq. IV was used depending on the correlation between load and flow; Eq.
IV is for the constituent which load has high correlation (exponentially) with flow. The least-
squares method was applied in the regression derivation.
The estimate of E*ORM (i.e., log[load]) is unbiased, however, the estimate of L*ORM (i.e., load) is
biased due to the transformation from log space to normal space.
3. URM
URM used ORM equations and an unbiasing correction over ORM's results: LAURM = (L*ORM)
(U) (Y), where U is a function of Q and t, and Y is the correction coefficient to be derived by
URM. The least-squares method was applied in the URM derivation.
A) TJRM1: The unbiasing was performed over ORM1 with the terms of U as l/exp(l/Q) +
(exp(t) + exp (-t))/2 + ln[Q] + t for sediment and other constituents which show significant
correlation of load with flow, or l/exp(l/Q) + (exp(t) + exp(-t))/2 for other cases.
B) URM2: The unbiasing was performed over ORM2 with the terms of U as l/exp(l/Q) + (exp(t)
+ exp (-t))/2 + sin(2irT) + cos(2TrT) for sediment or other constituents which show significant
correlation of load with flow, or l/exp(l/Q) + (exp(t) + exp(-t))/2 for other cases.
C) URM3: The unbiasing was performed over ORMS with the terms of U as l/exp(l/Q) + (exp(t)
+ exp (-t))/2 + ln[Q] + t for the constituents from the James River, or ln[Q] + sin(t) + cos(t) for
the constituents from the Susquehanna River.
Because the bias correction of URM directly applied load as the estimator with least-squares
regression derivation, the load estimate (L*URM) is unbiased.
Load Estimate
The loads or concentrations of PO4, NO22.3, TP, OrN, TN, and Sed for the Susquehanna River, and
PO4, NOX, TP, TKN and Sed for the James River, together with flow and time were input into the above
equations, using 1986-1990 data for the Susquehanna River and 7/88-6/92 for the James River to derive
regression, producing outputs of estimated daily loads in 1984-1992 for the Susquehanna River and 7/86-
6/94 for the James River.
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Results and Discussions
Introduction of Output Tables from Regression Results
The results of the regression for the selected constituents by each regression method are analyzed by
the comparison with observed loads. Residuals (i.e., simulation - observation) are the major parameters.
Therefore, only those days with observed data will be considered, and the set of all associated residuals is
considered as the whole sample and assumed to represent the whole population.
The whole period is divided into three sub-periods, the regression period, the lower extended period,
and the upper extended period as defined in the section of Selection of Observed Data. Due to limited
space of publication, only three parameters are listed for each sub-period: 1) SR: summation of residuals
(defined as £ ( est - obs ), 2) Bias (B = 100 [SR / £ obs]), and 3) SE: standard error of estimate (%/ £ (
est - obs )2/(n-2)), where obs is the observed value, est is the estimated value, and n is the number of
samples. The results are compared only for those days with observed data, which are regarded as
representative of the corresponding sub-periods.
SR represents the difference between the sum of estimated and the sum of observed, which has a
similar meaning as the bias. Based on the sign convention of this paper, a negative value of SR means
underestimates, overall. An SR can be very small when both under-estimates and over-estimates are very
big but are close. Therefore, a low SR may not guarantee a regression to be a good estimator. While the
standard error of estimate (SE) provides the statistics for deviations away from the regression line, it
measures the amount of spread of the sample points about the regression line. Although the multiple
coefficient of determination, R2, is also a useful statistic measure for multiple regression, which gives an
indication of how well the multiple regression equation actually fits the available sample data. Both SE
and R2 can be used to test the goodness of a regression with respect to "true" values. This discussion will
be primarily based on the SE values. Standard deviation (SD), which involves deviations away from the
mean for a set of samples or estimates, is also a useful statistic measure, but will not be discussed in this
paper either excepting when citing Cohn et al. (1992) paper.
Comparison of Bias
Cohn et al. (1992) compared bias of MVUE with other methods. The values of bias of MVUE for the
Susquehanna River in this calculation (Table 1) are similar to (but less than) those in Cohn et al. (1992).
The differences may be due to 1) different time periods were used for regression, and 2) Cohn et al.
statistics were based on the average B of subsamples, while this paper was based on all available
observed samples in a period. A similar conclusion to Cohn et al. (1992) was derived from our
calculations, in that in the regression period MVUE has lower bias than other regressions without bias
corrections. ORMs are usually underestimated due to Type-2 bias. These calculations further show that
the URM has virtually no bias, <0.01%, in the regression period, while MVUE still has considerable
calculated bias (most are positive bias).
Cohn et al. (1992) did not simulate for extended periods. It is conceivable that biases are usually
more significant in the extended periods than in the regression period by most methods. Tables 1 and 2
show that in the extended period, URMs have more or nearly equal cases with lower B values comparing
ORMs, and ORMs generally have more cases with lower B than MVUE.
SE versus SR as Evaluating Parameters
This section will use an example to show that a method with lower total SR may have more chances
of higher SR in split yearly or daily loads if SE is large.
m-287
-------
Let's pick out one sample set with lower SE but much higher SR in ORM (ORM3) than in MVUE,
such as TP from the James River. Table 2 also lists yearly SR for TP. In the regression period, SE is 9130
and 9549 for ORM3 and MVUE, respectively, and SR is -307341 and 49035 for ORM3 and MVUE,
respectively. Surely, for the total loads during the entire regression period (7/88-6/92), MVUE may have
a closer estimate because of a lower SR. However, if the period is split yearly (into four years), the
situation would be different. ORM3 has lower SR in two years (7/88-6/89 and 7/90-6/91), while MVUE
has lower SR in other two years (7/89 -6/90 and 7/91-6/92). This means that although MVUE has lower
SR in the entire regression period (7/88-6/92), if the period is subdivided into years, then ORM3 and
MVUE could have same chances to have lower yearly SR. If the period is subdivided into smaller steps
(monthly or daily), it is possible that ORM3 may have more chances to have lower SR than MVUE, as
long as ORM3 has lower SE than MVUE. Moreover, as ORM3 is lower in both SE and SR than MVUE
for TP from the James River in the extended years. Consequently, ORM3 has lower SRs in all the split
four extended years. The analysis on other parameters where both SR and SE are lower in URM or ORM
than in MVUE, the chances of lower yearly or monthly SR would be higher in the formers. Surely, a
higher chance of lower yearly SR does not means the corresponding method is better. The above analysis
is to show that SE is an important parameter for testing the goodness of a regression method, especially
for daily estimates.
Comparison of the Results From MVUE and ORMs
• In the regression periods: MVUE has more chances of lower SE than ORM1, while maintaining
nearly equal chances of lower SE to ORM2 and ORM3. MVUE usually has lower SR than
ORM1, ORM2 and ORM3.
• In the extended periods: ORM1, ORM2 and ORM3 generally have lower SE than MVUE, and
usually have lower SR than MVUE.
ORMs are usually fairly good in load estimates in the extended periods. A good estimation in
extended periods may be desirable in many cases. For example, there are sufficient data for storm flow
and base flows in 1986-1995, however, not for storm flows and base flows in 1984-85 and 1996. When
using a regression method to estimate loads during 1984-1996, it may be better not to include the 1984-
85 and 1996 data in the regression derivation, while 1984-85 and 1996 loads will be estimated from the
regression equation established by 1986-1995 data. In such cases, ORM would be preferred. Similarly, a
regression good in the extended period would enable users to use the established regression equations in
the publication (which were based on detailed study with sound samples in a certain period) for their
work.
Nevertheless, it should be noted, that ORMs generally yield an under-estimate in overall loads due to
the down bias by the transformation from logfload] space to load space. From the discussion of bias
generation it is understood that if log[load] can be closely estimated, then the bias due to the
transformation may be less significant.
Comparing URM with ORM and MVUE
• In the regression period: SRs of URM are always close to zero, indicating URM is a virtually
unbiased estimator; while SRs of MVUE are still high, yet generally lower than those of ORM.
SEs of URMs are lower than those of their ORM counterparts, indicating that URM improves the
regression over ORM after the unbiasing correction. Even though ORM1 for some constituents (e.g.,
sediment of the Susquehanna River, Table 1) has a higher SE than MVUE in the regression period, its
corresponding URM1 usually has lower SE than MVUE.
m-288
-------
• In the extended period: URM and ORM have nearly equal chances of lower SE and SR values,
and have more chances of lower SE and SR than MVUE.
The above demonstrates that the newly developed unbiasing methods (URMs) not only reduce SR,
but also reduce SE with respect to ORMs. Further more, SE and SR of URM are usually lower than those
of MVUE. Therefore URM is regarded as a better method. However, URM is sensitive to the correlation
of constituent loads with flow and time. Correlation analysis is important in selecting equations,
otherwise some unrealistic values may be generated in some cases.
Correlation Analysis Is Important in Regression Model
For a regression equation to be significant, it is better that the estimator has a significant correlation
with regressors. The correlation analysis from the earlier section indicates that log[load] is significantly
correlated with logfflow], while log[conc] is not. Therefore, the use of log[load] instead of log[conc] as a
regressor is recommended. However, the regressions with the estimator of either log[conc] or log[load] in
Eqs. I and n generate the same results. This does not mean that the correlation is not important. Because
in this specific equation, logfconc] on the right-hand side can be expressed with logfload] - log[flow],
and the regressors contain the log[flow] term on the left-hand side, therefore, Eq. I is equivalent to Eq. n
with one more unit of log[flow] which is included in coefficient p\. Therefore, the load estimate with
both equations would have no differences. This does not mean that the estimator-regressors correlation
condition is not important in the regression. If one removes log[flow] but keeps the other flow terms
(such as 0og[Q])2 and/or \/Q and/or 1/Q), then the load estimates would be different between the
estimators with log(C) and log(load). This demonstrates the importance of correlation (load, instead of
concentration, with flow) on regression.
The correlation analysis showed that the time terms may not affect on load as significantly as flow
does. ORM and URM were applied on the regressions without the regressors containing time. Some
results were close to those produced from the regression with all regressors, while some were not. This
may reflect the importance of load-time correlation. The Cohn's 7-parameter equation gave good
considerations on the variation of concentration with time.
Usually there is no unique regression equation for different constituents. Load-flow correlation could
be quite different among constituents depending on constituents behaviors and the overall characteristics
of the watershed. It is agreed with Preston et al. (1989) that the choice of an approach for estimation
should be based on the nature and characteristics of the data that will be utilized. It is recommended to
study the correlation of estimator and candidates of regressor when developing a regression model.
Recommended Method: URM, or a Combination of URM with ORM and MVUE
Because URM generally has lower SD and SR than ORM or MVUE, URM is considered to be a
better method. However, because the unbiasing is based on observed data, URM may generate greater
deviations than ORM in the extended periods in some cases. Therefore, a certain combination of URM
with ORM and MVUE may be applied.
This study is a preliminary one. A study on the applications of regression methods to various data
may be useful in further evaluation of the methods, and could provide suggestions to develop better
regression methods for load estimate.
m-289
-------
Conclusions
This study presents a new approach of a regression model to estimate fluvial loads based on the
correlation of load with flow and time. The URM is a virtual unbiased estimator with low SE and SR,
having improvement over ORMs and MVUE in some aspects. Because of the quality and representativity
of samples, and the correlationship of loads or concentrations with stream factors are different among
streams, there is no unique form of regression which is better than the others in estimating load of
constituents, especially if some of the effective stream factors are not (or cannot be) fully simulated.
Therefore, comparing a few methods and choosing a suitable one may be practical for more accurate
estimates. Nevertheless, the combination of URM with ORM and MVUE, is generally recommended.
References
Belval, D.L., P.J. Campbell, S.W. Phillips, and C.F. Bell. 1995. Water quality characteristics of five
tributaries to the Chesapeake Bay at the fall line, Virginia, July 1988 through June 1993. USGS
Water-Resources Investigations Report 95-4258, USGS, Richmond, Virginia, 71pp.
Bradu, D., and Y. Mundlak. 1970. Estimation in lognormal linear models. J. Am. Stat. Assoc. 65(329):
198-211.
Cohn, T.A., D. L. Caulder, E.J. Gilroy, L.D. Zynjuk, and R.M. Summers. 1992. The validity of a simple
statistical model for estimating fluvial constituent loads: an empirical study involving nutrient loads
entering Chesapeake Bay. Water Resour. Res. 28(9): 2353-2363.
Cohn, T.A., L. L. DeLong, E.J. Gilroy, R.M. Hirsch, and D.K. Wells. 1989. Estimating constituent load.
Water Resour. Res., 25(5): 937-942.
Duan, N. 1983. Smearing estimate: A nonparametric retransformation method, J. Am. Stat. Assoc.
78(383), 605-610
Gilbert, R.O. 1987. Statistic methods for environmental pollution monitoring. Nostrand Reinhold Co.,
NY. 313pp.
Gilroy E.J., R.M. Hirsch, and T.A. Cohn. 1990. Mean square error of regression-based constituent
transport estimates. Water Resour. Res. 26(9): 2069-2077.
Grewal, P.S. 1990. Methods of Statistical Analysis. Sterling Publishers, New York, New York, 1304pp.
Miller, C.R. 1951. Analysis of flow-duration, sediment-rating curve method of computing sediment yield,
report. U.S. Bur. of Reclam., Denver, Colo. 15 pp.
Preston, S.D., VJ. Bierman, Jr., and S.E. Silliman. 1989. An evaluation of methods for the estimation of
tributary mass loads. Water Resour. Res. 25(6): 1379-1389.
Walling, D.E. 1977. Assessing the accuracy of suspended sediment rating curves for a small basin. Water
Resour. Res. 13(3): 531-538.
Wang, P., L.C. Linker, and J. Storrick. 1998. Chesapeake Bay Watershed Model Application &
Calculation of Nutrient & Sediment Loadings, Appendix G: Observed data used for calibration, a
regression model, and a confirmation scenario of Phase IV Watershed Model. EPA/CBPO document
(in preparation; to be printed in 8/98).
Thomas, R.B. 1988. Monitoring baseline suspended sediment in forested basins: The effects of sampling
of suspended sediment rating curves. Hydrol. Sci., 33(5): 499-514.
m-290
-------
Table 1. Summary Analysis of Regression Results for Susquehanna River
MVUE
ORM1
OKM2
ORM3
URM1
URM2
URM3
1986
1990
1984
1985
1991
1992
1986
1990
1984
1985
1991
1992
1986
1990
1984
1985
1991
1992
1986
1990
1984
1985
1991
1992
1986
1990
1984
1985
1991
1992
1986
1
1990
1984
1985
1991
1992
Sediment
SR -41461
B (%) -2.72
SE 5441
SR
B (%}
SE
SR
B (%)
SE
TP
SR
B (%)
SE
SR
B (%)
SE
SR
B (%)
SE
-215857
-41.72
30928
-44961
-19.43
4533
50586
1. 85
8279
-319776
-55.98
44488
-163031
-44.22
8568
N02-3
SR 1066859
B (%) 2.09
SE 54040
SR
B {%)
SE
SR
B (%)
SE
P04
SR
B (%)
SE
SR
B (%)
SE
SR
B (%)
SE
OrN
SR
B (%)
SE
SR
B (%)
SE
SR
B (%)
SE
TN
SR
B (%)
SE
SR
B (%)
SE
SR
B (%)
SE
-1251263
-15.47
126364
-1078520
-9.75
200592
26155
7.45
1191
-26568
-34.16
2460
-39178
-45.27
2628
299553
2.68
77772
179608
28.54
72585
264146
36.08
29808
697864
0.92
85324
-2294232
-15.56
262134
420569
3.26
175605
-147828
-9.69
5594
-180743
-34.94
26247
-38538
-16.66
4245
-278516
-10.18
8964
-114589
-20.06
30452
-33449
-9.07
5985
-1000958
-1.96
53880
-828134
-10.24
122719
-498084
-4.50
192654
-73407
-20.90
1253
-28480
-36.62
2472
-39489
-45.63
2473
-1284436
-11.48
76463
70992
11.28
43293
125768
17.18
24351
-1615415
-2.12
85387
-2301751
-15.61
254135
376359
2.92
175125
-78108
-5.12
5074
171202
33 .09
44637
-39017
-16.86
4279
-314880
-11.51
8587
-151791
-26.57
38197
-43008
-11.67
5926
-1049959
-2.06
54217
-883883
-10.93
120605
-541081
-4.89
193263
-73233
-20.85
1250
-27943
-35.93
2507
-39112
-45.19
2479
-1304294
-11.65
76243
74661
11.86
43916
124179
16.96
24924
-1655845
-2 .17
85145
-2347030
-15.92
263020
354980
2.75
175629
-137124
-8.99
5289
-189936
-36.71
26944
-31452
-13.59
3853
-274996
-10.06
9001
-179279
-31.39
31379
44837
12.16
6576
-847178
-1.66
51419
-28318
-0.35-
140883
-993918
-8.98
215750
-71259
-20.29
1208
-34924
-44.90
2561
-22954
-26.52
3059
-1232086
-11.01
78410
-110785
-17.60
37197
152902
20.89
46704
-1404027
-1.84
84036
-2757639
-18.71
241133
1548894
12 .01
183615
-2
0.00
4005
-29952
-5.79
22358
32194
13.91
6492
-12
0.00
8872
-82394
-14.42
31222
17639
4.78
5941
-229
0.00
51627
-781002
-9.65
120896
-205480
-1.86
191178
-7
0.00
1197
-16377
-21.06
2381
-27341
-31.59
2389
2
0.00
75237
38041
6.04
25392
372871
50.94
32606
-57
0.00
84027
-2192558
-14.87
245994
795298
6.17
174523
-3
0.00
4934
165669
32 .02
41531
-17007
-7.35
4443
-13
0.00
8412
-103072
-18.05
36760
5251
1.42
5809
-231
0.00
51798
-835878
-10.33
117597
-209292
-1.89
191143
6
0.00
1196
-15868
-20.40
2446
-27000
-31.20
2392
6
0.00
75051
44865
7.13
25635
365411
49.92
32594
-63
0.00
83689
-2225755
-15.10
252472
787307
6.11
174663
-1
0.00
4081
-81061
-15.67
15227
15423
6.67
4171
-14
0.00
8918
-172162
-30.14
33788
68521
18.59
6186
-231
0.00
50461
-12863
-0.16
140535
-1179877
-10.66
213823
-2
0.00
1117
-27098
-34.84
2513
-14456
-16.70
3127
7
0.00
75600
-173444
-27 .56
68296
288572
39.42
62791
-65
0.00
84708
-2570606
-17.44
237529
1541215
11.95
179933
m-29i
-------
Table 2. Summary Analysis of Regression Results for James River
MVUE
ORM1
ORM2
ORM3
URMl
URM2
URM3
Jul-
Jun-
Jul-
Jun-
Jul-
Jun-
Jul-
Jun-
Jul-
Jun-
Jul-
Jun-
Sediment
-88 SR
B (%)
-92 SE
-86
-88
-92
-94
-88
-9V
-86
-R8
-92
-94
Yearly
7/86
7/87
7/88
7/89
7/90
7/91
7/92
7/93 -
Jul
Jun-
Jul-
Jun-
Jul-
Jun-
Jul-
Jun-
-88
-92
-86
-88
-92
-94
-88
-92
Jul-86
Jun-88
Jul-
Jun-
Jul-
Jun-
Jul-
Jun-
Jul-
Jun-
-92
-94
-88
-92
-86
-88
-92
-94
SR
B (%)
SE
SR
B (%)
SE
TP
SR
B (%)
SE
SR
B (%)
SE
SR
B (%)
SE
230091
26.30
16147
4439374
2573.59
1393751
40124
52.58
4922
49035
2.13
9549
2208164
1313.63
609131
1586797
194.41
44983
SR for TP
6/87 2194623
6/88 13541
6/89 175170
6/90 25608
6/91 -35338
6/92 -116405
6/93 326082
6794 1260715
N02-3
SR
B (%)
SE
SR
B (%)
SE
SR
B (%)
SE
P04
SR
B (%)
SE
SR
B (%)
SE
SR
B (%)
SE
TKN
SR
B (%)
SE
SR
B (%)
SE
SR
B (%)
SE
80682
2.86
5750
-95594
-65.91
19878
-235656
-22.92
6585
6703
1.76
1425
153352
679.99
22690
24851
32.68
674
67245
1.07
21386
2148608
770.59
597733
5208783
262.89
129642
-11906
-1.36
12755
222697
129.10
70282
-33917
-44.44
6908
-338520
-14.70
11818
299640
178.26
86482
-304017
-37.25
14444
303469
-3829
20381
-33397
-43721
-281783
-201186
-102831
-140153
-4.97
5829
-72489
-49.98
15189
91800
8.93
4530
-40172
-10.58
1430
29898
132.57
5158
-41730
-54.88
896
-835754
-13.24
24389
402573
144.38
126590
133443
6.73
17168
-44103
-5.04
8139
5225448
3029.30
1652226
-31090
-40.74
6757
-287336
-12.48
10786
444138
264.22
127975
-295305
-36.18
13936
447502
-3364
45079
-36430
-40398
-255587
-189957
-105348
-91435
-3.24
4679
14519
10.01
9387
51879
5.05
4827
-39526
-10.41
1436
35511
157.46
6534
-41978
-55.20
900
-639255
-10.13
22528
713450
255.88
215555
167131
8.44
19954
-2783
-0.32
11314
138862
80.50
44015
-27714
-36.32
5856
-307341
-13.34
9130
157042
93.42
47745
-42307
-5.18
11910
164050
-7008
-58102
-61837
-18872
-168530
-76432
34125
-136357
-4.84
6213
-59998
-41.36
13011
-121349
-11.80
5092
-65201
-17.17
1559
-7020
-31.13
942
39730
52.25
885
-834289
-13 .22
24701
428099
153.54
133618
6993
0.35
1666
-2
0.00
7013
345662
200.39
104080
-39733
-52.06
10811
-5
0.00
9384
647785
385.37
180076
3837
0.47
13283
634080
13705
67729
-49692
87373
-105415
-34841
38678
-7
0.00
4825
-31940
-22.02
8365
187800
18.27
5773
-14
0.00
1415
33237
147.38
5380
-28269
-37.17
770
22
0.00
21960
552046
197.99
167613
419267
21.16
26267
-2
0.00
6443
5441240
3154.40
1716039
-10335
-13.54
6249
-6
0.00
10214
554863
330.09
158361
-225945
-27.68
13852
555513
-650
170600
21622
-1555
-190673
-144751
-81194
-7
0.00
4580
25636
17.67
8392
70033
6.81
4716
-5
0.00
1422
37971
168.37
6561
-26478
-34.82
760
14
0.00
21957
794666
285.00
224903
356119
17.97
25692
_i
0.00
6907
200623
116.31
58504
-33409
-43.78
9821
-10
0.00
7485
307150
182.72
85411
176858
21.67
14856
301857
5293
11067
-35503
75538
-51112
42171
134687
-8
0.00
5254
-12348
-8.51
4838
-30467
-2.96
3980
-10
0.00
1511
-3292
-14.60
881
66226
87.09
1239
18
0.00
21689
679256
243.61
196467
590269
29.79
26901
HI-292
-------
Determining Comparability of Bioassessment Methods and Their Results
Jerome Diamond, James Stribling
Tetra Tech, Inc., Owings Mills, MD
Chris Yoder
Ohio EPA, Columbus, OH
Introduction
A "true" or accurate bioassessment is difficult to document, even for a specified time and place,
because of the heterogeneous spatial and temporal distribution of species present. Unlike chemical
analytical assessments, in which method accuracy can be verified in a number of ways, biological
assessment (i.e., field) accuracy can not be objectively verified; we are unable, for example, to conduct
meaningful "matrix spikes" for biological information in aquatic systems. Currently, a multitude of
biological collection and data interpretation methods are used by different organizations in the U.S. The
bioassessment information collected by these different organizations is useful almost exclusively to the
individual organization sponsoring the program. Monitoring groups outside the collecting agency,
typically find it difficult to know which bioassessment information may be used by them with
confidence. The result is limited data sharing across organizations because the quality of "foreign" data is
suspect or unknown. In some cases, different bioassessments yield conflicting interpretations at the same
sites, underscoring the accuracy of bioassessment results.
The current use of many bioassessment methods, with little or no information as to the comparability
of results obtained by these different methods, is a significant problem in three ways: (1) assessments of
aquatic resources on broad geographic scales (basins for example) or from state to state are not easily
feasible because different methods may be used in different parts of the region of interest, (2)
opportunities for increased resource efficiency or for minimizing duplication of efforts are missed, and
(3) depending on which bioassessment results are chosen, the quality of biological resources present,
and/or trends in the status of those resources over time, may be misinterpreted.
Bioassessments generally consist of three major facets: field data collection (sampling gear and
sampling protocol), data summarization and reduction (metric calculations, indices) and decision or
interpretive framework (ecoregional reference conditions, site-specific reference or control). Thus,
comparison of interpretive results among bioassessment methods, is underlain by many complex
interactions involving collection and analysis of data. We believe that the degree of data comparability
between bioassessment methods can be traced to the type of data collection methods used and their
performance characteristics. This paper presents a framework for characterizing and comparing
bioassessment methods that uses a performance-based system. We discuss the advantages of using such a
system and present a proposed framework for defining performance criteria and judging bioassessment
comparability. Lotic benthic macroinvertebrate bioassessment methods are used as examples in this
paper because the authors are most familiar with these methods and because benthic macroinvertebrates
are probably the most widely used bioassessment indicator in state and federal water quality programs in
the U.S. and in other countries including Canada, Great Britain, and Australia.
Definition and Components of a Performance—Based Methods (PBMS) Approach
There are two general approaches for acquiring comparable bioassessment data. One way is to have
every program use the same method. In the past, the US Environmental Protection Agency (USEPA) and
analogous agencies in other countries have attempted to pursue this option. The development of Rapid
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Bioassessment Protocols (RBPs) within the US EPA, for example, was an attempt, in part, to standardize
bioassessment methods in the U.S. However, forcing all collecting organizations to use a single bio-
assessment method, no matter how exemplary, is probably not feasible because different regions or lotic
habitats require different sampling methods and because it is not likely that the current establishment of
different methods can (or should) be reversed.
An alternative approach to acquiring comparable data from different organizations, and one
recommended by the Interagency Task Force on Water Quality Monitoring (USGS 1995) for all methods,
is to develop a performance-based approach for characterizing methods, and for setting quantifiable,
realistic performance criteria. In this approach, the data quality requirements for a particular bioassess-
ment program are specified in advance and the data collecting entity can select the appropriate method to
meet those specifications. This is termed a performance-based measurement system (PBMS). A PBMS is
defined as a system that permits the use of any appropriate sampling and analysis method that demon-
strates the ability to meet established data criteria and complies with specified data quality requirements
or data quality objectives (DQOs). DQOs include requirements for method precision, bias, sensitivity,
detection limit, and range of conditions or matrices over which the method yields satisfactory data. With
the successful introduction of the PBMS concept in laboratory analytical chemistry testing, and more
recently in laboratory toxicity testing (USEPA, 1994), it appears worthwhile to examine the possibility of
transposing such a system to the problem of bioassessment method comparability.
In order for the PBMS approach to work, some basic concepts must be defined including: data
quality objectives must be set that realistically define and measure the quality of the data needed;
reference (validated) methods must be made available that at least meet those data quality objectives; to
be considered satisfactory, an alternative method must be as good or better than the reference method in
terms of its resulting data quality characteristics; there must be proof that the method yields reproducible
results that are sensitive enough for the program or sponsor needs; and finally, the method must be
adequate over the prescribed range of conditions in which the method is to be used (USGS 1995). In a
bioassessment context, the above concepts imply that the quality of the data generated by a given
bioassessment method (i.e., its precision, sensitivity to different levels or types of impairment, range of
habitats over which the collection method yields a specified data precision or sensitivity) is known and
quantified (validated).
Table 1 summarizes some ways in which analytical chemistry methods define certain performance
characteristics. As an example, we compare these performance demonstration techniques with those that
have been used by different organizations to define performance characteristics for laboratory sorting and
taxonomic identification of benthic macroinvertebrate samples. It is evident that many of the same
method performance characteristics can be quantified for laboratory procedures used in bioassessments.
Although such validation has been performed by a number of organizations and for certain bioassessment
methods, rarely are the performance characteristics quantified for comparison purposes or to explicitly
demonstrate to prospective users that the method actually meets program DQOs. For example, a sorting
and identification method could, through repeated examinations using trained personnel, determine that
the rate of missed organisms is less than 10% of the sample and that taxonomic identifications (to the
genus level) have an accuracy rate of at least 90% ( as determined by check samples identified by
recognized experts). If such laboratory accuracy and completeness were believed to be necessary for a
given study, the study sponsor could require the above data quality characteristics as DQOs. In this case,
the above method meets the DQOs and could be considered the reference method. In a PBMS approach,
any other laboratory method that documented the attainment of at these DQOs would be yield data
comparable to the reference method and the results would therefore be satisfactory for the study.
The above example underscores the important issue of personnel training that is central to most data
collection methods, and bioassessment methods in particular. The performance of any method depends
on having adequately trained people. One way to document satisfactory training is to quantify
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performance characteristics of the method using newly trained personnel and comparing these
characteristics to those established previously and considered acceptable. While this is frequently done
for new field crews and new laboratory personnel in many organizations, rarely are the results of such
training documented or quantified. As a result, the organization can not assure either itself or other
potential data users, that different personnel performing the same method yield comparable results and
that data quality specifications of the method are being consistently met.
To demonstrate the PBMS framework in a bioassessment context, precision is taken as an example of
a performance characteristic. Method precision could pertain to many aspects or subprocedures used in
biological assessments. A key factor unique to developing a PBMS framework for bioassessment
methods is that bioassessments often consist of several subprocedures that are tightly linked (Figure 1).
Thus, a comprehensive characterization of a complete bioassessment method may entail a definition of
applicable performance characteristics for each sub-procedure. Precision with respect to sampling
procedures, for example, could be determined by examining specific metrics at a given site using
replicate samples taken from the site. Alternatively, precision of the interpretive bioassessment
framework might be determined by examining specific metrics or assessment scores across supposed
replicate reference sites in a given ecoregion and within a specific stream reach classification. Once data
precision is quantified for different bioassessment methods, it is possible to: (1) derive an overall
precision criterion, (2) designate a reference method that meets this criterion, and (3) assess the degree to
which different methods yield comparable data precision. Other performance characteristics such as
performance range, method interferences, and matrix applicability, also would be used to derive
performance criteria and quantify bioassessment comparability. While some of this information is
published for certain bioassessment methods, much of this knowledge is incorporated in an informal
manner and not quantified within the framework of the method itself (e.g., Peckarsky 1984; Resh and
Jackson 1993). This information needs to be more available to data users and organizations so that the
quality of data obtained by different methods is documented and one can then judge whether results
obtained from different methods are comparable.
In defining a reference method for a given bioassessment procedure, it is imperative that the specific
range of environmental conditions are quantitatively defined. In lotic benthic macroinvertebrate
bioassessment methods, the performance range or applicable environmental conditions for the method is
usually addressed qualitatively by including factors such as stream size, hydrogeomorphoric reach
classification, and general habitat features (riffle vs pool, shallow vs deep water, rocky vs silt substrate).
In a PBMS framework, different methods could be classified based on the methods' ability to achieve
specified levels of performance characteristics such as precision and sensitivity to impairment over a
range of appropriate habitats. In this way, the performance range of bioassessment methods can be
directly and quantitatively compared.
Advantages of a PBMS Approach for Characterizing Bioassessment Methods
In performing a benthic macroinvertebrate assessment, two fundamental concerns are of interest: that
the sample taken and analyzed is representative of the site or the population of interest and that the data
obtained are an accurate reflection of the sample collected and analyzed. The first concern is addressed
through appropriate field sampling procedures, including site selection, sampling device, and sample
preservation methods. These sampling methods will be dictated, to a certain extent, by the desired DQOs.
The second concern is addressed by using appropriate laboratory or analysis procedures. In a PBMS
framework, the appropriate question is what minimum precision (variance) and "accuracy" (value of the
metric or score compared to the "true" or unchanging value, given the bioassessment method) are
required for particular program or study needs? One could, for example, decide that the variance and
completeness generated on average by four surber samples at a site are acceptable because the increase in
data "accuracy" and precision from further sampling is not enough to warrant the increased effort and
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cost of obtaining those data; i.e., the bioassessment interpretation will not be changed substantially by the
increased effort.
Using a PBMS framework, the question is not which method is more "accurate" or precise but rather
what accuracy and precision level can a method consistently achieve and do those performance charac-
teristics meet the DQOs of the program such that bioassessment interpretations can be justified. Further-
more, once data precision and "accuracy" are quantified for a bioassessment method, error rates can be
estimated so as to determine whether the method will meet DQOs for a particular study or program. The
method may be modified perhaps (i.e., more replicate samples taken, larger samples taken) to improve
the precision and "accuracy" of the method, reduce Type II and Type I error rates, and therefore meet
more stringent DQOs of other programs or studies.
In benthic macroinvertebrate collection methods, many measures or metrics are potentially deter-
mined for the same sample. Together, these measures may form an index or score (IBI, ICI, references)
or alternatively, a multivariate analysis and, eventually, a narrative rating of status (Figure 2). Method
comparability could be determined if one knew how a particular metric of interest or assessment score
behaves under different environmental conditions (impaired vs. reference sites, different habitat types,
different seasons, or index periods) for each method. Such information (obtained through repeated
sampling at different times in the same location and sampling in different habitats and locations at the
same time) would yield estimates of method bias, precision, interferences, and performance range.
Objectives of the data users will define which measures(s) and what environmental conditions should
be used to determine comparability among methods. DQOs also will dictate how similar certain perfor-
mance parameters need to be to consider the data obtained from two different methods, comparable. It is
quite possible that two methods may be very comparable for certain measures of interest and not others.
Knowing this, one could use data for those measures where different methods are comparable. Alterna-
tively, two methods, differing only in their sample processing procedures, for example, can be relatively
easily compared over a broad range of field sampling conditions by knowing the performance character-
istics of the other procedures for either method. The key is that performance characteristics are defined
for each method and that the data user has access to comparability information when reviewing the data
or deciding whether to use data collected by another method.
The PBMS framework is especially useful for comparing bioassessment methods having different
collection methods and different metrics or indices. An example illustrating this issue is Ohio EPA's
comparison of data derived using Hester-Dendy (artificial substrate colonization) collection method and
a rigorous empirical classification/interpretation framework, and a volunteer monitoring method based on
kick net sampling. These methods examined different taxa and developed different measurements or
metrics. Comparison of results using the two methods at the same sites showed that the most informative
performance characteristics for comparison were sensitivity or discriminatory power among sites and
consistency or reproducibility among results within a site. The Hester-Dendy method showed greater
sensitivity and reproducibility compared with the kick-net method and the former method was therefore
judged to be a more appropriate method for the Agency's needs. However, the two assessment methods
yielded comparable results for sites that were significantly impaired. Thus, using a PBMS approach,
Ohio EPA could rely on the results of the volunteer monitoring method for sites that were judged as
impaired.
Suggested Approach for Defining Performance Characteristics for Bioassessment Methods
Bioassessments, regardless of the method, determine test site condition on the basis of some
reference condition or reference sites. The bioassessment reference condition consists of carefully chosen
sites or conditions that meet certain a priori criteria and is specific for a certain environmental stratum or
regime (ecoregion, habitat, season).
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An important first step towards defining performance characteristics for bioassessment methods is to
examine the data collected by the method for a given reference condition. This has been done, in part, by
several States, some USEPA programs, and the U.S. Geological Survey NAWQA Program. Within a
given ecoregion, several reference sites are sampled that have appropriate habitat for the sampling gear,
using a prescribed method. In some cases, sites are sampled more than once in a year so that a measure of
temporal precision could be obtained for each metric and the assessment score as a whole. Measures for
all reference sites within a given region are then compiled to derive the reference condition charac-
teristics for that region. If this approach is used in different ecoregions, one can obtain quantification of
several important performance characteristics: Precision for a given metric or assessment score across
replicate reference sites within an ecoregion; Temporal precision for a given metric or score for reference
conditions within an ecoregion; Bias of a given metric and (or) method owing to differences in eco-
regions or habitats; Performance range of a given method across different ecoregions; Potential inter-
ferences to a given method that are related to ecoregional or habitat qualities; and relative precision of a
given metric or score among reference sites in different ecoregions.
While sampling and evaluating reference sites is necessary to characterize bioassessment perfor-
mance, it is not sufficient. We also need to know how a given method performs over a range of impaired
conditions e.g., a method's sensitivity to impairment. As discussed earlier in this paper, sites do not have
known levels of impairment or analogous standards by which to create a calibration-curve for a given
bioassessment method. In lieu of this limitation, sampling sites are chosen that have known stressors (i.e.,
urban runoff, metals, grazing, sediments, pesticides). Because different sites may or may not have the
same level of impairment within a region (i.e., are not replicates), precision of a method in impaired sites
is examined by taking and analyzing multiple samples from the same site.
Table 2 illustrates the process by which a bioassessment method would quantify the necessary
performance characteristics using reference-condition and test-site data. Two different ecoregions or
habitat types are assumed in this process. More habitats or ecoregions would improve determination of
the performance range and biases for a given method. Five reference sites are assumed for each
ecoregion. This is a compromise between effort and cost required on the one hand, and resultant
statistical power gained on the other. More reference sites would further refine method precision,
performance range, and possibly discriminatory power of the method. At least three reference sites in a
given region should be considered a minimum to evaluate method precision. Given the usually wide
variation of natural geomorphic conditions and landscape ecology, even within supposedly "uniform"
ecoregions, it is desirable to examine 10 or more reference sites in a region (Barbour et al 1996 ).
A range of impaired sites within a region is suggested to sufficiently characterize a given method. It
is important that impaired sites meet the following criteria: They are very similar in habitat and geo-
morphometry to the reference sites examined; they are clearly receiving some chemical, physical or
biological stressor(s) and have for some time (months at least); and impairment is not obvious without
sampling; that is, the sites should not be heavily impaired. Widely different assessment procedures
typically yield the same interpretation at such sites. A much better test of method sensitivity, as well as
its performance range, is to examine sites with some, but not severe, stressors present. Ideally, it is
beneficial to examine several (^3) test sites in different regions, each with different stressors present and
(or) different levels of the same stressor. Such a sampling design would enable one to derive more
precise estimates of the performance range and any biases of the method or its assessment scoring system
due to the type of stressor or ecoregional characteristics.
Once performance characteristics are defined for each method, performance criteria can be
established, as well as scientifically feasible DQOs. If one method, for example, yields greater variability
(less precision) in the same measure or in assessment scores among reference sites within an ecoregion
than another method, then the precision exhibited by the less variable method may be used to define a
performance criterion for precision. A program or study can then require a method that meets that
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precision criterion, and the collecting agency can select an appropriate method with confidence. Another
collection method that demonstrates similar or better precision than the criterion as demonstrated in the
reference method, is comparable and data from the two methods can be used together with confidence.
In determining whether two collection methods give comparable results, note that method
comparability is based, for the most part, on the relative magnitude of the variances in measurements
within and between ecoregions. We explicitly are not basing comparability on the measurements
themselves because different methods may have different metrics or scoring systems. In addition, some
sampling methods may explicitly ignore certain taxonomic groups and metrics compared to other
methods. However, if one is especially interested in comparing the same metric among different methods,
this can be easily incorporated into the test design in Table 2 by comparing mean values for regional
reference or sites test using a paired t-test or non-parametric equivalent.
Relative accuracy of each method is addressed to the extent that the test sites chosen are likely to be
truly impaired on the basis of independent factors such as the presence of chemical stressors or
suboptimal habitat features. A method that exhibits low data precision (high score variability) among
ecoregional reference sites compared to another method suggests either uncertain method accuracy or
poor selection of reference sites. For some program goals and DQO, some certainty in the results may be
sacrificed if the method has other advantages, such as reduced costs and less effort to perform.
Another performance characteristic of interest to those using benthic macroinvertebrate
bioassessments is the sensitivity or discriminatory power of the method; that is, how well does a given
method detect marginally or moderately impaired sites? Actual mean scores of metric values are used to
determine method sensitivity in the form of a ratio between impaired sites and the regional reference
value. Because impairment can only be judged relative to a reference or attainable biological condition in
the relative absence of stressors, the score or metric at the test impaired site is not an absolute value and
must be related to the appropriate reference-condition value. A method that yields a larger ratio of test-
site score to reference score (m/^,, P//u.2, c/a, or q/a2, Table 2) would indicate less discriminatory power
or sensitivity; that is, the test site is perceived to be similar to or better than the reference condition and,
therefore, not impaired. If, however, the intent is to screen many sites so as to prioritize "hot" spots or
significant impairment problems in need of corrective management action, then a method that is
inexpensive and quick and tends to show impairment when significant impairment is actually present
(such as some volunteer monitoring methods) can meet prescribed DQOs with relatively little cost or
effort. In this case, the DQOs dictate a low priority for discriminatory power (high Type I error rate) and
a high priority for accuracy in the decision (low Type II error rate); that is, a truly impaired site has a
high probability of being categorized as such.
Applicable performance range and bias are two other important performance parameters that relate
directly to the overall utility of a given method and its comparability to other methods. The suggested
framework (Table 2) defined these two performance characteristics by sampling in different ecoregions
that have different physical habitat characteristics. A bioassessment method that shows a higher precision
among reference sites in one ecoregion or hydrogeomorphic basin as compared with another ecoregion or
basin type may be useful information for deciding where or when a given method should or should not be
used. Similarly, a metric or score that exhibits a consistent bias related to certain measured habitat
features would help a user decide the types of sampling situations in which a particular method may be
appropriate.
Acknowledgments
The ideas presented here represent a distillation of many discussions with members from the
Intergovernmental Task Force On Monitoring. We especially acknowledge the comments of Herb Brass
(USEPA), Russ Sherer (SCDHEC), and Jeroen Gerritsen (Tetra Tech, Inc.) The authors dedicate this
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work to Russ Sherer, former co-chair of the Methods and Data Comparability Task Group under the
ITFM who died on February 6, 1996.
References
American Society for Testing and Materials. 1993. Biological effects, 11.04 of Annual book of
standards: American Society of Testing and Materials. 1598 p.
Peckarsky, B. 1984. Sampling the steam benthos, in Downing, J., and Regler, F., eds. A manual or
methods for the assessment of secondary productivity in freshwater (2d ed.): Oxford, Blackwell
Scientific Publications, IBP Handbook 19. 501 p.
U.S. Environmental Protection Agency. 1989. Short-term methods for estimating the chronic toxicity of
effluents and receiving waters to freshwater organisms (2d ed.): Cincinnati, Ohio. U.S.
Environmental Protection Agency, Office of Research and Development. EPA/600-4-89-001. 334 p.
U.S. Environmental Protection Agency. 1990. Methods for measuring the acute toxicity of effluents and
receiving waters to aquatic organisms (4th ed.): Cincinnati, Ohio. U.S. Environmental Protection
Agency, Office of Research and Development. EPA/600-4-90-027. 293 p.
U.S. Geological Survey. 1993. Methods for sampling fish communities as a part of the National Water-
Quality Assessment Program. Report 93-104. Raleigh, NC. U.S. Geological Survey. 40 p.
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PRELABORATORY
LABORATORY
Data Quality Objectives
Site Selection/
Reference Condition
Habitat Selection for sampling
Sampling Method Device
Collection Procedure
Subsampling
Sample Preservation/
Transfer/Transport
Sample Processing
Sorting Subsampling
Laboratory Analyses
Taxonomic Identification
Enumeration
Data Analyses
Metric calculations
Index calculations
Statistical analysis
Assign Assessment Scores/Values
Data Transfer/Storage
Figure 1. Flow chart depicting the major methodological steps involved in all biological assessments.
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Figure 2. Data manipulation hierarchy of biological field methods.
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Table 1. Progression of a Generic Bioassessment Field and Laboratory Method
and Corresponding Steps Requiring Performance Criteria Characterization
Step
Procedure
Examples of Performance Criteria
Sampling device
Sampling method
Field sample processing
(subsampling, transfer,
preservation)
Performance range—Efficiency in different habitat types or substrates
Bias—Exclusion of certain taxa (mesh size)
Interferences—Matrix or physical limitations (current velocity, depth)
Performance range—Limitations in certain habitats or benthic substrates
Bias—Sampler (personnel) efficiency
Precision—Of metrics or measures among replicate samples at a site
Precision—Of measures among splits of subsamples
Accuracy—Of transfer process
Performance range—Of preservation and holding time
Laboratory sample
processing (sieving,
sorting)
Taxonomic enumeration
Precision—Among split samples
Accuracy—Of sorting method; equipment used
Performance range—Of sorting method depending on sample matrix
(detritus, mud)
Bias—In sorting certain taxonomic groups or organism sizes
Precision—Split samples
Accuracy—Of identification and counts
Performance range—Dependent on taxonomic group and (or) density
Bias—Counts and identifications for certain taxonomic groups
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Table 2. Recommended Process for Documentation of Performance Parameters
and Comparability of Two Different Bioassessment Methods
[Five reference sites are assumed in this layout, but one could have a minimum of three sites for each region]
Endpoint
Region 1
Region 2
Reference numbers 1-5 Impaired or test site Reference numbers 1-5 Impaired or test site
Method 1, Method 2, Method 1, Method 2,
mean variance mean variance Method 1 Method 2 mean variance mean variance Method 1 Method 2
Metric, M, ± S,
Metric,, X, ± q,
Assessment
score
/J2 ± S2 m p
X2±q2 z v
a,±d,
b,±f,
a2±d2
b,±f2
c q
e r
The following comparisons refer to the parameters specified above and are designed to yield various
performance characteristics of biological-field-collection method.
• Compare s, with s2 for a given metric to determine relative precision of the metric for the two methods and
an unimpaired condition.
• Compare st with dt and s2 with d2 to determine how metric variability may change with a region. A
relatively high variability in a given metric within a region or compared with another region for the same
method would suggest a certain performance range and bias for the metric.
• Compare m/^, with p/^2 to determine discriminatory power of a given metric by using the two methods in
region 1. A ratio closer to 1.0 would signify little difference in the metric between an impaired site and the
reference condition in region 1 for that method. The utility of the metric would be questionable in this case.
Do the same type of analysis by comparing c/a{ and q/a2 for region 2.
• Compare m/^, with c/a, and p/j.2 with q/a2 to determine relative discriminatory power, performance range,
and bases of a given metric and sampling method across regions. A similar ratio across regions for a given
metric may indicate the robustness of the method and the metric. A ratio near 1.0 in one region and not in
another for a given method and metric would indicate possible utility limitations or a limited performance
range for that metric.
• Compare q! with q2and f, with f2 to determine overall method variability at unimpaired sites in each region.
High variability in the score for one method compared to another method in a given region would suggest
lack of comparability and (or) different applicable data-quality operations for the two methods.
• Compare ql with f, and q2 with f2 to determine relative variability in assessment scores in the two regions.
A consistently low score variability for a given method across regional reference sites would suggest
method rigor and potential sensitivity.
• Compare resultant scores for a given method and region deleting apparently variable or insensitive metrics
to determine metric redundancy and to determine relative discriminatory power at impaired sites.
• Individual assessment scores for reference sites and impaired sites within each region can be compared
between methods by using regression to determine if there is a systematic relation in scores between the
two methods.
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Performance Based Methods System
Ann B. Strong, Chief, Environmental Chemistry Branch
U.S. Army Engineer Waterways Experiment Station
The Methods and Data Comparability Board (MDCB) of the National Water Quality Monitoring
Council advocates the promotion of a performance based methods system or measurement system
(PBMS) as one of its top priorities in providing a mechanism that will allow data comparability among
various monitoring programs and data bases. MDCB originally defined PBMS as a process that permits
the use of any appropriate method that demonstrates the ability to meet established criteria and complies
with specified data quality needs. In the 6 October 1997 Federal Register, the Environmental Protection
Agency (EPA) defines PBMS as "a set of processes wherein the data quality needs, mandates or limita-
tions of a program or project are specified, and serve as criteria for selecting appropriate methods to meet
those needs in a cost effective manner." A slightly different definition was presented by Kinney and
Caliandro (1998), "PBMS is a set of processes wherein a monitoring program's DQOs are designated
rather than specifying which approved analytical method must be used for monitoring. The analyst uses
these DQOs as the criteria for selecting appropriate methods to meet these needs..."
The MDCB feels that initial implementation of PBMS should embrace the following concepts:
• Data quality objectives must be set that realistically define and measure the quality of data
needed
• Reference(validated methods) must be made available that meet these objectives
• The selected method should be as good or better than the reference method
• There must be proof of method adequacy and ruggedness
To make PBMS a viable system for data comparability, minimum data base information is needed:
• Method(Specific Source)
• Deviations from method(explain)
• Method blank results
• Reference sample results
• Spike, duplicate spike and duplicate sample results
• Surrogate results (if applicable)
• Instrument tuning results to meet method specifications(if applicable)
• Calibration checks to meet specifications
• Sample data results(with qualifiers)
• Method detection limits
• Sampling and preservation methods
Method or measurement performance criteria must be defined to insure that data quality meets
project requirements:
• Precision obtained from replicate measurements and calibrations
• Bias obtained from spiked samples and standard reference materials
• Method detection limits determined over time
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• Performance range of method
• Interferences that can affect data reliability
• Multi-media applicability (water, soil, sediment, tissue)
Requirements for implementation of PBMS necessitate training- training- training for both data
generators and data users as the liability for appropriate data shifts to the site contractor or owner.
Training should include the sample collection process and pre-laboratory activities. Additional
prerequisites include matrix specific performance evaluation materials, a laboratory accreditation
process, and a systematic audit of activities.
There are many advantages to a PBMS such as
• Production of valid data based on scientific procedures rather than on specified methods
mandated by the regulatory process
• Incentive to develop innovative and better methods that are cost-effective
• Greater flexibility by the monitoring population to select the measurement that most effectively
meets their needs
« Consistency with "Total Quality Management"
Some disadvantages to implementation include:
• Potential challenges to data reliability
• Level of expertise needed for data validators is greater
• Initial resource and training requirements are extensive
Implementation within EPA, as the primary regulator requiring environmental monitoring, is
proceeding with a proposed streamlining process (USEPA, Mar 98) to reduce the regulatory burden of
prescriptive methods required under the Clean Water Act and the Safe Drinking Water Act and applies to
both chemical and biological methods. This proposals would allow analysts to use professional
judgement to modify and develop alternatives to established EPA methods. The streamlining process is
very similar to that originally proposed by the MDCB. EPA later published as notice of intent to
implement PBMS for all of its media programs (USEPA, Oct 1997) and developed generic check-lists
with program specific requirements (available on the Internet at http://www.epa.gov/pbms). The
requirement that only SW-846 methods be used for RCRA will be eliminated. In anticipation of this shift
to a performance based process, update 3 of SW-846 was written in PBMS format with method
performance criteria included. An initial and continuing demonstration of method performance are
required and must be documented. Implementation of PBMS does not negate the need or use of standard
or consensus methods; it only eliminates the mandate that they be used. The Deputy EPA Administrator
has issued a requirement that each EPA agency develop a PBMS implementation plan by 30 September
1998. Even with implementation by EPA, individual states may elect to continue to require "approved"
methods.
Whether we call PBMS a "methods" system or a "measurement" system, the basic goals are the
same—to provide information of known quality that will satisfy user needs. The MDCB endorses the
need for reference methods even though some of EPA's programs propose eliminating this requirement.
Incorporation of a full-blown PBMS raises concern with both the data producers and the data users that
EPA agencies will submit widely divergent implementation proposals for their various programs. There
is also great apprehension that the expertise required by auditors/evaluators will not be available.
Although initial implementation of PBMS is directed to laboratory operations, field methods and pre-
laboratory operations must be an integral part of the system if we are to meet our goal of producing
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comparable monitoring data of known quality that provide the answers we need to make adequate
environmental assessments.
References
Kinney, Andrea and Caliandro, Birgit. Apr 1998. EPA Shifts into Performance Gear. Today's Chemist at
Work. American Chemical Society. Vol 7, No. 4: 79-84.
U.S. Environmental Protection Agency. 28 Mar 1997. Guidelines Establishing Test Procedures for the
Analysis of Pollutants and National Primary Drinking Water Regulations; Flexibility in Existing Test
Procedures and Streamlined Proposal of New Test Procedures. Federal Register, Vol 62, No. 60:
14976-15049.
U.S. Environmental Protection Agency. 6 Oct 1997. Performance Based Measurement System, Federal
Register Vol 62, No. 193: 52098-52105.
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High-Resolution Water-Column Profiles of Chlorophyll
Fluorescence in Payette Lake, Idaho
Paul F. Woods, Hydrologist (Limnology)
U.S. Geological Survey
Abstract
A recently completed limnological study of Payette Lake, Idaho, documented substantial
hypolimnetic dissolved-oxygen deficits in the summer and autumn of 1995 and 1996. In contrast, the
20.5-square-kilometer lake was classified as oligotrophic during 1995-96 on the basis of concentrations of
total phosphorus, total nitrogen, and chlorophyll-a.
A new instrumentation package used for post-study monitoring of Payette Lake during 1997 has
yielded high-resolution water-column profiles useful for investigating the lake's hypolimnetic dissolved-
oxygen deficit in relation to oxygen demands from the decay of senescent phytoplankton. The new
instrumentation collects data at a rate of 8 scans per second for the following variables: depth,
temperature, conductivity, dissolved-oxygen concentration and saturation, pH, oxidation-reduction
potential, light transmissivity, photosynthetically active radiation, and chlorophyll fluorescence.
The chlorophyll fluorescence and light transmissivity profiles recorded large chlorophyll peaks within
the euphotic zone and metalimnion. More importantly, chlorophyll fluorescence peaks within the
hypolimnion also were recorded; these represented remnants of euphotic-zone chlorophyll pulses that
probably occurred between profiling dates. No instrumentation for in-vivo profiling of chlorophyll
fluorescence within or beneath the euphotic zone was available for the 1995-96 study; thus, chlorophyll
production in Payette Lake during 1995-96 was likely under-estimated. The underestimation of
chlorophyll-a may explain the discrepancy between the lake's oligotrophic level of chlorophyll-a and its
eutrophic level of hypolimnetic dissolved-oxygen deficit.
Introduction
Payette Lake is in Valley County, one of Idaho's rural, mountainous areas with a thriving
tourism/recreation industry (fig. 1). Concerns over water-quality degradation caused by lakeshore and
watershed development led to a series of water-quality studies between the late 1960's and the early
1980's (Idaho Department of Health, 1970; U.S. Environmental Protection Agency, 1977; Falter and
Mitchell, 1981; Falter, 1984). Evidence of cultural eutrophication and bacteriological contamination was
strong enough to prompt construction of a sewage-collection system in the early 1980's for the developed
part of the lake's shoreline. Water-quality concerns continued into the 1990's as residential development
and recreational use of Payette Lake continued to increase.
The Idaho State Legislature responded to these concerns by passing the Big Payette Lake Water
Quality Act in 1993. The Act mandated formation of a water-quality council and development of a water-
quality study of the lake and its watershed. The resulting study was a cooperatively funded effort by the
Idaho Division of Environmental Quality, which studied the watershed, and the U.S. Geological Survey,
which studied the lake. The purpose of the U.S. Geological Survey limnological study, conducted during
water years 1995 and 1996, was to determine Payette Lake's assimilative capacity for nutrients so that its
susceptibility to cultural eutrophication could be assessed. Five major tasks were undertaken: (1) Assess
physical, chemical, and biological characteristics of the limnetic and littoral zones of the lake; (2)
quantify loads of water and nutrients into and out of the lake; (3) develop an empirical nutrient load/lake
response model; (4) use the model to simulate the lake's response to hypothetical alterations in nutrient
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loads; and (5) estimate the nutrient loads added to the lake by 1994 forest fires that burned one-half of the
lake's watershed. The results of the 1995-96 study are described in a report by Woods (1997).
The 1995-96 study of Payette Lake documented substantial hypolimnetic dissolved-oxygen deficits
during the summer and autumn of both years. In contrast, the lake was classified as oligotrophic on the
basis of concentrations of total phosphorus, total nitrogen, and chlorophyll-a. The lake's propensity to
develop hypolimnetic dissolved-oxygen deficits, to the point of anoxia, was ascribed to physical
limnological factors coupled with a long-term accumulation of oxygen-demanding organic matter
produced within the lake or delivered by its watershed.
In water year 1997, the U.S. Geological Survey began limnological monitoring of Payette Lake to
provide trend information on trophic state variables (nutrients, secchi-disc transparency, and chlorophyll-
a) and the hypolimnetic dissolved-oxygen deficit. The monitoring employed a new instrumentation
package capable of high-resolution profiling of water-column variables. The purpose of this paper is to
describe how the enhanced resolution obtained with the new instrumentation has been used to investigate
the relation between the lake's hypolimnetic dissolved-oxygen deficit and oxygen demands from the
decay of organic matter produced in the euphotic zone by chlorophyll-based photosynthesis.
Description of Study Area
Payette Lake is a natural lake, formed by glacial activity, and is in the upper watershed of the North
Fork Payette River. Outflow from the lake is regulated for irrigation purposes by a small dam completed
in 1943; normal drawdown is 1.7 meters (m). The lake surface area and volume, excluding islands, are
20.5 square kilometers (km2) and 0.75 cubic kilometers (km3), respectively. Mean and maximum depths
are 36.8 and 92.7 m, respectively, and shoreline length is about 36 kilometers (km). The principal
tributary and outlet is the North Fork Payette River.
Payette Lake receives drainage from 373 km2 of heavily forested, mountainous terrain. Elevations
range from 1,520 m above sea level at the lake outlet to about 2,770 m. The geology is dominated by the
Idaho batholith, which is characterized by crystalline igneous rocks. The dominant vegetation is subalpine
fir, Engelmann spruce, and lodgepole pine. Major land uses are timber harvesting, recreation, residential
development, and sheep grazing. Mean annual precipitation at the lake outlet is 660 millimeters (mm)
and, at the watershed divide, is about 1,200 mm (U.S. Forest Service, 1995). Most precipitation is snow
during October to May. Ice normally covers the lake from late December to late April.
Description of Instrumentation
The new instrumentation package, a Sealogger1 (Seabird Electronics, Inc., model SBE-25), collects
water-column profile data at a rate of 8 scans per second for the following variables: pressure (depth),
temperature, conductivity, dissolved-oxygen concentration and saturation, pH, oxidation-reduction
potential, light transmissivity, photosynthetically active radiation, and chlorophyll fluorescence. At the
recommended descent rate of 0.25 m per second, the Sealogger can obtain about 2,960 data points for
each variable within a water-column profile extending the maximum depth of the lake. Either in real time
or immediately after the profile is completed, the data can be downloaded to a computer, processed, and
graphically plotted onsite.
1 Use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S.
Government.
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Water-Column Profiles, 1997 Water Year
The Sealogger was used to collect water-column profiles at limnetic stations 1,3, and 4 (fig. 1) on the
following dates in 1997: July 14-15, August 5 and 26, and September 16. The high-resolution, full-depth
profiles yielded evidence of wide variations in the profiled water-column characteristics over depth
increments of less than 1 m. Evaluation of the graphical plots provided better insight into the interaction
of physical, chemical, and biological processes throughout the water column.
Of particular interest were the profiles of chlorophyll fluorescence and light transmissivity because
they provided new information on the distribution of chlorophyll throughout the water column. Peaks in
chlorophyll fluorescence often were inversely related to light transmissivity (fig. 2), which corroborated
the presence of additional chlorophyll-bearing phytoplankton within the fluorescence peaks. Chlorophyll
fluorescence within the euphotic zone varied widely and frequently changed substantially over depth
increments of less than 1 m (fig. 3). Chlorophyll fluorescence tended to peak near the lower limit of the
euphotic zone (figs. 3 and 4), which ranged from 2.5 to 4.5 m beneath the thermocline. More importantly,
hypolimnetic peaks of chlorophyll fluorescence also were detected (figs. 5, 6, and 7); these represented
remnants of euphotic-zone chlorophyll pulses that occurred between profiling dates.
Application of 1997 Profile Results to 1995-96 Study
The high-resolution profiles of chlorophyll fluorescence obtained in 1997 prompted a reevaluation of
some of the results of the 1995-96 lake study. No instrumentation was available during that study for in-
vivo profiling of chlorophyll fluorescence or light transmissivity within or beneath the euphotic zone. On
the basis of the 1997 results, chlorophyll production in Payette Lake during 1995-96 was likely under-
estimated because the presence of metalimnetic and hypolimnetic chlorophyll layers was unknown.
The likely underestimation of chlorophyll production in 1995-96 may partly explain the discrepancy
between the lake's low biological production and its substantial hypolimnetic dissolved-oxygen deficit.
The annual geometric mean concentration of chlorophyll-a during 1995-96 was 1.3 micrograms per liter
(|J.g/L) (Woods, 1997); this concentration is within the oligotrophic range on the basis of Ryding and
Rast's (1989) open-boundary classification system for trophic state. In contrast, Payette Lake's average
hypolimnetic dissolved-oxygen deficit was 600 milligrams per square meter per day [(mg/m2)/d] during
1995-96 (Woods, 1997); according to Hutchinson (1957), a hypolimnetic dissolved-oxygen deficit in
excess of 550 (mg/m2)/d is within the eutrophic range.
The hypolimnetic dissolved-oxygen deficit calculated using the 1995-96 dissolved-oxygen data was
much larger than that predicted by the nutrient load/lake response model applied to Payette Lake (Woods,
1997). That model (Walker, 1996) empirically relates the hypolimnetic dissolved-oxygen deficit to
nutrient and chlorophyll-a concentrations within the epilimnion. The model would underestimate the
hypolimnetic dissolved-oxygen deficit because the underestimation of chlorophyll-a production would, in
turn, cause underestimation of the oxygen demand exerted by decomposition of senescent phytoplankton.
Implications for Sampling Design
Many sampling designs are available for evaluating chlorophyll-a concentrations in lakes.
Chlorophyll-a in Payette Lake was sampled during 1995-96 by compositing three volume-weighted,
point-depth samples collected from the euphotic zone with an opaque VanDorn sampler. For example, if
the euphotic-zone depth was 10 m (measured with a spherical quantum sensor), then point samples were
obtained at depths of 1.6, 5.0, and 8.4 m and composited. On the basis of figures 2-4, this sampling design
likely caused underestimation of chlorophyll-a concentrations because maximum concentrations were
often deep in the euphotic zone or were in the metalimnion. During 1997, a composite sample of
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chlorophyll-a was obtained through a weighted piece of tubing (12.5-mm diameter) lowered over the
depth of the euphotic zone. This sampling design accounted for the variation in chlorophyll-a within the
euphotic zone; however, it would not have provided a means for detecting the maximum chlorophyll-a
concentration beneath the euphotic zone, as illustrated in figures 5-7.
Deep-lying chlorophyll layers have been reported (Fee, 1976; Priscu and Goldman, 1983; Pick, et al,
1984; and Woods, 1992). In lakes sampled during these studies, as well as in Payette Lake during 1995-
97 studies, the euphotic zone was deeper than the epilimnion. Under that condition, the phytoplankton
circulating within the epilimnion remain exposed to amounts of photosynthetically active radiation
sufficient for photosynthetic production of carbon in excess of respiratory demands.
The designation of a lake's trophic state can be influenced by the effects of sampling design. Woods
(1986) used a large data base of chlorophyll-a concentrations from a 2-year limnological study of Big
Lake, in south-central Alaska, to illustrate the effect of three different sampling designs on designation of
trophic state. Design I was used at Big Lake; chlorophyll-a concentrations were measured biweekly using
a combination of in-vivo fluorometry and numerous discrete-depth samples. The range in concentration
for the 296 samples was 0.05 to 46.5 - g/L; the larger concentrations often were measured in samples
obtained near the lower limit of the euphotic zone or within the upper metalimnion. Designs II and III are
subsets of Design I. Design II consisted of three biweekly samples obtained from the upper, middle, and
lower depths of the epilimnion. Design III consisted of a single biweekly sample obtained near the lake
surface. On the basis of the open-boundary, trophic-state classification system of Ryding and Rast (1989),
results from Designs II and HI indicated an oligotrophic lake, whereas results from Design I indicated a
mesotrophic lake. The larger chlorophyll-a concentrations obtained by using Design I were attributable to
sampling of the deep-lying chlorophyll layers near the bottom of the euphotic zone.
Plans for Future Study
Chlorophyll-a concentrations in Payette Lake were underestimated during a 1995-96 study because
the sampling design used during the study did not provide the means to detect substantial chlorophyll-a
layers beneath the euphotic zone. A revised sampling design will be used to quantify chlorophyll-a
concentrations throughout the water column of Payette Lake. Full-depth profiles of chlorophyll
fluorescence will be obtained with in-vivo fluorometry. On the basis of the profile data, discrete-depth
samples will be collected for extractive analysis of chlorophyll-a to accurately quantify chlorophyll-a
throughout the water column.
Literature Cited
Falter, C.M. 1984. Nutrient and bacterial loading to Big Payette Lake, Valley County, Idaho, 1982.
Moscow, University of Idaho, 55 p.
Falter, C.M., and B.D. Mitchell. 1981. Limnology of Payette Lake with reference to sewer pipe line
construction. Moscow, University of Idaho, 41 p.
Fee, E.J. 1976. The vertical and seasonal distribution of chlorophyll in lakes of the Experimental Lakes
Area, northwestern Ontario-implications for primary production estimates. Limnology and
Oceanography 21(6), 767-783.
Hutchinson, G.E. 1957. A treatise on limnology-volume 1, geography, physics, and chemistry. New
York, John Wiley and Sons, Inc., 1015 p.
Idaho Department of Health. 1970. Payette Lakes, a water quality study, 1967-69. Boise, Idaho, 9 p., plus
apps.
Pick, F.R., C. Nalewajko, and D.R.S. Lean. 1984. The origin of a metalimnetic Chrysophyte peak.
Limnology and Oceanography 29(1), 125-134.
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Priscu, J.C., and C.R. Goldman. 1983. Seasonal dynamics of the deep-chlorophyll maximum in Castle
Lake, California. Canadian Journal of Fisheries and Aquatic Sciences 40(2), 208-214.
Ryding, S.O., and Walter Rast. 1989. Control of eutrophication at lakes and reservoirs, v. 1 o/Programme
on man and the biosphere series. Cambridge University Press, 295 p.
U.S. Environmental Protection Agency. 1977. Report on Payette Lake, Valley County, Idaho.
Washington, D.C., U.S. Environmental Protection Agency, National Eutrophication Survey Working
Paper no. 784, 17 p., 5 apps.
U.S. Forest Service. 1995. Blackwell post-fire landscape assessment working draft, February 15, 1995.
McCall, Idaho, Payette National Forest [variously paged].
Walker, W.W. 1996. Simplified procedures for eutrophication assessment and prediction-user manual.
U.S. Army Corps of Engineers, Waterways Experiment Station, Instruction Report W-96-2 [variously
paged].
Woods, P.P. 1986. Deep-lying chlorophyll maxima in Big Lake—implications for trophic-state
classification in Alaskan lakes, in Kane, D.L., ed., Cold Regions Hydrology Symposium, Fairbanks,
Alaska, 1986, Proceedings. Alaska Section, American Water Resources Association, 195-200.
1992. Limnology of Big Lake, south-central Alaska, 1983-84. U.S. Geological Survey Water-Supply
Paper 2382, 108 p.
1997. Eutrophication potential of Payette Lake, Idaho. U.S. Geological Survey Water-Resources
Investigations Report 97-4145, 39 p.
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North Fork
Payene River
-40-
IDAHO
EXPLANATION
Line of equal depth below lake surface
at a normal full-pool elevation of
1,519.7 meters—Interval is 10 meters
A
3
Location of measurement—Number is
depth, in meters
Limnetic station and number
44° 59'
Base from U.S. Geological Survey
digital data. 124,000. McCalI,1973
Universal Transverse Mercator
(UTM) projection, Zone 11
1 MILE
1 KILOMETER
Figure 1. Locations of limnetic sampling stations and bathymetry of Payette Lake, Idaho.
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CHLOROPHYLL FLUORESCENCE, IN RELATIVE UNITS
5 10 15 20
25
30
10
ta
5
2 20
O 30
Lower limit of
euphoric zone
40
10 20 30 40 ' 50 60 70
LIGHT TRANSMISSIVITY, IN PERCENT
80
90
100
Figure 2. Water-column profiles of chlorophyll fluorescence and light transmissivity at
limnetic station 1, Payette Lake, on July 15,1997.
100
10 15 20
CHLOROPHYLL FLUORESCENCE, IN RELATIVE UNITS
25
30
Figure 3. Water-column profile of chlorophyll fluorescence at limnetic station 3,
Payette Lake, on August 5,1997.
10 15 20
CHLOROPHYLL FLUORESCENCE, IN RELATIVE UNITS
25
Figure 4. Water-column profile of chlorophyll fluorescence at limnetic station 1,
Payette Lake, on September 16,1997.
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5 10 15 20
CHLOROPHYLL FLUORESCENCE, IN RELATIVE UNITS
Figure 5. Water-column profile of chlorophyll fluorescence at limnetic station 4,
Payette Lake, on July 14,1997.
30
10
20
30
40
50
60
70
80
Thermocline
_,
Lower limit of
euphoric zone
10 15 20
CHLOROPHYLL FLUORESCENCE, IN RELATIVE UNITS
25
30
Figure 6. Water-column profile of chlorophyll fluorescence at limnetic station 1,
Payette Lake, on August 5,1997.
0
10
§ 30
Z 50
S 60
Thennoeline
Lower limit of
euphoric zone
90
100
5 10 15 20 25
CHLOROPHYLL FLUORESCENCE, IN RELATIVE UNITS
Figure 7. Water-column profile of chlorophyll fluorescence at limnetic station 3,
Payette Lake, on August 26,1997.
30
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Comparison of Temporal Trends in Ambient and Compliance Trace Element and
PCB Data in Pool 2 of the Mississippi River, 1985-95
Jesse Anderson, Biologist
U.S. Geological Survey, 2280 Woodale Drive, Mounds View, MN 55112
Jim Perry, Professor, Department of Forest Resources
University of Minnesota, 1530 North Cleveland, St. Paul, MN 55108
Abstract
One goal of the Intergovernmental Task Force on Monitoring is to integrate the efforts of agencies
that collect ambient and compliance water-quality data. The similarity in temporal trends between
retrospective ambient (in-stream) and compliance (wastewater) water-quality data collected from Pool 2
of the Mississippi River was determined for 1985-95. Constituents studied included the following trace
elements: arsenic (As), cadmium (Cd), chromium (Cr), hexavalent chromium (Cr+6), copper (Cu), lead
(Pb), mercury (Hg), nickel (Ni), selenium (Se), zinc (Zn), and polychlorinated biphenyls (PCBs). Water-
column, bed-sediment, and fish-tissue (fillets) data collected by five government agencies comprised the
ambient data set; effluent data from five registered facilities comprised the compliance data set. The non-
parametric Mann-Kendall trend test indicated that 33% of temporal trends in all data were statistically
significant (p<0.05). Possible reasons for this were low sample sizes, and a high percentage of samples
below the analytical detection limit. Seven trace elements (Cr, Cd, Cu, Pb, Hg, Ni, and Zn) had statis-
tically significant decreases in wastewater and portions of either or both ambient water and bed sediment.
No trends were found in fish tissue. Inconsistency in trends between ambient and compliance data were
often found for individual constituents, making overall similarity between the data sets difficult to deter-
mine. Logistical differences in monitoring programs, such as varying field and laboratory methods among
agencies, made it difficult to assess ambient temporal trends. Trends in compliance data were more
distinct; most trace elements decreased significantly, probably due to improvements in wastewater
treatment.
Introduction
Ambient (instream) and compliance (wastewater) water-quality monitoring have been conducted in
separate, often unrelated programs for different purposes (U.S. Geological Survey, 1995). Comparison of
these data may be difficult because of varying field, analytical, and laboratory methodologies and
precision among the collecting agencies (Powell, 1995; Knopman and Smith, 1993; Ward et al., 1990). In
addition, interpretation of data from multiple agencies can be difficult because of variations in sampling
frequencies, sampling locations, uncertainty in measurement, multiple or changing censoring levels, and
outliers (Ward et al., 1990). These limitations can be especially pronounced when attempting to describe
trends in water-quality (Powell, 1995; Knopman and Smith, 1993).
The Intergovernmental Task Force on Monitoring (ITFM) was formed in 1992 to review and evaluate
water-quality monitoring activities nationwide and to recommend improvements (U.S. Geological
Survey, 1995). One recommended improvement is the linkage of ambient and compliance monitoring.
The ITFM suggests forming partnerships among ambient and compliance monitors; this cooperation can
lead to more cost-effective ways of protecting the environment. Often it is necessary to understand
contaminant loading effects on ambient conditions as well as the effects of ambient characteristics on
regulatory decisions and water uses (U.S. Geological Survey, 1995). The ITFM suggests studies to
determine whether ambient or compliance sampling can be reduced locally or nationally. The amount of
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reduction would depend on the degree of similarity between the ambient and compliance data sets. If
patterns in ambient and compliance water-quality data are similar, such as trends over time or direct
relationships, it may be concluded that the sampling frequency of ambient and/or compliance monitoring
may be reduced, saving the public and private sector considerable resources; or it may allow the water-
quality monitoring community to make better decisions with the same resources. Trend comparisons are
important water-quality evaluations. Numerous data have been collected in the past but more evaluations
of data would help the water management community avoid the "data rich but information poor" dilemma
(Ward et al., 1990).
To test the strategy of integrating ambient and compliance water-quality data, ITFM has encouraged
pilot studies in selected watersheds in the United States. This paper describes one such effort as part of
the U.S. Geological Survey's (USGS) National Water-Quality Assessment (NAWQA) Program. As part
of the Upper Mississippi River Basin NAWQA study, an ITFM pilot study was conducted that assessed
the temporal trends in ambient and compliance trace element and polychlorinated biphenyl (PCB) data in
Pool 2 of the Mississippi River (figure 1). This study assessed the similarity in temporal trends between
ambient and compliance trace element and PCB data in water, bed sediment, and fish tissue from 1985-
95. This time period was chosen because: 1) compliance data from the Permit Compliance System (PCS)
data base are available starting in 1985 (Mary Kimlinger, Minnesota Pollution Control Agency, oral
comm., 1996), 2) many years of data are needed to effectively detect statistically significant trends in
water-quality data sets (Gary Oehlert, Department of Applied Statistics, University of Minnesota, oral
comm., 1996), and 3) some ambient trace element and PCB water-quality samples prior to 1985 may have
been contaminated, resulting in data unsuitable for trend analysis.
The constituents addressed by this study were arsenic (As), cadmium (Cd), chromium (Cr),
hexavalent chromium (Cr+6), copper (Cu), lead (Pb), mercury (Hg), nickel (Ni), selenium (Se), zinc (Zn),
and PCBs. All have been classified as "category 1" priority pollutants because they are persistent in the
aquatic environment and may bioaccumulate or enter food chains (Chapman et al., 1982). Trace elements
and PCBs are ubiquitous in the modern industrial environment (Taylor and Shiller, 1995). Discharges
from wastewater and industrial activities cause increases in heavy metal concentrations in receiving
waters near major urban areas (Meade, 1995). Point-source discharges from industrial facilities and
municipal wastewater treatment plants are largely responsible for many of the toxic elements in the Upper
Mississippi River (UMR) system (Upper Mississippi River Water Quality Initiative, 1993). Despite being
banned in 1979, PCBs are still relatively widespread in the aquatic environment (Sullivan, 1988; Eisler,
1986).
Description of Study Area
The Minneapolis-St. Paul metropolitan area (TCMA) is bisected by the Mississippi River. It is one of
the largest population centers in the UMR Basin, with an estimated population of 2.3 million people
(Stark et al., 1996). The Mississippi River is an integral resource to the TCMA. It provides a public water
supply, electrical power, commercial transportation, wastewater dilution, a diverse fishery, and
recreational and aesthetic value. The UMR, the length of river between Minneapolis, Minnesota and St.
Louis, Missouri, contains a series of 29 locks and dams operated by the U.S. Army Corps of Engineers.
These locks and dams create 'pools', defined as the stretch of river between two adjacent locks and dams
(Wiener et al., 1984). Pool 2 of the Mississippi River extends from St. Paul, Minnesota to just upstream of
Hastings, Minnesota (Figure 1). This pool is located in the TCMA, and receives a majority of the major
industrial and municipal discharges to the UMR in the TCMA.
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Ambient Monitoring in the Study Area
Ambient monitoring describes instream water-quality conditions. Ambient monitoring can include
sampling for physical and biological parameters, in addition to chemical concentrations in water or bed
sediment samples. In the study area, ambient monitoring is conducted less frequently than compliance
monitoring (typically, quarterly or annually versus monthly for compliance monitoring). Ambient water-
quality samples typically are collected by Federal, state, and local government agencies. In the TCMA,
these agencies include the Metropolitan Council Environmental Services (MCES), Minnesota Pollution
Control Agency (MPCA), Minnesota Department of Natural Resources (DNR), U.S. Army Corps of
Engineers (COE), and the USGS. Data are stored in data bases such as the USGS National Water
Information System (NWIS), and the U.S. Environmental Protection Agency (USEPA) Storage and
Retrieval (STORET). Constituents measured and frequency of monitoring vary among agencies
depending on the scope of the monitoring program. As a result, monitoring programs are often not
coordinated among agencies.
Compliance Monitoring in the Study Area
The Federal Water Pollution Control Act of 1972 (in later laws renamed The Clean Water Act)
established the National Pollutant Discharge Elimination System (NPDES) to regulate and monitor
sources of wastewater to the Nation's waterways. Permits from the USEPA for discharge of contaminants
into National waters are required as part of the NPDES program; these permits require monitoring of
influent to and effluent from these facilities because municipalities and industries must have their
wastewater effluent comply with Federal or state standards (Tyler, 1992). Permitted facilities send
monthly Discharge Monitoring Reports (DMR) to a supervisory government agency; the DMR describes
the quality of the wastewater effluent. These data are then stored in the USEPA PCS data base. DMR data
serve the following management functions: 1) track permit issuance and reissuance, 2) identify effluent
limits and report violations, 3) determine compliance statistics at a state or national level, 4) track
enforcement actions and the resulting compliance schedules and interim limits, and 5) respond to requests
for information from Congress, state legislatures, and the general public (Mary Kimlinger, Minnesota
Pollution Control Agency, written comm., 1997).
The major facilities (those which discharge more than 1 million gallons per day) included in this
study, which discharge treated wastewater into Pool 2, are the Metropolitan Wastewater Treatment Plant,
Cottage Grove Wastewater Treatment Plant, Koch Refinery, Ashland Oil Company, and 3M Chemolite.
Methods of Evaluation
Retrospective ambient and compliance water-quality data, collected from 1985-95 in Pool 2 of the
Mississippi River, were obtained from various government agencies and industrial and municipal
facilities for the constituents of concern. To enhance the evaluation of ambient water-quality in the study
area, data were complied for three media: the water column (both filtered and unfiltered samples), bed
sediments, and fish tissue (fillets). Water-quality data were complied from the USGS and the MCES.
Surficial bed-sediment data were complied from the MCES and COE. Fish fillet contaminant data were
complied from the DNR and MCES for two species, common carp (Cyprinus carpio) and smallmouth
bass (Mlcropterus dolomieui). These two species were chosen because they are commonly found in the
study area, they have different diet and foraging patterns, and have dissimilar lipid contents in their
tissues, which is a factor in the bioaccumulation of contaminants.
The Mann-Kendall trend test (Mann, 1945; Kendall, 1975) was used to determine temporal trends in
the ambient and compliance data sets. The Mann-Kendall trend test can be stated most generally as a test
of whether Y (constituent concentration) values increase or decrease with time (Helsel and Hirsch, 1992).
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This nonparametric test has several advantages: 1) the data do not need to conform to a prespecified
distribution; 2) missing data are allowed in the data set if they are missing at random; and 3) data values
reported as 'less than the detection limit' can be used by assigning them a common value smaller than the
smallest measured value in the data set (Gilbert, 1987).
All 'below detection limit' observations were assigned a common value smaller than the smallest
detected value in the data set. In the event that multiple detection limits were present in a data set, all
nondetect values were assigned the highest detection limit.
Compliance water-quality data were reported in daily loads, often in kilograms of constituent
discharged per day. These data were converted to a concentration (mass of constituent per volume of
water), because ambient water data were expressed as concentrations, either milligrams per liter (mg/L),
or micrograms per liter (ug/L). Therefore, the Mann-Kendall trend test was computed on constituent
concentrations so direct comparisons between ambient and compliance data could be made. 'Below
detection limit' measurements in a compliance data set were often reported as a load or concentration of
zero. These data were considered as 'below detection limit' measurements in the evaluation.
A significance level of 0.05 was chosen for this evaluation. The graphing and data analysis program
Plotit®* (Scientific Programming Enterprises, 1994) was used to calculate Kendall's tau and its
associated p-value for all compiled data. The null hypothesis was that constituent concentrations did not
change significantly through time (i.e., no trend was evident).
Results
Selected ambient and compliance trace element and PCB data collected from Pool 2 of the
Mississippi River from 1985-95 were statistically analyzed. Analyses using the Mann-Kendall trend test
showed that there was a statistically significant trend (p<0.05,) in 33% of all tests (considering all
constituents, all media, and both ambient and compliance data).
Only 25% of all possible trends in ambient data were statistically significant. Statistically significant
trends were detected in ambient bed sediment for all constituents analyzed except PCBs, Cd, and Se.
Statistically significant decreasing trends were detected for the following constituents: Arsenic, Cr, Cu,
Pb, Hg, Ni, and Zn. One statistically significant increasing trend was detected: Cu in MCES data for
ambient bed sediment. Often in the ambient bed-sediment data set, for a given constituent, a significant
decrease wrs found in COE data, whereas no trend was detected in MCES data. In unfiltered ambient
water samples, statistically significant increasing trends were found for Arsenic, Hg, and Se; statistically
significant decreasing trends were detected in Cd, Cr, Cu, Pb, and Ni. Zn, Cr+6, and PCBs were the only
constituents for which statistically significant trends were not detected in unfiltered samples. One
statistically significant trend was found in filtered water, a decrease in Ni concentration. Fish tissue
contaminant data showed no statistically significant trends in smallmouth bass or carp fillets for PCBs or
any trace element.
More statistically significant trends were evident in the compliance data set (56% of tests).
Statistically significant decreasing trends were found for Cr, Hg, Se, and Zn in wastewater from more
than one facility. All statistically significant trends in this data set were decreasing, no statistically
significant increasing trends were detected. The effluent from 3M Chemolite had statistically significant
decreases in Hg, Se, and Zn. Statistically significant decreases in Cr, Cr+6, and Se were found in the
wastewater from Ashland Oil. The effluent from Koch Refinery had a statistically significant decrease in
Cr over the period of record. No statistically significant trends were detected in effluent data collected at
the Cottage Grove Wastewater Treatment Plant. The wastewater effluent data from the Metropolitan
The use of brand and firm names is for identification or informational purposes only and does not constitute
endorsement by the U.S. Geological Survey.
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Wastewater Treatment Plant had statistically significant decreases in all trace elements monitored except
As and Cr+6. The effluent also had a statistically significant decrease in total PCBs, the only detectable
trend in all analyzed PCB data.
The principal goals of this study were to determine if there were statistically significant temporal
trends in ambient and compliance trace element and PCB data, and if any temporal trends in these data
sets were statistically similar. For no single constituent were trends statistically and directionally similar
among all the ambient and compliance data sets. There were statistically significant decreases in the
concentrations of nine constituents in the compliance data set. Seven of these constituents (Cr, Cd, Cu,
Pb, Hg, Ni, and Zn) had similar statistically significant decreases in either or both ambient water and bed
sediment.
Discussion
Ambient Water Quality in Pool 2 of the Mississippi River
Only 25% of all possible trends in ambient water quality data were statistically significant. Three
possible reasons for this were: low sample size, the high percentage of 'below detection limit'
measurements in the data sets, or there were no temporal trends in the majority of the data.
One factor that affects statistical tests is the number of data points analyzed (sample size). As sample
size increases, a more accurate estimation of the true environmental condition emerges. When sample size
is low, differences may be real but masked by variance. In data analyzed for this study, sample size was
low (<30) for several constituents. This was most common in the ambient data set, especially in the fish-
tissue data, for which no trends were evident.
A high (>50) percentage of samples analyzed were below the detection limit. This also may have
been a factor in the instance of few detectable water-quality trends in the ambient data set. The occurrence
of values below the detection limit in environmental data sets is a major statistical complication (Newman
et al., 1989; Helsel and Cohn, 1988), and presents serious interpretation problems for data analysts
(Helsel, 1990). The constituents studied for this analysis were trace substances in the aquatic environ-
ment. More samples 'below detection limit' were found in filtered water samples. In these samples, water
was filtered before analysis, which removed particulate matter and its associated trace elements and
PCBs. This process may help explain why only one statistically significant trend was found in filtered
ambient water. Additionally, the majority of PCB samples compiled for this study were 'below detection
limit'. During 1985-95, PCBs were detected in 1% of ambient water samples and 6% of ambient bed-
sediment samples. The only detectable temporal trend in PCBs was in the effluent from the Metropolitan
Wastewater Treatment Plant. This data set had 52% of samples determined to be 'below detection limit';
however, there were no detected concentrations from August 1992 to December 1995. This pattern of
nondetection values in the latter portion of the data set is probably what led to the statistically significant
decrease.
An Example of Variability in Ambient Water Quality in Pool 2
The disagreement in trends among the ambient media was possibly due to a combination of logistical
differences in monitoring programs and natural variation in the river. Cu concentrations in ambient bed
sediment provide an example. From 1985 to 1995, COE data indicated a statistically significant decrease,
while MCES data indicated an increase. Data from these agencies are collected for different reasons. The
MCES has established sampling locations in the study area, while COE sampling locations vary with
navigation channel dredging activities. Additionally, during the period of record studied, the COE started
sampling bed sediment in 1988, this same year the MCES stopped bed-sediment sampling. COE's bed-
sediment monitoring program was not designed to detect spatial and temporal trends on a pool-wide basis
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(Dan Wilcox, U.S. Army Corps of Engineers, oral comm., 1997). Trends in these data should not be
expanded to make broad generalizations about the bed sediment quality of Pool 2. MCES bed sediment
data collection ended in 1988. Trends in these data should not be expanded to describe current trends in
the study area, or compared to COE data. Variability in trends in these data illustrate the difficulty in
compiling data from different sources in an attempt to determine holistic trends in Pool 2 water quality.
Compliance Water Quality in Pool 2 of the Mississippi River
Results from the compliance data were more statistically distinct than those from ambient data. A
total of 56% of all possible trends in the compliance data set were statistically significant, and all were
decreasing. Trends may have decreased because of improvements in wastewater treatment. Four of the
five major compliance facilities in the study area (3M Chemolite, Ashland Oil, the Metropolitan
Wastewater Treatment Plant, and Koch Refinery) made improvements in wastewater treatment during
1985-95. The 3M Chemolite plant upgraded its pH control system for better precipitation of zinc (Tom
Baltutis, 3M Chemolite, oral communication, 1997). Ashland Oil upgraded its wastewater treatment
facility in 1994 (Alan Mayo, Ashland Oil, oral communication, 1997). Koch Refinery has removed all
chromium from its cooling towers, and has decreased its usage of mercury instrumentation (Heather
Faragher, Koch Refinery, oral communication, 1997).
The MCES Industrial Pretreatment Program is another example of an improvement in wastewater
treatment. The load of trace elements to the UMR from the Metropolitan Wastewater Treatment Plant has
been substantially reduced in recent years because of the MCES Industrial Pretreatment Program. This
program controls and regulates facilities that discharge their effluent to the sewer system to ensure
compliance with local and Federal regulations (Leo Hermes, Metropolitan Council Environmental
Services, written comm., 1997). The load of trace elements to the river has been reduced by about 91,000
kg annually (Leo Hermes, Metropolitan Council Environmental Services, oral comm., 1997).
Comparisons Between Trends in Ambient and Compliance Water-Quality Data in Pool 2
In the majority of constituents studied here (7 of 11; Cd, Cr, Cu, Pb, Hg, Ni, and Zn), there were
statistically significant decreasing trends in parts of both the ambient and compliance data sets. For these
7 constituents, there were statistically significant decreases in either ambient water, bed sediment, or both,
and a parallel decrease in a portion of the compliance data set. The majority of trends in the compliance
data set were significantly decreasing. In the ambient data sets with larger sample sizes, results were
similar to those in wastewater (i.e., statistically significant decreases). For instance, COE bed-sediment
data had statistically significant decreases in 7 of 9 trace elements. Similarly, unfiltered-water data had
statistically significant trends in the majority of constituents, and most statistically significant trends were
decreasing.
This retrospective analysis indicates the difficulty in both the interpretation and detection of definitive
water-quality trends. No conclusive trend was the most common result in this analysis, and disagreements
in the statistical significance of trends in data collected by the various agencies or facilities were often
found (Table 1). Most trends in the ambient data set were not statistically significant, whereas a majority
of trends in the compliance data set were statistically decreasing (Table 1). On a broad scale, this fact
shows that the trends in the data sets were dissimilar. The fact that there was no statistical significance
among all ambient and compliance data for any single constituent reinforces this point (Table 1).
The numerous statistically significant decreases of constituents in wastewater (due to improvements
in wastewater treatment) apparently have not yet resulted in significant changes in ambient water, bed
sediment, and fish fillets. The dilution effects of the Mississippi River may be responsible for the lack of
trends in ambient water, and therefore the lack of similarity between ambient and compliance water-
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quality trends. It is possible that, in time, the reductions in wastewater loads will also be seen in bed
sediment and fish fillets.
This study indicated that trends between the ambient and compliance data sets were generally
different. Part of this may be due to the attributes of the ambient monitoring programs that collected trace
element and PCB data in Pool 2 of the Mississippi River from 1985-95. Many of these monitoring
programs contain data that may not be suitable for trend analysis in the study area. For example, COE
data was collected in the context of dredging operations. The fish fillet data were few and were not well
distributed over the 11-year period. The filtered-water data sets also were small. The largest ambient data
set (MCES unfiltered-water data) had data which was well distributed over the period of record, but the
samples were taken quarterly, often at several locations on the same day.
Continued ambient trace element and PCB monitoring, free of the shortcomings discussed previously,
will be useful for determining trends in ambient concentrations in Pool 2. A properly designed, holistic
monitoring program can help the water-management community better determine if, or how, ambient and
compliance monitoring data can be integrated.
National Implications of Ambient and Compliance Data Integration
Results from Pool 2 point to the importance of effective monitoring integration, especially if certain
analysis of the data (trends) are desired. The results from this study have implications beyond Pool 2 of
the Mississippi River. For integration among the data sets to be successful, the amount and usefulness of
ambient and compliance data should improve.
Collaboration is important because few single organizations can collect all the information needed for
informed decision making (U.S. Geological Survey, 1995). Data sharing can only be accomplished if
ambient and compliance data are readily available. Many Federal and state agencies store their water-
quality data in agency-specific data systems that other agencies cannot easily access or in files that are not
yet automated (Powell, 1995). Much of the compliance and ambient data generated by the regulated
community are unavailable for other uses because of differing designs and goals in collecting the data and
also because no one has asked for them in a systematic way beyond their narrow compliance context
(U.S. Geological Survey, 1995). Before ambient or compliance data can be shared and integrated, they
should be readily available.
Data produced by existing monitoring programs do not always meet existing needs, such as
determining ambient water-quality status and trends (Powell, 1995). If the ambient monitoring
community (Federal, state, and local government agencies) cannot meet current needs, the water-quality
data collected by compliance monitors may assist ambient monitoring efforts. Public access to these PCS
data is provided through the Freedom of Information Act (U.S. Environmental Protection Agency, 1993).
These data have implications beyond the immediate purpose of the compliance data. The quality of a
wastewater effluent can have profound consequences for the ambient conditions in a stream because
wastewater generally has much higher concentrations of dissolved constituents. These constituents can be
dispersed into the water column, assimilated into the bed sediment and biota, and possibly degrade the
ambient water quality. It is important to learn the amount and quality of wastewater being discharged to
an ambient receiving water. After reviewing these data it can be determined if, or how, they can
contribute to ambient monitoring efforts for determining status and trends. Accordingly, the use of
compliance water-quality data can be very beneficial in ambient monitoring efforts.
Ambient water-quality data from various local, state and Federal agencies can help the monitoring
community holistically determine the quality of our waters. Compiling ambient data from all available
sources can identify impediments to both the integration of ambient data and the possible integration with
compliance data. These data could reinforce or contradict established ambient trends, and put new results
in context with historical data, therefore facilitating better water-quality management.
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Improved data-sharing techniques can lead to another benefit: better communication among those
who monitor water quality on all levels. While gathering retrospective data from a particular organization,
ancillary information describing the scope of the monitoring program can be collected. Examples of
ancillary information include: specific reasons why the data were collected, sampling locations,
laboratory and field methodologies, quality-control and quality-assurance data, and planned analyses of
the data. This information can put the water-quality data in context with the data collected from other
sources. Additionally, these data may ultimately lead to reduced sampling duplication by those agencies
who monitor ambient water quality.
Acknowledgments
This effort was part of the Upper Mississippi River Basin study. Thanks to Jim Stark, Ginger Amos,
Dave Lorenz, George Garklavs, Keith Robinson, Paul Capel, Kathy Lee, James Fallon, and Paul Hanson
of the U.S. Geological Survey and Gary Oehlert of the University of Minnesota for reviewing this paper
and providing technical assistance. Also, thanks to the following individuals for providing data from their
respective agencies/facilities: Mary Kimlinger, Mary Dzerik, Linda Nelson, and Eudale Mathiason at the
Minnesota Pollution Control Agency; Mark Briggs at the Minnesota Department of Natural Resources;
Dennis Anderson at the U.S. Army Corps of Engineers; Jahna Lindquist at the Metropolitan Council
Environmental Services; Tom Baltutis at 3M Chemolite; Alan Mayo at Ashland Oil; and Heather
Faragher at Koch Refinery.
Literature Cited
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Ohio Journal of Science 5:153-163
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pollutants. Journal of the Water Pollution Control Federation 54:292-297.
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US Fish and Wildlife Service Biol. Rep. 85(1.7), 72 pp.
Frenzel, S.A. 1996. Occurrence of selected contaminants in water, fish tissue, and stream-bed sediments
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Gilbert, R.O. 1987. Statistical methods for environmental pollution monitoring. Van Nostrand Reinhold,
New York, 320 pp.
Helsel, D.R. 1990. Less than obvious statistical treatment of data below the detection limit.
Environmental Science and Technology 24:1766-1774.
Helsel, D.R., and T.A. Cohn. 1988. Estimation of descriptive statistics for multiply censored water quality
data. Water Resources Research 24:1997-2004.
Helsel, D.R., and R.M. Hirsch. 1992. Statistical Methods in Water Resources. Elsevier Science
Publishers, New York, 522 pp.
Kendall, M.G. 1975. Rank Correlation Methods, 4th ed. Charles Griffin, London, 202 pp.
Knopman, D.S. and R. A. Smith. 1993. Twenty years of the clean water act: Has U.S. water quality
improved? Environment 35:17-41.
Mann, H.B. 1945. Non-parametric tests against trend. Econometrica 13:245-259.
Meade, R.H. ed. 1995. Contaminants in the Mississippi River, 1987-92. U.S. Geological Survey Circular
1133, 140pp.
Newman, M.C., P.M. Dixon, B.B. Looney, and J.E. Finder III. 1989. Estimating mean and variance for
environmental samples with below detection limit observations. Water Resources Bulletin 25:905-
915.
Powell, M. 1995. Building a national water quality monitoring program. Environmental Science and
Technology 29: 458A-463A.
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Scientific Programming Enterprises. 1994. PlotlT for windows reference manual, Version 3.1.
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Lee. 1996. Water-quality assessment of part of the Upper Mississippi River Basin, Minnesota and
Wisconsin—environmental setting and study design. U.S. Geological Survey Water-Resources
Investigations Report 96-4098, 62 pp.
Sullivan, J.F. 1988. A review of the PCB contaminant problem of the Upper Mississippi River system.
Wisconsin Department of Natural Resources, 50 pp.
Taylor, H.E., and A.M. Shiller. 1995. Mississippi River comparison study: Implications for water quality
monitoring of dissolved trace elements. Environmental Science and Technology 29: 1313-1317.
Tyler, E.L. 1992. Reauthorizing the federal water pollution control act. Water Resources Update 88: 7-16.
U.S. Army Corps of Engineers. 1990. Mississippi River headwaters lakes in Minnesota low flow review,
variously paged.
U.S. Environmental Protection Agency. 1993. Permit compliance system, public access to PCS data
products. EPA/83l/F-93-001, 12 pp.
U.S. Geological Survey. 1995. The strategy for improving water-quality monitoring in the United
States-final report of the intergovernmental task force on monitoring water quality. U.S. Geological
Survey Open File Report 95-742, 25 pp.
Upper Mississippi River Water Quality Initiative. 1993. Report of the toxic pollution workshop, Feb 17-
18, 1993, Minneapolis, Minnesota, 14 pp.
Ward, R.C., J.C. Loftis, and G.B. McBride. 1990. Design of water quality monitoring systems. Van
Nostrand Reinhold, New York, 230 pp.
Wiener, J.G., Jackson, G.A., May, T.W., and Cole, B.P 1984. Longitudinal distribution of trace elements
(As, Cd, Cr, Hg, Pb, and Se) in fishes and sediment in the Upper Mississippi River, pages 139-170 in
J.G. Wiener, R.V. Anderson, D.R. McConville (eds). Contaminants in the Upper Mississippi River.
Butterworth Publishers, Boston, 368 pp.
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a
o\
Twin Cities
Metropolitan Area
. Lock and
Dam No, 1
n _...
Melropolilan
Wastewater
Treatment Plant
94° 45
MINNEAPOLIS
EXPLANATION
Pool 2 (river reach of study)
Compliance water monitoring site
and site name
U.S. Geological Survey gaging
station and station number
Cottage Grove Wastewater
Treatment Plant
3M Chemolite
Lock and
Dam No. 2
Twin Cities
_ Melropolilan
Area
5 KILOMETERS
J
Upper Mississippi River
NAWQA Study Unit
Base from U.S. Geological Survey digital data 1:100.000,
1990, Minnesota Department of Transportation
BaseMap '97 digital data. 1:24.000,1997; Albers
Equal-Area Conic projection. Standard parallels:
29°30' and 45°30'. central meridian: -93°00'
Figure 1. Map of study area, Pool 2 of the Mississippi River.
-------
Table 1. Comparison of Ambient and Compliance Trends
I An Increasing Trend T A Decreasing Trend N = No Trend Detected
More than one symbol Indicates a disagreement In statistical significance among agencies / facilities
-= not monitored
MEDIA
BED SEDIMENT
FILTERED WATER
UNFILTERED WATER
TISSUE- BASS
TISSUE- CARP
WASTEWATER
A/C1
A
A
A
A
A
C
As
1"
N
t
N
N
N
Cd
N
N
1
N
N
1
Cr
4"
N
1
N
N
1
Cr+6 Cu
- ti
N
" 4
N
N
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4 t 4
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1 A/C= Ambient or Compliance Monitoring, A=Ambient, C=Comp1iance.
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III-328
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Tailoring of Data Quality Objectives to Specific Monitoring Questions
Revital Katznelson, Ph.D., Senior Project Scientist
Woodward Clyde International-Americas, 500 12th Street, Suite 200, Oakland, CA 94607
Phone:(510)874-3048
Abstract
The use of simple field methods can be a cost-effective way to obtain very useful data for watershed
management, although they are not always supported with traditional quality assurance and quality
control (QA/QC) practices. Data quality objectives for field methods can be tailored to specific
monitoring questions, provided the issues of tolerable error, monitoring design, choice of operators, and
cost are addressed. Monitoring of water quality in urban creeks in California is used as an illustration of a
conceptual framework for tailoring of data quality. The first steps involve formulation of study questions,
selection of parameters, and developing sampling design. Next, the tolerable error is defined, methods are
selected, sources of error are identified, and operators are trained. In the example provided, the acceptable
magnitude of error that can be tolerated depends on the ecological significance of each water quality
parameter at the range of values that is critical for the health of the organisms in the creek. As to the
choice of operators, the key is training. Any non-professional monitors (staff or volunteers) can and
should be trained to identify the sources of error and uncertainty and to minimize both.
Introduction
Watershed information, including water quality data, is essential for guiding watershed management
decisions. Agencies allocate considerable resources for the acquisition of data of the best quality, using
sophisticated instruments and analytical methods; this approach is based on a widespread belief that
scientific measurements always have to be very accurate and precise, and that sophisticated instruments
which generate more digits and more decimal places make scientific measurements even better. Intensive
quality assurance/quality control (QA/QC) procedures are applied, and strict data quality objectives are
developed for data collection using these methods.
As young science students, we have been taught to replicate our measurements extensively in order to
provide formal estimates of error in our measurements and to provide statistically-robust data sets for
hypothesis testing. This discipline is extremely valuable for any person practicing scientific work. As
environmental scientists, we are obliged to use standard methods with rigorous quality assurance
procedures to obtain data that will be defensible in court. This is extremely important if the data are to be
used for determining if a hazardous-waste site should be cleaned, or if an effluent is violating discharge
permits that specify water quality criteria protective of aquatic life.
But because resources are never unlimited, in situations where representative data are more useful
than precise data we may opt to do 8 inexpensive field tests with 25 % error instead of 2 expensive
laboratory analyses with 5 % error. When we monitor the quality of water in our watershed to
characterize its conditions, to understand the processes going on in it, or to track changes that occur in it,
we can use methods that involve far less effort and expense than the state-of the-art methodology and still
obtain data that are good enough for what agencies want or need to know. The issues all boil down to:
What range of error can we tolerate?
Based on the concept that tolerable error is a function of the question we want to answer and the
importance (ecological, regulatory, or economic) of the tested parameter, this paper is an assortment of
thoughts, ideas, learning experience, and suggestions, to be shared with persons involved in monitoring as
"food for thought". A conceptual framework is described and specific examples, focused on field methods
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but including some laboratory methods, are provided. The intuitive, informal language that has been
developed by the author in numerous training sessions is used in this paper.
Definitions
Accuracy and Precision
The USEPA QA/QC guidance (USEPA 1996) provides excellent explanation for the concepts of
accuracy and precision, using intuitive "bull's eye" examples. Essentially, accuracy is a measure of how
close we are to the absolute true value, and can be assured or evaluated by analysis of standards from
different sources. Standards are also spiked into the tested sample matrix to provide information on matrix
effects, i.e., how far from the truth can our results be due to sample-specific interference. Precision is a
measure of how reproducible our measurements are, and can be evaluated by analysis of replicates from
the same sample and/or by repeated analyses of the same sample at different times.
Sensitivity, Resolution, and Detection Limit
When we talk about the resolution of a method, we make a statement about the smallest increment
that the method can discern with confidence. Detection limit is the lowest value that the method can
report as significantly positive (usually an indication that there is 95% probability that the value is indeed
positive). There is often a confusion between the two concepts (i.e., resolution and detection limit) owing
to the common use of the term "sensitivity" to describe both. For example, people refer to a method as
sensitive if it can detect low concentrations of an analyte in a sample, or if it can show minute differences
in concentrations between two samples.
Error and Uncertainty
Many workers have suggested rigorous definitions of error and uncertainty, and rigorous
methodology to quantify both (e.g., the strategy developed by the Intergovernmental Task Force on
Monitoring (ITFM 1995)). This formal derivation is essential in many fields, including ecological risk
assessment. However, as operators we intuitively use a practical distinction between error and
uncertainty: error we can do something about, uncertainty we have to live with. Another practical
distinction: error has to do with the quality of our measurements, uncertainty has to do with what they
represent. But in practice we can identify sources of both, both can be quantified (intuitively or formally),
and measures can be taken to diminish both; the level of effort has to do with the amount of error we can
tolerate.
For the purpose of this framework, the term "error" tells us how far our measurement could be from
the truth for that specific sample, either as percentage of the value (e.g., plus or minus ten percent, or
±10%) or as an increment of the value (e.g., + 0.3 pH units), depending on the method. This term
encompasses the concepts of accuracy, precision, and resolution. It does not deal with variability,
representativeness, comparability, and other attributes that are associated with natural variability, study
design, and choice of methods.
Data Quality and Reliability
People often confuse data quality with data reliability. They associate level of accuracy and precision
with quality: high precision and accuracy is considered high quality, and therefore reliable, data. In
reality, data are reliable if the values fall within the range of error specified for them, and it is much more
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difficult to obtain reliable data at high precision and accuracy (narrow range of error) than at a wide range
of error.
Conceptual Framework for Developing Data Quality Objectives
Monitoring is performed for a variety of reasons, in a variety of settings, to provide answers to a
variety of questions. Table 1 demonstrates an assortment of "Ammonia questions" that may be
encountered by an environmental scientist. It is apparent from Table 1 that study design, data quality
objectives, and methodology can and should be tailored to questions for cost-effective provision of data.
An approach to the process of tailoring data quality objectives to specific questions may include the
following steps which are described using a specific example below:
1. Formulating study questions
2. Selecting parameters and developing sampling design
3. Defining tolerable error for selected ranges of values
4. Determining the feasibility and cost-effectiveness of available methods
5. Exploring the sources of error and uncertainty associated with each method
6. Training operators to minimize error and uncertainty and to achieve data quality objectives
The first step may involve a complex process of formulating watershed management questions with
inputs from stakeholders to define the data needed, or it could be a simple question such as "can fish
survive in the creek?". The following steps assume that the latter is the question.
In the second step, we would examine what we know about the ecological requirements of fish in
creeks, list the factors and parameters that are ecologically significant for fish survival, and focus on those
that we think may pose problems, for example, dissolved oxygen (DO) depletion. The sampling design
may accommodate both routine monitoring of DO (e.g., every two weeks at 9-11 am at three fixed
stations in different creek segments) and worst-case scenario (e.g., DO measurements at dawn in the
remaining stagnant pools at the end of summer). Sampling for biochemical oxygen demand (BOD) will
help evaluate the potential for oxygen depletion. This step is iterative: the study design is periodically
refined based on review of the findings and parameters are added or deleted.
The third step would examine the range of DO values that is critical for the health of the fish in the
creek, and determine the amount of error that can be tolerated. For example, we can tolerate an error of
plus or minus 1 mg/1 DO around a value of 8 mg/1 (because values in the range of 7 to 9 mg/1 are
"healthy"), but not around 3 mg/1 (because the difference between 2 and 4 mg/1 means life or death to
many organisms.
The fourth step involves selection of method to measure DO. Table 2 lists the three major
methodologies for DO measurements and describes the principles and challenges associated with each of
them. Table 3 provides more specific information on cost and attainable data quality for available
methods. For our fish in creek example, the field kit utilizing the modified Winkler method with direct
titration can provide data of adequate quality.
In the fifth step, sources of error and uncertainty are explored. Table 4 provides a broad "checklist"
for the various methods. For our example, error and uncertainty associated with the use of the modified
Winkler field kits for DO measurement have to be examined. Experiments would show that contact with
air during sampling will introduce oxygen into the sample; this will probably have negligible effects on
the results if we are in the high range, i.e., close to oxygen saturation, but can introduce an error of 100%
(elevate the measured concentration from 1 mg/1 to 2 mg/1) at low DO values. Another source of potential
error is the dispensing of a fixed sample into the titration vial: should the 20-ml volume line lie below the
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meniscus or above it? This difference of almost 2 ml accounts for 10% of the volume. It must be noted
that evaluating the magnitude of error contributed by each source requires experimentation.
The sixth step is where the understanding of the sources of error and the magnitude of error they may
introduce can be translated into action. Operators trained in the use of the modified Winkler field kits for
DO measurement need to be made aware of the sources of error and develop a "feeling" of how confident
they are with the data they report. They have to watch for bubbles in the sample bottle and start all over
again if they see any. They have to be consistent (e.g., always have the bottom of the meniscus merge
with the 20-ml line) and confirm each reading by having two people "read" the output. They have to
repeat the titration if they think they overshot the endpoint. They have to duplicate measurements on a
regular basis (and always repeat the sampling, fixing and titration if the result "doesn't make sense").
They need to test the performance of the kit (e.g., measure DO in saturated clean water at a given
temperature) at regular intervals to account for gradual deterioration of the reagents with time, and
anytime a reagent has been replaced.
Example: Creek Monitoring in California
The Questions, the Parameters, and the Study Design
The study question in that case was: Are conditions in the creek suitable for year-round support of
fish populations? The parameters selected were dissolved oxygen, temperature, pH, electrical
conductivity, and turbidity. The initial monitoring/sampling design called for routine monitoring of these
parameters twice each month during the wet season (November through May) and into the dry season. As
the study progressed, it became apparent that flows during dry weather conditions were the critical factor,
particularly after the winter flows had subsided and water remained only in a few upstream segments of
the creek. Flow measurements had to be added to the list of parameters and the question was re-phrased :
"Are condition in the creek during the worst time of year and worst time of day still supportive of fish
survival?". Monitoring that describes the worst case scenario will provide an answer.
Experience shows that in order to describe the worst case scenario for a typical creek in the San Francisco
Bay Area in California, water quality parameters that change during the 24 hour period (in response to
solar radiation) need to be measured at the most critical time of day. Temperature and pH (also DO for
supersaturation effects) will be most extreme during the early afternoon, at about 14:00 or 15:00 summer
time, any day of the week, during August -September. Temperature fluctuation can best be evaluated
using an automatic data logger (hobo) that can be deployed in the creek for up to two months. The lowest
DO values (for evaluation of depletion) are likely to be measured at dawn or early morning, any day of
the week, during August-September when flow is minimal. Turbidity and electrical conductivity may not
show diurnal cycles but do fluctuate in urban creeks. Atypical dry weather values of these parameters
may be encountered during the weekends (more yard, garden, curbside activities in residential
watersheds, and more human and dog access to the creek). Turbidity questions relevant to fisheries are:
how often does the creek become turbid, and for how long. Drastic changes in conductivity, which could
indicate illicit discharges to the creek, may cause osmotic stress. However, the values for all five water
quality parameters are strongly dependent on flows, and flow should be evaluated any time measurements
are made.
Tolerable Error
Preliminary suggestions for tolerable error were set on the basis of ecological/physiological
significance, as follows:
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• Dissolved oxygen: a) an error of plus or minus 1 mg/1 in the ranges of 0-3 mg/1 and 8-10 mg/1
(because values in the range of 0-4 mg/1 are inadequate and values in the range of 7 to 11 mg/1 are
"physiologically comfortable"), b) an error of plus or minus 0.4 mg/1 in the range of 5-7 mg/1
(this is the critical zone for warm water fisheries and cold water fisheries).
• Temperature: an error of plus or minus 0.5 °C
• pH: plus or minus 0.5 pH units in the ranges 1-6, 7-8, and 9-14 (two uncomfortable zones and one
comfortable zone), and plus or minus 0.3 in the ranges of 6-7 and 8-9 (two zones of transition
between comfortable and uncomfortable).
• Electrical conductivity: error up to 30% of the measurement
• Turbidity: error up to 50% of the measurement (because the fish couldn't care less if it is 50 JTU
or 80 JTU, all they want to know is when this turbidity is going to go away).
• Flow: error of up to 100% of the estimate in the range of 1 to 30 gallons/minute, or up to 2 cubic
feet per second (cfs). The relevant factor during dry weather is the detention time of water in an
average pool, or how often the entire pool volume is replaced; information on turbulence
immediately upstream of the pool is relevant as well.
Training of Operators
Almost any person with minimal skills, be it an agency staff person, a volunteer, or a high school
student, can be trained to use pH probes, conductivity meters, thermometers, turbidity kits, and even the
modified Winkler field kits for DO measurement. But training has to go beyond the manufacturers
instructions contained in the kit itself. The most important elements of training are about including the
operators (those who are actually collecting the data in the field or in the lab) in the monitoring effort in a
way that shares the understanding of the objectives, promotes personal responsibility for the reliability
and usefulness of the data they generate, and creates a sense of ownership and participation of/in an
important process.
Training has to be conducted in phases that match the learning curve and the internalization of
concepts and "feeling". We can start with imparting awareness and intuitive understanding of error and
uncertainty. One way to do this is to ask the trainees what value they can "stick their neck out for" with
confidence, e.g., "if you report 4 mg/1, could it also be 3 or 5, or are you sure that it cannot be less then
3.5 or more than 4.5?". That may elicit their curiosity about their own performance and they will try to
find out for themselves, examining the sources of error and intuitively using all the quality assurance
procedures you can teach them. In this way, the operators define the data quality objectives (DQOs) for
themselves, based on their experience, and prove that they can achieve them (and if they cannot achieve
the DQOs tailored to the question, another method needs to be examined). And - if personal responsibility
for data reliability is promoted - operators need to be assured that it is sometimes OK to leave a blank
space in a data sheet ("if you cannot do it right, don't do it") because no data is much, much better than
wrong data, and that their honest "explanation notes" are their way of communicating their experience
with the data user.
Training has to remind the operators to use eyes, brain, and common sense in everything they do. It
has to teach and encourage operators to keep neat records, to pay close attention and be consistent when
dispensing volumes of liquids, to avoid contamination, to wait for instrument readings to stabilize, to
confirm each reading by having two people "read" the output, to repeat measurement on a regular basis,
to question if the result "makes sense", and to repeat measurements if it does not. It also has to reinforce
the awareness of measurement "drift", as instruments move away from calibration and reagents change
their reactivity over time, and encourage the operators to test the performance of kits and instruments at
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regular intervals to account for gradual deterioration of the reagents with time, and anytime a reagent has
been replaced.
Another important concept related to training for field work is that everything that applies to good
laboratory practices and to quality assurance is also valid in the field. This includes behavior/attitude
issues ("Quick and dirty" does not mean that we can tolerate contamination, and sloppy work is not
acceptable) as well as methodology (e.g., a run of the colorimetric salicilate field kit for ammonia with
multiple test tubes, to accommodate a few samples, reagent blank, and a calibration curve of three
standards). The concept that some samples may need to be re-analyzed, diluted in distilled water to fit a
range of color intensity increments that the human eye can perceive, also needs to be taught (and the
equipment to make dilution needs to be provided in the field kit).
Standard Operating Procedures (SOPs) may be used as a reminder but cannot replace training by a
person. At a later phase, trainees can be introduced to the more "traditional" QA/QC programs and
familiarize themselves with the formal elements of a QA/QC plan. However, the most elaborate QA/QC
plan will not assure data quality and reliability if the operators had not been properly trained. It is highly
recommended to construct monitoring programs in ways that allow direct contact between the data
collectors and the QA/QC officers (rather than have them several ranks removed), and to allow periodical
communication between the data collectors and the data users.
Acknowledgments
This article summarizes experience that has been acquired in numerous projects sponsored by The
Hebrew University of Jerusalem, Israel, Mekorot Water Company, Israel, the Alameda Countywide Clean
Water Program, Hay ward, CA, and the City of Newark, CA. Preparation or the article was supported in part
by the Alameda County Flood Control and Water Conservation District. The author wishes to thank Dr.
Marion Lamb, Dr. Peter Mangarella, and Ms. Arleen Feng for their helpful review comments.
References
Abeliovitch, A, and Y. Azov (1976) Toxicity of ammonia to algae in sewage oxidation ponds. Applied
Environ. Microbiol. 31:801-806
Intergovernmental Task Force on Monitoring Water Quality (ITFM). 1995. The strategy for improving
water-quality monitoring in the United States: Final report of the Intergovernmental Task Force on
Monitoring Water Quality. U.S. Geological Survey, Reston, Virginia.
Scheiner, D. (1976) Water Research 10:31-36.
U. S. Environmental Protection Agency (USEPA) 1996. The volunteer monitor's guide to quality
assurance project plans. Office of Wetlands, Oceans and Watersheds, Washington, D.C. EPA 841-D-
96-001.
Woodward-Clyde Consultants (WCC). 1997. Summer ecological study of Lakeshore Park, Newark.
Report prepared for the City of Newark, CA, April 1997.
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Table 1. Questions Related to Ammonia in Various Environmental Settings
Scenario or Setting
1 Illicit discharges to
urban creeks
2 Ammonia detected
in a creek
3 Ammonia
suspected/detected
in fisheries habitats
(creeks, rivers,
ponds, lakes)
4 Intensive
aquaculture tanks
5 Algal populations in
aquatic systems
Question
Is ammonia discharged
into the creek during dry
weather flows?
What is the source of
ammonia?
Is it toxic to fish?
What is the level of
ammonia in the tank at
any given point in time?
Does ammonia
accumulate to toxic
levels?
Is ammonia nitrogen
available?
Significance
Illicit connections,
broken sanitary sewers
malfunction of septic
systems
Source ID
Above 5 mg/1 at pH
above 8 may be a
problem
Unionized ammonia
above 0.2 mg/1 may be
a problem
Low importance (Even
if not available,
nitrogen is usually not
the limiting nutrient)
Design
anytime,
anywhere
different
watershed
locations,
same time
pulses in
rivers,
vertical
gradients
in lakes
continuous
monitoring
pulses,
gradients,
time-
course
Method1
and Cost2
Nessler reagent, one-
tube, 2 minutes test kit
$0.08+$2 / sample
Colorimetric 2-
reagents, 15 min, test
kit (e.g., salicilate), 2
sample dilutions +
blank + standard
$0.5+$10/ sample
Colorimetric (e.g.,
salicilate), test kit
$0.5+$10/ sample
Ion specific electrode,
on line, with recorder.
Cost varies.
Laboratory method
(e.g., indophenol3) with
concentration step
$50 / sample
Data Quality
Objectives1
DL 1 mg/1,
Error + 1 mg/1
DL 0.2 mg/1
Error ± 0.2 mg/1
DL 1 mg/1
Error ± 0.5 mg/1
DLO.l mg/1
unionized NHs
Error ±0.1 rng/1
NH3
DL 0.005 mg/1
Supporting
Information
City sewerage
maps
Watershed
activities and
storm drain maps
pH (± 0.3 units)
Temperature
(± 0.5°C)
Fish density,
feeding rate
Other N sources,
P, presence of
nitrogen fixer
1 Total ammonia (free ammonia, NHs, plus ammonium ion NUt*) is measured unless otherwise specified.
2 Cost is approximate and includes reagents plus labor (or both combined).
3 Scheiner 1976
4 Abeliovitch and Azov 1976
5 WCC 1997
-------
Table 1 (Continued)
Scenario or Setting
6 Urban or
agricultural storm
runoff
7 Total ammonia in
sediments
8 Ammonia in
sediment pore water
9 Nitrification in
sewage treatment
plants
10 Photosynthetic
activity in oxidation
ponds operating
under high organic
loads
Question
How much ammonia is
contributed by a given
watershed?
Sink or source
What proportion of the
ammonia is free, i.e.,
extractable in elutriate?
What is the rate of
ammonia removal?
Are the ammonia
concentrations high
enough to inhibit
photosynthesis?
Significance
Loads assessments
Nitrogen budget,
Potential toxicity
resulting from
resuspension or dredge
material disposal
Effectiveness of
nitrogen
transformation
processes
30-40 mg/1 ammonia
may inhibit Chlorella
photosynthetic
(oxygen-producing)
activity4
Design
flow-
weighed
storm
composite
flux,
gradients
different
locations,
times
different
locations
different
times of
day, photic
zone
Method1
and Cost2
Colorimetric, (e.g.,
indophenol3),
absorbance, calibration
curve
$30 / sample
Laboratory, distillation
$50 / sample
colorimetric, (e.g.,
salicilate), extraction
with H2O for free, with
KC1 for total
(freshwater sediments5)
$1 +$20 /sample
Colorimetric, (e.g.,
indophenol3),
absorbance, calibration
curve
$30 / sample
Nessler test kit, sample
dilutions
$0.16+$ 10 /sample
Data Quality
Objectives1
DL 0.05 mg/1,
Error ± 0.05
mg/1
DL1 mg/kg,
Error ± 1 mg/kg
DL 0.5 mg/1 in
extract,
Error ± 0.5 mg/1
DL 0.5 mg/1,
Error + 0.2 mg/1
DL 5 mg/1,
Error ± 2 mg/1
Supporting
Information
Storm event data
TOC
Sediment and
lake volume
loads, sludge age,
other process
info
chlorophyll a
1 Total ammonia (free ammonia, NH3, plus ammonium ion NH4+) is measured unless otherwise specified.
2 Cost is approximate and includes reagents plus labor (or both combined).
3 Scheiner 1976
4 Abeliovitch and Azov 1976
5 WCC 1997
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Table 2. Available Methods for Monitoring of Dissolved Oxygen
Group
Measurement Principle
Challenge
Application
Polarographic
Electrodes measure the flux of oxygen
across a membrane
Keep flushing sample liquid at
the membrane surface to
constantly replace the oxygen
consumed by the electrode.
(Rapid-Pulse and
microelectrodes exempted)
Measurement of DO along gradients or transects where
many samples are needed in a short time, measurements of
kinetics of change in DO concentrations, continuous
monitoring of DO (automatic data-logging Rapid-Pulse
probes), micro-scale DO gradients on sediment surface
(microelectrodes).
Colorimetric
Chemical reagents added in excess interact
with oxygen to form a colored product
(that absorbs light at a visible wavelength).
Color is proportional to oxygen
concentration.
Collect samples and introduce
reagents without contact with air
Screening for anoxic conditions, rough and rapid
measurements by non-professional operators, etc.
Titrimetric
Chemical reagents in excess interact with
oxygen to form a product, and another
chemical (the "titrant") is used
quantitatively to "neutralize" that product.
The amount of titrant needed is
proportional to oxygen concentration.
Collect samples and introduce
reagents without contact with air
Routine monitoring of DO in creeks, biochemical oxygen
demand (BOD) measurements, etc.
Laboratory applications: DO electrode calibration, BOD,
etc. Samples can be collected and fixed in the field, and
titrated later in the lab using high-precision burettes.
-------
Table 3. Properties of Oxygen Measurement Devices
Principle
Polarographic
Colorimetric
Titrimetric
Device
DO meter+electrode
Rapid-Pulse probe, for Sonde
Specialty, e.g., microelectrode
Reagent ampoules and comparator (e.g.,
"CHEMets")
Reagent ampoules and colorimeter or
spectrophotometer (e.g., "Vacu-Vials")
BOD bottle, reagents for fixing DO, vial,
indicator solution, titrant solution, and syringe
for titration
Error
±5%
±5%
varies
±2mg/l
± 1 mg/1 in the 0-10 range, or
±0.2 mg/1 in the 0-2 range.
±0.4 mg/1
Instrument/
kit cost
$800
-$10,000 for
entire Sonde
$1,00045,000
$20
$610
$40
Cost per
sample
$0.10
$0.10
NA
$0.50
$0.50
$0.20
Work
prep/calib 1 h
measure 0.5-3 min.
prep/calib 2 h
download 1 h
prep/calib 2 h
measure 2 min.
measure 2 min.
measure 5 min.
-------
Table 4. Sources of Error and Uncertainty in Oxygen Measurements
Sources of error and uncertainty
Electrode not assembled properly (e.g., air bubble trapped under membrane)
Electrolyte too weak
Instrument not calibrated correctly
Membrane not under equilibrium at recording time
Sample has been in contact with atmospheric air during collection
Constituents in the sample interfere with chemical reagents
Pigments in sample interfere with color absorbence measurements
Particles in sample interfere with color absorbence measurements
Color intensity keeps changing as a function of time and temperature
Human eye cannot distinguish small color increments; human eyes are not subjective
Dispensing of volumes is not accurate
Titration endpoint is not clear-cut, blue color of indicator reappears after a while.
Titration is performed too fast or too slow
Sample bottle and/or other kit utensils are contaminated with titrant, reagents, and/or
other interfering substances
Reagents and/or titrant are not reacting as specified
Polaro-
graphic
X
X
X
X
Colorimetric
visual
comparison
X
XX
XX
XX
X
XX
X
X
Colorimetric
absorbance
measurement
X
X
XX
XX
XX
X
X
X
Titrimetric
X
XX
X
XX
X
X
X
LO
VO
X - error/uncertainty can be diminished or controlled by operator (better training, more attention, more patience, fresh reagents)
XX - error/uncertainty due to nature of sample or operator and cannot be reduced.
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Quality Assurance/Quality Control Plan for Agricultural
Nonpoint Source Pollution Monitoring Research
Tamim Younos, Associate Director
Virginia Water Resources Research Center
Saied Mostaghimi, Professor
Carol Newell, Laboratory Manager
Phillip McClellan, Systems Analyst
Biological Systems Engineering Department, Virginia Polytechnic Institute and State University
Blacksburg, VA 24061
Abstract
Monitoring of surface runoff and stream discharge, and pollutant concentration and load in surface
water systems constitute major components of a surface water monitoring and nonpoint source pollution
research program. Advances in field instrumentation, data transfer, and laboratory analyses have made the
"real time" sampling and data processing feasible. However, there is a critical need for developing quality
assurance/quality control plans to improve on protocols for water sampling, sample custody, storage, and
analysis. The overall goal of a quality assurance/quality control plan is to create a high quality database.
The objective of this paper is to discuss major components of a quality assurance/quality control plan for
an agricultural nonpoint source pollution monitoring research program. The article is based on the
authors' collective experiences with developing quality assurance/quality control plans for several
nonpoint source pollution research projects and other relevant information.
Introduction
The objectives of a nonpoint source monitoring research program are to evaluate the extent and
characteristics of nonpoint source pollution (NFS), its short- and long-term impacts on receiving waters, and
the effectiveness of NPS control measures. To achieve these goals, field and laboratory measurements should
be designed to create a scientifically sound database. Important criteria for creating such a database are
conducting timely and accurate sampling, and implementation of standard protocols for sample custody,
storage, and analysis. Recent advances in field instrumentation, data transfer, and laboratory analyses have
made the "real time" sampling and data processing feasible. However, there is a critical need to improve on
protocols for water sampling procedures and analyses.
Recently, the U.S. Environmental Protection Agency (USEPA) published the monitoring guidance for
determining the effectiveness of NPS controls (USEPA 1997). The objective of this paper is to focus on
the standard protocols "quality assurance/quality control plan" for evaluation of agricultural NPS impacts
in a monitoring-research program. This article is based on the authors' collective experiences with
developing quality assurance/quality control plans for several nonpoint source pollution research projects
conducted at Virginia Tech (Mostaghimi 1989; Younos 1994) as well as other relevant information from
the literature.
Definition of Terms
Detailed descriptions of data quality terms relevant to nonpoint source pollution research can be
found in several publications (USEPA 1997; USEPA 1994; Erickson et al 1991). Briefly, Quality
Assurance (QA) is defined as an integrated system of management procedures designed to evaluate the
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quality of data and to verify that the quality control system is operating within acceptable limits. Quality
Control (QC) is defined as a system of technical procedures designed to ensure the integrity of analyses
by proper operation and maintenance of equipment and instruments.
The overall goal of a QA/QC plan is to create a high quality database that is characterized by
accuracy, precision, representativeness, comparability, and completeness. Accuracy is defined as the
degree of agreement of a measurement (or an average of measurements) with an accepted reference value
and is estimated by calculating the standard deviation of the differences between the measured and
referenced values over a typical range of data. Precision is a measure of mutual agreement among
individual measurements of the same property and is calculated in terms of the standard deviation of
various measurements. Comparability is the quality that makes data obtained from one study comparable
to data from other studies. For example, consistent sampling methodology, handling, and analyses are
necessary to ensure comparability. Representativeness is a measure of how the collected data is compared
to the value the same parameter has within the population being measured. Sampling must be designed to
ensure that the samples are representative of the population being sampled. Completeness is the amount of
valid data obtained from the measurement system (field and laboratory) compared to the amount that was
expected to be obtained under anticipated sampling/analytical conditions. It can also be expressed as
percent recovery.
Components of a QA/QC Plan
Major components of a QA/QC plan for NFS research include: project description and organization;
QA/QC objectives; sampling methods and sample custody procedures; procedures for instrument calibration
and maintenance; laboratory protocols; procedures for data reduction and validation; procedures for internal
quality control and auditing; guidelines for corrective action; and guidelines for report preparation. Details of
a QA/QC plan for NFS research are described below.
Project Description and Organization
A QA/QC plan shall contain a comprehensive description of the proposed project. The major
components of a proposed research are: Justification statement for the proposed research, review of literature
to support the stated justification, research goal and specific objectives, research methods (experimental
design, field and laboratory procedures, methods of computation and statistical analysis), plan of work,
expected results, and projected budget.
Project organization is essential to the success of a project. A working team should be formed at the
outset when the project is in the planning stage. Possible team members and their responsibilities are: project
director, responsible for overall project design and completion; project manager, responsible for daily
activities and project reports; quality assurance officer, responsible for the design and implementation of the
QA/QC plan; laboratory liaison officer, responsible for coordinating field and laboratory tasks; field
coordinator, responsible for the task coordination between researcher and the property owner/field operator;
project engineer, responsible for installing field instruments, and regular and/or emergency maintenance;
laboratory personnel, responsible for laboratory analysis and computations; data reduction personnel,
responsible for digitizing and analysis of rainfall and runoff charts; field observer, responsible for attending to
field instrumentation on a daily basis; sampling team, responsible for regular grab water sampling and
retrieval of auto-samples.
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QA/QC Objectives
The QA/QC objectives are to meet the data quality requirements (accuracy, precision,
representativeness, comparability, and completeness) described earlier. Tables 1 and 2 show the overall
data quality standards for field and laboratory data pertinent to a NFS pollution monitoring-research
program.
Sampling Methods and Sample Custody Procedures
Two types of samples are taken in a NFS research project, i.e., grab samples and auto-samples.
Usually, a grab sample indicates the ambient or baseline water quality condition while auto-samples
indicate the rainfall-runoff impact on a "real time" basis. Table 3 shows standards for sampling methods
and frequency, sample preservation and allowable holding times for a NFS research program.
Proper chain of custody is very important in order to maintain a high data quality. A custody form should
be developed for transferring the field samples to the laboratory. Separate forms should be developed for grab
sampling and auto-sampling. A sample custody form should contain the following information: watershed
identification; farm identification (farm address and owner or operator); field number; type of activity (animal
or cropping production); sampling site identification; sampling date and time; sample number; sample bottle
number; weather condition; name of sampler; name of individual who transported samples to the laboratory;
number of samples collected; number of samples delivered to the laboratory; sample arrival date and time in
the laboratory; and name of individual who received samples in the laboratory.
Use of triplicate sample custody forms is recommended. The first copy shall be retained at sampling
location for future reference and other two copies should be submitted to the laboratory when the sample is
delivered. The laboratory manager should sign off on the form, retain one copy and submit the second copy
to the project manager for record keeping.
Instrument Calibration and Preventive Maintenance
Essentially, three types of field data collection instruments are used in a NFS research program:
mechanical, electrical/mechanical, and electronic instruments. The mechanical instruments (for example, a
Universal rain gauge) use paper charts and are driven by wind-up or battery clocks. The electrical/mechanical
instruments (for example, an auto-sampler) contain electronic devices (battery powered) that control
mechanical devices (AC or battery powered). The electronic instruments (for example, a data logger) are
driven by battery power. Major laboratory instruments that require calibration and maintenance include
various types of auto-analyzers, the digestion block, and water bath.
To meet QA/QC requirements, before any data collection and analysis is initiated, all field and laboratory
instruments should be calibrated and a regular maintenance schedule for each should be established according
to standard procedures provided by the instrument manufacturer. After the project initiation, if the regular
instrument maintenance indicates that the required accuracy and precision limits are not met, the instrument
should be recalibrated and subsequently it should be regularly checked to ensure that it is functioning
properly.
Preventive maintenance of instruments should be conducted regularly to ensure that all instruments
remain in good working order. Preventive maintenance may include replacing or cleaning various
instrument parts, rejuvenation of electrodes, etc. Spare parts should be readily available so there will be
no interference with data collection in case of a breakdown. Instrument calibration and maintenance
procedures and maintenance schedule should be documented in detail and attached as an appendix to the
QA/QC plan.
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Procedures for Laboratory Analysis
Requirements established by the USEPA for laboratory analysis are documented in Table 2. All
laboratory analysis should be conducted in accordance to standard procedures and the procedures should be
documented and attached as an appendix to the QA/QC plan.
Procedures for Data Reduction and Validation
For a NFS research program, two types of field data are required, i.e., hydrologic (Table 1) and water
quality (Table 2). Recent advances in instrumentation allow direct hydrologic data transfer to the laboratory.
However, in many cases strip charts should be digitized in the laboratory. Protocols should be developed for
hydrologic data transfer, database management, and data interpretation and how the hydrologic data should
be integrated with the water quality data. As an example, Figure 1 shows the QA/QC data management
system developed at the Biological Systems Engineering Department at Virginia Tech.
Internal Quality Control and Auditing
Internal quality control is an integral part of determining the quality of both field and laboratory data.
Quality control checks for field instrumentation were discussed earlier. The quality control check for
hydrologic and quality data can be performed by a data management system (e.g. Figure 1). Procedures are
needed for the performance evaluation of the laboratory analysis to maintain precision and accuracy within
the required confidence level, In general, as a measure of precision, a duplicate sample analysis is
recommended. Duplicate samples are used to document the variance of the analytical results. The results of
duplicate testing are entered into an R control chart and the data is checked to make sure that the results fall
within the 95% confidence interval. As a measure of accuracy, either matrix spike (sample-standard samples),
check standards (standard-reference samples) or both can be used. The results are entered separately into X-
control charts for both matrix spikes and check standards to measure accuracy within the 95% confidence
interval. For performance evaluation of analytical methods, an independent laboratory should be sub-
contracted to evaluate the NPS laboratory's performance against other participating laboratories on the same
set of standards.
A system of semi-annual internal audits should be established to review and assess the ongoing assurance
practices for compliance with the quality assurance program. The audit should be undertaken by an 'Audit
Committee'. Its members should include the project director, project manager, laboratory liaison officer,
laboratory manager, project engineer, and quality assurance officer. The committee will be responsible for
verifying both compliance and performance and identifying discrepancies that may exist. This task can best
be achieved by developing a field and laboratory quality assurance audit form. Completed audit forms should
be reviewed to assure that a) all laboratory procedures are up-to-date and that control samples are analyzed at
the specified intervals, b) all field and laboratory equipment and instrumentation are checked and calibrated
according to the specified procedures, c) a logbook is maintained on problems encountered and corrective
measures taken, d) the precision and accuracy requirements for all data is enforced, and e) all reports to the
sponsoring agency are screened for QA/QC.
Corrective Action
If the audit review outlined in the previous section indicates that any of the QA/QC requirements are
violated, then corrective measures should be taken. Out-of-control situations may occur in the field or in the
laboratory as a result of instrument breakdown despite careful planning. The corrective action may include
repairing, recalibrating, or adjusting the malfunctioning instrument or substituting an alternative piece of
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instrument. Problems and corrective actions should be documented in the field logbook. Field and laboratory
personnel should be notified of any corrective action and changes in procedures.
Report Preparation
The project director in cooperation with the project manager should prepare a quarterly report for
submission to the sponsoring agency. Each quarterly report should address the following topics: a)
performance evaluation and system audit results, b) evaluation of compliance with the QA/QC goals, c)
evaluation of data quality measurement trends, and d) identification of problems, needs, and
recommendations for solutions.
Summary
In this article, data quality terms (accuracy, precision, representativeness, comparability, and
completeness) relevant to QA/QC plan for a NFS pollution research and monitoring program were
defined. Detailed descriptions were provided for major components of a QA/QC plan for agricultural NFS
pollution monitoring research program. Major components of a QA/QC plan include: project description
and organization; QA/QC objectives; sampling methods and sample custody procedures; procedures for
instrument calibration and maintenance; laboratory protocols; procedures for data reduction and
validation; procedures for internal quality control and auditing; guidelines for corrective action; and
guidelines for report preparation.
It is expected that this paper will serve as guideline for developing and implementing uniform QA/QC
plans for agricultural NFS research programs.
Literature Cited
Dux, J.P. 1986. Handbook of Quality Assurance for the Analytical Chemistry Laboratory. VNR Co., Inc.
Erickson, H.E., M. Morrison, J. Kern, L. Hughes, J. Malcolm, and K. Thornton. 1991. Watershed
Manipulation Project: Quality Assurance Implementation Plan for 1986-1989. EPA-600/3-91/008.
Mostaghimi, S. 1989. Quality Assurance/Quality Control Project Plan for Watershed/Water Quality
Monitoring and Evaluating BMP Effectiveness. Biological Systems Engineering Department, Virginia
Tech, Blacksburg, Virginia. Submitted to the Virginia Department of Conservation and Recreation,
Richmond, Virginia.
Taylor, J. K. 1987. Quality Assurance of Chemical Measurements. Lewis, Pub., Inc.
U.S. Environmental Protection Agency. 1979. Methods of Chemical Analysis of Water and Wastes. EPA-
600/4-79-020.
U.S. Environmental Protection Agency. 1994. Guidance for the Data Quality Objectives Process. EPA-
QA/G-4.
U.S. Environmental Protection Agency. 1997. Monitoring Guidance for Determining the Effectiveness of
Nonpoint Source Controls. EPA/841-B-96-004.
Younos, T. 1994. Quality Assurance/Quality Control Project Plan for the Dairy Loafing Lot Rotational
Management System - Nonpoint Source Pollution Assessment and Demonstration Project. Biological
Systems Engineering Department, Virginia Tech, Blacksburg, Virginia. Submitted to the Virginia
Department of Conservation and Recreation, Richmond, Virginia.
III-345
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[Hydrologic Quantity Data |
[Water Quality Data)
[Data Loggers] [Strip Charts] [Punched Paper Tape) [Automatic/Manual Sampling!
[Telephone] [Computer] [Digitizing] [Translation | [Sample Log Sheets/Samples)
[Editing, PC? Storage!
[Laboratory Analysis]
[Time vs. Value]
.[Entry, Sample Log & Analysis]
[Error Detection & Correction!
[REDUCE (Discharge, Intensity, Loading, etc/
I Summaries, Tables, Graphs] [Archiving, Recall, Magnetic Tape] [Utilities] [Modeling, Reports]
Figure 1. Database Management System
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Table 1. Data Quality Standards for Field Measurements
Parameter
Rainfall
Stage
Stream
Air Temperature
Water (Stream)
Temperature
Time Data
Archival
Time of rainfall
samples
Time of wet
weather stream
samples
Accuracy
4%
0.01 foot
1.6°C
2.0 °C
5 minutes
10 minutes
5 minutes
Precision
0.01 inch
0.0002 foot
0.1 °C
0.1 °C
1 second
1 second
1 second
Completeness
80%
95%
95%
80%
95%
90%
90%
Reference for accuracy
calculations
laboratory calibrated weights
graduated pipette (with an
equivalent 0.01" rainfall
graduation)
land surveyor's level hook gage
laboratory grade thermometer with
0.2 °C resolution
same as above
digital watch referenced to the
university mainframe computer
clock and observed to be accurate
within 1 second per month
same as above
same as above
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Table 2. Data Quality Standards for Laboratory Analysis
Parameter
Ammonia
(NH3-N)
Nitrate
N03-N
Orthophosphate
(P04 - P)
TKN
Total-P
Total
Suspended
Solids
COD
Fecal/Total
Coliform
Detection
Limit (mg/1)
0.01
0.05
0.01
0.10
0.05
0.02
10
MPNor
Membrane
Filtration
Recovery
98-102%
recovery
96-100%
89 - 94%
97-101%
91-94%
±5%
relative error
±5%
relative
error
95%
confidence
limit
Precision
(ms/I)
±0.06
± 0.026
±0.013
±0.126
± 0.056
±0.74
±4.23
J2C Protocol*
1 dup. per 20 samples
1 EPA QA-QC standards
per 40 samples
1 spike per 40 samples
1 blank run daily
1 dup. per 20 samples
1 EPA QA-QC standards
per 40 samples
1 spike per 40 samples
1 blank run daily
1 EPA QA-QC standards
per 40 samples
1 spike per 40 samples
1 blank run daily
1 dup. per 17.5 samples
2 EPA QA-QC standards
per 35 samples
1 blank per 35 samples
1 spike per 35 samples
1 dup. per 17.5 samples
2 EPA QA-QC standards
per 35 samples
1 blank per 35 samples
1 spike per 35 samples
1 dup. per 40 samples
1 blank per 40 samples
1 EPA standard per 200
samples
1 dup. for each sample
1 EPA standard per 20
samples
1 dup. for each sample
Method
EPA
350.1
EPA
353.2
EPA
365.1
EPA
351.2
EPA
365.1
EPA
160.2
EPA
410.4
APHA
909
*This QA/QC plan was developed using several references (USEPA 1979; Dux 1986; Taylor 1987).
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Table 3. Sampling Method, Frequency, Preservation, and Holding Times
Parameter
Ammonia
Nitrate &
Nitrite
Nitrate
TKN
Orthophosphate
Total-P
COD
TSS
Fecal Coliform
Total Coliform
Collection
Method
Automatic
Grab
Automatic
Grab
Automatic
Grab
Automatic
Grab
Automatic
Grab
Automatic
Grab
Automatic
Grab
Automatic
Grab
Grab
Grab
Collection
Frequency
Every 3 cm
change in stage
Bi-weekly
Every 3 cm
change in stage
Bi-weekly
Every 3 cm
change in stage
Bi-weekly
Every 3 cm
change in stage
Bi-weekly
Every 3 cm
change in stage
Bi-weekly
Every 3 cm
change in stage
Bi-weekly
Every 3 cm
change in stage
Bi-weekly
Every 3 cm
change in stage
Bi-weekly
Bi-weekly
Bi-weekly
Volume
Required
500ml
500ml
500ml
500ml
500ml
500ml
500ml
500ml
100ml
100ml
Container
Type
(polyethylene)
ISCO
Nalgene
ISCO
Nalgene
ISCO
Nalgene
ISCO
Nalgene
ISCO
Nalgene
ISCO
Nalgene
ISCO
Nalgene
ISCO
Nalgene
Nalgene
Nalgene
Preservation
Method
Immediately after
Sampling
Cool 4°C
(H2SO4 to pH <2)
Cool 4°C
(H2SO4 to pH <2)
Cool 4°C
Cool 4°C
(H2SO4 to pH <2)
Cool 4°C
Cool 4°C
Cool 4°C
Cool 4°C
Cool 4°C
(0.08% NA2S203)
Cool 4°C
(0.08% Na2S203)
Maximum
Holding
Time Prior
to Analysis
28 days
48 hours
28 days
14 days
48 hours
28 days
14 days
48 hours
28 days
28 days
14 days
7 days
24 hours
24 hours
III-349
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III-350
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Calabazas Creek Pilot Sediment Sampling Study
Terence D. Cooke, Consulting Scientist
Woodward-Clyde International Americas, 500 12th Street, Oakland, CA 94067-4014
Phone: (510) 874-1736
David D. Drury, Senior Civil Engineer
Santa Clara Valley Water District, 5750 Almaden Expressway, San Jose, CA 95118-3686
Phone:(408)927-0710
Abstract
The Santa Clara Valley Urban Runoff Pollution Prevention Program (Program) has been conducting
investigations of pollutant sources to urban stormwater runoff since 1987. Characterization of pollutants in
runoff indicated many of the metals and other pollutants are present as suspended sediment in receiving
streams during storm events. To better focus program resources a pilot sediment sampling study was
conducted to develop techniques for bed sediment characterization and examine the impact of urban
drainage on local waterways. The study developed field sampling and laboratory analysis methods to
estimate how sediment chemistry changes throughout the watershed and to relate these changes to different
drainage area characteristics (soil type, size, local sources, landuse). Prospective statistical power analysis
was used to ensure sufficient samples were collected for statistical comparisons. Sample compositing,
•replication, and field sieving was used to minimize variability and enable measurements of metals in the fine
sediment fraction. The laboratory testing program included techniques to distinguish labile metal (likely
associated with pollution inputs) from residual metals (likely associated with erosion of soil minerals).
Sampling and analysis protocols were tested by application to six stream reaches throughout the watershed.
The results were used to refine sediment sampling methods and to develop data analysis methods for
evaluation of the impacts of urban drainage on bed sediment concentrations, and to evaluate disposal
options for maintenance dredging of improved channels.
Study Background
The Santa Clara Valley Urban Runoff Pollution Prevention Program (Program) has been conducting
investigations of pollutant sources to urban stormwater runoff since 1987. Characterization of pollutants in
runoff indicated many of the metals and other pollutants are present as suspended sediment in receiving
streams during storm events. To better focus program resources a pilot sediment sampling study was
conducted to develop techniques for bed sediment characterization and examine the impact of urban
drainage on sediments in local waterways. The goals of the pilot sediment sampling study were to:
• Develop sampling and analysis methods estimate how sediment chemistry changes throughout a
watershed;
• Relate these changes to natural and man-made sources of pollution;
• Determine "Enrichment factor" = (concentration in the sediment)/(background or natural
concentrations).
Background is assumed to be represented by sediment samples taken in the upland portions of
streams where the catchment is primarily open space. The study results are presented in four sections:
Stream Reach Selection, Field Sampling, Laboratory Analysis and Data Analysis.
ffl-351
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Stream Reach Selection Process
The pilot study was implemented in the Calabazas Creek watershed. Calabazas Creek watershed is
highly developed (80% urbanized) and has significant flood control improvements in the most heavily
urbanized areas of the watershed. For this initial pilot study is was decided to gain a general overview of
sediment chemistry in the main channel (rather than urban tributaries) and to test the field and laboratory
approach. For these reasons stream reaches were selected in the main stem of the creek rather than in
tributaries.
Several different watershed conditions were considered during the stream reach selection process. The
reach selection process was used to identify specific stream reaches within the pilot watershed for sampling.
Stream reaches rather than specific locations within the stream were selected to enable field verification of
places within the stream where sedimentation occurs and therefore where sampling was feasible. Stream
reaches are defined as sections of the stream channel 50' to 200' in length. Within each reach, specific
stations were chosen for sampling during field reconnaissance. The general stream reach selection process
included the following steps:
1. Define and qualitatively classify the major subwatersheds within each watershed based on general
land use and topography as upland and urbanized.
2. Delineate major storm drains and tributaries
3. Determine important characteristics for each subwatershed which can influence sediment
composition and quantity including :
• Soil type
• Surface geologic formations
• Erosion potential (due to topography and soil types)
• Land use
• Localized Sources (mines, quarries, reservoir releases, areas of dumping)
• Chemical composition of stream sediment (based on previous data)
4. Select stream reaches.
Six stream reaches were selected for characterization. Five of the reaches were chosen in the nontidal
portion of the stream. A sixth reach was selected in the tidally influenced portion of the stream. Stream
reaches that drain different types of watershed areas (upland, and urban) were selected for characterization
in the nontidal areas. Upland areas were characterized as having steep slopes and primarily open
(undeveloped) land-use. Urban areas generally have lower slopes with a high percentage of residential,
commercial or industrial land-use. Figure 1 shows the location of the selected stream reaches.
Description of Reaches in Upland Areas
Two reaches were selected for characterization of Upland sediments. Reach Cl is located on Calabazas
Creek below Comers Debris Basin. The reach below the basin was selected because sediments settled in the
basin are removed by the District and therefore not a source to the Bay. Reach C2 is located on Prospect
Creek above its confluence with Calabazas. Prospect Creek is a major tributary to Calabazas, receiving
drainage largely from open land-use areas. The Prospect Creek tributary rather than Calabazas after the
confluence with Prospect was selected to enable comparison of sediment metals in the two major upland
tributaries.
ffl-352
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Description of Reaches in Urban Areas
Four reaches were selected along Calabazas Creek in the urban land-use area to determine if a gradient
in enrichment factors occurs as land use becomes more urbanized. The reaches were selected downstream
from major tributaries which drain urban land-use areas. Sites were selected such that the incremental
drainage areas were comparable. Reach C3 is located in Calabazas Creek downstream from the confluence
with Rodeo and Regnart Creek tributaries. These two tributaries drain primarily residential land-use areas.
Reach C4 is located in Calabazas Creek near Homestead Ave. This station is located downstream of
Interstate 280 and receives drainage from the Junipero Sierra storm drain and the industrial and residential
areas located between the two reaches. Reach C5 contains the Program storm water quality sampling station
(S3) located at Wilcox High School. Between Reach C4 and C5 Calabazas Creek receives drainage from
Residential, Commercial, and Industrial land-use areas. Reach C6 is located in Calabazas Creek at Alviso.
This station is located in the tidally influenced area and has been previously characterized by the District.
Between reaches C5 and C6 Calabazas Creek receives drainage from residential, commercial, and industrial
areas.
Field Sampling
The primary objective of the sediment sampling design was to provide a method for collection of
sediment from stream beds which would enable estimation of mean metals concentrations in fine and coarse
grain sized sediments. The study design focused on estimation of metals concentrations within stream
reaches which were defined as sections of the stream channel 50' to 200' in length.
Power Analysis
To estimate how many samples would be necessary to detect a given change in mean metal
concentration a statistical technique called a prospective power analysis was used. A power analysis predicts
the probability that a specific difference in concentration will be statistically significant, if present. The
number of samples necessary to prove significance depends on the variability of the data and the magnitude
of the difference. To estimate the number of samples necessary prior to collecting any data the expected
variability was estimated by analyzing existing sediment data collected by the program and USGS.
Variability can be characterized as the coefficient of variation (CV) which is:
CV = (s/x)
where:
s = standard deviation, and
x = mean
None of the existing data contained copper concentrations from multiple samples within one stream
reach. Therefore, data from all samples collected in Calabazas Creek were pooled and used to estimate mean
and standard deviation and calculate a CV. The CV calculated from the pooled data is higher than what
would be expected within each reach because data were collected over several years and from many
different reaches. Using the pooled data a CV of 0.5 is estimated. Using this CV in a power analysis with a
required confidence interval of 95% (o= 0.05) and a required power of 80% ((3=0.20) indicated that a 40%
difference in mean concentration could be detected if seven samples per reach were collected. For
comparison to detect a 30% difference between reaches would require 12 samples per reach while a 60%
difference in concentration could be detected with three samples per reach. Table 1 shows the results of the
power analysis for different values of CV and different minimum percent differences. Because the estimated
III-353
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CV was thought to be higher than what was expected in the current study, five stations were selected to be
characterized within each reach.
Compositing and Field Sieving
Chemicals in sediments can be highly variable on small spatial scales. Compositing was used to
minimize the effects of small scale variability. Field sieving was used to enable collection of sufficient
coarse grainsized sediment to enable separate laboratory analysis of the fine fraction. Figure 2 presents a
schematic of the field and laboratory sampling and compositing scheme. Briefly, six stream reaches were
sampled. Within each reach five stations were selected, generally separated by 20' to 50'. In each station,
three substations were sampled. Sampling each substation consisted of combining three or more scoops of
sediment into the field sieve (2 mm), wet or dry sieving (depending on the sediment moisture and
composition), and collection and transfer of the < 2 mm fraction into sample jars for transfer to the
laboratory. In the laboratory, composite samples from individual substations (e.g., Cl-l.l.comp, Cl-
1.2.comp and Cl-1.3.comp) were composited by mixing equal volumes in an acid rinsed plastic container
to produce each station composite (Cl-l.comp). The station composite was then split into four
subsamples. Subsample 1 was wet-sieved in the laboratory to separate the fine (< 63um) and coarse (63
um -2000 urn) fractions and each fraction was analyzed for total recoverable and 0.5 N HC1 leachable
metals and total volatile solids (fines only). Station composite subsample 2 was analyzed directly for total
recoverable metals, TOC, grain size, pH, moisture, and total sulfides.
Station composite subsample 3 was combined with subsamples from the five other station composites
collected from the same stream reach (e.g. Cl-2.comp, Cl-3.comp, Cl-4.comp, Cl-5.comp, Cl-6.comp)
to form one reach composite (Cl.comp). The reach composite was analyzed for Organics, BOD,
Nutrients, Volatile Solids. Station composite subsample 4 was frozen and archived for future analysis.
Laboratory Analysis
The primary objectives of the laboratory analysis were:
1. To provide a method for preparation and analysis of sediment samples that would enable a
technically defensible comparison of metals content of fine and coarse sediments.
2. To determine if a weak acid leaching method can be used in freshwater sediments to provide an
indication of potential biologically available metals and how these measurements compare to total
recoverable metals concentrations.
3. To determine if other constituents should be analyzed along with metals to aid in the
interpretation of the data.
Separation of Fine Fraction
A wet-sieving procedure was used in the laboratory to separate the fine (< 63 um) and coarse (63-
2000 um) fractions. The wet-sieving procedure consisted of placing a subsample of station composite
onto a 63 um mesh stainless steel sieve and washing the fine sediment through the sieve using reagent
water. The coarse sediment from the top of the sieve was transferred into sample containers using acid
rinsed plastic scoops. The rinsate water and fine sediment were collected in an acid rinsed plastic tub and
transferred into a acid cleaned glass beaker. The beaker was then placed onto a hot plate and heated at low
temperatures to evaporate most of the rinsate, leaving fine sediment and metals associated with the rinsate
water in the beakers. The rinsate water was included with the fine sediment to prevent loss of metals that
were weakly associated with the sediment which may have gone into solution as a result of the wet-
sieving. It should be noted metals weakly associated with the coarse fraction would also be included in
III-354
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the fine fraction. Loss of volatile solids (analyzed in the fine fraction) due to the evaporation procedure
was minimized through the use of low heat. However, some loss may have occurred.
Weak Acid Leaching Procedure
A weak acid leaching procedure used by researchers at University of California Santa Cruz (UCSC)
conducting sediment metals characterization studies was performed on the fine and coarse fractions of the
station composite samples to determine how the results compared to the total recoverable fraction and
provide an approximation of the potentially bioavailable metals fraction. ("Sediment Extraction for
Analysis of Bio-Available Metals Dry Leach Method, 4/1/91" Dr. Khalil Abu-Saba). The procedure
involves a mild acid digestion (0.5 N HC1, 24-hour, room temperature) designed to solubilize only 'labile'
metals.
Data Analysis
Three types of data analysis procedures used in the pilot sediment sampling study are described
below. The three types are: reach average enrichment factor calculations; enrichment factor analysis with
normalization to aluminum, and statistical hypothesis testing.
Enrichment Factor Calculations
The primary objective of the sediment sampling study was to estimate how sediment chemistry changes
throughout a watershed and to relate these changes to natural and man-made sources of pollution. One
measure of changes in sediment chemistry is defined by the "enrichment factor" which is the ratio of the
concentration of a given pollutant in the sediment (measured as milligrams of pollutant per kg of sediment -
mg/kg) at a given location within the watershed to the corresponding concentration at a location that
represents background or natural concentrations. In this case background is assumed to be represented by
sediment samples taken in the upland portions of streams where the catchment is primarily open space. An
alternative method which may be used to calculate enrichment is to 'normalize' the measured pollutant
concentrations in the sediment to the amount of native minerals. The advantage of the normalization
procedure is that variations in the amount of native mineral are accounted for, thereby reducing apparent
'false enrichment' due to sediments with high mineral content. Both types of enrichment calculations are
described below.
Reach Averages
Reach average enrichment factors can be calculated for each stream reach. The enrichment factors for
the reach are calculated as the average of the enrichment factors for each station within the reach as
follows:
EF D = avg(M 01.5 /avg (M u))
where:
EF D = average enrichment factor for the downstream reach of interest
M D.i.comp-o.s.comp = are metal concentration at downstream stations 1 through 5 in reach D
avg (M u) = average metal concentration of upstream stations
In the case of the pilot study only reach C1 was used as representative of the upstream areas of the
watershed based on apparent differences between copper concentrations in fine sediment from reach C2
as compared to reach Cl. Figure 3 shows the results of the calculations for copper. The highest
III-355
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enrichments were found for the fine grained sediment in the urban reaches. However, fine grain sediment
was less than two percent of the nongravel sediment in the urban reaches. Whole sediment was not
enriched in urban reaches as compared to the upland reach. Sediments in the tidally influenced reach (C6)
were 98 % fine grain sized. Fine sediment was only slightly enriched as compared to upland sediments.
However, because fines accounted for the majority of the sediment whole sediment in this reach showed
an apparent enrichment factor of 1.8. These results indicate enrichment factors for similar grain sizes
should be compared rather than whole sediment.
Normalization to Aluminum/Iron
Metal concentrations can also be normalized to other factors which are measured in the same sample.
Normalization allows determining how much a given metal is enriched beyond what might be expected
due to the erosion of minerals from the upland soils. Aluminum is a major component of clay minerals.
Iron is also a major mineral phase and many metals are known to adsorb onto iron oxide mineral phases.
Normalization to upstream aluminum or iron to metal ratios provides an indication if elevated metal
concentrations in downstream reaches are due to accumulation of upstream clay or iron minerals in
depositional environments or are actually enriched above what would be expected from upstream
minerals. Normalization is done using the following steps:
1. Perform a linear regression analysis with the upstream station data only using the mineral
indicator metal (aluminum or iron) as the x variable and the metal of interest as the y variable.
2. Plot the results of the regression.
3. Show the confidence limits of the linear regression fit on the plot.
4. Plot the downstream data on the plot.
5. Visually inspect the plot to determine if the downstream station data contain higher metal
concentrations than the upstream ratio data could support (data points are above the upper
confidence limits of the fit) or are depleted in metals as compared to the upstream data (data
points are below the lower confidence limit of the fit).
An example of the normalization procedure conducted for copper is presented in Figure 4. The
normalization procedure was conducted using reaches Cl and C2 as representative of the upstream ratios
(line on the linear regression plot) and by plotting the remaining reach stations individually. The figure
shows that all downstream reaches, with the exception of the intertidal reach (C6), contain stations with
elevated copper concentrations. The intertidal reach actually contained less copper than is expected based
on the aluminum data.
Statistical Hypothesis Testing
Statistical hypothesis testing is used to determine if observed differences between reaches and
fractions are significantly different within a desired confidence interval. Null hypothesis are tested to
determine if they can be rejected or accepted depending. An example of a null hypothesis is
"Copper concentrations in different size fractions are not significantly different from one another."
If the hypothesis is rejected the implication is that the copper concentrations in different size fractions are
significantly different.
III-356
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Comparison of Reaches and Fractions
Comparison of total and weak acid leachable metals concentrations in different size fractions and
among different stream reaches was accomplished using a one-way analysis of variance (ANOVA), if
multiple reaches were compared, or a t-test, if only two reaches were compared. Nonparameteric
statistical tests were used because the results of Levene's test showed that the variances were not equal (P
< 0.05). Figure 5 shows the results of the Wilcoxon Kruskal-Wallis Test performed using the copper data
from nontidally influenced reaches. The results of the testing indicate the copper concentrations in the
different sediment size fractions were different (p < 0.001). Examination of the percentile box plots and
score sum statistic in the Kruskal-Wallis Test shows that the < 63 um fraction is much higher than either
the >63 um or whole sediment copper concentrations.
Conclusions
The following conclusions were used to develop sediment sampling programs for addition watersheds
in Santa Clara Valley:
• Field and laboratory compositing methods proved useful in averaging out small scale spatial
variability within a given stream reach.
• Three to four composite samples per reach were necessary to detect 30% differences in metals
concentrations between reaches with 80% power.
• Tidally influenced reaches showed significantly different chemistry as compared to nontidally
influenced stream reaches.
• Fine sediment generally comprises a small fraction of the whole sediment in nontidal stream
reach segments.
• Fine sediment can have high enrichment factors in nontidal reaches that receive urban drainage.
• Aluminum and iron are useful indicators of mineral enrichment and can be used to normalize
metals concentrations to account for these differences.
The Santa Clara Valley Water District used the general field and laboratory testing methods
developed during the conduct of this study to improve the reliability of the chemical characterization of
sediments in stream channels that require maintenance dredging or realignment. Results from the analysis
of other watersheds confirmed the larger enrichment in tidally influenced stream reaches as compared to
nontidally influenced reaches.
III-357
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LEGEND
| | Soils of the Basins
| j Soils of Recent Alluvial Fans
IjgSggj Soils of Older Alluvial Fans
Soils of the Terraces
tC6 Stream Reach
SUNNYVALE
COUNTY
C5
SANTA CLARA
CUPERTINO
0 10,000 f«et
, i , i I
1 mile 2 miles
Figure 1. Stream reach locations in Calabazas Watershed.
III-358
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Reach Cl
Reach C2
Reach C3
Reach C4
Reach C5
Reach C6
Composite
Sample
Cl-l.l.comp
Composite
Sample
Cl-1.2.comp
Figure 2. Field sampling, sieving, compositing, and laboratory analysis scheme.
III-359
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DWhole
• > 63 urn
M< 63 urn
C-2 C-3 C-4 C-5
Stream Reach
C-6
Figure 3. Enrichment factor analysis for copper in three size fractions in Calabazas Creek Watershed. Copper
in upstream reach C-l was used to normalize downstream sediment copper concentrations.
COL.jmp(FRACTION=<63um-soil)
COPPER By ALUMINUM J
COPPER
190-
170 -
—
150-
130-
110-
90-
70-
50-
30-
10-
sa
Y
.--
10 15000
V
V K
• +..'
*J&'
3f&&r i**
%£"
25000 3501
DO
| ALUMINUM
EE Linear Fit
(Linear Fit I
Figure 4. Regression enrichment analysis for copper in fine sediments (mg/kg dry weight).
Linear regression shows copper/aluminum ratio in upstream reaches.
III-360
-------
COPPER By FRACTION
no •
100 -
90-
80-
70-
60-
50-
40-
30-
20
B
<63um-soll >63um-soll
FRACTION
[Quantiles
Level
<63um-soil
>63um-soil
Whole
minimum
38
21
22
10.0%
38.3
24.9
22.3
25.0%
55
27
26.75
median
70.5
29
28.5
75.0%
84.25
31
33
90.0%
96.8
35.7
34.4
maximum
100
39
45
Means and Std Deviations
Level
<63um-soll
>63um-soil
Whole
Number
22
22
22
Mean
69.0909
29.2727
29.5455
Std Dev
20.1634
3.8814
5.1337
Std Err Mean
4.2989
0.8275
1.0945
Wilcoxon / Kruskal-Wallis Tests (Rank Sums)
Level
<63um-soll
>63um-soll
Whole
Count
22
22
22
Score Sum
1213.5
492.5
505
Score Mean
55.1591
22.3864
22.9545
(Mean-MeanO)/StdO
6.488
-3.326
-3.156
1-way Test, Chl-Square Approximation
ChiSquare
42.1971
Of
2
Prob>ChlSq
0.0000
Figure 5. Results of nonparametric testing for differences among sediment fractions
(mg/kg dry weight) for the nontidally influenced reaches.
Table 1. Results of Generalized Power Analysis Showing the
Number of Samples per Reach Necessary for 80% Power at 95%
Confidence Based on the Expected Coefficient of Variability
% Difference to be
detected
20
30
40
50
60
cv
0.3
0.4
0.5
1.0
Samples per reach
9
4
2
2
1
17
7
4
3
2
26
12
7
4
3
105
47
26
17
12
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III-362
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Binational Water Quality Monitoring Activities Along the
Arizona-Sonora Border Region
Mario Castaneda, Water Border Technical Coordinator
Water Quality Division, Arizona Department of Environmental Quality
3033 N. Central Avenue, Phoenix, AZ 85012
Phone: (602) 207-4409
Abstract
Water pollution is one of the principal environmental and public health problems facing the
U.S./Mexico Border area. Deficiencies in the treatment of wastewater, the disposal of untreated effluent,
and the inadequate operation and maintenance of treatment plants result in health risks to border
communities. In some cases raw or insufficiently treated wastewater flows to surface and groundwater
sources in urban and rural areas. In addition, potential contamination to groundwater from point sources
exists in the area due to the increased industrial activity on both sides of the border. Groundwater is the
major drinking water source for most border communities. Binational efforts are being undertaken under
the U.S./Mexico Border 21 program to address these concerns. This binational program has also identi-
fied surface water and groundwater quality monitoring as an objective to characterize and determine the
status of and changes in water resources in the border area. Also, it has identified the need to collect and
analyze water quality data using standard sampling methodologies on both sides of the border. This paper
describes the efforts that the Arizona Department of Environmental Quality has undertaken in partici-
pating on binational water quality projects in the Arizona-Sonora border. Two different approaches for
binational cooperation on water quality monitoring will be presented. Water quality data collected and
analyzed by both countries using commonly agreed sampling methodologies and data quality objectives
will be presented and discussed.
Introduction
The growth of population and industry in Mexico's northern border region has put increased pressure
on state and municipal governments in the border region to provide effective and efficient public services
particularly in the area of potable water and wastewater. Water pollution is one of the principal environ-
mental and public health problems facing the U.S./Mexico border area. A border trade agreement and
changing economic conditions in Mexico increased industrial activity in the border region, more notably
in the Sonoran side. Labor and assembly costs are much lower in Mexico, and environmental regulations
in the past have not been as strict. Today, there are more than 2,200 maquiladoras (American assembly
plants) along the U.S.-Mexico border and about 80 just in Nogales, Mexico, where the population has
grown to nearly 400,000 people. Population growth in Nogales, Sonora is estimated at 4 percent per year.
Industry growth there overshadows Nogales, Arizona, with a population of less than 20,000.
Groundwater is the major drinking water source for most of the border communities in the Arizona-
Sonora border region. The lack of basic inventory and monitoring information pertaining to water
resources prevent a comprehensive understanding of watershed and regional natural resources issues.
Lack of quantitative information concerning the natural recharge and the possible limitations of many of
the groundwater supplies lead to uncertainties as to the future of these water resources. Binational efforts
are being undertaken under the U.S./Mexico Border 21 Program to address these concerns. The
U.S./Mexico Border 21 Program is a binational effort to work cooperatively toward sustainable
development through protection of human health and the environment as well as proper management of
natural resources in each country.
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The 1996 Border 21 Framework Document defined five-year objectives for the border environment
and described the mechanisms for fulfilling those objectives (EPA, 1996). One of the key objectives was
identifying surface water and groundwater quality monitoring to characterize and determine the status of
and changes in water resources in the border area. Another objective was the development of
environmental indicators to use in evaluating the effectiveness of border environmental policy. However,
the monitoring activity and the use of environmental indicators for the border area require sharing
environmental data by the U.S. and Mexico from the binational watersheds.
The Arizona Department of Environmental Quality (ADEQ) has been actively participating on the
EPA Border 21 activities (Castaneda, 1995). Several binational surface water and groundwater
monitoring projects have been already implemented along the Arizona-Sonora border. ADEQ has
recognized the importance of developing a consistent environmental policy in dealing with water quality
issues along the border and in participating on binational water quality projects since both surface water
and groundwater flow to both sides in these binational watersheds. However, this participation has been
complex and difficult because of the different legal environmental jurisdiction in both countries.
Binational water quality (and quantity) issues between both countries are dealt by the International
Boundary and Water Commissions (IBWC). Direct contact between the border states (i.e., Arizona and
Sonora) to exchange environmental information is difficult. In addition, surface water and groundwater
quality (and quantity) issues in Mexico are the sole responsibility of the Mexican federal government. The
Mexican states have had little or no jurisdiction on these matters. On the other hand, both the federal and
state governments in the U.S. deal with water quality issues but water quantity is handled by the states
alone.
It has been recognized in the U.S. that data comparability needs to be improved so that organizations
can use information from multiple sources (ITFM, 1995). Differences in methods used to collect and
analyze water quality samples frequently pose impediments to making full use of the data from other
sources. And even if the methods are compatible, adequate quality-assurance programs are needed to
quantify the precision, accuracy, and integrity of environmental data to ensure that these data can be used
for the appropriate application. The importance of these differences is compounded when environmental
data are exchanged between two nations.
In an effort to standardize water quality sampling methodologies along the Arizona-Sonora border
region, the Water Resources Research Center (WRRC) of the University of Arizona under contract with
ADEQ started developing a bilingual field manual for water quality sampling in 1993. Several U.S. and
Mexican institutions participated in the manual review process. The manual was finalized and printed in
March 1995. A second printing was made in July 1996 (WRRC, 1996). The need of implementing this
manual as an official guidance document by the States of Sonora and Arizona in all groundwater/surface
water quality sampling being performed in both border states was obvious.
This paper describes the efforts that ADEQ has undertaken in participating on binational water
quality monitoring projects in the Arizona-Sonora border. Two different approaches for binational
cooperation on water quality monitoring will be described: the formal approach that was coordinated by
the U.S. and Mexican federal governments through the IBWC umbrella and a more direct interaction of
ADEQ with the State of Sonora, Sonora local municipalities, a Sonoran non-governmental-organization
(NGO), and the University of Sonora.
The ADEQ Formal Binational Interaction
The formal ADEQ binational interaction can be exemplified by its participation on the Binational
Nogales Wash Groundwater Monitoring project. This is the first groundwater quality monitoring project
that has been implemented along the U.S.-Mexico border. Nogales Wash originates 7 miles south of the
U.S./Mexico International Boundary and flows north through Nogales, Sonora, Mexico and Nogales
Arizona, U.S.A. Perennial flow in Nogales Wash is fed by springs near its head. However, grey water and
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sewage has contributed to the flow. ADEQ has monitored surface water flows in the Nogales Wash in the
U.S. side and had documented high fecal coliform bacteria levels, ammonia, and heavy metals in the past
(Earth Technology, 1990, 1993). A disinfection system installed on the Mexican side of the border has
helped reduce the risk from raw sewage.
The wash joins the Santa Cruz river just upstream of the Nogales International Wastewater Treatment
plant discharge. This plant, located in Nogales, Arizona and owned and operated by the IBWC, treats
waste water from both Nogales, Sonora and Nogales, Arizona, AZ. Although the plant has been expanded
in one occasion, it has already exceeded its design capacity again and binational efforts are being made to
address this situation. An existing international wastewater trunk line runs along the wash. The condition
of this trunk line has deteriorated and infiltration/exfiltration problems have been reported by both cities.
A facility planning project is underway to fix these problems and to search for the best alternative to
satisfy the wastewater treatment needs of the region.
The regional groundwater flow is generally to the U.S. in this area. Previous binational water quality
monitoring activities that took place in the Nogales Wash area in 1990 showed concentrations of nitrates
and volatile organic compounds (VOCs) exceeding the Mexican water quality standards in the Nogales
Wash aquifer in Nogales, Sonora (The Udall Center, 1993). However, sampling methods and quality
assurance problems were cited by the IBWC and the water quality data were not considered valid by both
countries. As a result, official binational meetings were initiated in 1992 to address the growing concerns
about the groundwater quality in this area. Based on a binational agreement between the U.S. and Mexico,
the IBWC developed a joint report to allow for a joint U.S./Mexico groundwater quality study in the
Nogales Wash. Federal, state, county, and city representatives from both sides of the border participated
on this study. The project, funded by the Environmental Protection Agency (EPA) and its Mexican
counterpart was implemented to collect reliable soil and groundwater quality data from the vadose zone
and the alluvial aquifer along the wash. Data collection would document whether or not surface activities
and discharges to the Nogales Wash have significantly affected groundwater quality. Thirteen ground
water monitoring points within approximately 5 miles north and south of the International Boundary were
sampled. Groundwater monitoring wells were constructed for this purpose. Representatives of the partici-
pating agencies from the United States and Mexico selected and agreed upon sites for monitoring well
placement. All sites were located along the Nogales Wash or its tributaries in areas where the shallow
alluvial aquifer is present. Most of the sites lied down gradient of or adjacent to areas where past or
present land use included industrial activity or development that may have had an impact on groundwater
quality. In this manner, the location of potential sources of groundwater contamination could be narrowed
to a smaller region within the urban area for a more focused study by the appropriate authorities.
A well construction plan, developed in May 1993 by ADEQ and IBWC, was approved by the EPA in
January 1994. This work plan was negotiated with Mexico and agreed upon in October 1996. Drilling of
the monitoring wells in the U.S. side was completed in February 1996 and in Mexico in February 1997.
Although different drilling techniques were used by both countries, all monitoring wells were completed
similarly. A work plan presenting the proposed sampling procedures and quality assurance methods for
this study was initially developed by ADEQ specifically for this project in 1993. A revised version
containing the proposed U.S. sampling and analysis methodology was approved by EPA in September of
1995. Sampling of these monitoring wells was performed quarterly for one year and ended in February
1998. Although agreement was reached on the sampling methodologies to be used by the group prior to
sampling, no prior agreement was reached on the lab methodology and respective method detection limits
to be used for this study and each country analyzed the samples according to the best laboratory
methodology available to them. Negotiations to approve both documents took considerable amount of
efforts and time. A training session on the project sampling procedures was provided to all project
participants prior to the sampling activities. A binational project interim report containing sampling data
from the first two quarterly sampling activities was finalized in June 1998. A final report is being
developed by the participating agencies and is expected to be completed by December of 1998.
III-365
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Split samples were collected by both groups and tested for Volatile Organic Compounds (VOCs),
Total Petroleum Hydrocarbon (TPH), major cations and anions (MCAS), trace metals (26 constituents),
total and fecal coliforms, and field parameters. The laboratories responsible for the analyses conducted in
this project were certified by the respective country's laboratory certification process. Table 1 presents a
list of some of the parameters analyzed and their respective practical quantitation limits reported from
both labs.
Project quality control checks were performed using duplicate samples, field blanks, and trip blanks
collected by each group. Precision was determined through duplicate analyses and calculated as a relative
percent difference. Precision for field duplicate sample analysis were set to 30% or lower for VOCs and
to 35% or lower for metals. Accuracy was determined by the analyses of surrogate and matrix spiked
samples and calculated as percent recovery. Recovery was generally expected to be within 70-130%. The
limits for precision and accuracy were applied to any measurement that was at least ten times greater than
the background (noise) level of the detector or the detection limit of the method. Cation/anion balances
were calculated for each sample sent to the U.S. lab. The balance was not more than 10% discrepant. No
information on cation/anion balances has been provided by Mexico yet. Although performance standards
samples were proposed for this study, Mexico did not agree to incorporate them during the sampling
activity.
Figures 1, 2, and 3 present a comparison of the U.S. and Mexican data for some of the constituents
detected during the April 1997 sampling event. Mexico detected and reported higher values for silica,
alkalinity, and pH than the U.S. The U.S. detected and reported higher values of nitrates, arsenic, and
tetrachloroethylene (PCE) than the Mexican group. The reasons for these data differences are being
investigated by the binational group and will be included in the final project report.
Figure 4 presents a mean of the respective relative percent difference (RPD) for the constituents
analyzed by both labs (considered as duplicate samples). It also presents the mean of the individual RPDs
calculated from the duplicate samples collected by each group (two duplicates by each group for April
1997). Both sampling teams met the individual precision criteria established for this project. The use of
performance standard samples could have been an useful reference for the discrepant data.
Preliminary data from this project has shown the presence of groundwater contaminated with PCE
exceeding both U.S. and Mexican aquifer water quality standards in the Sonoran Nogales Wash aquifer.
This finding has been supported by lab data from both countries. Additional steps are being discussed
with Mexico to locate and assess potential PCE sources in the area.
The ADEQ Direct Binational Approach
A less formal but more direct approach to ADEQ binational activities can be exemplified by the water
quality monitoring activities being performed in the eastern part of the Arizona-Sonora border, in the
Douglas-Agua Prieta and Ambos Nacos area. These communities are presented with a variety of water
quality issues. Groundwater flows generally to Mexico. There is a groundwater sulfate plume produced
by leachate from mine tailings located in Arizona with concentrations exceeding the secondary drinking
water standards. Also, There is a wastewater treatment plant located in Douglas Arizona which treated
effluent is being discharged into Mexico without chlorination (at Mexico's request). In addition, there is a
wastewater treatment lagoon located in Naco, Sonora very close to the international boundary that has
overflowed into Arizona in the past.
With assistance and guidance from non-governmental organizations and the University of Sonora, the
Sonoran border municipalities of Cananea, Naco, and Agua Prieta implemented a water quality
monitoring project to assess the water quality of the three municipal areas, the San Pedro River (which
flows to the U.S.), and the Sonora River (not a binational river) to allow for a more thorough depiction of
water quality in the northern Sonora, Mexico. This effort, named the "Sonoran Regional Water Quality
III-366
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Sampling Project," was to provide a baseline of environmental information regarding surface water and
groundwater quality in the transboundary watersheds of the region. The University of Sonora's
Department of Scientific Research and Technology ( DICTUS), was subcontracted by the Sonoran
municipalities to carry out the water quality monitoring and assessment for this project. The U.S. Agency
for International Development (USAID) supported this project through a $100,000 grant administered by
the International City/County Management Association (ICMA).
The Sonoran municipalities requested ADEQ support in carrying out this project. ADEQ was
prepared to fulfill a supporting role for this Mexican project to enhance binational communication,
transboundary relationships, technology transfer, and to develop an enhanced understanding of the
environmental conditions in watersheds shared by Arizona and Sonora. ADEQ's role and participation in
this Mexican project was premised upon the prior awareness and approval of appropriate Mexican
agencies as demonstrated by the U.S. and Mexico Sections of the EBWC. The enhancement of
institutional capacity in Mexico was a primary objective of this project. By collaborating in this effort
with ADEQ and the Arizona State Laboratory of the Arizona Department of Health Services (ADHS),
expertise would be shared with the University of Sonora and the municipalities of Cananea, Naco, and
Agua Prieta to facilitate pursuit of environmental assessment and management efforts focused on water
quality.
The ADEQ provided laboratory in-kind services for this project for analytical capabilities which had
yet to be developed at the University of Sonora. Specifically this included analysis of volatile organic
compounds (VOCs) and pesticides in samples of water and sediment. The State of Arizona provided up to
$20,000 for such analysis of samples collected in Mexico and is helping DICTUS arrange a technology
transfer opportunity in Phoenix between personnel from the University of Sonora and the ADHS State
Laboratory. The purpose of this technology transfer opportunity is to provide guidance in the event that
the University of Sonora in Hermosillo decides to pursue such analytical capabilities.
Phase I of this project was implemented in 1997. Surface water, groundwater, and sediments were
sampled for VOCs, major cations and anions, and pesticides. Inorganics analysis were performed by
DICTUS. VOCs and pesticides analysis were performed by the ADHS State lab. A sampling plan was
developed by DICTUS with ADEQ support. The binational group used the ADEQ bilingual water quality
sampling manual as a basis for this sampling plan. Precision and accuracy objectives were set similarly as
those for the Nogales Wash groundwater project. An ADEQ-DICTUS specific Memorandum of
Understanding (MOU) that clarified the respective institutions roles and data confidentiality was
developed for this binational interaction. Because of the specific requirements of the ADEQ-DICTUS
MOU, no split samples were sent to Arizona for analysis. The final project report, which had been
scheduled for completion in April 1998, has been delayed and unfortunately, no data is available yet to
present in this technical paper.
Phase n of this project will expand the monitoring activities on the areas of concern that might be
detected during the Phase I. The ADEQ-DICTUS MOU has been amended to include inorganic lab
analysis support from the ADHS State lab for this project.
Final Remarks
These projects have provided an opportunity for ADEQ to collaborate in the understanding of the
water quality conditions at these binational watersheds. It also has provided the opportunity to exchange
and compare sampling methodologies with different Mexican federal and state regulatory agencies. There
were no major differences on the water quality sampling methodologies used by both groups. However,
major differences in laboratory analytical techniques need to be discussed and understood. There is a need
to use performance standard samples in these binational monitoring projects. This understanding will be
helpful when exchanging water quality data between both countries during the development of the border
water quality environmental indicators taking place within the U.S.-Mexico Border 21 process.
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References
Arizona Water Resources Research Center, July 1996. Field Manual for Water Quality Sampling. College
of Agriculture, The University of Arizona, Tucson, Arizona, WRRC IP #18, ADEQ TM-943.
Castaneda, Mario (1995): "U.S./Mexican Binational Ground Water Monitoring Activities In The Ambos
Nogales Border Region", XXVI Hydrologists International Congress, June 4-10, 1995, Edmonton,
Canada.
Intergovernmental Task Force on Monitoring Water Quality. February 1995. The Strategy for Improving
Water-Quality Monitoring in the United States, Final Report.
The Earth Technology Corporation, 1990. Water Quality Assurance Revolving Fund, Phase I Report,
Nogales Wash Study Area, Task Assignment E-3, Nogales, Arizona, Prepared for the Arizona
Department of Environmental Quality, Phoenix, Arizona, Final Draft, March.
The Earth Technology Corporation, 1993. Nogales Wash Water Quality Assurance Revolving Fund
Study Area, Monitor Well Installation and Groundwater Sampling Plan, Task Assignment ET-19,
Nogales, Arizona, Prepared for the Arizona Department of Environmental Quality, Phoenix, Arizona,
January.]
The International Boundary and Water Commission, May 1998. Binational Nogales Wash United
States/Mexico Groundwater Monitoring Program, Interim Report.
The Udall Center Studies in Public Policy. August 1993. Ambos Nogales Water Resources Study: Santa
Cruz Watershed and Nogales, Arizona.
U.S. Environmental Protection Agency. October 1996. U.S.-Mexico Border XXI Program Frame
Document.
HI-368
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CALCIUM
APRIL 1997
60 80 100
U.S. DATA (mg/I)
POTASSIUM
APRIL 1997
U.S. DATA (mg/I)
MAGNESIUM
APRIL 1997
20 30
U.S. DATA (mg/I)
SODIUM
APRIL 1997
10 20 30 40 50 60
U.S. DATA (mg/I)
SILICA
APRIL 1997
15 20 25
U.S. DATA (mg/I)
30
0.08
^,0.06
a 0.04
z
<
o
0.02
ARSENIC
APRIL 1997
0.02 0.04 0.06
U.S. DATA (mg/I)
0.08
Figure 1. U.S.-Mexico data comparison for some cations for the April 1997 sampling event.
III-369
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III-370
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Enhancements of Nonpoint-Source Monitoring Programs to Assess Volatile Organic
Compounds in the Nation's Ground Water
Wayne W. Lapham, Ground-Water Specialist
National Water-Quality Assessment Program, U.S. Geological Survey
MS 413, 12201 Sunrise Valley Drive, Reston, VA 20192
Phone: (703) 648-5805; Fax: (703) 648-6693; E-mail: wlapham.er.usgs.gov
Michael J. Moran, Hydrologist
NAWQA VOC National Synthesis, Rapid City, SD
Phone: (605) 355-4560 x244; Fax: (605) 355-4523; E-mail: mjmoran@usgs.gov
John S. Zogorski, Chief
VOC National Synthesis Project, U.S. Geological Survey, 1608 Mt. View Road, Rapid City, SD 57702
Phone: (605) 355-4560 x214; Fax: (605) 355-4523; E-mail: jszogors.cr.usgs.gov
Abstract
The U.S. Geological Survey (USGS) has compiled a national retrospective data set of analyses of volatile
organic compounds (VOCs) in ground water of the United States. The data are from Federal, State, and local
nonpoint-source monitoring programs and were collected during 1985-95. This data set will be used to
augment data collected by the USGS National Water-Quality Assessment (NAWQA) Program for
assessment of the occurrence of VOCs in ground water nationwide.
Eight attributes of the retrospective data set were evaluated to determine the suitability of the data to
augment NAWQA data in answering occurrence questions of varying complexity. The attributes include: the
VOCs analyzed for and the associated reporting levels, water use, well-casing material, well depth, depth
from land surface to the water level in the well, aquifer lithology, and distribution of sampling points. The
data set generally lacks documentation of some characteristics of each well sampled, such as casing material
and depth from land surface to the water level in the well. Only about 20 percent of the wells have associated
documentation of aquifer lithology.
More than 90 percent of the VOC data are suitable for use in addressing simple occurrence questions
relative to some current drinking-water regulations of about 5 micrograms per liter (|ig/L). However, only
about 15 percent of the data are suitable for addressing these simple occurrence questions at a much lower
assessment level of 0.2 fig/L. Enhancing monitoring-program data bases would greatly increase the
usefulness of these data in addressing complex occurrence questions, such as those that seek to explain the
reasons for VOC occurrence and nonoccurrence in the Nation's ground water. The three most important
enhancements to the data set would be: an expanded VOC analyte list, recording the reporting level for each
analyte for every analysis, and recording key ancillary information about each well.
Introduction and Purpose
In 1991, the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) Program
began full-scale implementation. The long-term goals of the NAWQA Program are to describe the status and
trends in the quality of a large representative part of the nation's surface-water and ground-water resources.
Another goal is to provide an improved understanding of the primary natural and human factors that affect
the quality of these resources. The NAWQA Program has two major operational components: 1) hydrologic
investigations of large river basins and aquifer systems, referred to as Study-Unit Investigations; and 2) a
National Synthesis that is organized to provide information about water-quality topics of national or regional
III-371
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concern. Volatile organic compounds (VOCs) are studied as part of the NAWQA Program because of the
occurrence of this constituent group in many of the Nation's water supplies (Office of Technology
Assessment 1984, Tennant et al. 1992, Pankow and Cherry 1996). To facilitate an assessment of VOCs in the
Nation's ground water, a "Ground-Water VOC Retrospective Data Set," herein referred to as the
retrospective data set, has been compiled.
The primary purposes of compiling the data set are: 1) to ascertain what high-quality, ambient-ground-
water-monitoring data exists so that NAWQA does not duplicate these efforts; 2) to augment data collected
by NAWQA; and 3) to provide data for areas that are not covered by NAWQA Study Units. The data set
contains information collected in nonpoint-source ground-water-quality monitoring networks by various
Federal, State and local agencies (Lapham et al. 1997). The locations of monitoring programs in the
retrospective data include areas in NAWQA Study Units that have not been sampled to date (1998) and areas
outside of the NAWQA Study Units. Lapham and Tadayon (1996) provided design elements for compilation
of this data set and developed criteria for screening data to ensure that their inclusion would be useful for
answering questions that involve the occurrence of VOCs in ground water at a national scale.
The purpose of this paper is to summarize eight key attributes of the retrospective data set and to discuss
the importance of these attributes in answering questions of occurrence, status, and distribution of VOCs in
ground water at a national scale. Suggested enhancements related to these attributes are given that would
improve our understanding of the occurrence of VOCs in ground water at regional and national scales.
General Characteristics of the Retrospective Data Set
Table 1 presents some general characteristics of the retrospective data set. Additional information about
this data set is provided in Lapham et al. (1997; table 5). NAWQA Study Units and the VOC National
Synthesis, in cooperation with State and local agencies, compiled the data set from 1995 to 1996. Currently
(1998), data for as many as 50 VOC compounds from 43 nonpoint-source-monitoring programs or networks
have been compiled into the retrospective data set (Lapham et al. 1997; table 5). About one-half of the wells
were sampled from 1985 through 1989 and the other one-half were sampled from 1990 through 1995. About
75 percent of the wells in the data set for which the use-of-water is known are either public- or domestic-
supply wells. The number of VOCs analyzed in a water sample range from 24 to 50 (table 1).
Answering VOC Occurrence Questions
There is a wide range in the complexity of questions related to VOC occurrence. For example, one of the
simplest, yet an important occurrence questions is "What percentage of the sampled wells contain a regulated
VOC at a concentration that exceeds its Maximum Contaminant Level (MCL)?" Answering this occurrence
question is relatively simple and requires only knowledge of the number of wells sampled, the number of
wells that had one or more VOCs detected above MCLs, and the laboratory's reporting level. An example of
a considerably more complex question is "Are VOCs found in the deeper parts of unconfmed aquifers or are
they only present in the upper, more vulnerable portions of these aquifers?" Answering this question requires
knowledge of the location of sampled wells and whether the aquifer in which each well is located is confined
or unconfrned. Additionally, information about the depth in the aquifer at which water is being withdrawn
needs to be known. This additional information and other information about well construction and aquifer
characteristics are essential to answer complex occurrence questions. An important effort of the NAWQA
Program is to fully populate ancillary data for each sampled well so that an attempt can be made to answer
complex, nationally relevant occurrence questions.
In addition to the need for ancillary data, answering occurrence questions about VOCs in ambient ground
water also requires large well networks and low reporting levels. These attributes are needed because VOCs
often are reported as nondetected (i.e. below today's analytical detection capability) and when detected, the
concentrations are typically low, at sub-microgram-per-liter levels. The benefit of large well networks is the
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establishment of significant statistical relations between VOC occurrence and hydrogeologic and
anthropogenic variables. There are, of course, practical limitations to the number of wells that can be sampled
in a national assessment. Resources available within the NAWQA Program today (1998) allow the sampling
of about 2,500 wells per 10-year cycle for a broad-scale occurrence assessment. As previously noted,
sampling by the NAWQA Program is being augmented with results of sampling of comparable monitoring
networks operated by State agencies and others. Data from a network of 5,000 to 10,000 wells or more, if
possible, is sought because of varied hydrogeologic settings, aquifer types, land-use practices, and chemical-
release patterns across the Nation.
The benefit of using a low analytical reporting level is an increase in the detection frequency. This is
illustrated in figure 1. This figure was developed from VOC analyses of water samples from wells with
shallow depths to the water table and within urban areas (urban land-use studies). The NAWQA Program
(Squillace et al. 1996) collected these data between 1993-95. All analyses of VOCs represented on this graph
were completed at the USGS National Water Quality Laboratory (NWQL) at a reporting level of 0.2
micrograms per liter (fig/L). Figure 1 was made by artificially censoring the data set at 12 different reporting
levels ranging from 0.2 to 10 |0.g/L. The percentage of wells that would have been recorded as having a
detection of one or more VOCs at each of these 12 reporting levels was calculated. Figure 1 indicates that the
percentage of wells in which one or more VOCs were detected at various reporting levels did not increase
notably as the reporting level decreased from 10 (J-g/L to about 2.0 |J.g/L. However, a notable increase in the
percentage of wells in which one or more VOCs were detected occurs as the reporting level decreased from
2.0 (J-g/L to 0.2 [ig/L. The percentage of wells in which one or more VOC were detected at a reporting level
of 2.0 |J.g/L is about 22 percent, but increases to about 54 percent at a reporting level of 0.2 p.g/L.
The NAWQA Program has lower detection levels for the analysis of VOCs in ambient ground water than
used in many other monitoring programs. For about the last 10 years, the USGS has analyzed VOC samples
at the USGS NWQL in Arvada, CO using an analytical method (Rose and Schroeder 1995) that has a
reporting level of 0.2 |J.g/L for most VOCs. This reporting level was decreased even further in 1997, and now
most VOCs can be detected at concentrations of 0.05 )J.g/L or less (Connor et al. 1998). Such low reporting
levels are possible because of the simple water matrix being analyzed in contrast to, for example,
contaminated ground water at a gasoline-spill site. Rigorous field and laboratory quality-control practices,
application of modern, commercially available analytical equipment, and well-trained analytical chemists
have been combined to achieve these low reporting levels.
Analysis of Eight Key Attributes of the Retrospective Data
Eight attributes associated with sampling wells for VOCs are considered to be key information necessary
for interpretation and explanation of VOC occurrence in ground water. Analysis of these 8 key attributes in
the retrospective data set provides insight into the data set's suitability to augment NAWQA data. These 8
attributes are:
Two attributes are related to laboratory analysis of VOCs in each ground-water sample:
1. VOCs analyzed
2. Reporting level associated with each analyte for each analysis
Five attributes of each well, which are assumed to be the minimum information that should be known
about a well before it is sampled:
3. Primary use of water from the well
4. Well-casing material
5. Depth of the well
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6. Depth from land surface to water level in the well
7. Lithology of the aquifer contributing water to the well
8. Distribution of sampling both nationally and in four major geographic regions of the Nation.
A large number (55) of VOCs are analyzed for by NAWQA because documenting which VOCs are not
detected in ground water is considered as important for a national occurrence assessment as documenting
which compounds are detected. An examination of frequency of analysis for 53 of those 55 VOCs in the
retrospective data set indicates that Federal, State, and local monitoring programs seldom analyze for the
number of VOCs analyzed by NAWQA. The frequency of analysis of an individual VOC generally seems to
be closely associated with the regulatory history of that VOC. More than one-half of the 53 compounds were
analyzed for in at least one-half of the samples, and 9 compounds were analyzed for in at least 90 percent of
the samples. The three most frequently analyzed compounds were 1,1,1-trichloroethane, tetrachloromethane,
and trichloromethane. Twelve of the 13 most frequently analyzed VOCs are in the use category of solvents,
industrial reagents, and refrigerants. Other compounds of current interest were not frequently analyzed such
as methyl tert-butyl ether (MTBE), a gasoline oxygenate, and other fuel oxygenates.
Assessment of VOC occurrence cannot be completed without knowledge of the reporting level for each
analyte for every analysis. The reason for this is that the level at which the assessment of occurrence is made
(i.e. the assessment level) needs to be prescribed, and all data need to be censored to this prescribed
assessment level before their use in an occurrence calculation. If the reporting level for each analyte for every
analysis is not known, it has to be inferred, or the analyte has to be deleted from the data set. Recording the
reporting level for each analyte for every analysis is particularly important when data are compiled into one
collective data set from many monitoring programs or networks. Recording reporting levels is particularly
important in this case because VOC analyses are done by many different laboratories possibly using different
analytical methods. For example, the VOC retrospective data set discussed in this article was compiled from
43 monitoring programs or networks in 22 States, and reporting levels for VOC analyses among those
programs vary by two orders of magnitude (Table 1).
Often the reporting level associated with nondetection of an analyte can be inferred because the
nondetection is reported as an unknown concentration less than the reporting level. The reporting level
associated with a detection of an analyte, however, usually is not recorded. This information, however, is
required when censoring data to the prescribed assessment level prior to an occurrence calculation. The
unrecorded reporting level for a detection of a VOC in the retrospective data set was inferred from less-than
values recorded for nondetections of the same VOC in samples collected from other wells in the same
monitoring program during the same round of sampling.
Nearly all (99 percent) of the data in the retrospective data set were analyzed at a reporting level of 5
lug/L or less, and about 70 percent of the data were analyzed at a reporting level of about 0.5 (ig/L or less.
However, only about 27 percent of the data were analyzed at a reporting level of 0.2 )J,g/L or less. Conse-
quently, most of the data can be used for an assessment of the occurrence of one or more VOCs at an
assessment level of 5 |ig/L or higher. Most NAWQA VOC data collected from 1993-95 were analyzed at a
reporting level of 0.2 |j.g/L. Therefore, at most, only about 27 percent of the retrospective data are suitable to
augment NAWQA data when addressing occurrence questions at the low assessment level of 0.2 |J.g/L.
As previously discussed, answering complex occurrence questions requires ancillary information. The
findings from other studies indicate that attributes of a well often help explain the occurrence of VOCs,
nitrate, or pesticides in ground water. Five of the most important attributes of a well are: 1) the use of the
water from the well; 2) the well-casing material; 3) the depth of the well (or a related attribute such as the
depth from land surface to the top or bottom of the interval(s) contributing water to the well); 4) the depth
from land surface to the water level in the well; and 5) the lithology of the aquifer at the interval contributing
water to the well. Accordingly, the degree to which these five attributes and combinations of these attributes
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are populated in the retrospective data set for each well will partly determine the usefulness of the
retrospective data in augmenting NAWQA data for occurrence assessment.
The percentage of wells that have the indicated attribute or combination of attributes populated in the
retrospective data set is shown in table 2. Data in this table demonstrates that the usefulness of the data set
decreases markedly as the information needs about each well increase. For example, about 74 percent of the
wells in the retrospective data set have information on well depth. Consequently, 74 percent or less of the
wells are potentially suitable for use in investigating the relation between VOC occurrence and well depth.
Casing material, depth from land surface to the water level in the well, and aquifer lithology are documented
in less than 25 percent of the wells, and combinations of two or more of these attributes generally are
documented in only a small percentage of the wells. Only about 7 percent of the wells have information on
well depth, use of water from the well, water level, and aquifer lithology. Consequently, only 7 percent or less
of the wells are potentially suitable for multivariate analyses, such as principal-components analysis, of the
relation between VOC occurrence and these four variables.
The percentages cited in table 2 were calculated for each of the five attributes or combinations of
attributes regardless of the reporting levels for the VOCs analyzed in samples from the wells. If the
assessment level selected for the occurrence assessment is low (for example, 0.2 (J-g/L), the percentage of
wells that have attributes of the well populated and also have VOC analyses at a reporting level less than or
equal to 0.2 (J.g/L would likely be lower.
An eighth consideration in evaluating the suitability of the data set for occurrence assessment is the
geographic distribution of the data. Although the data set might have an adequate distribution to address an
occurrence question at the national scale, distributions might not be adequate at smaller geographic scales.
Furthermore, as the occurrence questions become more complex and the assessment level is decreased, the
percentage of data in the data set that can be used to address those questions decreases. Examples of the
percentages of the retrospective data suitable to address a series of occurrence questions of increasing
complexity for various geographic regions at assessment levels of 5.0 and 0.2 |ig/L are given in table 3. The
assessment level of 5.0 |J.g/L is used for the first example because 5.0 |ig/L is the value of the MCL for
several VOCs regulated in drinking water. The assessment level of 0.2 u.g/L is used for the second example
because this is the assessment level currently used most often by NAWQA for analysis of VOC data.
Percentages in table 3 were based on the number of wells that have at least one VOC analyzed at or below the
indicated reporting level (i.e. the assessment level) and that also have the indicated supporting ancillary
information populated for the well.
Question 1 (table 3), the simplest question, addressed at the national scale only requires VOC
concentration data analyzed at or below the indicated reporting level. As indicated in table 3, data from nearly
all (4,894 of 5,320 or 92 percent) of the wells in the data set meet these criteria at an assessment level of 5.0
(ig/L. Answering this question at an assessment level of 0.2 |J.g/L, however, requires VOC analyses that were
done at a reporting level less than or equal to 0.2 (ig/L. Data from only about 15 percent of the wells meet this
requirement.
A requirement for inclusion of an analysis in the data set is the location of the sampled well by latitude
and longitude (Lapham et al., 1997). This information enables analysis of the VOC data by geographic
region. The wells suitable for answering question 1 at an assessment level of 5.0 (J.g/L are distributed fairly
evenly across the four geographic regions. Also, the number of wells in each of the four geographic regions is
fairly large: 1,543 wells in both the northeast and southwest regions, 1,010 wells in the southeast region, and
798 wells in the northwest region. Therefore, the retrospective data set is probably adequate to address simple
occurrence questions relative to drinking-water regulations at both the national and regional scales. Wells
suitable for answering question 1 at an assessment level of 0.2 |ig/L also are distributed fairly evenly across
the four geographic regions. However, the percentages of wells in the data set suitable to address question 1
at this low assessment level in each of the four geographic regions are small (5 percent or less).
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The most complex questions in table 3 (questions 4a - 4d) require the information needed to answer
question 1 plus knowledge of water use and whether or not the source of the water to the well is from a
consolidated or unconsolidated aquifer. Only a small percentage of the wells in the data set have this
information fully documented. For example, only 7.1 percent (or about 378) of the wells have information to
determine both the aquifer type and that the water use is for either domestic or public supply at an assessment
level of 5.0 |J.g/L. Additionally, only a few percent or fewer of the wells have this information documented
and have water samples analyzed for VOCs at an assessment level of 0.2 |ig/L for each of the four
geographic regions.
Suggested Enhancements to the Data Set
The utility of the retrospective data for occurrence assessment depends on the question being asked. The
utility of the data generally decreases as the complexity of the question increases, where complexity is
measured by the information required to answer the question posed. For example, information that would be
required to answer a relatively simple question about the occurrence of one or more VOCs in the Nation's
ground water would include the concentration(s) of the VOC(s) of concern and the laboratory reporting levels
associated with each VOC in each water sample. More complex questions also might require location
information and various combinations of ancillary information, such as the depth of each well sampled, the
depth from land surface to the water level in each well, the use of the water from each well, well-casing
material, and the type of aquifer contributing water to each well.
Analysis of the characteristics of the retrospective data described in this article indicates that the three
greatest limitations of the retrospective data for addressing occurrence questions are inadequate: 1) numbers
of VOC(s) of interest frequently analyzed for; 2) information on the reporting level for each VOC; and 3)
ancillary information about each sampled well. Therefore, the three most important enhancements to VOC
data collected in Federal, State, and local nonpoint-source monitoring programs for use in a national
assessment of VOC occurrence in drinking water would be an expanded VOC analyte list, recording the
reporting level for each analyte for every analysis, and recording key ancillary information about each well.
In addition to the limitations just cited, another possible limiting feature of the retrospective data for use
in augmenting NAWQA data is the relatively high laboratory reporting levels used by many laboratories for
analyses of VOCs in water samples, as compared to the reporting level commonly used by NAWQA. For
occurrence assessment at the assessment level of 0.2 p.g/L commonly used by NAWQA, only a small
percentage of the retrospective data is suitable to augment NAWQA data, even at the national scale.
However, further analysis would be useful in evaluating the relative benefit of lower reporting levels in
explaining the occurrence of VOCs in ground water.
Benefits of the Suggested Enhancements
Enhancing nonpoint-source ground-water monitoring programs for VOCs would enable the data to be
used collectively to answer a range of simple to complex questions about the occurrence of VOCs in ground
water at national and regional scales. Enhancements, such as measuring a larger number of VOCs at a
reporting level that is less than often used today and recording characteristics about each well and aquifer
sampled, would:
1. Result in a more complete assessment of the occurrence of VOCs in ground water nationwide by
documenting the absence as well as the presence of VOCs at a low reporting level.
2. Explain possible reasons for occurrence and nonoccurrence.
3. Likely provide an early warning about an unanticipated regional or national occurrence of a VOC.
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4. Provide data in support of the Safe Drinking Water Act on the occurrence of currently unregulated
VOCs that might be regulated in the future.
5. Provide VOC data that might be needed in the future to address unanticipated questions of regional
or national concern as they arise.
Acknowledgments
NAWQA personnel, in cooperation with State and local agencies, compiled most of the retrospective
data summarized in this paper during 1995. The cooperation of these agencies is greatly appreciated.
References
Connor, B.F., D.L. Rose, M.C. Noriega, L.K. Murtagh, and S.R. Abney. 1998. Methods of analysis by the
U.S. Geological Survey National Water Quality Laboratory—Determination of 86 volatile organic
compounds in water by gas chromatography/mass spectrometry, including detections less than
reporting limits. Open-File Report 97-829. U.S. Geological Survey. Denver, Colorado. U.S. Govt.
Printing Office.
Lapham, W.W., and Saeid Tadayon. 1996. Plan for assessment of the occurrence, status, and distribution
of volatile organic compounds in aquifers of the United States. Open-File Report 96-199. U.S.
Geological Survey. Rapid City, South Dakota. U.S. Govt. Printing Office.
Lapham, W.W., K.M. Neitzert, MJ. Moran, and J.S. Zogorski. 1997. USGS compiles data set for national
assessment of VOCs in ground water. Ground Water Monitoring and Remediation 17, no. 4: 147-57.
Office of Technology Assessment. 1984. Protecting the Nation's ground water from contamination. OTA-
O-233, ch. 1 and 2, October, Washington, D.C.: Office of Technology Assessment.
Pankow, J.F., and J.A. Cherry. 1996. Dense chlorinated solvents and other DNAPLs in ground water.
Portland, Oregon: Waterloo Press.
Rose, D.L. and M.P. Schroeder. 1995. Methods of analysis by the U.S. Geological Survey National Water
Quality Laboratory - Determination of volatile organic compounds in water by purge and trap
capillary gas chromatography/mass spectrometry. Open-File Report 94-708. U.S. Geological Survey.
Denver, CO: U.S. Govt. Printing Office.
Squillace, P.J., J.S. Zogorski, W.G. Wilber, and C.V. Price. 1996. Preliminary assessment of the
occurrence and possible sources of MTBE in groundwater in the United States, 1993-1994:
Environmental Science and Technology 30, no. 5: 1721-1730.
Tennant, P.A., C.G. Norman, and A.H. Vicory, Jr. 1992. The Ohio River Valley Water Sanitation
Commission's Toxic Substances Control Program for the Ohio River. Water Science and Technology
26, no.7-8: 1779-1788.
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2345678
Reporting level (micrograms per liter)
10
Figure 1. Relation between the percentage of urban land-use study wells sampled by the National Water-
Quality Assessment Program in which one or more volatile organic compounds (VOCs) were detected and
the reporting level.
III-378
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Table 1. Characteristics of the Ground- Water VOC Retrospective
[VOC: volatile organic compound]
Data-Set Characteristic
General information
Number of monitoring programs
Number of States included
Number of wells
Sampling period
Percentage of the wells that were sampled during the period 1985
through 1989
Percentage of the wells that were sampled during the period 1990
through 1995
VOC analyses
Range of the number of VOCs analyzed per sample
Range of reporting levels, in micrograms per liter
Percentage of analyses within indicated ranges of reporting levels (micrograms per liter):
<0.2
> 0.2 to < 0.5
>0.5 to < 1.0
> 1.0 to < 5.0
>5.0to< 10.0
Well characteristics
Range in depth of well below land surface, in feet
Median depth of wells below land surface, in feet
Percentage of wells screened in unconsolidated aquifers
Percentage of wells screened in or open to consolidated aquifers
Percentage of wells for which the above information was not recorded
Use of water from the well:
Percentage of wells used for public supply
Percentage of wells used for domestic supply
Percentage of wells that are unused (i.e., an observation well)
Percentage of wells used for irrigation
Percentage of wells used for industrial purposes
Percentage of wells used for commercial purposes
Percentage of wells used for purposes other than those above
Percentage of wells that do not have "use of water'' recorded
Data Set
Data-Set Value
43
22
5,320
46
54
24 to 50
0.1 to 10'
27
44
14
14
< 1
4 to 3,290
171
54
38
8
37
15
9
5
0.6
0.4
3
30
' Analyses that had a reporting level greater than 10 micrograms per liter were excluded from this data set
because they are believed to have been diluted prior to analysis.
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Table 2. Percentage of Wells That Have the Indicated Attribute or Combination
of Attributes Populated in the Retrospective Data Set
Ancillary Data
well depth
water use
casing material
depth from land surface to water level in the well
aquifer lithology
well depth & aquifer lithology
well depth
& water use & water level & aquifer lithology
Percent of All Wells
74
70
25
24
19
17
7
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Table 3. Percentages of Wells in the Retrospective Data Set With the Ancillary Information
Needed to Answer Indicated Occurrence Question at Assessment Levels of 5.0 and 0.2
Micrograms per Liter
[Total number of wells in the retrospective data set is 5,320; NE, northeast; SE, southeast, NW, northwest; SW,
southwest; |J.g/L, micrograms per liter]
Perce
Ancil
Regie
Ent
Question to be answered ^ni
Sta
(1) Percentage of wells with at least one
VOC detected at a reporting level of:
5.0\ig/L 92
0.2 jig/L 15
(2) Percentage of wells used for drinking-
water supply with at least one VOC
detected at a reporting level of :
5.0 ng/L 50
0.2 (ag/L 8
(3) Percentage of wells with at least one
VOC detected at indicated reporting
level and used for Domestic supply
5.0 jig/L 15
0.2 jig/L 5
Public supply
5 0 U2/L 35
— ' . VJ f-^-A' *—>
0.2 [ig/L 4
(4) Percentage of wells with at least one
VOC detected in
Domestic supply wells that withdraw
water from:
ntage of Wells in the Retrospective
lary Information Needed to Answer
n
ire Geographic
ted
tes NE SE
29 19
4 3
20 15
1 3
4 1
0.7 3
16 15
0.5 0
(a) Unconsolidated aquifers 1.3 0.4 0
5.0 ng/L 0.3 0 0
0.2(ig/L
(b) Consolidated aquifers 1-0 0-9 0
5.0 (J.g/L °
0.2 ^tg/L
Public supply wells that withdraw water
from:
0 0
\ 0.4 0
(c) Unconsolidated aquifers
5 0 LL2/L
no /T 3.4 0.1 2.5
°'2^g/L 27 0 24
(d) Consolidated aquifers
5.0 \igfL
0.2|ig/L
Data Set with the
Indicated Question by
Region
SW
29
5
8
2
5
1
2
1
0.9
0.3
0.1
0
0.2
0.1
0.4
0.3
NW
15
3
7
2
5
0.3
2
2.5
0
0
0
0
0.8
0.1
0.4
0
The States included in each of the four geographic regions of the United States for this table are: Northeast: Connecticut,
Delaware, Illinois, Indiana, Iowa, Kentucky, Maine, Maryland, Massachusetts, Michigan, Minnesota, Missouri, New
Hampshire, New Jersey, New York, Ohio, Pennsylvania, Rhode Island, Vermont, Virginia, West Virginia, Wisconsin;
Southeast: Alabama, Arkansas, Florida, Georgia, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee;
Southwest: Arizona, California, Colorado, Kansas, Nevada, New Mexico, Oklahoma, Texas, Utah; Northwest: Idaho,
Montana, Nebraska, North Dakota, Oregon, South Dakota, Washington, Wyoming.
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III-382
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Middle Gila River Watershed Water Quantity, Water Quality, and
Biological/Habitat Assessment Study, Phoenix, Arizona
Mike Gritzuk, Director of Water Services
Paul Kinshella, Wastewater Engineering Superintendent
Robert Hollander, Compliance and Regulatory Affairs Administrator
City of Phoenix, Arizona
Andrew Richardson, Partner
Frank Turek, Hydrologist
Juliet Johnson, Staff Engineer
Greeley and Hansen, Phoenix, Arizona
Abstract
The U.S. Environmental Protection Agency (EPA) plans to review NPDES permits on a watershed
basis rather than individually and to incorporate Total Maximum Daily Load (TMDL) waste load
allocations into future permits. This will allow EPA to assess NPDES permits and non-point source
discharges in a comprehensive framework. All NPDES permits and discharges within a defined watershed
will be evaluated concurrently in five year watershed cycles. The Arizona Department of Environmental
Quality (ADEQ) divided Arizona into 10 watersheds. The Middle Gila River Watershed (MGRW)
contains about 6,000 square miles including the Phoenix metropolitan area. ADEQ will initiate the
MGRW assessment in 1999; however, EPA needed the assessment information in 1997 to develop U.S.
Fish and Wildlife Service Endangered Species Act Section 7 consultations for the 91st Avenue
Wastewater Treatment Plant (WWTP) and other Phoenix area discharges.
The City of Phoenix and its Subregional Operating Group (SROG) partners, Glendale, Mesa,
Scottsdale and Tempe, operate the 91st Avenue WWTP which discharges highly treated effluent to the
Salt River. EPA approached Phoenix and requested SROG complete an assessment of the MGRW to
facilitate issuance of the 1997 NPDES permit. SROG recognized there were short-term benefits to
completing the assessment related to the 1997 NPDES permit. They also recognized a long-term benefit
that could be realized by providing good data that EPA could use as the foundation to establish TMDLs.
This benefit was demonstrated by SROG in a 1996 ultra-clean mercury analysis pilot study. EPA was
facing a lawsuit to set a mercury TMDL for the Salt and Gila Rivers based on the supposition of special
interest groups that the 91st Avenue WWTP was discharging excessive mercury. The ultra-clean program
demonstrated the actual mercury concentration was much less than the enforcement standard and
eliminated the need for SROG to construct $30 million in WWTP improvements to remove mercury.
EPA was given the data to develop a mercury TMDL based on good science. SROG recognized another
long-term benefit associated with developing stormwater pollutant models based on good data to maintain
BMPs rather than new TMDLs that would require treatment.
It was not realistic for SROG to assess the entire MGRW within the EPA's 1997 NPDES permit
schedule. The section of the MGRW selected for assessment was the river channel from Granite Reef on
the Salt River, 75 miles downstream to Gillespie Dam on the Gila River. This reach met the needs of EPA
and provided a foundation on which ADEQ could build the assessment for the entire MGRW in 1999.
SROG with input from EPA, ADEQ, and Arizona Game and Fish Department developed the scope for
the prototype watershed assessment model.
The MGRW assessment model used a GIS platform to inventory water quantity inflow into the study
reach, water quality analysis, habitat types, and endangered species. Monthly meetings allowed
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stakeholders and regulators to monitor the study progress and adjust the scope. Initial review of the
amount of water quality data allowed reduction to focusing on a list of 22 pollutants of concern.
Data assessment identified gaps where ADEQ needed to initiate sampling for the 1999 study. It also
showed where conventional analysis did not have the sensitivity to quantify concentrations and
recommended new monitoring programs to develop accurate data. The team approach to scope
development and study progress monitoring allowed for consensus building during the process and
facilitated NPDES permit preparation and review. It also resulted in a model that could be applied by
EPA to other watershed areas and provided a foundation for ADEQ to follow in the MGRW.
Keywords: watershed, GIS, NPDES, TMDL
Introduction
The U.S. Environmental Protection Agency (EPA) plans to review NPDES permits on a watershed
basis and to incorporate Total Maximum Daily Load (TMDL) waste load allocations into future NPDES
permits. This will allow the EPA to assess NPDES permits in a comprehensive framework. All NPDES
permits within a defined watershed will be evaluated in five-year watershed cycles. The City of Phoenix
and its Subregional Operating Group (SROG) partners, Glendale, Mesa, Scottsdale and Tempe, operate
the 91st Avenue Wastewater Treatment Plant (WWTP) which discharges highly treated effluent to the
Salt River. SROG and the 91st Avenue WWTP are in the Middle Gila River Watershed (MGRW).
SROG initiated the 91st Avenue WWTP Reclaimed Water Study in 1992 in response an increase in
Surface Water Quality Standards and NPDES permit monitoring requirements. The Reclaimed Water
Study was to identify cost effective, environmentally sound alternatives to constructed improvements at
the 91st Avenue WWTP. As a part of the Reclaimed Water Study, 63 effluent discharge alternatives were
evaluated including 45 that involved the Tres Rios Constructed Wetlands. The Tres Rios River
Management Plan team (TRRMP) was organized to study Tres Rios alternatives in more detail. The
TRRMP team consisted of representatives from Federal, State and local agencies, SROG, residents of the
Holly Acres area near the WWTP, and others. SROG and the TRRMP developed a large amount of
information about the 91st Avenue WWTP and the Tres Rios area, defined as the reach of the Salt River
extending 7 miles downstream from the 91st Avenue WWTP.
The EPA goal of reviewing NPDES permits on a watershed basis revolved around the five-year cycle
for NPDES permits. The 91st Avenue WWTP NPDES permit was to be reissued in 1997 and the EPA
wanted to conduct the review of the 91st Avenue WWTP and other Phoenix area discharges on a
watershed basis. The Arizona Department of Environmental Quality (ADEQ) divided Arizona into ten
watersheds, but the MGRW characterization by the ADEQ was scheduled for 1999. The ADEQ schedule
did not meet the needs of the EPA.
EPA approached Phoenix and requested SROG complete an assessment of the MGRW to facilitate
issuance of the 1997 NPDES permit. SROG recognized there were short-term benefits to completing the
assessment related to the 1997 NPDES permit. They also recognized a long-term benefit that could be
realized by providing good data that EPA could use as the foundation to establish TMDLs. This good data
benefit was demonstrated by SROG in a 1996 ultra-clean mercury analysis pilot study. EPA was facing a
lawsuit to set a mercury TMDL for the Salt and Gila Rivers based on the supposition of special interest
groups that the 91st Avenue WWTP was discharging excessive mercury. The ultra-clean program
demonstrated the actual mercury concentration was much less than the enforcement standard and
eliminated the need for SROG to construct $30 million in 91st Avenue WWTP improvements to remove
mercury. EPA was given the ultra-clean mercury study data to develop a mercury TMDL based on good
science. SROG recognized another long-term benefit associated with developing storm water pollutant
models based on good data to maintain BMPs rather than new TMDLs that would require treatment.
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Study Area
The MGRW defined by ADEQ contains about 6,000 square miles and was much larger than was
needed by the EPA to facilitate the 91st Avenue WWTP NPDES permit review. The initial study area
developed for this MGRW study focused on the Salt River extending from Granite Reef Dam
downstream to the junction with the Gila River and the Gila River from the Salt River junction
downstream to Gillespie Dam. The study area extends about 70 river miles and includes the lands within
the 100-year floodplain. The MGRW area was divided into five reaches to conform with designated use
standards defined in the Arizona Administrative Code, Title 18, Chapter 11. Each reach had different
designated uses and different standards for contaminants. The five reaches were:
• Reach 1 - Granite Reef Dam to 2 km downstream in the Salt River
• Reach 2-2 km downstream to the Interstate 10 Bridge over the Salt River
• Reach 3 - Interstate 10 Bridge to the 23rd Avenue WWTP outfall to the Salt River
• Reach 4 - 23rd Avenue WWTP outfall to the confluence of the Salt and Gila Rivers
- Reach 5 - Confluence of the Salt and Gila Rivers to Gillespie Dam on the Gila River
Stressors
Stressors that represented water quantity and contaminant inflow to the MGRW study area included:
Stream Flows
Stormwater (NPDES regulated)
Stormwater (unregulated)
WWTP (NPDES regulated)
Agricultural Storm Runoff
Dewatering Wells
Landfill Leachate
Groundwater
Sand and Gravel Mining
Concentrated Annual Feeding
Operations (CAPO)
• Agricultural Drainage
The total list of contaminants cited in NPDES permits and Surface Water Quality Standards was
reduced to 22 pollutants of concern. The list contained inorganic and organic contaminants, pesticides and
others, such as total dissolved solids selected because they often exceed standards, prompt fish
consumption advisory notifications, and were important to wildlife.
Inorganic Contaminants
Beryllium
Boron
Copper
Cyanide
Mercury
Nitrate
Selenium
Thallium
Pesticides
Chlordane
DDD
DDE
DDT
Diazinon
Dieldrin
Lindane
Toxaphene
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Organic Contaminants Other Parameter
• Bromodichloromethane • Dissolved Oxygen
• Bromoform • TDS
• Chloroform
• Chlordibromomethane
Analysis
A major effort in the MGRW study was to assess the quality of the available information—simply
stated, to determine if the information represented good data, bad data, or questionable data. The
laboratory techniques were not at issue, the quality of the data was evaluated as it related to identification
of exceedances of standards. Exceedances can trigger violations, enforcement, and development of more
stringent standards. The purpose of the analysis was to document the total number of potential
exceedances that could be projected and to divide them into questionable and bad data exceedances and
good data verifiable exceedances. Questionable data exceedances demonstrated the need for more
sensitive monitoring and laboratory analyses. This was an essential part of the study because the MGRW
study was provided to the EPA and ADEQ to establish future watershed water quality standards.
The MGRW study was a prototype for watershed characterization and a key was development of
databases for analysis and GIS platforms. Stressor data existed in paper and electronic mediums, in
different software formats and several GIS coordinate formats. Initial analyses involved converting the
data to a common format for analysis and screening the information to focus on the pollutants of concern.
A detailed analysis of the information in which the reported pollutant of concern concentrations were
compared to the standards documented that the majority of potential exceedances were not based on
verified concentration but rather were related to questionable data. These were concentrations where the
contaminant was detected and exceeded the standard, but the concentration was not at the quantitation
limit or where the concentration was reported as a "less than (<)" value that was greater than the standard.
The MGRW also required developing a format for GIS data modeling. GIS projects were developed
for:
• Designated Uses
• Surface Water Quality
• Stormwater Quality
• Agricultural Drainage
• Groundwater Quality
• Fish Tissue/Sediment Quality
• Surface Water Quantity
• Habitat/Species
Each pollutant of concern was identified as a layer in each project to allow analysis of the data related
to that specific pollutant of concern from all sources. GIS permitted analysis of pollutant of concern
trends through the MGRW study area and the database tables allowed monitoring changes in concentra-
tion over time. The GIS analyses demonstrated where data gaps existed. These were identified as
locations where new monitoring locations could be needed in future studies. The GIS analysis also
demonstrated areas where more detailed monitoring is needed to identify the source of contaminants.
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Results
The results of the MGRW study were:
• Development of an inventory of available water quantity, water quality and habitat/species data
and other information sources necessary to characterize existing water quality and degree of
beneficial use protection/attainment in the study area.
• Development of water quantity and water quality databases for pollutants of concern used to
conduct a trend analysis and other relevant statistical interpretations so as to characterize existing
and alternative future water quality conditions.
• Analysis of the sources and the fate and transport of pollutants of concern. Recommendations for
additional data/information needed to more precisely quantify/model these pollutants.
• Development of a database of species abundance and distribution (with particular emphasis on
threatened and endangered species), relative to habitat areas.
• A qualitative assessment of the adequacy of existing data to characterize existing water quality
conditions.
• Recommendations for additional data needs and potential data-gathering sources.
The initial purpose of the MGRW study was to provide the EPA with information needed to conduct
an Endangered Species Act Section 7 consultation with the U.S. Fish and Wildlife Service as a part of the
NPDES permit review. The MGRW study also provided information to meet the following additional
uses:
• Provide the GIS database framework to ADEQ to form the foundation for the characterization of
the Middle Gila River Watershed and a model to be used in other watersheds in Arizona.
• Provide a model for EPA to use to assess the quality of information available that EPA could use
to develop TMDLs.
• Provide a water quality, water quantity, and habitat database of the Tres Rios area that could be
used by the TRRMP Steering Committee and Technical Committees as a part of their analyses.
• Provide a water quality, water quantity, and habitat database to the U.S. Army Corps, of
Engineers for use in their Tres Rios, Arizona Feasibility Study.
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Environmental Monitoring Program to Support
the Rouge River National Wet Weather Demonstration Project
Louis C. Regenmorter, P.E., RPO Monitoring Coordinator
Camp Dresser & McKee
Vyto P Kaunelis, P.E., Chief Deputy Director
Wayne County Department of Environment
Abstract
The watershed-based approach being applied to the Rouge River in southeast Michigan under the
Rouge River National Wet Weather Demonstration Project (Rouge Project) has included an extensive
environmental monitoring program. Monitoring is considered a critical element to the Rouge Project
because it is used to: 1) establish baseline conditions; 2) support the development of watershed models;
3) identify problems and their sources; and 4) evaluate control programs.
The environmental monitoring conducted by the Rouge Program Office (RPO) has involved the
collection, management, and analysis of data on rainfall, stream flow, instream water quality, combined
sewer overflows (CSOs) and storm water discharges, biological communities and habitats, sediments,
toxics, aesthetics, and the performance of various control programs. Over 500 monitoring stations have
been established throughout the 438 square mile watershed. The RPO has collaborated with the EPA,
state, and local agencies in the development and execution of the monitoring program. Monitoring has
been performed on an annual basis since 1993.
Computer applications have been built to facilitate quality control, data storage, accessibility, and
analysis. Computer applications have been developed to display the results. The Rouge Project has also
developed a series of water quality indicators that serve as a means of communicating information on
water quality to the general public.
Introduction
The watershed-based approach is being applied to the Rouge River in southeast Michigan under the
Rouge River National Wet Weather Demonstration Project (Rouge Project). The Rouge Project includes
an extensive environmental monitoring program. This paper presents an overview of this watershed
monitoring program.
Monitoring is recognized as a critical element in watershed management. The Intergovernmental
Task Force on Monitoring Water Quality (ITFM) prepared the report, "The Strategy for Improving Water
Quality Monitoring in the United States" (ITEM, 1995). In the report, the ITFM recommends adopting
the use of goal-oriented monitoring as the means "to support sound water-quality decision-making." The
Urban Wet Weather Flows Federal Advisory Committee (UWWFAC) agrees with the ITFM report and
in their own report, "Draft Recommendations on Monitoring Requirements for Watershed Management
Programs" (Murray, et al., 1996), discusses how watershed-based monitoring plays an integral part by
tracking progress towards watershed objectives, while collecting more environmentally relevant data than
traditional compliance monitoring.
The Rouge River, a tributary to the Detroit River in southeast Michigan, has been designated as a
significant source of pollution to the Great Lakes system. (See Figure 1) The Rouge River Watershed is
largely urbanized, spanning 438 square miles, and is home to 1.5 million people in 48 communities and
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three counties. Sources of pollution to the river include CSOs, storm water runoff, resuspension of
contaminated sediment, inflow from abandoned dumps, and limited industrial and municipal point
sources.
The Rouge Project is a United States Environmental Protection Agency (USEPA) sponsored program
to manage wet weather pollution as the means for restoring the Rouge River. It is designed to identify the
most efficient and cost effective controls of wet weather pollution, while assuring maximum use of the
resource. A major element of the Rouge Project is CSO control. Controls that are being investigated
include retention treatment basins and sewer separation. Innovative storm water control technologies are
also being evaluated, including various best management practices (BMPs) and the development of a
general permit which will offer: discharge options; focus on hydrologic boundaries; and define
management activities. The results of the CSO and storm water control projects are being incorporated
into the detailed management plan for the Rouge River Watershed.
Methodology
Implementing the monitoring program to support the Rouge Project involved several elements. These
elements were derived from strategies for the implementation of monitoring plans that are presented in
the USEPA guidance document titled, "Guidance for the Data Quality Objectives Process" (USEPA,
1994) and in "Design of Networks for Monitoring Water Quality" (Sanders, et al., 1990). These elements
included:
1. establishing monitoring objectives;
2. defining data needs;
3. developing individual sampling programs;
4. data collection and handling; and
5. data management.
The first element in developing the monitoring program for the Rouge Project was to formulate an
overall direction for the program. This direction was expressed in terms of monitoring objectives. The
objectives were formulated in terms of the information requirements to support the other major
components of the Project.
The next element was to identify the data requirements. These requirements were expressed in terms
of the data needed to support the monitoring objectives. Estimates of the data needs were made by
identifying the database required to properly conduct a desired analysis and then evaluating the usability
of the current database for the Rouge River.
Based on the data requirements, individual sampling and monitoring programs were identified to
collect the data. A wide range of sampling programs were required for all the required data, such as water
quality and chemistry, habitat status, and public health concerns. Programs were developed based on
specific data quality objectives that were required to meet each objective.
Data collection and handling required that detailed information be provided to ensure all the
environmental measurements and related activities under taken by the staff were performed in a manner
consistent with the RPO's Quality Assurance Project Plan (QAPP). The QAPP defines the minimum
requirements for quality control and describes applicable quality assurance activities for the Rouge
Project. Field sampling plans (FSPs) were developed to provide this information. Specific activities that
were undertaken on a routine basis were also documented in Standard Operating Procedures (SOPs).
SOPs were available for laboratory methods (i.e., 5-day biochemical oxygen demand determination),
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field sampling (i.e., sediment core sampling technique), and data handling (i.e., data uploading into
database).
To manage the data, a database was needed with the capabilities to store the entire data set, given the
data sea was very large with various data types. In addition, a series of tools had to be developed that
would work within the database to locate the desired data and display the results using various format
options.
Results
The objectives developed for the Rouge Project monitoring program were to:
• establish baseline conditions;
• support the development of watershed models;
• identify problems and their sources; and
• evaluate control programs.
Based on these objectives, a wide range of sampling and monitoring studies were developed and
implemented to support each objective. These studies involved an extensive effort in the collection,
management, and analysis of data on rainfall, streamflow, instream water quality, CSO and storm water
quality, biological communities and habitat, instream bottom sediment, air deposition, and aesthetic
conditions. In addition, the monitoring program included measurement of the performance of various
structural controls, wetlands, and non-structural BMPs. Each of these individual programs are
summarized below.
Rain Gaging—Monitoring of rainfall amounts has been conducted throughout the Rouge River
Watershed using a network of 23 continuously recording gages. The data has been used to define wet
weather responses and spatial variation, and to drive the hydrologic models of the watershed.
Flow Monitoring—A program to monitor instream flow rates has been implemented by the RPO on
a seasonal basis since 1993. Flow levels are continuously monitored with readings taken and recorded
every 15 minutes. Networks of eight to 20 stations are established for a monitoring season, which
typically lasts six to nine months. Flow rates are then calculated from the level data using rating curves
that have developed at each station. The United States Geological Survey develops the rating curves for
the RPO using procedures that are consistent with the curve maintenance performed at their own gaging
stations. The data are used to establish baseline conditions, define trends, and assist with the
development of the hydraulic models of the Rouge River system.
Instream Water Quality Monitoring—A program to monitor instream levels of dissolved oxygen
(DO), temperature, pH, and specific conductance has been implemented by the RPO on a seasonal basis
since 1993. These parameters are continuously monitored with readings taken and recorded every 15
minutes. Networks of eight to 17 stations have been established six to nine months. The data are used to
establish baseline conditions, define trends, and assist with the development of the water quality models
of the Rouge River system.
Chemical Monitoring—Sampling programs have been performed to monitor instream levels of
selected pollutants under both dry and wet weather conditions. The chemical parameters include oxygen
demand, solids, nutrients, and metals (total and dissolved). Up to 60 sites are sampled over the same dry
period one to three times per year. Sampling during wet weather is performed at 10 to 30 stations. Both
autosamplers and manual grabs are employed to collect the samples at various times throughout a given
wet-weather event. Two to seven events are typically monitored at each site in a given season. The data
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are used to establish baseline conditions and assist with the development of the water quality models of
the Rouge River system.
Sediment Monitoring—Sediment monitoring has been conducted throughout the watershed to
characterize sediment quality. The monitoring involved the collection of a single sample at 182 locations
along the river. These samples were analyzed for metals, polychlorinated biphenyls (PCBs), and
polycyclic aromatic hydrocarbons (PAHs). Results were compared to a toxicity-based guideline as a
means of identifying those sites with the potential for impacting aquatic organisms. Another monitoring
program focused on defining the extent of PCB contamination in the sediments of Newburgh Lake. As a
result of this program, Newburgh Lake is being dredged in order to return it to a viable recreational
resource.
Bacteria Monitoring—Monitoring instream bacteria levels has been used to define the health risk
associated with public contact with the Rouge River. Monitoring has focused primarily on E. coli
bacteria under both dry and wet weather to establish existing levels. Detailed monitoring programs are
conducted in areas with elevated dry-weather levels in order for results to be compared to water quality
standards and to locate potential sources. Monitoring has also been used to define periods when certain
sections of the river will be safe for use as a recreational resource. Similar programs will be conducted
during wet weather to define the impacts that various CSO controls have on bacteria levels.
Aesthetic Monitoring—Each field visit to the Rouge River includes documentation of the aesthetics
at the site. The aesthetic indicators selected for the Rouge Project include water clarity, odor, water color,
visible debris, and signs of obvious pollution. Observations are documented on a standardized field sheet.
The results have been used to develop a numerical index of aesthetic quality. This index is used to locate
problem areas and track trends.
Toxics Assessment Plan—A toxicity evaluation of surface water, sediments and fish was conducted
in the Rouge River Watershed to determine baseline toxicity conditions, upon which treatment
alternatives could be measured. Many organic compounds and metals have been identified as parameters
of concern because of their toxic effects in the environment. The RPO is conducting a toxic monitoring
program to identify existing levels of organic compounds and metals in the water column and sediments.
The program also assesses the impacts on aquatic life and human health through studies on the
bioaccumulative and toxic properties of each compound.
Habitat Assessment—In 1996, the RPO performed an evaluation of habitat in the Rouge River
Watershed. Over 80 habitat monitoring stations were established. The purpose of the study was to
identify existing fish habitat in the watershed and those fish communities associated with a particular
habitat.
Modeling Special Studies—Several field studies have been developed and conducted by the RPO to
assist in the development of input values for the water quality models. These field studies include
sediment oxygen demand, stream reaeration, time of travel, and impoundment limnology.
CSO Control Technologies—Several technologies to control CSO pollution are being demonstrated
and monitored in the Rouge Project. These include several conventional detention basins and sewer
separation. The basins have been designed to test various design storms, shunt channels, first flush tanks,
and a vortex separator. These projects will help demonstrate how much treatment can be expected from
various sized retention structures and separate sewers. A two-year water quality benefit evaluation of
CSO basins constructed during Phase 1 will begin in June 1997.
BMP Evaluation Monitoring—Monitoring is used to evaluate how various BMPs perform for
storm water control. The BMPs tested by the RPO include structural (grassy swales, detention ponds, on-
line filters, sand filters), wetlands (existing, enhanced, constructed), and source control
(industrial/commercial and residential). Monitoring is performed either at the inlet and outlet of the
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structure or before and after implementation of the program. Treatment performance is based on
differences found in the concentration or loading of various pollutants.
Air Deposition Monitoring—The County is working with the University of Michigan in Ann Arbor
to measure the wet and dry deposition of toxic metals, PCBs and other contaminants from wet and dry air
deposition in the watershed.
Illicit Connections—The Rouge Project has implemented an aggressive program to detect and
eliminate improper connections to the storm sewer system. Several techniques are being used including
dye testing, infrared photography, isotopes, and indicators like ammonia and whiteners. As illicit
connections are found and eliminated, the volume and pounds of pollutants they contribute are calculated
and their impact on river water quality estimated.
Outfall Monitoring—Monitoring discharges from selected combined sewer and separated storm
water outfalls has been conducted throughout the watershed by the RPO. Flow rates are monitored
continuously with autosamplers used to collect a series of discrete samples throughout a given discharge
event. Samples are analyzed for a suite of parameters (oxygen demand, solids, nutrients, metals). Results
are used to define loading for the hydrologic models of the watershed and the impacts land use has on
quality. The results are also compared to the national databases for CSOs and storm water.
Outfall Inventory—Utilizing global positioning system (GPS) techniques, a pilot outfall inventory
was performed on the Bell Branch and Tarabusi Creek tributaries to the Rouge River. The techniques and
methods utilized were evaluated to develop a procedure for future outfall inventories conducted by
municipalities. These inventories can assist communities to more accurately define outfall information
for future use in the preparation of storm water discharge permits, and industrial discharges, as well as
conducting river model calculations.
Septic System Studies—Septic studies are being undertaken in the Rouge Project since failing septic
systems are believed to be a cause of elevated E. coli levels in the river during dry weather. Several
options are being developed to have local governmental units review septic systems on a regular basis or
at the time of sale of a property. If sanitary sewers are not available to a parcel served by a septic system,
a septic system maintenance program will be encouraged or required.
Discussion
Monitoring began in the fall of 1993 and has continued on an annual basis. All field programs are
conducted on a seasonal basis, typically when air temperatures remain above freezing and the water
temperature remains above 12 °C. The overall monitoring program for the Rouge Project has evolved
into a set of permanent and roving monitoring sites, depending on the data collection goals of each
individual study. Close to 600 individual monitoring sites have been established.
To date, over 5 million pieces of data have been generated from the field studies. The majority of the
data has come from the continuous monitoring of rainfall, river stage and flow, and water quality (DO
and temperature). This type of information is collected every 15 minutes, 24 hours per day during the
monitoring season.
Procedures have been established for maintaining this large data set and validating the accuracy of
the various data. The data handling and management procedures are summarized in Figure 2. Raw data
collected in the field or received from the laboratories are uploaded into the Rouge Project's ORACLE
database management system through a loading routine. The loading routine also checks the data to
ensure they are in the correct format, and generates reports and time-series plots that are used in the
quality assurance/quality control (QA/QC) process for validating the data. Once the data enters the
database, they are considered "preliminary." After loading, the data are sent through a rigorous QA/QC
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process whereby the validity of each data point is identified based on the data quality objectives (DQOs)
of the individual field study. These steps are shown on the left-hand column and bottom row of Figure 2.
Using specific QC data collected in the field and/or generated in the laboratories, the data are evaluated
in terms of precision and accuracy. When problems are found in the data that are caused by improper
field or laboratories procedures, this information is passed on to crew members or lab mangers so
corrective actions can be taken. The data are validated in terms of a "usability" factor. The basic
categories include acceptable, questionable, or rejected. Flags identifying the validity of each data point
are added to the database. Once the flags have been finalized, the data are considered final and available
for distribution.
Accessibility of the data was a very important consideration in the Rouge Project. Several computer
applications were developed that provided user-friendly interface with the database. DataView is one
application that queries the database based on user-specified criteria (monitoring station, parameter type,
dates) and displays the data in both tabular and graphical formats. The Rouge Information Manager is a
multimedia based tool to provide information on Rouge activities addressing GIS, database management,
CSO, nonpoint source, water quality modeling, and public involvement. This tool includes DataView in
its suite of programs, as well as, maps of monitoring sites, specific site information, and reports. This tool
is distributed on CD-ROM.
A method adopted by the Rouge Project to better communicate results of the monitoring program is
the use of "indicators." Using water quality indicators, river quality measurements and observations can
be expressed in fairly simple terms. This approach uses five water quality indicators: DO, river flow,
bacteria, aquatic life, and stream habitat to rate river quality based on defined public use categories such
as fishing, swimming, boating and aesthetics. Results are presented using graphical GIS displays which
include color-coded icons and river segments. Staff have used these indicators to identify and rate the
quality of river conditions which affect public uses of the Rouge River.
Total costs for Rouge Project monitoring program since 1993 and through 1998 has been $10
million. The majority of these costs have supported monitoring rainfall, flow, and water column
programs. Parameters such as flow, DO, water chemistry and bacteria were found to be highly variable in
the Rouge River and required large databases to accurately establish baseline conditions and identify
trends. Programs with smaller budgets may want to consider less emphasis on this type of information.
Future monitoring is targeted at developing a long-term monitoring plan for assessing the "health" of
the Rouge River. This program will be designed to assess long-term trends, document compliance with
the water quality standards, and will be consistent with upcoming policy from the USEPA regarding wet
weather monitoring for watershed planning. In addition, the Rouge Project is developing policies that are
based upon resource monitoring as a trade off to end pipe monitoring. The concepts have been accepted
by the UWWFAC and are in the process of being accepted by the local decision makers in the watershed.
Conclusions
The benefits gained from the intensive monitoring program implemented for the Rouge Project
included:
• extensive database of baseline conditions
• able to support water quality models of the watershed
• evaluating CSO demonstration control programs
• identify wet-weather impacts from non-CSO sources
• identify remediation needs of recreational resources
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A monitoring program designed in support of a watershed-based study must provide a broad range of
information. Monitoring several chemical parameters for compliance with site-specific standards or
criteria will not provide all the required information.
Planning a watershed-based monitoring program is very important. Without proper planning, the
range of desired information will lead to a very large and expensive program with little focus. Using a
goal- or objective-oriented approach has helped the Rouge Project to stay focused on the important
issues. Without this information, the limited resources for implementing the monitoring program cannot
be effectively applied.
Managing a natural resource on a watershed basis requires a comprehensive understanding of the
natural systems and the impacts of human activities. Monitoring must be integrated into watershed
management because it is a source of information that can provide answers to the complex questions that
come with trying to understand the problems and solutions associated with watersheds.
References
Intergovernmental Task Force on Monitoring Water Quality (ITFM), 1995. The Strategy for Improving
Water-Quality Monitoring in the United States. U.S. Geological Survey, Reston, VA.
Murray, J., K. Cave, J. Mancini, 1996. "Draft Recommendations on Monitoring Requirements for
Watershed Management Programs." Urban Wet Weather Flows Federal Advisory Committee
Watershed Subcommittee, Washington, DC.
Sanders, T.G., R.C. Ward, J. C. Loftis, T.D. Steele, D.D. Adrian, V Yevjevich, 1983. Design of
Networks for Monitoring Water Quality. Water Resources Publications, Littleton, CO.
USEPA, 1994. Guidance for the Data Quality Objectives Process. EPA A/G-4. USEPA, Washington,
DC.
USEPA, 1996. Environmental Indicators of Water Quality in the United States. USEPA 841-R-96-002.
Office of Water, USEPA, Washington, DC.
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ROUGE RIVER WATERSHED
Base Map
LEGEND
Watershed boundary
Major branches
Tributaries
Community boundaries
County boundaries
/gis/map/gra/asi2e/awshed/awbase.gra 05/16/95
Figure 1. Rouge River Watershed map.
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PROMPT
FEEDBACK
t
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COLLECTION
SITE
MANAGEMENT
DATA
EVALUATION
,
k.
QA/QC
REPORTS
.& TS PLOTS .
>
DATA
VALIDATION
^
RAW DATA
Y
LOADER
DATA
V
"X
PRELIMINARY
DATA
^/
/ DATA /
/ DISTRIBUTION/ >
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Figure 2. Data flow process.
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HI-398
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Translation of Water Quality to Usabilities for the Catawba River Basin
Carl W. Chen, Engineer
Joel Herr, Engineer
Laura Ziemelis, Engineer
Systech Engineering, Inc., 3180 Crow Canyon PL, Suite 260, San Ramon, CA 94583
Robert A. Goldstein, Project Manager
Electric Power Research Institute, P.O. Box 10412, Palo Alto, CA 94303
Larry Olmsted, Director of Scientific Services
Duke Energy Company, 13339 Hagers Ferry Rd., Huntersville, NC 28078
Abstract
The Watershed Analysis Risk Management Framework (WARMF) has been developed to provide a
road map that can help stakeholders organize themselves, develop a work plan, identify water quality
issues, propose management alternatives, and evaluate their effects on water quality. Stakeholders can
designate various parts of a river basin for beneficial uses such as drinking water supply, swimming, cold
or warm water fisheries, and aesthetic value. Parameters such as water depth, flow velocity, fecal
coliform, temperature, dissolved oxygen, suspended sediment, and chlorophyll level are used to set
criteria for each beneficial use. WARMF contains dynamic catchment, river, and reservoir models. These
models accept meteorological data, digital elevation maps, and land use information as inputs and
simulate hydrology, nonpoint source loads, and water quality throughout the basin. Monitoring data is
used to calibrate the models. The simulated results are processed to determine whether the water quality
meets the specified criteria. The locations that meet the criteria are painted green and the locations that do
not are painted red, in the GIS maps. WARMF, which makes intensive use of monitoring data, translates
scientific measures of water quality into usabilities. The methodology has been applied to the Catawba
River of North Carolina and South Carolina.
Introduction
EPA's concept of a watershed approach requires the involvement of local stakeholders in the
development of a management plan or a TMDL. The stakeholders must identify water quality issues in
the river basin, develop alternatives to solve the water quality problems, and reach consensus on a
management plan. They need data to make informed decisions. The data must be brought to stakeholders
in a logical and sequential manner and presented using management variables.
There are two types of data that can be used by stakeholders. One is monitoring data measured by
scientists, and the other is the simulation results from a model. Both data are in scientific terms, such as
the temperature of water in degree Celsius, the concentration of coliform bacteria in MPN per 100 ml, and
the concentration of dissolved oxygen in mg/1. These data are not very meaningful to stakeholders who
want to know whether the water is safe for swimming, acceptable for drinking water supply, not harmful
to fish, and/or aesthetically pleasing.
WARMF is a multi-tasking decision support system that can calculate TMDLs, guide stakeholders
through the consensus process, predict water quality improvement of a management scenario, and manage
data for a watershed. The focus of this paper will be to show how WARMF can translate monitoring data
and simulation data into usability terms.
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The stakeholders can use WARMF to display where the pollutants come from, how these pollutants
affect the water quality, and whether a management plan can render all sections of the river basin suitable
for intended uses. All examples will be drawn from the application to the Catawba River Basin of North
and South Carolina. Figure 1 shows the GIS map of the 5,000 square mile river basin. Both spatial and
temporal data will be displayed with the basin map on the computer monitor in colors. Unfortunately, the
colors cannot be seen in the black and white copies.
Methodology
Field measurement is expensive. It is impractical to have sufficient monitoring stations and samples
to characterize the entire watershed. Model simulation, on the other hand, can be performed for whatever
spatial resolution or time scale desired. In WARMF, available monitoring data is used to calibrate the
model. After a reasonable agreement is reached, the model is used to generate detailed simulation data for
further analysis.
As a step of the consensus process, stakeholders need to identify the intended uses of their watershed.
Stakeholders enter intended uses in a dialog box (see Figure 2). The intended uses in the Catawba basin
include swimmable waters, warm water fish habitat, cold water fish habitat, water supply, and aesthetic
enjoyment. Other uses such as ecosystem protection can be added to the list.
The stakeholders can select an intended use in the dialog box and then point and click at applicable
locations on the GIS map. The applicable locations do not have to be contiguous. For example, various
stream sections in the headwaters of the Catawba River can be chosen for coldwater fish habitat (see
Figure 2).
In the next step, the stakeholders can specify the criteria for each intended use. To do this, the
stakeholders will first select an intended use (e.g. swimmable) in the dialog box. They will then select a
parameter (e.g. coliform), a concentration (e.g. 1 per 100 ml), a calculation method (e.g. 7 day minimum
average), an exceedance rule (higher or lower), and a percent compliance (100, 90, or 80%).
For some uses, multiple criteria may be specified. For example, coldwater fish may require a 7 day
minimum average dissolved oxygen of more than 6 mg/1 and a 7 day average temperature of less than 22
degree Celsius. Figure 3 presents an example of the second criteria for coldwater fish habitat.
WARMF will process the simulated data according to the specified criteria. For an intended use, the
sections that meet the criteria are painted green and the sections that do not meet the criteria are painted
red. For an intended use with multiple criteria, a section has to meet all the criteria for it to be painted
green. In a GIS map, the stakeholders can easily spot the sections with water quality problems (i.e. the red
sections in Figure 4).
Results
Figures 5 to 8 compare some of the observed and simulated parameters at various points in the
Catawba River Basin. The figure heading shows the goodness of fit between observed data and simulation
results using percent error and a correlation coefficient.
Some of the fits are exceptionally good, while the others are not. It is very difficult to match the
timing of peaks and valleys to the observed data. A time delay of a day or two can make a big difference
on the statistics, even though the model follows the pattern of observed data. We conclude that the
simulated data is close to observed data and that the simulated data can be used to evaluate whether the
water is suitable for intended uses.
Figure 9 shows the spatial distribution of phosphate loading for two management scenarios. The
magenta is for point source and green is for nonpoint source loading. The key in the upper left indicates
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which simulations are represented by the loading bar Alternative 1 is the base case simulation which
represents existing conditions. Alternative 2 represents a management alternative with buffer strip
installation. With these displays, stakeholders can see where the pollution loads are and what impact the
management scenario has on the pollution load.
Figure 10 shows the usability (aesthetic value) of water sections under base case conditions. Figure
11 shows the usability of water sections under the management scenario of buffer strip implementation.
By comparing the two figures, stakeholders can see whether the management scenario has improved the
water quality by changing the color of water sections from red to green on the GIS maps.
Conclusion
It is concluded that WARMF is an effective communication tool to present data to the stakeholders.
The features of WARMF include:
1. Monitoring data is used to calibrate model. The calibrated model becomes a data gap filling tool,
which generates detailed hydrology and water quality data for all locations and times, regardless
of whether there is observed data available for comparison.
2. Stakeholders can assign intended uses to various parts of the river basin. They can also specify
water quality criteria for each intended use.
3. The simulated water quality is compared to the criteria of the intended uses. The sections that
meet the criteria are painted green. The sections failing to meet the criteria are painted red on the
GIS maps.
4. Stakeholders can input management plans into the model and view the effect on pollution loads
and resulting water quality in terms of usability.
III-401
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Lookout Shoals Lake
Lake Norman
Mountain Island
Lake
Lake Hickory
Lake Rhodhiss
NORTH CAROLINA
SOUTH CAROLINA
Fishing Creek
Lake
Great Falls
Reservoir
Cedar Creek
Reservoir
Lake Wateree
Figure 1. Map of Catawba River Basin in North and South Carolina.
III-402
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Xt Watetsned Analysts Bisk Management Fiamework - |C:\WARMI-\CATAWBA WSM]
0* £*,ifiew Mod« Scenario DSCU • Modjja
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Elevation (ft)
00
1200
2200
3200
4200
200
6200
Dais Lake
Lake Hickory
ake Rhodhlss
Enlet the intended ute below and
select the river* and letervoiit on
the map to which each intended
• ute applies.
Public water supply
Mountain Island
Lake
NORTH CAROLINA
SOUTH CAROLINA
Anal Rounded M
Figure 2. Assignment of the cold water fish habitat to sections of the Catawba River Basin.
Parameter
Temperature, C
is within water quality objectives when...
is
max. 7-day mean
below £f
22
4 002
80% of the time
OK
Figure 3. The Second Water Quality Criteria for cold water fish habitat.
III-403
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iM* -IfftxJ
tinma««.mwfcva.e«aa
Dissolved Oxygen, mg/l (min. 7-day meap]
10.0855
8
6.3314
Temperature, C (max. 7-day mian]
29.6454
22
8.46737
iAriel Rounded W | •;;
Figure 4. Display of suitable (green) and unsuitable (red) sections for cold water fish habitat
in Catawba River Basin.
Lake Wylie (Uppei) - Tempeiatuie. C
,--- . ...... —7
Elevation, m
Volume;
<;:,,- -:'"-
Ammonia, mfl/l M
Aluminum, mo/I
Calcium, mg/l
Magnostum, mg/l
Potatsium, mgi\
Sodiura, mg/l :
Sultoe. mg/!.S •:£:':
Nitrate, mg/! N
CWoiWe, mig/l
Phosphate^ mg/l P
Alkalinity, mg/l CaCO
Org. Caibon, mg/l : :
I notg. Carbon, mg/l
Total Algae;;ug/l Chl
Fecal CoHojro,tt/1 01
Sed Depostilion, at^
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— 42TEST « Observed
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Figure 5. Simulated and observed temperature for Lake Wylie
(8.9% error, correlation coefficient = 0.98).
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"ishing Creek - Dissolved Oxygen, mg/l
• -.iDfXl
Flow, c»« A
Depth, m
Velocity^ m/s
Temperature, C
pH
Ammonia, rng/l N
Aluminum, mg/i
Calcium, mg/l
Magnesium, mg/l
Potassium, mg/i
Sodium, mg/l
Sulfate, mg/l S
Nitrate, mg/l N.
Chloride, rag/I
Phosphate, mg/l P
Alkalinity, rog/l CaCO
Qrg. Carbon, mg/l
Inoig, Carbon, mg/l
Fecal Cofiform;«/1 01
'J '1'* H^1 BMil'i" M f 1 ' 1 ;
Total Phosphorus, mj
Total Nitrogen, mg/l
Total Qrg. Carbon, m —
Total Su*p. SeoL, rag -~
& Show Observed
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^ He^p
'«» ••?'' i'"' il
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Figure 6. Simulated and observed dissolved oxygen for Fishing Creek
(10.84% error, correlation coefficient = 0.75).
South Foik - Total Phosphorus, mg/l
Flow, cm*
Depth, m
Vetocity, m/s
Teaperature, C
PH
Ammonia, mg/l N
Aluminum, mg/l
Calcium, mg/l
Magnesium, mg/l
Potassium, mg/l
Sodium, mg/i
Sulfate, mg/l S
Nitrate, mg/I N
Chloride, mg/l
Phosphate, mg/l P
Alkalinity, mg/l CaCO
Org. Carbon, mg/l
(rung. Carbon, mg/l
Fecal Colif or m. 8/101
dissolved Oxygen m
Utf
Total Nitrogen, mg/l
Total OrgI Carbon, m
Total Susp. Sed_, mg
1/3 Show flbserved
— 42TEST
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I IT_»DI nAT > All consriluRnfit 4 ?TFC.T "v
AU constituents. 4 2TEST
Figure 7. Simulated and observed total phosphorus for South Fork Catawba River
(44.37% error, correlation coefficient = 0.67).
III-405
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[Lake Rhodhiss - Total Nitrogen, mg/I
Aluminum, mg/I/""; ' :;; *
Cakturo, sig/» tj*,
Magn«*iua, mg/t
, Pota**iu», rog/l.. ::•.-,/;:.
S odium, feg/l
Suffate, mg/I S ,r.
NUtate,mg/l N
Chloride, mg/I
Phosphate, mg/I P
Alkalinity, mg/I CaCO
Gtg.:';Catban,'mg/l -
Inoig. Caibon, »g/l 1^
: Total Algae, ug/l Chi ;
Fecal Colilorm, 8/101
Sed. Depositidn, aTl
Dissolved Oxy0en, «,
BlueGreetrt Algae, ug,
; Djalowtfug/l Chl-a-p
61 een Algae^ ug/l Cli
[DeuMut, mg/I C
Total Phoiphotus, IM
i Total Org> Catbon, m
1r\
.u -
e~
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I
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Figure 8. Simulated and observed total nitrogen for Lake Rhodhiss
(35.05% error, correlation coefficient = 0.58).
yr.Waletshed Analysis Bisk Management Ftasework -. IQ
SSISfil Si * i»^IJ3lfiSfJl WA
He!p:'..:|g|:
B»>lilgitl^}-;gj
Elevation (ft
200
1200ii
2200
3200
4200
5200,,
200|!
Lake Hiqkory
Lake Rhodhiss
,) Source EonSribulioru
Mon-poinl Souic0s
Point Sources
Lookout Shoals L
^-^
'Mountain Island
Lake
NORTH CAROLINA
SOUTH CAROLINA
Figure 9. Display of point and nonpoint phosphate load Alternative 1 (base case)
and Alternative 2 (buffer strip).
III-406
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VI Waletched At!. •.-.-.. isl MorMgenienl F'lamewoik - [C:\WAHMe'>CATAWllA.W'JM|
£te •'£* yiew "Mode S«n«lo; Djou M«W»
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Figure 10. Usability (aesthetic) of water sections under the base case condition.
Vt Waleithed Analiisi: Bisk Managemeni Fiamcwoik - |CAWARWF\WTAWBA,.WSH)
Figure 11. Usability (aesthetic) of water sections under the alternative with buffer strip.
III-407
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III-408
-------
Track C—Indicators and Reference Conditions
Biological Indicators and Reference Condition Development
Wetlands Indicators
Watershed Indicators
m-409
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m-410
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Biological Criteria Development for the Ohio River, USA
E. B. Emery
A. H. Vicory Jr.
Ohio River Valley Water Sanitation Commission
5735 Kellogg Avenue, Cincinnati, Ohio, USA
Abstract
There is a growing trend in the United States to develop biological methods to assess the status of the
nations surface waters. The Ohio River Valley Water Sanitation Commission has collected biological data
from the Ohio River since the 1950's. Beginning in 1990 the commission began to focus on the develop-
ment of biological criteria to more accurately monitor and measure improvements to the system. The
morphological and hydrological characteristics of the Ohio River require that current collection and
assessment methods used for smaller streams must be substantially modified for use on the Ohio River.
Sampling has revealed that spatial trends in the biota exist along the length of the river and within naviga-
tional pools. The micro-habitat present at each sampling location is also closely correlated to the composi-
tion of the biological fauna present. Temporally, improvements to the water quality of the system has
resulted in an improving biological community. Each of these patterns are being incorporated into the
formation of biological criteria. The riverwide application of biocriteria will allow the characterization
and comparison of the water resource quality of the Ohio River and establish biological goals, similar to
water quality goals.
Keywords: Biocriteria, fish, macroinvertebrates, Ohio River, ORSANCO
Introduction
The Ohio River Valley Water Sanitation Commission (ORSANCO) is an interstate agency created in
1948 for the purpose of identifying, monitoring and abating water pollution problems in the Ohio River
Basin. The value of monitoring biological populations to assess water quality conditions was recognized
by the Commission as early as 1957, with the implementation of lockchamber fish population surveys.
Today, these surveys are conducted annually on the Ohio River and continue to provide valuable
information on Ohio River fish populations and their response to natural and man-made environmental
and water quality conditions. While lockchamber fish surveys do provide insight into fish population
dynamics on a system-wide basis, they are biased in that they are capable only of assessing the fish
community condition within the lockchambers of the navigational lock and dams, an anthropogenic
feature of the Ohio River. The inapplicability of these surveys to other habitat types within the river
necessitated the formation of collection techniques which could be applied to multiple habitat types.
In response, the Commissions biological programs were expanded in 1990 to include the
development of an additional sampling technique, boat-mounted electrofishing. This technique provided
the mobility necessary to allow the sampling of fish populations at any near-shore location along the
river. This mobility marked the beginning of ORSANCO's characterization of the Ohio River and
provided the Commission a tool with which to begin biocriteria development.
Biological criteria can be used by States to confirm impairment from a known source of impact,
determine support of designated aquatic life use classifications for application in standards, and represent
a programmatic expansion from source control to resource management. Many states now use some form
of standardized biological assessment to determine the status of the biota of their waters. Some states
even use biological criteria to define aquatic life use classifications and to enforce water quality standards.
III-411
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Currently, three states have statutory biocriteria, 21 have management biocriteria, 23 are developing
biocriteria and only three are not using biocriteria at all.
The states of the Commission have established numeric biological criteria for the Ohio River as a
goal. An assessment tool is desired for detecting impairment from a known source of impact, as well as
aquatic resource characterization and use support assessments for the entire Ohio River.
The steps to achieving this goal are three-fold: 1) The individual components of the biocriteria must
be selected. 2) Current technologies must be modified for use on this large and dynamic system. 3) A
strategy must be developed for applying biocriteria to the inter-state waters of the Ohio River.
Methods
Study Area
The Ohio River (Figure 1) begins at the confluence of the Monongahela and Allegheny Rivers and
flows southwesterly to the confluence with the Mississippi River, covering 981 miles. The river is
contained within 20 navigational dams which provide a nine-foot minimum depth for navigation. The
study area dissects four ecoregions: the Western Allegheny Plateau, the Interior Plateau, the Interior River
Lowland and the Mississippi Alluvial Plain (Omernik, 1987).
ORSANCO'S Approach
In 1995 ORSANCO assembled a panel of biological and Ohio River experts to facilitate the
development of biological criteria for the fish population of the Ohio River. The charge of this panel is to
aid in the design of studies and interpretation of data necessary for the development of biocriteria. The
panel includes experts from academia, industry, state and federal agencies as well as ORSANCO staff.
In March of 1997, a second panel of experts was formed to assist in the development the
macroinvertebrate component of biocriteria. The charge of this panel was similarly focused, but directed
towards the second component of biocriteria, aquatic macroinvertebrates.
Components of Biocriteria
To date, two components of biocriteria have been chosen, fish and macroin vertebrates. Each is an
important component of the biota of the Ohio River and has been shown to be good indicator of water
quality conditions.
Fish
Fish correspond to the regulatory and public perceptions of water quality and reflect cumulative
environmental stress over longer time frames. Fish are often used by the public to arbitrarily judge the
quality of a waterbody and game species are sought after by thousands of fishing enthusiasts along the
river, with many consuming their catch. The public also weighs concerns over fish contamination and the
risks involved with consuming fish from the Ohio River.
Fish are relatively long-lived organisms which are easily used to detect impairments through
examining the structure and function of the community representing the area in question. ORSANCO has
chosen to use two types of descriptive indices to assess fish communities, the Index of Biotic Integrity
(IBI) and the Modified Index of Well Being (Mlwb). Each of these indices examines a different
III-412
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component of the structure and function of the fish community and will be modified for use on the Ohio
River.
Collection Methods
The collection method used for the biocriteria development process requires that standardized field,
laboratory, and data processing methods and procedures are followed according to ORSANCO (1996)
and OEPA (1987). Fish are collected using a boat electrofishing technique employed during the night
based upon the results of a day -vs- night comparison study (Sanders 1992). A two-person or three-person
crew manning an 5.5 meter aluminum John boat, equipped with a 5000-watt generator and a Smith-Root
Type VI-A Electrofishing unit providing pulsed DC current, samples each zone beginning no sooner than
30 minutes after sunset. Each zone is 500 meters in length and extended out from the shore/water
interface approximately 25 meters. This near shore area is utilized in order to stay in the shallower areas
where the sampling gear is more effective. The effective depth of the gear is 3-5 meters. Each zone is
fished for 2000 - 3000 seconds, depending upon the structure of the habitat being fished. The stunned fish
are netted, placed in an aerated holding tank, weighed, measured and returned safely to the water.
Samples are collected from July 1 through October 31 in order to maximize sampling efficiency by
collecting only during the stable flow conditions characteristic of the summer and early fall months. To
date, 340 collections have been made riverwide, at an average of 100 per year.
Macroinvertebrates
Macroinvertebrates include organisms such as crayfish, snails, clams, aquatic worms as well as the
larval and some adult forms of several insect orders. They form relatively immobile communities, are
easily sampled in large numbers and are quick to react to environmental change. They represent the
middle of the aquatic food web and are a major food source for many types of fish.
Macroinvertebrate species composition and community structure are very reflective of the
environmental conditions experienced throughout the life span of the organisms. A well balanced
macroinvertebrate community usually exists in areas of high water quality and suitable habitat and
polluted conditions bring about noticeable shifts within the community structure. Pollution tolerant
organisms gradually replace sensitive ones until under the most toxic conditions, all species of
macroinvertebrates are eradicated from the area.
Collection Methods
Macroinvertebrates are collected using a modified Hester-Dendy (H-D) multi-plate artificial substrate
quantitative sampling device. The device consists of eight 7.62 centimeter square, 31.75 millimeter thick
masonite hardboard with five 31.75 millimeter spaces, two 63 millimeter spaces and one 95.25 millimeter
space. The plates are then drilled and placed on a 63 millimeter eyebolt.
The H-D units are assembled into a cluster of five, attached to a cement block which acts as an
anchor, and are submerged into the near-shore area in approximately one meter of water. The units are
left in place for six to eight weeks to allow the macroinvertebrates to colonize the sampling unit, and at
the end of that time, are gently removed from the water and preserved for analysis. The cluster unit
provides a 0.45 square meter quantitative sample, which is used to represent the macroinvertebrate
community of the area in question.
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Other Components of Biocriteria
Algae, aquatic macrophytes and mussels are also being considered as components of biological
criteria for the Ohio River. Although, at this time all research efforts are being focused on the first two
components. It is planned that as the development of one component nears completion, the development
of the next will begin. In 1998 as the fish index nears completion, more and more resources will be
focused towards the second component, macroinvertebrates.
Modifying Current Technologies for use on the Ohio River
Current technologies available for the assessment of aquatic biota have not been modified for use on
the Ohio River. In fact, great river water resource biological integrity has only been recently recognized
(Simon and Lyons, 1995). Useful modifications of the IBI (Simon and Emery, 1995) and the Mlwb
(ORSANCO, 1992) for application to the Ohio River to assess stream resource quality have been
suggested. The Ohio EPA modified the Iwb, making it more sensitive to a wider array of environmental
disturbances, particularly those which result in shifts in composition without large reductions in species
richness, numbers, and / or biomass. The Modified Index of Well Being (Mlwb) retains the same
computational formula as the conventional Iwb, but any of the 13 highly tolerant species are eliminated
from the numbers and biomass components (ORSANCO, 1992). The modification prevents high Mlwb
scores from degraded sites with high numbers of pollution tolerant fish.
Suggested modifications to the IBI include selecting individual metric components sensitive to
anthropogenic changes associated with impoundment, channelization, dredging, siltation and industrial
and municipal dischargers. Biological reference condition expectations need to be developed within a
regional framework and metric performance tested on an individual impoundment basis (Simon and
Emery, 1995)
Throughout ORSANCO's biocriteria development process, researchers have revealed numerous
trends in the biological community which must be understood and interpreted in order to fully assess the
condition of the biota.
Riverwide Trends
Longitudinal patterns of the biota of the Ohio River (Figure 2) may be inconsistent with expectations
based upon Omernik's ecoregional boundaries (Simon and Emery, 1995). Macro-scale biogeographic
boundaries for the Ohio River will be established and incorporated into the biocriteria development
process. This will enable the formation of reference condition expectations within a regional framework.
Within Pool Trends
The navigational dams create varying hydrologic conditions throughout the pool. A rapid flowing
riverine condition exists at the headwaters of each pool, a transitional area exists in the middle area and
the lower area is more lacustrine. These varying hydrologic conditions cause a subsequent biological
gradient to exist. Stanford et. Al. (1988) indicated that hydrologic equilibrium is attained below dams as
energy is balanced along the river continuum. Certain species show a marked affinity for the riverene
areas (Figure 3), while others are more common in the lacustrine areas of the lower pool.
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Temporal Trends
Decades of improvements to the nations surface waters have provided a much more suitable
environment for the aquatic inhabitants. Although water quality following the Clean Water Act has
improved, the biological responses associated with these improvements is relatively undocumented or has
shown a decline for large river biological communities (Karr 1992). Figure 4 shows a clear biological
response to improving water quality. These responses must be revealed and closely monitored in order to
most properly assess the aquatic resource condition of the system.
Visions for Applying Biocriteria on the Ohio River
The riverwide application of biocriteria will allow the characterization and comparison of the water
resource quality of the Ohio River and establish biological goals, similar to water quality goals. It is
expected that permits for discharges will be limited to biocriteria in such a manner as to allow for permit
limitations to be adjusted, or retained based on downstream attainment or comparison upstream versus
downstream. In addition, use of biocriteria can identify inadequacies in chemical criteria and vice versa.
For example, continuing violations of chemical criteria established to protect aquatic life may be recorded
while criteria are being achieved. Such situations warrant investigation of the criteria comparatively.
Finally, use of biocriteria will do much to answer the question of the extent to which the waters are
meeting "fishable" goals of the Clean Water Act.
References Cited
Angermeir, P. L. and J. R. Karr. 1984. Relationships between woody debris and fish habitat in a small
warmwater stream. Trans. Am. Fish.Soc. 113: 716-726.
Karr, J. R. 1991. Biological integrity: a long neglected aspect of water resource management. Ecol. App.
1: 66-84.
Ohio EPA. 1987. Biological criteria for the protection of aquatic life: Vol II. Users manual for biological
field assessment of Ohio surface waters. Ohio EPA, Div Water Quality Monitoring and Assess.,
Surface Water Sect., Columbus, OH.
Omernik, J. M. 1987. Ecoregions of the conterminous United States. Ann. Assoc. Am. Geogr. 77: 118-
125.
ORSANCO (Ohio River Valley Water Sanitation Commission). 1992. Assessment of ORSANCO fish
population data using the Modified Index of Well Being (Mlwb). Ohio River Valley Water Sanitation
Commission, Cincinnati, Ohio.
ORSANCO (Ohio River Valley Water Sanitation Commission). 1996. Standard Operating Procedures
for Biological Collections. Ohio River Valley Water Sanitation Commission, Cincinnati, Ohio.
Sanders, R. E. 1992b. Day versus night electrofishing catches from near-shore waters of the Ohio and
Muskingum rivers. Ohioj. Sci. 92(3): 51-59.
Simon, T. P. and E. B. Emery. 1995. Modification and assessment of an index of biotic integrity to
quantify water resource quality in great rivers. Regulated Rivers: Research and Management 11: 283-
298.
Simon, T. P. and J. Lyons. 1995. Using fish community attributes for evaluating water resource integrity
in freshwater ecosystems, in Davis, W. S. and T. P. Simon, (Eds), Biological Assessment and
Criteria: Tools for water Resource Planning and Decision Making. Lewis, Ann Arbor, pp. 243-260.
Stanford, J. A., F. A. Hauer, and J. V. Ward. 1988. Serial discontinuity in a large river system. Verh.
Int. Verein. Theoret. Angew. Limnol23: 114-118.
Ward, J. V. and J. A. Stanford. 1995. Ecological connectivity in alluvial river ecosystems and its
disruption by flow regulation. Regulated Rivers: Research and Management 11: 105-119.
III-415
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NY
Figure 1. Map of the United States and corresponding enlarged view
of Ohio River and its tributaries.
m-416
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% Round-bodied Sucker Biomass
900
1000
Figure 2. A single metric component of the proposed biocriteria fish
index showing uneven distribution on a riverwide scale.
Relative Number of Round-bodied Suckers
Hannibal Pool
River Mils
Figure 3. Plot displaying single metric component of the proposed biocriteria fish index
showing uneven distribution within the Hannibal Pool of the Ohio River.
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Uniontown Lock and Dam
12
11
10
9
8
1970 1975 1980
Year
1985
1990
Figure 4. Plot displaying improving biological community over time at a single
location of the Ohio River.
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Rapid Bioassessment of Benthic Macroinvertebrates Illustrates Water Quality in
Small Order Urban Streams in a North Carolina Piedmont City
Kimberly Yandora
City of Greensboro, Storm Water Services, 401 Patton Avenue, Greensboro, NC 27406
Abstract
Rapid bioassessment of macroinvertebrates was conducted at thirty-one sites within the urban
watershed of Greensboro, North Carolina during the summer of 1997. Assessment at each site included
physicochemical parameters, habitat score, and the following indices: total taxa richness, North Carolina
biotic index value (NCBI), EPT (Ephemeroptera, Plecoptera, and Trichoptera) abundance, EPT richness,
ratio of EPT and Chironomidae, percent Tubificidae, and percent dominant species. Sites up-stream of
urban activity showed high diversity and richness of aquatic communities and overall good water quality.
Poor to fair water quality ratings were seen downstream of urban activity. However, the condition of
biotic communities was directly related to habitat and water chemistry. Habitat is degraded in urban areas
due to dredging, channelization, and impaired riparian buffer zones that contribute to poor species
diversity. The results of the bioassessment monitoring program lead us to the conclusion that physical and
chemical data of storm events and baseflow stream conditions cannot fully assess the effects of
urbanization on small order streams.
We have been monitoring storm water runoff for four years and ambient in-stream conditions for two
years to establish water quality history. Storm water runoff from developed land showed elevated levels
of pollution whereas ambient in-stream conditions showed much lower levels. Using benthic macro-
invertebrates as indicators of localized conditions aids interpreting water quality data because they lead
stationary lives and respond quickly to stress from storm events and illegal dumpings. This study
illustrates the importance of biological data in conjunction with physicochemical data to assess water
quality and to characterize impacts from urban runoff.
Introduction
Greensboro, North Carolina is located at the headwaters of the Cape Fear River Basin in the Piedmont
ecoregion and has 509 linear miles of streams (NCDEHNR 1995). Greensboro is a rapidly growing city
with a population of over 200,000 and an area of 109 square miles. The City of Greensboro was issued a
National Pollutant Discharge Elimination System (NPDES) Permit in 1994. This permit requires the
monitoring of storm water runoff within the city to characterize pollutant loading from different land uses
and to estimate annual pollutant loading. In addition, the permit focuses on the elimination of non-point
source pollutants through identification of every outfall in the city with a focus on industrial areas. The
City of Greensboro also conducted monitoring of ambient in-stream conditions to establish baseline water
quality and conducted acute toxicity testing on storm water. However, the effects of spills, illegal
discharges and other episodic events cannot be qualified or quantified by any of these methods.
Data on runoff from storm events indicated high levels of pollutants entering into the small streams of
Greensboro, but baseline in-stream data showed relatively low amounts of chemical pollutants. It was
determined that benthic macroinvertebrate would be good indicators of localized conditions. In addition,
many sites throughout the city could be studied with relative ease. Macroinvertebrates integrate the short-
term environmental variations because they respond quickly to stress from spills and storm events and
lead stationary lives. Macroinvertebrates, rather than fish, were selected because many of the first and
second order streams in Greensboro might not be able to support fish assemblages whereas invertebrates
are present in most streams (EPA 1996).
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Materials and Methods
Study Area and Site Selection
We sampled benthic macroinvertebrates at 31 sites within and draining into the watershed of the City
of Greensboro. North Buffalo Creek and South Buffalo Creek are located in the heart of the oldest and
most urbanized areas of the city with the highest amount of development and impervious area. Streams in
these sections often have been channelized and dredged in the past. South Buffalo Creek has the most
recent construction activity and as a result more sediment loading. Reedy Fork Creek is located in the
north reaches of the city limits and drains primarily undeveloped or agricultural areas. A series of five
reservoirs have been built on this stream to provide drinking water to Greensboro. In the last ten years,
urban sprawl has started spreading into these basins with increased residential, commercial and industrial
activities. In addition, the East Fork Deep River and Bull Run are within Greensboro City limits and
discharge to the City of High Point's water supply.
Emphasis was placed on the major tributaries supplying Greensboro's and High Point's drinking
water supplies. Efforts were made to spatially distribute sites over the entire area within the city limits of
Greensboro. Macroinvertebrate samples were collected on the North and South Buffalo Creeks at sites
that are monitored for storm water runoff and in-stream baseflow conditions. In addition, tributaries were
selected to provide basinwide coverage. Very little monitoring had been conducted on the Reedy Fork
Creek. All the main tributaries to the reservoirs and downstream of the reservoirs were sampled. In
addition, one site on East Fork Deep River and Bull Run were sampled to estimate the condition of these
streams as they leave the city limits of Greensboro.
Three sites were selected as reference sites in relatively undeveloped locations: Bryan Park,
Battleground, and McKnight Mill. Each is a first, second, and third order stream, respectively. These sites
are important since replicate sampling was not conducted and will be used for comparison.
Invertebrate Sampling
Benthic sampling followed a modified version of the EPA Rapid Bioassessment Protocol n using
single habitat approach with 1-meter kick net with 500-|0,m mesh openings (EPA 1996). A 100-m reach
representative of the stream was selected. All samples were collected from the riffle zones of streams in
areas where there was the best canopy coverage and side bank vegetation to portray the best overall
sample results. Whenever possible, the site was at least 100 meters upstream of roads or bridge crossings
and had no major tributaries discharging to the site. Two or three kicks were sampled at various velocities
within in the stream reach. Large rocks and logs in the area where dislodged and washed off within the
net. From the net, the sample was placed into a 500-|am opening sieve bucket where leaves, twigs and
other large debris were washed off and discarded. The remaining debris and sample was placed in plastic
containers and preserved in 90% ethanol. All organisms were sorted from debris in the laboratory and
then 100-organism sub-sample was randomly selected from a standardized grid. The one-hundred
organism sub-sample was properly labeled and preserved in glass containers in 90 % ethanol. Three of the
sites were samples twice to provide quality assurance.
Identification
Sorted samples were sent to a qualified contracted laboratory where organisms were identified to the
lowest practical taxon, usually species. Specimens too immature or damaged to identify below the level of
genus were reported to the lowest known level. Identifications were checked by having 10% of the
samples randomly selected and identified by another biologist. Tolerance values and functional feeding
groups were reported. Hilsenhoff tolerance values were used when North Carolina's tolerance values
III-420
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were not available. North Carolina's tolerance values range from 0 for organisms very intolerant of
organic wastes to 10 for organisms very tolerant to organic wastes.
Metrics
The following statistics were calculated for each site: total taxa richness, North Carolina biotic index
value (NCBI), EPT Abundance, EPT Richness, Ratio of EPT and Chironomidae, Percent Chironomidae,
Percent Tubificidae, and Percent dominant species (EPA 1996, NCDEHNR 1997, MCDEP 1996).
Richness
Taxa richness is the simplest measure of diversity. The total number of species collected in the 100-
organism subsample was recorded to measure taxa richness. Taxa richness decreases with a decrease in
water quality as the less tolerant species are eliminated. Bioclassification criteria developed by North
Carolina Department of Environment, Health, and Natural Resources (NCDEHNR) for the North
Carolina Piedmont for the standard qualitative sampling method is listed in Table 1. This bioclassification
criterion is based on values from summer collection (June - September). These ratings reflect effects of
chemical pollution but poorly assess the effects of sediment pollution.
Biotic Index Criteria
NCDEHNR developed the NCBI which accounts for differences in stream size, seasonal variations
and ecoregions to complement taxa richness (NCDEHNR 1997, Lenat 1993). The NCBI is intended to
examine the general level of pollution, regardless of source.
The NCBI is derived using the following formula:
NCBI =
TotalN
where TV; is the tolerance value of the ith taxa, Nj is the abundance of the ith taxa (1,3 or 10) and N is the
sum of the abundance values. The abundance information for each taxon is tabulated at either RARE (1-2
specimens), COMMON (3-9 specimens) or ABUNDANT (> 10 specimens) and given the value of 1, 3,
or 10 respectively. The bioclassification criteria developed by NCDEHNR for the NCBI (after seasonal
corrections) for the North Carolina Piedmont are listed in Table 2 (Lenat 1993).
Ratio of EPT and Chironomidae Abundance
Good biotic conditions would be reflected in communities with an even distribution among all four
major groups. Skewed populations having a disproportionate number of Chironomidae relative to the
more sensitive organisms (Ephemeroptera, Plecoptera, and Trichoptera) indicate environmental stress
(EPA 1989).
Percent Chironomidae
The percentage of the family Chironomidae in the sample represents whether a stream is oligotrophic
or eutrophic. A sample in which greater than 50% is Chironomidae suggests eutrophic conditions. Some
species of Chironomidae are also tolerant to heavy metals. Percentage of Chironomidae will increase with
a decrease in water quality.
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Percent Tubificidae
An abnormally high percentage of Tubificidae accompanied by abnormally low values for percent
Chironomidae indicates toxicity form urban runoff or insecticides that are toxic to arthropods. High
tubified percentages accompanied by large Chironomidae populations indicate a serious organic problem.
Percent Contribution of Dominant Species
A community dominated by relatively few species would indicate environmental stress. Dominant
species greater than 35% indicates poor water quality, between 23%-35% indicates fair water quality and
less than 25% indicates good water quality (EPA 1996).
Physical, Chemical, and Habitat Sampling
Water samples were collected in plastic containers from locations at the middle of stream prior to
macroinvertebrate sampling. Samples were preserved on ice and analyzed within 24 hours for nitrate-
nitrogen and reactive phosphorus using the Hach Portable DR2000 Spectrophotometer. Turbidity,
conductivity, pH, temperature and dissolved oxygen where measured at each site. Upon completion of
sampling, a habitat assessment of each site was conducted using a format developed by EPA Rapid
Bioassessment Protocol (EPA 1996). A numerical habitat score was calculated for each site. Habitat
assessments were summed to obtain overall habitat score: optimal (260-201), sub-optimal (200-136),
marginal (135-71), and poor (<70). Stream order for each site was determined using USGS topographic
maps.
Results
Baseline and Storm Data
Greensboro, NC annually receives over 38 inches of rain. Input of pollutants from storm water runoff
is frequent and a source of pollutant loading in our streams and waterways. This was evident in the results
from four years of land use storm water quality data that showed heavy metals, fecal bacteria, and solids
were the greatest impacts from urban runoff. These parameters frequently exceeded NCDEHNR action
limits and standards for in-stream concentrations. However, acute toxicity of first flush samples using the
fat head minnow (Pimephales promelas) showed no mortality. To complement this information, two
years ago monthly sampling of ambient stream conditions was started to determine baseflow conditions.
These samples were taken on the second Tuesday of each month to reflect any weather conditions but
most likely reflected dry weather conditions in the stream. Fecal coliform levels continued to be elevated
during baseflow conditions. Aluminum and iron, which were not tested in storm water runoff, were at
levels above the NCDEHNR's criteria, but were attributed to local soil types. The heavy metals, copper,
lead, and zinc, which were prevalent in storm runoff, were much lower in ambient conditions. It is
hypothesized that these particles quickly settle out of the water column into the sediment. As expected,
solids were much lower in ambient stream conditions as was biochemical and chemical oxygen demand
(BOD and COD, respectively).
Biological and Habitat Data
Selected results and the associated metrics are listed in Table 3. The purpose of biological sampling
was to sample citywide and to help determine the areas where more in-depth monitoring could be
conducted or sites where future development may adversely affect stream conditions.
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Physicochemical Data
At each site where macroinvertebrates were sampled, nitrate-nitrogen and reactive phosphorus, pH,
conductivity, turbidity, dissolved oxygen, and temperature was taken. Results were consistent with
ambient in-stream data. Nutrient levels were found at very low concentrations. Phosphorus ranged
between 0.0 mg/1 to 0.75 mg/1 and nitrate ranged from 0.2 mg/1 and 1.5 mg/1. Temperatures were slightly
elevated at sites with little canopy cover in comparison with site that had vegetative cover. Dissolved
oxygen (DO) was not below 4 mg/1 at any the sites. The lowest DO was 4.99 mg/1 and as high as 9.73
mg/1. Turbidity was below 25 NTU at all sites except three sites that ranged between 90 to 102 NTU.
These sites are associated with construction activities. Conductivity generally ranged from 64 (imhos/cm
to 506 |imhos/cm and one site with 996 (O.mhos/cm. Sites with high conductivity were observed to be
associated with industrial activities.
Discussion
Inadequacy of Chemical Data
The purpose of the storm water monitoring program is to assess the overall health of the streams in
Greensboro. NPDES permit requires monitoring of storm water runoff for the purpose of land use
characterization and pollutant load estimates. However, this does not adequately characterize the health or
condition of city streams. It does not take into account stream habitat or biological communities. This
monitoring provides instantaneous chemical and physical data but does not indicate long term or
continuous effects. Biological communities are directly affected by these parameters in addition to
upstream and downstream activities such as piping of streams, dams, impoundments, construction
activities, and stream crossings.
Role of Habitat
Great variation in habitat was seen throughout the city. For this study, the best overall habitat area
was sampled in the different stream reaches. Habitat scores were lower at sites that received good-fair and
fair biotic ratings in comparison to sites rated good and excellent. Some sites had good riparian buffers
and canopy cover but lacked adequate substrate and bank stability. Historic practice in the urban setting
was to channelize and dredge city streams to convey the water as quickly as possible out of the city to
minimize flooding. In addition, riparian zones were maintained mowed lawns and bank vegetation was
scarce. These practices have led to the destruction of biological communities. Current City policy is to
restore vegetative riparian zones and to stop dredging stream channels. Unfortunately, we are still left
with the damage from the past. Now the latest problem seems to be the result of construction and
development activities that continues to increase sediment and flow to the streams.
Evaluation of the Metrics
Endless number of metrics and indices exist to analyze macroinvertebrate samples (Resh et al 1995,
Thorne and Willliams 1997, Washington 1984). We have chosen to follow the procedures outlined by
NCDEHNR (1995). In particular, bioclassifications have been based on the NCBI to support water quality
assessment. Other metrics were used to help interpret the overall quality of the site. Of all the metrics
calculated, the most useful were NCBI, taxa richness, EPT abundance, and percent Chironomidae.
EPT Richness showed very little difference between individual site with NCBI ratings good, good-
fair, and fair. However, impaired and poor sites had EPT richness and abundance values of 1 and 0
III-423
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indicating absence of mayflies, stoneflies and caddisflies. However, this absence was already noted in the
NCBI and taxa richness. Therefore, we would not recommend using these metrics alone.
Tubificidae populations were rare, only being found at seven sites comprising less than 5% of the
community. Since Tubificidae were not present at many of the sites this metric is not useful. Similarly,
percent dominant species and ratio of EPT to Chironomidae did not produce distinct results. The greatest
tubificidae population was 26% of the community at Caesar, which had poor ratings from all metrics.
These results indicated the site suffers from sever organic pollution and eutrophic conditions.
Water Quality and Macroinvertebrates
South Buffalo Creek exemplifies the degradation of macroinvertebrate communities along the stream
continuum. Big Tree is a second order stream location on South Buffalo Creek with an excellent NCBI
rating influenced by residential land use. Its habitat score was rated sub-optimal with no channel
alterations. Boston Road is slightly downstream where South Buffalo Creek is a third order stream. This
site had a NCBI rating of good even though it is located downstream of two heavy construction areas with
high amount of sediment loading. Hillsdale is further downstream on South Buffalo Creek, which
receives runoff from an older commercial area with shopping mall, restaurants and office parks. The
stream channel has been dredged and the buffer zone and banks are mowed regularly. The NCBI value at
the Hillsdale site was only rated fair. Slight improvement was seen downstream at the Trestle site where
the NCBI rating was good-fair. Most likely the reason the NCBI rating was improved was better habitat
conditions and changes in land use. However further downstream on South Buffalo Creek at the
McConnell site, the location was rated fair with impaired substrate and quality even though the
surrounding land use was not developed. This site suffers from the affects of upstream urban activities.
Sites along North Buffalo Creek and its tributaries also showed degradation along the stream
continuum. The Arboretum site is the farthest site upstream on the main channel. This site location is a
third order stream where the NCBI rating was good-fair with habitat rating of sub-optimal. The stream
degraded slightly at Lake Daniel where the NCBI rating was only fair and the habitat score was reduced.
Surprisingly, the NCBI rating upstream of the City's wastewater treatment plant (WWTP) was rated
good-fair. Previous investigation by the NCDEHNR reported a poor NCBI rating. Water quality data
showed dissolved oxygen of 4.99 mg/1 and conductivity of 996 |J.mhos/cm. Two tributaries draining into
North Buffalo Creek at Caesar Park and White Street indicated poor NCBI ratings. Both are affected by
industrial as well as commercial runoff.
Sites located in the various tributaries to the water supply lakes were rated excellent, good and good-
fair by NCBI. EPT species were well represented and Chironomidae percentages were low at all sampling
locations. Development was restricted in these areas requiring best management practices on new
development. However, development has steadily increased. This preliminary data on benthic
communities serves as a baseline for change as development continues and the watershed changes.
Sediment loading occurs from construction activities especially during storm events. Benthic
macroinvertebrates are able to withstand short-term increases in suspended sediments; however,
continuous high levels of sediment may have adverse effects. The sediment effects macroin vertebrates by
changing substrate, causing respiration difficulties, lowering oxygen concentrations and reducing food
value. Chironomidae may increase because they use fine sediments in the construction of cases and tubes
(Wood and Armitage 1997). Therefore, higher percentage of Chironomidae would be expected in South
Buffalo downstream of construction activities. However, Boston, Hillsdale, and Trestle, which had high
turbidity, had relatively low Chironomidae percentages. Sediment was evident at most sites even in the
upper reaches of the water supply watershed that had almost no cobble substrate. Sediment in these areas
was from some construction and bank erosion. The effect of sediment and erosion of biota still needs to
be studied.
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Conclusions
It is not surprising that urbanization causes degradation of water quality, habitat and biotic
communities in streams. However, we have concluded that historic water chemistry monitoring does not
provide enough information to assess completely the condition of aquatic ecosystems. The rapid
bioassessment protocol indicated if water quality, substrate, riparian buffer, or channel alterations have
impacted the site. Sites thought to be severely degraded from the appearance and perceived water quality
actually indicated good-fair biotic communities. Conversely, sites in the water supply watershed of the
City thought to be relatively pristine having good water quality showed lower water quality, habitat and
biotic community diversity than expected. Macroinvertebrates are an important monitoring tool to
measure continuous and chronic effects from pollution, stream degradation from storm water runoff and
point source discharges, and indicators of stream recovery. Data on invertebrate communities in
conjunction with habitat and water chemistry data will provide the necessary tools for monitoring impacts
to streams and other aquatic systems.
References
Environmental Protection Agency. 1996. Revision to Rapid bioassessment protocols for use in streams
and rivers: periphyton, benthic macroinvertebrate, and fish. Assessment and Watershed Protection
Division, Washington, D.C. EPA/444/4-89-001.
Lenat, D.R. 1988. Water quality assessment of streams using a qualitative collection method for benthic
macroinvertebrates. Journal of North American Benthological Society. 12:222-233.
Lenat, D.R. 1993. A biotic index for the southeastern United States: Derivation and list of tolerance
values, with criteria for assigning water-quality ratings. Journal of North American Benthological
Society. 12:279-290.
Mecklenburg County Department of Environmental Protection. 1996. Mecklenburg County stream
bioassessment operating procedures.
NCDEHNR. 1995. Basinwide assessment report support document Cape Fear River Basin. Division of
Environmental Management.
NCDEHNR. 1997. Standard operating procedures biological monitoring. Division of Water Quality.
Resh, V.H. R.H. Norris, and M.T. Barbour. 1995 Design and implementation of rapid assessment
approaches for resources monitoring using benthic macroinvertbrates. Australian Journal of Ecology
220:108-121.
Thorne, R.S. and W.P. Williams. 1997. The response of benthic macroinvertebrates to pollution in
developing countries: a multimetric system of bioassessment. Freshwater Biology 37:671-686.
Washington, H.G. 1984. Diversity, biotic and similarity indices: a review with special relevance to
aquatic ecosystems. Water Resources 18(6):653-694.
Wood, P.J. and P.D. Armitage. 1997. Biological effects of fine sediment in lotic environment.
Environmental Management 21(2):203-217.
III-425
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Table 1. Bioclassification Criteria for Taxa Richness Values for the
North Carolina Piedmont for Standard Qualitative Sampling Methods
(Lenat 1988, NCDEHNR 1997)
>|Siff|tf|B
Excellent
Good
Good-Fair
Fair
Poor
>31
24-31
16-23
8-15
0-7
Table 2. Bioclassification Criteria for North Carolina Biotic Index for the
North Carolina Piedmont (NCDEHNR 1995)
lililSfMi^
Excellent
Good
Good - Fair
Fair
Poor
<5.19
5.19-5.78
5.79 - 6.48
6.49 - 7.49
>7.48
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Table 3: Selected Results of Biological Monitoring and Habitat Assessment
Site
Norffi Buffalo Creek
Willoughby
Caesar Park
Benjamin
Arboretum
White Street
*McKnight Mill
Lake Daniel
WWTP
South Buffalo Creek
Randleman
McColluck
Florida
Big Tree
Meadowview
Cypress Park
Gillespie
Boston
Hillsdale
RR Trestle
McConnell
Watef Sup'f ly .Wafers
King Edward
*Battleground
Church St.
*Bryan Park
Chimney Rock
Cotswald
Quaker Run
Bunch Road
Cardinal CC
1-29
Addams Farm
Piedmont Pkwy
Taxa
Richness
\ ^*
45
50
60
72
30
44
52
54
35
42
38
36
43
56
40
57
41
37
33
"" •'•'•&, ' >::l
65
62
60
62
54
62
50
31
37
54
44
50
EPT
Abundance
N<* * V
23
0
33
33
6
16
0
23
16
16
16
25
21
26
15
26
16
27
1
.-.'>'•'"
,$: !:v ••
20
16
18
13
22
13
18
1
13
23
25
23
NCBI
5.46
7.73
6.01
6.16
7.67
5.44
6.84
6.46
5.96
6.48
6.72
4.36
5.82
5.77
6.83
5.78
7.30
5.90
7.43
' " ' ','':'
5.81
5.24
5.00
5.88
6.29
5.73
5.5
6.3
6.46
5.54
5.54
6.08
NCBI
Classifi-
cation
Good
Poor
G-F
G-F
Poor
Good
Fair
G-F
G-F
G-F
Fair
Excellent
G-F
Good
Fair
Good
Fair
G-F
Fair
G-F
Good
Excellent
G-F
G-F
Good
Good
G-F
G-F
Fair
Good
G-F
EPT:C
16.33
0
8
2.3
0.16
2.5
0
6.24
5.53
3.2
3.16
19
1.97
11
0.54
3.56
1.73
6.23
0.01
,f/ -^
1.05
1.95
1.05
2.32
3.05
3.22
10.6
0.09
1
1.05
0
5.75
%
Chironomidae
3.33
61.8
8.94
18.18
80
12.8
76.74
11.81
"* . ,v,:;"*'<0
13.48
15
19
4.46
30.36
6.14
57.14
16.22
28.57
13.27
72.16
33.04
18.27
34.55
19.53
17.54
7.44
4.55
25.58
37.93
26.87
0
12.21
Stream
Order
, *>, ">
1
1
2
3
3
3
4
4
> - .' -.
1
1
1
2
2
2
2
3
3
4
4
1
1
2
2
2
3
3
3
3
4
3
2
Habitat
Score
178
162
191
170
151
183
144
180
140
157
144
181
121
141
109
160
155
181
138
168
177
152
170
137
130
120
119
110
170
198
193
*Reference Sites
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III-428
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Bioassessments in Arizona: What is Different about Biomonitoring
in Southwestern Streams?
Patti Spindler, Aquatic Ecologist
Arizona Department of Environmental Quality
There have been a number of issues to resolve in customizing bioassessments for Arizona. These are
issues of interest because they may be different than those of other states. Since 1992, biologists at the
Arizona Department of Environmental Quality (ADEQ) have been grappling with the problem of
developing the reference condition for macroinvertebrates and algae to be applied statewide (Spindler
1996). This is a difficult task in Arizona and other states having such varied topography and a diverse
mixture of stream types. There are mountain and desert stream types to differentiate and a range of flow
conditions, from perennial to ephemeral to effluent-dominated, to characterize. Slides shown during the
presentation are examples of Arizona's heterogeneous landscapes and streams and the mixture of
macroinvertebrates associated with them. In order to classify similar reference macroinvertebrate
communities in low order perennial streams, ecoregions (Omernik 1987) were tested to determine their
applicability to aquatic ecosystems in Arizona.
Approaches for classifying streams have been studied by numerous researchers. The paradigm most
commonly used in bioassessment work is the ecoregion approach which classifies landscapes using
multiple parameters including soils, land use, landform and vegetation (Figure 1; Omernik 1987).
Ecoregions work well for classifying streams in states where landscapes are relatively homogenous
(Hughes and Larsen 1988). Arizona geography is not homogeneous. For example, there are "sky-island"
mountains (Heald 1951) in desert areas in which all the life zones from desert to spruce-fir forest may lie
in close proximity to one another as you travel from the base to the top of these mountains along an
elevation gradient. Multivariate analyses and box plots were used to test ecoregions as a classifying tool
for Arizona macroinvertebrates. Data were collected from 68 reference sites statewide over a three year
period, 1992-4 (Meyerhoff and Spindler 1994). Multivariate analyses of these data showed that macro-
invertebrate communities do not group well according to ecoregions (Figure 2). Rather the community
pattern was characterized by two large clusters of sites generally explained by elevation on the x-axis and
stream size and type on the y-axis (Figure 3). This pattern of two macroinvertebrate groups when mapped
represents coldwater and warmwater stream communities at an apparent elevation cutoff of 5000' (Figure
4). Metric box plots also show how well this 2-group paradigm differentiates macroinvertebrate com-
munities compared to the ecoregion paradigm (Figure 5). As a result of these analyses on 1992-4 data-
sets, the coldwater/warmwater macroinvertebrate zones have been selected as the regional reference
condition paradigm for Arizona bioassessments.
Preparing for water quality standards development has presented another set of issues to resolve. The
issues include standardizing our sampling protocols and determining timing and what type of biocriterion
to place into the water quality standards. Sampling protocol issues include:
1. Seasonal index period; spring or fall,
2. Ability to detect impacts with family versus genus level taxonomy,
3. Riffle versus pool habitat, and
4. Analysis technique; multimetric or multivariate.
It would be preferable to allow sampling to occur in 2 index periods, spring and fall, if a metric scoring
system will produce similar results. If family-level taxonomy produces bioassessments similar enough to
genus-level taxonomy, then screening-level taxonomy and assessments could be performed by ADEQ
staff rather than having taxonomy for all assessments being performed by our professional taxonomist on
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contract. Choice of habitat (riffle or pool) is an important question because many desert streams dry
down to pool-only habitat after winter flows. Is pool data needed for all streams and does it differentiate
well between reference and non-reference sites? Both multimetric and multivariate techniques have been
published in the literature as reliable assessment methods (Reynoldson et al 1997). Both methods are
being examined for use in Arizona. We have not made final decisions on these issues, but are currently
investigating them.
ADEQ is in the process of beginning another triennial review of water quality standards and may
address the inclusion of biocriteria in some way. We are not ready to propose a narrative biocriterion in
this triennial review. However, we may include language which will allow use of the reference com-
munity metrics in support of other narrative criteria. In addition, the macroinvertebrate classification
scheme will be used to adjust aquatic & wildlife warmwater and coldwater designated uses on streams
named in the water quality standards.
There are many applications of bioassessments which are possible in Arizona. For instance one
project, a grazing exclosure study in an alpine meadow stream, examines the effectiveness of this "best
management practice". Another project compares and contrasts the results of bioassessments with
physical integrity assessments and site specific bioassessments are being done by two mining companies
in Arizona as a result of new NPDES permit requirements.
Watershed bioassessments will be prepared and reported in the 305b Water Quality Assessment
Report for Arizona. The Verde River watershed is our first watershed bioassessment and is providing a
dataset of 15 reference sites and 35 study sites for metric development needed to define attainment
classes. A series of Arcview maps were created for a sub-watershed of the Verde as an example of how
GIS can aid in communicating about watershed bioassessments. This "example bioassessment" is not
based on tested metrics and is not considered as a final result; it is only a sample of how GIS can be used
to support and display bioassessments.
For consistent bioassessments collaboration among biologists in different agencies within each state
is needed in order to agree upon regional reference conditions. Because we share ecoregions and similar
habitats, southwestern states can share reference site databases of macroinvertebrate and associated
information. We are already assisting staff from other local agencies (Bureau of Land Management) to
begin biomonitoring consistent with ADEQ methods. Watershed-wide bioassessments are being
incorporated into annual monitoring plans in Arizona and will become an important assessment tool in
years to come.
Literature Cited
Heald, W.F., 1951. Sky islands of Arizona. Natural History 60:56-63, 95-96.
Hughes, R.M. and D.P. Larsen, 1988. Regional reference sites: a method for assessing stream potentials.
Environmental Management 10(5):629-635.
Meyerhoff, R.D. and P.H. Spindler, 1994. Biological sampling protocols: reference site selection and
sampling methods. Arizona Department of Environmental Quality, Phoenix, AZ. 23 pp.
Omernik, J.M. 1987. Ecoregions of the conterminous United States. Annals of the Association of
American Geographers 77(1): 118-125.
Reynoldson, T.B., R.H. Norris, V.H. Resh, K.E. Day, and D.M. Rosenberg. 1997. The reference
condition: a comparison of multimetric and multivariate approaches to assess water-quality
impairment using benthic macroinvertebrates. Journal of the North American Benthological Society
16(4): 833-852.
Spindler, P.H. 1996. Using ecoregions for explaining macroinvertebrate community distribution among
reference sites in Arizona, 1992. Arizona Department of Environmental Quality, Phoenix. OFR 95-7.
41pp.
III-430
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Sampling $<($$ ty macroin vertebrate community typ*
_ AfllZOt^M^EW MEXICO MOUNTAINS
3 Afl IZOMM^EW MEXICO PU>iTEAU
33 COLOR ADO PU^TEAU
SOUTHERN BASIN
^ SOUTHERN DESERTS
Figure 1. Ecoregions and macroinvertebrate sampling sites (1992-4) in Arizona.
m-431
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100
o
o
tf>
(N
U)
'Si
20
AZNMM'
40 60
Axis 1 scores
. A7NIW£N|vM
100
Figure 2. Detrended correspondence analysis plot of macroinvertebrate communities found
at 68 reference sites displaying ecoregion associations.
100
20
40 60
Increasing Elevation
80
100
Figure 3. Detrended correspondence analysis plot of macroinvertebrate communities found
at 68 reference sites displaying warmwater and coldwater macroinvertebrate group
membership in circled areas and important environmental parameters determined by
multiple regression analysis.
m-432
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Macr oinvertetxate sampling sites
• W aim water sites
* Ctfdwater sites
Macroinver tebrate Regions {5000' contour interval)
[Q/V-^;;;'] Wgirnwater community
^M0ol*«/*8r community
Figure 4. Regions of similar macroinvertebrate community types and sampling sites in Arizona, 1992-4.
ffl-433
-------
% Shredders
1 2 V 4
Ecoregion
6
23456
Ecoregion
50
^_
S40-
oj
0)30-
2C-
1C
% Scrapers
4C
Q
^
(D
0123
Macroinvertebrate Region
% Shredders
0123
Macroinvertebrate Region
Figure 5. Comparison of ecoregions and macroinvertebrate regions with metric boxplots using average
macroinvertebrate abundances 1992-4 for 68 reference sites from Arizona streams (Ecoregions:
l=Arizona/New Mexico Mtns, 2=Arizona/New Mexico Plateau, 3=Southern Basin & Range, 4=Southern
Deserts, 5=Colorado Plateau; Macroinvertebrate regions: l=Desert warmwater, 2=Montane coldwater).
m-434
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Development of a Benthic Index of Biotic Integrity for Maryland Streams
James B. Stribling, Jeffrey S. White, Benjamin K. Jessup
Tetra Tech, Inc., 10045 Red Run Boulevard, Suite 110, Owings Mills, MD 21117-6102
Daniel Boward and Martin Hurd
Maryland Department of Natural Resources, Monitoring and Non-Tidal Assessment Division
Tawes State Office Building, 580 Taylor Avenue, C-2, Annapolis, MD 21401
Introduction
The Maryland Biological Stream Survey (MBSS) was established by the state Department of Natural
Resources (MDNR) to provide to the public and natural resource decisionmakers an accounting of the
biological status of streams and watersheds statewide (Kazyak and Jacobson 1994). Part of their impetus
was the Clean Water Act of 1972 and its directive "to protect and restore the chemical, physical, and
biological integrity of our Nation's waters" and the recognition that existing uses of stream assessment
data were not addressing biological integrity. Biological integrity is defined by Frey (1977) and Karr et
al. (1986) as the capacity of an ecosystem to support and maintain a biota that is comparable to that
found in natural conditions. Development of a biological indicator in this framework required objective
definition of reference conditions, and of the measurement characteristics that are used to describe the
biota.
For Maryland, a geographically-broad response to this need required an organized, systematic
sampling and analysis of indicators of stream quality (Index of Biological Integrity [IBI] for fish and
benthic macroinvertebrates) across the state, beginning with streams and watersheds. The MBSS has
proceeded with development of regionally calibrated indicators following four initial years of stream
surveys collecting physical, chemical, biological, and land use data. It has recently completed
development of a provisional indicator of biological condition using fish assemblage data (Roth et al.
1997) from those four years of sampling (1994-97). This report presents the process and results of
developing an IBI from the MBSS's benthic macroinvertebrate database.
Reference Conditions
Reference conditions, as used here, are numerical descriptions of the variability of biological
measurements taken from a composite of multiple reference sites (Gibson et al. 1996, Barbour et al.
1996). Reference sites are generally defined as those sites having minimal exposure to human activities
and are representative of the waterbody type and region of interest (Hughes et al. 1986). More
specifically, stream reference sites have in-channel physical, chemical, riparian condition, and land use
criteria that dictate their inclusion within a reference database. These criteria, which can exclude sites
from consideration as reference, vary by waterbody type and region and can be developed either a priori
or a posteriori (Gibson et al. 1996). The database of reference sites and the analyses performed in
developing and calibrating reference conditions provide an objective, interpretive framework for
determining ecological impairment or nonimpairment of streams. The IBI developed here will be useful
in assessing stream ecological conditions in individual streams, small watersheds, and large river basins
across the State.
Biological Measurements and Their Characteristics
Biota are affected by environmental conditions at multiple levels of organization including genes,
cells, individuals, species, assemblages, communities and populations (Karr 1991). Since different
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stressors can have variable effects on biota, response to changes in environmental conditions can be
reflected at any of these levels, and perhaps simultaneously at multiple levels. Because of this
complexity, it is desirable to use a method of characterizing components of the community or assemblage
that integrates and composites multiple, quantitative descriptors of that assemblage. Karr et al. (1986)
developed the multimetric approach (the Index of Biological Integrity or ffil) that combined a series of
metrics (biological descriptors) to characterize biological condition with fish assemblage data from
streams of the Midwestern U. S. There have been numerous adaptations of the approach using different
groups of organisms and calibrated for different geographic areas and waterbody types (Southerland and
Stribling 1995, Davis et al. 1996, U.S. EPA 1997). The approach has also been endorsed by the
Intergovernmental Task Force on Monitoring Water Quality (ITFM 1995) and the U. S. Environmental
Protection Agency (Gibson et al. 1996) as an appropriate means for assessing biological condition, and is
used by numerous states in water resource management and regulatory programs (Southerland and
Stribling 1995, Davis et al. 1996).
The MBSS sampled nearly 1,100 stream site locations across the state from 1994-1997. The database
for IBI development consisted of fish and benthic macroinvertebrate assemblage, chemical, physical
habitat, and land use data (Roth et al. 1997). Following the decision to use biological condition as the
principal indicator of ecological quality, and development of a fish IBI, the MBSS proceeded with
development of a benthic macroinvertebrate-based IBI and an indicator of physical habitat quality. All of
this research and the resulting indicators are based on the database assembled by the MBSS over four
years of field sampling and analysis. The purpose of this report is to document the analytical process and
the resulting benthic ffil that will be used to assess streams in Maryland.
Previous efforts at developing benthic macroinvertebrate-based multimetric indices in Maryland
(Stribling et al. 1989, 1996, Gerritsen et al. 1995, Van Ness et al. 1997, Maxted et al. 1998) used the
same conceptual framework but somewhat different approaches in field sampling methods, selection of
reference sites, and development of scoring criteria. The dataset used in this project is the largest of all of
these studies. It is statewide, encompassing much of Maryland's geographic range and physiographic
variability, has used consistent sampling and analytical methods, and uses sampling sites selected on a
probability basis. However, the number of stream site locations within this current database that met
reference criteria was fairly small (37) and was not distributed evenly across Maryland's physiographic
regions.
Methods
Reference and degraded sites were previously determined following establishment of criteria for
these two site groups. For this study, appropriate and relevant strata (or site classes) were determined by
examining the geographic variability of the biological data. Candidate metrics were calculated, and their
responsiveness to stressors evaluated, using data from reference and degraded sites. Multiple
combinations of metrics were tested for their efficiency in correctly categorizing known impaired and
nonimpaired sites, and a final index formed. The steps taken for development of a biotic index are as
follows:
Step 1. Developing the Database
Step 2. Identifying Reference and Degraded Sites
Step 3. Determining Appropriate Strata
Step 4. Compiling Candidate Metrics
Step 5. Testing Candidate Metrics
Step 6. Combining Metrics into an Index
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This project also presents the results of confirming the effectiveness of the index using an
independent data set. The index was developed using sampling results from 1994-95; confirmation relied
on 1996-97 results. Using the same data (1994-1995), we also developed a family-level index, allowing
coarse assessments to be performed at a greater cost efficiency. The approach followed in developing this
benthic IBI is identical to that used by the MBSS in development of the IBI using fish assemblage data
(Rothetal. 1997).
Step 1. Developing the Database
Benthic macroinvertebrates were sampled in the spring index period as outlined in the MBSS
Sampling Manual (Kazyak 1995). To summarize, a 600 micron mesh D-net was used to trap organisms
dislodged from approximately 20 square feet of multiple habitat types. Riffles and other productive
habitat types were sampled preferentially when available in the 75m sampling segment. The composited
sample was preserved and subsampled to approximately 100 individual macroinvertebrates. If a sample
was less than 80 organisms, it was not used in metric testing and evaluation. Most organisms were
identified to genus, if possible, using stereoscopes. Chironomidae were slide-mounted and identified
using compound microscopes. A list of taxa and their abundance within each subsample was generated
from laboratory identifications.
The database created for fish IBI development including water chemistry, physical habitat, and land
use (Roth et al. 1997) required no alteration for this research. Full descriptions of the parameters
measured and the data collection methods are not reiterated here.
Step 2. Identifying Reference and Degraded Sites
Reference and degraded sites used for selection and calibration of benthic macroinvertebrate metrics
are the same as those used for the Maryland Fish IBI (Roth et al. 1997). They were designated as
reference or degraded based on chemical and physical criteria that comprise a mixture of laboratory
analytical chemistry, field chemistry, visual-based physical habitat and riparian conditions, and land use
as determined by the MBSS.
Step 3. Determining Appropriate Strata
Detection of anthropogenic stresses on the benthic macroinvertebrate assemblage must occur
independently of inherent differences due to natural factors. Natural variability in community
composition across the state was explored using two analytical techniques: cluster analysis and
nonmetric multi-dimensional scaling (NMDS). Both techniques were used to compare measures of
similarity within and among groups of reference and other, minimally-impaired, sites. Physical and
geographic variables examined included stream order, catchment area, gradient, conductivity, ANC,
DOC, major river basin. Level IV subecoregion, and physiographic region, to determine their
appropriateness as natural strata.
Cluster Anahsis. Cluster analysis is a multivariate process for putting information into meaningful
groups in order to classify sites (van Tongeren 1987). Clusters produced by the taxon-abundance matrix
that align with physical attributes are interpreted as reflecting the natural variability of benthic
macroinvertebrate assemblages within a stratum, and provide supporting evidence for consideration as a
geographic stratum. The Jaccard coefficient (C) was selected to create a dissimilarity matrix. It is one of
the most widely-accepted measures used to examine similarity of pairs of sites in terms of taxa presence
and absence and is expressed as the percentage of ta.\a shared. Cluster analysis was performed on the
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Jaccard matrix using unweighted pair group averaging (UPGMA), from which sites were partitioned into
discrete clusters.
Non Metric Multidimensional Scaling. Ordination consisted of non-metric multidimensional scaling
(NMDS) using the Bray-Curtis index. The Bray-Curtis index or coefficient (also known as percentage
dissimilarity) is commonly used in ecology and was selected for creating the input matrix (Boesch 1977).
NMDS arranges sites along axes so points close together correspond to sites with similar taxonomic
composition, and points farthest apart are most dissimilar. The most widely used technique is based on an
ordination algorithm developed by Kraskal (Jongman et al. 1987). Each dimension explains variation in
the data, with the first explaining the most, continuing with the second in descending amounts of
explained variation. Dimension values are plotted as two- or three-dimensional graphs depending on the
view or perspective of the dimensions that best illustrate site classes or similarity groupings.
Step 4. Compiling and Calculating Candidate Metrics
Candidate metrics for testing and potential inclusion in the IBI, selected primarily from previous or
parallel studies or guidance documents (Barbour et al. 1996, Gibson et al. 1996, U.S. EPA 1997), are
grouped into five categories: richness, composition, tolerance/intolerance, feeding behavior or trophic
structure, and habit tendencies. A total of 57 metrics within these five categories were considered as
potential index components.
Taxonomic Richness. Metrics in this category are counts of the distinct number of taxa within
selected taxonomic groups. "Total taxa" and "EPT taxa" are broadly used metrics that provide
information on overall taxonomic diversity (at specified heirarchies), with the latter based on three insect
orders generally known to be sensitive to disturbance (Ephemeroptera [mayflies], Plecoptera [stoneflies],
and Trichoptera [caddisflies]).
Taxonomic Composition. These metrics are based on the proportion of individuals in a sample
belonging to a specified taxonomic group. Two exceptions are "% Orthocladiinae of Chironomidae" and
"% Tanytarsini of Chironomidae", each of which are the proportion of midges in a sample that are of this
subfamily and tribe.
Tolerance/Intolerance. Tolerance of a tax on is based on its ability to survive short- and long-term
exposure to physicochemical stressors that result from chemical pollution, hydrologic alteration, or
habitat degradation. Following the basic framework established by Hilsenhoff (Hilsenhoff 1982),
tolerance values were assigned to individual taxa on a scale of 0-10, with 0 identifying those taxa with
greatest sensitivity (least tolerance) to stressors, and 10, those taxa with the least sensitivity (most
tolerance) to stressors.
Trophic/Feeding. These metrics are based on mode of feeding, not the specific nutritional source or
benefits and include the following functional feeding groups: scrapers, predators, collectors, filterers and
shredders. Designations for each taxon were taken primarily from Merritt and Cummins (1996) and U. S.
EPA (1990 [draft]). When a taxon was not listed in either of these sources, it was assigned the feeding
type of its most closely-related taxon, an approach in agreement with other researchers (Merritt et al.
1996).
Habit. The habit of an organisms is determined by its locomotion or behavior in relation to their
habitat and substrate (Merritt and Cummins 1996, Merritt et al. 1996). Habit designations of burrower,
climber, clinger, sprawler, or swimmer were assigned to taxa using primarily Merritt and Cummins
(1996) as a reference.
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Step 5. Testing Candidate Metrics
Metric value distributions at reference and degraded sites were compared using two non-parametric
statistical tests. Differences of the medians were tested using Mann-Whitney U and differences in general
distribution characteristics were detected using the Kolmogorov-Smirnov test. The tests were applied
separately within each of the two strata (Coastal Plain and non- Coastal Plain). Metrics which passed
both of these tests (p<0.05) were retained for further analysis. Calculated metric values were converted to
metric scores of 5, 3 or 1 depending on their proximity to optimal values. Optimal and sub-optimal value
ranges were established as follows. The distribution of values in the reference sites of each stratum were
divided by percentiles; the 50th percentile (or median) and the 10Ih percentile were designated as threshold
values. Metric values above the median were scored as 5, metric values between and including the 10*
and 50th percentiles were scored as 3, and all metric values below the 10th percentile were scored as 1.
Those metrics that increase in response to perturbation (reverse metrics) were scored such that values
below the median received a score of 5, values between and including the 50th and 90* percentiles were
scored as 3, and values above the 90th percentile were scored as 1. Alternate scoring criteria based on the
50th percentile and a bisection to the lowest metric value, the 25th percentile and a bisection to the lowest
metric value, and a trisection of the 95th percentile were also tested. The degree of metric scoring
agreement with site reference and degraded status determined the responsiveness of the metric to
stressors, and was calculated as classification efficiency (CE). Criteria correctly classified sites
previously determined as "reference" if they produced metric scores of 3 and 5. Likewise, a metric score
of 1 indicated impaired biological conditions and correctly classified sites previously determined as
degraded. Incorrect classifications occurred when a reference site had a metric score of 1 and when an
impaired site had a metric score of 3 or 5. The CE of each metric in both strata was calculated as the
percentage of correct site classifications.
Step 6. Combining Metrics into an Index
The process by which metrics were chosen for the index required iterative testing of the CEs of
several metric combinations. At least one metric from each category (richness, composition,
tolerance/intolerance, functional feeding group, and habit) was included in every index combination
considered. Metrics with the highest CE per category per stratum were used in the preliminary
combinations. This preliminary combination was augmented in a stepwise manner until the highest index
CE attainable was determined. When more than one metric in a category shared the top rank in
efficiency, trials were run in a stepwise manner, with one, with the other, and with both (or all) until the
highest index CE was attained. Testing of metric combinations for the index continued in a stepwise
manner to include the second highest ranking metric in each category, and the third if appropriate. Upon
building a large metric set, trials continued, which selectively deleted metrics if deletion did not reduce
classification efficiency. Exceptions to this general format were the trials run without the habit metrics
and those with "total number of taxa" in the Coastal Plain, which had a low classification efficiency, but
contains essential information about the assemblage. The selected metric scores were averaged to yield a
single index value for each site. The CE of the index was calculated as above, with scores ^3.0 in the
reference sites and below 3.0 in the impaired sites considered correct and where the efficiency equals the
percentage of correctly classified sites.
Testing the Index Using an Independent Data Set
After developing the genus level index using 1994 and 1995 data, it was tested using 1996 and 1997
data. New reference and degraded sites were identified among the 1996 and 1997 sites using the
previously developed criteria. The index metrics were calculated in the Coastal Plain and non-Coastal
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Plain and metrics were scored using established scoring criteria. Metric scores were combined into an
index and the percentage of correct classifications (CE) determined as before.
Developing Indices for Use with Higher Level Taxonomic Identifications
A family level index was developed using much the same procedure as described above. Metrics that
were already part of the genus level index and are appropriate with family-level data were used.
Tolerance values were assigned to each family in the samples. Functional feeding group and habit
preference metrics were not used in the family level index. Metric scoring criteria were determined using
the 50th and 10th percentile thresholds. Various sets of metrics were tested starting with only those metrics
which were applied within the respective regions at the genus level. Metrics were subsequently added
and deleted from the trial sets, using all metrics in both regions, regardless of regional specificity at the
genus-level. The set of metrics chosen for the final index yielded the best CE in both physiographic
strata, i.e., only one set of metrics for the family level is proposed for both the CP and NCP regions. An
order level index was not pursued because only a limited number of metrics could be applied and the
information in those metrics had an insufficient range, e.g.; number of EPT orders ranges from 0 to 3.
Results
Determination of Strata
Both cluster analysis and NMDS suggested that the Coastal Plain (CP) and non-Coastal Plain (NCP)
have different benthic invertebrate assemblages. To a varying extent, site groupings are best reflected by
physiographic regions. This can be seen with both the dendrogram and the NMDS ordination plots
(Figures 1 and 2). Vertical lines drawn on Figure 1 demonstrate reasonable site groupings; small clusters
to the left of the numbered clusters are comprised of relatively heterogenous sites and are not numbered.
Based on the clusters, numbers of sites, and their distribution, the most sensible strata are NCP (clusters
1 and 3) and CP (cluster 2 and the heterogenous sites). Three distinct clusters were identified at the
linkage level of approximately 0.83. Cluster 1 consists mostly of sites from Washington and Allegheny
counties (22 out of 25); cluster 2 is comprised of 15 sites from southern Maryland and all are in the CP;
and cluster 3 is spread out geographically with 57 of the 61 sites occurring in counties that are within the
NCP. To the left of cluster 1 are heterogeneous sites based on taxonomic composition. Relative to sites
within clusters 1, 2, and 3 they have a large percentage of taxa not shared with other sites. Of these, 23
sites out of 29 occur in counties that are completely in the CP or have small areas that are transitional to
the Piedmont.
The NMDS plot of all 130 sites further supports the two distinct strata (Figure 2). The majority of
NCP sites in the top half of the graph, above 0.0 on the Y axis while the majority of CP sites are below
0.0 on the Y axis. After separation of CP from NCP sites, further subdivision using both clustering and
NMDS suggested site groups by physiographic province, ecoregion, and subecoregion (Omernik 1987,
Woods et al. 1996, White 1997). The most distinct ecoregions or subecoregions were Inner and Outer
Coastal Plain, Piedmont, Shale Ridges, and Limestone Valleys. However, due to insufficient number of
sites in many of these regions, their distinctness cannot be fully evaluated. None of the other physical,
chemical, or geographic variables including basin, stream order, and water chemistry showed as strong a
correspondence as geographic regions.
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Metric Evaluation
Of the 57 metrics evaluated, 16 in the Coastal Plain and 31 in the non-Coastal Plain passed both tests
of significance (Mann-Whitney U and Kolmogorov-Smirnov). Only two metrics in the richness category
("number of total taxa" and "number of EPT taxa") and one metric in the trophic/feeding category
("number of scraper taxa") in the Coastal Plain met this criterion for inclusion in the index. Of the 16 and
31 metrics, respectively, 12 were in common between the two regions. The 50* and 10* percentiles as
scoring criteria were more effective than other criteria tested, and were the only criteria used in
subsequent analysis. Of the metrics which passed both significance tests, CEs ranged from 47 to 84%. In
the Coastal Plain, 7 metrics had efficiencies greater than 70%; however, the two highest ("%
Hydropsychidae of Trichoptera" and "% Hydropsyche and Cheumatopsyche of EPT") were excluded
from the index groupings because a metric value of zero had uncertain meaning. A zero percentage of
tolerant individuals within a generally intolerant group (e.g. "Hydropsychidae of Trichoptera") could
signify either that no tolerant individuals are present (a potential indicator of reference conditions) or that
no tolerant or intolerant individuals are present (a potential indicator of stress). In the NCP, 16 metrics
had CEs greater than 70%, though two ("% Hydropsychidae of Trichoptera" and "% Baetidae of
Ephemeroptera") were excluded from index combinations for the reason mentioned above. Classification
efficiencies of two metrics showing statistical significance ("number of Crustacea or Mollusca taxa" and
"% Crustacea and Mollusca") could not be calculated because the range of values between the 50th and
10th percentiles was insufficient (both percentiles were zero).
Combination of Metrics into an Index
The metrics selected for the final indices included those that contained the most appropriate
ecological information and which, as a group, yielded the highest overall CE (Table 1). Three metrics
that were included are common to the indices of both strata: "number of total taxa", "number of EPT
taxa", and "% Ephemeroptera". Statistics and scoring criteria of the metrics included in the final indices
are shown in Table 2. The basic set of metrics in the preliminary index for the CP, composed of one
metric (of high CE) from each category, included the following five metrics; "number of EPT taxa", "%
Ephemeroptera", "the Beck's Biotic Index", "number of scraper taxa", and "% clingers". This
combination correctly classified 82% of the sites as reference or impaired. With two additional metrics
("number of total taxa" and "% Tanytarsini of Chironomidae"), the final index correctly classified 87%
of the sites, performing best within the degraded sites (Table 1). In the NCP, the preliminary index had a
CE of 82% and was composed of the following metrics: "number of Ephemeroptera taxa", "%
Ephemeroptera", "number of intolerant taxa", "% collectors", and "% swimmers". The final index
included five additional metrics ("total number of taxa", "number of EPT taxa", "number of Diptera
taxa", "% Tanytarsini", and "% tolerant individuals") and deleted the habit metric ("% swimmers"). This
final index had a CE of 88% overall and performed better within the reference sites (Table 1). Raw index
scores for the Coastal and non-Coastal Plain indices ranged from 7 to 35 and 9 to 45, respectively. To
facilitate statewide comparisons, these scores were adjusted to a common scale ranging from 1 to 5. The
relative separation of reference and degraded sites by total index score is shown in Figure 3.
Results of Index Testing with Independent Data Set
Classification efficiencies attained with the 1996-97 dataset were high. In the CP, correct
classification of reference and degraded sites occurred 72% of the time. When calculated on degraded
sites only, the CE was 94%. For the NCP, these statistics were 82 and 100%, respectively.
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Indices of Biological Integrity for Higher Taxonomic Levels
Several different combinations of family level metrics were tested for their effectiveness in correctly
classifying reference and degraded sites. At first, only those metrics which were used in the indices
developed at the genus level and which had significance at the family level were tested within each
region (CP and NCP). The addition of metrics which were used in either genus-level regional index
improved the overall efficiency of the family level index in both regions. The final index formulation has
the same metrics for both CP and NCP: "total number of families", "number of EPT families", "number
of Ephemeroptera families", "number of Diptera families", "% Ephemeroptera", and "Beck's Biotic
Index" The overall CE for the CP (71%) was not quite as high as it was for the genus-level index (87%);
however, for the NCP it was an identical (88%). The CP family level index performed best among
reference sites and the NCP family index performed equally well between reference and degraded sites.
Discussion
Maryland DNR has chosen to develop biological indices for both fish and benthic macroinvertebrates
because these indicators respond to both physical and chemical stressors to which they are exposed.
Also, a community- or assemblage-level measure (such as the multimetric Index of Biotic Integrity) that
integrates multiple types of responses (i. e., at different levels of biological organization) is more likely to
reflect those responses (Karr et al. 1986). Following Karr, there were several attempts at adapting the
multimetric approach to different areas of the country, or to different biological assemblages. Those
efforts met with variable success because they often would take guidance documents, such as Plafkin et
al. (1989), "off the shelf and attempt to apply methods without modification. There has been increasing
recognition that regional calibration of biological indices is necessary for improving their accuracy and
sensitivity (Fore et al. 1996, Kerans et al. 1992, Barbour et al. 1995, 1996, Maxted et al. 1998, Roth et al.
1997). The process used here is a direct effort to formulate the Maryland benthic IBI according to
specific regional conditions.
The majority of metrics selected for the two IB Is regionalized for the Maryland Coastal Plain and
non-Coastal Plain are supported by several demonstrations of their ability to discriminate between
reference and degraded conditions. A method used for testing the discriminatory power of candidate
metrics was statistical tests with subjectively selected significance levels (p-values). Additionally, the
effectiveness of the individual metrics and aggregated indices in correctly classifying reference and
degraded sites was documented by CE, which directly defines their ability to detect degradation. Because
the MBSS quantitatively defined reference and degraded sites using criteria for physical habitat quality,
water chemistry, and land use data, and the fact that individual metrics and indices are known to reflect
those criteria, it becomes less critical to be able to explain the direct modes of action of individual
stressors. The importance of this approach and the results is that metrics and index formulations are
reflecting the occurrence of multiple, site-specific stressors which accumulate from landscape-level
sources.
The initial compilation of candidate metrics and the process of metric selection and testing was in
part driven by a goal of representing different categories of ecological information. Effort was made not
only to maximize the effectiveness of detecting degradation, but also to communicate meaningful
ecological information. Table 3 provides a description of the ecological relevance of metrics that were
selected. The final suite of metrics in the MBSS CP IBI contained four of the five metrics used by
Maxted et al. (1998) in the Mid-Atlantic Coastal Plain. These include "number of total taxa", "number of
EPT taxa", "% Ephemeroptera", and "% clingers"; in that study, these metrics had mean CE's of 44%,
83%, 63%, and 65%, respectively. The largest difference in performance is with the metric "number of
EPT taxa", which produced a CE of only 55% in the MBSS CP. Differences in metric-specific and index
CEs can be attributed to a different set of reference sites and index period. For the latter, the Mid-
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Atlantic Coastal Streams Workgroup samples during the Fall (October 1 December 1), whereas the
MBSS index period is Spring. Seasonal differences in sampling can cause differences in metric
effectiveness. The ''HBF' metric that completes the index developed by Maxted et al. was replaced in this
study by "Beck's Biotic Index", which slightly outperforms the "HBI" in correctly identifying
degradation.
Of the nine metrics selected for the NCP index, four were also used by Smith and Voshell (1997) in a
10-metric index developed for the Mid-Appalachian Highlands. They were "number of EPT taxa",
"number of Ephemeroptera taxa", "% Ephemeroptera", and "number of intolerant taxa" Three metrics
used by Smith and Voshell ("% EPT", "HBI", and "% scrapers") were not selected for the MBSS index
because other metrics in the same categories either matched their CE or outperformed them. Habit
metrics were not used for the MBSS NCP index, while Smith and Voshell used a broader category of
habit (haptobenthos, or clean substrate inhabitors) to develop a useable metric. The indices in the CP and
NCP include three metrics in-common: "number of total taxa", "number of EPT taxa" and "%
Ephemeroptera". In the CP, "number of total taxa" and "number of EPT taxa" were the only metrics in
the richness category that significantly distinguished reference and degraded sites according to the
Kolmogorov-Smirnov test, and then their classification efficiencies were low (47 and 55%, respectively).
In the NCP, 6 richness metrics significantly discriminated, and those mostly had high CEs (56 to 82%).
Among other differences in the two physiographic regions it was noted that roughly twice as many
candidate metrics significantly discriminated reference and impaired sites in the NCP In the CP, this
may be an artifact of a smaller number of sites, or it may indicate either that benthic macroinvertebrate
assemblages are less responsive to environmental perturbation in this region, or that reference site
selection criteria were somehow inappropriate. Stressors seem to have a more profound ecological impact
in the NCP
The starting suite of metrics selected as the best performers in their metric categories had high
discriminatory power (84 and 82% in the CP and NCP, respectively). This was also recognized during
development of the fish IBI (Roth et al. 1997). As well as increasing the power, additional metrics in the
index serve to broaden the applicability of the index by capturing ecological information that may be less
common to all sites.
Although the CP and NCP division between the two indices may have been an intuitive result, the
exercises of performing cluster and ordination analyses were useful in investigating other potential site
classes. They also helped illuminate the need for additional sampling, since several finer regions were
under-represented by sites. Several of the sub-ecoregional site groupings (Inner and Outer Coastal Plain
[White 1997], Piedmont, Shale Ridges, Limestone Valleys [Omernik 1987, Woods et al. 1996]) may,
when represented by additional data from future sampling events, warrant consideration as separate site
classes.
Though substantial credibility was placed in the ability of each metric and the overall index to
discriminate degraded sites from nondegraded ones, other factors were also considered in the process of
determining whether metrics should be included. The metrics "number of total taxa" and "number of EPT
taxa" did not have extremely high discriminatory power from our tests; however, their near universal
recognition in benthic assessment efforts and value in communication balances that concern. The goal of
having metrics representative of the five categories necessitated inclusion of some metrics with relatively
low classification efficiencies. For example, in the CP, the metric "number of scraper taxa" had a CE of
only 55%, but it was also the only metric to pass other evaluation tests within the category of
trophic/feeding metrics. Similarly, the metric "% collectors" is the only trophic/feeding metric included
in the NCP index. Even with lower individual CEs, these metrics either helped improve those of the
overall indices, or did not lower them. Their inclusion will help with the interpretive power of
assessments.
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Integration of reference site and degraded site data from other programs (such as Montgomery
County and Delaware) would increase the sample size of the dataset and perhaps the sensitivity of the
biological indices. Use and integration of assessments from the previous indices with those from this
development effort should be subjected to a performance-based comparison to define their level of
comparability. The MBSS plans to update these indices with additional sites and sampling results in
future years, in essence, recalibrating the IBI with new information. It is recognized that new reference
sites may cause upward, or downward, adjustment of decision thresholds; they may also cause re-
evaluation of site classification or strata. This dynamic nature of reference conditions should not be seen
as a shortcoming to this process, rather, it should be seen as a means of improving the ecological
assessor's ability to recognize degraded conditions.
Literature Cited
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m-444
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F. R van Tongeren (editors). Data Analysis in Community and Landscape Ecology. Pudoc
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Environmental Effects Laboratory, Corvallis, OR.
m-446
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Tree Diagram for 130 Sites
Unweighted pair-group average
CfesimTanties from Jaxard matrix (Taxa >= 5 occ.)
Figure 1. Dendrogram results of cluster analysis produced through unweighted pair-group averaging (UPGMA) of biological dissimilarity distances
(benthic macroinvertebrates). Sites (n=130) are from 1994-95 MBSS sampling, and represent those identified as least impaired. Station identification codes
include county designations in the first two letters and physiographic region in the third letter. The Coastal Plain sites are denoted with N or S. Non-Coastal
Plain sites are denoted with A, P or V.
-------
0.8
0.4
s o.o
_«
I "°'4
-0.8
-1.2
-1.6
o%.
O.o
00,
<>,%»
*. • *°* ° «o °
° 00°
• •
• ^
o
o
Non-Coastal Plain
-1.6 -1.2 -0.8 -0.4 0.0 0.4 0.8 1.2 * Coastal Plain
Dimension 1
Figure 2. Results of multivariate ordination using nonmetric multidimensional scaling (NMDS) of Bray-
Curtis dissimilarity coefficients (benthic macroinvertebrates). Sites (n=130) are from 1994-95 MBSS sampling,
and represent those identified as least impaired.
Coastal Plain
Non-Coastal Plain
O O
o 3
T3
C
~l
c
N ~
r
D
- 13
c
:
N =
:
= 24
5
4
3
2
T
1
D
a
o
N = 24
N = 26
Reference Degraded
Reference Degraded
Figure 3. Comparison of overall index scores between reference and degraded
sites using 1994 -1995 data.
m-448
-------
Table 1. Classification Efficiencies for Various Combinations of Metrics
An "\" signifies inclusion of the metric in the index.
Overall classification efficiency
Efficiency in reference sites
Efficiency in degraded sites
Coastal
Preliminary
84
85
84
Plain
Final
87
77
92
Non-Coastal Plain
Preliminary Final
82 88
92 92
73 85
Richness Metrics
Number of total taxa
Number of EPTtaxa
Number of Fphemeroptera laxn
Number of Diplera taxa
Composition Metrics
°T- Ephcmcroptera
l~l- Tanytar. of Chiron.
l~- Tanytarsini
Tolerance/Intolerance Metrics
Number of intolerant taxa
l~- tolerant individuals
Beck's Biotic Index
Feeding Metrics
Number of scraper taxa
rr collectors
Habit Metrics
1T clingers
l~- swimmers (ceneral)
111-449
-------
Table 2. Descriptive Statistics of Reference Sites and Scoring Criteria for the Final Genus-Level
Index Metrics in the Coastal Plain and Non-Coastal Plain Regions
Coastal Plain (n = 13)
Metric
Number of total taxa
Number of EPT taxa
% Ephemeroptera
% Tanytarsini of Chiron.
Beck'sBiotic Index
Number of scraper taxa
% clingers
Statistic
min
8
2
0.8
0.0
2
0
20.0
10th
11
3
2.0
0.0
4
1
38.7
50lh
24
6
11.4
13.0
12
4
62.1
90th
32
11
46.2
46.2
16
6
86.1
max
36
13
47.7
100.0
18
8
99.2
Score
5
>24
>6
>11.4
>13.0
>12
>4
>62.1
3
11-24
3-6
2.0-11.4
>0.0-13.0
4-12
1 -4
38.7-62.1
1
<11
<3
<2.0
0.0
<4
<1
<38.7
Non-Coastal Plain (n = 24)
Metric
Number of total taxa
Number of EPT taxa
Number of Ephemeroptera taxa
Number of Diptera taxa
% Ephemeroptera
% Tanytarsini
Number of intolerant taxa
% tolerant
% collectors
Statistic
min
14
3
1
2
2.1
0.0
2
0.9
7.4
10th
16
5
2
6
5.7
0.0
3
1.1
13.5
50th
22
12
4
9
20.3
4.8
8
11.8
31.0
90lh
30
16
6
11
60.2
21.1
12
48.0
73.1
max
36
19
7
16
78.0
37.6
13
69.9
82.8
Score
5
>22
>12
>4
>9
>20.3
>4.8
>8
<11.8
>31.0
3
16-22
5-12
2-4
6-9
5.7 - 20.3
>0.0 - 4.8
3-8
11.8-48.0
13.5-31.0
1
<16
<5
<2
<6
<5.7
0.0
<3
>48.0
<13.5
ffl-450
-------
Table 3. Ecological Relevance of Metrics Used in the IBI
Metric
Description and rationale for use
Total number of
taxa
Number of EPT
taxa
Number of
Ephemeroptera
taxa
Number of
Diptera taxa
Percent
Ephemeroptera
Percent
Tanytarsini of
Chironomidae
Percent
Tanytarsini
Number of
intolerant taxa
Percent tolerant
Beck's Biotic
Index
Number of
scraper taxa
Percent
collectors
Percent clingers
The richness of the community in terms of number of taxa is commonly used as a
quantitative measure of stream water and habitat quality. Taxa richness generally decreases
as a stream ecosystem degrades; a factor of habitat elimination, competitive displacement by
opportunistic taxa following disturbance, and/or local extirpation of relatively intolerant
taxa.
The richness of the generally intolerant insect orders of Ephemeroptera (mayflies),
Plecoptera (stoneflies), and Trichoptera (caddisflies) can indicate stream condition, since
these taxa tend to become more scarce with increasing levels of disturbance.
The richness of mayfly taxa indicates the ability of a stream to support this generally
intolerant insect order. Mayflies have medium to high oxygen requirements and some taxa
need clean gravel substrate. They are mostly intolerant of organic enrichment and excess
fine sediment.
Diptera as an order are relatively diverse and many Diptera taxa are highly variable in their
tolerance to stress. Many taxa, especially Chironomidae, have cosmopolitan distributions
and may occur even in highly-polluted streams.
The degree to which mayflies dominate the community can indicate the relative success of
these generally pollution intolerant individuals in sustaining reproduction. The presence of
stresses will reduce the abundance of mayflies relative to other, more tolerant individuals.
The tribe Tanytarsini is a relatively intolerant group of midges. A high percentage of
Tanytarsini among the midges may indicate lower levels of anthropogenic stress. This
metric increases with high numbers of Tanytarsini (among all Chironomidae) and decreases
with high numbers of non-tanytarsine Chironomidae.
Tanytarsini as a percentage of the entire sample has a significance similar to the percent of
Tanytarsini of Chironomidae, except that other midges do not affect the metric value.
Intolerant taxa are the first to be eliminated by perturbations. Often, intolerant taxa are
specialists, and perturbations can disturb or eliminate specialized habitat or water quality
requirements.
As perturbation increases, tolerant individuals tend to predominate in the sample. Intolerant
individuals become less abundant as stress increases, leading to more individuals in
tolerant, opportunistic taxa.
The weighted enumeration of intolerant individuals in the community decreases in response
to stress.
High diversity of the herbivorous scraper fauna can indicate a lack of stressors. This metric
illustrates a food chain effect; these genera feed on periphyton and associated microfauna
which may themselves be more abundant under conditions of minimal perturbation.
Abundance of detritivores, which feed on fine particulate organic matter in deposits,
typically decreases with increased disturbance. This ecological response may be a food
chain effect, where organic material becomes scarce or unsuitable with increased
perturbation.
The taxa which cling to surfaces in fast moving water increase in abundance in the absence
of stressors. Stressors which adversely affect this metric are those that disturb high quality
habitat, such as clean gravel riffles.
ffl-451
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m-452
-------
The Influence of Land Use and Stream Morphology on Urban Stream Water Quality
Judith A. Gerlach Okay, Coordinator of the Difficult Run Project
Virginia Department of Forestry
Introduction
The Difficult Run watershed is the largest watershed in Fairfax County, Virginia. It has a 58 square
mile drainage. Difficult Run mainstem has ten tributaries (Fig. 1) which flow through a diversity of
landscapes comprised of farms, more suburban areas and highly developed urban centers. The stream side
forests in the Difficult Run watershed have become fragmented and the quality of the riparian forests have
been compromised. As development has taken place vegetated landscapes have been replaced with
impervious parking lots, walkways, roads and roofs. It is the purpose of this study to investigate the
relationship between riparian forest buffer width, land use density, stream morphology and
macroinvertebrate diversity.
Three streams from the Difficult Run watershed were selected for this study. The selection was based
on the density of development within the stream drainage basins. The width of riparian forest buffer was
also taken into consideration. Riparian forest buffers are an integral component of stream ecosystems and
perform valuable functions that influence water quality within streams (USDA Forest Service 1991).
Streamside tree canopy provides shade which moderates stream water temperatures (Boulton and Lake
1990). This is a very important attribute in urban/ suburban landscapes that have a high percentage of the
landscape covered with impervious surfaces which produce heat that is transferred to streams during
storm events (Schueler 1994). It has also been demonstrated that riparian forest buffers are the most
effective filters for nutrient management and the removal of sediment from surface runoff (USDA Forest
Service 1991). Litter and woody debris produced by streamside trees provide a food base for
macroinvertebrate species which shred the leaves after they are somewhat decomposed by bacteria
(Cummins et al. 1973).
Macroinvertebrate communities have been relied on as indicators of water quality, they are on the
lower end of the trophic level within streams providing a base for the instream food chain (Firehouck and
Doherty 1995). Species are categorized as pollution tolerant or pollution sensitive (intolerant) dependent
upon their ability to survive in water that provides less than ideal habitat. Schueler (1994) reports research
that correlates macroinvertebrate diversity and imperviousness, macroinvertebrates were replaced with
more pollution tolerant species when imperviousness increased. The premise of this study is that the
diversity of macroinvertebrate communities will decrease as imperviousness increases and riparian forest
buffers decrease.
Methods
Stream Morphology
The stretch of Little Difficult Run analyzed is surrounded by a 250 acre park with mixed hardwood
and pine forest. Beyond the park, the drainage basin is zoned at a maximum density of 1-5 acre lots.
Angelico Branch another Difficult Run tributary is located in Kemper Park, an eight acre park surrounded
by residential development zoned at a maximum density of 0.25 to 0.5 acre lots. A tributary of Colvin
Run referred to as Greenmont Court was the third stream selected, it flows between two townhome
communities. The communities have four - eight dwellings per acre, the drainage basin of this tributary is
zoned residential interspersed with commercial. All of the stream stretches studied are headwater areas.
The morphology and drainage basins are presented in Table 1.
III-453
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An analysis of stream morphology was performed to determine the similarities and differences of the
three streams. The method of stream analysis used employed techniques developed by Dave Rosgen
(1994). Characteristics considered are the stream width, bed to bank ratio, the bed substrate and the valley
slope. These measurements show the capacity and relative condition of the streams. All of the parameters
are measured using a rod and level with the exception of the pebble count. An actual random count of 100
pebbles, rocks, or boulders is done and each particle is measured (Harrelson et al. 1994). The particles are
placed in categories and the percent of each representative category is calculated. This analysis shows the
classification of the bed substrate which can range from <2 millimeters (sand) to 2048 -4096 millimeters
(very large boulder). Knowing the classification of the bed substrate is important because of the need for
stable macroinvertebrate community habitat (Death 10). Erosion of bed substrate and sediment deposition
create new geomorphic surfaces and have a disturbance effect on a stream ecosystem (Gregory 1991).
Macroinvertebrate Sampling
Macroinvertebrate samples were taken three times at each of the selected streams. The timing of the
sampling was set up to be seasonal; winter, summer and late autumn. Hydrology, temperature and
available food in each of these seasons differs (Gregory 1991). Deciduous leaf litter can remain instream
for up to six months as well as macroinvertebrate maturity can take from one to five years (Wetzel 1983).
Replicate sampling over a one year period is recommended by the Izaak Walton League of America
(IWLA) Save Our Streams program and this was the sampling protocol used.
The technique involved makes use of a kick seine set just below a riffle section of stream. The seine
is held by one person and another person moves up stream from the riffle turning rocks, stones and
pebbles with their hands and rolling the substrate with their feet. The sampling can be performed by one
individual using a small D-frame net and performing both the task of turning substrate and holding the net
in place below the riffle.
The net is removed from the stream and the organisms captured are counted and identified.
Organisms are classified as sensitive, somewhat-sensitive or tolerant according to the IWLA rating
system, which relies on response to pollution in the stream. The Environmental Protection Agency's
recommended Rapid Bioassessment Protocol (RBP) evaluation was used as an extension of the IWLA
rating. The RBP is a more intricate approach to water quality monitoring and requires species
identification of macroinvertebrates to the biological Family level (Hilsenhoff 1988). A reference stream
with a good water quality rating and located in the same physiographic province as the study sites is used
to compare and calculate the rating for the study streams. A rating of <21% is a severely impaired rating,
29-72% is a moderately impaired rating and > 79% is a non-impaired rating.
Sorenson's coefficient of similarity was applied to analyze the results of the macroinvertebrate
sampling. This analytical procedure takes into consideration the total number of species collected in each
sampling unit and the number of species the sampled sites have in common. The output is in the form of a
percent and displayed as clustering on a dendrogram. The highest value is 1.00 or 100% for those sample
units that are identical in the species they have in common. Sorenson's coefficient of similarity analysis
shows the macroinvertebrate similarities the three study streams have in common with the reference
stream and with each other.
Results
Stream Morphology
Stream classification is helpful in determining the vulnerability of a stream to increased stream bank
erosion and widening of the channel or entrenchment of the bed. The value that stream morphology
analysis has in this study is the ability to predict the stability of macroinvertebrate habitat in each stream
III-454
-------
analyzed. The findings of the analysis performed are displayed in Table 1. The stream beds ranged from
sandy - coarse gravel for Angelico Branch, large cobble - small boulder at the Greenmont Court site.
Little Difficult Run bed structure was a coarse gravel - small boulder. Of the three streams, Greenmont
Court has the most stable stream bed, it has actually downcut to the point that bedrock is visible in many
parts of the channel. The stream with the least stable bed structure is Angelico Branch which has a sandy
to coarse gravel bed.
The width/depth ratio is another morphologic feature important to macroinvertebrate species. Little
Difficult Run stream has a low width/depth ratio, the interpretation of this is that the banks are not as
steep revealing a certain amount of stability rather than undercutting and scouring. The stream at
Greenmont Court has the highest width/depth ratio which is interpreted to mean that the banks are steep
and depending on the soil structure can be unstable. Another stream element considered in this study is
the temperature of the stream water versus the ambient air temperature for each of the stream sample
sites. The air temperature was 12.7°C at both Angelico Branch and Greenmont Court sites, however, the
instream water temperatures were 6.4°C and 7. 7°C respectively. At the Difficult Run site the ambient air
temperature was 13.1°C and instream water temperature was 5.5°C. These temperatures are consistent
with air and water temperatures reported by water quality monitors in the local Soil and Water
Conservation District monitoring program (Penney 1997). Dissolved oxygen levels are dependent on
temperature and the rate of decay for leaf litter is also temperature dependent. It should be noted that
historical chemical monitoring data produced by the Fairfax County Health Department (1989-1994)
reveal some of the lowest nitrogen and phosphorous levels are present in the Difficult Run watershed as
well as consistently high dissolved oxygen levels. Stream monitors sampling different sites in the
Difficult Run watershed reported fair to moderate water quality (Penney 1997).
Macroinvertebrate Monitoring
Samples taken from the three streams over a one year period of time contained representatives from
13 Families of macroinvertebrates. The sample from the reference stream contained 14 Families. Of the
three study streams, the maximum number of macroinvertebrate Family representatives at any one stream
was 9 at each of the streams, but at different times. There are many variables that influence these numbers
and they will be addressed in the discussion section. The list of Families and their presence in the
respective streams can be found in Table 2.
The cluster analysis using Sorenson's coefficient of similarity shows both Greenmont Court and
Angelico Branch to be most closely related to the reference stream in terms of macroinvertebrate
community structure. However, the percent similarity to the reference stream for both Angelico Branch 2
(AB2) and Greenmont Court l(CRT) is only 0.522 or 52%. Cluster analysis results are presented in Table
3. This can be related to the Rapid Bioassessment Protocol evaluations. The ratings as mentioned earlier
are: < 21%, severely impaired; 29-72%, moderately impaired and > 79%, non-impaired. Little Difficult
Run received the lowest rating of severely impaired (21%) in January, 1997. The highest rating,
moderately impaired (43%) was also for Little Difficult Run in August, 1997, and Angelico Branch
received a 43% rating in December 1997. Greenmont Court remained consistent for all three sampling
periods with a moderately impaired rating of 36%.
The streams that have the highest coefficient of similarity (0.80) are Angelico Branch (AB1) and
Little Difficult Run (LDR1). Angelico Branch (AB1) and Greenmont Court (CRT) have a coefficient of
similarity of 0.70. This demonstrates which of the stream samples at each stream had similar macro-
invertebrate species. The dendrogram (Fig. 2) displays the cluster relationships between the various
stream samples.
III-455
-------
Discussion
The relationship between land use and water quality is not a concept to be easily explained or
understood. There are numerable variables that influence water quality. An attempt is made in this study
to isolate land use density and riparian forest buffers as influential elements that affect water quality.
Macroinvertebrate community structure is the tool used to evaluate the influence of land use density and
riparian forest buffers on water quality. It all seems very simple, but quickly becomes complicated
because stream ecosystems are just that, systems with many intricate elements.
The first element considered is the influence of stream morphology. Streams that are unstable and are
undergoing rapid change carry heavy sediment loads and have dynamic movement of the banks and bed.
The volume and velocity of the water carried by the stream will determine just how much structural
change will take place. In urban situations hydrology is very flashy and results in large volumes and swift
velocities, such is the case for the three streams in this study. Schueler (1994) reports that macro-
invertebrate diversity is inversely proportional to the amount of imperviousness in a watershed.
Of the three streams studied, Greenmont Court has the largest drainage area of imperviousness,
however, the macroinvertebrate samples collected proved to be the most closely related to the samples of
the good quality reference stream. Greenmont Court also has the poorest quality riparian buffer. This is
not the relationship expected at the onset of this study. One element very unique to Greenmont Court, not
found in the other streams, is the fact that the bed structure and general morphology of the stream indicate
that it has degraded to a point of stability. The large bedrock on the stream bottom and the cobble are
quite stable and possibly create good stable habitat for macroinvertebrates to colonize. Under the stress of
high volume and velocity flows, there is protection under the large rocks. A study by Death and
Winterbourn (1995) credit an increase in macroinvertebrate diversity to more stable stream structure.
Although analysis of Little Difficult Run stream morphology reveals that the banks are quite stable in the
stretch analyzed, the bed material, coarse gravel - small boulder can be moved by the sheer stress of large
volume and fast velocity flows. When the bed material rolls around, macroinvertebrates are buried,
washed out and generally lost (Schueler 1994). The same would be true for the morphology of Angelico
Branch, but there is an additive condition in this stream. In addition to an unstable bed, the banks are
extremely scoured and the stream carries a considerable amount of sediment. The sediment deposits not
only bury macroinvertebrates, but the movement of the sand and gravel, scrape the epidermis of the
organisms making them vulnerable to disease. Another point to consider is that many macroinvertebrate
species have gills that get clogged by sediment, making oxygen absorption and survival difficult. Death
(1995) reports a decrease in filter feeding macroinvertabrates at disturbed, unstable stream sites.
Little Difficult Run stream had the greatest number of macroinvertebrates in the January, 1997
sample, 202, representing 9 Families. The number is higher than the sample taken from the reference
stream which had 117, representing 14 families. The problem with the quality of the sample collected
from Little Difficult Run is the high number of tolerant species, particularly black fly larvae. The stream
was rated as severely impaired at this time. Greenmont Court had a total number of 21 macroinvertebrates
a considerably lower number than Little Difficult Run, but the proportion of pollution sensitive organisms
was high (7/8 Families) and a moderate impairment was recorded. Rainfall for the month of January
equaled 5.48 centimeters (cm.) The August sample numbers diminished for Little Difficult Run, 68 total
organisms, representing 6 Families. The total organisms for Greenmont Court differed by one organism,
20 for August versus 21 for January. Rainfall for the month of August was 13.51 cm., much higher than
for the month of January. The rainfall recorded for August would produce flashy hydrology in the urban
streams that were studied. It has been observed that 5.17 cm. of rain in a 24 hour period results in
overbank flooding in the Difficult Run watershed. The point to be made is that stream bed stability is a
likely explanation for the variance in numbers of organisms in Difficult Run from January versus August,
whereas, samples from Greenmont Court with a predominantly stable bed structure only varied by one
organism.
III-456
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All of the samples collected in December, 1997 were lower than at other times. Rainfall for the month
of December was 8.17cm. Although actual totals were lower, the quality of the samples all produced
moderately impaired ratings. Family representation was higher (9 Families) for Little Difficult Run and
lower for Greenmont Court (only 3 Families were represented). Angelico Branch followed the same
trends as the other streams with fluctuation in total numbers of organisms and Family representation with
the lowest numbers being recorded for December and the highest numbers recorded in January.
The riparian forest buffer widths along the study stretch of Little Difficult are ideal, both sides are
>30 meters. Angelico Branch has 9.0 meters on one side and 12.10 meters on the other, Greenmont Court
does not meet the minimum desired buffer width of 10.6 meters on either side of the stream. Although
there was less fluctuation in the number of macroinvertebrate species and the quality of the samples taken
at Greenmont Court were rated equal or higher than Little Difficult Run it must be noted that the sheer
numbers of organisms were much higher for Little Difficult Run for all three samples. The two positive
factors that can account for this are the riparian forest buffer and land use density.
The shade provided by tree canopy influences water temperature, particularly in August when
ambient air temperatures are higher. The plentiful leaf litter and woody debris provide food, cover and
nesting materials for many macroinvertebrate species. Caddisflies in particular rely on the woody debris
to form casings to protect eggs (Wetzel 1983). Although all of the stream buffers observed can provide
these materials, Little Difficult Run has the greatest potential to do so, because of its extensive riparian
buffers.
Conclusions
There are several points of interest presented by this study. A major concern is the reliance on one
parameter or tool for evaluation of water quality in urban streams. The interdependence of riparian forest
buffers, land use and stream morphology is a complicated issue, but certainly influential on the structure
of macroinvertebrate communities. If macroinvertebrate monitoring is the tool of choice for stream water
quality monitoring, stream morphology, land use and riparian forest, buffer quality should be included in
the study. It is not good enough to simply collect and interpret macroinvertebrate samples. The reasons
for their presence and absence transcends the whole ecosystem which includes the drainage basin, sources
of food, water temperature, shelter and nesting substrate as well as habitat stability.
The importance of urban riparian forest buffers was discussed in the context of typical stream
ecosystems. In this study a stream with a healthy riparian forest buffer produced sufficient litter to support
numbers of macroinvertebrates, but the numbers of sensitive organisms were lacking, evidence of poor
water quality. The stream with a minimal riparian forest buffer produced fewer numbers of macro-
invertebrates, but those present were of a sensitive rating, indicating a moderate water quality. Since
historical data and current water quality monitoring data records (chemical and physical) determine water
quality to be moderate to fair in Difficult Run watershed, it is not evident that the water quality is so poor
that sensitive organisms cannot exist. It is suggested that stream morphology in urban landscapes is an
integral component of macroinvertebrate community sustainability. It was demonstrated that a stream
with stable bed structure will support a more sensitive suite of macroinvertebrates, the organisms do not
have to contend with unstable bed structure and abrasive sediment. More tolerant organisms can
withstand these less than ideal conditions.
A shortcoming of this study is the length of time invested. It is difficult to draw firm conclusions with
one year's data set. Under the circumstances, trends cannot be set and statistical significance is difficult to
establish. Although the stream morphology and riparian forest buffers change slowly, other factors such
as seasonal hydrology fluctuates regularly, climate affects food production, and reproductive efforts can
be disrupted. A longer study period that includes litter accumulation data and more frequent sampling
would add more balance to the study.
III-457
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Acknowledgements
The Virginia Coastal Resources Program has granted funds for this study as a part of the Difficult
Run Riparian Project. Assistance with surveying and sampling was received from the following
individuals: Sam Austin, Larry Dunn, Maureen Kellogg and Barbara and Megan White. Their help and
dedication under less than ideal circumstances and weather conditions is appreciated.
Literature Cited
Boulton, AJ. and P.S. Lake. 1990. The ecology of two intermittent streams in Victoria Australia. I.
Multivariate analysis of physiochemical features. Freshwater Biology 24:123-141.
Cummins, K., M.S. Wilzbach, D.M. Gates, J.B. Perry and W.B. Taliaferro. 1989. Shredders and Riparian
Vegetation: Leaf litter that falls into streams influences communities of stream invertebrates.
BioScience 39(1):24-30.
Cummins, K., R.C. Petersen, P.O. Haward, J.C. Wuycheck and V.I. Halt. 1973. The Utilization of Leaf
Litter by Stream Detritivors. Ecology 54(2):336-345.
Death, R.G. 1995. Spatial patterns in benthic invertebrate community structure: products of habitat
stability or are they habitat specific? Freshwater Biology 33:455-467.
Death, R.G. and M.J. Winterbourn. 1995. Diversity patterns in stream benthic communities: The
Influence of Habitat Stability. Ecology 76(5): 1446-1460.
Fairfax County Health Dept. 1989. Stream Water Quality Report. Fairfax, VA.
Fairfax County Health Dept. 1994. Stream Water Quality Report. Fairfax, VA.
Fairfax County Health Dept. 1996. Stream Water Quality Report. Fairfax, VA.
Gregory, S., F.J. Swanson, W.A. McKee, and K.W. Cummins. 1991. An Ecosystem Perspective of
Riparian Zones. BioScience. 41(8):540-549.
Hilsenhoff, W.L. 1988. Rapid field assessment of organic pollution with a family level biotic index.
Journal of North American Benthal Soc. 7(l):65-68.
Izaak Walton League of America SOS Program. 1995. Firehock, Karen and J. Doherty. A Citizen's
Streambank Restoration Handbook.
Northern Virginia Soil and Water Conservation District. 1997. ed. Sherry Penney. First Round Results of
Surveys. Fairfax Watershed Watch. 1(2):4.
Rosgen, D.L. 1994. A Classification of Natural Rivers. Catena 22(3): 169-199.
Schueler, T. 1994. Feature Article. Watershed Protection Techniques 1(3): 104.
USDA. Forest Service. 1991. Riparian Forest Buffers. Radnor, PA. NA-PR-07-91.
USDA. Forest Service. 1994. Herrelson, C.C., C.L. Rawlins, J.P. Potyondy. Stream Channel Reference
Sites: An Illustrated Guide to Field Techniques. General Technical Report Rm. 245. 49-52.
Wetzel, R.G. 1983. Limnology. 2nd ed. Harcourt Brace Jovanovich College Publishers. New York. 760.
III-458
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Difficult Run Watershed
I I Watershed Boundary
f\/ Streams
^ DifTicull Run Mainstcm
S3 Tributaries
10123 Mile
Figure 1. Map of Difficult Run watershed with three study sites marked with dark filled circle.
III-459
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-Idr1
ab1
Idr3
ab3
crt3
ref
crt
ab2
Idr2
crt2
Figure 2. Dendrogram display of similarity analysis for study stream macroinvertebrate communities.
III-460
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Table 1. Features of Three Streams Selected for Study
FEATURES
width
mean depth
width/depth
valley slope
bed substrate
drainage basin
air temp/
water temp
forest buffer
width
rate of flow
LITTLE DIFFICULT RUN
2.15 meters
100.5 centimeters
3.16
2.5%
coarse gravel to small
boulder
275 acres
13. 1C/5.5C =7.6 degrees
diff.
30.30 meters/30.30 meters
2.16 cubic feet /sec
ANGELICO BRANCH
1.11 meters
1 1.43 centimeters
9.30
2%
sand-coarse gravel
108 acres
12.7C/6.4C = 6.3 degrees
diff.
6.06 meters/10.60 meters
1.50cubic feet/sec
GREENMONT COURT
2.89 meters
23.82 centimeters
11.90
6%
large cobble to small boulder
367 acres
12.7C/7.7C=5.0 degrees diff.
9.09 meters/3.03 meters
3.46 cubic feet/sec
Table 2. List of Macroinvertebrate Families Represented in Study Streams
SPECIES
Perilidae
Hydropsychidae
Philopotamidae
Athericidae
Red Chironomid
Simuliidae
Tipulidae
Limnodrilus
Corydalidae
Elmidae
Heptaginiidae
Gammarldae
Camaridae
Dryopidae
Chloroperlidae
Siphlonnuridae
Glossosomatidae
Sialidae
Psephenidae
Physa
Total
Little Difficult
Run
36
26
1
2
28
185
11
1
4
7
24
1
1
1
0
0
0
0
0
0
328
Angelico
Branch
31
2
0
1
26
5
2
5
1
4
0
2
0
1
0
0
0
0
0
0
80
Greenmont
Court
6
2
0
0
12
0
1
6
1
11
11
7
1
0
0
0
0
0
0
0
58
Lick Branch
Reference
0
0
31
0
9
7
4
0
3
1
0
1
1
0
31
13
9
4
2
1
117
m-461
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Table 3. Sorenson's Coefficient of Similarity for Study Streams and Reference Stream
Ref
.381
.400
.348
.455
.522
.300
.522
.222
.250
1.00
LDR1
1.00
0.380
0.625
0.800
0.500
0.462
0.500
0.364
0.222
0.381
LDR2
0.380
1.00
0.400
0.571
0.800
0.167
0.800
0.600
0.250
0.400
LDR3
0.625
0.400
1.00
0.588
0.556
0.800
0.556
0.308
0.364
0.348
AB1
0.800
0.571
0.588
1.00
0.706
0.429
0.706
0.500
0.200
0.455
AB2
0.500
0.800
0.556
0.706
1.00
0.400
1.00
0.615
0.364
0.522
AB3
0.462
0.167
0.800
0.429
0.400
1.00
0.400
0.200
0.500
0.300
CRT1
0.500
0.800
0.556
0.706
1.00
0.400
1.00
0.615
0.364
0.522
CRT 2
0.364
0.600
0.308
0.500
0.615
0.200
0.615
1.00
0.333
0.222
CRTS
0.222
0.250
0.364
0.200
0.364
0.500
0.364
0.333
1.00
0.250
LDR1
LDR2
LDR3
AB1
AB2
AB3
CRT1
CRT 2
CRT 3
REF
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Development of Biocriteria for Wetlands in Montana
Randall S. Apfelbeck, Water Quality Specialist
Monitoring and Data Management Bureau, Montana Department of Environmental Quality
2209 Phoenix Ave., P.O. Box 200901, Helena, MT 59620-0901
Phone: (406) 444-2709; Fax: (406) 444-5275; E-mail: rapfelbeck@mt.gov
Abstract
A goal of the Clean Water Act (CWA) is to restore and maintain the chemical, physical, and
biological integrity of the Nation's waters. Attainment of this goal includes development and
implementation of water quality standards for wetlands. States have been mandated by USEPA to
develop standards for wetlands with the following minimum requirements: 1) to include wetlands in the
definition of State waters, 2) establish beneficial uses for wetlands, 3) adopt existing narrative and
numeric criteria for wetlands, and 4) apply antidegradation policies to wetlands, and 5) adopt narrative
biological criteria for wetlands. The Montana Department of Environmental Quality (MDEQ) is
attempting to surpass the fifth requirement mandated by USEPA by means of developing numeric
biological criteria for wetlands.
Traditionally, MDEQ has relied upon chemical and physical parameters for water quality assessment
and standards development. However, MDEQ realizes that not all impacts can be detected by using
chemical or physical criteria. Therefore, the MDEQ is developing assessment protocols for monitoring
the presence or absence, density, and general health of the biota existing within aquatic habitats. These
protocols are being developed because biological data contribute important information necessary to
properly assess the condition of aquatic habitats and facilitates States compilation of necessary
information to meet the goals of the CWA.
The MDEQ is assessing wetland diatoms and macroinvertebrate communities which are considered
to be sensitive and responsive to changes in water quality. The presentation will include a description of
the study designs, sampling methods, approach to classification, and analytical methods (i.e., multimetric
and mulivariate) that are being used by MDEQ for the development of wetland biological criteria.
Results will be summarized and discussed.
Introduction
The State of Montana initiated the development of wetland biocriteria in 1992 through funding by
the United States Environmental Protection Agency (USEPA) State Wetlands Protection Program,
Section 104(b)(3) of the Clean Water Act. At this time, the State of Montana Department of Health and
Environmental Sciences (reorganized in 1996 as the Department of Environmental Quality (DEQ)) had
little information concerning the status or trends of the water quality of Montana's wetlands. In addition,
most of the water quality standards and biological criteria used by the State of Montana to protect the
beneficial uses (e.g., aquatic life) of state waters were developed for lakes, rivers and streams. Wetlands
were not considered state waters when these water quality standards and biological criteria were
developed; therefore, many of Montana's water quality standards and biological criteria were not
appropriate for most wetlands.
The Montana DEQ initiated the collection of wetland water quality data for several reasons:
8 To collect baseline data from a wide variety of wetlands located throughout the State of Montana
in order that the status and trends in wetland water quality could be determined.
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• To acquire an understanding of how climate, hydrologic controls, and geomorphic settings
influence wetland biological communities; in order that they can be stratified through
classification, as is required for the development of successful biocriteria.
• To develop biological measurements that reflect anthropogenic impacts to wetland water quality
that could be used as biological criteria.
Background of Wetland Biological Criteria
The main objective of the Clean Water Act (CWA) is to restore and maintain the chemical, physical,
and biological integrity of the nation's waters, including wetlands (Adamus 1996). Historically, methods
of assessing water quality and developing standards for aquatic habitats have typically focused on
chemical and physical parameters. However, not all impacts can be detected by using physical and
chemical criteria. The USEPA recognizes that assessing biology contributes data needed to evaluate the
condition of aquatic habitats. The agency is encouraging states to monitor the presence or absence,
density, and general health of the biota existing within aquatic habitats as an assessment tool in gathering
the necessary information to meet the goals of the CWA. The USEPA has also mandated that states
develop standards for wetlands (Adamus and Brandt, 1990).
Minimum requirements for these standards include the following:
• Including wetlands in the definition of state waters.
• Establishing beneficial uses for wetlands.
« Adopting existing narrative and numeric criteria for wetlands.
• Applying antidegradation policies to wetlands.
• Adopting narrative biocriteria for wetlands. Note: The Montana DEQ is attempting to surpass
this requirement by means of developing numeric biological criteria for wetlands.
Biocriteria can be either narratives or numbers that describe the aquatic communities of a healthy
ecosystem and provide a means to evaluate and protect aquatic life use (Davis et al. 1995). The term
reference condition refers to the best sites that can be found, those least disturbed. Researchers cannot
compare all reference sites as one group, they must be classified by their physical, chemical and
biological characteristics; for example a depressional wetland should not be compared with a riverine
wetland because water quality impairment cannot be detected where reference sites are biologically
different (Reynoldson et al. 1997). Biocriteria are developed to protect biological integrity which is the
ability of an aquatic ecosystem, to support and maintain a balanced, integrated, adaptive community of
organisms having a species composition, diversity, and functional organization comparable to that of the
natural habitats of the region (Karr et al. 1981). Assessing biological integrity requires comparing the
biological communities of reference sites to the biological community of the wetland being evaluated.
Theoretically, the two should be similar if the community is undisturbed.
Classification is an important component in developing biocriteria. Wetlands have biological
communities that reflect climate, hydroperiod, habitat, geomorphology, etc. Classification, a tool to
explain and sort out the natural variations in biological communities, has generally been used for spatial
variability. States often classify aquatic resources by regions that are ecologically similar (Omernik et al.
1997). Wetlands in the mountains, for instance, would be grouped or classified separately from wetlands
in the valleys. Temporal variability is usually controlled by developing a sampling index period. For
example, protocols may be developed where wetlands are only sampled for macroinvertebrates during the
summer, when the macroinvertebrate population is stable, because the population often change between
seasons.
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Study Areas and Designs
Montana's approach to developing biocriteria currently includes several study designs aimed at
developing tools to help detect human influence on wetland water quality. The original study was
designed in 1992 by DEQ Staff (Dr. Loren Bahls and myself) and involved sampling 80 wetlands
throughout Montana from April through September during 1993 and 1994 (Figure 1). Macroinvertebrates
(e.g., aquatic insects) and diatoms (algae), considered to be sensitive and responsive to changes in water
quality, were the biological communities sampled (Adamus 1996). I sampled a representative number of
wetlands from the Rocky Mountain, Intermountain Valley and Prairie Foothills, Glaciated Plains, and
Unglaciated Plains Ecoregions (Omernik 1986). To reduce seasonality, I attempted to sample all
wetlands within the same ecoregion during similar time periods. I sampled wetlands of the Plains
Ecoregion from early April through mid-June, wetlands of the Intermountain Valleys and Prairie
Foothills Ecoregion from mid-June until early August, and wetlands of the Rocky Mountain Ecoregion
from early July through September.
In order to develop a framework for classification, we made an effort to sample the full spectrum of
wetland types in Montana. These wetland types have open water for at least one season and are able to
support diatom and macroinvertebrate communities. Several of the wetland types targeted for sampling
were large, often identified as lakes on topographical maps.
We designed the study to sample approximately 75 percent reference sites and 25 percent impaired
sites. We found it useful to require the majority of the wetlands in the study design to be reference sites
for assessing the reference condition for a wide variety of wetland types. Requiring approximately 25
percent of the wetlands in the study design to be impaired provided us the opportunity to test biological
measurements for the ability to detect anthropogenic impacts on water quality. We also designed the
study to test and develop biological measurements that could detect multiple anthropogenic impacts such
as irrigation and silviculture, across numerous wetland types in an effort to develop a consistent approach
to assessing the water quality for all the wetlands evaluated.
If anthropogenic activities such as dryland agriculture, irrigation, feedlots, grazing, silviculture, road
construction, hydrologic manipulation, urban runoff, wastewater, mining, and oil and natural gas
production occurred in the wetland's watershed, we considered the wetland to be impaired. Some of the
potential causes of impairment included the following: elevated nutrients, salinity, organic carbon,
sediment, metals, and water temperature; introduction of exotic species; destruction of habitat; and
fluctuating water levels. We selected wetlands for sampling based on many variables, including the
availability of historical data, special interests by other entities, cooperation by land owners, and
accessability.
Wetlands evaluated for the study included those located within U.S. Fish and Wildlife Service
National Wildlife Refuges and Waterfowl Production Areas (41%), U.S. Forest Service Research Natural
Areas and special interest areas (24%), The Nature Conservancy preserves (4%), State of Montana
waterfowl production areas and Department of Transportation mitigation sites (13%), and industrial and
individual private lands (18%).
Two other studies are currently underway in Montana with the purpose of developing wetland
biocriteria. Both studies involve sampling depressional wetlands in the Ovando and Mission Valleys of
western Montana. We selected depressional wetlands for more intensive research because of the highly
variable hydrology, water-column chemistry and biological communities.
Researchers from Montana State University designed a study in 1997 that included the development
of vegetation biocriteria because many depressional wetlands are seasonally dry and cannot be easily
sampled for macroinvertebrates or diatoms (Borth 1997). Their study design included the sampling of
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vegetation, macroinvertebrates, and diatoms from 24 depressional wetlands with similar climate,
hydrology, and water chemistry. Their study also involved sampling across three levels of human
disturbances—minimally impacted, slightly impacted, and moderately impacted; it involved two
anthropogenic impairments as well—dryland agriculture and grazing.
The University of Montana is currently designing a study to provide information concerning the
spatial and temporal variability of macroinvertebrate and diatom communities of depressional wetland
reference sites (Hauer 1998). The objective of their study is to determine how chemical and physical
gradients, and seasonality influence the macroinvertebrate and diatom communities. Their study design
includes the sampling of macroinvertebrates and diatoms from 33 depressional wetlands of varying
alkalinity, salinity, and hydroperiod, with six of the wetlands sampled several times throughout the field
season. The study will assist DEQ in refining the wetland classification system and will aid in the
development of a sampling index period for western Montana depressional wetlands. These wetlands are
also being intensively sampled to develop and test a model for assessing depressional wetlands using the
Hydrogeomorphology (HGM) functional assessment approach (Federal Register, 1996). After this study
is completed, researchers could share data and link biological criteria to the HGM.
Sampling Methods (1992 Study Design)
Loren Bahls and I designed the sampling methods to allow data to be collected in the field within 1-2
hours. I collected samples of each wetland's water column, sediment, and macroinvertebrate and diatom
communities. In order to document impairments and for classification purposes, I collected water-column
and sediment samples for chemical analysis. I collected all the samples from a location that I determined
to be a representative site. These locations were restricted to areas that I found to be easily accessible
when wearing hip boots. I also recorded field chemical measurements, observations and photographs at
this location. To reduce sampler variability, I collected all the samples myself, and made all the
observations for all the wetlands evaluated.
I collected grab samples of each wetland's water column for analysis of common ions, nutrients, total
organic carbon, and total recoverable metals. Using a Horiba U-10 Water Quality Checker, I measured
field pH, conductivity, salinity, dissolved oxygen, turbidity, and temperature. I collected composite
sediment samples from each wetland's substrate for metal analyses. Using a log book, I recorded field
information and included the date and time samples were collected; wetland identification and location;
ownership; ecoregion; potential sources, causes, and degree of impairment; approximate wetland area
and maximum depth; substrate texture; percent open water; water color; vegetation in the upper
watershed; riparian and aquatic vegetation; anthropogenic activities within the watershed;
hydrogeomorphic features; and wildlife, macroinvertebrate and algae observations.
Using a 1 mm mesh D-Net in a sweeping motion, I collected macroinvertebrates from all
microenvironments in a sampling location. I composited macroinvertebrate samples and associated
materials such as vegetation and sediment, into a 1-liter plastic container and preserved them with
ethanol. I made an effort to collect 300 organisms from each location using a consistent method of
collection. To insure preservation, I refilled the sample with fresh ethanol several days after collection.
Tetra Tech, Inc. performed the subsampling and sorting of the macroinvertebrate samples (Stribling
et al. 1995). Because it took up to up to 18 hours to sort the macroinvertebrate samples as a 300-organism
subsample, Tetra Tech, Inc. modified the subsampling protocol to a 200-organism subsample. At 17 of
the 80 sites, there was an insufficient number of organisms present in the sample to permit subsampling
and the entire sample was sorted and identified. Tetra Tech, Inc. identified a lower threshold of 125
organisms based on a scatter plot of "Number of Individuals" vs. "Total Taxa". Seven wetland sites had
fewer than 125 organisms. I determined that there were so few organisms because the sites were either
saline, highly alkaline, ephemeral, or impaired.
IH-466
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Tetra Tech, Inc. identification the organisms in the wetland samples and standardized the taxonomic
level of identification based on Montana Stream Protocols (Bukantus 1994). Tetra Tech, Inc. eliminated
several taxa from consideration for metric development as they determined these taxa to be nonbenthic or
semi-aquatic surface dwellers and considered them uninformative in reflecting water quality. These taxa
included Gerridae, Collembola, Dytiscidae, Hydrophilidae, Ostracoda, Anostraca, Copepoda, Cladocera,
Notonectidae and Corixidae (Stribling et al. 1995).
Following methods of Bahls (1993), I collected diatoms as grab samples from the natural substrate
such as vegetation and sediment. I collected the diatom samples in a 250-ml plastic container and
preserved them with Lugol's solution. The Academy of Natural Sciences of Philadelphia (ANSP)
performed the subsampling, digestion, and mounting of the diatoms (Charles et al. 1996). The ANSP
identified all diatom valves to the lowest taxonomic level possible.
In order to classify or document impairment, Mark Shapley, a hydrogeologist for The Montana
Natural Heritage Program (MNHP), assisted DEQ in developing a wetland classification system through
summarizing and interpreting the physical and chemical data (Shapley 1995). Using topographic maps,
field observations, and information gathered from land management agencies, Shapley interpreted
geomorphic characteristics; collected geologic setting information; developed a hydrogeomorphic
database, and a bibliography that included all known sources of geologic, hydrologic, and water quality
information; collected interpolated mean annual climatic data including annual precipitation, estimated
mean annual evaporation and net precipitation from the Montana Agricultural Potentials System; and
generated maps for each wetland using Geographic Information System (GIS). Map features included
were hydrologic delineations, land management areas, counties, cities, major transportation corridors,
wetland watershed boundaries, and sampling locations.
Wetland Classification
Mark Shapley and I found that wetland biological communities varied widely between the reference
sites. We felt that the major causes of the variability of wetland biological communities were caused by
hydrologic content and functions, the source of the water, geomorphology, and climate. We evaluated the
following concepts and variables to developing a wetland classification system to stratify the natural
variability of biological communities.
According to Meeks (1990) the hydrologic content primarily affects the chemical and physical
aspects of wetlands that, in turn, affect the wetland biological component. Kantrud et al (1989) found that
the source of the water also plays an important role in determining wetland water quality and,
consequently, the biological component that lives there. For example, atmospheric water tends to be low
in dissolved salts, runoff tends to be intermediate, and groundwater, depending on the characteristics of
the substrate, tends to be high. Swanson et al. (1988) determined that wetland hydrologic functions
control the chemical characteristics of wetlands, and as a result, plant and invertebrate communities. For
example, closed basin wetlands (those without surface water outlets like a creek) that receive
predominantly precipitation tend to be dilute. Groundwater flow-through systems that are closed basins
tend to be higher in dissolved salts. At even higher salt concentrations, are closed basin wetlands that
lose water primarily to the atmosphere by evapotranspiration. According to Winter (1977), the most
important variables to consider in the hydrologic classification of lentic waterbodies such as lakes and
wetlands, to predict certain water-column chemistry are the following: dissolved concentration of
groundwater, precipitation-evaporation balance, streamflow inlet and outlet, the ratio of drainage basin
area to lake area, lake depth, local relief, regional slope, and regional position. Ecoregions are often used
by States to group or classify waterbodies from regions with similar variables such as climate, landforms,
hydrology, etc. (Omernik et al. 1997).
m-467
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In developing the Montana wetland classification system, we used ecoregions and hydro-
geomorphology to explain and sort out the natural variability of macroinvertebrate and diatom
communities. We used water chemistry such as pH, alkalinity and conductivity; and vegetation to assist
in delineating wetland classes. Both Stribling et al. (1995) and Charles et al. (1996) determined that
wetlands located in the Rocky Mountain, Intermountain Valley and Prairie Foothills, and Plains
ecoregions had significantly different biological communities. However, they detected no significant
differences between ecoregion subclasses.
Hydrogeomorphology has been used extensively to classify waterbodies (Rosgen 1993; Cowardin
1979; and Winter 1977). Brinson (1993) has developed and is continuing to refine a hydrogeomorphic
approach (HGM) to classify wetlands. Our approach to wetland classification follows similar concepts as
HGM, but our wetland classes were also evaluated to determine if reference sites within each class had
similar macroinvertebrate and diatom communities.
We currently have delineated ten wetland classes using hydrogeomorphology, ecoregions and water-
column chemistry. The classification system is likely to change in the future as more data is collected and
additional wetland reference sites are evaluated.
We classified wetlands using the following ecoregions: Rocky Mountain; Intermountain Valleys and
Prairie Foothills; and Plains. Using hydrogeomorphic features, we further classified wetlands as the
following: 1) Headwater wetlands, 2) Riparian Wetlands, 3) Open Lake Wetlands, and 4) Closed Basin
Wetlands. Headwater Wetlands are located at regionally high elevations like the headwaters of a stream,
receive mostly precipitation or snowmelt, and have low pH and conductivity. Riparian wetlands are
associated with a stream, large spring, or are calcareous fens, have relatively large watersheds or receive
groundwater. Consequently, Riparian Wetlands tend to have higher pH and conductivities than
Headwater Wetlands, because the water that it receives has longer contact with the surrounding soils and
geology. Open Lake Wetlands are ponded sites having an inlet and outlet, and relatively stables water
levels. Closed Basin Wetlands are lacking surface water outlets.
Closed Basin Wetlands include depressional wetlands and shallow closed basin reservoirs. We
refined the wetland classification system using several chemical and hydrogeomorphic variables such as
salinity, alkalinity, topographic position, hydroperiod and watershed/wetland area ratios. The following
are examples of the rationale that we used for classifying closed basin wetlands using chemical and
hydrogeomorphic variables: closed basin wetlands that receive predominantly groundwater and are at
their hydrologic endpoint, generally are at regionally lower elevations and are saline due to evaporation;
closed basin wetlands that have groundwater flowing through them tend to be alkaline; closed basin
wetlands that have large watershed/wetland area ratios tend to receive predominantly surface water and
often have widely fluctuating water levels during the course of a year; and wetlands that are ephemeral
tend to have a lower diversity of macroinvertebrates.
Two wetland types are difficult to classify. Wetlands such as potholes located in the arid west,
including Montana, are highly complex and difficult to classify due to both spatial and temporal
variability. For these wetlands, the hydrology, water chemistry and biology can change dramatically
throughout a season or from year to year as a result of climatic change. For example, the biological
community of a wetland often changes due to an increase in salinity or a decrease in water content
caused by drought. To account for extreme temporal variability like from year to year, a classification
system may need the ability to change based on climatic conditions such as drought. The second type of
wetland that is difficult to classify are large shallow reservoirs. These wetlands are hydrologically
manipulated with controlled outlets like dams or dikes. Large shallow reservoirs can be managed as
closed basins (no outlets) or as open lakes (with inlets and outlets). Because wetlands are classified based
on whether or not they have inlets or outlets, if a wetland can be changed it is difficult to classify.
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Analytical Methods
We used the multimetric approach to evaluate wetland macroinvertebrate communities and the
multivariate approach to analyze wetland diatom communities. Multivariate analysis compliments the
development of multimetric indices as it can be used by researchers to detect ecologically meaningful
patterns for the development of metrics and a classification system. For this reason, it is advantageous to
use both approaches in developing biocriteria (Gerritsen 1995; Karr et al. 1997; Norris 1995; Reynoldson
et al. 1997).
A metric is defined as an enumerated or calculated term representing some aspect of a biologic
community, such as structure, function, or other measurable feature that changes in a predictable way in
response to an anthropogenic stressor (Stribling et al. 1995). For example, a possible metric could be the
number of taxa living in the wetland or the relative abundance of a taxa that tolerates pollution.
Multimetrics are additive biological indices, meaning they are the sum of several metrics (Gerritsen
1995). Tetra Tech, Inc. assisted DEQ in developing wetland macroinvertebrate multimetric indices
(Stribling et al. 1995). They followed methods similar to those described by Karr and Chu (1997) with
the purpose of expressing and interpreting how similar a macroinvertebrate community at a wetland site
was to its potential if left undisturbed (i.e., to its reference condition).
One of the advantages of multimetric indices is that they are relatively simple to calculate from
collected data (Gerritson 1995). According to Norris (1995), one of the weaknesses of using multimetric
indices is that it requires a thorough understanding of each individual metric's ecological relationship in
order to evaluate water quality successfully by predicting anthropogenic stressors. For example, for most
wetland types a high diversity of macroinvertebrates may indicate reference condition; while for some
wetland types like forested wetlands a higher diversity of macroinvertebrates may indicate impairment. In
addition, metrics are often redundant in a combination index and errors can be compounded (Reynoldson
et al. 1997). Because of this we evaluated our proposed metrics in an attempt to develop an
understanding of ecological relationships, to test the metric's ability to predict various anthropogenic
stressors, and to test redundancy.
Multivariate analysis is a statistical approach used by biologists to determine relationships between
biota such as diatoms or macroinvertebrates, and environmental variables such as water-column
chemistry. Multivariate analyses require the sampling of many reference sites for two things: biota, and a
wide range of environmental variables like water-column chemistry. These sites are then classified or
grouped based on biota uniformity, which can be used to describe the environmental variables (Norris
1995). Multivariate analysis is a useful exploratory approach that can help uncover patterns when only a
little is known about the natural history of a place or biological community, and can play a significant
role in the classification of aquatic resources for developing multimetric indices (Gerritsen 1995).
Multivariate methods are attractive because they require no prior assumptions either in creating reference
site groups or in comparing test sites with reference groups. Unfortunately, potential users may be
discouraged by the complexity of multivariate techniques (Reynoldson et al. 1997).
The multivariate approaches that the ANSP used to investigate relationships between Montana
wetland diatom assemblages and environmental variables (mostly water-column chemistry) was
Detrended Canonical Correspondence Analysis (DCCA) and two-way indicator analysis (TWINSPAN).
The ANSP graphically displayed clusters of diatoms with similar composition using DCCA (Charles et
al. 1996). Using DCCA, ANSP displayed and labeled vectors to illustrate the relationship between
diatom assemblages and environmental variables. Longer vectors show us a stronger correlation among
diatom assemblages and environmental variables (Figure 2). The ANSP used envelops to graphically
enclose all reference sites using the wetland class delineations (Figure 3).
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Results
With assistance from Tetra Tech, Inc., I developed seven macroinvertebrate biological measurements
(i.e., proposed metrics) that appeared to be useful for developing multimetric indices for detecting
wetland water quality impairment (Table 1). I chose biological measurements as proposed metrics if they
appeared to detect impairment of several types of anthropogenic stressors for the majority of the wetland
classes. These metrics did not appear to work for saline or highly alkaline wetlands for two major
reasons: 1) Metrics could not be calculated, because few of the counted taxa were found in saline
wetlands, 2) Due to highly variable macroinvertebrate communities, the reference condition could not be
determined for highly alkaline wetlands. I also found it difficult to detect impairment for shallow
depressional wetlands as I believe that they tended to have highly variable macroinvertebrate
communities due in part to widely fluctuating water levels and salinities. I did find the macroinvertebrate
indices most useful for detecting impairment of perennial wetlands with relatively stable water levels and
salinities, and at the same time the alkalinity and salinity is low.
I did not score the proposed metrics using 1-3-5 where severely-impaired is scored 1, slightly-
impaired is scored 3 and least-impaired is scored 5 (Gerritson 1995; Karr 1986). Instead, I designed the
metric scores to reflect slight changes in the macroinvertebrate community and weighted them according
to their ability to respond to anthropogenic stressors. For example, the "Number of Taxa" and "Percent
Dominance" metrics appeared to be the most responsive to stressors; therefore, their scores were given
the greatest weight. The "number of individuals" metric was the least responsive to stressors; therefore,
its score was weighted least.
For several of the metrics, the maximum scores were theoretically infinite, but the maximum score
for these metrics did tend to level off as it reached its maximum potential (reference condition). For
example, the maximum "Number of Taxa" score (potential) for a riparian wetland and ephemeral
depressional wetland was approximately 28 and 15, respectively. Since each metric had a different
potential for each wetland class, the multimetric macroinvertebrate index scores for reference condition
also differed between wetland classes.
I developed decision thresholds of optimal, good, fair, and poor using best professional judgement to
delineate wetland biological condition (Figures 4 and 5). The ability to develop decision thresholds
varied with each wetland class as it depended in part on the number of reference and impaired sites
sampled. I rated the reference sites as optimal. I further evaluated wetland sites that were considered
reference condition but had an index score different from other reference sites within the same class to
determine if they were misclassified, impaired, resulting from sampling or analytical errors, or influenced
by seasonality or anthropogenic stressors. The delineation of the remaining decision thresholds (i.e.,
good, fair, and poor) incorporated the evaluation of sites with documented impairment and the variability
of reference site index scores within each wetland class. Physicochemical and historical data, field notes
and photographs assisted in the delineation of the decision thresholds.
The ANSP analyzed Montana's wetland diatom assemblages and associated environmental variables
such as water-column chemistry using the multivariate analyses DCCA and TWINSPAN (Charles et al.
1996). Using DCCA, the ANSP determined that phosphorus, conductivity and pH which had the longest
vectors were strongly correlated with diatom assemblages. There also appeared to be a correlation
between diatom assemblages and calcium and lead; however, the vectors are shorter (Figure 2). In 1995,
Stevenson and Yangdong also discovered that diatoms strongly correlated to phosphorus and
conductivity in wetlands located in western Kentucky, and in 1996, McCormick et al. determined that
periphyton taxonomic composition related strongly to total phosphorus in the Florida Everglades.
The ANSP used both DCCA and TWINSPAN to test and refine the wetland classification and for
detecting water quality impairment. The ANSP categorized the wetland sites according to the
predetermined wetland classes. Envelops enclosed all of the reference sites within each class in the
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DCCA graphs. I evaluated outlier sites, such as WET59 and WET63 to determine if they were impaired
or misclassified (Figure 3).
The ANSP determined that for most of the predetermined classes, diatom assemblages clustered
fairly well using DCCA, showing that they are biologically similar (Charles et al. 1996). I further
evaluated wetland classes with high variability in diatom assemblages to determine if wetlands were
misclassified or if additional class delineations were necessary. If significant overlap occurred between
classes, I evaluated them further to determine if they could be combined.
Graphs generated using DCCA to analyze diatom assemblages demonstrated that three wetland
classes—Riparian, Open Lakes and Ground water Recharge Closed Basin—had significant overlap of
their reference sites. Upon further evaluation, I determined that although these wetlands differed
hydrologically, the water-column chemistry, diatom assemblages, and macroinvertebrate multimetric
indices were similar for these classes. For this reason, I concluded that these classes could be combined,
though I felt that more wetlands should be sampled and evaluated to confirm these conclusions.
Graphs generated using DCCA also illustrated that the Alkali Closed Basin wetland class had two
reference wetland sites that were outliers. It appeared that these wetlands were hydrologically similar, but
they differed from the other reference sites biologically, and chemically; consequently, the classification
system may need to be refined in the future.
We considered the outliers of the enveloped reference sites in the DCCA graphs as impaired (Figure
3). The greater the distance between the outlier site and the enveloped centroid of the reference sites, the
greater the impairment. The location of the outlier sites often indicates the cause of impairment. For
example, outlier sites located in the far upper right quadrant may be receiving acid mine drainage, while
salinity might be impacting outlier sites located toward the left side of the enveloped reference sites
(Figure 3).
The TWTNSPAN analysis produced seven distinct wetland diatom assemblage groupings similar to
DCCA clusters. The TWTNSPAN analysis, however, did not distinguish ephemeral from perennial
wetlands. Probably, hydrologic content is less important to diatoms than to macroinvertebrates since
diatoms have shorter life spans. TWTNSPAN also appeared to group wetlands that had high sediment.
The sediment likely came from two sources: highly variable water levels that exposed the sediment or
intensive agriculture that resulted in sediment from runoff. The ANSP characterized this group by an
abundance of motile diatom taxa, particularly Nitzschia and Navicula (Charles et al. 1996).
Conclusion
In most cases, both the multimetric approach using macroinvertebrates and the multivariate approach
using diatoms identified similar wetlands as impaired. Neither approach detected impairment in saline or
highly alkaline wetlands although the multivariate approach using diatom assemblages appeared to be the
most promising. Macroinvertebrate and diatom biocriteria are not likely to be the most useful for
wetlands lacking open water environments as they would be difficult to sample. Vegetation biocriteria is
likely to be the most appropriate tool for assessing the biological condition of these wetland types. Due to
longer life spans, macroinvertebrates appeared to be most useful for detecting habitat impairment as they
are more capable than diatoms of integrating impairment over time. Diatoms appeared to be most
responsive to excessive nutrients, salinity, sediment, acidity, and metals in the water column that existed
during the time of sampling or that occurred 2-3 weeks prior to sampling. Neither diatoms or
macroinvertebrates could detect elevated trace metals in the sediment using either method.
The multivariate approach was a useful tool for detecting water quality impairment, and therefore
may also be useful for detecting ecologically meaningful patterns that can be used for developing
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metrics. We found DCCA to be useful as it often indicated the environmental variables causing
impairment. It was, however, difficult for us to evaluate the degree of impairment.
Classification is one of the most important components for developing successful wetland biocriteria.
Wetland hydrology, water-column chemistry, and biological communities tend to be highly variable both
spatially and temporally. It is difficult to develop a classification system that will stratify the natural
variability of wetland biological communities so that anthropogenic impacts can be consistently detected.
Hydrogeomorphology appears to be a useful approach to wetland classification. Still, the relationship
between wetland biological communities such as diatoms and macroinvertebrates and hydrogeomorphic
functions is not well understood.
Multivariate analysis is an important tool for wetland classification because it is an objective
approach that detects patterns in biological assemblages such as wetland site groupings caused by natural
variability. Consequently, a researcher can use multivariate analysis to initiate the development of a
classification system without any bias; or to test conceptual approaches to wetland classification like
hydrogeomorphology or ecoregions.
Literature Cited
Adamus, P.R. and K. Brandt. 1990. Impacts on water quality of inland wetlands of the United States: A
survey of indicators, techniques, and applications of community-level biomonitoring data. EPA/600/3-
90/073. U.S. EPA, Environmental Research Laboratory, Corvallis, OR.
Adamus, P.R. 1996. Bioindicators for assessing ecological integrity of prairie wetlands. EPA/600/R-
96/082. U.S. EPA, Environmental Research Laboratory, Corvallis, OR.
Bahls, L.L. 1993. Periphyton bioassessment methods for Montana streams. Montana Department of
Health and Environmental Sciences, Water Quality Bureau, Helena, MT.
Borth, C. 1997. Development of vegetation indices for water quality and hydroperiod in depressional
wetlands in western Montana. Masters Thesis proposal submitted to DEQ. Montana State University,
Bozeman, MT
Brinson, M. 1993. A hydrogeomorphic classification of wetlands. Wetlands Research Program, U.S.
Army Corps of Engineers, Technical Report WRP-DE-4.
Bukantis, R. 1996. Rapid bioassessment macroinvertebrate protocols: Sampling and sample analysis
SOPs. MT Dept. of Environmental Quality, Planning, Prevention and Assistance Division, Helena,
MT.
Charles, D., F. Acker, and N.A. Roberts. 1996. Diatom periphyton in Montana lakes and wetlands:
Ecology and potential as bioassessment indicators. A report submitted to the Montana DEQ. Patrick
Center of Envir. Research, Environmental research Division, The Academy of Natural Sciences,
Philadelphia, PA.
Cowardin, L.M., V. Carter, F.C. Golet, and E.T. LaRoe. 1979. Classification of wetlands and deepwater
habitats of the United States. U.S. Fish and Wildlife Biological Service Program FWS/OBS-79/31.
Davis, W.S. and T.P. Simon, eds. 1995. Biological assessment and criteria: tools for water resource
planning and decision making. CRC Press, Inc., Lewis Publishers, Boca Raton FL.
Federal Register: August 16, 1996. Regulatory Program of the U.S. Army Corps of Engineers. National
action plan to develop the hydrogeomorphic approach for assessing wetland functions. 61(160):
42593-42603.
Gerritsen, J. 1995. Additive biological indices for resource management. BRIDGES—Integrating basic
and applied benthic science. J. of the N. Amer. Benthological Soc. 14(3):440-450.
Hauer, F.R. 1998. Linking macroinvertebrate biocriteria and the hydrogeomorphic approach to the
functional assessment of wetlands: Analysis of reference pothole wetlands in western Montana.
Proposal. Univ. of Montana Flathead Lake Biological Station, Poison.
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Kantrud, H.A., G.L. Krapu, and G.A. Swanson. 1989. Prairie basin wetlands of the Dakotas: A
community profile. USFWS, Northern Prairie Wildlife Research Center—Biological Report 85 (7.28).
Karr, J.R. and D.R. Dudley 1981. Ecological perspective on water quality goals. Environmental
management 5: 55-68.
Karr, J.R., K.D. Fausch, P.L. Angermeier, P.R. Yant, and I.J. Schlosser. 1986. Assessing biological
integrity in running water. A method and its rationale. Illinois Natural History Survey, Special
Publication 5.
Karr, J.R. and C.W. Chu. 1997. Biological monitoring and assessment: using multimetric indexes
effectively. EPA 235-R97-001. University of WA. Seattle, WA.
McCormick, P., P.S. Rawlik, K. Lurding, E.P. Smith and F.H. Sklar. 1996. Periphyton-water quality
relationships along a nutrient gradient in the northern Florida Everglades. J. N. Amer. Benthological
Soc. 15(4):433-449.
Meeks, G. and C.L. Runyon. 1990. Wetland protection and the states, national conference of state
legislatures. ISBN 1-55516-425-0.
Norris, R.H. 1995. Biological monitoring; the dilemma of data analysis. BRIDGES—Integrating basic
and applied benthic science. J. of the N. Amer. Benthological Soc. 14(3):440-450.
Omernik, J.M. 1986. Ecoregions of the coterminous United States. U. S. EPA Corvallis Environmental
Research Station RID 8769170 (map).
Omernik, J.M. and R.G. Bailey. 1997. Distinguishing between watersheds and ecoregions. J. Amer.
Water Res. Assoc. 33( 5):935-949.
Reynoldson, T.B., R.H. Norris, V.H. Resh, K.E. Day, and D.M. Rosenberg. 1997. The reference
condition: a comparison of multimetric and multivariate approaches to assess water-quality
impairment using benthic macroinvertebrates. J. N. Am. Benthol. Soc. 16(4):833-852.
Rosgen, D. 1996. Applied river morphology. Wildland Hydrology Consultants. Pagosa Springs, CO.
Library of Congress Card No. 96-60962.
Shapley, M.D. 1995. Geologic, geomorphic and chemical characteristics of wetlands selected for use in
biocriteria development by the Montana Department of Environmental Quality. A report submitted to
the Montana DEQ. Montana Natural Heritage Program, Helena, MT.
Stevenson, R.J. and P. Yangdong. 1996. Gradient analysis of diatom assemblages in western Kentucky
wetlands. J. Phycol. 32, 222-232.
Stribling, J.B., J. Lathrop-Davis, M.T. Barbour, J. White, and E.W. Leppo. 1995. Evaluation of
environmental indicators for the wetlands of Montana: The multimetric approach using benthic
macroin vertebrates. A report Submitted to Montana DEQ. Tetra Tech, Inc., Owings Mills, MD.
Swanson, G.A., T.C. Winter, V.A. Adomaitis, and J.W. LaBaugh. 1988. Chemical characteristics of
prairie lakes in South-Central North Dakota—their potential for influencing use by fish and wildlife.
U.S. Dept. of the Interior Fish and Wildlife Service, Northern Prairies Research Center. Fish and
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Water Resource Research USGS Vol. 13, No.4.
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Figure 1. Montana Department of Environmental Quality
wetland characterization sites.
Phosphorus
Pb
Detrended Canonical Correspondence Analysis
(DCCA)
Figure 2. Ordination of Montana wetland diatom
assemblages and environmental variables that account for
most of the assemblage variability.
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PHOSPHORUS
t
O x
WET59
O CT
• SALINE
O HEADWATER AND DILUTE CLOSED BASIN
^ EPHEMERAL
Detrended Canonical Correspondance Analysis
(DCCA)
Figure 3. Diatom assemblage clusters for Montana
Headwater and Dilute Closed Basin, Saline, and
Ephemeral wetlands, and environmental variables that
accounted for most of the assemblage variability.
Reference sites are enveloped. Outlier sites are impaired.
Impairments were also detected using macroinvertebrates
multimetric indices (Figures 4 and 5).
METRICS
0 HILSENHOFF BOTIC INDEX
• LEECH/SPONGE/CLAM
B CRUSTACEA/MOLLUSCA
• CHIHONOMIDAE (MIDGE)
• # INDIVIDUALS
3 PLEC/OOON/EFH/TRJC
D INVERSE % DOMINANCE
S * TAXA
WETLAND CODE NAMES
Figure 4 Macroinvertebrate multimetric index scores for
Headwater and Dilute Closed Basin Wetlands. WET59
was impacted by acid mine drainage. NOTE: The metric
"Hilsenhoff Biotic Index" is no longer recommended.
HI-475
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METRICS
a HILSENHOFF BIOTIC INDEX
• LEECH/SPONGE/CLAM
H CRUSTACEA/MOLLUSCA
• CHIRONOMIOAE (MIDGE)
• # INDIVIDUALS
B PLEC/ODON/EPH/TRIC
D INVERSE % DOMINANCE
B # TAXA
WETLAND CODE NAMES
Figure 5. Macroinvertebrate multimetric index scores for
Ephemeral wetlands. WET63 was impacted by agriculture
induced saline seeps. NOTE: The metric "Hilsenhoff Biotic
Index" is no longer recommended.
Table 1. Proposed Metric, Proposed Metric Calculations and Score Calculations Used for
Developing Wetland Macroinvertebrate Indices
Prosposed Metrics
Number of Taxa
Percent Dominance
POET
Number of Individuals
Chironomidae
Crustacea/Mollusca
Leech/Sponge/Clam
Proposed Metric Calculation
Count taxa to lowest level possible
Accumulative total of percent 1 , 2, and 5
most dominant taxa
Count Plecoptera, Odonata, Ephemeroptera
and Tricoptera taxa (species)
Count individuals in total sample
(maximum count of 300)
Number of Chir taxax(100-%chir+50)x
(((%Orthocladiinae to total ChirH-100)+0.5)
Number of Crustacea and Mollusca Taxa
x(100-%Crustacea to total Mollusca
Taxa+50)
Count Hirudinea, Porifera and Sphaeriidea
Taxa to lowest level possible
Score Calculation
(Number of Taxa)x0.75
((300- percent Dominance)-=-25)x3
(POET)x3
(Number of Individuals^- 100)x3
(Chironomidae-^250)x3
((Crustacea/MolluscaH 1 00)x3
(Leech7Sponge/Clam)x3
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Indicators of Reservoir Ecological Condition
Don L. Dycus, Technical Specialist
Tennessee Valley Authority
Introduction
The Tennessee Valley Authority (TVA) began a program to systematically monitor the ecological
condition of its reservoirs in 1990. Previously, reservoir studies had been confined to reservoir specific
assessments to meet specific needs as they arose.
Objectives of TVA's monitoring efforts, termed Vital Signs Monitoring, are to provide information
on the "health" or integrity of the aquatic ecosystem in major Tennessee Valley reservoirs. Ecological
monitoring activities provide the necessary information from key physical, chemical, and biological
indicators to evaluate conditions in reservoirs and to target detailed assessment studies if significant
problems are found. In addition, this information establishes a baseline for comparing future water quality
conditions.
This paper describes the monitoring and data evaluation processes used to evaluate the overall
ecological health of reservoirs. It summarizes 1997 data as an example of the mechanics of the ecological
health scoring system used in the process.
The reservoir ecological health evaluation system is reviewed each year seeking areas in need of
improvements. Initially, numerous improvements were made based on experienced gained from working
with this new system and input from other professionals. Each year, progressively fewer changes have
been needed.
Study Design Considerations
This monitoring program was designed based on several fundamental premises.
1. Ecological health evaluations must be based on physical, chemical, and biological components of
the ecosystem.
2. Monitoring must provide current, useful information to resource managers and the public.
3. Monitoring program design must be dynamic and flexible, rather than rigid and static, and must
allow adoption of new techniques as they develop.
4. Monitoring must be sustained for several years to document the status of the river/reservoir
system, determine its year-to-year variability, and track changes through time.
5. Addressing specific cause/effect mechanisms is not the primary purpose of monitoring. While
monitoring may provide information to identify cause/effect relationships, more detailed
assessment investigations usually are required.
With these premises in mind, our challenge has been to develop a sustainable monitoring effort that
collects the right kinds of physical, chemical, and biological data to provide enough information to
reliably characterize ecological health. Study design must carefully consider selection of important
ecological indicators, representative sampling locations, and frequency of sampling, all in light of
available resources. Following are some of the basic study design decisions made in developing this
program.
Ecological Indicators—Physical, chemical, and biological indicators (dissolved oxygen, chlorophyll,
sediments, benthos, and fish) were selected to provide information from various habitats or ecological
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"compartments". For example, the open water or pelagic area in reservoirs is represented by chlorophyll
and dissolved oxygen (DO) in midchannel. The shoreline or littoral area is evaluated by sampling the fish
assemblage. The bottom or benthic compartment is evaluated using two indicators: quality of surface
sediments in midchannel (determined by chemical analysis of sediments) and examination of benthic
macroinvertebrates from a transect across the full width of the sample area (including overbanks if
present).
Sampling Locations—Three areas were selected for monitoring: the inflow area, generally riverine in
nature; the transition zone or mid-reservoir area where water velocity decreases due to increased cross-
sectional area, suspended materials begin to settle, and algal productivity increases due to increased water
clarity; and the forebay, the lacustrine area near the dam. Overbanks, basically the floodplain which was
inundated when the dam was built, are included in transition zone and forebay areas. Embayments,
another important type of reservoir area, also were considered. Previous studies (Meinert et.al., 1992)
have shown that ecosystem interactions within an embayment are mostly controlled by activities and
characteristics within the embayment watershed, usually with little influence from the main body of the
reservoir. Although these are important areas, monitoring of hundreds of embayments is beyond the scope
of this program. As a result, only four, large embayments (all with drainage areas greater than 500 square
miles and surface areas greater than 4500 acres) are included in this monitoring effort.
Sampling Frequency—Sampling frequencies (indexing periods) must consider the expected temporal
variation for each indicator. Physical and chemical components vary in the short term so they are
monitored monthly from spring to fall. Biological indicators better integrate long-term variations and are
sampled once each year. Fish assemblage sampling is conducted in autumn (September-November). From
1990 through 1994 benthic macroinvertebrate sampling was conducted in early spring (February-April) to
avoid aquatic insect emergence. Beginning in 1995, sampling was switched to late autumn/early winter
(November and December). The problem with spring benthos sampling was that results were reflective of
conditions from the previous year. This caused results for this indicator to be out of synch with those from
the other indicators. This change is more thoroughly discussed in Dycus and Meinert (1996).
Another design issue dealing with sampling frequency is year-to-year variation. Meteorological
conditions (particularly runoff from rainfall and its influence on flows) have a great effect on reservoirs
and can vary substantially from year-to-year. To account for this variation, our design specifies that a
reservoir be sampled for five consecutive years. Following that, sampling occurs on an every other year
basis.
Data Evaluation Considerations
Like most evaluations, results for ecological integrity studies must be compared to some reference or
yard stick to determine if monitoring results are indicative of good, fair, or poor conditions. In streams
this is usually accomplished by studying a site that has had little or preferably no alterations due to human
activities. Observations at that site provide the reference conditions or expectations of what represents a
site with good/excellent ecological health. Given that reservoirs are not natural systems, this approach is
inappropriate. Other potential approaches include historical or preimpoundment conditions, predictive
models, best observed conditions, or professional judgment. Preimpoundment conditions are inappro-
priate because of significant habitat alterations. For the most part, models are of limited value for many
indicators because of spatial and temporal variations within and among reservoirs. Spatial variation exists
within in the multiple zones (e.g., forebay, transition zone, inflow, and embayments) of a reservoir.
Further, each zone responds differently to different stimuli. Temporal variations are introduced because
reservoirs are controlled systems with planned annual drawdowns in elevations ranging from only a few
feet to close to a hundred feet. This leaves best observed conditions and professional judgment as the
most viable alternatives for establishing appropriate reference conditions or expectations for reservoirs.
Our process uses a combination of these two approaches.
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A preliminary step to developing reference conditions is to examine the need to separate the
reservoirs under study into separate classes so that appropriate , "apples-to-apples" comparisons can be
made. Like streams, important considerations for classing reservoirs include size, gradient/depth,
ecoregion, etc. In addition, reservoirs are managed systems and management objectives must considered.
A lesson we learned early in this process was that the issue of classification and its influence on
determining reference conditions differed among the environmental indicators. A fundamental question
that had to be addressed separately for each indicator was—Should reservoir ecological health evaluations
be based on:
1. ideal conditions (basically a subjective determination; for example, a very low DO concentration
is an unacceptable ecological condition regardless of any classification issue); or
2. the best conditions expected/observed given the environmental and operational characteristics of
the dam/reservoir (for example, very low DO concentrations are acceptable in many tributary
reservoirs because they are expected due to water management practices, withdrawal schemes,
stratification, etc.)?
Our response (opinion) was that ideal conditions should be expected for DO and Sediment Quality.
That is, poor DO is unacceptable regardless of type of reservoir or dam operation. Sediments should not
have high concentrations of metals, should have no or at most very low concentrations of pesticides, and
should not pose a toxic threat to biota. In this situation, there is no need for classification because the
same conditions are desired for all reservoirs.
For chlorophyll, the classification scheme that has evolved is somewhat of a combination of the two
approaches. First the geological characteristics (primarily erodablility and nutrient level of soils) of the
watershed were examination. Then a conceptual/subjective decision made as to the concentrations
indicative of good, fair, and poor conditions. Two classes of reservoirs were developed - reservoirs in
watersheds draining nutrient poor soils, basically those in the Blue Ridge Ecoregion (i.e., expected
oligotrophic reservoirs); and reservoirs in watersheds draining soils which are not nutrient poor (i.e.,
expected mesotrophic reservoirs).
For the benthic macroinvertebrate community and fish assemblage, the "best expected/observed
conditions" approach was selected initially. Basically, this means the data base from the existing
population of reservoirs is examined to determine the range of conditions for each community
characteristic or metric (e.g., number of taxa). The process is to first omit outliers (defined as more than
three standard deviations from the mean), then trisect the range of remaining values. These three ranges
represent good, fair, and poor conditions and form the reference conditions or expectations for each
metric. This is still the basic approach used for these two indicators, but experience has shown best results
can be obtained by including professional judgment in the process. Cutoff points are examined closely
and adjusted, if appropriate, based on professional judgment. This approach is discussed in detail in
Dycus and Meinert (1998).
Reservoirs were divided into four classes to evaluate the benthos and fish. One class includes the
reservoirs on the Tennessee River plus the two navigable reservoirs on tributaries to the Tennessee River
(loosely termed run-of-river reservoir). This group of reservoirs has relatively short retention times and
little winter drawdown. The remaining tributary reservoirs were separated into three classes: those in the
Blue Ridge Ecoregion, those in the Ridge and Valley Ecoregion, and those on the Interior Plateau
Ecoregion. The run-of-the-river reservoirs were not subdivided by ecoregion because most of the water
flowing through each reservoir comes from upstream and does not originate within the ecoregion where
the reservoir is physically located.
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Ecological Health Rating Methods
In absence of universally accepted guidelines to evaluate reservoir ecological health, we had to
develop an evaluation methodology for reservoirs included in this program. The ecological health
evaluation system examines each indicator separately and then combines those ratings a single, composite
score for each reservoir.
Dissolved oxygen—The rating criteria represent a multidimensional approach that includes dissolved
oxygen levels both throughout the water column (WCDO) and near the bottom (BDO) of the reservoir. The
DO rating (ranging from 1 "poor" to 5 "good") at each sampling location is based on monthly
measurements during April through September for the run-of-the-river reservoirs and May through
October for the tributary reservoirs. This is the period when maximum thermal stratification and
maximum hypolimnetic anoxia are expected. The WCDo Rating is the six-month average of the
proportion of the reservoir cross-sectional area at the sample location that has a DO concentration less
than 2.0 mg/L. The BDO Rating is the six month average of the proportion of the reservoir cross-sectional
bottom length that has a DO concentration less than 2.0 mg/. The final DO rating is the average of these
results.
Chlorophyll—Scoring criteria were developed separately for each of the two classes of reservoirs.
Reservoirs expected to be oligotrophic receive highest ratings at low chlorophyll concentrations.
Reservoirs expected to be mesotrophic receive highest ratings for an intermediate range of concentrations.
Figure 1 shows the sliding scale used to evaluate the seasonal average chlorophyll concentration for each
reservoir class. For reservoirs expected to be mesotrophic, the rating is reduced at low chlorophyll
concentrations because some environmental factor (e.g., turbidity, toxicity, retention tine) must be
inhibiting primary production.
Sediment quality—Initially, the scoring criteria for sediment quality was based two components:
sediment toxicity tests and sediment chemical analyses for ammonia, heavy metals, pesticides, and PCBs.
Since 1995, the sediment quality scoring criteria have been based only on sediment analyses for metals
(As, Cd, Cr, Cu, Pb, Hg, Ni, and Zn), organochlorine pesticides, and PCBs. Sediment toxicity tests were
discontinued primarily because of budget reductions, but also because frequent changes in toxicity testing
methods made year-to-year comparisons difficult. The sediment quality rating compares results for metals
analyses to sediment guidelines we adapted from EPA Region 5 (EPA, 1977). Presence of any of the
organic analytes is deemed undesirable so results are compared to laboratory detection limits. If none of
the metals exceed these guidelines and no PCBs or pesticides are detected, the site would receive the
highest sediment quality rating. Occurrences of analytes above these standards lowers the rating.
Benthic Macroinvertebrate Communities—Scoring criteria were developed from the data base on
TVA reservoirs, as described above. Seven metrics or characteristics are used to evaluate the benthic
macroinvertebrate community. The benthic macroinvertebrate rating is derived from the total of these
metrics.
1. Taxa richness—The average total number of taxa per sample at each site.
2. EPT—The average number of Ephemeroptera, Plecoptera, and Trichoptera taxa per sample at
each site.
3. Long-lived species—The proportion of samples with at least one long-lived organism (Corbicula,
Hexagenia, mussels, and snails) present.
4. Percentage as Tubificidae—The average percentage of tubificids in each sample at each site.
5. Percentage as dominant taxa—The average percentage of the two most abundant taxa in each
sample.
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6. Total abundance excluding Chironomidae and Tubificidae—The average number of organisms
excluding chironomids and tubificids per sample at each site.
7. Proportion of samples with no organisms present—Proportion of samples with no organisms
present.
Fish Assemblage—Twelve metrics or characteristics are used to derived the Reservoir Fish
Assemblage Index (RFAI) which forms the basis for evaluating the fish assemblage (Hickman and
McDonough, 1995).
Species Richness and Composition Metrics
1. Total number of species
2. Number of piscivore species
3. Number of sunfish species
4. Number of sucker species
5. Number of intolerant species
6. Percentage of tolerant individuals (excluding Young-of-Year)
7. Percentage dominance by one species
Trophic Composition Metrics
8. Percentage of individuals as omnivores
9. Percentage of individuals as insectivore
Reproductive Composition Metrics
10. Number of lithophilic spawning species
Abundance Metrics
11. Total catch per unit effort (number of individuals
Fish Health Metrics
12. Percentage individuals with anomalies
The ecological health scoring process is designed such that four of the indicators (DO, chlorophyll-a,
benthos, and fish) are given equal weights and assigned a rating ranging from 1 (poor) to 5 (excellent).
The other indicator, sediment quality, is given only half the weight of the other indicators and assigned a
rating ranging from 0.5 (poor) to 2.5 (excellent). (Note: Prior to 1995, sediment quality had been rated on
the full 1 to 5 range, same as the other indicators. But, discontinuance of sediment toxicity testing, which
had contributed half the sediment quality rating, resulted in the rating for this indicator being reduced by
one half). Ratings for the five indicators are summed for each site. Thus, the maximum total rating for a
sample site would be 22.5 (all indicators excellent) and the minimum 4.5 (all indicators poor).
To arrive at an overall health evaluation for a reservoir, the sum of the ratings from all sites are
totaled, divided by the maximum possible rating for that reservoir, and expressed as a percentage. It is
necessary to use a percentage basis because the number of sites monitored varies according to reservoir
size and configuration. Only one site, the forebay, is sampled in small tributary reservoirs, and up to four
sites (forebay, transition zone, inflow, and embayment) are sampled in selected run-of-the-river
reservoirs. Also, the number of indicators varies from three to five at different sites. Chlorophyll and
sediment quality are excluded at the inflows on run-of-the-river reservoirs because in situ plankton
production of chlorophyll does not occur significantly in that part of a reservoir and because sediments do
not accumulate there. As a result, the number of scoring possibilities may be as few as 5 indicator ratings
for a small reservoir sampled only at the forebay. Or, as many as 18 indicator ratings for a large reservoir
ffl-481
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sampled at the forebay, transition zone, inflow, and embayment. The total score for the small reservoir
would be 22.5 if all indicators rated excellent, whereas, the total score for the large reservoir would be
82.5 if all indicators rated excellent. Hence, using a percentage basis allows easier comparison among
reservoirs.
This approach provides a potential range of scores from 20 to 100 percent and applies to all reservoirs
regardless of the number of indicators or sample sites. To complete the ecological health scoring process,
the 20-100 percent scoring range must be divided into categories representing good, fair, and poor
ecological health conditions. This was achieved as follows:
1. Results for each year were plotted, examined for apparent groupings, and compared to previous
years.
2. Next, the groupings were compared to a trisection of the overall scoring range and to known, a
priori conditions for each reservoir.
3. Ranges representing good, fair, and poor conditions were then established. A final fine-tuning of
scoring ranges was needed in a few cases to ensure a reservoir with known conditions fail within
the appropriate category. This was done only in circumstances where a nominal adjustment was
necessary.
Currently used reservoir scoring criteria are:
Run-of-the-river reservoirs Tributary, storage reservoirs
Poor Fair Good Poor Fair Good
<52 52-72 >72 <57 57-72 >72
This ecological health scoring process has been in use for several years. Each year, slight
modifications may be made in the overall evaluation process or in the numerical rating criteria for the five
ecological health indicators based on experience gained from working with this process, review of the
evaluation scheme by other state and federal professionals, and results of another year of monitoring.
The difference in the poor scoring range between the two types of reservoirs exists because two
storage reservoirs with known poor conditions rated slightly higher than the boundary for the lower (poor)
grouping on the run-of-the-river reservoirs. Hence, the high end of the lower scoring range for storage
reservoirs was shifted upward from 52 to 56 percent to accommodate these reservoirs with known poor
conditions.
An example that illustrates the overall reservoir health evaluation methodology is presented in
Table 1. Fort Loudoun Reservoir, the example used, has five aquatic health indicators at two locations
and two indicators at another location.
Reservoir Ecological Conditions—1997 Results
Combining all the aquatic ecosystem indicator ratings to determine the overall ecological health for
each of the 17 reservoirs sampled in 1997 shows the following:
• 6 of the 17 rated good (4 mainstream reservoirs and 2 tributary reservoirs);
• 6 of the 17 rated fair (2 mainstream reservoirs and 4 tributary reservoirs); and
• 5 of the 17 rated poor (all tributary reservoirs).
The ecological health ratings for all reservoirs during 1997 and earlier years are presented by
classification unit in Table 2. Comparisons show that 10 of the 17 reservoirs sampled in 1997 scored within
two points of their long term average, 3 scored higher, and 4 scored lower than their long term average.
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Meteorological (rainfall and runoff) and hydrological (retention time) condition contribute greatly to year-to-
year variations.
References
Dycus, D.L., and D.L. Meinert. 1996. "Aquatic Ecological Determinations for TVA Reservoirs—1995.
An Informal Summary of 1995 Vital Signs Monitoring Results and Ecological Health Determination
Methods." April 1996. Tennessee Valley Authority, Water Management, 1101 Market St. (CST 17-
D) Chattanooga, TN. 37402
Dycus, D.L., and D.L. Meinert. 1998. "Aquatic Ecological Determinations for TVA Reservoirs—1997.
An Informal Summary of 1997 Vital Signs Monitoring Results and Ecological Health Determination
Methods." April 1998. Tennessee Valley Authority, Water Management, 1101 Market St. (CST 17-
D) Chattanooga, TN. 37402
EPA, 1977. "Guidelines for the Pollutional Classification of Great Lakes Harbor Sediments." USEPA,
Region V, Chicago.
Hickman, G. D., and T. A. McDonough. 1995. "Assessing the Reservoir Fish Assemblage Index A
Potential Measure of Reservoir Quality." Published in Proceedings of Third National Reservoir
Symposium, June 1995. American Fisheries Association. D. DeVries, Editor.
Meinert, DL., S.R. Butkus, and T.A. McDonough. 1992. "Chickamauga Reservoir Embayment Study -
1990." TVAAVR-92/28. Tennessee Valley Authority, Water Management, 1101 Market St. (CST 17-
D) Chattanooga, TN. 37402
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Chlorophyll-a Scoring Methods, Mesotrophic Reservoirs
2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Average Summer Chlorophyll-a Concentration
Chlorophyll-a Scoring Methods, Oligotrophic Reservoirs
(Hiwassee and Little Tennessee River Watersheds)
34567
Average Summer Chlorophyll-a Concentration
Chlorophyll-a Rating - The chlorophyll-a rating at each sampling location Is based on the average summer concentration
(of monthly photic zone composite samples). If triplicate samples are collected at a sampling location, only the median
value of the triplicate Is used In the calculation of the summer average and the maximum. If a monthly chlorophyll-a sample
has a concentration that exceeds 30 ug/l, the value is not Included In the calculation of the summer average, however, the
final chlorophyll-a rating Is decreased one unit, (I.e. 5 to 4, or 4 to 3, etc.) for each sample that exceeds 30 ug/l.
' If nutrients are present (e.g. total phosphorus greater than about 0.01 mg/L and nltrate+nitrite-nltrogen
greater than about 0.05 mg/L) but chlorophyll-a concentrations are generally low (e.g. < 3ug/L), other
Figure 1. Sliding scale used to determine appropriate score for chlorophyll concentrations
in reservoirs that are expected to be either oligotrophic or mesotrophic.
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Table 1. Computational Method for Evaluation of Reservoir Health: Fort Loudoun Reservoir—1997
(Run-of-the-River Reservoir)
Aquatic Health Indicators
Chlorophyll-a
Summer Average, p.g/1
Maximum Concentration
Dissolved Oxygen
Percent less than 2 mg/l :
X-Sectional Area
Bottom X-Sectional Length
Sediment Quality
Metals/Pesticides/PCBs
Benthic Community
Total Score - Seven Metrics
Fish Community
Total Score - Twelve Metrics
Overall Reservoir Evaluation \
Observations
Forebay
Transition
Inflow
15.7
21.0
16.2
23.0
No Sample
No Sample
0(5)
0(5)
0(5)
0(5)
No Sample
No Sample
chlordane
chlordane
No Sample
17
29
13
42
37
22
Sampling Location Sum
Reservoir
Sum
Overall Reservoir
Evaluation
Ratings
Forebay
Transition
Inflow
1.2 (poor)
1.0 (poor)
No Rating
5.0 (good)
5.0 (good)
No Rating
1 .5 (fair)
1.5 (fair)
No Rating
2 (fair)
4 (good)
2 (poor)
4 (fair)
3 (fair)
2 (poor)
13.7 of 22.5
14.5 of 22.5
4 of 10
32. 2 of 55 (58%)
"fair"
-------
Table 2. Reservoir Ecological Health Scores 1991-1997
Reservoir Class
Reservoir
Run-of-the-River Reservoirs
Kentucky Reservoir
Pickwick Reservoir
Wilson Reservoir
Wheeler Reservoir
Guntersville Reservoir
Nickajack Reservoir
Chickamauga Res.
Watts Bar Reservoir
Fort Loudoun Reservoir
Melton Hill Reservoir
Tellico Reservoir
Blue Ridge Ecoregion
Hiwassee Reservoir
Chatuge Reservoir
Nottely Reservoir
Blue Ridge Reservoir
Ocoee No. 1 Reservoir
Apalachia
Fontana Reservoir
Ridge & Valley Ecoregion
Cherokee Reservoir
Fort Pat. Henry Res.
Boone Reservoir
South Holston Res.
Watauga Reservoir
Douglas Reservoir
Norris Reservoir
Interior Plateau Ecoregion
Normandy Reservoir
Beech Reservoir
Tims Ford Reservoir
Bear Creek Reservoir
Little Bear Creek Res.
Cedar Creek Reservoir
Area
(Acres)
160,300
43,100
15,500
67,100
67,900
10,400
35,400
39,000
14600
5700
15,900
6,100
7,100
4,200
3,300
1,900
1,100
10,600
30300
900
4300
7600
6,400
30,400
34200
3,200
900
10,600
700
1,600
4,200
Reservoir Ecogical Health on 1 997 Criteria
1991*
69
77
58
70
84
87
83
72
63
67
61
72
59
60
87
74
N/A
57
N/A
53
63
75
60
71
N/A
N/A
N/A
N/A
N/A
N/A
1992*
87
80
67
76
85
81
88
79
63
65
57
71.
79
61
83
74
N/A
57
N/A
63
59
72
54
72
N/A
N/A
63
N/A
N/A
N/A
1993*
81
70
76
72
79
87
86
76
56
66
63
69
79
62
91
67
71
66
86
58
66
63
60
69
62
69
60
64
68
64
1994
75
82
73
74
83
91
86
73
64
75
74
62
72
56
80
67
75
48
56
56
66
63
62
65
64
54
58
60
69
72
1995
72
N/A
N/A
69
N/A
92
79
N/A
49
N/A
53
N/A
N/A
49
89
71
72
51
51
52
N/A
N/A
L45
61
59
46
56
46
64
60
1996
N/A
73
75
N/A
86
N/A
N/A
70
52
73
N/A
62
84
N/A
N/A
N/A
62
49
59
N/A
55
72
N/A
N/A
69
51
53
47
64
68
1997
78
N/A
N/A
76
N/A
88
88
N/A
58
N/A
62
N/A
N/A
48
82
71
73
N/A
N/A
56
55
N/A
N/A
54
64
N/A
N/A
N/A
42
64
69
1993-97
Averaqe
77
75
75
73
83
90
85
73
56
71
63
64
78
54
86
69
73
70
54
62
55
62
66
55
65
64
55
57
52
66
67
*1991, 1992, and 1993 are scored on 1997 criteria for 4 of the 5 indicators. A change in processing of
benthic macinvertebrate samples beginning in 1994 prevents appropriate scoring of the earlier results
on the latter criteria.
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Evaluation of Wet Weather Pollution Sources on Large Rivers
Utilizing Biological Communities
Geoffrey M. Edwards, Environmental Specialist
Peter A. Tennant, P.E., Deputy Executive Director
John T. Lyons, P.E., Water Pollution Control Programs Manager
Ohio River Valley Water Sanitation Commission (ORSANCO)
5735 Kellogg Ave., Cincinnati, OH 45228
Abstract
This paper presents findings of biological studies associated with a two-year, $2 million national
demonstration project conducted on a segment of the Ohio River, which includes the Cincinnati/Northern
Kentucky metropolitan area. Physical and chemical monitoring were also included in the project, which
identified fecal coliform bacteria as the parameter with the most frequent and excessive violations of
water quality standards. Biological community monitoring was undertaken to determine if direct impacts
on aquatic life could be identified and attributed to wet weather sources.
Key Terms: Biological community monitoring, wet weather pollution sources, combined sewer overflows
(CSOs), electrofishing, Hester-Dendy sampler unit
Introduction
The primary objective of the study's biological component was to utilize existing methods of
biological sampling to determine the effects of wet weather pollution on the biological community of a
large river. To achieve this goal both fish and macroinvertebrate populations were sampled.
Surveys were conducted in a segment of the Markland Pool-Ohio River Mile Points (ORMP) 462 to
492. Figure 1 displays the study area in relationship to the Ohio River Basin.
The scope of work for this study included a fish population assessment and the collection of
macroinvertebrate samples on the Ohio River at designated locations in Markland Pool. The fish
population surveys were conducted in three rounds of sampling at 21 sites. The macroinvertebrate
samples were collected in four rounds of sampling, each round consisting of an eight-week colonization
period. Objectives of the biological monitoring were to: (1) provide upstream background data, (2)
examine the effects of major identifiable pollutant inputs (clusters of combined sewer overflows, sanitary
sewer overflows, wastewater treatment plants, and tributaries) within the Greater Cincinnati/Northern
Kentucky urban area, and (3) investigate the level of downstream recovery relative to upstream conditions
within the confines of the study area.
Sampling Method
Fish Population Assessment
The fish population assessment conducted for this component consisted of sampling 21 sites during
Rounds One and Three, and 23 sites during Round Two (Table 1). Sites were chosen to produce optimum
coverage for the study area. The surveys focused on sampling similar habitat areas (mud/gravel substrate)
to reduce environmental variability as much as possible.
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The fish population assessment was conducted in cooperation with the Ohio Environmental
Protection Agency (Ohio EPA). Sampling was conducted from September 18 - 26, 1995 (Round One),
August 13 - 29, 1996 (Round Two), and October 7 - November 6, 1996 (Round Three). Sites were
approximately 500 meters in length, and were sampled at night to optimize catch abundance and diversity
(Ohio EPA, 1987). Fish collected were counted, measured and identified to the lowest practical
taxonomical level on site. All minnows and questionable identifications were preserved on site and later
identified by Ohio EPA staff.
Macroinvertebrate Sampling
Macroinvertebrate samples were obtained using Hester-Dendy artificial substrate multi-plate sampler
units. Sampler units consisted of five individual samplers/sampler unit. Each unit of five was anchored to
a cement block at the sampling site to stabilize and submerge the unit. Macroinvertebrate sampling
consisted of three distinct phases:
Phase One established 16 macroinvertebrate "longitudinal" sampling stations consisting of one
Hester-Dendy sampling unit per station within the study area (Table 2). Sites between ORMP 462 and
492 were sampled at regular intervals (approximately two miles) for four rounds, each round lasting eight
weeks.
Phase Two established three macroinvertebrate "cluster" sampling stations consisting of five Hester-
Dendy sampling units per station (Table 2). Four rounds of sampling were also conducted at these sites,
each round lasting eight weeks. The objective was to identify the extent of natural variability in
macroinvertebrate populations within the study area.
Phase Three isolated the near-field effects of individual CSOs within the study area. In the first year
of sampling (1995) individual sampling units were placed above and below each of three CSO discharges.
In the second year the number of sites was expanded to six. Dye tests were used to determine the location
of the sampling sites (Table 2). Each CSO was monitored for overflow frequency and duration. Sites were
sampled for a total of three rounds, each round lasting eight weeks. The objective was to determine if
overflows produce a measurable near-field impact on macroinvertebrate populations below the outfall.
The colonization period for macroinvertebrate samples was eight weeks. Sampling was conducted
July 12 - September 5, 1995 (Round One), August 31 - October 26, 1995 (Round Two), July 9 - August
29, 1996 (Round Three), and August 27 - October 16, 1996 (Round Four). Recovery rates of the sampler
units were as follows: Round One - 27 of 34 (79.4%), Round Two - 20 of 38 (52.6%), Round Three - 37
of 44 (84.1%), and Round Four - 41 of 44 (93.2%). Table 2 lists the specific units recovered for each
round. Once retrieved, the individual plates from each sampler unit were processed in the field and the
resulting composite of organisms stored in a preservative for shipment. Composites were sent to an
independent laboratory, where they were counted and identified to the lowest practical taxonomical level.
Field measurable water quality parameters were collected at each site at the time of placement and
retrieval of sampler units. Temperature, pH, dissolved oxygen and conductivity were recorded using a
Hydrolab Model H-20 instrument. The Hydrolab instrument was pre- and post-calibrated to ensure the
accuracy of the data collected.
Total precipitation was measured at gauges throughout the study area during Rounds One through
Four, and is expressed below as an average of all the gauges within the study area (Table 4).
Total flow from the CSOs where sampler units were placed was monitored during Rounds Two
through Four (CSO samplers were not placed during Round One). An example of the information
collected at each of the sampling locations is expressed in Table 5.
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Data Assessment
Fish Population Assessment
Sites were evaluated with the help of Ohio EPA personnel who calculated an Index of Biotic Integrity
(IBI) and a Modified Index of well-being (Mlwb). It should be noted that both indices were designed to
evaluate fish populations in inland streams and waterways. Since an index designed and calibrated
specifically to evaluate fish populations for a large river like the Ohio River has not been developed, the
IBI and Mlwb were utilized in their present form.
Index of Biotic Integrity (IBI)
The IBI is a multi-metric approach to evaluating fish populations and was originally described by
Karr (1981) for use in Illinois streams. Ohio EPA uses a modified version of the IBI which takes into
account regional differences between the fish populations of Ohio and Illinois. It consists of twelve
metrics which are compared to the value expected at a reference site and then rated either a 5 (value
approximates), 3 (deviates somewhat from) or 1 (strongly deviates from the value expected). The
maximum IBI score is 60 and the minimum score is 12. Metrics used by Ohio EPA are: total number of
species, sunfish species, sucker species, intolerant species, round body suckers, simple lithophils, tolerant
fishes, omnivores, top carnivores, insectivores, DELT anomalies (Deformities, Eroded fins, Lesions, and
Tumors), and relative number minus tolerant species (Ohio EPA, 1987).
IBI values expected at a reference site for the study area (Interior Plateau Ecoregion) for an Ohio
inland stream would have a mean value of 43, a standard deviation of 1.1, and a range of 32 - 52 (Ohio
EPA, 1987). Results from the Ohio River samples collected in 1995 (Round One) and those collected in
1996 (Rounds Two & Three) are displayed below (Table 6).
Modified Index of Well-being (Mlwb)
The Mlwb is also a multi-metric approach to evaluating fish populations and was originally
developed as the Index of well-being (Iwb) by Gammon (1976) for use on the Wabash River in Indiana.
The Iwb consists of four measures offish communities: numbers of individuals, biomass, Shannon
Diversity based on numbers, and Shannon Diversity based on weight. Ohio EPA modified the Iwb by
eliminating any of 13 highly tolerant species, hybrids, or exotic species from the numbers and biomass
components of the Iwb, but not from the Shannon components (Ohio EPA, 1987). A minimum Mlwb
score is 0 and the maximum is 12.
Mlwb values expected at a reference site for the study area (Interior Plateau Ecoregion) for an Ohio
inland stream would have a mean value of 9.2, a standard deviation of 0.1, and a range of 8.5 - 10.2 (Ohio
EPA, 1987). Results from the Ohio River samples collected in 1995 (Round One) and those collected in
1996 (Rounds Two & Three) are displayed below (Table 7).
Macroinvertebrate Sampling
A total of 125 composite samples was collected over four rounds of sampling (Table 2) and was
evaluated with the help of Ohio EPA staff. The following indices were calculated for each composite
sample: total number of organisms, taxa richness, percent Dominant taxa, percent Chironomids,
Chironomid richness, percent EPT (Ephemeroptera, Plecoptera, and Trichoptera), EPT richness,
EPT/Chironomid ratio, Modified Hilsenhoff s Biotic Index, Community Loss Index, Jaccard Coefficient,
Ohio EPA's Invertebrate Community Index (ICI) and various analyses associated with the Zebra Mussel
population. It should be noted that these indices were designed to evaluate macroinvertebrate populations
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in inland streams and waterways and were used in the absence of an index specifically designed and
calibrated to evaluate macroinvertebrate populations for a large river like the Ohio. Sites were compared
statistically based on a mean value at each cluster location and standard deviation at 95 percent
confidence level. The expectations prior to the initiation of sampling was that the indices would reflect a
higher biological integrity at the upstream sites as compared to the sites within the urban area. In addition,
it was expected that the downstream sites would display a recovery in the composition of the
macroinvertebrate community which would more closely represent the upstream conditions. Similarly,
the expectation was that indices would reflect higher biological integrity upstream of the CSO sites than
immediately downstream. CSO site O-3 offered two years of results which are used below as
representative of all of the CSO locations studied.
Total Number of Organisms
This index is simply a count of the organisms found in each macroinvertebrate sample. The
expectation prior to sampling was that the total number of organisms would be highest at the upstream
sites, show a decrease through the urban area and a recovery at the downstream sites. At the CSO sites,
the expectation prior to sampling was that the total number of organisms upstream of the overflow would
be higher than the number downstream. Results are summarized as follows:
• Cluster and Longitudinal site sampling resulted in the expected trend (Figure 2).
• The number of organisms above CSO O-3 was statistically the same as the number below the
outfall during Rounds Two through Four. This does not confirm the expectation prior to
sampling.
Taxa Richness
Taxa richness is simply a count of the taxa found in each macroinvertebrate sample. In this case, all
roundworm taxa were counted as only one taxa per Ohio EPA protocol. The expectation prior to sampling
was that the total number of taxa would be highest at the upstream sites, show a decrease through the
urban area, and a recovery at the downstream sites. At the CSO sites the expectation prior to sampling
was that the total number of taxa upstream of the overflow would be higher than the number downstream.
Results are summarized as follows:
• The number of taxa at Cluster and Longitudinal sites show a steady, increasing trend throughout
the study area which does not conform to expectation.
• The site O-3 CSO samplers recovered in Rounds Two through Four either show an increase in
number of taxa from upstream to downstream of individual outfalls or remain statistically the
same - opposite of the expectation. However, of the additional taxa below the outfall, a majority
are of the family Chironomidae which are generally considered to be pollution tolerant
organisms.
Percent Chironomids
This index is a measure of the percentage of the Family Chironomidae (Midges) within the
community found at each site. These organisms are generally tolerant of pollution and their numbers tend
to increase in degraded conditions. The expectation prior to sampling was that the percentage of
chironomids would be lowest at the upstream sites, show an increase through the urban area and decrease
at the downstream sites. At the CSO sites, the expectation prior to sampling was that the percentage of
III-490
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chironomids upstream of the overflow would be lower than the percentage downstream. Results are
summarized as follows:
• The Cluster site samples conformed to expectation, however the Longitudinal site samples do not
meet the expectation (Figure 3).
• Of the site O-3 CSO samplers recovered in Rounds Two through Four, the mean Percent
Chironomids above the CSO was 36.84 and the percentage below the outfall was 51.66
(statistically significant) indicating the expected performance. This may suggest that a CSO can
have a quantifiable effect upon near-field macroinvertebrate communities even on large rivers
where tremendous dilution can occur.
Percent Ephemeroptera, Plecoptera and Trichoptera (EPT)
This index is a measure of the percentage of the Orders Ephemeroptera (Mayflies), Plecoptera
(Stoneflies), and Trichoptera (Caddisflies) within the community found at each site. These organisms are
generally considered to be pollution-sensitive species. The presence of EPT organisms at a site is
generally an indicator of good water quality, since their sensitivity precludes them from inhabiting
degraded areas. The expectation prior to sampling was that the percentage of EPT will be highest at the
upstream sites, show a decrease through the urban area and a recovery at the downstream sites. At the
CSO sites the expectation prior to sampling was that the percentage of EPT upstream of the overflow
would be higher than the percentage downstream. Results are summarized as follows:
• The number of taxa at Cluster and Longitudinal sites show a steady, increasing trend through the
study area which does not conform to expectation.
• Of the Site O-3 CSO samplers recovered in Rounds Two through Four, mean percentage of EPT
above the CSO was 23.42 and the percentage below was 12.35. Based on this information, the
difference in the percentage of EPT was statistically significant and indicated the expected
performance.
Modified Hilsenhoffs Biotic Index (HBI)
This family level index was originally developed by Hilsenhoff (1977) for use in Wisconsin streams.
The original tolerance classifications were based on a numerical range of 0 to 5 and later modified by
Hilsenhoff (1987) to use a 0 to 10 scale. However, similar results can be obtained using an index value of
either 0 to 5 or 0 to 10, and adequate information is not available for several species that would allow use
of the more definitive 0 to 10 tolerance range (U.S. EPA, 1990). Therefore, a 0 to 5 scale was chosen as
modified by the Maryland Department of the Environment (1992). Higher index values indicate a more
pollution tolerant macroinvertebrate community, and generally a lesser degree of water quality. A score
of:
0 to 1.75 = excellent water quality
1.76 to 2.50 = good water quality
2.51 to 3.75 = fair water quality
3.76 to 4.00 = poor water quality
> 4.00 = serious water quality problems
The expectation prior to sampling was that the HBI score would be lowest at the upstream sites, show
an increase through the urban area and a decrease at the downstream sites. At the CSO sites the
expectation prior to sampling was that the HBI score upstream of the overflow would be lower than the
score downstream. Results are summarized as follows:
• Cluster and Longitudinal site sampling resulted in the expected trend.
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• Of the Site O-3 CSO samplers recovered in Rounds Two through Four, mean HBI score above
the CSO was 3.33 and the mean below was 3.66 (statistically significant) indicating the expected
performance.
Percent Mussels
This index is a measure of the percentage of the Zebra Mussels (Dreissena polymorpha) and Asiatic
Bivalves (Corbicula fulminea) within the community found at each site. Since the mussels were the
largest contributors of organisms to the population in many samples, this index may also be considered
the percentage of dominant taxa for those samples. These two mussel taxa are generally tolerant of
pollution and their numbers tend to increase in degraded conditions. The expectation prior to sampling
was that the percentage of mussels would be lowest at the upstream sites, show an increase through the
urban area and decrease at the downstream sites. At the CSO sites, the expectation prior to sampling was
that the percentage of mussels upstream of the overflow would be lower that the percentage downstream.
Results are summarized as follows:
• The Cluster and Longitudinal sites displayed the opposite of the expected trend with high
numbers upstream, lower numbers through the urban area, and increasing numbers at downstream
sites.
• At the Site O-3 CSO samplers recovered in Rounds Two through Four, the percentage of mussels
above the CSO was 18.63 and the percentage below the outfall was 0.86. This seems to confirm
that these mussels may be more sensitive to urban influences than originally expected.
Discussion and Conclusions
The indices used to evaluate the fish population assessment and the macroinvertebrate collections
conducted in 1995 and 1996 were designed to evaluate inland streams as opposed to a large river like the
Ohio River. Given that basis, the results of these analyses must be viewed with a certain amount of
caution. ORSANCO is well aware that any attempt to evaluate water quality conditions using biological
populations on the Ohio River must be conducted with new indices designed for, or existing indices
calibrated for, the special conditions which exist on large rivers (i.e., large amounts of flow, transient
sediments, etc.). However, biological populations have been valuable assessment tools for smaller
streams, and quite probably will prove to be of similar value on large rivers in the future. In the interim,
biological results from this project did show some interesting results using available methods of
evaluation.
Fish Population Assessment
In general, for the fish population assessed during this study, neither of the two indices was able to
demonstrate any consistent, statistically reasonable difference between the upstream sites, the urban sites,
and the downstream sites. It is important to note that the standard deviation for both the IBI and Mlwb
was rather high. As sampling efforts continue both river-wide and in the study area, and as the sample
size becomes more robust, the standard deviation should be compressed for both of the indices.
Macroinvertebrate Sampling
In Rounds One through Four, several indices performed as expected. In particular, total number of
organisms, percent chironomids, Hilsenhoff s Biotic Index, and percent mussels showed clear statistically
III-492
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significant results over the study area. As with the fish population indices, it is clear that a larger, more
robust sampling size is important to compress the standard deviations for many indices.
Of the CSO samples recovered in Rounds Two through Four, several indices performed as expected.
In particular, percent Chironomids, percent EPT, EPT/Chironomid ratio, Hilsenhoff s Biotic Index and
the Invertebrate Community Index showed clear statistically significant differences in the makeup of the
macroinvertebrate populations above and directly below particular CSOs. Future sampling efforts should
focus on sampling at a variety of outfalls and at different seasons. There is evidence that, at least at these
sites, CSOs have a quantifiable impact on the near-field populations of organisms irrespective of the
unique qualities that large rivers, like the Ohio River, possess. Once these impacts are defined, efforts
could be focused on examining the length of the impact in terms of distance downstream.
Acknowledgments
The authors would like to acknowledge the efforts of a number of individuals from Ohio EPA
particularly, Chris Yoder and Jeff DeShon, who provided a great deal of assistance toward the completion
of the biological component of this study. In addition to actually participating in the electrofishing
surveys, representatives from OEPA also conducted specific evaluations of both the fish and
macroinvertebrate data which proved extremely valuable in efforts to interpret the information collected.
Special thanks to Bob Ovies of ORSANCO for assistance with graphics and HTML formatting.
We would also like to recognize the tremendous amount of in-kind and monetary resources dedicated
to this study from the US EPA, the Metropolitan Sewer District of Greater Cincinnati, and the Sanitation
District No. 1 of Northern Kentucky.
Literature Cited
Ohio Environmental Protection Agency, 1987. Biological Criteria for the Protection of Aquatic Life:
Volume II: Users Manual for Biological Field Assessment of Ohio Surface Waters. Division of Water
Quality Monitoring and Assessment, Surface Water Section, Columbus, Ohio.
United States Environmental Protection Agency, 1990. Macroinvertebrate Field and Laboratory Methods
for Evaluating the Biological Integrity of Surface Waters. Office of Research and Development,
Washington, D.C. EPA/600/4-90/030
United States Environmental Protection Agency, 1993. Fish Field and Laboratory Methods for Evaluating
the Biological Integrity of Surface Waters. Office of Research and Development, Washington, D.C.
EPA/600/R-92/111
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STUDY AREA
GREATER CINCINNATI / NORTHERN KENTUCKY
| Cincinnati MSD WWTPs
• Cincinnati MSD CSOs*
Great Miami R
• Little Miami R
SD#1 WWTP
NKY CSOs*
Water Intakes
Ohio River
460
Licking R
*Illustrates distribution, not actual number or specific location of CSOs
Figure 1. Ohio River basin.
III-494
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8000
7000
6000 - -
E
(0
1 5000
O)
2 4000
o
V
J3 3000
3
2 2000 - -
1000 --
460
465
470 475 480
River Mile
485
490
495
" Longitudinal Sites ° Cluster Sites
Figure 2. 1995 to 1996 Macroinvertebrate results—total number of organisms.
100.0% :
90.0% \-
80.0% :-
70.0% \
eo.0% ;-
50.0% i-
g 40.0% i
o
o 30.0% i
Q.
20.0% i
10.0% i
0.0%
-------
Table 1. Electrofishing Survey Site Locations
Station ID
E-l
E-2
E-3
E-4
E-5
E-6
E-7
E-8
E-9
E-10
E-ll
E-12
ORMP & Bank *
459.0 RDB
463.0 LDB
463.3 LDB
464.0 LDB
466.6 LDB
467.5 RDB
468.2 RDB
469.0 RDB
469.4 RDB
469.3 LDB
47 1.5 LDB
472.1 LDB
Station ID
E-13
E-14
E-15
E-16
E-17
E-18
E-19
E-20
E-21
E-22
E-23
ORMP & Bank *
472.8 RDB
473.6 RDB
476.5 LDB
478.7 LDB
480.7 RDB
483.0 RDB
486.0 LDB
487.2 RDB
488.2 RDB
489.8 LDB
49 1.3 LDB
*Bank refers to the descending bank or the relative position of the bank as seen while traveling downstream.
LDB is the Left Descending Bank (Kentucky side) and RDB is the Right Descending Bank (Ohio/Indiana side).
III-496
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Table 2. Macroinvertebrate Sampler Location and Units Recovered
Phase One - Longitudinal Sites
Station ID
L-l
L-2
L-3
L-4
L-5
L-6
L-7
L-8 A
L-8B
L-9
L-10
L-ll A
L-ll B
L-12
L-l 3
L-14
L-15
L-16 A
L-16B
ORMP & Bank *
462.25 LDB
464.1LDB
466.0 LDB
467. 9 LDB
469.75 LDB
472.1 LDB
474.0 RDB
476.0 LDB
475.9 RDB
478.1 RDB
480.0 LDB
48 1.9 RDB
482.0 RDB
483.9 RDB
485.9 LDB
488.0 LDB
490.0 LDB
49 1.9 LDB
49 1.9 LDB
Round One
X
X
X
X
X
X
X
X
X
X
X
X
Round Two
X
X
X
X
X
X
N/P
N/P
X
X
Round Three
X
X
X
X
X
X
N/P
X
X
X
N/P
X
X
X
X
X
X
X
Round Four
X
X
X
X
X
X
N/P
X
X
X
N/P
X
X
X
X
X
X
X
Phase Two - Cluster Sites
Station ID
C-l
C-2
C-3
ORMP & Bank *
462.25 LDB
474.0 RDB
490.0 LDB
Round One
X
X
X
Round Two
X
X
Round Three
X
X
X
Round Four
X
X
X
Phase Three - CSO sites
Station ID
O-l A
O-l B
O-2 A
O-2B
O-3 A
O-3B
O-4 A
O-4B
O-5A
O-5B
O-6 A
O-6B
ORMP & Bank *
467.15 RDB
467.20 RDB
47 1.6 RDB
47 1.65 RDB
472.0 LDB
472.25 LDB
472.3 RDB
472.35 RDB
475.8 RDB
475.85 RDB
48 1.9 RDB
48 1.9 RDB
Round One
N/P
N/P
N/P
N/P
N/P
N/P
N/P
N/P
N/P
N/P
N/P
N/P
Round Two
N/P
N/P
X
X
N/P
N/P
N/P
N/P
N/P
N/P
Round Three
X
X
X
X
X
X
X
X
X
Round Four
X
X
X
X
X
X
X
X
X
X
X - Indicates retrieval of macroinvertebrate sampler unit, blanks indicate sampler units which were not recovered.
N/P - Indicates sampler unit was Not Placed.
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Table 3. Range of Physical Parameters
Parameter
Temperature (C)
pH
Dissolved Oxygen (mg/1)
Conductivity (umhos/cm)
Round One
27.83-31.93
6.76 - 8.60
6.77 - 12.38
290-443
Round Two
16.89-31.33
6.80 - 8.60
6.73 - 12.38
290 - 596
Round Three
26.82 - 28.87
6.73 - 9.03
6.69 - 8.98
253 - 432
Round Four
18.05-28.28
6.73 - 9.03
7.46-9.61
253 - 550
Table 4. Precipitation
Parameter
Total Precipitation (in)
Number of Storms
Avg. Precipitation/Storm (in)
Round One
6.84
14
0.49
Round Two
6.24
15
0.42
Round Three
4.52
12
0.38
Round Four
6.38
8
0.80
Table 5. CSO Overflow at Site O-3
Parameter
Total Flow (mgd)
Number of Overflows
Avg. Flow/Overflow (mgd)
Round Two
1.60
11
0.15
Round Three
0.84
8
0.11
Round Four
1.42
8
0.18
Table 6. Index of Biotic Integrity Results
Parameter
Mean
Standard Deviation
Range
Round One
42.95
3.91
36-52
Round Two
36.00
2.09
26-46
Round Three
36.40
1.60
32-44
Table 7. Modified Index of Well-Being Results
Parameter
Mean
Standard Deviation
Range
Round One
9.04
0.56
8.80 - 9.90
Round Two
8.46
0.36
6.90 - 9.70
Round Three
8.49
0.30
7.20-9.60
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Evaluation Monitoring as an Alternative to Conventional Water Quality
Monitoring for Water Quality Characterization/Management
Anne Jones-Lee, PhD, Vice President
G. Fred Lee, PhD, PE, DEE, President
G. Fred Lee & Associates
ElMacero,CA95618
Abstract
Conventional "water quality" monitoring frequently involves measuring a suite of chemical
constituents at various locations at a fixed, somewhat arbitrary sampling frequency over a period of one
year. The data is then compared to US EPA water quality criteria and/or state standards based on these
criteria. Exceedance of the criterion values is judged to represent an "impaired" waterbody which
requires corrective action to eliminate the exceedance. The US EPA has been using this approach to
inform the public about the current state of water pollution control within the US through its biennial
reports to Congress in which urban area and highway stormwater runoff associated chemical constituents
is reported to be one of the primary causes of water pollution in the US. A critical review of the tradi-
tional water quality monitoring programs shows that it is not possible to reliably assess true water quality
use impairments to aquatic life resources based on chemical concentration measurements. Further,
exceedance of a water quality criterion in an ambient water is rarely a reliable indicator of real water
quality use impairments of concern to the public. This paper presents an alternative approach to conven-
tional water quality monitoring (Evaluation Monitoring) which focuses monitoring resources on defining
the real water quality use impairments that are occurring in a waterbody associated with urban area and
highway stormwater runoff. Emphasis is given to assessing chemical impacts rather than chemical con-
centrations or loads. An Evaluation Monitoring program is being applied to the Upper Newport Bay,
Orange County, California and the Sacramento River watersheds.
Introduction
With increased attention being given to fully implementing the requirements of the Clean Water Act
(CWA), especially for the control of "toxics," it is appropriate to examine the adequacy of today's con-
ventional ambient water quality monitoring. The typical "water quality" monitoring program involves
measuring a suite of chemical constituents and some biological indicator parameters on a fixed
monitoring grid where samples are taken at a somewhat arbitrarily established frequency, usually based
on funding available for a period of a year or so. At the end of this period, the collected data is examined
with respect to exceedances of US EPA water quality criteria and state standards based on these criteria.
If there are exceedances of the standards by any magnitude with a frequency of more than once in three
years, the waterbody is considered to be "impaired" and it is supposed to be listed on the state's 303(d)
list of impaired waterbodies. This listing sets off a regulatory process that ultimately leads to the
establishment of total maximum daily loads (TMDLs) for the sources of the constituents responsible for
the exceedance of the water quality standard.
While, for a number of years, the initiation of the TMDL process for the control of toxics was not
implemented in accord with regulatory requirements, through litigation initiated by environmental
groups, the US EPA is entering into consent decrees which require that TMDLs be established in the next
few years to control the violations of water quality standards that are occurring in ambient waters. In
California, the State and Regional Water Boards must develop over 1,400 TMDLs in the next 13 years,
based on the recently adopted 303(d) list of impaired waterbodies. This situation is causing regulatory
IH-499
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agencies, the regulated community and others to critically examine the adequacy and reliability of the
water quality monitoring database that was used to establish the 303(d) listing. Frequently this database
is based on a typical routine water quality monitoring program. Such programs, however, typically fall
far short of providing the information needed to:
• define the real significant water quality use impairments that are occurring in the waterbody of
interest,
• define the specific constituents responsible for the use impairment and the sources of these
constituents, and
• serve as a valid basis for developing a TMDL based water pollution control program.
Basically, most of today's water quality monitoring programs are patterned after 1960s/70s programs
designed to address BOD/TSS-type problems. This type of program is deficient in providing the informa-
tion needed to define the water quality significance of potentially toxic regulated constituents such as
heavy metals and organics, nutrients and sediment associated constituents, etc.
A common, highly significant error that is made by many of those who work in the water quality
evaluation and management field is to assume that a table of chemical constituent concentrations
represents an assessment of a waterbody's water quality. As discussed below, there is no way to reliably
translate chemical concentration data to water quality-use impairment impacts. The typical water quality
monitoring program conducted today, while often said to be generating "chemistry" information,
generates some chemical characteristic information and largely ignores the information that has been
developed in the aquatic chemistry field over the last 30 years on how chemical constituents impact water
quality-beneficial uses that are essential to developing technically valid, cost-effective water pollution
control programs. The net result is that TMDL programs are being developed today, at great cost to the
public, that do not adequately or reliably cost effectively control real water quality use impairments of
concern to the public, who must ultimately pay for the water quality management programs.
Deficiencies in Current Stormwater Runoff Water Quality Monitoring
hi order to determine whether a chemical constituent at a certain concentration in stormwater runoff
causes a water quality problem in the receiving waters for the runoff, it is necessary to understand how
chemical constituents impact the designated beneficial uses of the receiving waters for the stormwater
runoff. The factors that need to be considered in making this type of evaluation are listed in Table 1.
Without an adequate understanding of these issues, it is not possible to reliably estimate whether a
potentially toxic heavy metal or other constituent present in runoff waters or in ambient waters is adverse
to the aquatic life-related beneficial uses of a waterbody.
Aquatic life and most other designated beneficial uses are impacted by the concentration of
toxic/available forms of chemical constituents in the immediate vicinity of the aquatic organisms and by
the duration of organism exposure to the toxic/available form. This relationship has been described by
Lee et al. (1982), Lee and Jones (1991) and Lee and Jones-Lee (1995a,b) and is presented in Figure 1.
The stippled area on the figure is an area of adverse impact. If the concentration/duration of exposure
relationship is outside of the stippled area, then there is no adverse impact on the aquatic organisms.
Duration of Exposure Issues. Of importance to assessing the water quality impacts of stormwater
runoff is that, in most situations, the duration of exposure that aquatic organisms can receive associated
with a stormwater runoff event is short-term and episodic. This means that elevated concentrations
relative to worst case water quality criteria/standards of toxic/available forms of chemical constituents
can be present in receiving waters for stormwater runoff without adversely affecting aquatic life. The US
EPA water quality criteria, including the one-hour acute criterion, are not reliable for estimating critical
HI-500
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concentrations of constituents in stormwater runoff that may be adverse to receiving water water quality.
As discussed by Lee and Jones-Lee (1991, 1995a,b, 1998a), with few exceptions, the US EPA water
quality criteria tend to significantly overestimate adverse impacts and therefore can lead to the
unnecessary construction of structural BMPs for managing constituents in urban area and highway
stormwater runoff.
Aquatic Chemistry Issues. Another component of basic information that must be available to relate
chemical concentration data in stormwater runoff to water quality impacts in the receiving water is the
concentration of toxic/available forms at the point of measurement in the runoff waters, as well as at the
point of concern, i.e. in the sphere of influence surrounding an aquatic organism that could be impacted
by the constituent. Figure 2 shows the general aquatic chemistry system that must be considered in
translating the measured concentration of a constituent in runoff waters to a concentration of a
constituent that adversely impacts aquatic life-related beneficial uses in the receiving waters. Many
chemical constituents exist in several oxidation states which, in turn, determine their basic aquatic
chemistry, i.e. the reactions which the chemical constituent enters into in the runoff waters and in the
receiving waters determine the actual chemical species present. There are eight basic types of chemical
reactions that a chemical in a particular oxidation state may enter into. Aquatic chemistry focuses on
determining the kinetics (rates) and thermodynamics (energetics-positions of equilibrium) of the
reactions that determine the chemical species that will be present in a particular waterbody, including the
waterbody's sediments. It is the actual chemical species present that determines whether a potentially
toxic chemical is toxic to aquatic life under a particular exposure scenario.
Stormwater Runoff Water Quality Impact Management
The current typical water quality monitoring program is an outgrowth of compliance monitoring for
domestic and industrial wastewater discharges where there is interest in determining if these discharges
contain chemical constituents at concentrations that violate NPDES permit conditions. However, as
discussed by Lee and Jones (1991), and Lee and Jones-Lee (1995a, 1996b), the typical wastewater
discharge compliance monitoring is not a reliable approach for monitoring the stormwater runoff from
urban and rural areas with respect to defining what, if any, real water quality use impairments are
occurring in the receiving waters for the runoff. In August 1994, the Engineering Foundation held a
stormwater NPDES-related monitoring needs conference which focused on the current state of
knowledge of the monitoring of urban area street and highway stormwater runoff for water quality
impacts on the receiving waters for the runoff. Urbanos and Torno (1994), in an overview summary of
the conference, discussed that little is known about the water quality impact of urban stormwater runoff-
associated chemical constituents. They stated,
"If we are to acquire this understanding, we must stop wasting monitoring resources on the 'laundry
list' type of monitoring encouraged or required by our current regulations. We must instead move
towards well-designed and adequately funded national and regional scientific study programs and
research efforts."
The situation is not simply one of shifting urban area and highway stormwater runoff monitoring from
edge-of-the-pavement, end-of-the-pipe monitoring to traditional receiving water monitoring. The
traditional approach for such monitoring involves collecting a number of samples of receiving waters to
determine their physical, chemical and biological characteristics. This is usually done on a more or less
mechanical basis in which fixed-period sampling, such as once a month at a number of sampling stations,
is conducted. At the end of the study period, the data that have been collected are examined for the
purpose of attempting to discern water quality impacts caused by stormwater runoff-associated chemical
constituents. Such programs frequently fail to provide reliable information on the water quality use
impairments associated with chemical constituents in urban area and rural stormwater runoff.
m-501
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The technically valid and cost-effective approach for managing real water quality use impairments
(pollution) caused by urban area and highway, as well as rural area, stormwater runoff is to find real
water quality problems in the receiving waters for the runoff, determine the specific cause of this
problem(s), and develop site-specific source control methods to control the problem to the maximum
extent practicable. The Evaluation Monitoring (EM) program discussed in this paper is specifically
designed to develop this type of information. The EM program was developed to determine, on a site-
specific basis, whether chemical constituents and pathogenic organisms in urban area street and highway
stormwater runoff are significantly adverse to the beneficial uses of the receiving waters for the runoff.
The EM approach shifts the emphasis in the monitoring of the receiving waters from chemical
constituent monitoring to highly focused event based water quality problem indicator monitoring, that
specifically addresses stormwater runoff events as sources of potential pollutants.
The importance of reliably defining the real water quality use impacts of urban area and highway
stormwater runoff as part of developing water quality management programs can be understood from the
high cost associated with applying current water quality management programs for domestic and
industrial wastewater discharges to urban area and highway stormwater runoff. Herricks (1995), editor of
the Engineering Foundation Stormwater Impact conference proceedings, stated,
"...best management practices need to be holistic, and that any control strategy needs to be a
reasoned application based on scientific understanding, not rule of thumb practice."
Davies (1995) reviewed many of the issues that need to be addressed in evaluating and controlling
nonpoint-source stormwater runoff impacts. He stated,
"It is generally agreed that NFS [nonpoint source] problems are unique and complex, and they will
not be resolved as easily as the relatively simple treatment and standard compliance approaches
used in the PS [point source] program. NFS programs will require development and application of
innovative and imaginative control strategies, and the program will cost much more than the PS
program."
The general conclusion from the conference was that there has been far too much use of rule-of-
thumb/standard-practice approaches in stormwater quality evaluation and management. Rather, there is
need to focus on finding real water quality problems and solving them in a technically valid, cost
effective manner.
Roesner (1994), a session chair for the Engineering Foundation stormwater NPDES-related
monitoring needs conference stated, as part of the closing session for this conference,
"Throughout the course of this conference, it has become increasingly apparent to me that the
course we are taking with the NPDES stormwater permitting program is going to cost municipalities
a lot of money, but is not going to result in any significant improvement in the quality of our urban
receiving water systems."
Jones-Lee and Lee (1994, 1998) have reviewed the ability of conventional stormwater runoff best
management practices, such as detention basins, filters, etc., to treat urban area and highway stormwater
runoff to ultimately meet current regulatory requirements of not more than one exceedance of any
magnitude in the stormwater runoff every three years. They conclude that conventional BMPs will not
treat urban and highway stormwater runoff adequately to meet these requirements. As Jones-Lee and Lee
discuss, advanced wastewater treatment practices, such as reverse osmosis, ion exchange, activated
carbon, etc., will need to be used to achieve the required degree of treatment to prevent causing
violations of water quality standards by stormwater runoff associated constituents. The cost of
retrofitting the Los Angeles urban area and highway stormwater runoff conveyance structures with this
treatment is estimated to be on the order of $60 billion. Obviously, with these kinds of costs, it is
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essential that real, significant water quality use impairments be found through appropriately conducted
water quality monitoring/evaluation programs.
The US EPA (1990) defined the goal of its urban stormwater runoff water quality management
program as the control of pollution of waters through the use of best management practices (BMPs) to
the maximum extent practicable. It is important, when considering the potential impact of chemical
constituents in urban area and highway stormwater runoff, to not label chemical constituents "pollutants"
unless there is reasonable evidence to indicate that a particular chemical constituent in stormwater runoff
is, in fact, or has a significant potential to be, significantly adverse to the beneficial uses of the receiving
waters for the runoff, i.e. causing pollution. With few exceptions, most of what are called "pollutants" in
urban area and highway stormwater runoff are more properly labeled "chemical constituents." Typically,
individuals working in the water quality field label chemical constituents "pollutants." However, legally,
under the Clean Water Act, pollution is defined in terms of use impairments, and not chemical concentra-
tions. "Pollution" should only be used under conditions where well-defined impairment of uses has been
documented or can reasonably be expected. "Pollutants" are those chemical constituents that are respon-
sible for pollution. It is technically wrong, and highly misleading, to call all chemical constituents
"pollutants." This is especially true when developing management programs for urban area and highway
stormwater runoff.
Evaluation Monitoring as a Framework for Water Quality Problem
Identification and Management
Evaluation Monitoring as developed by Lee and Jones-Lee (1996a) shifts the water quality
monitoring resources from measuring source and/or ambient water chemical concentrations and loads to
assessing chemical impacts in the receiving waters, focusing on finding real significant water quality use
impairment problems. Where significant water quality problems are found, Evaluation Monitoring
focuses on determining their cause, and defining the sources of the constituents responsible. This
approach was developed about four years ago by Lee and Jones Lee (1996a) associated with water
quality studies in the Upper Newport Bay watershed in Orange County, CA. As discussed by Lee and
Jones-Lee (1996a, 1997a,b,c), Evaluation Monitoring is a watershed-based, technical stakeholder-driven
water quality problem definition and control program. It serves as the basis for addressing the overly
protective nature of US EPA water quality criteria and state standards based on these criteria. It also
serves as the basis for regulating chemical constituents for which there are no water quality
criteria/standards, as well as those situations where US EPA water quality criteria exists, such as
chlorpyrifos, that are not adopted by the states as standards.
The mechanical application of US EPA criteria as state ambient water quality standards will, for
many if not most waterbodies, be overly protective. If there were an infinite amount of money that could
be spent to control chemical constituents within a waterbody's watershed, then working toward a goal of
achieving these criterion values would be appropriate, provided that there were not other significant
social problems which needed funding. However, today, with a large number of social problems that
need funds, and limited funding for water quality management, it is important to focus water quality
management programs on solving real, significant water quality use impairments that significantly
adversely impact the beneficial uses of a waterbody. Table 2 lists the typical water quality use
impairments of concern to the public. The impairment of beneficial uses of a waterbody for aquatic life-
related uses should focus on finding significantly altered numbers, types and characteristics of desirable
forms of aquatic life, or cause aquatic organisms that are used as food to have excessive concentrations
of hazardous chemicals in their edible tissue through bioaccumulation.
Basically, the EM approach focuses the monitoring resources available on determining what, if any,
real water quality use impairments are occurring in the receiving waters for the stormwater runoff. This
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problem definition phase of the EM program is conducted as a cooperative effort among the stormwater
quality management agencies, industry/commercial stormwater dischargers, point source NPDES
permitted dischargers, highway departments, regulatory agencies, agricultural interests, the public and
others interested in water quality and appropriate use of public funds. When real, significant water
quality use impairments are found, then efforts should be directed to determining the cause(s)/source(s)
of constituents/ materials that are causing the use impairment(s). Once the cause and source of the
impairments have been defined, then efforts are directed towards controlling the water quality use
impairment, preferably at the source through source control.
Some of the basic questions that need to be addressed in evaluating whether stormwater runoff-
associated constituents from a particular area are adversely impacting the beneficial uses of a waterbody
include:
• Is there significant toxicity in the receiving waters that is associated with stormwater runoff
events that could be adverse to aquatic life populations in the receiving waters?
• Are there closed shellfish beds, swimming areas, etc. that could be impacted by stormwater
runoff-associated pathogenic indicator organisms?
• Is there excessive algal/aquatic weed growth that could be stimulated by aquatic plant nutrients
(nitrogen and phosphorus) in the stormwater runoff waters?
• Is there litter and debris that is derived from stormwater runoff?
• Do the fish and/or shellfish contain excessive concentrations of hazardous chemicals that could
be derived from stormwater runoff?
• Is the receiving water for the stormwater runoff excessively turbid during a runoff event?
• Is there shoaling, burial of spawning areas, shellfish beds, etc. occurring in the receiving waters
due to the transport of suspended sediment in the stormwater runoff waters?
• Is there an accumulation of oil and grease in the receiving waters that is either aesthetically
unpleasing and/or adverse to aquatic life?
• Are domestic or other water supplies experiencing treatment problems, excessive costs, etc. due
to stormwater runoff-associated constituents?
The initial phase of the EM program involves determining how each of these use impairments could be
detected in the receiving waters for the stormwater runoff where they are listed as a designated beneficial
use of a waterbody of concern.
For many of the impacts, such as impairment of drinking water raw water quality, excessive
bioaccumulation, excessive suspended and deposited sediments, excessive pathogenic organism
indicators, low dissolved oxygen, and aesthetic impacts from litter, debris, oil and grease, etc., it is
possible, through direct measurements of the receiving waters at the point of concern, to determine if
there is a use impairment. For example, for excessive bioaccumulation, collecting edible organisms from
the receiving waters and determining whether the tissue contains excessive concentrations of hazardous
chemicals is straightforward and can be readily accomplished. Similarly, excessive concentrations of
pathogenic organism indicators on a particular beach or within a shellfish population is also readily
discernible. Therefore, for most of the use impairments, direct measurements of the impairment are
readily possible by selected sampling of the receiving waters at the point of concern.
One of the more important, but difficult to assess, water quality problems is toxicity to larval forms
of fish and other small aquatic life, such as zooplankton, which serve as important food for higher trophic
level organisms. While it is relatively easy to detect large-scale acute impacts to adult, large forms of
aquatic life, such as is associated with a fish kill, detecting adverse impacts on smaller forms is difficult.
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In order to do this, it becomes necessary to assess whether toxicity under standard test conditions is
found in the receiving waters that is of sufficient magnitude, areal extent and duration to be significantly
toxic to larval forms of fish and/or smaller forms of aquatic life, such as zooplankton which are key
components of larval fish food.
Traditionally, water quality monitoring programs have focused on measuring the concentrations of a
constituent and, if flow data are available, the load of the constituent passing a particular point and then
try to extrapolate as to whether the constituent of concern at a particular concentration is adverse to the
beneficial uses of a waterbody. Toxicity to aquatic life is one of the primary areas of concern for many
chemical constituents. Evaluation Monitoring, rather than trying to extrapolate from chemical concen-
trations to toxicity, focuses on measuring toxicity directly and then determining through Toxicity
Investigation Evaluations (TIEs) the cause of the toxicity and, through forensic analysis, its source.
Similarly, rather than trying to extrapolate from chemicals that are potentially bioaccumulatable to
excessive tissue residues, Evaluation Monitoring measures directly whether excessive bioaccumulation
has occurred in edible organisms in the receiving waters and then where such problems are found,
through forensic studies, determine the source(s) of constituents responsible. This is the approach that is
being used to a considerable extent in the Sacramento River watershed first year monitoring through the
implementation of the Evaluation Monitoring approach.
Development and Implementation of a Water Quality Monitoring Program
Far too often water quality monitoring programs are implemented without proper planning of the
program prior to initiation of the sampling. As discussed by NRC (1990) and Lee and Jones-Lee (1992),
the key to development of a credible water quality monitoring program is the appropriate definition of the
objectives of the program and the characteristics of the waterbody being investigated that need to be
considered in achieving these objectives. Lee and Jones-Lee (1992) have provided guidance on the issues
that should be considered in formulating the basic components of a water quality monitoring program,
including defining the objectives of the monitoring program and how reliably the objectives of the
monitoring program are to be assessed, selection of sampling stations and approaches, selection and
evaluation of analytical methods, the use of clean "non-contaminating" sampling and sample handling
procedures, QA/QC, data storage and retrieval and data interpretation, etc.
The Evaluation Monitoring approach is more difficult to implement than the traditional monitoring
approach since it requires a high degree of expertise in understanding how chemical constituents in
runoff waters, or within a waterbody, impact the beneficial uses of the water. Further, and most
importantly, Evaluation Monitoring is based on an event based highly directed selective sampling of the
runoff and receiving waters, focusing on runoff events and their near term and long er term impacts on
receiving water beneficial uses. By focusing the sampling on these events it is possible to more reliably
determine whether chemical constituents in the runoff are potentially adverse to the beneficial uses of the
waterbody.
Implementation of an Evaluation Monitoring Program
Lee and Jones-Lee (1997c) have provided detailed discussion of the approach that should be used to
implement an EM program. A summary of some of the key issues that need to be considered is presented
in this section.
Review of Existing Water Quality Characteristic Data. The first phase of the Evaluation Monitoring
program should be devoted to a critical review of the existing database on the water quality
characteristics of waterbodies and their tributaries. Based on this review, information gaps on current
water quality use impairments of the type listed in Table 2 should be defined, and the monitoring
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program then focused on filling these gaps. The initial purpose of the data review would be to critically
evaluate the reliability of the existing data and compile a credible database. Once this database has been
compiled, a critical review of the reliable data should be conducted to determine what water quality
problems have been potentially identified as well as confirmed through the existing database. This should
then be presented to the watershed stakeholders for their review and comment. Associated with this
presentation should be a discussion of the areas that need further attention, with specific
recommendations on the kind of monitoring/evaluation program that should be conducted to fill the
information gaps.
Once a comprehensive set of data from past studies, as well as from any current monitoring
programs, has been collected and a report prepared on this database, then a stakeholder-developed
consensus should be formulated on what real water quality use impairments exist in the various parts of
waterbodies within the watershed of concern. When the water quality use impairment problems have
been defined, then, if the cause of these impairments has not been determined, site-specific studies
should be undertaken to determine the cause, i.e. the specific chemical constituents responsible for the
use impairments.
A use impairment should be a designated beneficial use impairment of the waterbody that is
perceivable by the public. Not included in this definition is an exceedance of a water quality standard/
objective. The water quality significance of exceedance of a water quality standard/objective should be
addressed as a separate issue, where specific studies are conducted to determine the relationship between
the exceedance of the objective and the impairment of the beneficial uses of the waterbody of concern for
the public. Also, specific evaluations should be made of the improvement in the designated beneficial
uses of the waterbody that would accrue through controlling the input of the constituent responsible for
the water quality objective exceedance to a sufficient extent to eliminate the exceedance so that it occurs
no more than once every three years, i.e. current CWA requirements. The emphasis in defining the cause
of the water quality problem should not be on the total constituent, such as total copper, cadmium, lead,
etc., but on the specific forms of the constituent responsible for the toxicity, excessive bioaccumulation
or other use impairment, such as available forms of nutrients that impact excessive fertilization of a
waterbody.
When the specific constituents responsible for the use impairment have been identified, then through
forensic studies, the specific sources of the constituents responsible for the use impairment should be
determined. Again, the focus should not be on all sources of total copper or other constituents; it should
be on those sources of copper, mercury, PAHs, etc. that are adverse to the beneficial uses of a particular
part of the waterbodies in a watershed of concern.
Addressing Exceedances of Water Quality Criteria/Standards
An important component of a watershed-based water quality management program should be devoted
to determination of what the exceedance of a water quality standard/objective means to the beneficial
uses of a part of the watershed where the exceedance occurs and in downstream waters. The US EPA
water quality criteria and state standards (objectives) based on these criteria assume worst-case or near
worst-case conditions in developing the specific chemical numeric criterion values. The chemical
constituents of potential concern are assumed to be in toxic/available forms and present in the vicinity of
the organism for sufficient periods of time to cause chronic toxicity. The US EPA's regulatory approach,
however, tends for many waterbodies, but not all, to over-regulate chemical constituents since many
waterbodies contain constituents that detoxify or otherwise make unavailable, chemical constituents of
concern. The US EPA water quality criteria were never intended to be implemented as mechanical, not-
to-be-exceeded values. The US EPA site-specific criterion adjustment approach, such as the Water
Effects Ratio approach, only partially adjusts for the aquatic chemistry of constituents in aquatic systems
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that impact their toxicity/availability. This approach does not allow adequate time for chemical
equilibrium to be reached and fails completely to address the key issue of the impact of the form of the
constituent of concern added to the waterbody on its toxicity/availability.
The current implementation approach of assuming that US EPA water quality criteria are appropriate
state standards leads to significant over-regulation of most regulated constituents, i.e. those constituents
for which there is a water quality criterion, for most waterbodies. This will certainly be the case for most
watersheds. In some cases, much higher concentrations of constituents of concern than the water quality
standard can be present without adversely impacting the designated beneficial uses of a waterbody.
Today, any so-called "water quality" study of heavy metals and other potentially toxic chemical
constituents that does not include aquatic life toxicity measurements as an integral component of the
study is of little or no value in providing the kinds of information that is needed in a true water quality
study. Aquatic life toxicity measurements must be the foundation of any credible water quality study
where measurements of heavy metal or other potentially toxic constituents are being made. Similarly, a
"water quality" study of mercury, without measuring edible fish tissue concentrations of mercury, is not a
credible water quality study. This is the kind of study that is typically associated with compliance
monitoring, which has been well known for many years to be of limited reliability in assessing water
quality issues.
Detection of Subtle and New Water Quality Problems
A key component of the Evaluation Monitoring approach is a periodic review of the water quality
characteristics of the waterbody of concern to determine whether the previous review failed to detect an
incipient - unknown water quality problem as well as any new water quality problems that have
developed since the last review due to the expanded use or new use of chemicals in the watershed that are
adverse to the beneficial uses of a waterbody. It is suggested that a five year review cycle be used. This
period of time is coincident with the duration of NPDES permits covering waste water and stormwater
discharges. During this five year review, funds should be made available to examine the waterbody for
more subtle water quality use impairments than were detected in the previous review.
Water Quality Significance of Aquatic Life Toxicity
Another issue that needs to be addressed as part of developing a water quality management program
for a particular watershed or waterbody is the development of an approach for assessing the water quality
significance of aquatic life toxicity of the type being found in many surface waters that receive some
agriculture and urban stormwater runoff. Organophosphate pesticides such as diazinon and chlorpyrifos
are being found to cause acute toxicity to Ceriodaphnia and some other forms of aquatic life. There is
need to provide guidance on how to determine what represents excessive aquatic life toxicity within a
particular waterbody that is adversely impacting the beneficial uses of the waterbody. As described by
Lee and Jones-Lee (1998b) an expert panel should be appointed and provided with the necessary
resources to formulate approaches that can be brought to the stakeholders that can be used to determine
the water quality significance of toxicity to certain organisms at certain locations.
Once the overall guidance approach is defined, then site-specific application of this approach should
be initiated for various parts of the watershed where toxicity has been identified and its magnitude, areal
and volumetric extent, and duration is evaluated with respect to its potential significant to the beneficial
uses of the waterbody. There will likely be need to conduct additional site-specific studies focusing on
the relationship between the measured aquatic life toxicity in tributary waters and mainstem waters on
aquatic organism assemblages within these waters. This type of information will ultimately become the
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key information needed to determine whether measured toxicity is a significant cause of a water quality
use impairment at any location within a waterbody.
Formulation of Water Quality Management Programs
Once the true water quality problems have been defined and the source of the specific constituents
responsible for the problem identified, then there is need to begin to formulate water quality use
impairment management plans. As part of formulation of the potential water quality management plans,
there is need to incorporate high-quality current science and engineering into determining the potential
benefits of controlling the input of a constituent responsible for a water quality use impairment to a
particular degree on the beneficial uses of a particular part of a waterbody. This determination should
include consideration of impacts near the point of discharge/runoff (near field impacts) and on the overall
beneficial uses of the waterbody (far field impacts). Typically today, water quality management programs
for specific constituents in the current point source discharge management program, as well as for
watershed based water quality management programs, are formulated without adequate incorporation of
aquatic chemistry and aquatic toxicology information into the program. The mass load approach for
managing water quality in a waterbody, which is based on total constituent loads, is an example of a
technically invalid approach for formulating a watershed based water quality management program.
Not all sources of a constituent of concern contribute the constituent in toxic/available forms.
Further, even the discharge of a toxic/available form in one part of a watershed does not lead to that
constituent being toxic/available throughout the downstream waters. An example of this situation is
copper in the Sacramento River system discharged by the Iron Mountain Mine in the upper part of the
watershed. While there is toxicity due to copper near the point of discharge, this toxicity is rapidly lost in
the Sacramento River system even though the total copper is still present at concentrations above water
quality standards. It is inappropriate to assume that the copper present in the Sacramento River system
which exceeds the copper water quality objective is adverse to the beneficial uses of all downstream
waters associated with the exceedance of the objective. This issue has been reviewed by Lee and Jones-
Lee (1997d).
While the Iron Mountain Mine contributes to the copper concentration that is part of the cause of the
water quality objective exceedances that occur in San Francisco Bay, the San Francisco Estuary Institute
(SFEI 1997) has published the results of the 1996 Regional Monitoring Program. This report indicates
that after three years of monitoring which included toxicity testing using the same test organism as was
used to develop the national as well as the San Francisco Bay site-specific water quality standard
(objective), the exceedance of the copper water quality objective is not associated with aquatic life
toxicity in San Francisco Bay waters or sediments.
Lee and Jones-Lee (1996c), discuss the importance of using current readily available science and
engineering in identifying water quality problems in a watershed and for formulating technically valid,
cost-effective control programs for these problems. As discussed, these control programs should focus on
real, significant water quality use impairments and not divert the limited financial resources available to
chasing ghosts of problems that arise out of overly protective approaches associated with the US EPA's
ill-founded Independent Applicability Policy. This Policy requires that chemical specific numeric
criteria/standards must be met for potentially toxic constituents, even though properly conducted toxicity
tests show that the constituents are in non-toxic, non-available forms. For further discussion of the
inappropriateness of this Policy, consult Lee and Jones-Lee (1995b, 1996d). As they discuss, from a
watershed-based water quality management program approach, the US EPA water quality criteria should
be used as a trigger to conduct further work to define the water quality significance of exceedance of a
water quality objective.
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Conclusion
The current water quality monitoring programs in which a suite of chemical constituents and selected
biological indicators are measured periodically at selected stations in a waterbody is typically an
unreliable approach for defining the water quality use impairments of a waterbody. This approach
evolved out of waste water treatment plant discharge compliance monitoring, where the objective of the
monitoring program is to determine whether there has been an exceedance of an NPDES permit condi-
tion. Such an approach relies on the use of exceedances of US EPA worst case based water quality
criteria and state standards based on these criteria as a reliable measure of a water quality use impair-
ment. However, with few exceptions, the exceedance of a US EPA water quality criterion is not a reliable
basis for determining whether a real water quality use impairment of a waterbody of concern to the
public. Also, this approach fails to address the water quality problems caused by those constituents for
which there are no water quality criteria or for which states do not use the available water quality criteria
as a basis for developing a water quality standard. The current US EPA approach for defining water
quality use impairments based on exceedance of water quality standards is not a technically valid
approach for developing the 303 (d) list of impaired waterbodies, and for formulating TMDLs. Through
the use of this approach, the US EPA and state pollution control agencies are providing unreliable
information to Congress and the public on the magnitude of the water quality use impairments of the
nation's waters caused by urban area and highway stormwater runoff associated chemical constituents.
The Evaluation Monitoring approach shifts the monitoring emphasis from chemical concentrations
and loads to chemical impacts - use impairments. This is a far more reliable approach for using the
monitoring resources available for detection of real significant water quality use impairments than
traditional water quality monitoring. With increasing emphasis being placed on managing the subtle
impacts of potentially toxic constituents, it is essential, if the public funds are to be used wisely in
controlling real significant water quality problems, that a significantly different approach be used to
define water quality problems and to develop appropriate water quality management programs to control
these problems in a technically valid cost effective manner.
Additional information on the implementation of the Evaluation Monitoring approach is available in
the comprehensive guidance provided by Lee and Jones-Lee (1997c). This guidance, as well as the other
papers and reports developed by the authors listed in the Literature Cited section, are available from the
authors' web site, http://members.aol.com/gfredlee/gfl.htm.
Literature Cited
Davies, P.H. 1995. "Factors in Controlling Nonpoint Source Impacts," In: Stormwater Runoff and
Receiving Systems: Impact, Monitoring, and Assessment, CRC Press Inc., Boca Raton, FL, pp. 53-64.
Herricks, E.E. 1995. Stormwater Runoff and Receiving Systems: Impact, Monitoring, and Assessment,
CRC Press, Inc., Boca Raton, FL.
Jones-Lee, A., and Lee, G.F. July, 1994. "Achieving Adequate BMPs for Stormwater Quality
Management," Proc. 1994 National Conference on Environmental Engineering, "Critical Issues in
Water and Wastewater Treatment," American Society of Civil Engineers, New York, NY, pp. 524-
531.
Jones-Lee, A. and Lee, G.F. June, 1998. "Stormwater Managers Beware of Snake-Oil BMPs for Water
Quality Management," Submitted for publication.
Lee, G.F., Jones, R.A. and Newbry, B.W. 1982. "Water Quality Standards and Water Quality," Journ.
Water Pollut. Control Fed. 54: 1131-1138.
Lee, G.F. and Jones, R.A. 1991. "Suggested Approach for Assessing Water Quality Impacts of Urban
Stormwater Drainage," In: Symposium Proceedings on Urban Hydrology, American Water
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Resources Association Symposium, November 1990, AWRA Technical Publication Series TPS-91-4,
AWRA, Bethesda, MD, pp. 139-151.
Lee, G.F. and Jones-Lee, A. July, 1992. "Guidance for Conducting Water Quality Studies for Developing
Control Programs for Toxic Contaminants in Wastewaters and Stormwater Runoff," Report of G.
Fred Lee & Associates, El Macero, CA, 30pp.
Lee, G.F. and Jones-Lee, A. 1994, 1995a. "Stormwater Runoff Management: Are Real Water Quality
Problems Being Addressed by Current Structural Best Management Practices? Part 1," Public Works,
125:53-57,70-72. Part Two, 126:54-56.
Lee, G.F. and Jones-Lee, A. 1995b. "Independent Applicability of Chemical and Biological
Criteria/Standards and Effluent Toxicity Testing," The National Environmental Journal, 5(1): 60-63,
Part H, "An Alternative Approach," 5(2): 66-67.
Lee, G.F. and Jones-Lee, A. 1996a. "Assessing Water Quality Impacts of Stormwater Runoff," North
American Water & Environment Congress, Published on CD-ROM, Amer. Soc. Civil Engr., New
York, 6pp.
Lee, G.F. and Jones-Lee, A. 1996, 1996b. "Stormwater Runoff Quality Monitoring: Chemical
Constituent vs. Water Quality," Public Works, Part 1147:50-53, Part H 147:42-45, 67.
Lee, G.F. and Jones-Lee, A. 1996c. "Aquatic Chemistry/Toxicology in Watershed-Based Water Quality
Management Programs," In: Proc. Watershed '96 National Conference on Watershed Management,
Water Environment Federation, Alexandria, VA, pp. 1003-1006.
Lee, G.F. and Jones-Lee, A. 1995, 1996d. "Appropriate Use of Numeric Chemical Water Quality
Criteria," Health and Ecological Risk Assessment, 1: 5-11. Letter to the Editor, Supplemental
Discussion, 2: 233-234.
Lee, G.F. and Jones-Lee, A. 1997a. "Evaluation Monitoring as an Alternative to Conventional
Stormwater Runoff Monitoring and BMP Development," SETAC News, 17(2):2Q-21.
Lee, G.F. and Jones-Lee, A. November, 1997b. "Evaluation Monitoring for Stormwater Runoff Water
Quality Impact Assessment and Management," Presented at Society of Environmental Toxicology &
Chemistry 18th Annual Meeting, San Francisco, CA.
Lee, G.F. and Jones-Lee, A. June, 1997c. "Development and Implementation of Evaluation Monitoring
for Stormwater Runoff Water Quality Impact Assessment and Management," Report of G. Fred Lee
& Associates, El Macero, CA.
Lee, G.F. and Jones-Lee, A. June, 1997d. "Regulating Copper in San Francisco Bay: Importance of
Appropriate Use of Aquatic Chemistry and Toxicology," Presented at the Fourth International
Conference on the Biogeochemistry of Trace Elements, Berkeley, CA.
Lee, G.F. and Jones-Lee, A. June, 1998a. "Appropriate Application of Water Quality Standards to
Regulating Urban Stormwater Runoff," Submitted for publication.
Lee, G.F. and Jones-Lee, A. June, 1998b. "Development of Regulatory Approach for OP Pesticide
Toxicity," Presented at NorCal SETAC meeting, Reno, NV.
NRC. 1990. Managing Troubled Waters: The Road of Marine Environmental Monitoring, National
Research Council, National Academy Press, Washington, D.C.
Roesner, L. 1994. Section XI, closing session discussion, In: Stormwater NPDES Related Monitoring
Needs, Proc. Engineering Foundation Conference, American Society of Civil Engineers, New York,
NY pp. 537.
SFEI. 1997. "Regional Monitoring Program for Trace Substances, 1996 Annual Report," San Francisco
Estuary Institute, Richmond, CA.
Urbanos, B. and Torno, H.C. 1994. "Overview Summary of the Conference," In: Stormwater NPDES
Related Monitoring Needs, Proc. Engineering Foundation Conference, American Society of Civil
Engineers, New York, NY pp. 1-5.
U.S. Environmental Protection Agency. November 16, 1990. "National Pollutant Discharge Elimination
System Permit Application Regulations for Stormwater Discharges; Final Rule," US Environmental
Protection Agency, 40 CFR Parts 122, 123, and 124, Federal Register 55(222):47990-48091.
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Concentration of
Available Forms
of Contaminant
Impact Arefl
US EPA
Criterion
96
Duration of Exposure
Y-
„
Exceedance of US EPA Water Quality Criteria for
Acute (1 hr. ave.) and Chronic (4 day ave.) Over
estimates impacts on beneficial uses.
Figure 1. Aquatic toxicology.
>XI+C
Biochemical
Transformation
I?
//
n
A A
1 !
! !
Precipitation
i i
! !
V V
•».
^\ Sorption
\
\
\ ^
\ 0-x+
QX-
MX* MX-
Figure 2. Aquatic chemistry of chemical contaminants.
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Table 1. Factors that Must Be Considered in Translating Runoff Measured Concentrations of a
Constituent to Potential Aquatic Life Water Quality Impacts
The information needed to determine whether a potentially toxic constituent causes an impairment of the
beneficial uses of receiving waters for stormwater runoff includes the following.
Stormwater runoff
• measured concentration of constituent during runoff event - concentration time profile
discharge of the runoff waters during runoff event - hydrograph
• analytical chemistry of the method used for analyses - what chemical species are measured
Receiving waters
Physical factors:
Currents, tides - transport-advection
Mixing-dispersion
Biological factors:
Duration of organism exposure to toxicant
Organism movement - locomotion
Diel migration
Sensitivity to toxicants
Organism assemblages - resident populations relative to habitat characteristics
Chemical factors:
Aquatic chemistry
Kinetics and thermodynamics of reactions
Additive, synergistic and antagonistic reactions and impacts
Toxic/available and non-toxic, non-available forms
Background concentrations of constituents of concern
Table 2. Water Quality Use Impairments
Aquatic life toxicity - water column,
Sediment toxicity that impairs water quality - beneficial uses,
Excessive bioaccumulation of hazardous chemicals,
Dissolved oxygen depletion,
Domestic water supply water quality,
Groundwater recharge,
Eutrophication - excessive fertilization,
Sanitary quality impairment - contact recreation and/or shellfish harvesting,
Suspended sediment impacts and accumulation,
Oil and grease accumulation, and
Litter accumulation.
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The Index of Watershed Indicators—An Evolving National Tool
Chuck Spooner and Sarah Lehmann
U.S. Environmental Protection Agency, Office of Water
Abstract
Water quality managers increasingly recognize watersheds as important geographical areas on which
to base the assessment, restoration, and protection of our water resources. The Environmental Protection
Agency's (EPA) Office of Water has designed the Index of Watershed Indicators (IWI or Index) to
organize aquatic resource information and present it on a watershed scale. The Index uses data from
diverse sources and portrays both the condition and vulnerability of watersheds across the United States.
EPA's Office of Water first published the Index in October 1997 as a national portrayal of the health
of watersheds. EPA and its many partners are now working to improve the Index by adding missing data
layers, refreshing current data layers with more recent information, and providing additional tools for
interpreting the IWI assessment. We are also striving to include finer resolution data where available.
Future versions of the IWI will continue to build upon our experiences and the experiences of water
quality mangers and the public who utilize the IWI.
Introduction
Assessments of our water resources can occur at many scales. More and more, water quality
managers at all levels recognize watersheds as important geographic areas on which both the assessment,
restoration, and protection of waters can and should occur. The Clean Water Action Plan1, for instance,
recognizes the watershed approach as the "key to setting priorities and taking action to clean up rivers,
lakes and coastal waters." Thinking at the watershed scale rather than at the scale of the individual
waterbody or at politically defined scales (e.g., states or federal regions) allows us to focus on resources
rather than programs. It also necessitates that all partners and stakeholders work together to identify
problems and implement solutions to those problems.
One way in which the Environmental Protection Agency's Office of Water is attempting to inform
the discussion on watershed management is through the Index of Watershed Indicators (IWI or Index).
The IWI is a broad, national assessment of the condition and vulnerability of watersheds across the
country. This assessment is based on 15 indicators of aquatic resource health (see figure 1). EPA
published the first IWI assessment report in October 1997 and at the same time made this and additional
information available to the public via the Internet (). Using the Index of
Watershed Indicators, we are able to present a variety of water information geographically and to
combine that information to present a general indication of the health of watersheds2
The Clean Water Action Plan charts a course for federal agencies toward fulfilling the original goal of the Clean
Water Act—"fishable and swimmable" waters for all Americans as requested by Vice President Albert Gore, Jr. It
can be accessed via the World Wide Web at http://www.epa.gov/cleanwater.
2
The attempt to derive general figures for watershed health lends itself to the question of how meaningful these
figures are in assisting water quality mangers and the public to improve watershed conditions. Although discussing
the estimation of environmental benefits, an interesting commentary on this question is provided by Myrick A.
Freeman. Freeman wrote, "National estimates of environmental benefits [watershed health] share many of the same
virtues and defects as gross national product. They are both single-valued numbers used to summarize and describe
complex phenomena. Actual estimates may be derived from samples of or extrapolation from limited information.
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The Index of Watershed Indicators uses information from a variety of sources and agencies. The
most heavily weighted data layer3 in the IWI is derived directly from information submitted by states,
tribes, territories, and interstate commissions under section 305(b) of the Clean Water Act. This data
layer reflects the most current information submitted by the states. The Index also uses statistically
sampled information from the United States Department of Agriculture's Natural Resource Inventory to
predict potential agricultural runoff of nitogen, pesticides, and sediment. Another data set used by the
Index which is based on a statistically based sample is the Census data used to calculate an urban runoff
potential.
Results from the first IWI assessment indicate that 21% of the watersheds in the United States have
serious water quality problems; 36% have some water quality problems; and 16% of the watersheds have
better water quality. One in eight watersheds in the country showed signs of being highly vulnerable to
future degradation. It is important to note that the strength of monitoring programs across the country is
highly variable. Areas with strong monitoring programs may show more problems (and are more
accurate) than those areas with weaker monitoring programs.
Continuing Improvement
In the first Phase of the IWI, EPA and its federal, state, tribal, and other partners emphasized that we
need to improve many of the data layers used to create the overall score. Additionally, we did not include
a few aquatic resource indicators in the first assessment due to data limitations. The second phase of the
Index of Watershed Indicators is focused on improving the IWI's assessment and adding to its
capabilities to support other assessments at a variety of scales. All of the indicators used in the first IWI
will be updated on the Internet with more recent data if it is available. Additionally, Assessed Waters
Meeting All Designated Uses Set in State/Tribal Water Quality Standards 1994/1996 (Indicator 1), Index
of Agricultural Runoff Potential 1990-95 (Indicator 12) and Estuarine Pollution Susceptibility Index
1989-1991 (Indicator 15) will be altered substantially for the fall 1998 release of the IWI. New indicators
are being considered for inclusion in the Index to complement and improve the watershed assessment.
Finally, EPA is working to present aquatic resource information at a finer scale that will be of even
greater use to water managers and the public in their day-to-day decision making.
Indicator Refreshes
US EPA and its partners are updating the data for the following indicators during 1998. Once
updated, states, tribes, and federal agencies will have 30 days in which to review the new data before its
release to the public (see Table 1).
Analysts may be more interested in examining components of the aggregate measures. But the estimating procedure
may preclude disaggregration of the form and to the extent desired by the analysts." Freeman, Myrick A. Benefits of
Environmental Improvement: Theory and Practice. Johns Hopkins University press. 1979. Pg. 2.
By providing information not only on the overall watershed scores but also on the individual indicators and the
more specific data underlying the indicators, EPA has allowed for disaggregation of the IWI information.
All of the data layers in the Index of Watershed Indicators are weighted equally except for data layer 1, which is
derived from State Water Quality Reports (305(b) Reports). Data Layer 1 is weighted six times as heavily as the
other data layers. When there is insufficient 305(b) data available in a watershed for the purposes of IWI, all other
indicators are increased in weight by a factor of 3.
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Indicator Alterations
As noted above, we will substantially change indicators 1,12 and 15 from what was presented in
Phase I of the Index of Watershed Indicators. The revised indicators will be made available for the
October release of IWI. For example, IWI Indicator 1, based on State/Tribal Water Quality Reports
(section 305(b) of the Clean Water Act), did not include assessments of lakes, reservoirs, or estuaries.
EPA is working to update this data layer to include 1996 or 1998 data (where states, tribes, territories, or
interstate commissions have submitted updated information), and to increase the coverage beyond rivers.
Adding lakes, reservoirs and/or estuaries to the IWI will give a fuller picture of water resources within
the watershed and may eliminate some watersheds from the insufficient data category.
The Agricultural Runoff indicator estimate was based on 160,000 Natural Resources Inventory
(1992) points. It can generally be considered an "edge-of-field" risk component of the IWI. EPA and the
U.S. Department of Agriculture will work together to expand and refine the estimate. For example, the
methodology used for Phase I IWI did not include such things as leaching and stream transport/inputs.
This effort will also increase the number of land use types considered in the estimate. EPA will also look
into ways to use local sources of information for crop need and nutrient applications. Other possible
sources of information that will be considered for Phase I IWI includes tillage information, livestock
management issues, and the export of nutrients and other agriculture pollutants from the watershed.
Finally, EPA and the National Oceanic and Atmospheric Administration will work to expand the
Estuarine Pollution Susceptibility Index to include not only vulnerability information but also
information on the condition of the nation's estuarine areas.
New Indicators
During Phase I, EPA committed to adding additional indicators that would round-out the assessment
of watershed condition and vulnerability. The introduction of new indicators will improve the IWI in two
integral ways. First, and most obviously, the second version of the IWI report will include new such
indicators as ground water condition, biological integrity, terrestrial condition, atmospheric deposition
and coastal conditions4.
Second, we are developing and compiling other indicators and associated maps as part of a "map
library" (see figure 2). They will be available to anyone wishing to use them for watershed level
analyses. These maps will also provide interpretive mechanisms for the IWI assessment. Examples of
indicators under consideration include such things as wild and scenic river locations, national estuary
program locations, and sources of atmospheric emissions. We will expand this map library as EPA,
states, tribes, other federal agencies, and other organizations uncover and make available new sources of
information.
New Indicator Categories
The first version of the Index of Watershed Indicators published in 1997 dealt with two separate
components of watershed health: condition and vulnerability. While the addition of a vulnerability
segment was a major step forward in national assessments of aquatic resource health, the Office of Water
is continuing to move into new areas. As part of Phase II and beyond, we will be adding indicators in two
Sensitivity analyses are still being performed on these indicators. Therefore, all of them may not end up in the IWI
scoring methodology.
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new categories: program response and resource characteristics. The addition of these new categories
provides a "four dimensional" framework for the Index of Watershed Indicators.
The program response category is designed to capture what we, as agencies and society, are doing to
address conditions and vulnerabilities identified in watersheds. For example, indicators under
consideration include waterbodies named on state 303(d) lists, watersheds identified by states and tribes
as needing protection or restoration efforts through the Clean Water Action Plan, the number of acres
registered in the Conservation Reserve Program, and specially designated areas (such as wild and scenic
rivers). This type of indicator captures the activities we undertake to improve or maintain aquatic
resource health. Adding this category to the IWI is important so that the assessment more accurately
reflects mitigation efforts in place in the watershed.
Second, the IWI will also include indicators of resource characteristics. These indicators are intended
to portray how systems function and the effect those functions have on watershed health. For example,
one function of watershed systems is that they drain to watersheds and/or waterbodies downstream.
Using a model from the United States Geological Survey called SPARROW (SPAtially Referenced
Regressions On Watershed Attributes,)5 IWI will look at the amount of nutrients that each watershed
exports to other watersheds. High levels of nutrient export suggest that the watershed may be
contributing to problems downstream even if those problems are not manifested in the original
watershed. Other indicators which fall in this category include atmospheric emissions and uses of ground
water.
Finer Resolution
One of the drawbacks to any national level assessment is the scale at which information is presented.
The IWI, while taking great strides in refining our ability to present nationally consistent information
more locally, still present information on a relatively gross scale. Many of the cataloguing units are as
large or larger than U.S. counties. Because of this, problems in one part of a watershed can cause the
whole watershed to be categorized as having poor water quality or conversely, waters with many water
quality problems may be masked in the analysis. Both of these cases present unique problems and need to
be addressed.
EPA is piloting the inclusion of more detailed state and local information into the IWI. Areas under
consideration for this type of analysis include the Willamette River in Washington and the Raccoon
River near Des Moines, Iowa. EPA is also working with USGS, other federal agencies, and states to
complete a nationally consistent watershed delineation at the 11 and 14 digit cataloguing unit. This will
provide us with the basis for greatly refining the IWI assessment.
Conclusion
The Index of Watershed Indicators is a powerful tool for displaying, assessing, and explaining
complex water quality information. In order to continue to expand its utility, EPA and its many partners
are working to improve the quality and breadth of coverage as well as the presentation of information in
reports and on the world wide web. The IWI will continue to evolve and improve overtime as we are able
to build upon earlier versions, and as water quality managers and the public discover new applications to
which the Index can be put.
For more information on the SPARROW model, see the USGS website:
http://wwwrvares.er.usgs.gov/nawqa/sparrow/
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Index of Watershed Indicators
Condition Indicators
• Assessed Waters Meeting All Designated Uses Set in
State/Tribal Water Quality Standards 1994/1996
• Fish And Wildlife Consumption Advisories 1995
• Indicators of Source Water Condition for Drinking Water
Systems 1990-96
• Contaminated Sediments 1980-93
• Ambient Water Quality Data—Four Toxic Pollutants
1990-95
• Ambient Water Quality Data—Four Conventional
Pollutants 1990-95
• Wetland Loss Index 1982-92; 1780-1980
Vulnerability Indicators
• Aquatic/Wetland Species at Risk 1996
• Pollutant Loads Discharged Above Permitted Limits—
Toxic Pollutants 1995
• Pollutant Loads Discharged Above Permitted Limits—
Conventional Pollutants 1995
• Urban Runoff Potential 1990
• Index of Agricultural Runoff Potential 1990-95
• Population Change 1980-1990
° Hydrologic Modification Caused by Dams 1995-96
• Estuarine Pollution Susceptibility Index 1989-1991
Maps Developed by Others
Mapping Software and Services
Figure 2. Map library.
Figure 1. IWI indicators.
Table 1. Schedule of Updates
Map Title
Condition
Update^
1
2
3
5
6
9-10
12
13
15
Assessed Rivers Meeting All Designated Uses
Fish & Wildlife Consumption Advisories
Indicators of Source Water for Drinking Water
Ambient Water Quality Data — 4 Toxics
Ambient Water Quality Data — 4 Conventionals
Vulnerability
PCS Loads Over Limits (Toxic/Conventional)
Agricultural Runoff Potential
Population Change
Estuarine Pollution Potential
June
July
July
June
June
1996 update completed Jan
1997 update June
*
August
July
* = Date to be set pending further review of methodology or future base data update/availability
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An Analysis of Long-Term Water Quality Trends in Virginia
Carl E. Zipper
Department of Crop and Soil Environmental Sciences
Virginia Polytechnic Institute and State University
Golde I. Holtzman and Patrick Darken
Department of Statistics
Virginia Polytechnic Institute and State University
Pamela Thomas and lason Gildea
Department of Crop and Soil Environmental Sciences
Virginia Polytechnic Institute and State University
Leonard Shabman
Virginia Water Resources Research Center
Virginia Polytechnic Institute and State University
Introduction
The quality of water in Virginia's rivers and streams affects the health and welfare of Virginia's
citizens, quality of life available in the Commonwealth's communities, and the state's economic
development potential. Each year, large amounts of money are spent by both the state and the private
sector to protect, improve, and monitor the quality of Virginia's waters. Yet, little information is available
on the long-term success of these water-quality-protection expenditures.
The research described in this report was the first-phase of a two-phased, multi-year effort to enhance
Virginia Department of Environmental Quality's (DEQ) capability to detect and interpret long-term
water-quality trends. This report summarizes results of long-term trend analyses of Virginia water-quality
monitoring data collected over varying periods at individual monitoring stations.
Research Objective
The objective of this research was to evaluate Virginia surface-water quality monitoring data
statistically for trend.
Research Methods
We addressed the research objective by performing a statistical analysis of water quality data (9
variables) from 180 monitoring stations maintained by Department of Environmental Quality (DEQ), and
11 additional stations maintained by U.S. Geological Survey (USGS) using the seasonal Kendall
technique. Four of the USGS stations are located out of state.
The Data Set and Data Acquisition
Water quality data (9 variables) for 191 water-quality monitoring stations were downloaded from the
STORET data base by Virginia Tech personnel under the direction of Virginia DEQ. The stations
included 180 Virginia monitoring stations maintained by Virginia DEQ, 7 Virginia monitoring stations
maintained by USGS, and 4 out-of-state monitoring stations maintained by USGS.
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DEQ personnel identified monitoring stations to be included in this study based on two primary
criteria:
• availability of water quality data at a frequency sufficient to support the Seasonal Kendall
analytical technique, and
• locations that provide statewide geographic representation.
Virginia DEQ monitoring station data extend from the earliest availability of data through early 1997.
Most DEQ data sets begin in the early 1970s, although a few also contain data from the late 1960s; most
extend through early 1997. Availability of some data for some USGS monitoring stations (notably, pH)
extend as far back as the 1940s. In order to assure that the results of USGS monitoring-station analyses
would be roughly comparable to those of the DEQ stations, data prior to 1966 were excluded from these
analyses. Most stations were sampled monthly, although data sets for some stations show multiple-month
and/or multiple-year gaps in the data record. A small number of stations lacked data for one or more
variables.
DEQ personnel collected water samples from stream centers, generally using a bridge or boat and
following EPA protocol. This activity occurred during the course of the agency's ongoing surface water-
quality monitoring program. Laboratory analyses were conducted by the Virginia Division of
Consolidated Laboratory Services (DCLS) using methods based on U.S. Environmental Protection
Agency's "Methods for Chemical Analysis of Water and Wastes." Analytical methods used by DCLS
(including detection limits and precision) changed during the period of analysis, as new instrumentation
and more advanced analytical methods became available.
Precise flow data are not available for most monitoring stations. Therefore, statistical-analysis
procedures did not include an adjustment for flow.
Water-Quality Variables Studied
Water quality variables analyzed are listed below, with the STORET codes in parentheses.
• DO—Dissolved Oxygen Saturation (00301). DO saturation values were calculated from
measured values of DO concentration and water temperature by Virginia DEQ.
• BOD—Biochemical Oxygen Demand (00310).
• PH—pH (00400). The pH data for the early years were listed with a precision of one-tenth pH
unit (e.g., 7.1), whereas modern pH values were listed at a precision of one-one hundredth of a
pH unit (e.g., pH 7.12). We rounded all pH values off to one-tenth of a pH unit prior to
conducting seasonal-Kendall analyses.
• TR—Residue Total (00500).
• NFR—Non-Filterable Residue (00530).
• NN—Nitrate-Nitrite Nitrogen (00615 + 00620; 00630). NN values were determined using two
separate methods. For some sampling dates at most sampling locations, the DEQ data included a
single value representing nitrogen concentration in the nitrate and nitrite form (Storet Code
00630). In other cases, two separate analyses were performed, both nitrate (00615) and nitrite
(00620); in these cases, the two values were summed to calculate NN. This process created some
statistical complications when either one of these values was listed as a lower-detection limit.
• TKN—Total Kjeldahl Nitrogen (00625).
• TP—Total Phosphorous (00665).
• FC—Fecal Coliform (31616).
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Data Screening
The statistical analysis (seasonal Kendall analysis) and data-screening procedures were automated by
means of an original algorithm using SAS/IML (SAS/IML is a registered trademark of SAS Institute,
Inc., Gary, NC) programming code. The programming code used in our earlier work (Rheem and
Holtzman, 1990; Zipper et ai, 1992) has been replaced by a more efficient program written by P. Darken
and G. Holtzman called WQ1.
Values lying outside the analytical limits of the laboratory procedures, or beyond the boundaries of
what would be reasonably expected to occur in natural waters even under extreme conditions, were
discarded prior to statistical analysis. Limits used to identify values eliminated as outliers are written in
the output for each station. These limits were identified by DEQ personnel.
Because of the wide range of conditions present, we were unable to automate the process of
identifying erroneous observations. Erroneous values were identified by visual inspection on a station-
specific basis. Virginia Tech personnel identified apparent outliers as suspect values through review of
graphical and tabular output subsequent to a preliminary statistical analysis. DEQ personnel reviewed the
raw data for each suspect variable; those values identified by DEQ personnel as erroneous were
eliminated from the data set prior to statistical analysis, and identified as erroneous values in the program
output.
Statistical Methods
The data consist of measurements of 9 water-quality variables made at 191 monitoring stations. The
measurements were made over varying time periods between the late 1960s and early 1997 at intervals of
varying lengths, but, generally, at three- to four-week intervals. The data were analyzed for trend using a
seasonal Kendall Tau rank correlation test. Statistical procedures utilized are described in Zipper et al.
(1998aandl998b).
Some months had multiple observations. In each of those months, the median of the observations was
chosen as a representative value for that month so that the data set would consist only of monthly
observations.
STORET remark codes are single-character variables that accompany water-quality values entered in
the STORET database. Non-blank remark codes typically indicate a condition that should be placed on
interpretation of the associated data value. Data with non-blank remark codes to indicate conditions other
than detection-limits or calculated values ($ - DO only) were eliminated from the trend analysis. Below
each graphic display (Zipper et al, 1998a), those observations eliminated from the analysis due to remark
codes are listed. Remark codes other than those indicating detection limits occurred only rarely.
For each variable at each monitoring station, all observations remark-coded as being at a lower
detection limit were treated as tied. Similarly, all observations remarked as being at an upper detection
limit were treated as tied. Values less than or equal to a lower detection limit (and similarly, greater than
or equal to an upper detection limit) were treated as tied with values designated as being at the detection
limit.
This procedure was complicated for the nitrate-nitrite nitrogen (NN) variable, because in some cases
two values were added to create the NN value used in this analysis. This analysis required development of
statistical procedure designated as the "epsilon method" Usage of the epsilon method is documented in
the remark-code sections of the output for the NN variable at each monitoring station (Zipper et al,
1998a).
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Results
On a statewide basis, significant and apparent trends indicating water quality improvement
outnumbered significant and apparent trends indicating water-quality deterioration for BOD, TP, FC,
NFR, and DO. For BOD, NFR, and TP, trends representing water-quality improvement outnumbered
trends representing water-quality deterioration by ratios exceeding 3:1. For BOD, declining trends
representing water-quality improvement were predominant statewide.
For both NN and TKN, increasing trends outnumbered declining trends; increasing levels of nitrogen
are generally interpreted to indicate deteriorating water quality. On a statewide basis, there is a tendency
for increasing NN trends to occur at stations with relatively high medians.
Declining pH trends outnumbered increasing pH trends by a slight margin. Excluding coalfield
stations (where acid-mine-drainage treatment may be responsible for the predominance of increasing pH
trends), declining pH trends outnumbered increasing trends by a margin of nearly 2-to-l. Increasing TR
trends and decreasing TR trends occurred in approximately equal numbers.
Because of several factors inherent in the data set, simple numerical comparisons of increasing and
decreasing trends cannot be interpreted as a direct representation of general statewide change in water
quality. For example, we have no basis for evaluating the extent to which the monitoring stations
analyzed may or may not collectively represent statewide water quality. Whereas some monitoring
stations may be located such that watershed characteristics are the predominant influence on water
quality, others are located such that point-source discharges are the predominant influence. Similarly,
because the time periods analyzed were based on data availability, rather than a fixed time period, not all
trends represent change over the same time period. The fact that broad gaps of data coverage occurred for
some variables at some stations between the starting and ending dates must also be considered in
comparing findings of trend vs. no-trend among two or more stations.
We also confronted several other analytical issues during the course of this research. Many instances
were present where significant or apparent trends were found to be present but the best-estimate of
median-change per year (slope) is zero. This condition typically occurs in cases where many pairs of
observations are interpreted as being tied (i.e., insufficient evidence is present to allow determination of
which is the higher value), and was quite common for variables which yielded high numbers of
observations at or below detection limits, including TP, BOD, FC, TKN, and NFR. Without flow data, we
are unable to determine whether detected trends were the direct result of actual changes in water quality,
or if they were an indirect result of differences in the distribution of high (or low) flow-volumes-at-
sampling throughout the monitoring period. The accuracy of our analyses is dependent upon the
assumption that variations of flow-volume are randomly distributed throughout the monitoring period.
More complete results for individual monitoring stations can be accessed on the internet (Zipper et
al, 1998a).
Conclusions
On a statewide basis, significant and apparent trends indicating water quality improvement
outnumbered significant and apparent trends indicating water-quality deterioration for BOD, TP, FC,
NFR, and DO. For both NN and TKN, increasing trends outnumbered declining trends; increasing levels
of nitrogen are generally interpreted to indicate deteriorating water quality. Declining pH trends
outnumbered increasing pH trends by a slight margin. Increasing TR trends and decreasing TR trends
occurred in approximately equal numbers. Because of uncertainties regarding the ability of the monitoring
stations chosen to adequately represent statewide water quality and variations in periods of data coverage,
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simple numerical comparisons of increasing and decreasing trends cannot be interpreted as a direct
representation of general statewide change in water quality.
Acknowledgments
This paper's content is derived from a research report submitted to Virginia Department of
Environmental Quality (Zipper et ai, 1998b). This research was supported with funding provided by
Virginia Department of Environmental Quality to the Virginia Water Resources Research Center. The
authors express sincere thanks to all Virginia DEQ personnel who worked with us, including Ron
Gregory, Roger Stewart, Stuart Torbeck, and David Lazarus.
References
Rheem, S. and G.I. Holtzman. 1990. A SAS program for seasonal Kendall trend analysis of monthly
water quality data. Proceedings, Sixteenth Annual SAS Users Group International (SUGI)
Conference, February 17-20, 1991, New Orleans.
Zipper, C.E., G. Holtzman, S. Rheem, and G. Evanylo. 1992. Surface Water Quality Trends in Southwest
Virginia, 1970 - 1989: Seasonal Kendall Analysis. Virginia Water Resources Research Center
Bulletin 173. 99 pages.
Zipper, C.E., G.I. Holtzman, and P. Darken. 1998a. Long Term Water Quality Trends in Virginia
Waterways, . Virginia Water Resources Research Center.
Zipper, C.E., G. Holtzman, P. Darken, P. Thomas, J. Gildea, L. Shabman, and T. Younos. 1998b. Long-
Term Water Quality Trends in Virginia's Waterways: Final Report, by a report submitted to Virginia
Department of Environmental Quality by Virginia Water Resources Research Center in June, 1998.
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Loads and Yields of Suspended Sediment and Nutrients for Selected Watersheds in
the Lake Tahoe Basin, California and Nevada
Timothy G. Rowe, Hydrologist
U.S. Geological Survey
Abstract
The U.S. Geological Survey, in cooperation with the Tahoe Regional Planning Agency, has
monitored tributaries in the Lake Tahoe Basin since 1988 to determine streamflow and concentrations of
sediment and nutrients contributing to loss of clarity in Lake Tahoe. Loads and yields of suspended
sediment and nutrients for 10 selected watersheds totaling nearly half the area tributary to Lake Tahoe
(152 square miles [mi2]) are described. The size of the watersheds ranges from 2.15 mi2 (Logan House
Creek) to 56.5 mi~ (Upper Truckee River).
The Upper Truckee River had the largest median loads of sediment (7.2 tons per day [ton/d]) and
nutrients, in pounds per day (Ib/d): total ammonia plus organic nitrogen (TKN), 110; dissolved nitrite plus
nitrate (NO2+NO3), 7.7; total phosphorus (TP), 31; and total bioreactive iron (Fe), 400 Ib/d. Logan House
Creek had the smallest loads of sediment (<0.01 ton/d) and nutrients (TKN, 0.26; NO2+NO3, 0.02; TP,
0.02; and Fe, 0.09 Ib/d).
Third Creek had the largest yield for sediment (0.32 (ton/d)/mi2) and Fe (13 lb/d/mi2), Ward Creek for
TKN (3.4 lb/d/mi2) and TP (1.1 lb/d/mi2), and Blackwood Creek for NO2+NO3 (0.68 lb/d/mi2). Logan
House Creek had the smallest yield for sediment (<0.01 ton/d/mi2) and nutrients (TKN, 0.12; NO2+NO3,
0.01; TP, 0.01; and Fe, 0.04 lb/d/mi2).
Introduction
Lake Tahoe is an outstanding natural resource and famous for its alpine setting and deep, clear
waters. Protection of this renowned clarity has become very important in the past half century, as the
clarity has been decreasing by about 1 foot per year (Goldman and Byron 1986). This decrease is due
mainly to human activities, which have increased dramatically in the Lake Tahoe Basin since 1960.
Increased nutrient concentrations within Lake Tahoe are considered the primary cause of algal
growth, and thereby loss of clarity, in the lake. Suspended sediment also is of concern, because nutrients
attach to and are transported by sediment particles. Within the Lake Tahoe Basin, stream discharge is
suspected of being one of the major pathways for nutrient and sediment transport to the lake. Increased
development has accelerated this transport through urbanization of wetland areas, added erosion from
development of steep mountain sides, and discharge by septic and sewage systems within the basin.
Public concern for the clarity of Lake Tahoe also has increased over the years. As an example, voters
in Nevada passed bond acts in 1986 and 1996 to fund construction projects in Nevada to reduce erosion
and the transport of nutrients and sediments to Lake Tahoe.
The Tahoe Regional Planning Agency (TRPA), the U.S. Geological Survey (USGS), the Tahoe
Research Group of the University of California, Davis (TRG), and State and local agencies have been
monitoring the Lake Tahoe Basin for nutrients and sediments since the 1970's. One cooperative program,
a tributary-monitoring study by the USGS and TRPA, began in the 1988 water year. The primary purpose
of the study was to provide a long-term data base for monitoring local water-quality thresholds and
estimating the loads of nutrients and sediment from selected Lake Tahoe tributaries. This study initially
included four Lake Tahoe Basin watersheds and has expanded over the years. The current network
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includes 32 stream sites in 14 of the 63 Lake Tahoe watersheds where sediment, nutrient, and streamflow
data are collected (fig. 1 and Boughton et al 1997).
This paper presents findings from the cooperative study for 10 near-mouth sampling sites in 10
watersheds of the Lake Tahoe Basin during water years 1988-96. For this report, the period of record for
four sites is 1988-96, and for six sites is 1993-96, although the data-collection effort is ongoing. All years
referred to are water years—October 1 through September 30.
Nutrients sampled are total ammonia plus organic nitrogen (TKN), dissolved nitrite plus nitrate
(NO2+NO3), total phosphorus (TP), and total bioreactive iron (Fe) (iron that is biologically available to
phytoplankton). Suspended-sediment and nutrient data used in this report are from instantaneous samples
collected during the day throughout the entire water year.
Description of Study Area
Lake Tahoe, the highest lake of its size in the United States, with an average lake-surface altitude of
6,225 ft above sea level, is about 22 miles (mi) long and 12 mi wide. The average depth of the lake is
about 1,000 ft and the deepest part is 1,646 ft (fig. 1). The basin area is 506 square miles (mi2), consisting
of 192 mi2 in lake-surface area and 314 mi2 in surrounding watershed area (Crippen and Pavelka 1972).
The highest altitude in the watershed is in the Trout Creek Basin (10,881 ft).
The 10 watersheds sampled for this study compose nearly half (152 mi2) the watershed area. The size
of the selected watersheds ranges from 2.15 mi2 (Logan House Creek) to 56.5 mi2 (Upper Truckee River).
The main stream channel lengths range from 3.30 mi (Logan House Creek) to 21.4 mi (Upper Truckee
River).
Precipitation, which falls mostly as snow from November into June, varies across the basin, from 30-
40 inches per year (in/yr) on the eastern side to 70-90 in/yr on the western side (Crippen and Pavelka
1972). Annual precipitation in the basin was below normal for 6 years (1988-92 and 1994) and above
normal during the remaining 3 years (1993, 1995, and 1996) of the study (Dan Greenlee, Natural
Resources Conservation Service, oral commun., 1996).
Methods
Streamflow was measured and gaging stations were operated according to USGS guidelines
(Buchanan and Somers 1969; Kennedy 1983). All streamflow data are available in USGS electronic data
bases and USGS published annual Water Resources Data Reports for Nevada and California.
Drainage areas for sampling sites and total watershed areas (table 1) were reported by Carrier et al
(1995), and channel lengths were reported by Jorgensen et al (1978).
Water-quality samples were collected using USGS guidelines (Edwards and Glysson 1988). The
samples were analyzed for nutrients and iron by TRG laboratories in Davis and Tahoe City, Calif.,
according to procedures described by Hunter et al (1993). The samples were analyzed for suspended
sediment by the USGS California Sediment Laboratory in Salinas, Calif., using USGS guidelines (Guy
1969). All water-quality data are available in USGS data bases and in published annual Water Resources
Data Reports for Nevada and California.
Daily loads of suspended sediment and nutrients were calculated by multiplying the instantaneous
nutrient and suspended-sediment concentration values by the instantaneous streamflow value and
converting the product to tons per day or pounds per day.
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For each watershed, summary statistics were calculated for loads of suspended sediment and the four
nutrients using methods described by Helsel and Hirsch (1992) and are shown in figure 3; median daily
loads are presented in table 3. Median values were chosen as preferable summary values because they are
not strongly influenced by a few extreme values.
Median loads were normalized to a common unit (square miles), and the resulting yields were ranked
for each of the 10 sampled watersheds, with a rank of 1 assigned to the highest median yield and 10 to the
lowest. Rankings were then summed up for all sediment and nutrients and divided by five to give an
overall general ranking of the sampled watersheds for yields.
Results
Instantaneous streamflow at the time of sample-collection visits ranged from 0 cubic feet per second
(ft3/s), at two sites during low base-flow periods in July 1988 and August 1994, to 1,750 ft3/s at Upper
Truckee River during a rain storm at the spring snowmelt-runoff peak in May 1996. The highest median
streamflow value for sampling visits was 158 ft3/s at Upper Truckee River. The lowest median
streamflow value was 0.20 ft3/s at Logan House Creek (table 2).
For periods of record discussed herein, the Upper Truckee River had the highest average annual daily
mean streamflow, 123 ft3/s, and highest average annual runoff, 89,000 acre feet (acre-ft), and Logan
House Creek had the lowest at 0.30 ft3/s and 221 acre-ft, respectively. The highest average annual unit
runoff, 2,860 acre-ft/mi2, was in Blackwood Creek and the lowest, 106 acre-ft/mi2, was in Logan House
Creek.
The hydrograph of daily mean streamflow for Incline Creek (fig. 2A) for 1996 shows a seasonal
pattern that is typical of streams in the Lake Tahoe Basin. Most runoff is during the April-through-June
snowmelt period. Sharp peaks represent fall and early winter rains (December), rain-on-snow storms
(February), and summer thunderstorms (May and July).
The longer term hydrograph (fig. 2B) for Incline Creek for the 9-year period of record discussed
herein clearly shows the effects of drought (water years 1988-92 and 1994), as compared to years in
which runoff was above normal (1993, 1995, and 1996). The average annual daily mean streamflow for
the 9 years is 6.26 ft3/s.
Instantaneous measurements of suspended-sediment concentrations from the 10 stream sites ranged
from <1 milligrams per liter (mg/L) at many sites during the summer to 3,930 (mg/L) at Third Creek
during a rainstorm on snowpack in March 1993 (table 3). Median values ranged from 3.0 mg/L at Logan
House Creek, to 80 mg/L in Third Creek.
Median suspended sediment loads ranged from <0.01 ton per day (ton/d) for Logan House Creek to
7.2 ton/d in the Upper Truckee River. Median yields of sediment showed different results—from 0.01 ton
per day per square mile (ton/d/mi2) for Logan House Creek to 0.32 ton/d/mi2 for Third Creek. When
yields were ranked, Third Creek had the highest rank (1) and Logan House Creek had the lowest (10;
table 3).
Instantaneous measurements of nutrient concentrations varied throughout the basin (table 3). For
TKN, the range was <0.01 mg/L-24 mg/L, both at Third Creek, with the highest during a summer
thunderstorm in July 1990. Median TKN values ranged from 0.12 mg/L in Ward and General Creeks to
0.23 in Third Creek. For NO2+NO3, the range was from <0.001mg/L for two sites to 1.25 mg/L at
Glenbrook Creek during a rainstorm on snowpack in March of 1993. Median NO2 + NO3 values ranged
from 0.005 mg/L in General Creek to 0.031 mg/L in Incline Creek. For TP, the range was from <0.001
mg/L at Logan House Creek to 9.42 mg/L at Third Creek during the summer thunderstorm in July 1990.
Median TP values ranged from 0.020 mg/L in Logan House Creek to 0.052 mg/L in Incline Creek. For
Fe, the range was from 8 micrograms per liter ()-lg/L) to 33,900 |ig/L, both at Ward Creek, with the
III-527
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highest during a rainstorm in October 1994. Median Fe values ranged from 74.5 |J.g/L in Logan House
Creek to 1,360 (ig/L in Third Creek.
The Upper Truckee River had the largest median daily load of all nutrients (TKN, 110; NO2+NO3,
7.7; TP, 31; and Fe, 400 Ib/d), whereas Logan House Creek had the smallest (TKN, 0.26; NO2+NO3,
0.02; TP, 0.02; and Fe, 0.09 Ib/d). Summary statistics for sampled loads for the 10 watershed sites are
depicted by box plots in figure 3.
Median daily yields for TKN ranged from 0.12 Ib/d/mi2 at Logan House Creek to 3.4 lb/d/mi2 at
Ward Creek. NO2+NO3 ranged from 0.01 lb/d/mi2 at Logan House Creek to 0.68 lb/d/mi2 at Blackwood
Creek. TP ranged from 0.01 lb/d/mi2 at Logan House Creek to 1.1 lb/d/mi2 at Ward Creek. Fe ranged
from 0.04 lb/d/mi2 at Logan House Creek to 13 lb/d/mi2 at Third Creek.
Median daily yields were ranked for each constituent by watershed. These rankings represent degree
of potential constituent contribution to Lake Tahoe, per unit area of watershed, with 1 indicating the
highest contribution and 10 the lowest. For TKN, Ward Creek ranked highest and Logan House Creek the
lowest; for NO2+NO3, Blackwood Creek was the highest and Logan House Creek the lowest; for TP,
Ward Creek was highest and Logan House Creek the lowest; and for Fe, Third Creek was the highest and
Logan House Creek the lowest. When the ranks of yields for suspended sediment and the four nutrients
were averaged, Blackwood Creek was highest and Logan House Creek lowest. The overall ranking, from
highest to lowest, (fig. 3), was Blackwood Creek, Ward Creek, Third Creek, Upper Truckee River, Incline
Creek, General Creek, Trout Creek, Edgewood Creek, Glenbrook Creek, and Logan House Creek.
Discussion
Concentrations of suspended sediment and nutrients varied widely in the sampled watersheds of the
Lake Tahoe Basin. This variation is largely due to differences in weather patterns, precipitation amounts,
and natural conditions across the basin. For example, more precipitation falls on the western side of Lake
Tahoe, and the streamflow runoff and sediment and nutrient loads reflect that. The years of drought
conditions also reduced both nutrient and sediment loads in the watersheds.
When the concentrations are flow-weighted and loads are calculated, the largest loads are in the
Upper Truckee River watershed. This is solely because the Upper Truckee River is the largest watershed
and delivers the greatest annual runoff to Lake Tahoe. The smallest loads are from Logan House Creek,
which is the smallest of the 10 sampled watersheds and delivers the least annual runoff to the lake.
Third Creek has the highest sediment and Fe yield, which is due to the exposed soil caused by the
large snow and rock avalanche of February 17, 1986, in the upper reach (Bill Quesnel, Incline Village
General Improvement District, oral commun., 1992). Ward Creek had the highest yield for TKN and TP
and Blackwood Creek the highest for NO2+NO3, possibly because of human activities in the area.
The ordered ranks show that the largest yields of sediment and nutrients were in Blackwood Creek,
followed by Ward Creek, Third Creek, Upper Truckee River, and Incline Creek. The watersheds with the
smallest yields are Glenbrook and Logan House Creeks. This ranking agrees with a suspended-sediment
study on nine Lake Tahoe Basin watersheds (eight of which are included here) between 1981-85 by Hill
and Nolan (1988). They found that the highest annual suspended-sediment yields were from Blackwood
Creek, Ward Creek, Upper Truckee River, and Third Creek.
For the 10 selected watersheds, the higher yields were from six watersheds on Lake Tahoe's western,
southern, and northern sides, all of which receive greater precipitation and are more developed and
affected by human activities. The lower yields were from four watersheds on the eastern side, which
receive less precipitation and are somewhat less developed.
III-528
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Literature Cited
Boughton, C.J., T.G. Rowe, K.K. Allander, and A. R. Robledo. 1997. Stream and ground-water
monitoring program, Lake Tahoe Basin, Nevada and California. U.S. Geological Survey Fact Sheet
FS-100-97.
Buchanan T.J., and W.P. Somers. 1969. Discharge measurements at gaging stations. U.S. Geological
Survey Techniques of Water-Resources Investigations, book 3, chap. A8.
Cartier, K.D., L.A. Peltz, and Katie Long. 1995. Hydrologic basins and hydrologic-monitoring sites of
Lake Tahoe Basin, California and Nevada. U.S. Geological Survey Open-File Report 95-316.
Crippen J. R., and B.R. Pavelka. 1972. The Lake Tahoe Basin, California-Nevada. U.S. Geological
Survey Water-Supply Paper 1972.
Edwards, T.K., and G.D. Glysson. 1988. Field Methods for measurement of fluvial sediment. U.S.
Geological Survey Open-File Report 86-531.
Goldman, C.R., and E.R. Byron. 1986. Changing water quality at Lake Tahoe—The first five years of the
Lake Tahoe Interagency Monitoring Program. Tahoe Research Group, Institute of Ecology,
University of California, Davis.
Guy, H.P., 1969. Laboratory theory and methods for sediment analysis: U.S. Geological Survey
Techniques of Water Resources Investigations, book 5, chap. Cl.
Helsel, D.R., and R.M. Hirsch. 1992. Statistical methods in water resources. Studies in Environmental
Science 49. Amsterdam, Elsevier.
Hill, B.R., and K.M. Nolan. 1988. Suspended-sediment factors, Lake Tahoe Basin, California-Nevada, in
I.G. Poppoff, C.R. Goldman, S.L. Loeb, and L.B. Leopold, eds. International Mountain Watershed
Symposium Proceedings, Lake Tahoe, June 8-10, 1988. South Lake Tahoe, Calif., Tahoe Resource
Conservation District.
Hunter, D.A., I.E. Reuter, and C.R. Goldman. 1993. Standard operating procedures—Lake Tahoe
Interagency Monitoring Program. University of California, Davis, Tahoe Research Group.
Jorgensen, A.L., A.L. Seacer, and SJ. Kaus. 1978. Hydrologic basins contributing to outflow from Lake
Tahoe, California-Nevada. U.S. Geological Survey Hydrologic Investigations Atlas HA -587.
Kennedy, E.J., 1983, Computation of continuous records of streamflow. U. S. Geological Survey
Techniques of Water- Resources Investigations, book 3, chap. A13.
Rowe, T.G., and J.C. Stone. 1997. Selected hydrologic features of the Lake Tahoe Basin, California and
Nevada. U.S. Geological Survey Open-File Report 97-384.
Rush, F.E., 1973, Bathymetric reconnaissance of Lake Tahoe, Nevada and California. Nevada Division of
Water Resources, Information Report 17, 1 sheet.
III-529
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120° 15'
120° 00'
119° 45'
39° 15'
39° 00'
38° 45'
DOUGLAS
Minden
Gardnerville
Base from U.S. Geological Survey digital data, 124,000 and 1:100,000, Bathymetric contours from Rush, 1973. Compiled from soundings
1969-85. Universal Transverse Mercator projection, Zone 11 soundings made by the U.S. Coast and Geodetic Survey (1923)
EXPLANATION
— ••— Boundary of Lake Tahoe Basin '•" Bathymetric contour, In feet below highest
Hound.™ 0« ,Uhh».in_MamB '"fl"1 ***+•"«*<» altitude (6,229.1 feet above
Boundary of subbasln-Name U.S. Bureau of Reclamation datum of 1929)
of subbasin Is indicated _ . . u
T Surface-water sKe
Figure 1. Geographic setting, hydrologic basins, bathymetry, surface-water sampling sites, and selected
watersheds in the Lake Tahoe Basin (modified from Rowe and Stone 1997).
HI-530
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OCT NOV DEC
1995
JAN FEB MAR APR
MAY JUNE JULY AUG SEPT
1996
1988 1989 1990 1991
1992 1993
WATER YEAR
1994 1995 1996
Figure 2. (A) Daily mean streamflow for Incline Creek during 1996 water year, a representative stream in the
Lake Tahoe Basin; (B) Daily mean streamflow for Incline Creek, 1988-96 water years, representing years of
drought and above-normal runoff.
III-531
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120° 15'
120° 00'
119° 45'
39° 15' -
39° 00' -
38° 45' —
Base from U.S. Geological Survey digital data, 154,000 and 1:100,000,1969-85
Universal Transverse Mercator projection, Zone 11
EXPLANATION
Boundary of LakeTahoe Basin
Boundary of subbasln
Overall yield rank
i—90th Pereentile
-,-7Sth Percentile
I—10th Pereantile
Figure 3. Daily suspended-sediment and nutrient loads depicted by box plots and yield ranks for selected surface-
water sampling sites in the Lake Tahoe Basin, 1988-96 (modified from Boughton et al 1997).
m-532
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Table 1. Sampling-Site Information for Selected Lake Tahoe Basin Watersheds
Sampling site
(figure 1)
Third Creek near Crystal Bay, Nev.
Incline Creek near Crystal Bay, Nev.
Glenbrook Creek at Glenbrook, Nev.
Logan House Creek near Glenbrook, Nev.
Edgewood Creek at Stateline, Nev.
Trout Creek at South Lake Tahoe, Calif.
Upper Truckee River at South Lake Tahoe, Calif.
General Creek near Meeks Bay, Calif.
Blackwood Creek near Tahoe City, Calif.
Ward Creek near Tahoe Pines, Calif.
Total watershed
drainage area
(square miles)"
6.05
6.70
4.11
2.15
6.64
41.2
56.5
7.63
11.2
9.75
Sampling-site
drainage area
(square miles)
6.02
6.69
4.10
2.09
5.61
40.4
54.0
7.39
11.1
9.73
Main channel
length(miles)b
7.05
4.66
3.92
3.30
5.53
10.7
21.4
9.17
6.20
5.90
"From Cartier et al 1995.
^From Jorgensen et al 1978.
Table 2. Streamflow Information for Selected Lake Tahoe Basin Watersheds
[Abbreviations: acre-ft, acre-feet; ft3/s, cubic feet per second; ft, feet; mi2, square miles.]
Sampling site
Third Creek
Incline Creek
Glenbrook Creek
Logan House Creek
Edgewood Creek
Trout Creek
Upper Truckee River
General Creek
Blackwood Creek
Ward Creek
Range and median
of sampled
streamflow" (ft3/s)
0.93- 118(6.0)
.56-71 (5.7)
0 - 35 (0.88)
0 - 7.9 (0.20)
1.1 25(3.6)
3.2 - 305 (49.5)
.70 - 1,750C (158)
.41 -559(30.5)
1.1-936(60.0)
.22 - 950 (47.5)
Period of Average annual Average
record mean daily annual
(water streamflow runoff
years) (ft3/s) (acre-ft)
1988-96
1988-96
1988-96
1988-96
1993-96
1993-96
1993-96
1993-96
1993-96
1993-96
6.68
6.26
1.30
.30
3.72
44.8
123
20.2
44.0
32.1
4,830
5,040
943
221
2,690
32,400
89,000
14,700
31,800
23,200
Average
annual yieldb
(acre-ft/mi2)
802
753
230
106
480
802
1,650
1,990
2,860
2,380
aMedian, in parentheses, equals 50-percent value.
bYield is annual runoff divided by sampling-site drainage area.
°Bold indicates highest value.
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Table 3. Suspended-Sediment and Nutrient Information for Selected Lake Tahoe Basin Watersheds
[Nutrient concentrations from Tahoe Research Group, University of California, Davis (1996). Abbreviations: mg/L,
milligrams per liter; ton/d, tons per day; ton/d/mi2, tons per day per square mile; Ib/d, pounds per day; lb/d/mi2,
pounds per day per square mile; mg/L, micro grams per liter]
A. Suspended sediments
Sampling site
Third Creek
Incline Creek
Glenbrook Creek
Logan House Creek
Edgewood Creek
Trout Creek
Upper Truckee River
General Creek
Blackwood Creek
Ward Creek
Sampling site
Third Creek
Incline Creek
Glenbrook Creek
Logan House Creek
Edgewood Creek
Trout Creek
Upper Truckee River
General Creek
Blackwood Creek
Ward Creek
Instantaneous
Concentration
range
(mg/L)
1 - 3,930e
1 1,840
1-606
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Table 3 (continued)
B. Nitrogen and Phosphorus (continued)
Sampling site
Instantaneous measurement
Median
loadb
(Ib/d)
Median
yield0
(lb/d/mi2)
Yield
rankd
Concentration Median
range concentration"
(mg/L) (mg/L)
Total phosphorus
Third Creek
Incline Creek
Glenbrook Creek
Logan House Creek
Edgewood Creek
Trout Creek
Upper Truckee River
General Creek
Blackwood Creek
Ward Creek
Sampling site
Third Creek
Incline Creek
Glenbrook Creek
Logan House Creek
Edgewood Creek
Trout Creek
Upper Truckee River
General Creek
Blackwood Creek
Ward Creek
0.002 - 9.42
.004- 1.12
.008- 1.98
<.001 - .160
.008 - .507
0.003 - 0.393
.004 - .222
.007 - .275
.010 -.994
.008 - 2.02
Instantaneous
Concentration
range
(mg/L)
219-33,300
226 - 28,500
43 - 27,700
18-2,750
34 - 6,540
137 - 8,750
53-4,210
32-7,650
103 - 14,800
8 - 33,900
0.051
.052
.039
.020
.041
0.041
.030
.021
.031
.032
C. Total bioreactive
measurement
Median
concentration"
(mg/L)
1,360
1,060
504
74.5
607
620
394
101
440
159
2.2
2.0
.15
.02
1.2
15
31
2.9
9.5
11
iron
Median
loadb
(Ib/d)
77
65
3.7
.09
15
230
400
15
110
44
0.37
.29
.04
.01
.21
0.36
.57
.39
.86
1.1
Median
yield0
(lb/d/mi2)
13
9.8
.89
.04
2.7
5.6
7.4
2.1
10
4.5
5
7
9
10
8
6
3
4
2
1
Yield
rankd
1
3
9
10
7
5
4
8
2
6
a Median equals 50-percent value.
b Median load equals 50-percent value. Load = concentration x streamflow x load factor (0.0027 for ton/d; 5.394 for
Ib/day).
c Median yield is median load divided by sampling-site drainage area.
d Rank from 1 to 10: 1 indicates highest contribution of constituent and 10 lowest contribution. Overall rank for all
constituents: (1) Blackwood Creek, (2) Ward Creek, (3) Third Creek, (4) Upper Truckee River, (5) Incline Creek, (6)
General Creek, (7) Trout Creek, (8) Edgewood Creek, (9) Glenbrook Creek, and (10) Logan House Creek. See
Figure 3.
e Bold indicates highest value.
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III-536
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Track D—Linking Monitoring to Environmental
Management and Decision Making
Vulnerability Assessment
Section 305(b)
Monitoring for TMDLs
Source Water Issues, Both Surface and Ground Water
Successful Program Collaboration
HI-537
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IH-538
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Use of a Numerical Rating Model to Determine the Vulnerability of Community
Water-Supply Wells in New Jersey to Contamination by Pesticides
Eric F. Vowinkel, Hydrologist
U.S. Geological Survey, 810 Bear Tavern Road, Suite 206, West Trenton, NJ 08628
Phone: (609) 771-3931; E-mail: vowinkel@usgs.gov
Abstract
A numerical rating model was developed to assess the vulnerability of water from community water-
supply wells in New Jersey to contamination by pesticides. The model was used to rank the community
water-supply wells into groups of low, medium, and high vulnerability to pesticides on the basis of
hydrogeologic-sensitivity and pesticide-use-intensity variables. Sensitivity variables used in the model were
distance of the well from the outcrop area of the aquifer from which water is withdrawn, soil organic-matter
content at the wellhead, and depth to the top of the open interval of the well. Pesticide-use-intensity variables
used in the model were predominant land use surrounding the wellhead, distance of the well from agricultural
land, and distance of the well from a golf course.
Of the 1,945 wells for which sufficient hydrogeologic and land-use data were available, the vulnerability
index calculated with the model was low for 26 percent, medium for 70 percent, and high for 4 percent of the
wells. To test the reliability of the model, 90 wells were randomly selected for sampling, and water samples
were analyzed for 140 pesticides and nutrients. Pesticides were detected in 6 of the 90 wells. None of the five
pesticides that were detected was present at a concentration that exceeded a U.S. Environmental Protection
Agency or New Jersey Department of Environmental Protection (NJDEP) maximum contaminant level for
drinking water. The frequency of pesticide detection in water from wells by vulnerability index was 0 percent
in the low-vulnerability group, 5 percent in the medium-vulnerability group, and 19 percent in the high-
vulnerability group. The median nitrate concentration in water from wells in the low-vulnerability group was
less than the detection level of 0.1 milligrams per liter.
The NJDEP used the results of the vulnerability study to evaluate strategies for monitoring
requirements for pesticides. Monitoring requirements can be waived or reduced for wells that are not
vulnerable to contamination. The NJDEP estimated that monitoring waivers for community water-supply
wells will save water purveyors about $5 million in analytical costs annually.
Introduction
About 50 percent of the 8 million people in New Jersey obtain their drinking water from ground-water
sources—about 39 percent from community supply wells and 11 percent from privately owned domestic
supply wells. In 1995, about 400 Mgal/d (million gallons per day) of water was withdrawn from more than
2,300 community water-supply wells. Regulations enacted by the U.S. Environmental Protection Agency
(USEPA) and the New Jersey Department of Environmental Protection (NJDEP) require water companies to
monitor well water for pesticides routinely (Louis et al 1994). NJDEP determined that 10 of the 23 regulated
pesticides are used in New Jersey and have been detected in water supplies; therefore, some monitoring is
required (Louis et al 1994). Monitoring requirements for pesticides in water samples can be waived if (1) the
part of the aquifer from which the water is withdrawn is insensitive to contamination by pesticides or (2) the
aquifer is sensitive to contamination but pesticides are not used in the area near the wellhead. A screening
tool was needed for evaluating the vulnerability of wells to pesticides at a statewide scale.
The U.S. Geological Survey (USGS), in cooperation with the NJDEP, developed a geographic
information system (GIS) data base and a numerical rating model to provide a measure of the vulnerability of
water from community water-supply wells to contamination by pesticides (Vowinkel et al 1994; Vowinkel et
III-539
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al 1996). The GIS data base and the rating model were used to rank community water-supply wells (fig. 1)
into groups of low, medium, and high vulnerability. To test the results of the model, water samples were
collected from 90 wells and analyzed for pesticides, nitrate, and other nutrients. Results of a previous
investigation indicate that concentrations of nitrate in water from domestic wells were larger in those wells in
which pesticides were detected than in those wells in which no pesticide was detected (Vowinkel and Tapper,
1995). The frequencies of detection and concentrations of the pesticides and nitrate in water from wells were
compared among the vulnerability groups. This paper describes the method and results of the application of
the numerical rating model used to predict vulnerability of water from wells to contamination by pesticides.
New Jersey's principal aquifers can be classified into three groups: (1) unconsolidated sediments of the
Coastal Plain, (2) unconsolidated glacial sediments, and (3) bedrock containing fractures and (or) solution
cavities. The aquifers of the Coastal Plain differ in area! extent and thickness, and generally are permeable
units of unconsolidated sand and gravel that are separated from each other by less permeable units of silt and
clay. Aquifers in the Coastal Plain generally are confined except where they crop out. The glacial aquifers are
mostly valley-fill deposits consisting of narrow, belt-like deposits scattered throughout New Jersey, most
commonly north of the terminal moraine of the Wisconsinan glaciation. The bedrock aquifers include
fractured shale and sandstone units of the Newark Supergroup in the Piedmont physiographic province,
weathered and fractured crystalline rocks in the New England province, and sedimentary rocks in the Valley
and Ridge province (fig. 1).
Types of land use and land cover in New Jersey are diverse. In the early 1970's, the land was about 24
percent urban (16.5 percent residential and 7.5 percent nonresidential), 25 percent agricultural, and 51 percent
undeveloped forests and wetlands. Some areas that were agricultural and undeveloped during the 1970's have
since been developed. Because ground water moves slowly in most aquifers in New Jersey, however, the
effects of land use on ground-water quality may be more closely related to previous land use than to current
land use at wells where land use has changed. Digital land-use data from the early 1970's were used to test
hypotheses concerning relations of land use to ground-water-quality data that were collected mainly during
the 1980's and 1990's.
Pesticide use in agricultural, golf-course, and residential areas was estimated from available data
collected as part of the NJDEP Pesticide Control Program (R.L. Myers, New Jersey Department of
Environmental Protection, written commun., 1997). Most of the agricultural land in New Jersey is cropland,
with smaller amounts of pasture and orchards. The amount of permitted active pesticidal ingredients applied
in 1985 in agricultural areas was reported to be about 3.8 x 105 kg (kilograms), at a rate of about 420 kg/km2
(kilograms per square kilometer). In 1990, reported pesticide use at 202 golf courses in New Jersey was about
2.0 x 104 kg, at a rate of about 442 kg/km2. Pesticide use in residential areas is difficult to estimate. Permitted
applications of pesticides by registered lawn-care companies were about 1.7 x 105 kg in 1995; application
rates are unknown. The amount of pesticides applied by homeowners is unknown.
Methodology
The vulnerability of a public supply well to contamination is defined by the equation
Vulnerability = Sensitivity + Intensity.
A numerical rating model was developed to rank wells by using variables selected to describe (1) the
sensitivity of the aquifer to contamination from the land surface and (2) the intensity of pesticide use in
areas where the aquifer is sensitive to contamination. The sensitivity and intensity variables were coded
such that larger scores for the sensitivity of the aquifer and intensity of pesticide use indicate greater
vulnerability to contamination by pesticides. Individual sensitivity and intensity variables were coded
from a low of 0 to a high of 5 (table 1). Possible sensitivity and intensity scores for a given well ranged
from 0 to 15.
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The sensitivity of an aquifer to contamination was defined by properties describing position within the
ground-water flow system that control the introduction and transport of contaminants into the system. Ground
water is most sensitive to contamination by pesticides in shallow parts of the outcrop area and in soils
containing little organic matter, and least sensitive in deep parts of an aquifer downdip from the outcrop area.
Hydrogeologic sensitivity of a well was based on three variables—distance from the outcrop area, soil
organic-matter content, and depth to the top of the open interval. A numerical score of 0 was classified as low
sensitivity, whereas a score of 10 or greater was classified as high sensitivity.
Because the intensity of pesticide use is not uniform among land-use types, the potential for
contamination of ground water by pesticides is greater in recharge areas and areas in which the land use is
predominantly agricultural or urban-residential than in discharge areas and areas in which the land is
undeveloped. Pesticide-use-intensity scores for wells that are in sensitive parts of an aquifer were based on
three variables-surrounding land use, distance from the nearest agricultural land, and distance from the
nearest golf course. Agricultural land use was weighted higher than residential land use because most of the
regulated pesticides are used for agricultural purposes. A well with a numerical score of 0 was classified in
the low-intensity group, whereas a well with a score of 10 or greater was classified in the high-intensity
group.
The wells were grouped into a three-by-three matrix of low, medium, and high sensitivity and low,
medium, and high pesticide-use intensity. To simplify the grouping of wells, wells in the low-sensitivity and
low-intensity groups were rated as low vulnerability, whereas wells in the high-sensitivity and high-intensity
groups were rated as high vulnerability (fig. 1). All other wells were rated as medium vulnerability (Vowinkel
et al 1996).
This model differs from other vulnerability models in that wells in the low-sensitivity group were not
assigned an intensity rating because the water in the wells was not believed to be hydraulically connected
to water at land surface that could be affected by the land use at the wellhead. The confining units above
the open intervals of the wells are sufficiently thick to prevent the movement of contaminants from the
land surface to the well or the times of travel of water from the outcrop area to the wells most likely are
greater than 50 years and pesticides were not used prior to the time the water recharged the aquifer.
Water samples were collected from a stratified set of 90 randomly selected wells to test the accuracy of
the results of the numerical rating model (Clawges et al in press). Samples were analyzed for 140 pesticides
(Mogadati et al 1994) at Rutgers University laboratory and for nutrients at the USGS National Water Quality
Laboratory (NWQL). Historical nitrate water-quality data for samples from all 640 community water-supply
wells previously collected and analyzed by the USGS also were used to test the results of the model.
Results and Discussion
Of the 1,945 community water-supply wells for which data were available, the aquifer sensitivity was
low for 26.3 percent, medium for 15.8 percent, and high for 57.9 percent (fig. 2A). All of the wells in the
low-sensitivity and low-vulnerability groups are screened in confined aquifers in the Coastal Plain. Most
wells in the bedrock or glacial aquifers were classified in the high-sensitivity group because they are in the
outcrop area of the aquifer in which the well is screened and the depth to the top of the open interval of the
well is less than 20 meters from the land surface. About 4.5 percent of the wells were grouped in the high-
pesticide-use-intensity group (fig. 2B). The 77 wells (4 percent) classified in the high-vulnerability group (fig.
2C) typically are in outcrop areas with shallow depths to the top of the open interval and are in predominantly
agricultural areas.
Pesticides were detected in water samples from 6 of 90 wells. Five pesticides—atrazine, dinoseb,
simazine, metolachlor, and metalaxyl—were detected. Concentrations ranged from 0.01 to 2.2 g /L
(micrograms per liter). No pesticide concentration exceeded any U.S. Environmental Protection Agency
(USEPA) maximum contaminant level (MCL). Concentrations of nitrate were significantly larger (p =
III-541
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0.005) in water from those wells in which a pesticide was detected than in those wells in which no
pesticide was detected (fig. 3).
Pesticides were not detected (fig. 4A) and concentrations of nitrate were smallest (fig. 4B) in water
from wells in the low-vulnerability group. Pesticides were detected in about 5 percent of the wells in the
medium-vulnerability group and about 19 percent of the wells in the high-vulnerability group (fig. 4A).
Similarly, nitrate was not detected in water from any of the wells in the low-vulnerability group (fig. 4B).
The distributions of nitrate concentrations are significantly different (p < 0.001) among the three
vulnerability groups. The 75th-percentile concentration of nitrate (as N) in water from wells in the
medium-vulnerability group is 2.1 mg/L (milligrams per liter) compared to 4.3 mg/L in water from wells
in the high-vulnerability group.
The distributions of historical concentrations of nitrate in water from community water-supply wells
were compared by vulnerability group (fig. 5). The median concentration of nitrate (as N) in water
samples from wells in the low-vulnerability group was less than 0.1 mg/L and the nitrate concentration
was larger than 0.5 mg/L in water from only 2 of 157 wells (1.3 percent). The 75th-percentile
concentration of nitrate was less than 2 mg/L in water from wells in the medium-vulnerability group. The
median concentration of nitrate (as N) was greatest in water from wells in the high-vulnerability group
(2.0 mg/L).
On the basis of the current and historical data analyses, the likelihood is small of detecting a pesticide or
nitrate in community water-supply wells classified in the low-vulnerability group. The results indicate that
nitrate concentration in water from a well may be a crude predictor of possible contamination of the water by
pesticides.
The numerical rating model is a means for conducting a preliminary assessment of the potential
vulnerability of water from community water-supply wells to contamination by pesticides. The numerical
rating model has several limitations that result from the necessary simplifying assumptions made in the
analysis. These model assumptions are that (1) the solubilities of all pesticides and nitrate in water are similar;
(2) pesticides are applied uniformly in agricultural, residential, and golf-course areas; and (3) differences in
the scales of the GIS hydrogeologic and land-use data do not affect the results of the statistical analyses.
The NJDEP used the results of the vulnerability study to evaluate strategies for monitoring
requirements for pesticides. Monitoring requirements can be waived or reduced for wells that are not
vulnerable to contamination. The NJDEP estimated that monitoring waivers for community water-supply
wells will save water purveyors about $5 million in analytical costs annually (Vowinkel et al 1996).
References
Clawges, R.L, Oden, T.D., and Vowinkel, E.F., in press: Water-quality data for 90 community water-
supply wells in New Jersey, 1994-95, U.S. Geological Survey Open-File Report 97-625.
Louis, J.B., Sanders, P.P., and Bono, P.M., 1994. Implementation of a program to assess the vulnerability
of New Jersey's drinking water supplies to pesticide contamination, in Weigman, D.L., ed., New
directions in pesticide research, management, and policy, November 1-3, 1993. Virginia Water
Resources Research Center, Blacksburg, Virginia, p. 167-183.
Mogadati, P.S., Wang, Sensui, Roinestad, K.S., and Rosen, J.D., 1994, Significant improvements in
multiresidue pesticide analysis, in Weigman, D.L., ed., New directions in pesticide research,
management, and policy, November 1-3, 1993. Virginia Water Resources Research Center,
Blacksburg, Virginia, p. 409-427.
Vowinkel, E.F., Clawges, R.L., and Uchrin, C.G., 1994, Evaluation of the vulnerability of water from
public supply wells in New Jersey to contamination by pesticides, in Weigman, D.L., ed., New
directions in pesticide research, management, and policy, November 1-3, 1993. Virginia Water
Resources Research Center, Blacksburg, Virginia, p. 495-510.
III-542
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Vowinkel, E.F., Clawges, R.L., Buxton, D.E., and Stedfast, D.A., 1996, Vulnerability of drinking water
supplies in New Jersey to contamination by pesticides, U.S. Geological Survey Fact Sheet FS-165-96,
2 p.
Vowinkel, E.F., and Tapper, R.J., 1995, Indicators of the sources and distribution of nitrate in water from
shallow domestic wells in agricultural areas of the New Jersey Coastal Plain, U.S. Geological Survey
Water-Resources Investigations Report 93-4178, 48 p.
III-543
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75
74
EXPLANATION
LOCATION OF WELL
AND VULNERABILITY
TO CONTAMINATION
BY PESTICIDES
° LOW
« MEDIUM
• HIGH 40'
Figure 1. Results of numerical rating model showing vulnerability of water from wells in New Jersey to
contamination by pesticides.
(A) SENSITIVITY
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Figure 2. Results of numerical rating model showing distribution of wells in New Jersey by (A) aquifer sensitivity,
(B) pesticide-use intensity, and (C) vulnerability to contamination by pesticides.
m-544
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PESTICIDES DETECTED
EXPLANATION
Number of observations
Maximum value
Figure 3. Distribution of nitrate concentration in water from 90 community water-supply wells
in New Jersey by detection or nondetection of pesticides.
(A) PESTICIDE DETECTION
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VULNERABILITY INDEX
EXPLANATION
(10) Number of observations
Maximum value
75th percentile
Median
25th percentile
Minimum value
Figure 4. (A) Frequency of pesticide detection and (B) nitrate concentration in water from 90 community
water-supply wells in New Jersey by vulnerability index.
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Figure 5. Historical nitrate concentrations in water from 640 community water-supply wells
in New Jersey by vulnerability index.
m-545
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Table 1. Description of Sensitivity and Intensity Variables and Scores Used in the Numerical Rating
Model to Determine the Vulnerability of Water from Community Water-Supply Wells to
Contamination by Pesticides
Modified from Vowinkel and others, 1994, p. 505.
[>, greater than; >, equal to or greater than; - -, this numerical score not assigned to this variable]
Score
Variable
After sensitivity:
Distance from outcrop area, in >1.6
kilometers
Lower extreme of soil organic- >2.0
matter content, in percent1
Depth to the top of the open > 100
interval of the well, in meters'
Pesticide-use intensity:
Predominant land use within an Undevel-
800-meter-radius buffer zone of oped
the wellhead1
Distance of the well from the > 1.6
nearest agricultural area, in
kilometers'
Distance of the well from the > 1.6
nearest golf course, in
kilometers1
>0.8-1.2 >0.4-0.8 >0to0.4 0
1.0-1.9
75-99 50-74 25-49 10-24
Urban- Urban-
nonresi- residential
dential
>0.8-1.2 >0.4-0.8 >0-0.4
>0.8-1.2 >0.4-0.8 >0-0.4
Agricul-
tural
This variable was applied only to wells in the outcrop area.
III-546
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The Puget Sound Ambient Monitoring Program-Case Study of Coordinated
Regional/State Monitoring
Scott Redman, PSAMP Science Coordinator
Puget Sound Water Quality Action Team
Abstract
The Puget Sound Ambient Monitoring Program (PSAMP) is a multi-agency, multi-disciplinary effort
to assess the health of Puget Sound. This program was designed in the late 1980's to evaluate the
effectiveness of the Puget Sound Water Quality Management Plan, to investigate long-term trends in
environmental quality, to improve decision making, and to prevent overlaps and duplications in
monitoring activities.
Through this program, state, federal and local agencies coordinate their efforts to assess the quality of
marine and fresh waters and marine sediments; evaluate toxic contaminants and their effects in fish,
marine birds, and marine mammals; and study the status of nearshore habitats and marine bird and
mammal populations. PSAMP is built around a coordinating structure that involves the staff and accesses
the resources of eight implementing agencies, study designs developed from common goals to measure
status and trends, and Puget Sound protocols for field and laboratory work.
PSAMP scientists and managers are currently implementing suggestions from a 1995 program review
to improve the integration and breadth of PSAMP's assessments and improve PSAMP's links to manage-
ment decisions. At the heart of the improved integration among monitoring efforts and between moni-
toring and decision-making is the articulation of a conceptual model of Puget Sound and the effects of
human activities on the Puget Sound environment. To broaden the program's assessments of ecosystem
health and to move toward the vision of comprehensive evaluations, PSAMP scientists are placing new
emphasis on efforts to share information with other scientists and researchers in the region.
Introduction to the PSAMP
The Puget Sound Ambient Monitoring Program is a long-term, comprehensive program to assess the
health of Puget Sound and its resources. Approximately 10 years ago, the PSAMP was adopted to evalu-
ate the effectiveness of the Puget Sound Water Quality Management Plan, to assess long-term trends in
environmental quality, and to improve decision-making and prevent overlaps and duplication in
monitoring efforts.
The PSAMP is implemented as coordinated studies by Washington state departments of Ecology, Fish
and Wildlife, Health, and Natural Resources and King County's Department of Natural Resources and the
U.S. Fish and Wildlife Service. The Puget Sound Water Quality Action Team coordinates the program
with the assistance of representatives of the implementing agencies and the U.S. Environmental
Protection Agency.
Through the studies that comprise the PSAMP, data on marine and fresh waters, fish, sediments, and
shellfish in Puget Sound have been collected since 1989; surveys of nearshore habitat have been
conducted since 1991; marine bird populations have been surveyed since 1992; and marine bird
contamination has been studied since 1995.
The Puget Sound Ambient Monitoring Program (PSAMP) capitalizes on the expertise and efforts of
individual agencies to answer questions about the health of the Sound. It is important to recognize,
111-547
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however, that this monitoring program cannot hope to contain all scientific knowledge and investigation
concerning Puget Sound. Therefore the PSAMP acknowledges the importance of expanding upon its
resources and incorporating and synthesizing information from a variety of sources.
A Conceptual Model of Puget Sound
Through day-to-day observations, many residents of and visitors to the Puget Sound basin are familiar
with the remarkable diversity of Puget Sound's shorelines, waters and living resources. This diversity
provides vitality to the Puget Sound region and contributes to the high quality of life that residents and
visitors enjoy in this unique natural environment. However, this diversity also complicates the answer to
our questions about the health of Puget Sound.
To make sense of the many varied connections between human actions and their effects on the
environment, PSAMP scientists drafted a "conceptual model" that describes their understanding of and
assumptions about these connections. Figure 1 provides a general overview of this model. This model is
based on fundamental relationships between humans and the environment—human actions can stress the
environment; these stresses can alter parts of the ecosystem; and management activities can moderate the
effects of human actions on the environment. In its more detailed forms, the conceptual model helps
identify ecosystem components and environmental stresses that are important to monitor. It also helps
communicate the relevance of monitoring results to managers of Puget Sound's environment and
resources.
Based on the model, the PSAMP identified monitoring topics that relate to specific ecosystem
characteristics or human-influenced stresses on the environment:
• Biological Resources: What are the status and trends of Puget Sound's biological resources?
• Physical Environment: Are the physical environments of Puget Sound changing and, if so, how
do these changes affect Puget Sound's biological resources?
• Toxic Contamination: What are the status and trends of toxic contamination in Puget Sound?
How does toxic contamination affect the Sound's biological resources and humans who consume
them?
• Pathogens and Nutrients: What are the status and trends of pathogens and nutrients in Puget
Sound? How do they affect the Sound's biological resources?
• Human Health: What are the risks to human health from consuming seafood from Puget Sound?
These are big questions and broad topics. Each topic relates to many human actions and reflects on a
number of management programs aimed at restoring and protecting Puget Sound. Monitoring for each
topic is addressed through multiple PSAMP studies and additional assessments by other programs not
affiliated with the PSAMP. Table 1 illustrates how the PSAMP integrates studies by monitoring topic.
This integration supports synthesis of results and allows a variety of scientific perspectives to be
presented in program reports.
As an ambient monitoring program, the PSAMP focuses on describing the state of the environment.
This program cannot, and is not be expected to, delve deeply into questions about why the environment is
in the state that we observe. When monitoring results point out significant changes in the health of
organisms, the scientists involved in the PSAMP hope to communicate this information so that other
programs can initiate research projects and specific investigations to uncover causes and better evaluate
the nature of problems. Where information is available, the PSAMP can help begin this process by
discussing "associated" findings that may relate to underlying causes. When possible, PSAMP reports
III-548
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present information on this type of association, identify suggested or known causes of observed problems,
or refers to the work of others who are following up on the results of monitoring.
PSAMP Reports
The PSAMP communicates its findings through the following publications and presentations:
• Agency reports—as part of their participation in the PSAMP, member agencies produce and
disseminate technical reports on their PSAMP efforts. These reports are the primary publications
of PS AMP findings.
• Puget Sound Update—the sixth Update (1998) has recently been completed. This report is
intended to be accessible, yet comprehensive documentation of recent findings of the PSAMP.
New editions of the Update will be produced every two years.
• Presentations at Puget Sound research conferences and at other scientific meetings,
• Puget Sound Notes—an occasional, technical newsletter for Puget Sound scientists.
• Sound Waves—a bi-monthly or quarterly newsletter for citizens of the Puget Sound region. Since
late 1996 this newsletter includes a page devoted to recent findings of the PSAMP on the health
of Puget Sound.
Data developed from PSAMP studies are also available to scientists and citizens upon request.
Various electronic and hard copy formats are available via request to the principal investigator.
Examples of Findings from the PSAMP
The results of PSAMP studies are used (with information from other sources) to address the five
monitoring topics identified above. An example of the types of results developed by PSAMP's studies
under each of these topics can be seen in the titles of presentations made by program scientists earlier this
year at the 1998 Puget Sound Research Conference.
Biological Resources
Marine Birds
• Status and Trends for Selected Diving Duck Species Examined by the Marine Bird Component of
PSAMP
Marine Mammals
• Disease Screening of Harbor Seals (Phoca vitulina) from Gertrude Island, Washington
Nearshore Vegetation
• Floating Kelp Resources in the Strait of Juan de Fuca and the Pacific Coast of Washington
• Mapping Shorelines in Puget Sound II. Linking Biota with Physical Habitats
• Mapping Shorelines in Puget Sound III. Management Applications for Inventory and Monitoring
• Puget Sound Intertidal Habitat Inventory: Vegetation Mapping
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Benthic Macroinvertebrates
• The Distribution and Structure of Soft-bottom Macrobenthos in Puget Sound in Relation to
Natural and Anthropogenic Factors
• Marine Benthic Invertebrate Communities near King County's Wastewater Outfalls
Physical Environment
Shoreline
• Mapping Shorelines in Puget Sound I. A Spatially Nested Geophysical Shoreline Partitioning
• Probability-based Estimation of Nearshore Habitat Characteristics
• Puget Sound Intertidal Habitat Inventory: Shoreline Characteristics Mapping
Marine Water Physical Character
• "The Puget Sound Signal" in the Public Evening Session on Local Effects of El Nino
• Variations in Residence (Flushing) Time in the West Bay of Budd Inlet, 1992-1994 Hydrographic
Studies
• Assessing Sensitivity to Eutrophication Using PSAMP Long-term Monitoring data from the
Puget Sound Region
Toxic Contaminants
Sediments
• Toxicity of Sediments in Northern Puget Sound—A National Perspective
• Response of the P450 RGS Bioassay to Extracts of Sediments Collected from Puget Sound,
Washington
Shellfish
• Trace Metal Contamination in Edible Clam Species from King County Beaches
Fish
• Factors Affecting the Accumulation of Polychlorinated Biphenyls in Pacific Salmon
• Persistent Pollutants and Factors Affecting Their Accumulation in Rockfishes (Sebastes spp.)
from Puget Sound Washington
• Geographic and Temporal Patterns in Toxicopathic Liver Lesions in English Sole (Pleuronectes
vetulus) from Puget Sound and Relationship with Contaminant Concentrations in Sediments and
Fish Tissues
Marine Birds and Mammals
• Contaminant Monitoring of Surf Scoters near Tacoma, Washington
• Elevated PCB Levels in Puget Sound Harbor Seals (Phoca vitulina)
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Pathogens and Nutrients
• Long-term Trends in Fecal Coliform Levels in Three South Puget Sound Bays and Links to
Watershed Remedial Action
• Variation in Primary Productivity of Budd Inlet
• Sources of Variability in Water Quality Monitoring Data
Human Health
• Temporal and Spatial Distribution of PSP Toxin in Puget Sound
What's Been Learned and What's Next for the PSAMP?
In addition to the scientific findings of the PSAMP, the program has also provided some lessons
related to the design and implementation of a coordinated multi-agency monitoring program. For
example,
• Clearly state our goals and objectives for monitoring (e.g., what are we measuring and why?) and
then evaluate monitoring efforts against these objectives to determine how well the monitoring is
working.
• Strive to combine input from technical (scientific) and management perspectives to ensure that
studies use the best possible designs (e.g., measure things that are important to people in ways
that will provide useful information) given available budgets.
• Grow beyond characterizations of the current condition of the environment and geographic
patterns (e.g., within Puget Sound) to improve descriptions of patterns over time, especially those
that may be related to changes in resource management. For the PSAMP, discerning temporal
trends has proven difficult over short time periods because of the "noisy" signal (variability)
observed in many aspects of the Puget Sound natural environment.
• Communicating monitoring findings is crucial—a monitoring program's work isn't complete
until results have been shared.
To continue efforts to improve the design and implementation of the program, the PSAMP
committees look forward to advancing in new directions, including:
• Improving and expanding our collaboration with others, because the PSAMP alone cannot fully
assess the health of Puget Sound.
• Reviewing the program every two years to allow the PSAMP to adapt to its findings and establish
new directions and priorities in the Puget Sound Management Plan and Puget Sound Water
Quality Work Plans.
, Focusing our reporting on monitoring topics and specific aspects of monitoring components that
are directed to the diversity of PSAMP audiences.
III-551
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regulates '•,
informs.
Monitoring
(PSAMP, etc.)
assesses
Figure 1. Conceptual model for Puget Sound ambient monitoring program.
III-552
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Table 1. Relationships between PSAMP Topics and Monitoring Components/Agencies
Agency & Component Study
Washington Department of Ecology
Marine Waters
Washington Department of Ecology
Sediments & Benthic Organisms
Washington Department of Ecology
Fresh Water
Washington Department of Fish and
Wildlife
Fish
Washington Department of Fish and
Wildlife
Birds and Marine Mammals
Washington Department of Health
Shellfish
Washington Department of Natural
Resources
Nearshore Habitat
King County Department of Natural
Resources
Marine Water, Sediment, and
Other Studies
U.S. Fish and Wildlife Service
Birds
Topic
Status
and
Trends of
Biological
Resources
X
X
X
X
X
X
X
X
Changes in
Physical
Environment
X
X
X
X
X
X
Toxics
X
X
X
X
X
X
Nutrients
&
Pathogens
X
X
X
X
X
Human
Health
X
X
X
X
X
X
X= major aspect of component
x = minor aspect of component
Blank cells indicate limited or no involvement of a study in a topic.
III-553
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III-554
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Integrating Ambient and Compliance Monitoring in the
Kennebec River Basin, Maine
Keith Robinson, Hydrologist
U.S. Geological Survey, 361 Commerce Way, Pembroke, NH 03275
Phone: 603-226-7809
David Courtemanch, Bureau Chief
Maine Department of Environmental Protection, State House Station #17, Augusta ME 04333
Phone: 207-287-7789
Abstract
One of the recommendations of the national Interagency Task Force on Water-Quality Monitoring
(TTFM) is to better integrate ambient (stream monitoring to assess general water-quality conditions) and
compliance (effluent-quality monitoring for regulatory purposes) monitoring. An integrated monitoring
program can improve the understanding of stream-water-quality conditions, while still tracking effluent as
required by discharge-permit limitations. Over the past 2 years, the U.S. Geological Survey, Maine
Department of Environmental Protection, U.S. Environmental Protection Agency, New England Interstate
Water Pollution Control Commission, and a number of permitted wastewater dischargers in the Kennebec
River Basin have been developing an integrated monitoring strategy for the Basin. Participating wastewater
dischargers include a number of pulp and paper mills, municipal wastewater treatment facilities, and
hydropower facilities.
Activities included an inventory of current and past ambient and discharge-permit related water-
quality monitoring in the Basin, a survey of resources and expenditures for current ambient and
compliance-monitoring activities, and identification of the important water-quality issues in the Basin.
During the summer of 1997, the first coordinated water-quality sampling of the Kennebec River took
place. A long-term, integrated ambient-compliance monitoring plan is under development with the goal of
redirecting monitoring activities to areas where gaps in knowledge exist, without increasing the resources
spent on monitoring. A creative working environment has developed between the participants so that
resources are shared among the Federal and State agencies and dischargers to accomplish mutual goals of
the plan. Overall, participants are genuinely interested in improving the understanding of river-water
quality and its management.
Introduction
In 1992, the Intergovernmental Task Force on Monitoring Water Quality (ITFM) was created to develop a
national integrated monitoring strategy that would assist in facilitating defensible water-quality programs and
decision making (Intergovernmental Task Force on Monitoring Water Quality, 1994). The ITFM recognized
that there are limited resources available for environmental-monitoring programs and that these programs need
to be conducted as effectively and efficiently as possible. A number of recommendations were made by the
ITFM in 1995 to enhance the effectiveness of water-quality-monitoring programs across the Nation
(Intergovernmental Task Force on Monitoring Water Quality, 1995). One recommendation was to better
integrate ambient and compliance monitoring activities in watersheds. Ambient monitoring activities of
streams assess general water-quality conditions. This monitoring typically is performed by government
agencies and citizen groups and is often a discretionary activity. On the other hand, the regulated community,
which includes public water suppliers, wastewater-treatment facilities, and industrial facilities, conducts
compliance monitoring. Compliance monitoring is subject to regulatory directions.
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The ITFM recognized that the regulated community conducts more water-quality monitoring for
compliance purposes than government agencies do to assess general water-quality conditions
(Intergovernmental Task Force on Monitoring Water Quality, 1995). As a result, water-resource managers
have a more thorough understanding of regulated activities than they do of the water resources being
affected. The ITFM felt that partnerships between the ambient and compliance monitors would make the
data from both activities more usable and accessible. If such partnerships are established, more thorough
assessments of stream water-quality conditions can be done without increasing overall monitoring costs. In
return, environmental protection agencies would offer the regulated community adjustments to their
compliance monitoring as compensation for the overall benefits from the partnership.
To test the concept of integrating ambient and compliance monitoring, the U.S. Geological Survey
(USGS) proposed pilot studies in conjunction with National Water-Quality Assessment (NAWQA)
Program study units in 1995. Three of these pilot studies were ultimately established. One of these pilot
studies was in the Kennebec River Basin in central Maine; the Kennebec River is part of the New England
Coastal Basins (NECB) NAWQA study (figure 1). This paper describes the results to date of efforts to
develop an integrated ambient and compliance monitoring framework for the Kennebec River.
The Kennebec River ambient-compliance-monitoring integration study initially began in 1995 when
the Maine Department of Environmental Protection (MEDEP) expressed interested in working with USGS
to develop the pilot study. This was followed in 1995 with a series of meetings between State and Federal
monitoring and water pollution control agencies and wastewater permittees in the basin. Currently (1998),
the process is still on-going. The goal of the study is to increase knowledge of water quality in the
Kennebec River Basin by integrating ambient and compliance monitoring in a comprehensive manner
without an increase in resources allocated to monitoring. Only compliance monitoring performed by
wastewater treatment facilities on their influent and effluent is included in the study. Other compliance
monitoring in the basin, such as that related to public water supplies or waste disposal sites, was not
considered. The study area includes the part of the Kennebec River Basin from Madison, Maine to
Richmond, Maine (figure 1). The study focuses on the mainstem of the Kennebec River and not on
tributaries; tributaries are considered as point-source contributors to the mainstem.
Description of the Study Area
The 145-mile-long Kennebec River in central Maine drains an area of 5,890 square mile (mi2), the
second largest drainage basin in the State. The basin is 82 percent forested, 10 percent water, 6 percent
agriculture, and 2 percent urban. The Kennebec River drains into Merrymeeting Bay before flowing into
the Atlantic Ocean. Timber harvesting is prevalent in the northern half of the basin, whereas agriculture,
industries, and scattered population centers are found in the southern half of the basin. Pulp and paper
mills and impoundments are found along the Kennebec River and tributaries. Below Augusta, the river is a
freshwater tidal estuary.
Approximately 50 miles of the Kennebec River are within in the study area. Major tributaries to the
Kennebec in this section include Sandy River, Wesserunsett Stream, Sebasticook River, Messalonskee Stream,
and Cobbosseecontee Stream (figure 1). Six dams are located along the Kennebec River in the study area;
these dams are used primarily for generation of hydropower. Population centers in the study area include
Madison, Skowhegan, Fairfield, Waterville, Augusta, and Gardiner. Eight major wastewater treatment
operations or industrial facilities discharge directly to the Kennebec River in the study area; of these
discharges, three are municipal, three are industrial, and two are municipal with industrial contributions
comprising a majority of the total wastewater flows (table 1).
Water quality in the Kennebec River has improved over the past 30 years. Before the mid-1970s,
untreated and incompletely treated wastewaters from cities and industries resulted in a highly degraded
river. In the 1960s and early 1970s, a pulp and paper facility at Winslow discharged organic-enriched
wastewaters that were equal to a city of 2 million people (New England River Basins Commission, 1979).
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Since the mid-1970s, however, water quality has steadily improved as a result of improved wastewater
treatment. In 1994, MEDEP classified the Kennebec River between Madison and Fairfield as Class B
waters, and between Fairfield and the Edwards Dam as Class C waters (Maine Department of
Environmental Protection, 1994). Class B waters are suitable for recreation in and on the water, fishing,
drinking and industrial water supplies, navigation, and unimpaired habitat for fish and other aquatic life;
Class C waters have similar designated uses except that these waters only support habitat for fish and other
aquatic life and have lower water-quality criteria than Class B waters (Maine Department of
Environmental Protection, 1994). Below Fairfield, the water in the Kennebec River has been classified as
Class C because of reduced dissolved oxygen as a result of industrial wastewater discharges and
impoundments used for hydropower. In addition, this section of the river is under a fish consumption
advisory because of the presence of dioxin in fish tissues. The dioxin is a consequence of past wastewater
discharges from pulp and paper facilities in the basin. A part of the river between Waterville and Augusta
is Class B as a result of recovery from upstream wastewater discharges. The freshwater tidal estuary is
Class C.
Agencies and Organizations Involved in the Study
USGS and the MEDEP- Bureau of Land and Water initiated the study in 1995 and have taken the lead
in arranging meetings and setting goals for the study. Representatives of the major wastewater treatment
facilities (permittees), the U.S. Environmental Protection Agency (USEPA) - Region 1, and New England
Interstate Water Pollution Control Commission were invited to participate in the study in 1995. Since then,
the number of participants has increased and currently (1998) totals about 15 individuals representing 12
different agencies and organizations. Agencies and organizations currently participating in the study are
shown in table 2.
All operators of major and minor wastewater treatment facilities were invited to participate; major
facilities are distinguished from minor facilities on the basis of flows and amounts of chemicals discharged.
All major and a few of the minor permittees participated, either by attending meetings or requesting to be on
the mailing list, at sometime during the 3 years of the study. Although there are no citizen watershed groups
specific to the Kennebec River, two citizen/environmental advice groups have requested to be informed about
the progress of the study.
Progress Towards Integrating Ambient and Compliance Monitoring in the Kennebec River Basin
MEDEP operates a program of revolving studies of the major river basins in the State. These revolving
studies are conducted on 5-year intervals and include monitoring of water quality, wasteload allocation
modeling, and re-issuance of wastewater discharge permits and licenses. (MEDEP issues licenses for all
wastewater discharges in the State; these licenses are in addition to the National Pollutant Discharge
Elimination System permits issued by the USEPA.) Watershed studies for the Kennebec River Basin were
planned for 1997-98 with revised wastewater discharge licenses/permits issued in 1998. Initially, the plans
for integrating ambient and compliance monitoring were to be completed by 1997 so that the plans could
be incorporated into license/permit re-issuance in 1998. However, MEDEP is currently about 1 year
behind in their schedule for completing the Kennebec River Basin watershed study and re-issuance of
licenses. USEPA has completed its re-issuance of discharge permits. Plans for fully integrating ambient
and compliance monitoring were not completed in 1997 and are still under development. Despite delays in
completing an integrated monitoring strategy, progress has been made; this progress is presented in the
following sections.
The government agencies and permittees involved in this study have been participating because they
feel it is a worthwhile activity. No special funding has been obtained to pay for the work involved in the
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study. As a result, the work has occasionally become a low priority, which has delayed the completion of
the 3-year study.
The Process
The following six-step process was developed for integrating ambient and compliance monitoring:
• Identify and describe ongoing and historical monitoring activities in the study area.
• Identify the existing resources that various organizations apply to monitoring.
• Identify, assess, and prioritize water-quality problems and data needs for the study area.
• Develop an 'idealized' integrated monitoring approach for the study area of the Kennebec River
based on the water-quality issues and priorities.
• Identify how existing monitoring programs/requirements can be modified to support an integrated
monitoring plan.
• Develop action plan and necessary logistics for implementing program changes needed to achieve
an integrated monitoring program.
The first three steps were completed in the first 2 years of the study. These steps included an inventory of
current and past ambient and discharge-permit related water-quality monitoring in the basin, a survey of
resources and expenditures for current ambient and compliance-monitoring activities, identification of the
important water-quality issues in the basin, and the use of existing data for various river-management
activities. The results of these activities are described in the following paragraphs.
During the summer of 1997, the first integrated water-quality sampling of the Kennebec River took
place; a second integrated sampling is planned for 1998. Subsequent to this sampling activity, a long-term,
integrated ambient-compliance-monitoring plan will be developed with the goal of redirecting monitoring
activities to areas where gaps in knowledge exist without increasing the resources spent on monitoring. A
creative working environment has developed between the participants so that resources are shared among
the Federal and State agencies and permittees to accomplish mutual goals of the plan.
Ambient and Compliance Monitoring
Descriptions of the ambient- and compliance-monitoring activities in Kennebec River were gathered
early in the study. Information on the types and frequency of monitoring and the annual costs associated
with the monitoring were collected. A review of ambient monitoring of the Kennebec River from 1980 to
the present (1998) found that there is no current on-going, continuous monitoring underway (table 3). Both
MEDEP and the USGS formerly performed routine water-quality monitoring of the Kennebec River;
MEDEP's routine water-quality monitoring was discontinued in 1989 and USGS's routine monitoring
ended in 1994. MEDEP currently focuses their monitoring on toxic substances and benthic invertebrates.
The toxic monitoring consists of annual collections of fish tissue and bed sediments at two locations on the
Kennebec River. Dioxins, polychlorinated biphenyl's, pesticides, trace metals, and polyaromatic
hydrocarbons are monitored. In addition, MEDEP monitors water quality of the Kennebec River once
every 5 years to collect data necessary for the re-issuance of discharge permits; these data are collected
under low-flow conditions. USGS does not currently (1998) monitor water quality in the study area. The
only other in-stream water-quality monitoring that was identified is that done by the Kennebec Sanitary
Treatment District (KSTD). River samples are analyzed for E. coli bacteria because the District has
combined sewer overflows; this monitoring is required as part of their discharge permit.
Information on the compliance-monitoring activities of the four largest permitted discharges in the
study area was compiled. The remaining discharges did not provide information but comprise a small
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fraction of the total waste discharged to the Kennebec River. The compliance-monitoring requirements for
all permittees can be obtained from the NPDES permit or state license issued to the facility. (The costs
associated with the compliance monitoring, however, could not be obtained from the permit/ license.)
Most of the major permittees contain multiple discharges and typically include separate process
wastewaters and stormwater discharges. Compliance monitoring of the process wastewaters of major
facilities generally includes bioassay tests and the analysis of chemical constituents and flow.
The annual costs associated with ambient- and compliance-monitoring activities vary by activity and
agency/organization. In addition, some of the costs are only estimates. This is especially true for costs
associated with the discontinued ambient-monitoring activities; these estimates are based on costs during
the last year of monitoring, and as such, are outdated. In 1996, the year this information was collected,
about $35,000 was spent annually by the MEDEP to monitor the Kennebec River in the study area. Of this
amount, $20,000 was from fees collected by MEDEP associated with the licenses of 3 permittees in the
basin—KSTD and 2 pulp-and-paper mills (SD Warren Co. and Kimberly-Clark)—to assess dioxin effects.
Estimates of the costs of compliance monitoring by permittees are difficult to compare to one another
because of the variability of the information provided by each. One permittee estimated annual costs to be
near $150,000; this figure includes all staff salaries. Other permittees included only direct laboratory
costs—these varied from approximately $9,000 per year to $25,000 per year. If one assumes that annual
laboratory costs average $15,000 per year per major permittee, then approximately $120,000 is spent for
direct laboratory costs associated with compliance monitoring. This compares to $35,000 annually spent
on ambient monitoring by the State.
Water-Quality Issues in the Kennebec River and Utility of the Ambient and Compliance Data for
River Assessments
In 1996, participants in the study developed a matrix that defined the uses of past and present ambient-
and compliance-data-collection activities in terms of the water-quality issues affecting the Kennebec River
(table 4). Types of monitoring, uses of the data, and water-quality issues are included in table 4. The
purpose of compiling this information was to determine which forms of ambient and compliance
monitoring were most valuable for assessing a variety of water-quality issues and management programs.
This information would then to used to identify those monitoring activities that should be part of a long-
term monitoring plan for the Kennebec River.
The water-quality issues found to be affecting the Kennebec River include a variety of chemical
constituents and pollutants and contributing sources of the pollutants. Important water-quality issues
identified included an understanding of the general water-quality characteristics of the Kennebec River,
such as color, odor, bacterial quality, and nutrients, knowledge of the sources of contaminants, and how
the river assimilates and transports contaminants.
Three types of monitoring were identified as generating the most useful data to assess water-quality
issues and resources in the study area: (1) fixed-site river monitoring of chemical constituents that occurs
throughout the year, (similar to monitoring by the former USGS National Stream Quality Accounting
Program); (2) monitoring of toxic substances in fish tissues and sediments; and (3) effluent data from the
major permittees. All three of these monitoring efforts provide data necessary for determining the status of
water-quality conditions and trends in conditions over time. Currently (1998), toxics and effluent
discharges are being monitored, but year-round, fixed-site chemical monitoring of the Kennebec River is
not being done.
Integrating Ambient and Compliance Monitoring - An Initial Test
In the summer of 1997, MEDEP and USGS conducted a 3-day intensive monitoring program for the
Kennebec River to collect water-quality data necessary for a wasteload-allocation model developed by
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MEDEP. This monitoring consisted of the collection of samples at multiple sites along the river itself and
at the outlet of major tributaries, and collection of effluent samples from permittees. Samples were
analyzed for dissolved oxygen, nutrients, biological oxygen demand, and E. coli bacteria. Participants in
the ambient-compliance-integration study felt that this 3-day monitoring effort would be a reliable test to
see if monitoring agencies and permittees could work together to monitor the river. The monitoring plan
prepared by MEDEP with the assistance of USGS had the following integrated components (Miller, 1997):
• Personnel from the permittees would participate in river monitoring with MEDEP and USGS staff;
• The KSTD would analyze all E. coli bacteria samples, thus reducing laboratory analysis costs for
MEDEP; and
• The USEPA and MEDEP would waive effluent compliance monitoring requirements for
permittees participating in the river monitoring.
Personnel from the four major permittees in the study area participated in the monitoring effort along
with staff from MEDEP and USGS. Overall, this arrangement worked well and saved MEDEP resources
that would have been needed to monitor the river. MEDEP felt that without the support from the
permittees, the monitoring could not have been done in 1997 because of other work commitments.
MEDEP also joined into a cooperative agreement with the local office of the USGS to coordinate the
operation of the monitoring program.
MEDEP and USEPA informed all the permittees in the study area that compliance-monitoring
requirements would be waived if the facility participated in the monitoring, although none of the
permittees requested the waiver. The permittees felt that it would be easier to maintain their monitoring
schedule since they already had the staff and equipment than to stop monitoring altogether, some of which
was necessary for plant operation.
MEDEP is planning to conduct a second 3-day intensive monitoring program for the Kennebec River
in the summer of 1998 to gather additional data for their wasteload-allocation model. Based on the overall
success of the monitoring in 1997, permittees will be participating again. Offers to waive compliance
monitoring will once again be made.
Developing a Long-term Integrated Monitoring Plan for the Kennebec River
Progress has been slow on the development of a long-term integrated monitoring plan for the
Kennebec River. Reasons for this include (1) resources allocated to develop a long-term plan are minimal
within the agencies participating and work on the plan must be done in addition to day-to-day assignments;
(2) the focus of the participants over the past year has been on completing the summer intensive
monitoring; and (3) existing data on water-quality, compliance monitoring, and the watershed have not
been fully analyzed. In May 1998, MEDEP, USGS, and USEPA agreed that if a long-term integrated
monitoring plan is to be developed and implemented, then reliable resources need to be allocated for its
development. Special funding will be pursued for 1999 to complete the plan. Activities that will need to be
conducted include the following:
• Determine the distribution of nonpoint sources of pollution in the study area,
• Determine the spatial and temporal degradation in the river water quality based on all existing
data,
• Identify gaps in our knowledge exist, for instance, nutrient loadings,
• Assess State dioxin monitoring for trends and variations from year to year,
• Examine results of existing water-quality models for timing and causes of degraded water quality,
• Complete assessment other water-quality studies conducted in the basin,
m-560
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• Compare known point-source loadings with ambient loads in the river,
• Gather information on watershed characteristics, such as nutrient and pesticide loading estimates,
water use, flow modifications, known nonpoint-source locations,
• Work with permittees and enforcement agencies to review compliance monitoring strategies and
make a final determination if compliance monitoring can be altered, and
• Determine how ambient and compliance monitoring can be integrated while maintaining or
reducing the total resources spent on these activities.
Since 1995, some of these activities have been started, but are not fully completed and need to be
completed as part of the development of the long-term integrated monitoring plan.
Conclusions
Currently (1998), all participating agencies and permittees continue their interest in achieving the
long-term goal of an integrated monitoring program for the Kennebec River. The past 3 years have shown
that sensitive issues related to compliance monitoring and private-sector resources can be tabled when the
goal is better environmental data and decisionmaking. In addition, a creative working environment has
developed between the participants so that resources are shared among agencies and permittees to
accomplish these mutual goals.
It remains to be seen, however, if substantial revisions to compliance-monitoring requirements will be
implemented. Regulatory staff appear somewhat reluctant to reduce the monitoring frequency and
coverage over an extended time period, even if it means improved river monitoring. At the same time,
permittees feel their wastewater operations are vulnerable to public scrutiny if their monitoring is not
perceived by the public to be adequate.
As these issues are addressed and resolved, an increased understanding of the quality of the Kennebec
River will be reached. This long-term monitoring study is evolutionary in design, not only in identifying the
monitoring needs for the river, but also in developing the relationships among the parties to address the
monitoring needs. At the same time, the quality of the river is changing as the relative effect of different
stressors and sources of pollutants also are changing. All these factors must be incorporated long-term
monitoring programs for the Kennebec River.
Literature Cited
Intergovernmental Task Force on Monitoring Water Quality. 1994. Water-quality monitoring in the United
States: 1993 report of the Intergovernmental Task Force on Monitoring Water Quality. U.S.
Geological Survey, Reston, Va., variously paginated.
1995. The strategy for improving water-quality monitoring in the United States. U.S. Geological
Survey, Reston, Va., 25 p.
Maine Department of Environmental Protection. 1994. Appendices to the State of Maine 1994 water
quality assessment. Bureau of Land and Water, Augusta, Maine, variously paginated.
Miller, David. 1997. Kennebec River Basin work plan, May, 1997. Bureau of Land and Water, Maine
Department of Environmental Protection, Augusta, Maine, 16 p.
New England River Basins Commission. 1979. Kennebec River Basin overview. 159 p.
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70° 30
EXPLANATION
H Kennebec River Basin
study area
L3 New England Coastal
Basins National Water
Quality Assessment
Program study unit
BasefromU.S.GeologicalSurveydigi1aldata,1:2.000,000,1972 .'/ . '
Albers Equal-Area Conic projection 440 —,' •—
Standard parallels 29-30' and 45-30- and central meridian 71° ^ '
Iwards'Dam
rdiner
/
Richmond
10
EXPLANATION
Drainage basin
20
30
40 50 MILES
0 10 20 30 40 50 KILOMETERS
Figure 1. Map of the Kennebec River Basin.
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Table 1. Wastewater Permittees Discharging to the Kennebec River Study Area, Maine
Permitted flows Major or
Type of (million gallons per minor
Facility facility/wastewater day) facility
Anson-Madison Sanitary
District
Kennebec Sanitary Treatment
District
Augusta Sanitary District
Gardiner Wastewater
Treatment Facility
Skowhegan Wastewater
Treatment Facility
S.D. Warren Co. (Sappi)
Kimberly Clark (discontinued
operation in 1998)
Statler Industries (discontinued
operation in 1998)
Norridgewock Wastewater
Treatment Facility
Skowhegan Package Plant
Municipal-industrial
Municipal-industrial
Municipal
Municipal
Municipal
Industrial process and
stormwater
Industrial process and
stormwater
Industrial process
Municipal
Municipal
7.8
12.7
8.0
1.7
1.4
47
11
6.0
0.2
—
Major
Major
Major
Major
Major
Major
Major
Major
Minor
Minor
Richmond Utilities District
Central Maine Power Co -
Merimel Hydroelectric
Central Maine Power Co -
Weston Hydropower
Central Maine Power Co -
Shawmut Station
Madison Paper Industries
Municipal
Industrial stormwater
Industrial stormwater
Industrial stormwater
Industrial stormwater
0.3
Minor
Minor
Minor
Minor
Minor
• = No data available.
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Table 2. Organizations and Agencies Participating in the Kennebec River
Ambient-Compliance-Monitoring Integration Study
Organization/Agency
Maine Department of Environmental Protection
U.S. Geological Survey
U.S. Environmental Protection Agency
New England Interstate Water Pollution Control Commission
Kennebec Sanitary Treatment District
Kimberly Clark
Madison Paper Industries
Central Maine Power Co.
S.D. Warren Co. (Sappi)
Anson-Madison Sanitary District
Somerset County Soil and Water Conservation District
Friends of Merrymeeting Bay
Table 3. Ambient River Monitoring Activities in the Kennebec River Study Area, Maine, 1980-1998
Monitoring program
and agency
National Stream
Quality Accounting
Network/USGS
Continuous
Monitor/USGS
Period of
data
collection
1978-93
1979-94
Number of
sites on the
Kennebec
River
1
1
Constituents monitored
Water column: nutrients, trace
elements, field parameters, bacteria,
suspended sediment
Water column: specific conductance,
pH, temperature, dissolved oxygen
Purpose of
monitoring
National water-
quality network
Assess
wastewater
Primary Monitoring
Network/MEDEP
Dioxin
Monitoring/MEDEP
1974-89
1988-present
Biomonitoring/MEDEP 1988-present
Surface Water Ambient 1994-present
Toxics/MEDEP
4
2
Water column: DO., temp., fecal
coliform, pH, color, priority
pollutants
TCDD, TCDF in fish tissue
Benthic invertebrate community
Fish tissue: metals, PCBs, pesticides,
polyaromatic hydrocarbons (PAHs)
discharge impacts
Establish baseline
conditions, assess
trends, and assess
use attainment
Assess
classification/
designated use
attainment
Assess designated
use attainment
Assess designated
use attainment
USGS, United States Geological Survey
MEDEP, Maine Department of Environmental Protection
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Table 4. Components of the Matrix Relating Monitoring Activities to Use of the
Monitoring Data and Water-Quality Issues for the Kennebec River Study Area, Maine
Type of monitoring activity Uses of the monitoring data
Water-quality issues in the
study area
• Ambient
• Compliance
Streamflow measurements
Enforcement/compliance
Water-quality conditions
assessment
Models/predictive studies
Wasteload allocations
Public health advisories
Time trends
Planning water uses/water
management decisions (both
public and private)
Resource assessment
Time trends
Planning water uses/water
management decisions (both
public and private)
Planning water uses/water
management decisions (both
public and private)
Resource management
Fisheries management
Waste water treatment
process controls
Third party legal actions
• Chronic toxicity of aquatic
life and bioaccumulation
(esp. chlorine and metals)
• Assimilative capacity and
background concentrations
for toxics, conservative
pollutants, and nutrients
• Pathogens/E. coli bacteria
• Understanding background
conditions in the river
• Understanding the relative
contribution of point and
nonpoint sources to total
loads in the river
• Contaminated sediments
• How flow regulation affects
water quality and aquatic
habitat
• Color/odor/foam/total
suspended solids
• Health of aquatic
life/biomonitoring
• Fate and transport of metals.
nutrients, sediments, and
constituents of concern
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III-566
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Evaluation of Nutrient Loads and Sources in the Ohio River Basin
Deborah M. Olszowka, Environmental Specialist
Jason P. Heath, Water Quality Monitoring & Assessment Programs Manager
Peter A. Tennant, P.E., Deputy Executive Director
Ohio River Valley Water Sanitation Commission (ORSANCO)
Abstract
The seasonal development of a zone of hypoxia in the Gulf of Mexico, which poses a threat to
commercial aquatic communities, has been associated with nutrient loadings from the Mississippi River.
This oxygen-depleted zone occurs in bottom-waters and currently includes an area of approximately 7000
square miles, extending from the mouth of the Mississippi River, westward along the coast towards the
Texas border. The Ohio River Basin constitutes approximately 20 percent of the Mississippi Watershed,
and contributes about 35 percent of the river's total flow. It is therefore logical to suspect that nutrient
loadings from the Ohio River could be contributing to eutrophication problems in the Gulf. In the fall of
1997, the Ohio River Basin Water Sanitation Commission (ORSANCO) began conducting a study to
evaluate nutrient loads and sources in the Ohio River Basin. The objectives of this project are to
document present water quality conditions concerning nutrients in the Ohio River and its major
tributaries, to quantify nutrient loads from major sub-basins to the Ohio River through water quality
monitoring activities, and to assess the relative magnitude of nutrient sources (i.e., agriculture, POTWs,
industrial discharges, urban runoff, etc.). In addition, the identification of priority watersheds within the
Ohio River Basin will provide a basis for implementing and measuring the effectiveness of control
programs to reduce stream nutrient loads in the Ohio River Basin and, ultimately, to the Gulf of Mexico.
Introduction
The Ohio River Valley Water Sanitation Commission
The Ohio River Valley Water Sanitation Commission (ORSANCO) is an interstate water pollution
control agency that was created in 1948 to administer an agreement among eight states. That agreement, the
Ohio River Valley Water Sanitation Compact, committed the states to certain goals for the water quality of
the Ohio River and its tributaries, and established the Commission as a body corporate with regulatory
powers to oversee its execution. The Compact was signed by the Governors of the eight states- Illinois,
Indiana, Kentucky, New York, Ohio, Pennsylvania, Virginia, and West Virginia, and was approved by the
United States Congress.
In the Compact's key provisions, the states pledge action to clean up the Ohio River and protect it from
further abuse with the Commission providing information and a forum for the states to coordinate their
activities. A guiding principle of the Compact is that pollution originating from one state shall not injuriously
affect the waters of another state. Therefore, programs carried out by the Commission are designed to
complement efforts by the states. Today, ORSANCO manages and operates programs for water quality
monitoring and assessment, assists in emergency response management, establishes pollution control
standards for the Ohio River, and facilitates interstate cooperation and coordination through an extensive
committee structure.
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The Ohio River Basin
The Ohio River Basin encompasses portions of 14 states with an area of more than 200,000 square
miles, constituting over five percent of the total United States land mass. The mainstem itself forms in
Pittsburgh at the confluence of the Allegheny and Monongahela Rivers. Extending 981 miles, the Ohio
River forms the border between Ohio, Indiana, and Illinois to the north, and West Virginia and Kentucky
to the south. When the Ohio River joins with the upper Mississippi River at Cairo, IL, it provides
approximately two-thirds of the total flow of the Mississippi River at the confluence. As such, the Ohio
River watershed may have a substantial influence on water quality of the lower Mississippi River, and
subsequently the Gulf of Mexico.
Problem Description
Ohio River Nutrients
Historically, nutrients have not been considered a water quality problem in the Ohio River. Data
collected by ORSANCO from 1980 to 1990 for total phosphorus, nitrate/nitrite nitrogen, and ammonia
nitrogen indicates either no change or decreasing concentrations in nutrient parameters at most of the 16
Ohio River sampling locations. However, while nutrients have not been seen as an Ohio River problem,
they have been identified as a concern on tributaries, hi 1994 305(b) reports, states' collectively ranked
nutrients as the fourth greatest cause of water use impairment in the Basin, hi addition, public water
suppliers are reporting increased algal populations and trends toward earlier seasonal algal blooms on
some portions of the Ohio River and major tributaries. Therefore, further investigation of nutrients is
necessary to quantify loadings from major tributaries that are not routinely monitored, estimate the
relative contributions of point and nonpoint sources, and determine the level of reduction that is being
achieved by current control programs within the Basin.
Gulf of Mexico
A zone of hypoxia in the Gulf of Mexico has recently been associated with nutrient loadings,
primarily nitrogen, from the Mississippi River. Hypoxia occurs when dissolved oxygen concentrations
occur at less than 2.0 mg/L. hi the Gulf of Mexico this condition has been found to develop as early as
April and continue into as late as October in bottom waters (Sabins, 1992). The size of the zone varies
each year but typically extends from the mouth of the Mississippi River, westward towards the Upper
Texas Coast. Since the Ohio River Basin constitutes approximately 20 percent of the Mississippi
Watershed, and contributes about 35 percent of the river's total flow, it is therefore logical to suspect that
nutrient loadings from the Ohio could be contributing to the problems in the Gulf.
Development of the Project
hi the fall of 1997, ORSANCO received a $100,000 grant from the United States Environmental
Protection Agency to evaluate nutrient loads in the Ohio River Basin. The purpose of this project is to
quantify nutrient loads from major Ohio River sub-basins and to assess the relative magnitude of point
and nonpoint sources. Ultimately, this data will document baseline water quality conditions upon which
to measure future improvements and identify what is needed to achieve meaningful reductions in nutrient
loading from the Ohio River Basin and eventually, the Gulf of Mexico. In order to achieve the project
objectives five tasks have been established.
1. Compile and assess existing stream nutrient data.
2. Monitor tributaries to quantify loads where existing data is not sufficient.
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3. Generate a mass balance of nutrient loads.
4. Estimate relative load contributions by source type.
5. Inventory control programs in the Ohio River Basin.
Project Implementation Status
Compile and Assess Existing Data
The purpose of this task is to assist in designing a sampling program to supplement existing data.
Historical data for nutrients were compiled from five of ORSANCO's bimonthly sampling stations for the
period 1990 through 1996. From Figure 1 it is apparent that nutrient loads increase by more than 50
percent between mile points 279.2 and 531.5 over the seven-year period. On a temporal scale the mean
monthly loading for total nitrogen increases significantly in December and tails off in June (Figure 2).
On major tributaries, average daily nitrogen loads were estimated over a seven-year sampling period
(Figure 3). Based upon this data, loading is typically higher on major tributaries in the lower portion of
the Ohio River indicating a correlation between loading and agricultural land use. Overall, the largest
loadings originate from the Wabash River, Tennessee River, Great Miami River, and the Scioto River.
Monitor to Quantify Loads
The objective of this task is to monitor significant tributaries during periods of higher nutrient
loading. Existing ambient monitoring data was used to develop a monitoring program for the Ohio River
Basin. Additional monitoring is necessary to supplement existing data to more accurately assess and
estimate nutrient trends and loadings from Ohio River sub-basins. Data indicated that the upper portion of
the Ohio River and several major tributaries were not significant due to relatively small loadings.
Therefore, nutrient monitoring is being conducted in the lower two-thirds portion of the Ohio River at
four mainstem sites and on 12 major tributaries (Figure 4). Biweekly samples are being collected and
analyzed for total phosphorus, ammonia-nitrogen, nitrate-nitrogen, nitrite-nitrogen, and total kjeldahl
nitrogen. Sample collection is being conducted during months when average monthly loads are relatively
high. Therefore, sample collection took place from March through May, 1998 and will resume again in
November, 1998 until February, 1999.
Estimate Relative Contributions by Source Type
The objective of this task is to estimate relative contributions of nutrients from point and nonpoint
sources for each eight digit hydrologic unit within the study area. Currently, there are no adopted water
quality criteria for phosphorus concentrations in the Ohio, while the criterion for nitrate/nitrite nitrogen is
10 mg/L based upon the protection of water supplies. A review of NPDES permits for direct discharges in
the Ohio River Basin in 1996 indicated that of approximately 11,000 permits, 2220 contained limits for
ammonia nitrogen, 224 contained limits for total kjeldahl nitrogen, 34 contained limits for nitrate, 31
contained limits for nitrite, and 228 contained limits for total phosphorus. Using this information loadings
for nutrient parameters will be calculated and incorporated into a Geographic Information System (GIS)
format.
Better Assessment Science Integrating Point and Nonpoint Sources (BASINS) software is being used
to estimate nonpoint source contributions within the Basin. BASINS integrates GIS, data analysis, and
modeling systems to support a watershed based analysis.
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Inventory Control Programs
The purpose of this task is to identify locations where control programs exist and to ascertain their adequacy
in reducing nutrient loading. In the Ohio River Basin thirty-one non-point source (NFS) control programs
focusing on the abatement of water quality problems associated with nutrients have been identified. Programs
are being implemented in all five Ohio River states on small watersheds through partnerships among state
and local governments, environmental groups, and communities. Typically, programs focus on public
education activities and the implementation of Best Management Practices (BMPs).
Future Work
In order to complete the project objectives the following work remains need to be completed.
• Continue monitoring for nutrients in the Fall of 1998.
• Estimate nutrient loadings from major Ohio River sub-basins using additional monitoring data.
» Estimate point source contributions using Permit Compliance System (PCS) data.
• Estimate nonpoint source contributions of nutrients BASINS software.
« Compile data and results into a final report.
Reference
Sabins, Dugan S., 1992. "Introduction and Status of the Impacts and Effects of Nutrient Enrichment in the
Gulf of Mexico." Proceedings of the Gulf of Mexico Symposium, Tarpon Springs, Florida, December
10-12, 1992.
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1.8E+06
Nitrogen*
Total Phosphorus
O.OE+00
918.5
720.7 531.5 279.2
Ohio River Mile Point
126.4
Figure 1. Average nutrient loadings from selected Ohio River mainstem sites,
1990-1996.
4.E+06
3.E+06
a
^
1/3
2.E+06
•c
a
o
l.E+06
O.E+00
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Figure 2. Ohio River mean monthly total nitrogen load, 1985-1996.
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Wabash
-r 500000
I Cumberland
Green
Tennessee
-- 500000
Figure 3. Total nitrogen* loading from selected tributaries, 1990-1996.
'IF Sampling Locations
Figure 4. Nutrient sampling locations in the Ohio River Basin.
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Institutional Challenges in Monitoring—
Stream Gaging as an Example
Dr. Emery T. Cleaves, State Geologist and Director
Maryland Geological Survey, 2300 St. Paul Street, Baltimore, MD 21218-5210
Phone:(410)554-5503
Abstract
Environmental issues throughout our nation involve interactions of aquatic resources, land use,
atmospheric conditions and geologic framework. Water and its movement is key to understanding such
interactions. Streamflow data provided by stream gages is one of the informational keystones. Major
institutional challenges at federal, state and local levels revolve around more effective linkages between
users of streamflow data and providers of the data and effective long-term funding.
Networks of stream gage stations at the federal, regional, state and local levels are necessary for
assessments of surface water resources. These networks are operated by the U.S. Geological Survey
(USGS) primarily through its Cooperative Water Program. The USGS provides a standard data collection
methodology, an effective data distribution network, data comparability, and effective QA/QC.
One federal agency collects and distributes the data to a multitude of users and this has created a
"linkage" challenge. We are faced with a "tyranny of the commons": Users rely on the public data
provided by an obscure federal bureau in the Department of the Interior, but many users do not directly
support data collection and distribution. Many users are unaware of how the networks are funded and
that stream gages are becoming an endangered species.
What do we do? We can work through the USGS Cooperative Water Program and its cooperators
and data users to reinvigorate the program, or change our current concept and practice and establish
independent federal, state and local networks, or some combination of the two. Some states are presently
establishing their own networks. USGS, through the Advisory Committee on Water Information, and
other avenues, is moving to reinvigorate its gaging program. The Maryland Water Monitoring Council, in
collaboration with USGS and Maryland users aims to review and reinvigorate the state gaging networks.
These efforts aim to establish effective linkages between provider - cooperator - other users; the goal is to
develop long-term institutional, financial and political support for stream gaging networks at the national,
state and local levels.
Institutional challenges to effective monitoring abound! They are inherent in important issues such as
integrating surface and groundwater monitoring, linking compliance and ambient monitoring, and
implementing comparable field methods and comparable laboratory methods. Institutional challenges to
effective monitoring exist at the federal level, at the state level, and at the local level, and wherever the
three levels seek to interact with each other. Major challenges also emerge as governmental agencies
interact with the private sector and with volunteer monitoring groups.
Because water is a keystone resource, these institutional challenges are critical in water quantity and
water quality monitoring. Water supplies our cities, our industries and our crops. Without water, there are
deserts. To know what water we have, we measure it. We measure the water that rains down on us, that
flows in our streams, and that moves through the ground. In particular, we monitor stream flow. We do so
in order to predict and respond to emergencies, to describe status and trends, to describe existing and
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emerging problems, to address design, management and regulatory problems, and to evaluate the
effectiveness of programs.
To put a "face" on monitoring challenges, I will use the stream gage which is used to measure the
flow of water. In visualizing a stream gage, you probably call to mind the stream gage house. The stream
gage house looks remarkably like the outhouse (or backhouse) many of our grandparents used. Both
houses protect vital functions. Modern municipal sewer systems and septic tank systems have replaced
the outhouse; but, nothing has yet replaced the stream gage.
Why pick the stream gage as an example of institutional challenge? Environmental issues throughout
the Nation involve interactions of aquatic resources, land use, atmospheric conditions and geologic
framework. Water and its movement is key to understanding such interactions. And, streamflow data
provided by stream gages is one of the basic informational keystones.
Streamflow data provided by gages has many uses: water quality evaluation, contaminant load
estimation, ecosystem concerns and watershed-based water resource planning and management. Other
equally important uses include regional hydrologic assessment, highway and bridge design, model
calibration, water supply evaluation and prediction, flood prediction and warning, drought prediction,
and recreation—canoeing, boating and fishing.
The networks of gages which provide the streamflow data for these many uses (national, regional,
state and local levels) are operated mainly by the U.S. Geological Survey primarily through its
Cooperative Water Program. The U.S. Geological Survey operates some 7,000 full-service gages in
cooperation with other federal agencies, interstate compacts, state, county and local government
agencies, and non-government groups.
The institutional challenge is awesome!: many important uses for the stream data combined with
multitudinous customers. We are faced with a "tyranny of the commons." The many users rely on the
public data provided by an obscure federal bureau in the Department of the Interior. But, many of these
users who depend on the data do not directly support data collection and its distribution. Many users are
unaware of how networks are funded and that stream gages are slowly becoming an endangered species.
What can be done?
Do we continue our present system, with the U.S. Geological Survey being the primary surface water
data collection agency? There is much favoring such continuation: USGS provides a standard data
collection methodology, an effective data distribution network, data comparability and effective QA/QC.
However, some states and individual users are developing their own networks, primarily because of
perceived cost benefits.
In Maryland, through the Maryland Water Monitoring Council, we are evaluating the state's stream
gaging network with the goal of developing a strategy for collecting and distributing streamflow data and
also funding the network. We are analyzing the physical matrix of the network and analyzing the
stakeholders who use the network. As an example of institutional challenge, information from a recent
"Gaging Station User" questionnaire illustrates the situation.
Maryland currently has a gaging network of 82 full service gages. We have identified 20 different
uses for the streamflow data, such as modeling, water supply, baseline water quality data, research, and
recreation. To date, 53 stakeholders have responded to the questionnaire. Responders include 10 federal
agencies, 12 state units, 10 regional groups, 10 counties and cities, and 11 other groups. Some users
utilize flow data from all 82 gaging stations, and a few use information from only one gage. Of the
twenty identified uses, eighteen are common to all 82 gages. To emphasize the obvious, Maryland
streamflow data has many uses and a broad range of customers.
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Financially, however, of the 53 identified customers, only 14 provide funds to support the gaging
network. Financial support flows from 2 federal agencies, 3 state units, 3 regional groups, and 6
county/city units. A major objective of the Maryland network analysis is to recommend a strategy
relating cost sharing to federal, state, regional and local goals, interests and responsibilities.
With multiple goals, a broad spectrum of customer objectives and a virtual alphabet soup of federal,
state, local and other data users, we have little choice but to coordinate, collaborate and communicate if
we are to reinvigorate our endangered species, the stream gage. To meet this challenge, we must establish
effective linkages between data provider and customer and financial supporter. These efforts aim to
develop a rational strategy for the gaging networks; networks that meet the needs for streamflow data,
and long-term institutional financial, and political support. This is beginning to happen in Maryland
through the Maryland Water Monitoring Council and it is beginning to happen nationally as the U.S.
Geological Survey, through the Advisory Council on Water Information, is moving to reinvigorate the
national stream gaging network program.
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m-576
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Integrating Upland and In-Channel Monitoring Results
to Improve Ecosystem Condition at Heavenly Ski Resort
Sherry Hazelhurst, Hydrologist
USDA Forest Service, Lake Tahoe Basin Management Unit
Phone:(530) 573-2655
Introduction
Heavenly Ski Resort lies in the southeastern corner of the Lake Tahoe Basin, on the east slope of the
central Sierra Nevada mountains. Encompassing about 4,000 hectares in California and Nevada, the resort
is one of the largest in the area operated on National Forest System lands. Emerald and sapphire waters of
Lake Tahoe shimmer in the sun, reflecting upward onto Heavenly's snow capped peaks. The Lake's
designation as an Outstanding Natural Resource Water affords strict water quality standards for its
tributaries, including those originating from Heavenly. Consequently, maintaining water quality at the
resort is a high priority, and has been the focus of traditional monitoring programs. After analyzing eleven
years of water quality data on one creek, results indicate that suspended sediment and nutrient
concentrations were affected by ski resort development, however specific causes were lacking.
Heavenly has been a Special-use permittee of the US Forest Service since 1955. To date, there are
approximately 628 acres of ski runs, 25 ski lifts, and 30 miles of roads within the resort (Heavenly 1996).
Many run surfaces were created by bulldozing a swathe down steep hillsides, resulting in removal of all
vegetation, rocks, woody debris, and often a loss of the shallow topsoils. Numerous roads were also built
to install lifts, interrupting drainage patterns with bare, compacted surfaces. The loss of soil cover and
alteration of the topography caused accelerated erosion throughout the resort, although the relative
contribution from individual sources was not identified through water column analyses. Similarly,
beneficial effects of revegetation and other mitigation projects prior to 1991 could not be detected using
traditional monitoring.
Regulatory agencies and the public are demanding quantitative data showing ecosystem condition at
Heavenly. Compliance with state standards and the ease of obtaining water samples have been the
primary reasons for emphasis on measuring water quality. These monitoring results show the cumulative
effect of management on Heavenly Valley Creek, however, many questions regarding other watersheds
and ecosystem processes within them remain unanswered. Recent planning efforts at Heavenly allowed
the Forest Service and other interested parties to adapt the traditional monitoring program by expanding
its scope to include a more holistic sampling of the ecosystem.
A steering committee, including representatives from regulatory agencies and Heavenly, was asked to
show how past and future development and mitigations may impact water quality and ecosystem
processes at the resort. Current conditions were addressed in the Master Plan (Heavenly 1996) and
Environmental Impact Statement (USDA FS 1996a).
Baseline Conditions
Water quality data for the period-of-record, 1981 through 1991, was analyzed to illustrate nutrient
and sediment concentrations relative to state standards. Heavenly Valley Creek, the largest watershed at
the resort, originates from springs on the upper California side and drains about 531 hectares. Water
quality had been monitored at three stations, the most reliable of which was Patsy's (elevation 2,444 m).
This station depicts cumulative effects fairly well because it is located just below ski area development in
the watershed. Sample collectors and water analyses varied over this period, so some constituent values
were missing and others were extrapolated using a larger data set.
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Analysis showed that nutrient and sediment concentrations were often greater than the state standard,
with a trend toward increasing values. Table 1 displays annual average concentrations of total Nitrogen
(N), total Phosphorus (P), and suspended sediment. State standards for these constituents were exceeded
in 7, 11, and 6 years, respectively, between 1981 and 1991. Soluble reactive phosphorus (SRP) showed a
similar increasing trend for the years sampled. SRP represents that fraction of P that is readily available
for plant uptake. It is an important management indicator, since it is thought to be a limiting nutrient for
planktonic algal production in Lake Tahoe (Goldman and Byron 1986; Byron and Goldman 1989).
Table 1 also shows that average flow began a significant decline in 1986, beginning a seven-year
drought period. The drought likely affected nutrient and sediment concentrations beyond 1986, in that
soils were less apt to detach from water erosion and lower stream flows were incapable of transporting
large loads.
Physical and biological attributes of all seven streams draining the resort were assessed between 1990
and 1991. Channel stability was evaluated using the Pfankuch method (Pfankuch 1978). Most of the
streams received fair to poor stability ratings. Common findings included bank cover lacking, sediment
filling pools, downcutting, obstructions causing cutting, and ineffective project mitigations. A fisheries
survey found that most of the stream area within the resort lacked migratory and resident fish populations
due to downstream barriers and low instream flows during portions of the year. Some resident fish
occurred in lakes, and mostly consisted of planted species (USDA FS 1996).
Upland conditions on Heavenly*s ski runs, roads, and structures were documented and quantified in
the first attempt to illustrate an individual project's effect on water quality. Developments were assessed
for soil cover, drainage, infiltration, and best management practice application. The 68 runs and 30 miles
of roads were field surveyed and divided into 385 and 395 segments, respectively. Soil loss was
calculated for each segment using a modified version of the universal soil loss equation. Soil erosion rates
ranged between 0.02 and 462 T ha"1 yr"1 on ski runs and 3.5 to 1070 T ha"1 yr"1 on roads. The average soil
loss rate was 46 T ha"1 yr"1 from ski runs and 156 T ha"1 yr"1 from roads. Stream sedimentation for the run
and road segments was then estimated using a sediment delivery model. Calculations show that ski runs
contributed slightly more sediment to streams than roads, although significantly more soil erodes from
road surfaces. Sedimentation rates averaged 2.7 T yr"1 for ski run segments and 2.5 T yr"1 for road
segments. Maps illustrate the location of segments with the greatest sediment yield, and these segments
were scheduled for mitigation. The mathematical models were also used to predict the outcome of
prescribed measures.
Heavenly's EIS was the first document to address physical, chemical, and biological impacts of ski
area development on its ecosystem resources. Although the resort has over 40 years of operational
history, much of the quantitative information regarding watershed condition had been speculative until
this analysis. Data gathered during the EIS process provides a baseline against which future management
may be measured. As such, the monitoring program needed to be revised to encompass a broader view of
ecosystem processes. Heavenly's planning process was guided by a steering committee comprised of
members from Heavenly Ski Resort, USDA Forest Service, Tahoe Regional Planning Agency, El Dorado
County (California), City of South Lake Tahoe (California), and Douglas County (Nevada). The steering
committee asked Forest Service specialists to prepare a watershed monitoring program that would begin
to track progress of past and future mitigations as well as development.
Monitoring Program
The first step in building a new monitoring program was listing all the questions that were being
asked about watershed condition. Water quality remained a primary concern due to state standard
attainment and cumulative effects measurement. Specific sources of water quality fluctuation had been
less well monitored, thus more effort was needed to document these processes. The resulting program
combines as many physical, chemical, and biological parameters as possible to gain a more holistic view
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of watershed processes. Soil cover, best management practices, and riparian conditions are three areas
impacting water quality at Heavenly that were selected for additional monitoring. Each of these areas
affects the others, so a condition and trend analysis ties all of the individual parts together to show
interactions and opportunities for adaptive management.
Cover prevents highly erodible, bare soils from becoming easily detached and deposited into streams.
There are several objectives for collecting information about the type, amount, and distribution of plants,
organic material, and rocks on ski runs and roads. These measurements will be used to determine cover
types and quantities most effective for preventing soil movement on a variety of slopes. Information from
revegetated sites may begin to indicate favorable seed mixtures, fertilizers, irrigation regimes, and site
preparation methods. Over time, plant successional patterns are expected to become clearer. Management
practices and mitigation prescriptions will be adapted to enhance conditions most successful in preventing
soil erosion. Finally, assumptions made in the erosion and sediment delivery models will be adjusted as
needed.
Best management practices (BMPs) are measures used to prevent adverse water quality impacts from
temporary or permanent soil disturbing activities. Numerous assumptions are associated with the
planning, implementation, maintenance, and effectiveness of BMPs prescribed for all such projects.
Monitoring these phases of BMP development is intended to identify both strong and weak points in the
process, helping to improve success on subsequent projects.
Riparian areas function as transition zones between uplands and channels, linking terrestrial and
aquatic ecosystem processes. Their position in the landscape often affords immediate and measurable
effects from changes on either side. It is this sensitivity that makes riparian areas ideal for interpreting
how management is affecting the ecosystem over both short and long temporal scales.
Condition and trend summaries unite observations from all watershed attributes monitored into a
broad description of ecosystem processes. Inferences can be made about cause and effect relationships,
and the data can show where positive and negative impacts are occurring. Further, this information can be
used in future projects to create successful outcomes and correct shortcomings. Reports summarize all
data collected annually, and a cumulative report will more thoroughly analyze data after five years.
The new monitoring program incorporates both quantitative and qualitative measures where
appropriate. Attributes that can be measured with repeatability are quantified. Similarly, estimations,
recommendations, photos, and comments can be taken almost everywhere. Both measures are expected to
be useful for presenting facts, correcting problems, and showing changes over time. Such latitude to
measure and describe various ecosystem attributes will enhance our understanding of how things are
connected and work together.
Heavenly's steering committee, other government agencies, and the public had opportunities to
comment on draft versions of the monitoring program. These groups provided helpful insights that
improved the final product. Heavenly made the commitment to begin monitoring in 1995, and the new
program was adopted as part of the Master Plan in 1996.
Evaluating Progress
Several years of mitigation projects and favorable growing conditions have resulted in improved
ecosystem conditions throughout Heavenly. Monitoring over the past three years shows increases in soil
cover and BMP effectiveness, with corresponding improvements riparian condition and water quality.
Half of all the ski runs assessed in 1991 have been reevaluated in the past three years. Soil cover on
these runs ranges between 18 and 78%, and averages 57%. Vegetation and organic material cover an
average 28% and 21% of the run surface, with the balance comprised of rocks. These values show
increases of 10% total cover, 4% vegetation, and 6% organic material since 1991. Additionally, two miles
III-579
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of road have also been abandoned or obliterated, increasing soil cover and infiltration properties. Several
common notes accompany these evaluations: soil cover should still be improved on most runs;
concentrated flow from roads and other areas is entering run surfaces, often accelerating erosion; and
waterbars or other drainage structures need to be repaired or added.
All permanent structures have been evaluated for applicable BMPs, and areas needing improvement
have been identified. Sites have been ranked for treatment according to high, moderate, or low priority
based on proximity to active channels and extent of erosion source(s). The highest priorities have been
scheduled for implementation during 1998 and 1999. Common BMP needs at lifts and other structures
include increasing plant, organic material, or rock cover and adding infiltration systems.
Construction sites have been monitored at least once weekly for BMP compliance. Temporary BMPs
are required to be in place prior to soil disturbance and are evaluated by Forest Service and other agency
officials. Monitoring has shown that most BMPs are applied correctly and are effective. Problems tend to
develop if they are not maintained or if storms exceed the design limitations of the measures.
Riparian condition and channel stability have improved significantly over the past six years,
particularly on Heavenly Valley Creek. Stream bank stability has improved dramatically, with 18 of 23
reaches, 74% of the channel length, rated good or excellent. Only 31% of the channel was rated good to
excellent in 1990. Almost every reach showed improvement in the following ways: riparian plant vigor
and density increased; more bed and bank materials stabilized; and there was less downcutting and bar
development. Two, Rosgen AB@ type reaches (Rosgen 1996) have been intensively inventoried using
Forest Service, Region 5 protocols (USDA 1996c). Both reaches had consistently lower pool depths and
more fine particles in the pool tails than similar reaches in undeveloped watersheds. However, the upper
reach showed signs of improvement with high sinuosity (1.46), low width to depth ratio (11), and an
increasing number of pools. The lower reach was recovering from a large influx of sediment due to an
earthen berm failure in 1995. Fish communities on this creek are still lacking within the ski resort,
however macroinvertebrates are common.
Changes in soil cover, BMP effectiveness, and riparian condition are reflected in improved water
quality. The drought beginning in 1987 lasted until 1994, with a brief reprieve in 1993. Nutrients and
sediment would have been expected to increase to previous levels with the return of greater than average
runoff after 1994. However, this was not the case. Figure 1 shows how nutrient and sediment loads
measured from 1992 through 1997 decline proportionally with flow. It also illustrates the magnitude of
change over the period-of-record. Suspended sediment loads dropped most dramatically during non-
drought years, from a high of 1074 T ha"1 yr"1 in 1983 to a low of 26.8 T ha"1 yr"1 in 1996. Total N and P
loads continue to decrease, with lows of x and y recorded. Annual average nutrient and sediment
concentrations are also declining. Nitrogen was below the state standard in 2 of 6 years, while suspended
sediment was always less (see Table 1). Total P values do not meet state standards at this site, nor at any
other comparable sites in undeveloped watersheds. The regional water board is aware of this result, and
will be reviewing data to revise the total P standards for all tributaries within the Lake Tahoe Basin. It is
relevant to note that total P concentrations once averaged above 0.178 mg/L and is currently below 0.040
mg/L. SRP remains fairly constant throughout the period, with fluctuations probably limited more by
dissolution in flow than in total P within the stream. Another constituent of note is turbidity. This measure
of water clarity has improved 90%, averaging less than 2 ntu in each of the last three years following
annual averages of 32 ntu in 1981 and 1985.
These preliminary results from the new monitoring program appear to be promising. Land
management practices at Heavenly have improved over the last decade for a variety of reasons, not the
least of which include new products, technologies, education, and commitment. The first steps have been
taken, and we hope to see even greater improvement a decade from now. The broader perspective on
processes is beginning to cast a new light on relationships among ecosystem elements. The Forest
Service, Heavenly, and their other partners are learning so many new things that adaptive management is
no longer a goal but reality.
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Providing Feedback for Adaptive Management
Learning from our successes and failures sounds trite, however, monitoring emphasizes these lessons.
We are probably most limited by the amount of time we can spend observing things. Almost
instantaneous lessons can be learned during a significant event, such as a thundershower that causes
runoff to breach a waterbar. Some lessons are apparent after a season's worth of observation, such as seed
germination success at a revegetated site. Still other lessons take longer periods of time to understand,
such as plant succession and resulting impacts on water quality.
The temporal scales involved in these lessons affect the forms of feedback, and ultimately adaptive
management, that is achieved. Fixing a breached waterbar may require verbally notifying a few people,
and expecting a result in a matter of minutes or hours. Seed germination results over one season may be
documented the first year and for several subsequent years, affecting revegetation projects for years to
come.
Written reports provide everyone with the same information, improving group understanding and
communication. The annual report provides a review opportunity for those conducting and using the
monitoring results. Lessons shared can improve current and future planning efforts by demonstrating
effective management tools and positive project outcomes. Reports also serve as a tracking device for
identifying needed projects and highlighting accomplishments. Whether informally or formally noted,
monitoring feedback can be used to adapt management practices over short or long periods. A critical step
in the feedback loop is educating others about the things we have learned. This education phase is often a
disconnect for many monitoring programs, particularly if the results go unrecognized by those for whom
they are intended to help. One reason this monitoring program has been so successful is due to a strong
and committed partnership.
The Benefits of Partnership
Heavenly recognized the benefits of establishing partnerships with interested groups early in the
planning process. Through mutual agreement, committees were formed tapping resources from many
other agencies. The collaborative work of many groups including regulatory, governmental agencies, and
universities provides a wide array of knowledge from which to make the best decisions possible.
Cooperators receive a copy of the reports and meetings are held to discuss the issues presented. The five-
year review period will occur in 2000 with the report analyzing changes in condition and trend throughout
the resort. This review will offer the group a chance to evaluate the success of existing programs and
rectify any shortcomings or redundancy.
A major beneficial effect of this partnership has been an improved relationship between Heavenly,
various agencies, and the public. Frequent cooperator meetings and project site visits have fostered
significant beneficial effects. There is greater respect and understanding among Heavenly's employees,
various agency specialists, and the public. Heavenly's employees have higher awareness of environmental
protection and land management, with particular regard for how their specific job duties affects such
issues. The role of regulator is becoming easier since everyone has a vested interest in improve manage-
ment outcomes. Removing the adversarial relationships has resulted in more open and productive discus-
sions about problems that arise. Better solutions are being reached through consensus among trusting
participants. Ultimately, the benefits of partnership are improving everything from inter-personal
communication to ecosystem condition. I'm proud to work with such a dedicated group of people.
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Literature Cited
Byron, E. R. and C. R. Goldman. 1989. Land-use and water quality in tributary streams of Lake Tahoe,
California-Nevada. Journal of Environmental Quality 18:84-88.
Goldman, C. R. and E. R. Byron. 1986. Changing water quality at Lake Tahoe: the first five years of the
Lake Tahoe Interagency Monitoring Program. University of California, Davis, CA. 12 p.
Heavenly Ski Resort. 1996. Heavenly Ski Resort Master Plan. South Lake Tahoe, CA. 500 p.
Pfankuch, D. J. 1978. Stream reach inventory and channel stability evaluation. USDA Forest Service,
Northern Region. Missoula, MT. 1978-797-059/31. 26 p.
Rosgen, D. L. 1996. Applied river morphology. Wildland Hydrology, Pagosa Springs, CO. 37 Ip.
USDA Forest Service. 1996. Final Heavenly Ski Resort Master Plan EIR/EIS/EIS. South Lake Tahoe,
CA. 4000 p.
USDA Forest Service. 1996b. Stream channel inventory protocols for Region 5. Version 3.4. USDA
Forest Service, Pacific Southwest Region. San Francisco, CA. 80 p.
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Total Nitrogen & Flow 1981-1997
Heavenly Valley Creek
1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
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0
£ e
1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
I I Flow I I Constituent
Figure 1. Nutrient and sediment loads relative to flow, as measured at Heavenly Valley Creek
from 1981 to 1997.
III-583
-------
Table 1. Annual Average Nutrient And Sediment Concentrations Measured at
Heavenly Valley Creek from 1981 to 1997
Year
State Standard
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
Flow
rfc
CIS
—
3.07
2.85
4.35
0.50
2.57
0.31
0.32
0.13
0.36
0.13
0.85
4.5
1.5
5.1
4.63
5.6
0.77
Total N
0.19
0.11
0.14
0.14
0.36
0.48
0.52
0.50
0.48
0.35
0.42
0.15
0.282
0.363
0.165
0.210
0.144
0.214
Annual t
Total P
m,
i
0.015
0.021
0.032
0.040
0.020
0.052
0.120
0.142
0.168
0.032
0.023
0.078
0.072
0.178
0.060
0.032
0.042
0.047
\verages
Soluble
Reactive P
rr/l
g/i
—
0.007
0.012
0.018
0.010
0.011
0.019
0.030
0.032
—
—
0.018
0.023
0.028
0.011
0.004
0.009
0.009
Suspended
Sediment
60
139
54
138
96
65
83
103
2
3
—
37
55
14
3
12
6
7
Values in bold type exceed the annual average state standard.
III-584
-------
Monitoring Ground Water Quality
A. Roger Anzzolin, Environmental Engineer/Hydrogeologist,
U.S. Environmental Protection Agency
Office of Ground Water and Drinking Water
Mary Siedlecki, Research Geochemist
Research Triangle Institute
Ground water is a vital national resource. In many parts of the Nation, ground water serves as the
only reliable source of drinking and farm and livestock irrigation water. Unfortunately, this vital resource
is vulnerable to contamination and ground water contamination problems are being reported throughout
the country.
To ascertain the extent to which our Nation's ground water resources have been impacted by
contamination problems, Section 106(e) of the Clean Water Act (CWA) requests that each State monitor
ground water quality and report the findings to Congress in their biennial 305(b) State Water Quality
Reports. Recognizing that an accurate representation of our Nation's ambient ground water quality
condition is a complex problem, the U.S. Environmental Protection Agency (EPA), in partnership with
interested States, critiqued the existing guidelines and proposed changes to the guidelines that would
enhance assessment of ground water quality within the 305(b) program. The new guidelines were
introduced to States for the 1996 305(b) reporting cycle.
The most significant change in the 1996 guidelines was the request that States report ground water
quality data for selected aquifers or hydrogeologic settings (e.g., watersheds) within the State. The focus
on specific aquifers or hydrogeologic settings provides for a more quantitative assessment of ground
water quality than was possible prior to 1996.
State response to the new ground water guidelines was excellent. Thirty-three States reported ground
water quality data. In total, data were reported for 162 aquifers and other hydrogeologic settings. States
used data from ambient monitoring networks, public water supply systems (PWS), private and
unregulated wells, and special studies. Finished water quality data from PWS were the most frequently
used source of data. Ambient monitoring networks and untreated water quality data from PWS and
private and unregulated wells were the next most frequently used sources of data.
Ground water data reported by States in their 1996 305(b) State Water Quality Reports reflected the
diversity of our Nation's individual ground water management programs. This diversity presents a
challenge in assessing ground water quality on a national basis. For preparation of the National Water
Quality Inventory 1996 Report to Congress, ground water quality assessments were performed using
comparable data groupings. Data most closely approximating actual ground water quality conditions (e.g,
untreated ground water) were given special consideration in the assessments.
Guideline Development
In 1972, Congress enacted the first comprehensive national clean water legislation in response to
growing public health concern over serious and widespread water pollution. The CWA is the primary
Federal law that protects the health of our Nation's waters, including lakes, rivers, and coastal areas.
Beginning in 1974, Section 106(e) of the CWA required States to carry out as part of their programs:
(1) the establishment and operation of appropriate devices, methods, systems, and procedures
necessary to monitor, and to compile and analyze data (including classification according to
ffl-585
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eutrophic condition), the quality of navigable waters and to the extent practicable, ground waters
including biological monitoring; and provision for annually updating such data and including it
in the report required under Section 305 of this Act.
Monitoring data gathered by States under Section 106(e) of the CWA were submitted to the
Administrator in biennial State Water Quality Reports as required under Section 305(b) of this same act.
The first such report was submitted to the Administrator in 1974. This report consisted primarily of data
derived from water quality databases in existence at the time (e.g., U.S. EPA STORET). Data evaluation
was negligible and ground water quality data were absent from the report. Similar reports were produced
for 1975 and 1976. Although a hiatus in report production occurred between 1976 and 1982, reports have
been generated biennially since 1982.
Ground water was included in the biennial reports for the first time in 1982. It was introduced as one
of "two additional water quality-related issues most commonly cited by States" as of concern. The half
page of text devoted to these additional issues pointed out that ground water contamination was widely
reported in over half the States.
Given that States were expressing concern over ground water related issues, a set of guidelines was
developed to assist States in reporting ground water information. The first set of guidelines was
developed for the 1986 biennial reporting cycle. In these guidelines, States were provided with minimum
ground water reporting criteria, which focused on identifying the major sources of contamination
impacting ground water resources. The 1986 biennial report was formatted into four sections: ground
water use in the United States, major sources of ground water contamination and contaminants of
concern, ongoing State programs for ground water protection, and current Federal initiatives designed to
assist States in developing their ground water protection programs. All subsequent biennial reports
contain these same sections.
For the 1988 biennial report, several States broadly described their ground water resources as being
"excellent" to "good" in quality. Based on this information, it was concluded that the Nation's ground
water resources were "quite good." Similar conclusions were drawn in subsequent biennial reports.
Broad generalizations concerning the quality of State ground water resources failed to provide either
a complete or an accurate representation of ambient ground water conditions (i.e., background or baseline
water quality conditions). However, assessing the quality of our Nation's ground water resources is no
easy task. An accurate and representative assessment of ambient ground water conditions ideally requires
a well-planned and well-executed monitoring plan. Such plans are expensive and may not be compatible
with State administrative, technical, and programmatic initiatives. As a consequence, EPA, in partnership
with interested States, critiqued the existing guidelines and proposed changes to the guidelines that
would improve assessment of ground water quality within the 305(b) program.
The most significant change for 1996 was the request that States provide ground water information
for selected aquifers or hydrogeologic settings (e.g., watersheds) within the State. The focus on specific
aquifers and hydrogeologic settings provides for a more quantitative assessment of ground water quality
than was possible in previous reporting cycles. The new guidelines were introduced to States as part of
the 1996 305(b) reporting cycle.
1996 Guidelines for Assessing Ground Water Quality
To ensure consistency with previous 305(b) reporting cycles, the 1996 Guidelines incorporated all of
the components requested during previous 305(b) reporting cycles. Specifically, the 1996 Guidelines
continued to request an overview of ground water contamination sources and a description of State
programs dedicated to ground water protection. In addition, the 1996 Guidelines contained two new table
formats encouraging States to report data for selected aquifers or hydrogeologic settings within the State.
m-586
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States reported on the type and number of contamination sites per aquifer or hydrogeologic setting
having the potential to adversely impact ground water quality using Table 1. Specifically, States were
asked to identify the type and number of contaminant source(s) present in the reporting area (e.g., NPL,
LUST, RCRA, Superfund), the number of sites that are listed or have confirmed releases, and the number
of sites with confirmed ground water contamination. The data reported in Table 1 provide a measure of
aquifer vulnerability.
States reported ground water monitoring data for selected aquifers or hydrogeologic settings using
Table 2. States compared quantitative ground water monitoring data to water quality standards.
Depending upon the results of the comparison, the data were summarized into major categories,
including "not detected at or above the method detection limit (MDL)," "exceeding the MDL but less
than the maximum contaminant level (MCL) defined under the Safe Drinking Water Act," and
"exceeding the MCL." This type of data provides a measure of the condition of the aquifer.
Because the concept of reporting quantitative data for specific aquifers within a State was new, EPA
recommended that ground water quality be assessed incrementally. For 1996, States were encouraged to
set a priority for reporting results for areas of greatest ground water demand and/or vulnerability. With
each subsequent 305(b) reporting cycle, States were encouraged to continue the process through the
assessment of progressing numbers of aquifers or hydrogeologic settings. In this way, an increasingly
greater area of each State will be assessed.
1996 305(b) Results as Reported Using Table 2
Reporting ground water quality data on an aquifer-specific basis was new to the 305(b) program in
1996. Due to differences in State programs and priorities, it was anticipated that there would be wide
variation in reporting style among the States. Still, State response to the new Guidelines was excellent
and it was evident that States welcomed the changes made in the program.
Diversity in Reporting Units
Thirty-three States reported data summarizing ground water quality. In total, data were reported for
162 specific aquifers and other hydrogeologic settings. States that were unable to report ground water
data for specific aquifers assessed ground water quality using a number of different hydrogeologic
settings or "reporting units," including statewide summaries, counties, watersheds, basins, and special
monitoring areas within a State that stood out due to potential vulnerability. Figure 1 presents an
overview of the States that were able to provide ground water quality data for specific or "differentiated
hydrogeologic units" within the State.
Ground Water Quality Data Sources
Data collection and organization vary among the States. Furthermore, a single data source for
assessing ground water quality did not exist for purposes of the 1996 biennial report. EPA suggested
several types of data that could be used for assessment purposes (e.g., ambient ground water monitoring
data, untreated water collected from private or unregulated wells, untreated water collected from public
water supply wells, and special studies).
States were encouraged to use available data that best reflected the quality of the resource.
Depending upon data availability and the judgment of the ground water professionals, one or multiple
sources of data were used in the assessments. The majority of the States opted to use multiple sources of
m-587
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data. As shown in Figure 2, States used data collected from ambient monitoring networks, public water
supply systems, private and unregulated wells, facility monitoring wells, and special studies.
Finished water quality data from public water supply systems were the most frequently used source
of data (Figure 3). Ambient monitoring networks and untreated water quality data from private and
unregulated wells were the next most frequently used sources of data.
Parameter Groups/Analytes
The primary basis for assessing ground water quality using Table 2 is the comparison of chemical
concentrations measured in ground water to water quality standards. For 1996, EPA suggested that States
consider using MCLs defined under the Safe Drinking Water Act (SDWA). In general, most States used
the MCL concentrations for comparison purposes. Exceptions occurred when State-specific standards
were available.
It was not possible for States to sample and analyze ground water for every known constituent. For
ease of reporting, EPA suggested that the ground water quality data be summarized into parameter groups
(e.g., VOCs, SVOCs, and nitrate). These three groups were recommended because they are generally
indicative of contamination originating as a result of human activities. States were also encouraged to
report data for any other constituents of interest.
Nationally, more States reported data for volatile organic compounds (VOCs), semivolatile organic
compounds (SVOCs), nitrates, and metals than any other constituent or group of constituents. Parameter
groups and individual constituents identified by States in their 1996 305(b) reports are summarized in
Table 3. As shown, States reported data for a wide variety of constituents. Organic as well as inorganic
and microbiai constituents were included in the ground water assessments depending upon State interests
and priorities. Although the greatest quantity of data was reported to nitrate and VOCs, it was clear that
States were also concerned with SVOCs, pesticides, metals, and bacteria.
Assessment of Ground Water Quality
Ground water quality data reported by States in 1996 represented difference sources, often with
different monitoring purposes. As a consequence, national comparisons were not appropriate. Rather,
ground water quality comparisons were performed using comparable data groupings. Data most closely
approximating actual ground water quality conditions (e.g., untreated ground water) were given special
consideration in the assessments.
The 1996 biennial report presented tabulations of data. Table 4 presents an example of ground water
quality data reported for nitrate. As shown, 15 States reported nitrate data for ambient monitoring
networks. Nitrate was measured at concentrations exceeding the MCL of 10 mg/L in 8 of the 15 States
for a total of 26 units and 267 wells impacted by nitrate. Thus, approximately 50% of the reporting States
indicated elevated levels of nitrate in ground water collected from ambient monitoring networks. This
percentage was even higher for States reporting data for untreated water from PWS systems and from
private/unregulated wells (i.e., nitrate levels exceeding the MCL were reported by five out of seven
States for untreated water from PWS and by nine out of ten States for untreated water from
private/unregulated wells).
Conclusion
The most significant change in the assessment of ground water quality within the 305(b) program
was the focus on assessing ground water quality for specific aquifers and/or hydrogeologic settings. The
EI-588
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positive response from States indicated that they welcomed the changes made in 1996 and were
responding by developing and implementing plans to report more aquifer-specific information in the
future. Still, ground water data reported in 1996 were too diverse and too sparse to be used to
characterize ground water quality nationally for the 1996 305(b) reporting cycle. Despite this
shortcoming, a framework for reporting ambient ground water quality data on a biennial basis has been
developed under the 305(b) program, and it is expected that data diversity will decrease with each
successive 305(b) cycle as the direction and focus of the program become clearer to both States and EPA.
Thus, with additional 305(b) reporting cycles, the data set will achieve the necessary maturity to
characterize our Nation's ground water resources in a meaningful fashion.
Next Steps
With a framework for reporting ground water quality data on a national basis in place, the next step
is to begin accumulating sufficient data to characterize the condition of our Nation's ground water
resources. States have begun reporting data for the 1998 305(b) cycle. Although States are increasing
their individual coverages by assessing aquifers not previously assessed during the 1996 cycle, national
coverage will still not be achieved in 1998. However, based on their State Water Quality Reports, it is
evident that some States have achieved a level of sophistication to allow the assessment of ground water
quality on a statewide basis.
Idaho is one State that currently uses a geographic information system (GIS) dataset and displays the
data spatially. The State of Idaho supplied coverages in the form of hydrogeologic subareas, major
aquifer flow systems, and statewide monitoring well locations. Nitrate concentrations measured in 1995
and 1996 in monitoring wells constituting the State monitoring network were also supplied by Idaho. The
concentration measured in each of the wells is presented graphically in Figure 4. This same information
is summarized in Figure 5 according to the number of wells in an aquifer having a certain percentage of
nitrate concentrations exceeding background levels.
As shown in the attached set of figures, it is possible to provide ground water layers on a
state-by-state basis given that the individual States can provide both the aquifer coverage and associated
data. The 1998 Report to Congress will highlight and illustrate individual State efforts.
With individual States leading the way, it is clear the meaningful and representative assessment of
ground water quality can be achieved within the 305(b) program in the near future.
m-589
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DC
^Hawaii
•o American Samoa
Virgin Islands
<^ Puerto Rico
1996 305(b) Ground Water Report Not Provided
Differentiated Into Hydrogeologic Units Within the State
Not Differentiated, Reported on a Statewide Basis
Tabulated Ground Water Monitoring Data Not Provided
Figure 1. Summary of reporting units.
American Samoa
Puerto Rico
A Finished Water from PWS Wells
• Untreated Water from PWS Wells
• Ambient Monitoring Networks
• Other Ground Water Monitoring Data
• Untreated Water from Private or Unregulated Wells
•k Special Studies
T Facility Monitoring Wells
SSI 1996 305(b) Ground Water Report Not Provided
IB Tabulated Ground Water Monitoring Data Not Provided
Figure 2. Sources of ground water data.
% Total
Ambient Monitoring Network
Untreated Water from PWS
Untreated Water from Private
or Unregulated Wells
Finished Water Quality Data
from PWS Wells
Special Studies
Not Specified
10 20 30 40 50 60
Percentage of States
Note: Percentages based on a total of 33 States submitting data. Some States utilized multiple
data sources.
Figure 3. Aquifer monitoring data.
m-590
-------
State of Idaho
Figure 4. Nitrate concentrations.
Nitrate ConcentraUons
Statewide Monitoring
Network 1995 & 1996
Legend
JED
Figure 5. Assessment of nitrate concentrations.
m-591
-------
Aquifer
Description
Aquifer Setting
Table 1. Ground Water Contamination Summary
Counties (optional)
Longitude/Latitude
(optional)
Data Reporting Period
SourceType
NPL
CERCLIS
(non-NPL)
DOD/DOE
LUST
RCRA Corrective
Action
Underground
Injection
State Sites
Nonpoint
Sources"
Other (specify)
Totals m
' ~ • '
Present in
reporting area
; (circle) ^
Yes/No
Yes/No
Yes/No
Yes/No
Yes/No
Yes/No
Yes/No
Yes/No
<".
Number of
sites in
• area
Number of sites
that are listed
and/or have .
.confirmed
releases
Number with
confirmed
ground water
contamination
Contaminants'61
Number of site
investigations
(optional)
Number of *
sites that have
been stabilized
or have had
the source
removed :
(optional) .
. Number of
sites with
corrective
action plans
(optional)
Number of
sites with
active
remediation
(optional)
Number of
sites with
cleanup
completed
(optional)
H
to
NPL
CERCLIS (non-NPL)
DOE
DOD
LUST
RCRA
National Priority List
Comprehensive Environmental Response, Compensation, and Liability Information System
Department of Energy
Department of Defense
Leaking Underground Storage Tanks
Resource Conservation and Recovery Act
-------
Aquifer
Description
Aquifer Setting
Table 2. Aquifer Monitoring Data
Counties (optional)
Longitude/Latitude
(optional)
Data Reporting Period
... • :.-: ' '
Monitoring
Data Type
Ambient
Monitoring
Network
(Optional)
Raw Water
Quality Data
from Public
Water Supply
Wells
Finished
Water
Quality Data
from Public
Water Supply
Wells
Total No. of
Wells Used
in the •
Assessment ^
•-': r .
Parameter
, Groups
VOC
SOC
NO3
Other"5'
VOC
SOC
NO3
Other (15>
VOC
SOC
NO3
Other "S)
Number of Wells
No detections of
parameters above
MDLsor
background levels
NOW
Number of
wells in
sensttiveor
vulnerable
areas*
(optional}^
No detections of parameters
above MDLs or background
levels and/or located in
areas that are sensitive or
vulnerable and nitrate
concentrations range from
background levels to less
than or equal to 5 mg/L.
ND/Nttrate
< 5mg/Lw
Number of
wells in
sensitive or
vulnerable
areas
(optional) m
Parameters
are detected at
concentrations
exceeding the
MDLbutare
less than or
equal, to the
MCLs and/or
nitrate ranges
from greater
than 5 to less
than or equal
to10mg/L(10)
Parameters
are detected at
concentrations
exceeding the
MCLs1'"
-Removed
from
service"2*
Special
Treatment03*
Background
parameters
exceed
MCLs <">
-------
Table 3. Summary of Parameter Groups/Analytes Reported by States in 1996
Nitrate
voc
SVOC
Bacteria
Pesticides
Radioactivity
Inorganics
^^
Arsenic
' Antimcny '
Baium
Beryllium
Gxrrium '
Chromium
Ccfcdt
Copper *
Iron
Lead
Manganese
Mercury '
Molybdenum
Nickel
Selenium
Silver
Strontium
thdlium
Vcncdum
Zinc
y
~^
Alkdinity* , ' '
(Alunrinufr) * ,
Biocrbonote
Boron
BromlcB" ,<* ' «>
Cddum
CNaicte
> Fluoricfe .4 'i ,,
Hachess,
Lilhium iv*
McgTesium *
PotoBsiumT
Silica ' ' s
"• QrvH i \m - ^ >•
OULI Ul 1 1 ri ^
, SpedficConcuctivity
Totd Dissolved Sdicfe
, f
Table 4. Nitrates
Monitoring Type
Ambient
Monitoring
Network
Untreated Water
from PWS
Untreated Water
from Private/
Unregulated
Wells
Finished Water
from PWS
Special Studies
States
Reporting
15
7
10
18
2
States
Reporting
MCL
Exceedances
8
5
9
11
2
Units
Impacted by
MCL '
Exceedances
26
5
10
18
4
*f t,
Wells ,
Impacted by*
MCL
Exceedances
267
85
2,233
230
309
Highest
Number of
Wells that
Exceeded the
»MCL within
a Single Unit
81 out of 681
38 out of 346
2,000 out of
250,000
101 out of
2,806
288 out of
9,000
* Average
Number of *
, Wells that^
Exceeded the
MCL within
a Single Unit
10
17
23
13
No
meaningful
average
MCL = Maximum contaminant level.
PWS = Public water supply.
IE-594
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Developing a Multi-Agency 305(b) Monitoring
Program for the Coastal Waters of Alabama
Kevin Summers
U.S. Environmental Protection Agency
Gulf Ecology Division, Gulf Breeze, FL
John Carlton
Alabama Department of Environmental Management
Field Operations Division, Mobile, AL
Steve Heath
Alabama Department of Conservation and Natural Resources, Mobile, AL
Abstract
"How can you plan responsibly for your future if you do not know where you are in the present?"
With the ability of many federal agencies to maintain long-term coastal monitoring in jeopardy due
to shrinking budgets, many states are beginning to re-examine their coastal and freshwater monitoring
programs. This re-examination focuses primarily on the potential to design or re-design programs to
attain multiple objectives through multi-agency interactions. This presentation documents the efforts of
Alabama resource agencies to incorporate the monitoring activities of several freshwater and coastal state
agencies and private industries into a coordinated, comprehensive water quality monitoring program. A
key element of the comprehensive monitoring plan includes the use of biological indicators in addition to
the tradition physical and chemical variables measured in the past. A key part of this effort involved the
interactions between state and federal agencies, their interactions with academic researchers and the
public to draw upon as large a knowledge base as possible. The process by which a multi-faceted, multi-
objective comprehensive monitoring plan for Alabama waters was developed and implemented is
described.
Introduction
Whether you live or visit coastal Alabama, you are stuck by the high quality of life that exists here.
White sand, clean water, a multitude of wildlife, and affordable and available land all combine to make
this the fastest growing area in Alabama. With this growth comes increased pressures on local natural
resources that have historically played an important role in the economic health of the area and of the
State. Minerals, natural gas, timber, navigable waterways, and abundant viable habitats supporting the
many commercial and recreational fisheries and shellfisheries all provide an important economic base for
all of Alabama.
Accompanying this rapid increase in population is a growing public perception that the environment
of coastal Alabama is threatened by the lack of a consistent, coordinated, management effort. Warning
signs include: fish kills, shellfish closures, reduction in shrimp and fish harvests, continued losses of
wetland acreage, large scale reductions in the biodiversity of the Mobile Bay watershed and the increased
areal extent of summer hypoxia (i.e., low dissolved oxygen zones) where few if any organisms can
survive. A comprehensive, scientific assessment of these problems is not possible as the state of Alabama
does not utilize an ecosystem-based monitoring approach to detect changes in the natural environment.
Ecosystem-based approaches to monitoring environmental "health" or condition are designed to assess
m-595
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not only site-specific threats to the environment but also more subtle environmental changes resulted in
the cumulative effects of both man-made and natural processes through a watershed. This approach is
being successfully utilized in other areas of the country and is currently being considered by many Gulf
states (Grumbine 1994, McKenna 1994, Axelrad 1995, Cross 1996). While many characterization studies
have been conducted in coastal Alabama waters, most have been compliance-driven and have been
designed specifically to meet the requirement of regulatory agencies. These studies often have limited
sampling periods, use different sampling protocols, and analyze and record data with different methods
and formats. A more comprehensive and integrated approach to monitoring that focuses upon interagency
cooperation is necessary before we can better understand how coastal Alabama water function under
natural conditions or respond to man-made stressors.
Realizing the limitations of the current and historic monitoring efforts and the need for a
comprehensive, integrated, ecosystem-based plan to serve as a foundation for more effective coastal
management efforts, a group of scientists, planners, and engineers representing academia, government,
and the private sector met to list criteria for the development of a cooperative regional plan. A
representative "Strategy Subcommittee" of this group was formed to incorporate the results of the
meeting into a final working strategy. This manuscript is a result of their efforts and attempts to address
the pressures that an expanding population places on an ecosystem and to better utilize the diminishing
flow of government funding allocated to manage our natural resources. This strategy incorporates both
wide-scale baseline assessments of ecological condition, site-specific issues related to human activities
and their influence on coastal waters, and enhanced cooperation and coordination of state agencies
activities. The strategy is meant to be comprehensive in its approach to the issues, enable coastal
managers to identify accurately long- and short-term environmental trends, and maintain the flexibility
necessary to address future environmental issues.
Overall Program Objective(s)
The overall objective for the comprehensive monitoring plan is to: Assess the "health" or condition
of the coastal waters of Alabama and track changes in that condition through time. As it is obvious that
this statement has a different meaning for different individuals, most of the discussion can be reduced to:
(1) A baseline of condition of all coastal waters, (2) A baseline of condition for selected coastal waters,
and/or (3) paired sites of affected and unaffected locations within coastal waters.
It is clear that any monitoring strategy will have to address both wide-scale baseline assessment of
condition and site-specific issues regarding anthropogenic influences on coastal waters. The Alabama
Monitoring and Assessment program (ALAMAP) is designed to incorporate all of these issues through a
multi-tiered design that addresses baseline ecosystem-level conditions, long-term trends, and hypothe-
sized environmental problems; and yet, remains flexible enough to be useful in addressing future
problems.
Assessment Questions
The selection of assessment questions is one of the most important activities in the design of a
comprehensive monitoring plan. This list of questions becomes the roadmap for the design of the spatial
and temporal attributes of the plan as well as the selection of appropriate indicators. Unlike objectives,
which can include intangible concepts, assessment questions must be constructed to represent measurable
quantities (e.g., concentrations) or, at least, represent second-order measurable quantities (e.g., diversity).
In August 1996, a workshop was held in Fairhope, Alabama to ascertain the assessment questions an
Alabama Monitoring and Assessment Program would have to address. After several presentations
regarding the development of these types of questions in other programs, workshop members split into
m-596
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two sub-groups—ecosystem and site-specific—to discuss the assessment question pertinent to
ALAMAP. While many of the questions created by the two sub-groups were similar, three points became
clear. (1) The site-specific group wanted the flexibility to address numerous problems as they arose;
therefore, a fixed station design did not seem appropriate. (2) However, this group wanted a baseline of
information regarding all Alabama coastal waters (and sub-systems) that could be used for comparison to
the problematic sites. (3) Like the site-specific group's second need, the ecosystem group desired a
broad-scale monitoring program that would characterize the "coastal waters of Alabama" rather than just
subregions or "hot spots" where problems occurred. While the final lists of questions from each group
are important to ascertain the details of the design and the appropriate indicators, the primary finding of
the workshop was that the ALAMAP design must be flexible and address overall condition of Alabama
coastal waters as well as provide specific monitoring information for observed or hypothesized
environmental problems.
Conceptual Model
Developing a conceptual model is an important step in constructing a monitoring plan in that it
represents our present understanding of the manner in which Alabama coastal ecosystems function, the
interaction of its components, and the potential effects of anthropogenic inputs to the system (e.g.,
effluents, atmospheric deposition) or outputs from the system (e.g., harvest, public recreation). To
construct a conceptual model one says, "This is how I think my system operates." This process may be
based on pure logic or based on experience and available data, but it represents our interpretation of the
ecosystem to be monitored. Very often a conceptual model is depicted by a diagrammatic model such as
those shown in Figure 1 and can vary greatly in the amount of detail depicted in their structures. All
conceptual models are simplifications and idealizations; yet we accept these models as useful because
they adequately express the most important aspects of the system with regard to the objectives of our
monitoring plan and the assessment questions being asked.
Since ecological systems comprise many components that are highly interactive, the reduction of the
number of components is necessary and by definition part of developing a conceptual model. The
complexity of real world systems is usually simplified by the aggregation of processes and components
that are similar into functional groups such as trophic levels, particle size, functional "guilds" and so
forth. This consolidation is often a major factor involved in the construction of a conceptual model. The
conceptual model constructed to represent Alabama coastal waters is shown in Figure 1.
Development and Selection of Indicators
This section provides an overview of a general strategy for indicator development and selection for a
comprehensive monitoring program. The overall process of indicator development and selection consists
of six phases:
(1) Identify environmental values, apparent stressors, and assessment endpoints;
(2) Develop a set of candidate indicators that are linked to the identified endpoints and are expected
to be responsive to stressors;
(3) Screen the candidate indicators to identify those with reasonably well established databases,
methods, and responsiveness to be further evaluated as research indicators;
(4) Quantify the expected performance of research indicators to identify developmental indicators;
(5) Quantify the performance of developmental indicators on appropriate geographic scales to select
core monitoring indicators; and
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(6) Re-evaluate and modify the set of core monitoring indicators as necessary.
The first two phases of indicator development and selection process are meant to generate ideas for
endpoints and indicators. The processes used in these phases should therefore encourage broad-scale,
lateral thinking with the focus on breadth rather than depth of coverage. Phase 1 (identifying
environmental values, potential stressors and endpoints) requires a broad perspective on both desired
ecosystem attributes (as expressed by resource managers, scientists, private industry, legislators, and the
general public) and ecosystem stresses (which may occur on local to global spatial scales and over short-
to long-term temporal scales). Proper identification of assessment endpoints and questions requires well-
developed conceptual models of all important aspects of the ecosystem of concern (i.e., Alabama coastal
waters), to ensure that identified endpoints are connected to the current and anticipated stresses of
concern.
Phase 2 (identifying candidate indicators) similarly requires a broad sampling of scientific opinion,
through both detailed literature reviews and interactions with scientists and resource managers
conducting relevant research and monitoring. As this is a continuing process where scientific and
technological advances occur, new candidate indicators can be generated or existing indicators can be
improved.
The next three phases of indicator development and selection provide critical evaluation and iterative
filtering of the set of candidate indicators to obtain a defensible, practical set of core monitoring
indicators. Whereas Phases 1 and 2 are designed to include all possible relevant indicators, the next three
phases are designed to systematically exclude indicators that fail to satisfy specific criteria for adoption,
or are not amenable to complete evaluation.
The list of indicators should be as comprehensive as possible and a strawman list of candidate
indicators for monitoring Alabama coastal Waters is presented in Table 1. The process of testing and
prioritization of these candidate indicators is guided by a set of criteria for indicator selection coupled
with peer reviews of these decisions. The use of clearly defined criteria increases the objectivity,
consistency, and depth of indicator evaluations. Criteria for selection are shown in Table 2 and their
application should not be too restrictive at this stage. A list of indicators to be measured at all baseline
sites is shown in Table 1 as bolded entries. Indicators for the site-specific surveys will be specific to the
site and question being assessed and examples of these types of indicators are denoted as SS in Table 1.
Design of Spatial and Temporal Aspects of Monitoring Plan
The assessment question workshop resulted in the development of two primary approaches: (1) Site-
Specific and (2) Ecosystem. The site-specific approach requires a delineation of specific hypotheses to be
tested by the monitoring activity (e.g., a comparison of selected areas receiving anthropogenic impacts to
reference or unaffected sites). The ecosystem approach, if to be applicable to all coastal waters, requires
the probabilistic approach. Both approaches require that the boundaries of the monitored population (i.e.,
Alabama coastal waters) be determined, acceptable uncertainty criteria be ascertained, and the
appropriate design and reporting strata be determined.
The spatial and temporal aspects of a monitoring design are derived from the assessment questions
and the variation associated with the selected indicators. The ecosystem-level assessment questions
tended to call for monitoring results that apply to "all" Alabama coastal waters. A probabilistic design is
required to meet this need although thousands of probabilistic options are available. The site-specific
group's questions called for results that would differentiate among selected sites or test working
hypotheses. As a result, a set of judgmental sites would be required to address each hypothesis. Because
both forms of questions were posed then a multi-tier design needs to be incorporated to include aspects of
both approaches.
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Uncertainty criteria must be defined and agreed upon to select a monitoring design that has the
appropriate power to address the assessment questions. For example, one assessment criteria might be
that all status or "health" assessments have 95% confidence intervals of ±10% such that an assessment of
estuarine sediments with contaminant concentrations greater than criteria A would be X%±10% (e.g.,
35±10% of all Alabama coastal sediments). This type of uncertainty pertains to probabilistic statements.
Site-specific assessments also would require uncertainty criteria at primarily the level of discrimination
often referred to as a p-level. For example, the uncertainty level for discrimination between affected and
reference sites might be a 95% chance of discerning a difference if a difference exists between the sites
(i.e., p<0.05).
Appropriate design strata can include many approaches. Basically, a rule of thumb is that if you wish
to answer an assessment question with regard to a strata with the desired level of certainty then that strata
should be designed into the overall monitoring plan. However, if the strata simply represents geographic
units that you want information on (e.g., by habitat unit, use type, etc.) but do not care whether the design
certainty level is met, then the strata should not be incorporated into the monitoring design.
In general, the use of strata within a sampling design enhances the power to detect differences
because it optimizes the design based on the natural variability characteristics of what is being measured.
However, in broad scale-monitoring designs where many indicators are being utilized, what is optimal for
one indicator is often not optimal for another. In addition, to design a monitoring plan based on strata that
represents the entire resource (i.e., "all" Alabama coastal waters) requires that the physical distribution of
the selected strata be known and the variability of the indicators in question be known. Often this is not
the case. While much information is known concerning potential strata in Alabama waters, rarely can a
known distribution be determined for all strata variables without preliminary sampling.
Several options were discussed with regard to stratification for both site-specific and ecosystem-wide
monitoring. Potential site-specific strata included:
(1) Site-specific question (e.g., nutrient additions, contaminant additions, bacterial additions, etc.),
(2) Base geography (e.g., river mouths, upper, mid-, and lower Mobile Bay, Mississippi Sound, and
Perdido Bay)
(3) Bay spine (e.g., multiple sites along the geographic centerline or "spine" of Mobile Bay)
Potential ecosystem-wide strata included:
(1) Habitat Type (i.e., Marsh Type, Bottom Type, Vegetation Type)
(2) Classification Use Categories (e.g., Industrial, Shellfish Harvest, Recreational, Wildlife)
All of these strata represent reasonable approaches to developing a spatial sampling design. The key
to selecting the appropriate strata is a determination of the needs of the monitoring program, the avail-
ability of data on the distribution and boundaries of the strata, the availability of data on the spatial
variability of indicators of interest within the strata, and the ramifications of multiple strata on sampling
size (i.e., reduces sampling size for site-specific monitoring and increases sampling size for ecosystem-
wide sampling).
The actual placement of sites and the total number of sites is also based on the assessment questions.
Since many of these questions require assessments for "all" Alabama coastal waters then the sampling
design must be extrapolable and thus probabilistic in nature. This does not necessarily mean that the sites
are randomly placed, although that type of placement is one possibility. Probabilistic simply infers that
the sites are representative and not biased. If the sites can be placed judgmentally (i.e., based on
experience and knowledge) so that they are representative of selected strata (e.g., habitats, use zones),
then the requirement for a probabilistic nature for the design will be met.
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Designing temporal aspects of the sampling plan also relate directly to the initial set of assessment
questions. If the monitoring program is to fully characterize short-term status and trends, then monthly
sampling may be required. If the question lends itself to longer-term assessment of status and trends, then
seasonal or annual samples may be more appropriate. The choice of a temporal framework should not be
based on the fact that the values of the indicators change with time, but rather, what is the time scale of
interest. If the purpose of the monitoring program is to "understand" coastal phenomena in relation to
within-year changes associated with environmental forcing functions, then shorter-time scale sampling is
appropriate that will include monthly and/or seasonal variation. However, if the desire of the planning
group is to ascertain changes in overall status and trends over longer temporal scales (e.g., years), then
annual or semi-annual time scales tend to be more useful.
Many of the proposed indicators exhibit large intra-annual variability (i.e., they are seasonal) (Oviatt
and Nixon 1973, Jefferies and Terceiro 1985, Grassle et al. 1985, Holland et al. 1987). Generally,
monitoring programs do not have the monetary resources to characterize this variability or to assess
status in all seasons for "all" resources (i.e., all Alabama coastal waters). Therefore, sampling has often
been limited to a confined portion of the year (i.e., an index period) when indicators are expected to show
the greatest response to anthropogenic and climatic stress.
For most coastal ecosystems in the Northern hemisphere, mid-summer (July-August) is the period
when ecological responses to pollution exposure are likely to be most severe. During this period,
dissolved oxygen concentrations are most likely to approach stressful, low values (USEPA 1984, Officer
et al. 1984, Oviatt 1981). Moreover, the cycling and adverse effects of sediment contaminant exposure
are generally greatest at the low dilution flows and high temperatures that occur in mid-summer (Connell
and Miller 1984, Sprague 1985, Mayer et al. 1989).
Multi-Tier Monitoring Design
The assessment questions raised by the majority of the workshop participants require a base
generalized, probabilistic design as one element of ALAMAP. The overall design needs to include both
ecosystem-wide and site-specific elements collected during at least two index periods per year (spring
and late summer). The ecosystem-level monitoring design tier stratifies based on water quality use
categories with built-in design characteristics that would permit changing the strata to habitat-based after
six years of data collection. In each case, site placement is probabilistically-based and includes 59 sites
spread over nine water quality use areas sampled quarterly. Several of the water quality use areas that
have the same use have been combined to ensure and efficient characterization of condition. The
recommended approach for the annual baseline environmental characterization is shown in Figure 2.
The ecosystem-wide surveys are supplemented by two intensive surveys conducted biennially in two
regions of Alabama's coastal waters (Figures 3-8). The two regions include: (1) Mobile Bay proper
(Regions 1, 2, 3 and 4) and (2) the coastal areas outside Mobile Bay including Perdido Bay, Mobile
River, Tensaw River, Blakely River and Mississippi Sound (Regions 5, 6, 7, 8, and 9). The two-year
period conforms to the 305(b) reporting schedule and would permit two full data collections per 305(b)
cycle and will permit re-designs, if desired, on even numbered years from two-year to ten-year
increments. The intensive surveys will be performed in a late summer index period only.
The ecosystem-wide sampling is supplemented by site-specific surveys used to examine specific
environmental issues. This site-specific survey element is be based on impact and issue strata (i.e., what
types of anthropogenic effects are of interest and what specific geographic areas are desired to examine
whether management practices are successful). The design phase of this survey is dependent upon the
identification of sites of interest and the questions posed. Their timing would be irregular based upon the
timing of specific issues.
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The proposed comprehensive coastal monitoring design combines the strengths of probabilistic
sampling with hypothesis testing and permits:
(1) A baseline characterization of the ecological condition of all Alabama coastal waters (59 sites
samples quarterly);
(2) An intensive baseline characterization of each of six water quality use areas every other year
corresponding to the two-year cycle inherent in the 305(b) reporting requirements (approximately
30-40 sites per area);
(3) An indirect assessment of effects within selected key coastal habitats that can be determined
from the above baseline characterizations by sub-population estimation (number cannot be
determined until habitats are mapped after 6 years);
(4) A long-term assessment of seasonal dynamics in Alabama coastal waters and their trends (59
sites samples four times per year);
(5) An assessment of shrimp stocks to manage fisheries opening and closing dates (number of sites
and timing varies) that is incorporated into the annual monitoring design; and,
(6) A direct assessment of key anthropogenic issues, as determined by resource managers and the
public, to determine if management strategies are working at directed locations (Number of sites
and timing depends on question posed). These results can be compared to the overall baseline
assessment conducted by the ecosystem-wide surveys.
The two-year rotational cycle is depicted in Table 3 and allows ADEM to meet the requirements of
the 305(b) program for coastal waters. In addition, the first sampling cycle will permit the mapping of
bottom habitats such that the sampling stratification could be altered to represent habitats in the future
with the water quality use areas being determined by sub-population estimation. All ecosystem-wide
estimates for collected indicators will be estimated in terms of areal distribution with an uncertainty of
±10%.
Assessment of Quality Needs for Monitoring Results
Monitoring programs that involve multiple organizations and laboratories, as well as multiple
individuals in the field, frequently encounter problems in obtaining data that are comparable among the
many individuals and laboratories involved (Taylor 1978, 1985; Martin Marietta Environmental Systems
1987; NRC 1990). Such problems usually result because, in the haste to initiate data collection programs,
the participating organizations and their staffs are not adequately trained in applying standardized
collection methods, and the comparability of the laboratory processing methods and capabilities are not
evaluated (Taylor 1985).
The proposed comprehensive monitoring program for Alabama coastal waters should implement a
quality assurance program to ensure that the data produced are comparable with known and acceptable
quality. The program will consist of two distinct but related sets of activities: quality control and quality
assurance.
Quality control includes design, planning, and management actions to ensure that the types and
amounts of data are collected in a manner required to address the monitoring objectives. Examples of
some quality control activities that could be employed are the use of standardized sample collection and
processing protocols, and the requirement of specific levels of group training for field crews and
technicians who will collect and process samples. The goals of quality control procedures are to ensure
that:
m-6oi
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(1) Collection, processing, and analysis techniques are accomplished consistently and correctly
(2) The number of lost, damaged, and uncollected samples is minimized
(3) The integrity of the data record is maintained and documented from sample collection to entry
into the data record
(4) The data are comparable with similar data collected elsewhere
(5) The study results are reproducible.
Quality assurance activities should be implemented to quantify the effectiveness of the quality
control procedures. These activities ensure that measurement error and bias are identified, quantified, and
accounted for or eliminated (if practical). Quality assurance consists of both internal and external checks
including: repetitive measurements, internal test samples, interchange of technicians and equipment, use
of independent methods to verify findings, exchange of samples among laboratories, use of standard
reference materials, and audits (Taylor 1985, USEPA 1984).
The proposed monitoring program will implement a quality assurance/quality control program based
on measurement quality objectives based on estimates of achievable data quality associated with
attributes of any data. These attributes are: representativeness, completeness, comparability, accuracy
and precision.
Reporting
Reporting of the results of any monitoring program should be timely and portrayed in a way that is
useful to its audience (e.g., resource managers, decision makers, scientific community, the public). Every
effort should be made to report on the findings in written form within 6-12 months of a survey's
completion. Unfortunately, this is often where reporting issues end.
The most important aspect of reporting is generally ignored by most programs—that is the
development of an integrated information management system that makes the data readily available to
most potential users and acts as an archive. One of the primary assessment requirements listed in an
earlier section is the determination of trends. Trends determination requires consistent, long-term data
collection. Invariably, over the course of a long-term study, participants come and go and key
information concerning the available data (metadata); often even the data itself cannot be retrieved. Data
collectors are no longer available or can not be found, database constructors have moved on, program
planners are planning other programs. In short, no one remembers.
To avoid this all too common occurrence, the proposed monitoring program will develop a
standardized, integrated information management system with the personnel responsible for that system
involved in all early aspects of planning the program. A clear and concise set of responsibilities of this
system (including metadata) should be determined early in planning. A good rule of thumb in developing
such a system is called the twenty-year rule. In short, what information would you need in twenty years
to reconstruct earlier activities if no one were available to ask. That's the information that should be
contained in your long-term information management system.
Conclusions
ALAMAP represents an early adaptation by a state of probabislistic approaches to collect the
information necessary to meeting its 305(b) requirements. ALAMAP in 1997 expanded its probabilistic
sampling to include all freshwater resources in Alabama, except lakes. In 1998, the Gulf of Mexico
program initiated an effort to help the Gulf States adopt a core, comprehensive, integrated coastal
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monitoring approach that includes probabilistic sampling as one of its elements. Early discussions by this
group have praised Alabama's efforts and described their program as a framework upon which a Gulf-
wide consistent monitoring program could be based. In 1998, the Mobile Bay National Estuary Program
adopted ALAMAP as its programmatic monitoring approach.
In 1996, EPA's 305(b) Working Group described probabilistic sampling approaches as an acceptable
approach for collecting environmental condition data and reporting 305(b) results. At present, twelve of
the 23 coastal states are adapting their coastal monitoring programs to utilize probabilistic surveys as an
element of their overall programs.
Literature Cited
Axelrad, D.A. 1995. Viewpoint: Sustainable development and ecosystem management. Florida
Department of Environmental Protection Newsletter, August, 1995. Tallahassee, FL.
Connell, D.W. and GJ. Miller. 1984. Chemistry and Ecotoxicology of Pollution. New York: John Wiley
and Sons.
Cross, J.N. 1996. Southern California Coastal Water Research Project. Annual Report 1994-1995.
Westminister, CA.
Grassle, J.F., J.P. Grassle, L.S. Brown Leger, R.F. Petrecca and N.J. Copely. 1985. Subtidal
macrobenthos of Narragansett Bay. Field and mesocosm studies of the effects of eutrophication and
organic input on benthic populations, pp. 421-434. In: J.S. Gray and M.E. Christiansen (eds.). Marine
Biology of Polar Regions and Effects of Stress on Marine Organisms. New York: John Wiley and
Sons.
Grumbine, R.E. 1994. What is ecosystem management? Conservation Biology 8:27-38.
Holland, A.F., A.T. Shaughnessy, and M.H. Hiegel. 1987. Long-term variation in mesohaline
Chesapeake Bay macrobenthos: Spatial and temporal patterns. Estuaries 10:227-245.
Jefferies, H.P. and M. Terceiro. 1985. Cycle of changing abundances in the fishes of Narragansett Bay
area. Mar. Ecol. Prog. Ser. 25: 239-244.
Martin Marietta Environmental Systems. 1987. Statistical and deliverable analytical support contract:
Final report. Prepared for the Chesapeake Bay Program Water Quality Data Analysis Working
Group.
Mayer, F.L., L.L. Marking, L.E. Pedigo and J.A. Brecken. 1989. Physiochemical factors affecting
toxicity: pH, salinity, and temperature, Part 1. Literature review. U.S. Environmental Protection
Agency, Office of Research and Development, Gulf Breeze Environmental Research Laboratory.
McKenna, J. 1995. Final report of the research sub-committee on ecosystem management. Florida
Department of Environmental Protection, Tallahassee, FL.
NRC. 1990. Managing Troubled Waters: The Role of Marine Environmental Monitoring. Washington,
DC: National Academy Press.
Officer, C.B., R.B. Biggs, J.L. Taft, L.E. Cronin, M.A. Tyler, and W.R. Boynton. 1984. Chesapeake Bay
anoxia: Origin, development, and significance. Science 223: 22-27.
Oviatt, C.A. 1981. Some aspects of water quality in and pollution sources to the Providence River.
Report for U.S. Environmental Protection Agency, Region I, September 1979-September 1980.
Oviatt, C.A. and S.W. Nixon. 1973. The demersal fish of Narragansett Bay: An analysis of community
structure, distribution, and abundance. Est. Coast. Mar. Sci. 1: 361-378.
Sprague, J.B. 1985. Factors that modify toxicity. pp. 124-163. In.: G.M. Rand and S.R. Petrocelli (eds.).
Fundamentals of Aquatic Toxicology: Methods and Applications. New York: Hemisphere
Publication Corp.
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Taylor, J.K. 1978. Importance of inter-calibration in marine analysis. Thai. Jugo. 14:221.
Taylor, J.K. 1985. Principles of quality assurance of chemical measurements. NBSIR 85-3105.
Gaithersburg, MD: National Bureau of Standards.
USEPA. 1984. Chesapeake Bay: A Framework for Action. Prepared for the U.S. Congress by the U.S.
Environmental Protection Agency, Region in, Philadelphia, PA.
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o\
o
Higher Trophic Levels
Suborganism
.Biochemical
.Stress
.Genetic
.Other
Organism
.Pathology
.Growth
.Reproduction
.Other
Population
•Size
•Biomass
.Recruitment
.Other
Primary Producers
•Phytoplankton
Aquatic Macrophytes
.Communities
.Composition
.Diversity
•Abundance
.Other
-x-
Dissolved Oxygen
.Water Column
.Sediment
Biotic Integrity
.Composition
.Abundance
.Health
Human Use
.Consumption
.Swimming
.Aesthetics
Figure 1. Conceptual model of estuarine ecosystem.
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Alabama Coastal Waters
15 Miles
97-5-096
Figure 2. Ecosystem-wide sampling site design for Alabama Monitoring and Assessment Program (ALAMAP).
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Alabama Coastal Waters
Region 1
Legend
B Trawl Sites
© Region 1 Sites
97-5-090
Figure 3. Intensive site survey to be conducted in even numbered years in Region 1 to
supplement ALAMAP base design.
m-607
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Alabama Coastal Waters
Region 2
Legend
B Trawl Sites
© Region 2 Sites
Figure 4. Intensive site survey to be conducted in even numbered years in Region 2
to supplement ALAMAP base design.
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o
VO
Alabama Coastal Waters
Region 3 & 4
Legend
(3 Trawl Sites
© Region 3 & 4 Sites
97-5-092
Figure 5. Intensive site survey to be conducted in even numbered years in Regions 3 and 4 to supplement ALAMAP base design.
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ON
t—I
O
Alabama Coastal Waters
Region 5
G9 Trawl Sites
© Region 5 Sites
97-5-093
Figure 6. Intensive site survey to be conducted in odd numbered years in Region 5 to supplement ALAMAP base design.
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Alabama Coastal Waters
Region 6
HJ Trawl Sites
® Region 6 Sites
8 Miles
97-5-094
Figure 7. Intensive site survey to be conducted in odd numbered years in Region 6 to supplement ALAMAP base design.
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Alabama Coastal Waters
Regions 7, 8, 9
Legend
B Trawl Sites
© Region 7, 8,9 Sites
8 Miles
97-5-095
Figure 8. Intensive site survey to be conducted in odd numbered years in Regions 7,8, and 9
to supplement ALAMAP base design.
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Table 1. Candidate Indicators for Monitoring Coastal Alabama Coastal Waters
(SS denotes variables taken in site-specific surveys)
Physicochemical Variables
Dissolved Oxygen
Salinity / Conductivity
pH
Temperature
River Discharges (from USGS)
Water Depth
Nutrients
Nitrogen Species
Phosphorus Species
Carbon (Particulate and Dissolved)
Chlorophyll
Sediment Oxygen Demand (SS)
Biological Oxygen Demand (SS)
Contaminant Loadings
Sediment Contaminant Concentrations
Tissue Body Burdens
Acid Volatile Sulfides (SS)
Atmospheric Deposition (SS)
Toxicological Bioassays (SS)
Habitat Modification
Acreage of Wetlands
Acreage of Oyster Reef Habitat
Acreage of Submersed Aquatic Vegetation
Acreage of Open Water Sand Bottom
Acreage of Open Water Mud Bottom
Turbidity
Total Suspended Solids
Nephelometry
Secchi Disk Depth
Hydrologic Modifications
Major Tributary Discharge Rates
Circulation Patterns (SS)
Stream and Creek Channels (SS)
Shoreline Modifications (SS)
Dredging Activity (SS)
Dredge Spoil Disposal (SS)
Living Resources
Fisheries-Dependent Catch (SS)
Size Distribution (SS)
Fisheries Effort (SS)
Species Composition
Fisheries-Independent Abundance
Fish Community Diversity
Benthic Community Composition
Benthic Community Abundance
Fecal Coliform Concentrations
Fish Pathologies
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Table 2. Indicator Selection Criteria
Regionally Responsive
Unambiguously
Interpretable
Simple Quantification
Index Period Stability
Low Year-to-Year
Variation
Environmental Impact
Sampling Unit Stable
Available Method
Historical Record
Retrospective
Anticipatory
Cost Effective
Critical Criteria
Must reflect changes in ecosystem condition, and respond to stressors on concern
across most resource classes and habitats within the monitored region
Must be related unambiguously to an assessment endpoint relevant exposure or
habitat variable that forms part of the ecosystem's overall conceptual model of
ecological structure and function
Can be quantified by synoptic monitoring or by cost-effective automated monitoring
Exhibits low measurement error and stability of regional cumulative frequency
distribution during index period (low temporal variation in regional statistics)
Must have sufficiently low natural year-to-year variation to detect ecologically
significant changes within a reasonable time frame
Sampling must have minimal environmental impact
Desirable Criteria
Measurements of response indicator taken at a sampling unit (site) should be stable
over the course of the index period (to conduct associations)
Should have a generally accepted, standardized measurement method that can be
applied on a regional scale
Has an historical data base or a historical data base can be generated from
accessible data sources
Can be related to past conditions
Provides an early warning of widespread changes in ecosystem condition or
processes
Has low incremental cost relative to its information
Table 3. Spatial and Temporal Design Aspects of a Typical Two Year Cycle
Number of sites includes both intensive biennial surveys and quarterly baseline surveys.
Area 1998 1999 2000 2001 2002 2003
NWBay
NEBay
SW Bay/ Bon Secour
Miss. Sound
Perdido Bay
Mobile Tribs.
Overall
50
54
99
40
28
28
287
15
24
72
63
58
51
281
50-60*
50-60
90-100
40
28
28
278-308
15
24
72
60-70
50-60
50-60
266-296
50-60
50-60
90-100
40
28
28
278-308
15
24
72
60-70
50-60
50-60
266-296
Issue-Based
As Needed
As Needed
As Needed
As Needed
As Needed
As Needed
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Important Concepts and Elements of an Adequate State Watershed
Monitoring and Assessment Program
Chris O. Yoder
State of Ohio Environmental Protection Agency, Division of Surface Water
1685 Westbelt Drive, Columbus, OH 43228
Introduction
Watershed-based approaches are gaining widespread acceptance as a conceptual framework from
within which water quality management programs should function. However, overall reductions and
inequities in State ambient monitoring and assessment programs jeopardize the scientific integrity of
watershed-based approaches. This also has had the undesirable effect of failing to properly equip the
States and EPA to adequately meet the challenges posed by recently emerging issues such as cumulative
effects, nonpoint sources, habitat degradation, and interdisciplinary issues (e.g., TMDLs) in general.
Unfortunately, the chronic shortfall in ambient monitoring and assessment resources is not new—the
ITFM (1995) reported that of the funding allocated by state and federal agencies to water quality
management activities, only 0.2% was devoted to ambient monitoring. As the need for adequate supplies
of clean water increases, concerns about public health and the environment escalate, and geographically
targeted watershed-based approaches increase, the demands on the water quality monitoring
"infrastructure" will likewise increase. These demands cannot be met effectively nor economically
without fundamentally changing our attitudes towards ambient monitoring (ITFM 1995). An adequate
ambient monitoring and assessment framework is needed to ensure not only a good science-based
foundation for watershed-based approaches, but water quality management in general. This paper
attempts to describe the important elements, processes, and frameworks that need to be included as part
of an adequate State monitoring and assessment program and how this should be used to support the
overall water quality management process. Furthermore, it is a goal of this effort to highlight the need to
revitalize monitoring, assessment, and environmental indicators as an integral part of the overall water
quality management process.
Monitoring and assessment information, when based on a sufficiently comprehensive and rigorous
system of environmental indicators, is integral to protecting human health, preserving and restoring
ecosystem integrity, and sustaining a viable economy. Such a strategy is intended to achieve a better
return on public and private investments in environmental protection and natural resources management.
In short, more and better monitoring and assessment information is needed to answer the fundamental
questions that have been repeatedly asked about the condition of our water resources and shape the
strategies needed to deal with both existing and emerging problems within the context of watershed-
based management.
The long-term vision is to develop a process for the comprehensive assessment of the waters of each
State by producing and implementing a multi-year monitoring and assessment framework at relevant
geographic scales to support all water quality management objectives (including risk-based decision
making). Some of the key elements of this approach are:
0 development and implementation of a statewide monitoring strategy.
• publishing existing monitoring and assessment results from all relevant sources (e.g., Water-shed
specific reports, State 305[b] reports).
• performance of data storage, retrieval, and management.
• taking appropriate regulatory and management actions based on those results.
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These efforts would fall short if a linkage between program management and monitoring and
assessment were not made part of the overall water quality management process (Figure 1). This, too, is
part of the long range vision for revitalizing the role of water quality monitoring nationwide.
Goals of an Adequate State Monitoring and Assessment Program
The following is a compilation of the major program goals that should shape the design of an
adequate State monitoring and assessment program and thus become the identifiable characteristics.
While much of this is patterned after the major
monitoring and assessment compendia and
program guidance that has recently been
developed (TTFM 1995; U.S. EPA 106 Program
Guidance), the specifics of implementation lie
within the custodial responsibilities of State water
quality management programs.
1. The 18 national water indicators and
the goals each measures (U.S. EPA
1995a; see inset p. 3) are employed as the
core indicators with additional area
and/or resource specific goals and
indicators as needed to fulfill the
following purposes:
• conserve and enhance public health.
• conserve and enhance ecosystems.
• support uses designated by
States/Tribes in Water Quality
Standards (WQS).
• conserve and improve ambient
conditions.
• reduce or prevent loadings and other
stressors (e.g., habitat degradation).
Taken together, all of the above should
lead to achieving healthy watersheds.
2. Assess all water resource types within
an organized time frame (e.g., rotating
basin approach) by employing the
following approaches:
• achieve virtually 100% coverage
through a mix of different spatial
schemes, i.e., targeted sites, rotating
basin cycles, and/or probabilistic
design.
• utilize appropriate and robust
techniques for extrapolation and
stratification of monitoring and
The U.S. EPA National Indicators for Water and
the Goals Each Supports
Conserve & Enhance Public Health:
1. Population served by drinking water systems in
compliance with health-based standards.
2. Population served by drinking water systems at
risk from microbial contamination.
3. Population served by drinking water systems
exceeding lead action levels.
4. Number of drinking water systems with source
water protection.
5. Percentage of waters with fish consumption
advisories.
6. Percentage of estuarine and shellfish waters
approved for harvest for human consumption.
Conserve & Enhance Ecosystems:
7. Percentage of waters with healthy aquatic com-
munities (i.e., biological integrity).
8. Percentage of imperiled aquatic species.
9. Rate of wetland acreage loss.
Support Designated Uses:
10. Percentage of waters meeting designated uses:
a. Drinking water supply
b. Fish and shellfish consumption
c. Recreational
d. Aquatic life
Conserve & Improve Ambient Conditions:
11. Population exposed to chemical pollutants in
ground water.
12. Trends in surface water pollutants.
13. Concentrations of selected pollutants in shellfish.
14. Trends in estuarine eutrophication.
15. Percentage of waters with chemically contami-
nated sediments.
Reduce Loadings & Prevent Other Stressors:
16. Point source loadings to surface and ground
water.
17. Nonpoint source loadings to surface and ground
water.
18. Marine debris.
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assessment results (i.e., every mile of every stream need not be monitored to achieve the
100% coverage goal).
• maximize interagency and inter-organizational cooperation and collaboration.
• when appropriate, make use of volunteer organization results.
3. Produce a "better" 305b report:
• national statistics are currently biased by wide differences between State approaches to
monitoring & assessment including indicators usage and calibration—one result is widely
divergent state estimates of impaired waters (generally overly optimistic estimates of the full
attainment of aquatic life uses).
• assignment of impairment (or lack thereof) to associated causes and sources also reveals the
inconsistent usage of indicators and indicator frameworks—e.g., habitat has been under-
reported by most states (almost one-half of states reported zero impaired miles for rivers &
streams in 1992).
4. Support the emerging watershed
approaches:
• reductions in State monitoring &
assessment programs jeopardize the
science basis for successfully
implementing watershed-based
approaches which are ostensibly based
(in part) on addressing previously
overlooked or under-emphasized
problems.
• management applications most
commonly take place at the watershed
level thus monitoring & assessment
must be relevant to this level of
management and be capable of
detecting impairments and
characterizing aquatic resources at this
scale.
5. Satisfy basic questions that are frequently
encountered by water quality program
managers:
• what is the condition of surface,
ground, estuarine, and coastal waters?
• how and why are conditions changing
over time?
• what are the associated causes and
sources of impairment?
• are water quality management
programs producing the desired
results?
Water Quality-Based Decisions That
Would Benefit From Better Monitoring &
Assessment Information
Water Quality Standards:
• Refined and stratified designated uses and criteria
• Biological criteria
• Site-specific applications (e.g., dissolved metals
translators, design temperature & pH, hardness)
• Water effect ratios
• Antidegradation
• Ground truthing revisions to water quality criteria
TMDLs:
• Delineating impaired segments and associated
causes & sources
• Wasteload allocation (model calibration &
verification
NPDES Permits:
• Impact assessment
• Toxicity assessment (i.e., WET testing)
• Overall permit program effectiveness
Nonpoint Sources:
• Delineating impaired segments and prioritization of
watersheds
• Database for State Nonpoint Source Assessments
404/401 Dredge & Fill:
• Improved site-specific review and approval criteria
• Minimize exemptions via nation-wide permits
Ground Water:
• Development of ambient background characteristics
Wetlands:
• Improved wetlands classification and delineation
criteria
ffl-617
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• are state and national water quality goals being attained?
Each of the above can be subdivided into issue specific questions that are commonly encountered
by water quality managers (see inset on previous page).
6. Integrate the water resource integrity concepts that have been developed during the past 10-
15 years into monitoring and assessment approaches, environmental indicators, and watershed-
based programs:
« the five factors that determine the integrity of water resources (Figure 2; Karr et al. 1986)
should be used to guide the development of environmental indicators—indicators that both
represent or extend to each major factor and that reflect the integrity of the water resource as
a whole (e.g., composite measures, indices) are needed.
• follow the stressor, exposure, response paradigm for determining the most appropriate roles
for individual indicators—avoid the inappropriate substitution of stressor and exposure
indicators for response indicators.
• utilize appropriate regionalization schemes (e.g., ecoregions, subregions) to stratify and
partition natural variability for ambient indicators.
• incorporate tiered and refined use designations in the State WQS as appropriate.
• use the water indicators hierarchy (Figure 3) as an operational framework for State water
quality management programs—make linkages between administrative activities and
indicators of stress, exposure, and response.
State Monitoring & Assessment Program Objectives
The following are some of the major objectives that State monitoring & assessment programs should
have as priorities. Fully meeting some of these objectives will require time to acquire and develop the
necessary database, indicators, and staff expertise. However, this will be partly dependent on the status of
existing and past State monitoring and assessment efforts. Nevertheless, using the following objectives
provides a basis for determining the adequacy of a given State program. A well-rounded approach to
indicators and monitoring design utilizing a core set of chemical, physical, and biological indicators
should provide the information needed to simultaneously meet these objectives without the need to
redesign the approach for each different objective.
1. Baseline characterizations of surface water resources:
• status and trends information.
• aquatic resource characterization.
2. Identification and characterization of existing and emerging problems:
selection of indicators and the overall indicator framework will strongly influence the
adequacy of problem identification and characterization (we cannot address problems that we
do not know about or adequately understand).
• the indicator framework and monitoring design must be prepared to provide information and
insights to problems that may not yet be understood or even recognized.
• there will be a need to go beyond point source paradigms.
* make better linkages between designated uses and indicators.
3. Guide and evaluate the water quality management and regulatory process:
IH-618
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• monitoring & assessment information should drive the regulatory and management processes
from problem identification to assessing the effectiveness of these efforts.
• the 305[b] process (i.e., Water Body System) should be the central reporting mechanism for
State programs—this will further benefit the national assessments compiled by EPA, other
federal agencies, and private organizations.
• support the development and refinement of aquatic life and other designated uses in State
WQS.
• examples of other regulatory and management programs that can be influenced include
303[d] listing, TMDLs, water quality-based permitting, compliance and enforcement,
prioritizing grants and other financial assistance, the State nonpoint source assessment (319
program), etc.
• monitoring and assessment information should provide the impetus for "new" regulatory or
program management directions (e.g., initiatives to restore and protect riparian habitat,
nutrient criteria, sediment criteria, stream protection, antidegradation) and enhance existing
efforts (CSOs, stormwater, 404/401 program, chemical criteria validation, biological
criteria).
4. Evaluation of overall water quality management program effectiveness:
• demonstrate the effectiveness of 25+ years of CWA program implementation.
• establish linkages between administrative activities (i.e., "bean counts") and environmental
results (i.e., ambient chemical, physical, and biological indicators).
• which actions worked and which ones did not?—provide insights on why and suggest what
specific program and/or resource adjustments might be needed.
5. Responding to emergencies, complaint investigations:
• quantify environmental damages on a spatial and/or temporal basis.
• characterize resources at risk.
• define the magnitude of apparent problems.
6. Identify and characterize reference conditions:
• baseline for development of indicator benchmarks for evaluating designated use attainment/
non-attainment (e.g., biological criteria) and other management objectives.
• this functions as a long-term data source for characterizing ambient biological, chemical, and
physical conditions through time.
Monitoring & Assessment Program Design Issues
Monitoring and assessment program design includes the different types of indicators and the frame-
works within which each is developed and used. This in turn determines the different types of data that
will need to be collected and synthesized into information in order to successfully realize the previously
stated goals and objectives. Spatial considerations about the basic design of the monitoring program are
also included and will be most influenced by the overall program goals and objectives of each State.
State monitoring and assessment programs serve multiple needs and must function across multiple scales
(i.e., local watershed, basin/subbasin, statewide), thus consideration of more than one approach will
likely be needed.
HI-619
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Environmental Indicators for Surface Waters
1. The most appropriate roles of indicators are defined as follows:
« Stressor Indicator—measures of activities that have the potential to impact the environment
(e.g., pollutant loadings, land use characteristics, habitat changes).
• Exposure Indicator—measures of change in environmental variables which suggest a degree
(magnitude and duration) of exposure to a stressor (e.g., chemical pollutant levels in water
and sediment, toxicity response levels, habitat quality indices, biomarkers).
• Response Indicator—usually a composite measure or other expression of an integrated or
cumulative response to exposure and stress (e.g., biological community indices, status of a
target species, etc.).
• The problem nationally with inconsistent 305 [b] statistics (and by extension inconsistent
303[d] and 304[1] lists, etc.) is usually the result of the inappropriate substitution of stressor
and/or exposure indicators in the place of response indicators—this is commonly due to the
lack of information about response indicators.
• The exclusion of response indicators and the inappropriate substitution with exposure and/or
stressor indicators ultimately influences what States report in terms of waters meeting
designated uses. An example of this is illustrated in Figure 4 where some State estimates of
aquatic life use attainment based on surrogate approaches are much different than estimates
based primarily on biological assessments (U.S. EPA 1996).
2. Use the EPA hierarchy of indicators (U.S. EPA 1995b; Figure 3) as a template to improve the
integration of administrative actions and measures with environmental indicators within the State
water quality management process:
• The EPA hierarchy of surface water indicators links traditional administrative approaches
(permitting, funding, compliance, enforcement) with environmental indicators which
simultaneously sequences stressor, exposure and response indicators—six levels (Figure 3).
• The six level hierarchy can become an operational template for implementing environmental
indicators and monitoring information within a State water quality management process via a
watershed approach. This will facilitate the development of case histories about what works
and what does not, showing where information gaps exist, and providing opportunities for
feedback throughout the process.
Monitoring Design Approaches
A key issue facing the States and EPA is selection of an appropriate monitoring design. It has been
recognized for some time that the traditional fixed station design (e.g., NAWQMN, NASQAN) common
to many State monitoring networks is alone insufficient to meet the above-stated objectives. However,
State monitoring and assessment resources even under the best of circumstances have been limited and
therefore must be prioritized. Thus, selection of the most cost- and information-effective spatial design is
a critical step in the process. Two approaches, a synoptic, targeted design commonly referred to as a
rotating basin approach and the probabilistic design developed by the U.S. EPA EMAP program are
summarized here. The strengths and weaknesses of each are indicated with respect to the multiple issues
that State monitoring and assessment programs must address.
ffl-620
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Rotating Basin Approach
1. Strengths:
organized, systematic approach based on accumulating assessment information at a local
scale over a fixed period of time, usually 5 or 10 years.
coincides with various management programs which are supported by the monitoring &
assessment information (i.e., NPDES permit reissuance, basinwide water quality planning,
proposed 5-year 305b reporting cycle).
provides monitoring & assessment information at a local or reach specific scale so that the
many issues which occur at this level can be addressed while providing the opportunity to
aggregate upwards to a watershed, regional, statewide, or national scale once sufficient data
exists.
there is more opportunity to define gradients of specific human disturbances with assessment
information (e.g., Karr's human activity "dose"—ecological response curve).
develop and maintain tabs on reference condition in a predictable and standardized time
frame.
2. Weaknesses:
visiting a basin/segment/watershed only once in 5 or 10 years may not be sufficient to satisfy
all needs.
• larger scale assessment information (i.e., in support of a valid statewide assessment) is
generally not available for 5-10 years.
Probabilistic Design
1. Strengths:
• statistically robust design.
• "faster" route to a statewide assessment—aggregate to national scale.
• transcends State boundary limitations—can facilitate collaborative monitoring between
States.
2. Weaknesses:
lacks site-specific/issue-specific resolution.
• logistics are potentially more difficult (i.e., more difficult access to remote monitoring sites).
• reference condition may be more difficult to define on probability basis alone.
• local scale issues may be overlooked.
Aquatic Resource Characterization
Defining the different aquatic resource types that a State program must address is a critical step in the
process. This includes the major aquatic ecosystem types such as flowing waters (i.e., rivers and
streams), lakes and reservoirs, coastal waters, great lakes, estuaries, or wetlands. Further stratification
within each is possible (e.g., headwater streams, wadable streams, large rivers, depressional wetlands,
m-621
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riparian wetlands, etc.) and may be accounted for a priori or as part of the indicator development and
calibration process. Other stratification elements, which include watershed driving factors (e.g.,
ecoregions) and other physical vectors, are incorporated as well. Designated aquatic life uses provide an
additional layer of stratification. Taken together, all of these processes should result in more finely tuned
indicator expectations or benchmarks against which management program success will ultimately be
judged.
State Monitoring & Assessment Components and Resources
State monitoring and assessment programs need to include the appropriate ambient measurements in
order to adequately meet the previously stated goals and objectives. The Intergovernmental Task Force
on Monitoring Water Quality (ITFM 1995) recommended the minimum elements of an adequate
monitoring and assessment program that will support meeting the previously stated goals and objectives
(Table 1). This also represents the elements essential to implementing the hierarchy of water indicators
framework (Figure 3) which, in turn, is needed not only to demonstrate program effectiveness, but also to
provide opportunities for feedback resulting in future program improvements.
The ITFM (1995) concluded that the implementation of the ITFM recommendations and strategy
would result in an adequate information base to achieve the environmental protection and natural
resource management goals and objectives established for the nation's aquatic resources. However, it
was also recognized that full implementation of the strategy could not be achieved "overnight" and that
the necessary capacity and resources (i.e., the monitoring and assessment "infrastructure") will need to
be acquired over a reasonable period of time. Nevertheless, monitoring organizations, including States,
will need to review, update, and/or revise their monitoring strategies in a series of deliberate steps. The
demands that are increasingly being placed on our water resources at all scales require that past
approaches to monitoring be significantly improved in terms of both quality and quantity. Some of the
steps toward a more comprehensive and effective approach to ambient monitoring include the following
which also summarizes the major points of this document:
1. Develop a goal-oriented approach to monitoring, assessment, and indicators development where
indicators are sufficiently specific so as to explicitly measure the identified national goals and
those relevant to State WQS.
2. Evaluate information priorities and identify existing information gaps.
3. Develop a comprehensive and flexible approach that addresses all relevant scales and aquatic
resource types.
4. Take advantage of inter-organizational collaboration whenever appropriate.
5. Link traditional compliance monitoring with watershed-based ambient monitoring.
6. Deal effectively with methods comparability to maximize the flexibility in monitoring and
assessment approaches while producing data and information of known quality and power of
assessment.
7. Automate and streamline data and information management including data entry, storage, and
retrieval.
8. Develop better assessment and reporting at all relevant scales; publish results on a regular basis.
9. Promote the development of incentives and the elimination of disincentives to the development
of better State ambient monitoring programs and indicators.
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Simply upgrading the monitoring program to include more and better measurements and the better
conversion of data to information, while important, is alone insufficient. To achieve the overall goal of
improving the use of monitoring and assessment information in the emerging watershed approach, water
quality management must mature to focus primarily on the condition of the environment as the overall
measure of program success (Figure 5). Whereas the performance of the "program" was once the
principal measure of effectiveness, the program must be viewed as a tool to be used along-side
monitoring and assessment and environmental indicators to improve the quality of the environment.
References
tTFM (Intergovernmental Task Force on Monitoring Water Quality). 1995. The strategy for improving
water-quality monitoring in the United States. Final report of the Intergovernmental Task Force on
Monitoring Water Quality. Interagency Advisory Committee on Water Data, Washington, DC +
Appendices.
Karr, J. R., K. D. Fausch, P. L. Angermier, P. R. Yant, and I. J. Schlosser. 1986. Assessing biological
integrity in running waters: a method and its rationale. Illinois Natural History Survey Special
Publication 5: 28 pp.
U.S. Environmental Protection Agency. 1995a. Environmental indicators of water quality in the United
States. EPA 841-R-96-002. Office of Water, Washington, DC 20460. 25 pp.
U.S. Environmental Protection Agency. 1995b. A conceptual framework to support development and use
of environmental information in decision-making. EPA 239-R-95 012. Office of Policy, Planning,
and Evaluation, Washington, DC 20460. 43 pp.
U.S. Environmental Protection Agency. 1996. Summary of state biological assessment programs for
streams and rivers. EPA 230-R-96-007. U. S. EPA, Office of Policy, Planning, & Evaluation,
Washington, DC 20460.
m-623
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MANAGEMENT MONITORING
^
i
T
ACTIONS
Awareness of
Problems/Issues
F
Analyze for Man-
agement Options
Choose Courses of
Action
r
Design & Imple-
ment Programs
Evaluate Program
Effectiveness
Make Adjustments '
in Priorities & Pro-
grams
V J
L ^
««
k '
>»>
««
k. ^
PURPOSES
Define Water Re-
source Conditions
A
Characterize Exist-
ing & Emerging
Problems By Type,
Magnitude, & Geo-
graphic Extent
Provide Information
. Base for Designing
Abatement, Control,
& Management
Strategies
Provide Information
for Evaluating Pro-
gram Effectiveness
Reveal Trends in '
Water Resource
Quality
^ J
k
k.
k
Figure 1. The relationship between management actions and the purposes monitoring
and assessment (after ITFM 1995).
m-624
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Figure 2. The five major factors that determine the integrity of the water resource
(modified after Karr et al. 1986).
m-625
-------
a
3
•J
^
in
f>
-i
w
»
jj
a
rn
3
•1
3
3
-------
(0
i
2
CO
O)
O
Q.
0)
DC
Biological Integrity B "Aquatic Life Use" |
0 20 40 60 80 100
% Miles Attaining Designated AL Use
Figure 4. Miles of rivers and streams reported as fully supporting designated aquatic life uses based on
varying methods used by 11 states in their 305[b] reports (light shading) compared to that based on
biological assessments (after U.S. EPA 1996).
Two Approaches to Watershed-Based
Water Quality Management
PROGRAM
FOCUSED
APPROACH
Goal: Program Performance
Measures: Administrative Actions
Results: Improve Programs
RESOURCE
FOCUSED
APPROACH
Environmental Performance
Indicator End-points
Programs are Tools to
Improve the Environment
Figure 5. The goals, measures, and results of program-based and resource-based approaches to water
quality management. State programs will evolve toward a resource-based approach by developing and using
a sufficiently comprehensive and rigorous system of environmental indicators.
m-627
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Table 1. Summary Matrix of Recommended Environmental Indicators for Meeting Management
Objectives for Status and Trends of Surface Waters
(X indicates recommended as a primary indicator after ITFM 1995: other recommended indicators are indicated
by /). The corresponding EPA indicator hierarchy level is also listed between indicator groups.
Categories of Management Objectives
Indicator Group
Human
Health Ecological Health Economic Concerns
_ D ... Recrea- Aquatic/ Industry/
Consumpjon Pubic ^^
«°K^K T min9-fish- a"uatic TransP°r-
./Shellfish Supply £ q ^
Agricul-
ture/
Forestry
Biological Response Indicator (Level 6)
Macroinvertebrates
Rsh
Semiaquatic Animals
Pathogens
Phytoplankton
Periphyton
Aquatic Plants
Zooplankton
X
X
X
X
XXX
X X
X X
X
X X X X
X
X X X X
XXX
X
X
X
X
X
X
Chemical Exposure Indicator (Level 4&5)
Water chemistry
Odor/Taste
Sediment Chemistry
Tissue Chemistry
Biochemical Markers
X
X
X
X
V
X X X X
X X
X X X X
X V X X
V V V
X
X
V
Physical Habitat/Hydrologic Indicator (Levels 3&4)
Hydrological Measures
Temperature
Geomorphology
Riparian/shoreline
Ambient Habitat Quality
^x
X
X
X
V
X X X X
X X X X
X X X X
X V X X
V V V V
X
V
X
X
V
Watershed Scale Stressor Indicators (Levels 3,4&5)
Land Use Patterns
Human Alterations
Watershed Impermeability
X
X
V
X X X X
X X X X
V V V V
X
V
V
Pollutant Loadings Stressors (Level 3)
Point Source Loadings
Nonpoint Source Loadings
Spills/Other Releases
V
V
V
V V V V
V V V V
V V V V
V
V
V
IH-628
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Linking Water Quality Data for
Source Water Protection Assessments
Steven P. Roy, Associate Director
Tetra Tech, Inc., 10306 Eaton Place, Suite 340, Fairfax, VA 22030
E-mail: royst@tetratech-ffx.com
Abstract
Source Water Assessments will require states and water utilities to compile and review large volumes
of information related to water system operation, hydrogeology, hydrology, potential contamination
sources, and water quality. Many state and federal environmental databases exist in electronic formats
that can provide valuable information for states and water utilities conducting source water assessments.
Easy linkage to databases supported by the U.S. Environmental Protection Agency and the U.S.
Geological Survey can supply vast amounts of data related to water quality, point source discharges,
solid and hazardous waste facilities, soils, geology, topography, land use, in stream water quality
violations, etc. EPA's Index of Watershed Indicators and Surf Your Watershed can provide information
on potential source water pollution on a geographic basis. This paper will describe some of the data
requirements necessary to conduct a source water assessment and how accessing existing federal
databases can form the basis of a source water assessment.
Introduction
Public water supply development, delivery, and management is a locally driven effort in the United
States. Because of the diverse nature of drinking water management, source water assessments will be
most effective when initiated locally. Most public water supplies that have experienced water quality
contamination have identified either nonpoint sources of contamination or unregulated activities such as
spills, leaks, or illegal disposal as the source of contamination. However, a contamination assessment
would not be complete without first identifying those potentially polluting activities that are permitted.
The following sections describe the nature of public water supply in the United States, review the
source water assessment requirements, identify federal contaminant data sources and systems, and
presents possible systems that could be used by states to assist in conducting source water assessments.
Public Water Supply in the United States—The Need for Source Water Protection
There are over 180 thousand water systems in the United States that serve over 250 million people
(USEPA, 1997a). This estimate includes community water systems, transient noncommunity water
systems, and nontransient noncommunity water systems (Table 1). This number is important for source
water assessment purposes because each of these systems must have an assessment completed within a
window of 3-1/2 years. Another way to look at this volume of work is 51,532 assessments per year, or
277 assessments per day, or 5.3 assessments/state/day. This workload points to the need for some level of
automation.
Fifty-three percent (53%) of the U.S. population obtains its drinking water from groundwater sources
(USEPA, 1997b). Groundwater is the predominant source of supply for community water systems. Close
to 80% of the community water systems rely upon groundwater as their primary source (Table 2), and
most of these systems (85%) are small, serving less than 3,300 persons each.
HI-629
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Source Water Assessments—The 1996 Safe Drinking Water Act Amendments (PL 104-182)
Water utilities, local governments, state governments, and the federal government share the
responsibility for developing measures and programs to protect drinking water quality. Drinking water
suppliers have been guided in their efforts due primarily to source protection provisions of The Safe
Drinking Water Act (SDWA). The SDWA requires the USEPA to set standards for maximum
contaminant levels (the maximum permissible level of contaminant in water delivered to any user of a
public water supply system) in public drinking water supplies, regulate underground injection of wastes
into groundwaters, and establishes public water supply protection programs. In 1986, Amendments to the
SDWA were passed increasing the authority and responsibility for drinking water protection. The
Amendments created the Sole Source Aquifer Demonstration Program and the Wellhead Protection
Program. Subsequent amendments in 1996 further strengthened drinking water protection with the
establishment of the Source Water Protection Program.
The 1996 Amendments to the Safe Drinking Water Act (SDWA) created the requirement that all 180
thousand public water supplies be covered under a source water protection program (SWAP). The basic
assumption for the implementation of source water protection programs is that multiple-barrier protection
of public water supplies will provide for high quality water supplies and protect public health. The
establishment of multiple barriers that include source water protection, treatment, distribution system
maintenance, and monitoring, are proposed to protect the quality and safety of drinking water supplies.
Source water assessment and protection represents the first step in protecting public water supplies.
What Are Source Water Assessments?
Source water assessments are the centerpiece of the new SDWA's focus on prevention. Source water
assessments identify the potential threats to the source of a community's drinking water. States can use
the assessments to issue monitoring waivers for many chemicals regulated under the Act. Over the next
four years, the States are directed to develop source water assessments for all public water supplies
which will include the following:
1. Delineate the ground water area or surface watershed contributing water to the water supply
intake;
2. Inventory the contaminant sources in the delineated water supply area;
3. Determine the susceptibility of the water system to contamination; and
4. Make the assessment available to the public.
How Are Source Waters To Be Delineated?
Section 1453 (2)(A) of SDWA requires that the states delineate the boundaries of the source water
protection areas (SWPA) from which one or more public water systems receives its drinking water
supply. U.S EPA's Source Water Protection Final Guidance Document specifies the following
delineation approaches:
For surface water systems, the SWPA should include the entire watershed area upstream of the
Public Water System's (PWS) intake, up to the state border. The states can choose to segment large
watersheds into sub-basins or buffer zones to provide for more cost-effective contaminant source
inventories and susceptibility analyses (EPA, 1997c, d, e).
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For ground water systems, delineations should be conducted using one or more of the following
methods: (1) arbitrary radii; (2) calculated fixed radii; (3) simplified variable shapes; (4) analytical
methods; (5) hydrogeologic mapping; and (6) numerical flow/transport models (EPA, 1987, EPA,
1997a).
How Are Contaminant Sources To Be Identified?
States are required to identify the sources of contaminants regulated under SDWA for which
monitoring is required (or any unregulated contaminants selected by the State that may present a threat to
public health). These contaminants include those regulated under the SDWA (contaminants with a
maximum contaminant level (MCL), contaminants regulated under the SWTR, and the microorganism
Cryptosporidium.) A variety of both regulated and unregulated contaminant sources are found within
source water protection areas, including aboveground and underground storage tanks; animal feedlots;
agricultural chemicals, underground injection wells; chemical processing facilities; transportation and
road maintenance facilities; septic systems; pipelines; and waste transport, storage, and disposal
facilities.
Contaminant source inventories of permitted facilities can be conducted using existing data sources
(e.g., U.S. EPA's Envirofacts data base), land record searches, and local surveys or canvassing efforts.
How Is a Drinking Source Water's Susceptibility to Contamination Assessed?
Susceptibility assessments are the least understood aspect of source water protection, yet the most
important. Assessments are intended to identify the contaminant sources that pose the greatest threat to
the drinking water supply so that they can be targeted for management. Assessment methodologies
include simple, analytical techniques such as hydrogeologic and hydrologic mapping to identify the
relative vulnerability of ground water and surface water supplies, as well as complex contaminant
transport models linked to risk assessment matrices. Once a source water's susceptibility is assessed, a
vulnerability determination must be produced and made available to the public.
What About Management of the Sources of Contamination?
Implementation of management controls is not required under the SDWA amendments of 1996,
however many water suppliers and local governments are actively controlling existing contamination
sources and preventing new contamination sources from threatening drinking water quality, through the
implementation of various management controls.
Regulated Contaminant Identification—Federal Databases
Under Section 1453 (2) (B) of the Safe Drinking Water Act, states are required to identify the
sources of contaminants regulated under the Act for which monitoring is required (or any unregulated
contaminants selected by the State that may present a threat to public health). To the extent practical, the
state must identify the source of the contaminants within the SWPA to determine the susceptibility of the
public water systems to contamination.
A variety of both regulated and unregulated contaminant sources are found within SWPAs. These
include aboveground and underground storage tanks; animal feedlots; underground injection wells
.(especially Class V wells); agricultural chemical storage and processing facilities; transportation and
road maintenance facilities; septic systems; pipelines; and waste transport, storage, and disposal
m-63i
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facilities. The range of potential contaminants and contaminant sources are described in several EPA
publications (EPA, 1990; EPA, 1991a; EPA, 1991b).
Because of the wide variety of potential contaminant sources that may be found in SWPAs, a
hierarchical approach to source identification is appropriate and most cost effective. The information
sources available to support this approach are summarized below.
Step 1—Search Federal and State Relational Data Bases to Identify Regulated Sources
The U.S. EPA Envirofacts Warehouse allows for on-line retrieval of environmental information from
Seven EPA databases on Superfund sites (Comprehensive Environmental Response, Compensation, &
Liability Information System, CERCLIS), drinking water (Safe Drinking Water Information System,
SDWIS), toxic and air releases (Toxic Release Inventory, TRI), hazardous waste (RCRIS), and water
discharge permits (Permit Compliance System, PCS), and grants information. As a result, an Envirofacts
search allows for rapid identification of existing and abandoned waste management facilities within the
SWPA and other ongoing hazardous substance releases to the air, land, or water. In addition, certain
states may maintain additional searchable data on underground injection wells and underground storage
tanks. The U.S. EPA is currently working on improving and enhancing many of its environmental
databases. During 1998 and 1999, many significant changes in these systems will result in easier assess
for the public and better locational information.
Step 2—Collect Existing Information From Local Land Records, Sanitary Surveys, or Public
Health Records
Information can be collected from local government records, including operating, discharge, and
disposal, construction, and other permitting information; zoning records; real estate titles and
transactions; and health department records. Maps, aerial photographs, telephone directories, and historic
records can be used to locate particular land uses that have been or are continuing to threaten the SWPA.
Step 3—Collect New Information on Past Land Use Practices or Contaminant Sources That Have
Not Been Identified
Because many sources, such as product storage facilities, failing septic systems, or abandoned
underground storage tanks, may not be tracked at the local level, additional information collection is
often needed to adequately characterize threats to the PWS. Such information can be collected by door-
to-door canvasing within the SWPA, windshield surveys, mail surveys, or general public education and
outreach to request and gather new information. A number of communities have successfully used local
volunteers to collect such information.
Step 4—Target Significant Sources for Further Investigation
EPA (1991b) provides worksheets to help target more significant contaminant sources based on the
type and volume of materials managed at the sources and the proximity and vulnerability of the drinking
water intake. For those sources that are deemed most significant, the accuracy and reliability of the
gathered information should be verified. Ground-truthing or field-checking the data will help support the
SWPA susceptibility analysis.
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Vulnerability Assessments/Susceptibility Determinations
Vulnerability and susceptibility assessments are perhaps the least understood concept related to
source water protection. Many states and water utilities have developed approaches to assess the
vulnerability of drinking water sources to contamination. Methodologies have ranged from risk matrices
to complex contaminant transport models.
Many states have prepared methods to assess ground water vulnerability. Many of these methods
address a specific contaminant or hydrogeologic setting. The EPA is currently considering a decision-tree
approach to predict microbial contamination in ground water systems. Surface water assessments have
relied on predictive models such as Qual2E, and SWMM. Packaged assessment products such as Better
Assessment Science Integrating Point and Nonpoint Sources (BASINs) developed by EPA, Office of
Science and Technology present a new method to incorporate both point source and nonpoint source
pollution into source water assessments.
BASINS supports the development of total maximum daily loads (TMDLs), which require a
watershed-based approach that integrates both point and nonpoint sources. BASINS can support this type
of approach for the analysis of a variety of pollutants. It can also support analysis at a variety of scales,
using tools that range from simple to sophisticated. BASINS was originally released in September, 1996
(BASINS 1.0), version 2.0 is scheduled for release in the summer of 1999. The foundation of BASINS is
the interrelated components essential for performing watershed and water quality analysis. These
components are grouped into four categories:
1. National databases with local data import tools;
2. Assessment tools (TARGET, ASSESS, and Data Mining) that address needs ranging from
large-scale to small-scale;
3. Watershed and water quality models including NPSM (HSPF), TOXIROUTE, and QUAL2E;
and
4. Post-processing output tools.
BASINS was used to query the type and number of facilities within the drainage area of Lake Harsha
in Clermont County, OH. The results include: 5 wastewater discharge facilities; 3 toxic release inventory
sites; 28 water quality monitoring stations; and 11 bacteria monitoring stations. This query resulted in the
ability to access a large amount of actual water quality monitoring data, as well as identify the location
and type of potential point sources of pollution. A good first step in conducting a source water
assessment for Lake Harsha.
Summary
States, local governments and water suppliers face a significant challenge to implement the source
water assessment requirements of the SDWA. Tools are available to assist in the first step of identifying
water quality information and point source discharge information. Envirofacts and BASINS provide
information and map tools to initially assess threats and vulnerability of public water supplies to
contamination. These tools should only be used as an initial evaluation approach, supplemented with
more detailed state and local information. Nonpoint sources of pollution including septic systems and
agricultural impacts can not be derived from these systems. Locally-derived information is critical to
conduct accurate source water assessments. Completed source water assessments will provide the
opportunity for the synthesis, analysis, and central display of public water supply water quality and
potential water quality threats. A mechanism must be developed to ensure that source water assessments
are integrated with other monitoring and assessment efforts.
ffl-633
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References
U.S. Environmental Protection Agency. 1985. Protection of Public Water Supplies from Ground-Water
Contamination, Seminar Publication. EPA 625-4-85-016. Center for Environmental Research
Information.
U.S. Environmental Protection Agency. 1987. Guidelines for Delineation of Wellhead Protection Areas.
EPA 44016-87-010. Office of Ground-Water Protection.
U.S. Environmental Protection Agency. 1988. Developing a State Wellhead Protection Program: A
User's Guide to Assist State Agencies Under the Safe Drinking Water Act. EPA 440/6-88-003. Office
of Ground-Water Protection
U.S. Environmental Protection Agency. 1989. Wellhead Protection Programs: Tools for Local
Governments. EPA 440-6-89-002. Office of Water.
U.S. Environmental Protection Agency. 1990. A Review of Sources of Ground Water Contamination from
Light Industry. EPA 440/6-90-005.
U.S. Environmental Protection Agency. 1991a. Guide for Conducting Contaminant Source Inventories
for Public Drinking Water Supplies. EPA 570/9-91-014.
U.S. Environmental Protection Agency. 1991b. Managing Ground Water Contamination Sources in
Wellhead Protection Areas: A Priority Setting Approach. EPA 570/9-91-023. Office of Ground
Water and Drinking Water.
U.S. Environmental Protection Agency. 1993. Wellhead Protection: A Guide for Small Communities,
Seminar Publication. EPA 625/R-93/002. Office of Research and Development and Office of Water.
U.S. Environmental Protection Agency. 1997a. The 1995 Community Water Supply Survey. Office of
Water.
U.S. Environmental Protection Agency. 1997b. Water on Tap: A Consumer's Guide to the Nation's
Drinking Water. EPA 815-K-97-002. Office of Water.
U.S. Environmental Protection Agency. 1997c. State Source Water Assessment and Protection
Programs—Final Guidance. EPA 816-R-97-009. Office of Water.
U.S. Environmental Protection Agency. 1997d. Guidelines for Wellhead and Springhead Protection Area
Delineation in Carbonate Rocks. EPA 904-B-97-003. Region 4, Groundwater Protection Branch.
U.S. Environmental Protection Agency. 1997e. Delineation of Source Water Protection Areas, A
Discussion for Managers; Part 1: A Conjunctive Approach for Ground Water and Surface Water.
EPA 816-R-97-012. Office of Water.
ffl-634
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Figure 1. A watershed area (EPA, 1997e).
Segment 4
, N Segment 1
-'Mntake
Figure 2. Watershed area—segmented for
assessments (EPA, 1997b).
Table 1. Number of Public Water Systems in the United States
", r/ ''*''- * ''"' $[ System Type
Community Water Systems
Nontransient Noncommunity Water Systems
Transient Noncommunity Water Systems
Total Systems
Number -/: ','$$
50,289
23,639
106,436
180,364
•»f *"*4fy% of Total " *
/J '/X~ -< r
28%
13%
59%
700%
Table 2. Community Water Systems By Source in the United States
'fy, fy **, Sburce^Type
Primarily Groundwater
Primarily Surface Water
Primarily Purchased
Total
Number
40,123
4,832
5,334
50,259
• -a^'.'-XT- ?$,• *^*r • '•„ ...
•^v-\^ % of Total
79.8%
9.6%
10.6%
700%
IH-635
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Internet Web Sites to Support Source Water
Assessments and Protection
Better Assessment Science Integrating Point and Non-Point Sources (BASINS)
http://www.epa.gov/OST/BASINS
Clean Water Needs Survey
http://www.epa.gov/OWM/uc.htm
Effluent Guidelines Studies
http://www.epa.gov/ostwater/prodesum.html
Environmental Monitoring Methods Index
http://www.epa.gov/OST/pc/ds.html
Grant Information and Control System—Construction Grants
http://www.epa.gov/enviro/htm/gics/gics_cgl.html
Index of Watershed Indicators (IWI)
http://www.epa.gov/surf/IWI/
Ocean Data Evaluation System
http://www.epa.gov/OWOW/watershed/tools/ref.htrrM30
Permit Compliance System
http://www.epa.gov/owmitnet/pcsguide.htm
Reach File
http://www.epa.gov/OWOW/NPS/rf/rfindex.html
Safe Drinking Water Information System/Federal Version
http://www.epa.gov/ogwdwOOO/datab/sfed.html
Safe Drinking Water Information System/State Version
http://www.epa.gov/ogwdwOOO/sdwis_st/sdwis.html
STORET
http://www.epa.gov/OWOW/STORET/
STORET X (Modernized STORET)
http://www.epa.gov/OWOW/STORET/sthp.html
Surf Your Watershed (SURF)
http://www.epa.gov/surf
The Waterbody System
http://www.epa.gov/OWOW/NPS/NBSFlash/NBSFlash.html
EPA, Office of Water Information Systems, Models and Tools
National Listing of Fish and Wildlife Consumption Advisories
http://www.epa.gov/OST/fishadvice/
National Sewage Sludge Survey
http://earth 1 .epa.gov/earthl 00/records/i 10625.html
National Volunteer Monitoring Directory
http://www.epa.gov/owow/monitoring/dir.html
Personal Computer/Complex Effluent Toxicity Information System (PC-CETIS)
http://www.ntis.gov/fcpc/cpn4834.htm
National Small Flows Clearinghouse List Server
http ://w ww.estd. w vu. edu/nsfd
Watershed Information Resources System (WIRS) Bibliographic Database
http://www.terrene.org/wirsdata.htm
Land Cover Digital Data Directory for the United States
http://www.epa.gov/owow/watershed/wacademy.htm
Office of Science and Technology (OST) Clearinghouse
http://www.epa.gov/OST/pctoc.html
Beach Watch
http://www.epa.gov/OST/beaches
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CORMIX (Cornell Mixing Zone Expert System)
http://www.epa.gov/OWOW/watershed/tools/model.htmW3
DYNTOX
http://www.epa.gov/OWOW/watershed/tools/model.htmltt5
HSPF
http://www.epa.gov/OWOW/watershed/tools/model.htmlttl2
QUAL2E Enhanced Stream Water Quality Model User Interface
http://www.epa.gov/ostwater/QUAL2EJAriNDOWS/metadata.txt.html
SWMM Storm Water Management Model User Interface
http://www.epa.gov/ostwater/SWMMJNINDOWS/metadata.txt.html
PRELIM Version 5
http://www.epa.gov/owrmtnet/pipes/prloclim.htm
Other Water Information Systems, Models and Tools
USGS Water Resources Scientific Information Center (WRSIC)
http://www.uwin.siu.edu/databases/wrsic/index.html
National Water-Use Information Program
http://water.usgs.gov/public/watuse/wunwup.html
National Oceanic Data Center (NODC)
http://www.nodc.noaa.gov
National Ground Water Information Center
http://www.h2o-ngwa.org/about/
Agriculture Research Service (ARS) Water Data Base
http://hydrolab.arsusda.gov/arswater.html
AQUatic Toxicity Information REtrieval (AQUIRE) database
http://www.epa.gov/earthlOO/records/a00120.html
Chemical Hazards Response Information System and the Hazard Assessment System (CHRIS/HACS)
http://www.ccohs.ca/products/databases/chris.html
EPA Spatial Data Library System (ESDLS)
http://www.epa.gov/enviro/htmyesdls/esdls_over.html
Estuarine Living Marine Resources (ELMR)
http://www.epa.gov/ces/guide/prog(19).htm
Integrated Risk Information System (IRIS)
http://www.epa.gov/iris
Integrated Taxonomic Information System (ITIS)
http://www.itis.usda.gov/itis/index.html
Land Use and Land Cover Digital Data
http://map.usgs.gov/mac/isb/pubs/factsheets/fs05294.html
Marine Pollution Retrieval System (MPRS)
http://www.epa.gov/ces/guide/prog(53).htm
National Coastal Pollutant Discharge Inventory Program (NCPDI)
http ://www. epa. go v/ces/guide/prog(22). htm
National Coastal Wetlands Inventory
http://www.neonet.nl/ceos-idn/datasets/NOS00038.html
National Contaminant Biomonitoring Program (NCBP) Data Base
http://www.epa.gov/ces/guide/prog(38).htm
National Estuarine Inventory (NEI)
http://www.neonet.nl/ceos-idn/campaigns/NEI.html
National Heritage Network
http://www.heritage.tnc.org/
National List of Vascular Plant Species That Occur in Wetlands
http://www.nwi.fws.gov/ecology.html
National Resources Inventory
http://www.ftw.nrcs.usda.gov/nrLdata.html
IE-637
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National Shellfish Register
http://www-orca.nos.noaa.gov/projects/95register
National Status and Trends Data Base (NSTDB)
http://seaserver.nos.noaa.gov/projects/nsandt/nsandt.html
National Water Information System (NWIS)
http://h2o.usgs.gov/public/nawdex/wats/intro.html
National Water-Use Data System (WUDS)
http ://water.usgs. gov/public/watuse/guidelines/awuds .html
Toxic Chemical Release Inventory System (TRIS)
http://www.epa.gov/enviro/html/tris/tris_query.html
Wildlife Refuge Management Information System
http://www.fws.gov/pullenl/cais/rmis.htm]
Ground Water On-Line
http://www.h2o-ngwa.org/gwonline/index.html
WATERNET
http://www.awwa.org/waternet.htm
Earth Sciences Data Directory (ESDD)
http://204.32.12.2/cdrates/rec00001/r0000401.htm
Master Water Data Index (MWDI)
http://water.usgs.gov/public/nawdex/mwdi.html
NOAA Environmental Services Data Directory (NOAADIR)
http://www.esdim.noaa.gov/NOAA-Catalog/
National Environmental Data Referral Service (NEDRES)
http://www.esdim.noaa.gov
Ecological Sensitivity Targeting and Assessment Tool (ESTAT)
http://www.esri.com/base/comrnon/userconf/proc97/PROC97/ABSTRACT/A459.HTM
National Wetlands Inventory Digital Data Base
http://www.nwi.fws.gov/data.html
National Wetlands Research Center Data Base (NWRCDB)
http://www.nwrc.gov/sdms/sdmsmain.html
Envirofacts Warehouse
http://www.epa.gov/enviro/indexjava.html
Maps On Demand (MOD)
http://www.epa.gov/enviro/html/mod/index.html
NOAAServer
http://www.esdim.noaa.gov/NOAAServer/
Government Agencies
United States Environmental Protection Agency (EPA) http://www.epa.gov/
EPA Office of Water http://www.epa.gov/ow/
OW Water Resource Center waterpubs@epamail.epa.gov
American Indian Environmental Office http://www.epa.gov/indian/
Office of Ground Water and Drinking Water http://www.epa.gov/OGWDW/
Office of Science and Technology http://www.epa.gov/OST
Office of Wastewater Management http://www.epa.gov/OW-OWM.html
Office of Wetlands, Oceans, & Watersheds http://www.epa.gov/OWOW/
Region 1—CT, MA, ME, NH, RI, VT http://www.epa.gov/epahome/images/region01/
Region 2—NJ, NY, Puerto Rico and the Virgin http://www.epa.gov/epahome/images/Region2/
Islands
Region 3—DE, MD, PA, VA, WV, and DC http://www.epa.gOv/epahome/images//region03/
Region 4—AL, FL, GA, KY, MS, NC, SC, and TN http://www.epa.gOv/epahome/images//region4/reg4.html
Region 5—IL, IN, MI, MN, OH, and WI http://www.epa.gOV/epahome/images//Region5/
Region 6—AR, LA, NM, OK, and TX http://www.epa.gov/epahome/images/earthlr6/index.htm
Region 7—IA, KS, MO, and NE http://www.epa.gov/epahome/images/rgytgrnj/
Kt-638
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Region 8—CO, MT, ND, SD, UT, and
Region 9—AZ, CA, HI, NV, Guam & American
Samoa
Region 10—AK, ID, OR, and WA
Chesapeake Bay Program
Coastal America
Great Lakes Program
Great Lake's SPATIAL DATA
Great Lakes Information
Gulf of Mexico Program
National Oceanic and Atmospheric Administration
(NOAA)
Climate Diagnostics Center
Climate Monitoring and Diagnostics Laboratory
Environmental Research Laboratories (ERL)
Hydrologic Information Center NWS, NOAA
National Centers for Enviromental Prediction
(NCEP)
National Climatic Data Center
National Oceanographic Data Center
National Weather Service (NWS)
NOAA Network Information Center
Army Corps of Engineers
United States Army Corps of Engineers, St. Paul
District
United States Army COE—Water Control Page
U.S. Army COE—Waterways Exp. Station (WES)
United States Fish & Wildlife Service
U.S. FWS National Wetlands Inventory
United States Geological Survey (USGS)
Federal Geographic Data Committee
National Water Conditions
National Water Data EXchange (NAWDEX)
USGS—Water Resources Division
USGS Node National Geospatial Data
Clearinghouse
National Center for Atmospheric Research (NCAR)
National Operational Hydrologic Remote Sensing
Center
National Rural Water Association (NRWA)
National Science Foundation
Natural Resources Conservation Service (NRCS)
Rural Utilities Service, Water And Waste Program
(USDA)
United States Bureau of Reclamation
http://www.epa.gov/epahome/images/unix0008/
http://www.epa.gov/epahome/images/region09/
http://www.epa. go v/epahome/images/r 1 Dearth/
http://www.epa.gov/r3chespk/
http://www.epa.gov/owow/oceans/coastam/
http://www.epa.gov/glnpo/
http://epawww.ciesin.org/glreis/nonpo/spatial/spatial.html
http://www.great-lakes.net/
http://pelican.gmpo.gov/gulfofmex/gmpo/gmpo.html
http://www.noaa.gov
http://www.cdc.noaa.gov/
http://www.cmdl.noaa.gov/
http://www.erl.noaa.gov/
http://hsp.nws.noaa.gov/
http://nic.fb4.noaa.gov/
http://www.ncdc.noaa.gov
http://www.nodc.noaa.gov/
http://www.nws.noaa.gov
http://www.nnic.noaa.gov
http://www.usace.army.miy
http://www.ncs.usace.army.mil
http://www.ncs-wc.usace.army.mil
http://www.wes.army.mil/WES/weIcome.html
h ttp: //w w w. fws. go v/
http://www.nwi.fws.gov/
http://www.usgs.gov/
http://fgdc.er.usgs.gov/fgdc.html
http://nwcwww.er.usgs.gov:8080/NWC/html/NWC.html
http://h2o.er.usgs.gov/public/nawdex/nawdex.html
http://h2o.usgs.gov/
http://nsdi.usgs.gov/nsdi
http://http.ucar.edu/metapage.html
http://www.nohrsc.nws.gov
http://www.cais.com/nrwainfo
http://www.nsf.gov
http://www.ncg.nrcs.usda.gov
http://www.usda.gov/rus/water/water.htm
http://www.usbr.gov
State Environmental Agencies
Alabama
Alaska Dept of Env. Conserv.
Arizona Fish and Game
Arizona Water Resources Research Center
Arkansas
Arkansas Dept of Pollution Control and Ecology Regs
http://alaweb.asc.edu/govern.html
http://www.state.ak.us/local/akpages/ENV.CONSERV/
home.htm
http://www.state.az.us/game
http://ag.arizona.edu/AZWATER
http://www.state.ar.us/
http://www.adeq.state.ar.us/regs/regsmain.htm
HI-639
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California
California EPA
California Department of Water Resources
California Watershed Projects Inventory (CWPI)
California Rivers Assessment (CARA)
Colorado Water Resources
Colorado Springs Utilities—Water Resources
Department
Colorado State Univ.—Water
Colorado Water Resources Research Institute
Colorado Department of Public Health and
Environment
Colorado Southwestern Water Conservation District
Connecticut Department of Environmental Protection
Delaware
Delaware Dept. of Nat. Res. and Environmental
Control
Florida Department of Environmental Protection
Florida Department of Environmental Protection
(Gopher)
Georgia DNR
Georgia Home Page
Hawaii Department of Land and Natural Resources
Idaho Department of Health and Welfare
Idaho Emergency Response Commission
Illinois EPA
Indiana Water Resources Research Center
Indiana Department of Natural Resources
Iowa Department of Natural Resources
Kansas Department of Health and Environment
Kansas Northwest Goundwater Mgmt. District #4
Kentucky Environmental Quality Commission
Louisiana Department of Environmental Quality
Maine Department of Environmental Protection
Maine DEP, Bureau of Land & Water Quality
Maryland Department of the Environment
Maryland Department of Natural Resources
Massachusetts Department of Environmental
Protection
Michigan Department of Environmental Quality
Minnesota
Mississippi
Missouri Department of Conservation Home Page
Missouri DNR, Division of Environmental Quality
Missouri Freshwater
Montana Dept. of Water Resources
Montana Natural Resource Information System
Montana GIS Data Library
Nebraska Natural Resources Commission
Nebraska Water Center / Environmental Programs Unit
Nevada
http://www.water.ca.gov/www.gov.sites.html
http://www.calepa.cahwnet.gov
http ://wwwdwr. water.ca. gov/
http://ice.ucdavis.edu/California_Watershed_Projects_
Inventory/
http://ice.ucdavis.edu/California_Rivers_Assessment/
http://srvldcolka.cr.usgs.gov/
http://www.csu.org/Water/Waterhmp.htm
http://www.lance.colostate.edu/depts/ce/netscape/special
_programs/wcenter/
http://yuma.acns.colostate.edu/Depts/CWRRI/
http://www.state.co.us/gov_dir/cdphe_dir/cdphehom.html
http://web.frontier.net/SCAN/wip/wiphome.html
http://dep.state.ct.us/
http://www.state.de.us/govern/intro.htm
http://www.dnrec.state.de.us/
http://www.dep.state.fl.us/
gopher://gopher.dep. state.fl .us/
http://www.dnr.state.ga.us/
http://www.state.ga.us/
http://www.htdc.org/~dlnr/divisions.html
http://www.state.id.us/dhw/hwgd_www/home.html
http://www.state.id.us/serc/serchome.htm
http://www.epa.state.il.us/
http://ce.ecn.purdue.edu/wrrc.html
http://www.state.in.us/acin/dnr/index.html
http://www.state.ia.us/government/dnr/index.html
http://www.ink.org/public/kdhe
http://colby.ixks.com/~wbossert
http://www.state.ky.us/agencies/eqc/eqc.html
http://www.deq.state.la.us/
http://www.state.me.us/dep/mdephome.htm
http://www.state.me.us/dep/mdep604b.htm
http://www.mde.state.md.us
http://www.dnr.state.md.us/
http://www.magnet.state.ma.us/dep/dephome.htm
http://www.deq.state.mi.us/
http://www.dnr.state.mn.us/
http://www.state.ms.us/
http://www.state.mo.us/conservation/welcome.html
http://www.state.mo.us/dnr/deq/homedeq.htm
http://www.umsl.edu/~joellaws/ozark_caving/springs/
jspring.html
http://nris.msl.mt.gov/wis/wisl.html
http://nris.msl.mt.gov/
http://nris.msl.mt.gov/gis/mtmaps.html
http://www.nrc.state.ne.us/
http://ianrwww.unl.edu/ianr/waterctr/wchome.html
http ://ww w.state. n v.us/
m-640
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New Hampshire Department of Environmental
Services
New Jersey Department of Environmental Protection
New Mexico Environment Department
New Mexico Water Resources Research Institute
New York State Department of Environmental
Conservation
North Carolina Dept. of Env., Health and Nat. Res.
North Carolina GIS Database
North Carolina - Division of Water Resources
North Carolina Water Resources Research Institute
North Dakota State Water Commission
North Dakota Geological Survey Division
Ohio Environmental Protection Agency
Oklahoma Conservation Commission
Oklahoma Department of Environmental Quality
Oregon Department of Fish and Wildlife
Oregon Department of Environmental Quality
Pennsylvania Department of Environmental Protection
Pennsylvania Dept. of Conservation and Natural
Resources
Rhode Island
South Carolina Department of Natural Resources
South Carolina Dept. of Health and Environmental
Control
South Dakota Dept. of Environment and Natural
Resources
Tennessee Department of Environment and
Conservation
Texas Natural Resources Conservation Commission
Texas State Agencies
Texas Environmental Center
Utah Water Research Laboratory
Utah Department of Environmental Quality
Utah GIS Database
Vermont Agency of Natural Resources
Virginia Department of Environmental Quality
Washington State Department of Ecology
Washington Department of Transp Env. Affairs Office
Washington -University of WA's Wetland Ecosystem
Team
West Virginia Division of Env. Protection
Wisconsin State Agencies
Wyoming Department of Environmental Quality
Wyoming Water Resources Center
Powell Consortium. (AZ, CA, CO, NM, NV, UT &
WY)
http://www.state.nh.us/des/descover.htm
http://www.state.nj.us/dep/
http://www.nmenv.state.nm.us/
http://wrri.nmsu.edu/
http://www.dec.state.ny.us
http://www.ehnr.state.nc.us/EHNR/
http://cgia.cgia.state.nc.us/
http://149.168.114.60/dwr/dwr.htm
http ://w ww2. ncsu .edu/ncsu/CIL/WRRI
http://www.swc.state.nd.us
http://www.state.nd.us/ndgs/NDGS.HomePage.html
http://www.epa.ohio.gov/
http://www.oklaosf.state.ok.us/~conscom
http://www.deq.state.ok.us/home.html
http://www.dfw.state.or.us/
http://www.deq.state.or.us/
http://www.dep.state.pa.us/
http://www.dcnr.state.pa.us
http://www.doa.state.ri.us/info/exec.htmtfdeparts
http://water.dnr.state.sc.us/www/dnr/dnr.html
http://www.state.sc.us/dhec/eqchome.htm
http://www.state.sd.us/state/executive/denr/denr.html
http://www.state.tn.us/environment/
http://www.tnrcc.texas.gov/
http://www.texas.gov/
http://www.tec.org/guestbook-noforms
http://publish.uwrl.usu.edu/
http://www.eq.state.ut.us/
http://dpagr6.state.ut.us/
http://www.state.vt.us/anr/
http://www.deq.state.va.us
http://www.wa.gov/ecology/
http://www.wsdot.wa.gov/eesc/environmental/
http://www.fish.washington.edu/people/asif/WET.html
http://charon.osmre.gov/
http://badger.state.wi.us/departments.html
http://www.state.wy.us/state/government/state_agencies/
deq.html
http://www.wwrc.uwyo.edu/
http://wrri.nmsu.edu/powell
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International Environmental Organizations
Environment Australia
Division of Water Resources CSIRO
International Groundwater Modeling Center (IGWMC)
Middle East Water Information Network
National Water Research Institute (Canada)
University of Western Australia—Centre for Water
Research
WQ Branch, BC Ministry of Env., Lands & Parks
Water Resources Systems Research Unit
Environment Canada
http://www.environment.gov.au/
http://www.dwr.csiro.au/
http://igwmc.mines.colorado.edu:3851/
http://www.ssc.upenn.edu/~mewin/
http://www.cciw.ca/nwri/intro.html
http://www.cwr.uwa.edu.au
http://www.env.gov.bc.ca/~wqb/
http://wrsru7.ncl.ac.uk/
http://http://www.ec.gc.ca/envhome.html
Private/Industry/Academic Organizations
American Water Resources Association (AWRA)
American Water Works Association (AWWA)
Farm*A*Syst/Home*A*Syst
National Drought Mitigation Center
National Institutes for Water Resources
Pipe Association Global
The Riess Institute
Wasser & Boden (Water & Soil) (German)
Watershed '98,Water Environment Federation
http://www.uwin.siu.edu/~awra
http://www.awwa.org/
http://www.wisc.edu/farmasyst
http://enso.unl.edu/ndmc
http://wrri.eng.clemson.edu/
http://www.pag.org
http://www.riess.org
http ://www.blackwis .com/wabo.htm
http://www.wef.org/docs/watershed.denver.html
Collections of Water Information and Data Sources
Air and Water Quality (Environment) Directories
Biodiversity and Ecosystems Information
Bottled Water Web
Browse EPA Topics
Cadillac Desert Online
Encyclopedia of Water Terms
Engineers Online
Environment Online
Environmental Law
Environmental Professional's Homepage
EPA Watershed Tools Directory
Global Change Master Directory
Groundwater Remediation Project, Environment
Canada
Hydrogen Peroxide Online
Inter-American Water Resources Network (IWRN)
Lifewater International
National Extension Water Quality Database
Pollution Online
Public Works Online
Selected Links to Hydrological and Related Servers
Selected Info. Res. for NFS Poll. Reduction for MN
River Basin
Selected Water Quality Related WWW
http://www.einet.net/galaxy/Community/Environment/Air
-and-Water-Quality.html
http://straylight.tamu.edu/bene/home/bene.information.
html
http://www.silcom.com/~water
http://www.epa.gov/epahome/browse.htm
http://www.crpi.org/cadillacdesert/
http://www.tec.org/tec/terms2.hrml
http://www.engineersonline.com
http://www.environmentonline.com/
http://www.webcom.com/~staber/welcome.html
http://www.clay.net/
http://www.epa.gov/OWOW/watershed/tools/
http://gcmd.gsfc.nasa.gov/gcmdhome.html
http://gwrp.cciw.ca/index_e.html
http://www.h2o2.com
http://www.uwin.siu.edu/WRN/orgs/US/data/
http://earthview.sdsu.edu/lifewater/lifewater.html
http://hermes.ecn.purdue.edu:8001/server/water/water.
html
http://www.pollutiononline.com
http://www.publicworks.com
http://wrsru7.ncl.ac.uk/links.html
http://www.soils.agri.umn.edu/research/mn-river/doc/
edinfowb.html
http://www.inform.umd.edu/EdRes/Topic/AgrEnv/Water/
.www.html
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Sewage Net http://www.sewage.net
Software for Ground Water Scientists http://gwrp.cciw.ca/internet/software.html
Solid Waste Online http://www.solidwaste.com
The EnviroWeb http://envirolink.org/
Universities Water Information Network (UWIN) http://www.uwin.siu.edu
US National Biological Survey http://www.nfrcg.gov/
Universities Water Information Network (UWIN): http://www.uwin.siu.edu:80//WaterSites/browse.html
Wetlist
Universities Water Information Network (UWIN): http://www.uwin.siu.edu/databases/wrsic/index.html
USGS WRSIC Research Abstracts
Water Resources Databases http://www.nal.usda.gov/wqic/dbases.html
Water Quality Topics http://hammock.ifas.ufl.edu/text/wq/19634.html
Water Quality Information Center http://www.inform.umd.edu/EdRes/Topic/AgrEnv/Water
Water Online http://www.wateronline.com
Water Resources Discussion List http://www.inform.umd.edu/EdRes/Topic/AgrEnv/Water/
Iist3.txt
Water Publications Digest http://alpha.wcoil.com:80/~waterdig/index.html
Waterloo's Environmental Information Systems Project http://bordeaux.uwaterloo.ca/
WaterWiser: The Water Efficiency Clearinghouse http://www.waterwiser.org/
WaterWiser - The Water Efficiency Clearinghouse http://www.waterwiser.org
WWF Global Network http://www.panda.org/
WWW Virtual Library: Environment http://ecosys.drdr.virginia.edu/Environment.html
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m-644
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Section IV
Conference Wrap-up/Summary of Open Discussion
The final portion of the conference was a general discussion by the entire attendance, where
participants were encouraged to highlight issues that had not received much attention throughout
the meeting. Additionally, the session was designed to encourage feedback from participants on
the structure and design of the conference. A show of hands revealed that approximately 80
percent of the audience were from federal, state, and tribal agencies. The remaining 20 were an
even mixture of academia, private industry, and environmental organizations.
Two areas that were brought up as having received insufficient or only minimal airtime
during the conference were tribal monitoring activities and monitoring in coastal and marine
systems. Valid tribal monitoring data and interpretation results need to be given attention and
integrated into broader geographic assessments. There also needs to be increased awareness of
and effort in using the results from coastal and marine monitoring activities. For example, there
is substantial high-quality research and monitoring that is ongoing in coral reef systems through-
out national and territorial waters. Attention given these areas by the Council needs to be
heightened, possibly by having designated leads appointed.
Goals were suggested for the Council. Some ideas included a public outreach goal that
emphasized the usefulness of monitoring. A suggestion for a technical goal was that the Council
should set up a clearinghouse for data and information. Should a clearinghouse be set up,
someone or some agency needs to be responsible for creating a structure for interpretation, as
noted by a conference participant.
There were advocates of volunteer monitoring who reminded the group that volunteers can
collect reliable data. Volunteer programs, in many cases, need agency, or technical, support and
good training. One obstacle to volunteer monitoring is resistance to incorporating volunteer-
collected data into data systems.
There was discussion around the issue of how Council recommendations should be framed;
that is, whether to have them strictly technically oriented or "science-based," or to make efforts
to have them obviously applicable to policy and socioeconomic decisions. Both opinions were
voiced, but it was generally felt that policy decisions need to be made using sound science and
that reliance strictly on political needs should be minimized.
Most members of the audience felt that conferences such as this need to continue and that
emphasis needs to be placed on balancing the number of sessions with opportunities for interac-
tions, thematic presentations of information, increased discussions on sharing of information, and
information formats that allow monitoring and assessment results to be used for management and
policy decision making.
IV-1
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Appendix A
Agenda
A-l
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A-2
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Monitoring: Critical Foundations to Protecting Our Waters
First National Monitoring Conference • July 7-9,1998
Silver Legacy Resort Casino • Reno, Nevada
FINAL AGENDA
TUESDAY, July 7
8:00 - 8:45 am Registration/Coffee
8:45 - 9:00 am Welcome to the first National Monitoring Conference
>• Mark Schaefer, USGS
9:00 - 9:30 am Overview of National Water Quality Monitoring Council (NWQMC)
> Elizabeth Fellows, U.S. EPA, and John Klein, USGS
9:30 - 9:45 am Overview of Conference Goals & Structure
" Rodney DeHan, FL DEP
9:45 - 11:45 am Monitoring: A New Paradigm (Panel Discussion)
»• Moderator: Herb Brass, U.S. EPA
> Panelists: Ellen McCarron, Florida Department of Environmental Protection;
Robbie Savage, Association of State and Interstate Water Pollution Control
Administrators; George Ice, National Council of the Paper Industry for Air and
Stream Improvement, Inc.; Robert Ward, Colorado State University; Steve Paulsen,
U.S. EPA National Health and Environmental Effects Research Lab; Barbara
Erickson, Arizona Department of Health Services
ll:45am-l:00pm LUNCH
1:00 - 2:30 pm Concurrent Workshop Session a
al/A Room 1 Monitoring Design I (Track A)
*• Moderator: Tom Sanders, CSU • Facilitator: Chris Victoria, Tt
— A Locally Designed Watershed Monitoring Program, John Cavese, Ottawa
River Coalition
— Design of Stream Sampling Networks and a GIS Method for Assessing Spatial
Bias, Alison C. Simcox, Dept. of Civil & Env. Eng, Tufts U.
— Mid-Atlantic Regional Pilot for Integrated Monitoring and Research, Pixie
Hamilton, USGS
a2/B Room 2 Data Comparability & Collection Methods I (Track B)
> Moderator: Fred Banach, CT DEP • Facilitator: Paul Jehn, GWPC
— Collecting Nationally Comparable and Defensible Water-Quality Data,
Franceska Wilde, USGS
— A Comparison of Water-Quality Sample Collection Methods Used by the U.S.
Geological Survey and the Wisconsin Department of Natural Resources,
Herbert Garn, USGS
— Quantification ofDioxin Concentrations in the Ohio River Using High Volume
Water Sampling, Samuel A. Dinkins, ORS ANCO
A-3
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a3/C Room 3 Biological Indicators and Reference Condition Development I (Track C)
•• Moderator: Chris Yoder, OH EPA • Facilitator: Sam Stribling, Tt
— Biological Criteria Development for the Ohio River, USA, Erich B. Emery,
ORSANCO
— Rapid Bioassessment ofBenthic Macroinvertebrates Illustrates the Degradation
of Water Quality in Small Order Urban Streams in a North Carolina Piedmont
City, Kimberly Yandora, City of Greensboro
— Bioassessments in Arizona: What Is Different about Biomonitoring in
Southwestern Streams?, Patti Spindler, AZ DEQ
— Developing Stream Reference Conditions for Maryland Using a Benthic Index
of Biological Integrity, Sam Stribling, Tt
a4/D Room 4 Vulnerability Assessment (Track D)
> Moderator: Dennis Helsel, USGS • Facilitator: Steve Roy, Tt
— Aquifer Vulnerability: Now You See It; Now You Don't, Dennis Helsel, USGS
— Nitrate in Ground Waters of the United States, Bernard T. Nolan, USGS
— Use of a Numerical Rating Model to Determine the Vulnerability of Community
Water-Supply Wells in New Jersey to Contamination by Pesticides, Eric F.
Vowinkel, USGS
— A "DRASTIC" Improvement to Ground-Water Vulnerability Mapping, Michael
G. Rupert, USGS
— Assessing Ground Water Vulnerability to Nitrate in the Puget Sound Basin
Using Logistic Regression, Anthony J. Tesoriero, USGS
a5/D Room 5 Monitoring Partnerships Lead to Success I (Track D)
*• Moderator: Linda Green, URI • Facilitator: Sue Laufer, Tt
— Eutrophication and Recovery of Yawgoo and Barber Ponds, Linda Green, URI,
Watershed Watch
— Organizing Regional Citizen Volunteer Monitoring Networks, Geoff Dates,
RWN
— The Puget Sound Ambient Monitoring Program - Case Study of Coordinated
Regional/State Monitoring, Scott Redman, Puget Sound Ambient Mon. Pro.
2:30-3:00pm BREAK
3:00 - 4:30 pm Concurrent Workshop Session b
bl/A Room 1 Monitoring Design II (Track A)
•> Moderator: Rodney DeHan, FL DEP • Facilitator: Paul Jehn, GWPC
— Sampling Designs for Pesticides During Stable and Unstable Hydrologic
Conditions in the San Joaquin River Basin, California, Charles R. Kratzer,
USGS
— Interagency Binational Monitoring Program for Toxic Substances in the New
River and Lower Colorado Rivers along the U.S./Mexico Border, Edwin H. Liu,
U.S. EPA
— Designing a Comprehensive, Integrated Water Resources Monitoring Program
for Florida, Rick Copeland, FL DEP
A-4
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52/B Room 2 Data Comparability and Collection Methods II (Track B)
* Moderator: Michal Harthill, USGS • Facilitator: Jerry Diamond, Tt
— An Alternative Regression Method for Constituent Loads From Streams, Ping
Wang, MD DNR
— Determining Comparability of Bioassessment Methods and Their Results, Jerry
Diamond, Tt
— Performance Based Methods System, Ann B. Strong, US ACE
— Comparison of Temporal Trends in Ambient and Compliance Trace Element
and PCB Data in Pool 2 of the Mississippi River, 1985-95, Jesse Anderson,
USGS
b3/C Room 3 Biological Indicators And Reference Condition Development II (Track C)
> Moderator: Mike O'Neil, Utah SU • Facilitator: Chris Victoria, Tt
— Regional Scale Results and Utilization of a Probability-Based Monitoring
Design and Ecological Indicators in Regional and State Environmental
Assessments, Lyle Cowles, U.S. EPA
— The Influence of Land Use and Stream Morphology on Urban Stream Water
Quality, Judith A. Gerlach Okay, VA Dept. Forestry
— The Development of Environmental Indicators in Support of State Water
Quality Management Program Objectives, Chris O. Yoder, OH EPA
b4/D Room 4 Monitoring Partnerships Lead to Success II (Track D)
>• Moderator: Fred Van Alstyne, NY DEC • Facilitator: Steve Roy, Tt
— Integrating Ambient and Compliance Monitoring in the Kennebec River Basin,
Maine, Keith Robinson, USGS
— Evaluation of Nutrient Loads and Sources in the Ohio River Basin, Samuel
Dinkins, ORSANCO
— Institutional Challenges in Monitoring - Stream Gaging as an Example,
Emery T. Cleaves, MD GS
b5/A Room 5 Monitoring Coastal Systems I (Track A)
>• Moderator: Chuck Spooner, U.S. EPA • Facilitator: Mike Paque, GWPC
— Southern California Bight Regional Monitoring, Janet Y. Hashimoto, U.S. EPA,
and Stephen Weisburg, SCCWRP
— NOAA's Mussel Watch Project: 11 Years of Coastal Monitoring for Chemical
Contaminants, Gunnar Lauenstein, NOAA/NOS/ORCA
— Prioritizing Coastal Watersheds to Support USGS Fall Line Monitoring Results
from a Federal/State Working Group, S. Paul Orlando, Jr., NOAA/ORCA
6:00 - 8:00 pm Reception and Social Hour
WEDNESDAY, July 8
8:30 - 10:00 am Concurrent Workshop Session C
cl/A Room 1 Monitoring Coastal Systems II (Track A)
» Moderator: Paul Orlando, NOAA/ORCA • Facilitator: Mike Paque, GWPC
— Trends in Water and Bottom-Sediment Quality in the San Juan Bay Estuary
System, Puerto Rico, 1925-95, Richard M.T. Webb, USGS
— Warm Season Algal Populations in Four Long Island Sound Harbors, Steven
Yergeau, Save The Sound Inc.
— Performance-based Quality Assurance—the NOAA National Status and Trends
Program Experience, Gunnar Lauenstein, NOAA/NOS/ORCA
A-5
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c2/B Room 2 Data Comparability and Collection Methods HI (Track B)
>• Moderator: Linda Green, URI • Facilitator: Paul Jehn, GWPC
— Real Time Automated Biomonitoring for the Rapid Detection of Waterborne
Toxicants, Jason Ezratty, PA State U.
— High Resolution Water-Column Profiles at Payette Lake, Idaho, Paul F. Woods,
USGS
c3/A Room 3 Nonpoint Source Monitoring I (Track A)
>• Moderator: Dan Smith, USDA • Facilitator: Jerry Diamond, Tt
— Trend Detection in Land Treatment and Water Quality Data for the Herrings
Marsh Run Watershed, Jean Spooner, NC State U. Water Quality Monitoring
Group
— Pesticides in Surface Waters in the Mid-Atlantic Integrated Assessment Region,
Scott Ator, USGS
— Alternatives for Evaluating Water Quality and BMP Effectiveness at the
Watershed Scale, George Ice, National Council of the Paper Industry
c4/C Room 4 Wetlands Indicators (Track C)
» Moderator: Michal Harthill, USGS • Facilitator: Sam Stribling, Tt
— Developing Indicators of Biological Condition for Montana Wetlands,
R. Apfelbeck, Montana DEQ
— Bioassessment of Wetlands, Thomas J. Danielson, U.S. EPA-Wetlands Division
c5/D Room 5 Monitoring Partnerships Lead to Success III (Track D)
>• Moderator: Chuck Spooner, U.S. EPA • Facilitator: Sue Laufer, Tt
— Integrating Upland and In-Channel Monitoring Results to Improve Ecosystem
Condition at Heavenly Ski Resort, Sherry Hazelhurst, USDA
-— Collaborative Efforts in Regional Environmental Assessment Among Federal
Agencies in the Mid-Atlantic Region, Joel Blomquist, USGS
— Water Quality Monitoring: "Back to the Future", Robert C. Ward, CSU
— Northwest Biological Assessment Workgroup, Gretchen Hayslip, U.S. EPA
10:00 - 10: 30 am BREAK
10:30 am - 12:00 pm Concurrent Workshop Session d
dl/A Room 1 Monitoring Wetlands (Track A)
> Moderator: Ellen McCarron, FL DEP • Facilitator: Chris Victoria, Tt
— Water Quality Monitoring in a Developing Coastal Region: Fear and Loathing
in Calabash, North Carolina, Janice Nearhoof, UNC
— Spatial and Temporal Trace Level Monitoring Study of South San Francisco
Bay, Daniel Watson, City of San Jose, Environ. Services Dept.
— Water Quality Assessment Program in the Indian River Lagoon, Florida: II.
Redesigning of Monitoring Network, Gilbert C. Sigua, St. John's Water
Management Dis.
d2/B Room 2 Quality Assurance/Quality Control for Monitoring Programs I (Track B)
> Moderator: Elizabeth Herron, URI • Facilitator: Paul Jehn, GWPC
— Tailoring of Field-Methods Data Quality Objectives to Specific Monitoring
Questions, Revital Katznelson, Woodward Clyde
— Pollutant Databases and Data Gathering for Effluent Guidelines Development,
William A. Telliard, USGS
— Quality Assurance/Quality Control Plan for Monitoring Agricultural Non-point
Source Pollution, Tamim Younos, Virginia Tech
A-6
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d3/D Room 3 Monitoring for TMDLs (Track D)
>• Moderator: Don Brady, U.S. EPA • Facilitator: Sue Laufer, Tt
— TMDLs, Don Brady, U.S. EPA
— USGS Role in TMDL Monitoring, Allison A. Shipp, USGS
— Data and Information in TMDL Development, Fred Andes
— State Perspectives on TMDL Monitoring,
d4/C Room 4 Biological Indicators and Reference Condition Development III (Track C)
- Moderator: Don Dycus, TVA • Facilitator: Sam Stribling, Tt
— Indicators of Reservoir Ecological Condition, Don Dycus, TVA
— The Evaluation of Wet Weather Sources of Pollution on Large Rivers Utilizing
Biological Communities, Erich Emery, ORSANCO
— Evaluation Monitoring as an Alternative to Conventional Water Quality
Monitoring for Assessing the Water Quality Characteristics of a Waterbody,
Fred Lee, G.Fred Lee & Associates
d5/D Room 5 305(b) (Track D)
*• Moderator: Emery Cleaves, MD GS • Facilitator: Steve Roy, Tt
— QA/QC Assessment of Lay Monitoring in Rhode Island, Elizabeth M. Herron,
URI Watershed Watch
— Monitoring Ground Water Quality Within the 305(b) Program, A. Roger
Anzzolin, U.S. EPA
— Developing a Multi-Agency 305(b) Monitoring Program for the Surface and
Coastal Waters of Alabama, Kevin Summers, U.S. EPA
— Important Concepts and Elements of an Adequate State Water Quality
Monitoring and Assessment Framework, Chris O. Yoder, OH EPA
12:00 -l:00pm LUNCH
1:00 - 2:30 pm Concurrent Workshop Session e
el/A Room 1 Monitoring Urban Stormwater and Sewer Discharges I (Track A)
> Moderator: Lynn Singleton, WA Dept. Ecol. • Facilitator: Jerry Diamond, Tt
— Improving Indicator Selection for Regional Stormwater Monitoring, Broch
Bernstein, Ecoanalysis, Inc.
— Storm Water Metals - Issues and Historical Trends, Erich R. Brandstetter,
Lawrence Livermore National Lab
— Stormwater Monitoring, Sarah Meyland, NY Institute of Technology
— A Comprehensive Approach to Urban Storm Water Impact Assessment, Scott
Bryant, City of Greensboro
e2/B Room 2 Quality Assurance/Quality Control for Monitoring Programs II (Track B)
> Moderator: Herb Brass, U.S. EPA • Facilitator: Sue Laufer, Tt
— Calabazas Creek Pilot Sediment Sampling Study, Terrence D. Cooke,
Woodward-Clyde
— Binational Water Quality Monitoring Activities Along the Arizona/Sonora
Border Region, Mario Castaneda, AZ DEQ
A-7
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e3/B Room 3 Tools for Communicating Monitoring Results I (Track B)
» Moderator: Nancy Lopez, USGS • Facilitator: Paul Jehn, GWPC
— Using Data from Existing Monitoring Programs for National Assessment of
Volatile Organic Compounds in Ground Water: Enhancements for an Improved
Understanding of Occurrence and Spatial Distribution, Wayne Lapham, USGS
— Middle Gila River Watershed Assessment Study, Phoenix, Arizona, Juliet
Johnson
— Environmental Monitoring Program to Support the Rouge River National Wet
Weather Demonstration Project, Louis C. Regenmorter, Camp, Dresser and
McKee
e4/A Room 4 Nonpoint Source Monitoring II (Track A)
>• Moderator: Bob Ward, CSU «Facilitator: Mike Paque, GWPC
— Change on the Range - Water and Habitat Monitoring for Management
Practice Effectiveness, Morro Bay National Monitoring Program, Karen R.
Worcester, Regional Water Quality Control Board
— Nitrate Loads Contributed by Springs Along the Suwannee River, John R.
Pittman, USGS
— Identifying the Potential for Nitrate Contamination of Shallow Ground Water
and Streams in Agricultural Areas of the United States, David K. Mueller,
USGS
e5/C Room 5 Watershed Indicators (Track C)
> Moderator: Carol Keppy, CTIC • Facilitator: Sam Stribling, Tt
— The Index of Watershed Indicators - An Evolving National Tool, Sarah
Lehmann, U.S. EPA
— Great All American Secchi Dip-In, Bob Carlson, Kent State U.
— Long Term Surface-Water Quality Trends in Virginia, Carl Zipper, Virginia
Tech
— Daily Loads and Yields of Suspended Sediment and Nutrients for Watersheds in
Lake Tahoe Basin, California and Nevada, Timothy Rowe, USGS
2:30 - 3:30 pm Poster Session (with authors present)
3:30 - 5:00 pm Concurrent Workshop Session f
f I/A Room 1 Multidimensional Watershed Monitoring (Track A)
•• Moderator: Rodney De Han, FL DEP • Facilitator: Chris Victoria, Tt
— Key Monitoring Questions: Designing Water Quality Monitoring and
Assessment Systems to Meet Multiple Objectives, James E. Harrison, U.S. EPA
— Monitoring the Collective Human "Footprint" on Water Quality, Adrienne
Greve, CSU
— Quantifying Interactions Between Ground Water and Surface Water in Karst
Systems, Florida, Brian G. Katz, USGS
— Contamination Sources and Restoration Method for a Polluted Stream in a
Karst-Water Environment, Tamim Younos, Virginia Tech
f2/A Room 2 Monitoring Urban Stormwater and Sewer Discharges II (Track A)
>• Moderator: Geoff Dates, RWN • Facilitator: Jerry Diamond, Tt
— Water Quality Monitoring for Integrated Wastewater and Stormwater
Management, Lawrence B. Cahoon, UNC Wilmington
— Monitoring the Beneficial Impacts ofCSO Control Implementation, Carol L.
Hufnagel, Mcnamee, Porter & Seely, Inc.
A-8
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f3/A Room 3 Nonpoint Source Monitoring III (Track A)
>• Moderator: Tony Wagner, CMA • Facilitator: Mike Paque, GWPC
— Evaluating Nonpoint-Source-Pollution Loading from Ground to Surface Water
Using Shallow Monitoring-Well Networks, Michael E. Serfes, NJ GS
— A Reconnaissance for New, Low-Application Rate Herbicides in Surface and
Ground Water in the Midwestern United States, 1998, William A. Battaglin,
USGS
— Nitrate and Selected Pesticides in Ground Water of the Mid-Atlantic Region,
Joel Blomquist, USGS
f4/B Room 4 Tools for Communicating Monitoring Results II (Track B)
> Moderator: Fred Banach, CT DEP • Facilitator: Sue Laufer, Tt
— Road Map for Consensus in Watershed Management ofCatawba River, Carl W.
Chen, Systech Engineering, Inc.
— Environmental Indicators for Evaluating Progress: Using Historical Data for
Comparisons or Baselines?, Jon Harcum, Tt
f5/D Room 5 Source Water Issues, Both Surface and Ground Water (Track D)
•• Moderator: Dave Denig-Chakroff, AMWA - Facilitator: Steve Roy, Tt
— Linking Water Quality Data for Source Water Assessments, Steven P. Roy, Tt
— The U.S. Geological Survey's Drinking Water Initiative: Science,
Policy, and Management, Mike Focazio, USGS
6:00-8:00 pm Reception and Social Hour
8:00 -10:00 pm Track Meetings/Work Sessions
(These work sessions will involve track coordinators, workshop facilitators/moderators
and others interested in developing the presentations for the Track Reports on Thursday
morning.)
THURSDAY, July 9
8:30-9:00 am Final Announcements and Introduction of Track Reports
9:00 - 9:45 am Reports from Tracks A and B
»• Paul Orlando—Track A • Nancy Lopez—Track B
9:45-10:15 am BREAK
10:15 -11 :00 am Reports from Tracks C and D
» Michal Harthill—Track C • Dave Denig-Chakroff—Track D
11:00 am - 12:00 pm Open Mike Session—Discussion of Track Reports and Conference
*• Moderator: Linda Green, URI
12:00 - 12:30 pm Next Steps and Adjournment
>• John Klein, USGS, and Chuck Spooner, U.S. EPA
A-9
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Appendix B
List of Participants
B-l
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B-2
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NWQMC National Monitoring Conference -1998
Name
Jacqueline
Greg Aamodt
Kip Allander
Bruce Anderson
Duane Anderson
Jesse Anderson
Mark Anderson
Fred Andes
Roger Anzzolin
Randall Apfelbeck
Jeffery Armbruster
Don Arnold
Lorenzo Arriaga
Donna Ashizawa
Mark Asmundson
Scott Ator
William Backous,
P.E.
Bruce Baker
Teena Ballard
Title
Environmentalist II
4ydrologist
Hydrologist
Manager
Biologist
Environmental
Specialist
Geologist
Water Quality
Specialist
Asst. Reg.
Hydr/NAWQA
Biologist
US/ Mexico Border
Coordinator
Volunteer
Coordinator
Mayor
Program Manager
Director, Bureau of
Water Resources
WQ Monitoring
Coordinator
Affiliation
Friends of The Los Angeles
River
Carver County Environmental
Services
USGS, WRD
Land and Water Consulting
nc.
MN Pollution Control Agency
USGS, WRD
Alaska Dept. of
Environmental Conservation
Sonnenschein, Nath &
Rosenthal
U.S. EPA
MT Dept of Environmental
Quality
USGS
City of San Jose
Bureau of Reclamation
Kailua Bay Advisory Council
City of Bellingham,
Washington
USGS-WRD
WA Department of Ecology
Dept. of Natural Resources
Union Soil & Water
Conservation District
Address
115 W California
Blvd#183
600 East Forth St.
333 W Nye In.
P.O. Box 8254
520 Lafayette Rd.
North
2280 Woodale Dr.
Air & Water Quality
410 Willoughby Ave.
Ste105
8000 Soars Tanor
401 "M" St., SW
(WH-550)
2209 Phoenix Ave.
3850 Halcomb Br.
Rd., Suite 160
4245 Zanker Rd.
700 E. San Antonio
Ave., Rm B-318
45-270 William Henry
Rd., Rm201
210 Lottie Street
8987 Yellow Brick
road
P.O. Box 47600
101 S.Webster St.
Ao/5
10507N. McAlister
Rd.
City/State
Pasadena, CA
91005
Chaska, MN
55318-2158
Carson, NV
89428
Missoula, MT
59807
St. Paul, MN
55155
Mounds View,
MN55112
Juneau, AK
99801-1795
Chicago, IL
60606-6404
Washington, DC
20460
Helena, MT
59620-0901
Norcross, GA
30092
San Jose, CA
95134
El Paso, TX
79901
Kaneohe, HI
96744
Bellingham, WA
98225
Baltimore, MD
21237
Olympia, WA
98504-7600
Madison, Wl
53707
LaGrande, OR
97850
Phone
(626) 794-0487
(612)361-1804
(702) 887-7675
(406)721-0354
(612)297-1831
(612)783-3230
(907) 465-5274
(312)876-2559
(406) 444-2709
(770) 409-7704
(408) 945-3740
(915)534-6324
(808) 204-0702
(360) 676-6979
(360) 407-6699
(608) 226-2104
(541)963-0724
Fax
(626) 794-0487
(612)361-1828
(702) 887-7629
(406)721-0355
(612)297-2343
(612)783-3103
(907) 465-5274
(312)876-7934
(406) 444-5275
(770) 409-7725
(408) 934-0476
(915)534-6299
(808) 234-0645
(360)738-7418
(360) 407-6884
(608) 267-2800
(541)963-4201
E-mail
jsbish@aol.com
gaamodt @ co.carver.mn.us
kalland@usgs.gov
anderson ©landwater.net
JESSEAND@USGS.GOV
manderso@evircon.state.ak.us
rapfelbeck@mt.gov
armbruster@usgs.gov
don.arnold@ci.sj
Larriaga@uc.usbr.gov
ashizawa@lava.net
masmundson @ cob.org
swator@usgs.gov
bbac461 ©ecy.wa.gov
ballart@orlagrande.fsc.usda.gov
-------
ta
Name
Fred Banach
Pete Baranov
Tim Bartish
William Battaglin
Arthur Becker
Robert Bennet
Marie Bennett
Brian Bennon
Brock Bernstein
Patricia Billingsley
Debra Bills
Sue Blake
Rick Blaskovich
Joel Blomquist
Stephen Blumer
Dragomir
Bogdanic
David Bolin
Erich Brandstetter
Herbert Brass
David Braun
Teresa Brock
Title
Chemist
Biologist
Hydrologist
Environmental
Engineer, Water
Program MGR
President
Water Quality
Specialist
Hydrologist
Fish and Wildlife
Biologist
Whatcom County
Water Resources
Hydrologist
Supervisory
Hydrologist
Environmental
Engineer
Asst. Oil & Gas
Supervisor
Co-Chair, Methods
Board
Sr. Scientist
Affiliation
Connecticut Department of
Environmental Protection
Sweetwater Authority
USGS
USGS
Naval Air Station North Island
Bennet Sample Pumps
Bennett Sample Pumps
Gila River Indian Community
Oklahoma Corporation
Commission
U.S. Fish and Wildlife
Service, DOI
Whatcom County
Bureau of Reclamation
U.S. Geological Survey
USGS
California Department of
Transportation
Alabama Oil & Gas Board
Lawrence Livermore National
Laboratory
U.S. EPA
The Nature Conservancy
Lockheed Martin Idaho
Technologies
Address
79 Elm Street
100 Lake vie wAve.
4512 McMurray Ave.
25046 MS 406
P.O. Box 357040
(Code18e)
6325-B Star Lane,
P.O. Box 7644
P.O. Box 7644
P.O. Box 370
308 Raymond St.
P.O. Box 52000-
2000
2321 W. Royal Palm
Road, Suite 103
5280 NW Drive,
Suite B
8987 Yellow Brick
Road
6520 Mercantile
Way, Suite 5
Office of
Environmental
Engineering, 111
Grand Avenue
P.O. Box "0"
P.O. Box 808, L-627
26 W M.L. King Drive
P.O. Box 1625
City/State
Hartford, CT
06106-1632
Spring Valley,
CA91977
Fort Collins, CO
80525-3400
Denver, CO
80225
San Diego, CA
92135-7040
Amarillo, TX
79114
Amarillo, TX
79110
Sacaton, AZ
85247
Ojai, CA 93023
Oklahoma City,
OK 731 52-2000
Phoenix, AZ
85021
Bellingham, WA
98226
Billings, MT
Baltimore, MD
21237
Lansing, Ml
48911
Oakland, CA
94612
Tuscaloosa, AL
35486-9780
Livermore, CA
94513
Cincinnati, OH
45268
Idaho Falls, ID
83415-4110
Phone
(860) 424-3020
(619)475-9047
(970) 226-9483
(619)545-3024
(806) 352-0264
(806) 352-0264
(520) 562-3203
(805) 646-3849
(405)521-2500
(602) 640-2720
(360) 676-6707
(406) 247-731 1
(410)238-4264
(517)887-8922
(510)286-5669
(205) 349-2852
(925) 424-4961
(513)569-7936
(208) 526-8855
Fax
(860) 424-4055
(619)479-6271
(970) 226-9230
(619)545-2717
(806) 352-0264
(806) 352-0264
(520) 562-3994
(405) 522-0757
(602) 640-2730
(406) 247-7338
(410)238-4210
(517) 887-8937
(510)286-5728
(205) 349-2861
(925) 422-2748
(513)569-7191
(208) 526-2448
E-mail
pbaranov® wq.sweetwater.org
tim_bartish @ usgs.gov
Debra_Bills@FWS ,gov
dblake @ parificrion.net
spblumer® usgs.gov
dbogdani@trmx3.dot.ca.gov
dbolin @ ogb.gsa.tuscaloosa.al.us
brandstetterl @ 1 1 n1 .gov
tb3@inel.gov
-------
Cd
Name
Robert Broshears
Richard Brown
Scott Brown
Anthony Brunetti
Scott Bryant, P.E.
Jeffery Bryant
William Bryson
Benjamin Budka
Maude Bullock
Chris Bunck
Daniel Butler
Anthony Bynum
Lawrence Cahoon
Sharon Campbell
Italo Carcich
Bob Carlson
Christine Carson
Mario Castaneda
William Cauthron
John Cavese
Title
Regional Water
Quality Specialist
Watershed
Coordinator
Consumer Safety
Officer
Chief Engineer
Member Services
Director
Petroleum Policy
Analyst
Environmental
Specialist
Head, Water and
Laboratory Issues
Senior Biologist
Environmental
Scientist
Aquatic Ecologist
Bureau Director
Biohydrology
Senior
Environmental
Specialist
Environmental
Specialist — Lima
Affiliation
USGS
Lawrence Livermore Natl
Laboratory
Montana Salinity Control
Assoc.
U.S. Food & Drug
Administration
City of Greensboro
GWPC
Kansas Geological Survey
King County
Office of the Chief of Naval
Operation
U.S.G.S.
Oklahoma Conservation
Commission
Yakama Indian Nation
Dept. of Biological Sciences
U.S.G.S.
New York State Dept. of Env.
Cons.
Kent State University
The Nature Conservancy
ADEQ
Oklahoma Water Resources
Board
BP Chemicals, Inc
Address
MS 406 Denver
Federal Center
P.O. Box 808
P.O. Box 909
200 C Street, SW
P.O. Box 3136
827 NW 63rd St.,
^Suite 103
709 Lawrence Ave.
322 W. Ewing Street
Code N457D,
Rm 644, 2211 S.
Clark Place
6006 Schroeder Rd.
413N.W. 12th Street
P.O. Box 151
601 South College
Rd.
P.O. Box 25007,
D-80220
50 Wolf Road
P.O. Box 5190
1815 N. Lynn St.
3033 N Central Ave.
3800 N. Classen
Boulevard
P.O. Box 628
City/State
Denver, CO
80225
Livermore, CA
94551
Conrad, MT
59425
Washington, DC
20460
Greensboro, NC
27402-3136
Oklahoma City,
OK 731 16
Lawrence, KS
66049
Seattle, WA
98119
Arlington, VA
22244-5108
Oklahoma City,
OK 731 03
Toppenish, WA
98948
Wilmington, NC
28403-3297
Denver, CO
80225-0007
Albany, NY
12233-3502
Kent, OH
44242-0001
Arlington, VA
22209
Phoenix, AZ
85012
Oklahoma City,
OK 731 18
Lima, OH 45802
Phone
(303) 236-5950
(925) 423-461 1
(406) 278-3071
(202)418-3186
(336)373-2126
(405) 858-9566
(785) 842-8250
(206) 684-2328
(703)602-1738
(608) 270-2407
(405) 979-2200
(509)865-5121
(910)962-3706
(303) 445-2208
(518)457-7488
(330) 672-3849
(703)841-8744
(602) 207-4409
(405) 530-8800
(419)226-1666
Fax
(303)236-5919
(925) 422-2748
(406) 278-3071
(202)418-3198
(336) 373-2988
(405) 848-0722
(785)864-5317
(206) 684-2345
(703) 602-5547
(608)270-2415
(509) 865-5522
(910)962-4066
(303) 445-6328
(330) 672-3713
(703) 247-3674
(602) 207-4528
(405) 530-8900
E-mail
vebroshe @ usgs.gov
brown95@llnl.gov
MSCA@3Rivers.net
Tbrunett@Bangate.fda.gov
scott.bryant@ci.greensboro.nc.us
Jeff @ gwpc.site .net
ben .budka @ netrokc.gov
bullockm@n4.opnau.navy.mil
dbutler@occwq. state. ok.us
cahoon@uncwil.edu
igcarcic@gw.dee.state.ng.us
ccarson@tnc.org
wlcauthron @ owrb.state.ok.us
-------
w
Name
Rebecca
Challender
Bob Charbonneau
David Charkroff
Richard Charter
Liann Chavez
Carl Chen
David Chestnut
Kratai Chidee
Emery Cleaves
Richard Cobb
Elizabeth Cody
Bryce Cole
Russ Collett
Phil Conrad
Victoria Conway
Terence Cooke
Rick Copeland
Denise Coutlakis
Lyle Cowles
Title
Water Quality
Specialist
Environmental
Assessment
Coordinator
Manager
Owner/Director
Sr. Engineering
Geologist
Senior Scientist
Division Buyer
State Geologist
Manager, Ground
Water Section
Ground Water
Coordinator
Assistant Professor
Water Resource
Program Leader
Coordinator,
Ground-Water
Monitoring
Networks
Supervising
Engineer
Administrator,
Ambient Monitoring
3rogram
Environmental
Scientist
Affiliation
USDA
University of California
Madison Water Utility
R. Charter Associates
RWQCB
Systech Engineering, Inc.
SC Dept. of Health and
Environmental Control
City of Austin Resources
Management Division
Maryland Geological Survey
Illinois EPA
Boise City Public Works
Walla Walla College
NRCS Oregon
Kentucky Geological Survey
Los Angeles County
Sanitation Districts
Woodward-Clyde International
Americas
State of Florida, Department
of Environmental Protection
U.S. EPA
U.S. EPA
Address
21 21 -C Second
Street
1111 Franklin St.
523 East Main Street
6947 Cliff Ave.
73-720 Fred Waring
Dr., Suite 100
3 180 Crow Canyon
Place, Suite 260
2600 Bull Street
P.O. Box 1088
2300 St Paul Street
1021 N. Grand Ave.
P.O. Box 19276
P.O. Box 500
1 1 1 SW 3rd Street
101 SWMain,
Suite 1300
228 Mining & Mineral
Resources Bldg.
P.O. Box 4998
500 12th Street
2600 Blair Stone
Road
401 M Steet, SW
(MS-4606)
25 Funston Rd.
City/State
Davis, CA
95616
Oakland, CA
94607
Madison, Wl
53703
Bodesa Bay,
CA 94923
Palm Desert,
CA 92260
San Ramon, CA
94586
Columbia, SC
29201
Austin, TX
78767
Baltimore, MD
21218
Springfield, IL
62794-9276
Boise, ID
83701-0500
College Place,
WA 99324
Portland, OR
97204
Lexington, KY
40506-0107
Whitter, CA
90607
Oakland, CA
94067-4014
Tallahassee, FL
32399-2400
Washington, DC
20460
Kansas City, KS
66115
Phone
(530) 757-8264
(510)987-9594
(608) 266-4652
(707) 875-2345
(803) 734-5393
(217) 785-4787
(208) 384-3901
(509) 527-2295
(503)414-3204
(606) 257-5509
(562)699-7411
(850)921-9421
(202) 260-5558
(913)551-5042
Fax
(530) 757-8217
(510)987-0752
(608) 266-4426
(707) 875-2947
(803) 734-4435
(217) 782-0075
(208) 384-3905
(509) 527-2867
(503)414-3103
(606)257-1147
(562)692-5103
(850)921-5655
(202) 260-0273
(913)551-5218
E-mail
rchallender@ca.nrcs.usda.gov
ddenigehakroff @ ci.madison.wi
waterway @ monitor.net
chavl @ rb67.swrcb.ca.gov
systecheng @ compuserve.com
chestnde@columb32.dhec.state.sc.us
ecleaves @ mgs.dnr.md.gov
epa31 88® epa.state.il. us
ecody @ pobox.ci.boise.id.us
colebr@wwc.edu
rcollett@or.nrcs.usda.gov
vconway@lacsd.org
copeland-R @ dep.state.pl. us
-------
Name
Jim Cox
Richard Craig
Janet Crockett
Joel Cross
Patricia Crowley
Allen Gulp
Marc Dahlberg
James Daily
Thomas Dallarie
Tom Danielson
Geoff Dates
Jeffrey Davies
Donna
DeFrancesgo
Rodney DeHan
Charles Demas
David Denig-
Chakroff
Richard Denton
Leslie DeSimone
Dave DeTullio
Jerome Diamond
Samuel Dinkins
Title
Environmental
Coordinator
Sr. Ground Water
Quality Analyst
Planning Section
Manager
Ag Hydrologist
Wildlife Specialist
Resource Inventory
Specialist
Environmental
Analyst
Ecologist
Science Director
Environmental
Scientist
Research
Specialist
Senior Research
Scientist
QW Specialist
Manager
Monitoring Section
Manager
Hydrologist
Water Resources
Pol. Coor.
Environmental
Scientist
Affiliation
NASCA
Confederated Tribes of Warm
Springs
daho Dept. of Water
Resourses
llnois EPA
Kieser & Associates
The Nature Conservancy
Arizona Game and Fish Dept.
USDA/NRCS
Mass. DEP
U.S. EPA
River Watch Network
Alabama Department
Environmental Mgt.
University of Montana
Florida Geological Survey
USGS
Madison Water Utility
Utah Division of Water Quality
USGS
USDA-NRCS
TetraTech, Inc.
ORSANCO
Address
Rt 1 Box 81 2
P.O. Box C
1301 N. Orchard
1021 N. Grand Ave
East
301 E. Michigan
Ave., Suite 505
201 Fvazier Ave.
2221 W. Greenway
5301 Longley Lane
Bldg. F, Suite 201
627 Main Street,
2nd Floor
401 M Street, SW
(4502F)
RR 1 , Box 209
2204 Perimeter Rd.
School of Forestry,
Univ. of Montana
903 W. Tennessee
3535 S. Sherwood
Forrest Blvd.
523 East Main Street
288 N. 1460W
SLC41
28 Lord Rd.,
Suite 280
5301 Longley Lane
Bldg. F, Suite 220
10045 Red Run
Blvd., Suite 110
5735 Kellogg Avenue
City/State
Tappahannock,
VA 22560
Warm Springs,
OR 97761
Boise, ID 83706
Springfield, IL
62702
Kalamazoo, Ml
99007
Phoenix, AZ
85023
Reno, NV
89511-1805
Worcester, MA
01608
Washington, DC
20460
Hartiand, VT
05048
Mobile, AL
36633
Missoula, MT
Tallahassee, FL
32304-7700
Baton Rouge,
LA 7081 6
Madison, Wl
53703
Salt Lake City,
UT 84 11 4-4870
Martborough,
MA 01752
Reno, NV
89511
Owings Mills,
MD 21117
Cincinnati, OH
45228
Phone
(804)443-1527
(541 ) 553-3462
(208) 327-5445
(217)782-3362
(616)344-7117
(602) 789-3260
(702) 784-5208
(508) 767-2744
(202) 260-5299
(802) 436-2544
(334) 450-3400
(406)243-4813
(850) 488-9380
(504) 389-0281
(608) 266-4652
(801)538-6055
(508)490-5010
(702) 784-5863
Fax
(804)443-1751
(541)553-1994
(217)785-1225
(616)344-2493
(602) 789-3265
(702) 784-5939
(508)791-4131
(202) 260-8000
(802) 436-2544
(334) 479-2593
(406) 243-2571
(850) 488-8086
(504) 389-0706
(608) 266-4426
(801)538-6016
(508) 490-5068
(702) 784-5939
E-mail
Jcox® Access. DiGex. Net
jcrocket® idwr.state.id.us
epa1252@state.il.us
kieser@net-link.net
bdaily@nv.nrcs.usda.gov
thomas.dallarie-eqe@state.ma.us
geoff@rwn.igc.org
defrace @ selway. umt.edu
Dehan_R@DEP.state.fl.us
crdemas@usgs.gov
ddenigchakroff@ci.madison.wi.us
rdenton@deq.state.ut.us
ddetulli @ NV.nrcs.usda.gov
erryd@ccpl.carr.lib.md. us
-------
Name
Anne Dix
Wayne Dreyer
David Drury
Judy Dunscomb
Jack Dutra
Don Dycus
Pat Earley
Vance Eflin
Erin Eid
Erich Emery
Bob Erickson
Barbara Erickson,
Ph.D
Susan Pagan
Amanda Fawley
Elizabeth Fellows
Michael Focazio
Tori Foerste
Theresa
Fogelsong
John Gallagher
Chris Gannon
Linda Gardner
Title
Environmental
Division Chief, LTC
Senior Civil
Engineer
President
Aquatic Biologist
Environmental
Scientist
Chief of
Surveillance
Env. Scientist
Bureau Chief
Deputy Director
Hydrologist
Soil Scientist
Surface Water
Quality Investigator
Affiliation
UVG
Nevada Army National Guard
SCVWD
The Nature Conservancy
J.D. Information Services, Inc.
Tennesee Valley Authority
SPAWAR/CSC
Guam Environmental
Protection Agency
Minn. Pollution Control
Agency
OH River Valley Water
Sanitation Comm.
U.S. EPA
Bureau of State Lab Services
U.S. EPA
ADEQ
Office of Ground Water &
Drinking Water-U.S. EPA
U.S. Geological Survey
U.S. Army Corps of Eng.
USGS
National Bureau of Standards
Confederated Tribes of Warm
Springs
Seattle Public Utilities
Address
P.O. Box 025345
1530 Grand Summit
#141
5750 Almaden Exwy
9804 W. 101st Street
1101 Market Street
CST17D
1 369 Elevation Road
P.O. Box 22439
GMF
520 Lafayette Road
North
5735 Kellogg Ave.
999 18th Street,
Suite 500
1520 West Adams
401 M Street, SW
3033 North Central
Ave.
401 M Street, SW
EPA 401 M. Street
S.W., Suite 1219,
MC 4607
Fret of Arsonal St.
333 W Nyelane
9226 Mellenbrook
Rd.
P.O. Box C
7102nd Ave.
City/State
Miami, FL
33102-5345
Reno, NV
89523
San Jose, CA
95118
Overland Park,
KS 662123343
Chattanooga,
TN 37402
San Diego, CA
92110
Barrigada,
Guam 67921
St. Paul, MN
Cincinatti, OH
45228
Denver, CO
80202-2466
Phoenix, AZ
85007
Washington, DC
20460-4503
Phoenix, Az
85012
Washington, DC
20460
Washington, DC
20460
St. Louis, MO
63118
Carson City, NV
89706
Columbia, MD
21045
Warm Springs,
OR 97761
Seattle, WA
98104
Phone
(502)361-7001
(702) 787-5694
(408)927-0710
(913)492-5759
(423)751-7322
(619)553-2768
(671)475-1664
(303)312-7012
(602)542-1194
(202) 260-9477
(602) 207-0000
(202) 260-7082
(202) 260-3080
(314)263-4008
(702) 887-7649
(541)553-3462
(206) 386-4024
Fax
(502)361-7011
(408) 268-7687
(913)492-2881
(423)751-7648
(619)553-5404
(671)477-9402
(303)312-6017
(602) 542-0760
(202)260-1977
(602) 207-4528
(202) 260-4383
(202) 260-3762
(703) 648-6684
(541)541-1994
(206) 684-8529
E-mail
adix@kirika.uvg.edu. gt
wdreyer@sierra-emh.army.mil
davedrur@scvwd.dst.ca.us
JPDUTA19@idt.Net
dldycus@tua.gov
earl@spwar.navy.mil
emery@orsanco.org
fagan.susan @ epa.gov
fellows.elizabeth.epa.gov
focazio.mike @ epamail.epa.gov
linda.gardner@ci.seattle.wa.us
-------
Name
Herbert Garn
Ron Gauthier
Ellen Geismar
Randy Glover
Robert Gomez
Maria Gomez-
Taylor
Omar Gordillo
Chad Gourtey
Dave Grabiec
Linda Green
Adrienne Greve
Brian Griffin
Greg Gross
Benjamin
Grunewald
Robert Hall
Pixie Hamilton
John Harcum
Sandra Harrell
Title
Chief, Hydrologic
Studies and Data
Collection
Environmental
Scientist
Environmental
Scientist
Sales Manager
Environmental
Specialist
Chemist
SR. Source Control
Insp.
Director
Graduate Research
Assistant
Secretary of
Environment
Supervisor
Associate Director
Environmental
Scientist
Principle Engineer/
Hydrogeologist
Scientist
Affiliation
USGS
U.S. Navy Marine
Environmental Support Office
City of Austin — Watershed
Protection
Hydrolab Corp.
faos Pueblo Environmental
Office
U.S. EPA
The Nature Conservancy
The Nature Conservancy
City of San Jose— ESD
Watershed Watch
Colorado State University
State of Oklahoma
MN Pollution Control Agency
GWPC
U.S. EPA
USGS
Tetra Tech
Space and Naval Warfare
Systems Center, San Diego
Address
8505 Research Way
SPAWAR Systems
Center San Diego
53475 Strothe Rd.
Code D3621
P.O. Box 1088
12921 BumetRd.
P.O. Box1846
401 M Street, SW
10495475 W
4245 Zanker Road
21 OB Woodward
Hall
Chem. & Bioresource
Engr., Rm 100,
Glover, CSU
3800 N. Classen
Boulevard
520 Layfayette Road
North
827 NW 63rd, Suite
103
75 Hawthorne St.
WTR-2
3600 West Broad
Street, Room 606
10045 Red Run
Blvd., Suite 110
Marine
Environmental
Support Office
D3621 53475 Strothe
Road
City/State
Middleton, Wl
53562
San Diego, CA
92152
Austin, TX
78767
Austin, TX
78727
Taos, NM
87571
Washington, DC
20460
Farmington, UT
84025
San Jose, CA
95134
Kingston, Rl
02881-0804
Ft. Collins, CO
80523
Oklahoma City,
OK 731 18
St. Paul, MN
55155
Oklahoma City,
OK 731 16
San Francisco,
CA94105
Richmond, VA
23230
Owings Mills,
MD 21117
San Diego, CA
92152-6326
Phone
(608)821-3828
(619)553-2808
(512)499-6572
(512)255-8841
(505)751-4601
(202)260-1639
(801)451-2501
(408) 945-3028
(401)874-4561
(970)491-1141
(405) 530-8995
(612)297-1831
(405) 848-0690
(415)744-1936
(619)553-2906
Fax
(608)821-3817
(619)553-5404
(512)499-2846
(512)255-3106
(505)751-3905
(202)260-7185
(408) 934-0476
(401)874-4561
(970)491-7369
(405) 530-8999
(612)297-2343
(405) 848-0722
(405)744-1078
(619)553-5404
(916)985-4301
E-mail
hsgarn@usgs.gov
gauth @ spwar.navy.mil
robtgom @ laplaza.org
chadgour@earthlink.net
aig @ lamar.colostate.edu
bcgriffin @ owrb.state.ok.us
ben@gwpc.site.net
hall.robertk@epa.gov
pahamilt® usgs.gov
sandi@nosc.mil
-------
td
Name
James Harrington
Jim Harrison
Martha Harrison
Michalann Harthill
Janet Hashimoto
Gretchen Hayslip
Sherry Hazelhurst
Dennis Helsel
Judith Henderson
Kenneth
Henderson
Carolyn Henne
Allison Hensel
Chris Herrington
Elizabeth Herron
Sarah Hippensteel
Kari Holder
Gary Holm
Title
State Water Quality
Biologist
Environmental
Research
Specialist
Ecologist
Chief, Monitoring
and Assessment
Office
Hydrologist
Project Director
Chief Deputy
Marketing Director
Watershed
Coordinator
Engineering
Associate
Program
Coordinator
Technical Writer
Project Scientist
Affiliation
California Fish and Game
Department
U.S. EPA
Tulane University School of
Medicine
USGS/Biological Resources
Division
U.S. EPA
U.S. EPA
USDA FOREST SERVICE
USGS
CYCLE.
California Department of
Conservation
GREEN
Missouri Com Growers
City of Austin
University of Rhode Island-
Watershed Watch
YSI, Incorporated
International Technology
Corporation
Address
200 S Nimbus Rd.
61 Forsyth Street,
Atlanta Federal
Center
1430 Tulane Ave.
12201 Sunrise Valley
Dr.
75 Hawthorne Street
(WTR-2)
1200 Sixth Avenue,
OEA-095
870 Emerald By
Road, Suite 1
P.O. Box 25046,
MS-415
342-1 1th St
801 K St., 20th Floor
(MS 20)
206 South Fifth Ave.,
Suite 150
3702 W Truman
Blvd., Suite 100
P.O. Box 1088
CE Education Center
- East Alumni Ave.
1700 Brannum Ln.
482 Constitution
Way, Suite 1 1 1
Commander Code
1115, Pudget Sound
Naval Shipyard, 1400
Farragut Avenue
City/State
Rancho
Cordova, CA
95670
Atlanta, GA
30303
New Orleans,
LA 70112
Reston, VA
20192
San Francisco,
CA94105
Seattle, WA
98101
South Lake
Tahoe, CA
96150
Denver, CO
80225
Richmond CA
94801
Sacramento,
CA 9581 4-3530
Ann Arbor, Ml
48104
Jefferson City,
MO 65709
Austin, TX
78767
Kingston, Rl
02881
Yellow Spring,
OH 45387
Idaho Falls, ID
83402
Bremerton, WA
98314-5001
Phone
(916)358-2858
(404) 562-9271
(504)585-6145
(703) 648-4077
(415)744-1933
(303)236-2101
(510)233-1415
(916)323-1777
(734)761-8142
(573)893-4181
(512)499-2840
(937) 767-7241
^(208)524-9162
(360) 476-0456
Fax
(404) 562-9224
(504) 587-7641
(703) 648-4238
(415)744-1078
(510)233-1415
(916) 323-0424
(734)761-4951
(573)893-4612
(512)499-2846
(937) 767-9320
(208)524-9166
(360) 476-3477
E-mail
jharr@sna.com
harrison.jim@epamail.epa.gov
mharris @ mailhost.tcs.tulane.edu
michalann_harthill @ usgs.gov
hashimoto.janet@epamail.epa.gov
dhelsel @ usgs.gov
cycle @msn com
khender® consrv.ca.gov
ahensel @ hotmail.com
riww@uriacc.uri.edu
shippensteel @ YSI.com
kholder@ida.net
-------
Name
Wayne Hood
Jeff Homton
Louise Hotka
Mike Houts
Carol Hufnagel
George Ice
Gary Ingman
George Ivey
Ivan James
Lucy Jao
W
^ Paul Jehn
Carolyn Jenkins
Patricia Jennings
Cyril Jim
Forrest John
Juliet Johnson
Lee Johnson
Lynette Johnson
Nils Johnson
Toni Johnson
Title
Water Monitoring
Coordinator
Program Asst.
Administrator
Forest Hydrologist
Bureau Chief,
Monitoring & Data
Man. Bureau
Assistant Regional
Hydrologist,
NAWQA
Lab Manager
Technical Director
Sr. Env. Analyst
Environmental
Engineer
Water & Soil
Technician
Engineer
Water Quality
Specialist
Intern/Grad Student
Information
Coordinator
Affiliation
Arizona Department of
Environmental Quality
The Nature Conservancy
Minnesota Pollution Control
Agency
Oklahoma Department of
Environmental Quality
McNamee, Proter & Seeley,
nc.
NCASI
Montana Department of
Environmental Quality
The Nature Conservancy
USGS
City of Los Angeles
GWPC
NEIWPCC
EPA/OPP
The Confederated Tribes of
Warm Springs
U.S. EPA
Greeley and Hansen
ADEQ
Nevada Department of
Transportation
The Nature Conservancy
USGS Water Info Coord Prog
Address
3033 N. Central
520 Lafayette Road
707 N. Robinson
P.O. Box 1677
220 Bagley,
Suite 710
P.O. Box 458
P.O. Box 200901
Box 25046, MS 406,
Bldg. 53, Room
F-1200
12000 Vista Del Mar
P.O. Box 91 6
255 Ballardvale
5308 Jerrell Ct.
P.O. Box C
1445 Ross Avenue
426 N. 44th St.,
Suite 400
3033 North Central
Ave.
1263 South Stewart
Street
MS 440 National
Center
City/State
Phoenix, A2
85012
St. Paul, MN
55155
Oklahoma City,
OK 731 01 -1677
Detroit, Ml
48226
Corvallis, OR
97339
Helena, MT
59620-0901
Denver, CO
80225
Playa Del Rey,
CA 90293
Republic, WA
99166
Wilmington, MA
01887
Burke, VA
22015
Warm Springs,
OR 97761
Dallas, TX
75202
Phoenix, AZ
85008
Phoenix, AZ
85012
Carson City, NV
89712
Reston, VA
20192
Phone
(612)296-7223
(405)702-8184
(541)752-8801
(406) 444-5320
(303) 236-5950
(310)648-5262
(509) 775-3247
(978) 658-0500
(403) 308-8569
(541)553-3462
(214) 665-8368
(602) 275-5595
(602) 207-4520
(702) 888-7690
(919)419-8533
(703)648-6810
Fax
(612)297-2343
(405)702-8101
(541)752-8806
(406) 444-5275
(303)236-5919
(310)648-5060
(978) 658-5509
(403) 305-6309
(541)553-1994
(214)665-6689
(602)267-1178
(602) 207-4528
(702)888-7104
(703) 648-5644
(416)441-1829
E-mail
hotka@pca.state.mn.us
michael.houts@deqmail.state.ok.us
Hufncaro@mcnamee.com
Gice @ wcrc-ncasi.org
gingman@mt.gov
ijames@usgs.gov
pauljehn @ televar.com
cjenkins @ neiwpcc.org
Jennings.patricia@epamail.epa.gov
john.forrest@epamail.epa.gov
naj2 @ acpub.duke.edu
tjohnson @ usgs.gov
-------
Name
Michelle Joliat
Kathy Jordan
Celeste Journey
James Kaap
Charles Kanetsky
Brian Katz
Revital Katznelson
Richard Kelley
Dan Kelly
Karol Keppy
Bob King
John Klein
Karen Klima
Margery Knight
Gary Kohlhepp
Charlie Kratzer
Joe Kukaus
Joanne Kurklin
Title
Program Specialist
Hydrologist
Water Quality
Specialist
Regional
Monitoring
Coordinator
Research
Hydrologist
Program
Consultant
Project Manager,
Know Your
Watershed
Assistant Regional
Hydrologist for
External Programs
National Programs
Director
Aquatic Biologist
Hydrologist
Senior
Environmental
Engineer
Hydrologist
Affiliation
University of Waterloo
Nevada Division of
Conservation District
USGS
Natural Resources
Conservation Service
EPA Region 3
USGS
Woodward Clyde Consultants
University Hygenic Laboratory
The Nature Conservancy
Cons. Tech. Info. Center
U.S. EPA
U.S. Geological Survey
U.S. EPA
Environmental Alliance for
Senior Involvement
Mighigan Department of
Environmental Quality
USGS
Concurrent Technologies
Corp.
USGS
Address
4 Spinney Ct.
2350 Fairtane Dr.,
Suite 120
65 15 Watts Road
041 Chestnut Bldg.
227 N. BronoughSt.,
Suite 301 5
500 12th Street,
Suite 100
H.A. Wallace
Building, 900 E.
Grand Avenue
1258 Buck Island
Drive
1220 Potter Dr.,
Room 1 70
401 M Street SW
Placer Hall, 6000 J
Street
8733 Old Dumfries
Rd.
P.O. Box 30273
Placer Hall, 6000 'J'
Street
510 Washington
Avenue, Ste 120
202 NW 66th, Bldg. 7
City/State
Don Mills
Ontario,
Canada
M3A3142
Montgomery,
AL 36054
Madison, Wl
53719-2726
Philadelphia,
PA 19107
Tallahassee, FL
32301
Oakland, CA
94607
Des Moines, IA
50319
Klamath Falls,
OR 97601
West Lafayette,
IN 47906-1 383
Washington, DC
20460
Sacramento,
CA 9581 9-61 29
Catlett, VA
20119
Lansing, Ml
48909
Sacramento,
CA95S19
Bremerton, WA
98337
Oklahoma City,
OK 731 16
Phone
(519)886-9037
(334)213-2332
(608) 276-8732
(215) 566-2735
(850) 942-9500
(510)874-3048
(515)281-5371
(541) 882-5406
(765) 494-2238
(202) 260-7028
(916)278-3031
(540) 788-3274
(517)373-1289
(916)278-3076
(360) 475-6906
(405)810-4408
Fax
(334)213-2348
(608) 276-5890
(215)566-2782
(850) 942-9521
(510) 874-3268
(515)243-1349
(541)884-1869
(765) 494-5969
(202)260-1977
(916)278-3045
(540) 788-9301
(517) 373-9958
(916)278-3017
(360) 475-6901
(405)843-7712
E-mail
joliat@bordeaux.uwaterloo.ca
kaap@wi.nres.usda.gov
Kanetsky .charles @ epamail.epa.gov
bkatz@usgs.gov
rxkatznO@wcc.com
richard-kelley @ uiowa.edu
kellyd@celltech.com
keppy@ctic.perdue.edu
king.robert@epa.gov
jmklien@usgs.gov
mknighteco @ aol.com
kohlhepg@state.mi.us
zukaus@ctc.com
jkurklin @ usgs.gov
-------
Name
Vern LaGesse
Eugene Lampi
Jessica Landman
Wayne Lapham
Andrea LaPlante
Christina Lasch
David Lasier
Gunnar Lauenstein
Sue Laufer
Jane Lavelle
Lin Lawson
Kevin Leary
David Lee
G. Lee
Robert Legare
Sarah Lehmann
Vanessa Leiby
Michele Leslie
Mary Ley
Cindy Lin
Title
Project Engineer
Senior Attorney
Hydrologist
WQ/Nutrient
Management
Specialist
Director
Manager, Mussel
Watch Project
Environmental
Specialist
Fluvial
HydroBiologist
Hydrogeologist/Soil
Scientist
President
Water Quality
Tech.
Enviro. Specialist
Protection
Executive Director
Quality Assurance
Officer
Environmental
Scientist
Affiliation
The Nature Conservancy
City of Portland
Natural Resources Defense
Council
USGS
Tioga County Soil & Water
Conservation District
The Nature Conservancy
Friends of Lake Keowee
Society
NOAA National Ocean
Service
Tetra Tech, Inc.
City of San Jose-
Environmental Services Dept.
Arizona Department
Environmental Quality
U.S. DOE
YSI Incorporated
G. Fred Lee & Associates
Montana Salinity Control
Association
U.S. EPA
ASDWA
The Nature Conservancy
Interstate Commission on the
Potomac River Basin
U.S. EPA
Address
1211 SWSth
Avenue, Rm 800
1200 New York
Avenue, NW
12201 Sunrise Valley
Dr.
56 Main Street
Calle 31 #503 x 60 y
60A, Alcola Martin
P.O. Box 239
1305 East West
Highway
10306 Eaton Place
777 North First St.,
Suite 450
400 W. Congress
#433
232 Energy Way
1725 Brannum Lane
27298 E El Macero
Dr.
P.O. Box 909
401 M St. SW
(4503f)
1 1 20 Connecticut
Ave., N.W.,#1060
61 1 0 Executive
Boulevard, Suite 300
75 Hawthorne Street
(WTR-2)
City/State
Portland, OR
Washington, DC
20005
Reston, VA
20192
Owego, NY
13827
Merida,
Yucatan,
Mexico 97050
Salem, SC
29676
Silver Spring,
MD20910
Farfax, VA
22030
San Jose, CA
95112
Tucson, AZ
85701
North Las
Vegas, NV
89030
Yellow Springs,
OH 45387
El Macero, CA
95618
Conrad, MT
59425
Washington, DC
20460
Washington, DC
20036
Rockville, MD
20852
San Francisco,
CA94105
Phone
(503) 823-7097
(202) 289-6868
(703) 648-5805
(607) 687-3553
11529-9202
(864)944-1056
(301)713-3028
(703) 385-6000
(408) 277-5533
(520) 628-6739
(702)295-0184
(937) 767-7241
(530) 753-9630
(406) 278-3071
(202) 260-7021
(202) 293-7655
(410)267-5729
(415)744-1950
Fax
(503) 823-5344
(202)289-1060
(703) 648-6693
(607) 687-6162
(864) 944-8369
(301)713-4388
(703) 385-6007
(408) 277-3606
(520) 628-6745
(702)295-1153
(937) 767-9320
(530) 753-9956
(406) 278-3071
(202)260-1977
(202) 293-7656
(301)984-5841
(415)744-1078
(530) 283-5465
E-mail
eugenel @ bes.ci.portland.or.us
jlandman@nrdc.org
wlapham @ usgs.gov
clThaler@aol.com
laufesu@TetraTech-ffx.com
jane.lavelle@ci.sj.ca.us
Lawson, Lin® ev. state. az.us
lean/® nv.doe.gov
gfredlee@aol.com
MSCA@3rivers.net
Lehmann.sarah@epa.gov
asdwa@interramp.com
Ley.mary@epamail.epa.gov
lin. cindy @ epamail.epa.gov
-------
Cd
Name
Donna Lindquist
Nancy Lopez
Walton Low
Mike Lyday
Cadie MacDonald
Joe Makuch
Jennifer Maloney
Abby Markowitz
Donald Mason
Ruth Matthews
Tom Matzen
Morris Mauney,
Ph.D
Susan McAlpine
Ellen McCarron
Patrick McCarthy
Michael McCarthy
Sylvia McCollor
Robert McConnell
Monty McDaniel
Anne McFartand
Dr. Eugenia
McNaughton
Title
Program
Coordinator
Chief, Water Info.
Coordination
Program
Staff Hydrologist
Environmental
Scientist
Coordinator, Water
Quality Information
Commissioner
Advisory Scientist
Chief Wetlands
Branch, WES
Supervisor, Data
Mgmt and
Monitoring Unit
Monitoring Unit
Manager
Surface Water
Quality Investigator
Environmental
Scientist
Affiliation
Plumas Corporation
USGS
National Water Quality
Assessment Program-USGS
City of Austin
The Nature Conservancy
National Agricultural Library
Minnesota Pollution Control
Agency
Tetra Tech
PUCO
The Nature Conservancy
Lockheed Martin Idaho
Technologies
Waterways Experiment
Station, USAGE
The Nature Conservancy
Florida Department of
Environmental Protection
The Nature Conservancy
Research Triangle Institute
Minnesota Pollution Control
Agency
Colorado Department of
Health & Environment
Seattle Public Utilities
Texas Institute for Applied
Environmental Research
U.S. EPA
Address
P.O. Box 3880
417 National Center
413 National Center
P.O. Box 1088
10301 Baltimore
Blvd., 4th Floor
520 Lafayette Rd.
10045 Red Run
Blvd., Suite 110
180 East Broad
Street, 12th Floor
P.O. Box 876
P.O. Box 1625 MS
4660
3909 Halls Ferry
Road
2600 Blairstone
Road
RTI — Durham Office,
P.O. Box12194
520 Lafayette Rd.
4300 Cherry Creek
Dr., South
7102nd Ave.,
Suite 510
Tarleton State
University — Mail
StopT-0410
75 Hawthorne St.
City/State
Quincy, CA
95971
Reston, VA
20192
Reston, VA
20192
Austin, TX
78767
Beltsville, MD
20705
St Paul, MN
55155-4194
Owings Mills,
MD 21117
Columbus, OH
43215
Apalachicola,
FL 32329
Idaho Falls, ID
83415
Vicksburg, MS
38103
Tallahasse, FL
32399
RTP, NC 27709
St. Paul, MN
55155
Denver, CO
80246
Seattle, WA
98104
Stephenville,
TX 76402
San Francisco,
CA94110
Phone
(530) 283-3739
(703)648-5014
(703) 648-5707
(512)499-2956
(301)504-6077
(410)356-8993
(614)466-3905
(850) 653-31 1 1
(208) 526-7507
(601)634-4258
(850)921-9472
(919)990-8638
(612) 296-7249
(303) 692-3578
(206) 684-7790
(415)744-1162
Fax
(703) 648-5644
(703) 648-6693
(512)499-2846
(301)504-7098
(410)356-9005
(614)466-7366
(850) 653-31 1 1
(208) 526-2448
(601)634-3205
(850)921-5217
(919)990-8639
(612)297-2343
(303) 782-0390
(206) 470-6871
(415)744-1078
E-mail
plumasco @ psln.com
nclopez@usgs.gov
wlow@usgs.gov
jmakuch @ nal.usda.gov
don.mason@puc.state.oh.us
rmatthews@tnc.org
zen@inel.gov
jmm@rti.org
sylvia.mccollor@pca.state.mn.us
robert.mcconnell@state.co.us
monty.mcdaniel @ ci.seattle.wa.us
mcfarla @ tiaer.tarleton.edu
mcnaughton.eugenia@epamail.epa.gov
-------
Name
Robert Mendoza
Robert Mendoza
Dean Messer
Sarah Meyland
Stan Miller
Timothy Miller
Michael Miller
Francelia Miller
Jan Miller
Kelly Mills
Jill Minter
Randy Minyen
Tammie Mirabal
Davika Misir
David Moldal
Darla Montgomery
Cindy Moore
Title
Chief, Water
Supply Section
Director , Rhode
sland State
^rogram
Associate
Professor
Program Manager
Chief, National
Water Quality
Assessment
3rogram
Aquatic Biologist
Environmental
Education
Coordinator
Environmental
Specialist
Sr Geologist
Monitoring
Coordinator
Environmental
Program
Administrator
Environmental
Specialist
Chemical Engineer
Environmental
Administrator/
Scientist
Environmental
Engineer
Water Quality
Protection Manager
Affiliation
U.S. EPA
U.S. EPA
Larry Walker Associates
New York Institute of
Technology
Spokane County Aquifer
Protection
USGS
Wisconsin Department of
Natural Resources
Confederated Tribes
St. Johns River Water Mgt.
District
TNRCC
U.S. EPA, Region 8
Oklahoma Corporation
Commission
Taos Environmental Office
ADI Technology Corporation
Charlette Harbor National
Estuary Program
Puget Sound Naval Shipyard
Washington State Department
of Agriculture
Address
JFK Federal Bldg.
(WGP-445)
J.F. Kennedy Federal
Building
509 Fourth Street
225-A Main St
1 026 W Broadway
12201 Sunrise Valley
Drive
P.O. Box 7921
P.O. Box C
P.O. Box 1429
P.O. Box 13087
Mail Code
8epr-ep/999, 18th
Street, Suite 500
2101 N. Lincoln Blvd.
P.O. Box 1846
2345 Crystal Drive,
Suite 909
4980 Bayline Drive,
4th floor
1400 Farragat Ave.,
Code 106.3
WSDA Pesticide
MGMT Division,
P.O. Box 42589
City/State
Boston, MA
02203-221 1
Boston, MA
02203-2211
Davis, CA
95616
Farmingdale,
NY 11 735
Spokane, WA
99260
Reston, VA
20192
Madison, Wl
53707
Warm Springs
Reservations,
OR 97761
Palatka, FL
32178-1429
Austin, TX
78711-3087
Denver, CO
80202
Oklahoma City,
OK 731 05
Taos, NM
87571
Arlington, VA
22202
Fort Myers, FL
33917-3909
Bremorten, WA
98337
Olympia, WA
98504-2589
Phone
(617)565-3597
(530) 753-6400
(509) 456-6024
(703) 648-6868
(608) 267-2753
(904) 329-4869
(512)239-4512
(313)312-6084
(405)521-4683
(505)751-4601
(703)416-0613
(941)995-1777
(360) 476-2630
(360) 902-2047
Fax
(617)565-9360
(530) 530-7030
(703) 648-6693
(608) 267-2800
(904) 329-4329
(512)239-4450
(313)512-6071
(405)521-4945
(505)751-3905
(703)416-0182
(941)656-7724
(360) 476-5295
(360) 902-2093
(541)553-1994
E-mail
deanm @ lwdavis.com
tlmiller@usgs.gov
millema@dnr.state.wi.us
Jan_miller@ district.sjrwmd.state.fi. us
minter.jill @ epamail .epa.gov
r.minyen@occmail.occ.state,ok.us
mirabel @ laplaza.org
davika.misir@adtech.com
chnep-moldal @ mindspring .com
cmoore ©agr.wa.gov
-------
Name
Chris Morris
Doenee Moscato
Dave Mueller
Karl Muessig
Evan Murray
Arleen Navarret
Janice Nearhoof
Kenneth Neely
William Nelson
Bernard Nolan
Karen Northup
Francisco Nunez
Michael O'Neill
Edward Oaksford
Judith Okay
John Olson
Paul Orlando
Lynn Orphan
Fredrick Ousey
Dave Owen
Title
Hydrologist
Environmental
Protection
Specialist
Hydrologist
Bureau Chief
Resource
Conservationist
Senior Marine
Biologist
Research
Associate
Hydrologist
Environmental
Scientist
Associate
Professor
Supervisory
Hydrologist
Coordinator,
Difficult Rain
Project
Environmental
Specialist
Physical Scientist
Environmental
Engineer
President
System Water
Quality Supervisor
Affiliation
The Confederated Tribes of
Warm Springs
US Army Environmental
Center
USGS
New Jersey Geological
Survey
NRCS
San Francisco Public Utilities
UNC- Wilmington
Idaho Department of Water
Resources
Army Environmental Center
USGS-WRD
ADEQ
The Nature Conservancy
Utah State University
USGS-WRD
Virginia Dept. of Forestry
Iowa Dept. of Natural
Resources
NOAA/National Ocean
Service
Kennedy/Jenks Consultants
Enviro-Tech Services
Company
East Bay Municipal Utility
Dist.
Address
P.O. Box C
911 ClymerCt.
P.O. Box 25046
P.O. Box 427
100E. B Street Rm
3124
3500 Great Highway
601 S. College Road
1301 N. Orchard
USAEC SFIM-AEC-
ETP
M.S. 41 3 National
Center
3033 N Central
Utah State University
227 North Bronough
Street, Ste3015
12055 Government
Ctr. Pkwy, Suite 904
Wallace State Office
Building
1305 East-West
Hwy, SSMC-4
51 90 Neil Rd., #300
1125-B Arnold Dr.,
#161
P.O. Box 24055
City/State
Warm Springs,
OR 97761
Belair, MD
21015
Lakewood, CO
80225
Trenton, NJ
08625
Casper, WY
82609
San Francisco,
CA94132
Wilmington, NC
28403
Boise, ID 83705
APG.MD 21010
Reston, VA
20192
Pheonix, AZ
L85012
Logan, UT
84322-5240
Tallahassee, FL
32301
Fairfax, VA
22035
Des Moines, IA
50319
Silver Spring,
MD 20910
Reno, NV
89502
Mortinez, CA
94553
Oakland, CA
94623-1055
Phone
(541)553-3462
(410)671-1221
(330)236-2101
(609) 484-6587
(307)261-6480
(415)242-2201
(910)962-7338
(208) 327-5455
(410)671-1675
(703) 648-5666
(602) 207-4524
(435) 797-2465
(850) 942-9500
(703) 324-1408
(515)281-8905
(301)713-3000
(702) 827-7900
(510)370-1541
(510)287-1831
Fax
(410)671-1675
(609)633-1004
(307)261-6490
(415)242-2285
(910)962-4066
(208) 327-7866
(410)671-1675
(703) 648-6693
(602) 207-4528
(435) 797-4048
(850) 942-9521
(703) 324-3914
(515)281-8895
(301)713-4384
(702) 827-7925
(510)370-8037
(510)287-1155
(405) 848-0722
E-mail
dlmoscato @ aec.apgea.army.mil
karlm@njgs.dep. state. nj. us
emurry@wy.nrcs.usda.gov
Nearhoof J. @ uncwil.edu
kneely@idwr,state.id.us
wcnelson@aec.apgea.army.mil
btnolan@usgs.gov
mikeo@ext.usu.edu
oaksford@usgs.gov
Jokay@gmu.edu
jolson @ max.state.ia.us
paul.orlando @ noaa.gov
lavastone@aol.com
dowen @ EBMUD.com
-------
tei
Name
Michel Paque
Vicki Paque
Dave Paradies
Steve Paulsen
Betsy Pearce
Jon Peckenpaugh
Joel Pedersen
Maria Peek
Hope Pennell
Charles Peters
Greg Pettit
Robert Pigg
Kevin Piper
David Pollison
Jim Porter
Scott Redman
Louis Regenmorte
David Renstrom
Peter Richards
Title
Executive Director
Director
vlonitoring
Supervisor
Environmental
Scientist
Environmental
Scientist
Environmental
Scientist
Hydrologist
Water Quality
vlonitoring
Manager
Groundwater
Monitor Coordinator
Project Manager/
Coordinator
Head-Planning
Branch
Interagency Data
Management
Coordinator
Science
Coordinator
Water Quality
Program
Coordinator
Senior Research
Scientist
Affiliation
GWPC
Bay Foundation
U.S. EPA
City of Greensboro
U.S. EPA
U.S. EPA
American Farm Bureau
Federation
Bonneville Power Admin.
USGS
Oregon Department of
Environmental Quality
Michigan Department of
Agriculture
Middle Carson River Mgt.
Group
Delaware River Basin
Commission
Minnesota Pollution Control
Agency
Puget Sound Ambient
Monitoring Prog
Rouge Program Office
City of Federal Way, Surface
Water Mgmt.
Water Quality Lab, Heidelberg
College
Address
827 NW 63rd, Suite
103
909 Santa Ysabel
200 SW 35th St.
401 Patton Ave.
401 M. Street, SW,
Mailcode 7507c
75 Hawthorne Street
(WTR-2)
2501 N. Stiles
West 707 Main,
Suite 500
8505 Research Way
1712 SW 11th
611 W.Ottawa,
P.O. Box 30017
P.O. Box 3543
P.O. Box 7360
520 Lafayette Road
P.O. Box 40900
220 Bagley Ave.,
Suite 920
33530 1st Way South
310 East Market
Street
City/State
Oklahoma City,
OK 731 16
Oklahoma City,
OK
Los Osos, CA
93402
Corvallis, OR
97700
Greensboro, NC
27406
Washington, DC
20460
San Francisco,
C A 94 105
Oklahoma City,
OK 731 05
Spokane, WA
99201
Middleton, Wl
53565
Portland, OR
97201
Lansing, Ml
48909
Carson City, NV
89702
W. Trenton, NJ
08828
St. Paul, MN
55155
Olympia, WA
98504-0900
Detroit, Ml
48226
Federal Way,
WA 78003-6221
Tiffin, OH 44883
Phone
(405) 848-0690
(805) 528-0221
(541)754-4428
(336) 373-2707
(703)305-5128
(415)744-1950
(405) 523-2437
(509) 358-7476
(608)821-3810
(517)373-9893
(702) 883-3525
(609) 883-9500
(612)296-8859
(253)661-4074
(419)448-2240
Fax
(541)754-4716
(336) 373-2988
(703) 305-6309
(415)744-1078
(405) 523-2362
(509) 353-2909
(608)821-3817
(517)335-3329
(702) 883-3525
(609) 883-9522
(612)297-2343
(253)661-4129
(419)448-2124
E-mail
mike @ gwpc.site.net
Betsy.johnson@ci.greensboro.nc.us.com
peckenpaugh.jon@epamail.epa.gov
pedersen.joel@epamail.epa.gov
marla_peek@okfb.fb.com
hepennell@bpa.gov
capeters @ usgs.gov
piggr@state.mi.us
pollison@drbc.state.nj.us
Jim. porter® pca.state.mn. us
srpswqat@usgs.gov
regenmorterlc@cdm.com
prichard@mail.heidelberg.edu
-------
ca
Name
Amanda
Richardson
Holly Richter
Michael Rigney
Andrew Robertson
Keith Robinson
Anne Rogers
Dominic Roques
Patrick Roques
Jack Rowe
Timothy Rowe
Steven Roy
Mike Rupert
Jose Saez
Jayne Salisbury
Judy Salvo
Thomas Sanders
Leo Sarmiento
Roberta Savage
Mateo Scoggins
Title
Director,
Freshwater
Intiative
Watershed
Program Director
Chief CMBAD
Aquatic Scientist
Aquatic Scientist
Product Line
Manager
Associate Director
Hydrologist
Senior Engineer
Director
Associate
Professor of Civil
Engineering
Industrial Waste
Investigator
ASIWPCA
Executive Director
Environmental
Scientist
Affiliation
TetraTech, Inc.
The Nature Conservancy
Coyote Creek Riparian
Station
NOAA
USGS
Texas Natural Resource
Conservation Comm.
Roques Wildland Resources
Texas Natural Resource
Conservation Comm.
Systems Management, Inc.
USGS
Tetra Tech, Inc.
USGS
Los Angeles County
Sanitation Districts
Oklahoma State University
USGS
Colorado State University
City of Palo Alto
ASIWPCA
City of Austin
Address
10045 Red Run
Blvd., Suite 110
27 Ramsey Canyon
Rd.
4316 Bayne Place
NOAA/NOS,
N/ORCA2, 1305
East-West Highway
361 Commerce Way
P.O. Box 13087
MC-150
1600ShattuckAve.
#222
P.O. Box13087,
MC-150
10946 Golden West
Drive, Suite 100
333 W Nye Lane
10306 Eaton Place
230 Collins Road
P.O. Box 4998
12201 Sunrise Valley
Dr., MS 4400
2501 Embarcadero
Way
750 First St., NE,
Suite 910
P.O. Box 1088
City/State
Owings Mills,
MD21117
Hereford, AZ
85615
San Jose, CA
95130
Silver Spring,
MD20912
Pembroke, NH
03275
Austin, TX
78711-3087
Berkeley, CA
94709
Austin, TX
78711-3087
Hunt Valley, MD
21031-0238
Carson City, NV
89706
Fairfax, VA
22030
Boise, ID 83703
Whitter, CA
90607
Stillwater,
Oklahoma
74078-301 1
Reston, VA
20192
Fort Collins, CO
80523
Palo Alto, CA
95823
Washington, DC
20002
Austin, TX
78767
Phone
(410)356-8993
(520) 803-0882
(408) 262-9204
(301)713-3032
(512)239-4597
(510)644-0186
(512) 239-4604
(410)229-7546
(703) 385-6000
(208)387-1323
(562) 699-741 1
(703) 648-5645
(970)491-8652
(650) 329-2292
(202) 898-0905
(512)499-1917
Fax
(410)356-9005
(520) 803-0883
(408) 263-3523
(301)713-4388
(512)239-4420
(510)644-1074
(512) 239-4420
(410)229-7603
(703) 385-6007
(208)387-1372
(562) 692-5103
(405) 744-7673
(703) 648-5645
(970)491-7727
(650) 494-3531
(202) 898-0929
(512)499-2846
E-mail
AmandaR824@aol.com
brichter@tnc.org
mike@coyotecreek.org
Andrew.robertson @ NOAA.gov
anrogers@tnrcc.state.tx.us
roqueswild @ vdn.com
proques@tncc.state.tx.us
jrowe@awi-smi.com
royst @ tetratech-ffx.com
mgrupert® usgs.gov
jsaez@lacsd.org
jfsalvo@usgs.gov
tgs @ engr.colostate.edu
leo_sarmiento@city.palo-alto.ca.us
admin 1 @ asiwpca.org
-------
Name
Billy Scott
Elmer Scott, JR.
Randall Seelbrede
William Segars
Deepak Sehgal
Michael Series
Larry Serpa
Matthew Setty
Mikel Shakarjian
Lisa Shevenell
Brian Shin
Allison Shipp
Merle Shockey
Tom Shugrue
Mary Siedlecki
Nicole Silk
Alison Simcox
Michael Simkins
Title
CWA Program
Manager
Water Master
Resource
Conservationist
Water Quality
Coordinator
Water & Soil
Resources Mgr.
Staff Hydrologist
River Conservation
Hydrogeologist
Environmental
Engineer
Chief, Production
Program, NWQL
Staff Scientist
Research Geo
Chemist
Director, Fresh
Water Learning
Center
Environmental
Health Specialist
Affiliation
USAEC
The Confederated Tribes of
Warm Springs
Matural Resources
Conservation Service
Univ. of Georgia
Confederated Tribes of Warm
Springs
Mew Jersey Geological
Survey
The Nature Conservancy
Kennedy/Jenks Consultants
South Carolina Department of
Natural Resources
Nevada Bureau of Mines &
Geology
Naval Facilities Engineering
Service Center
Dept. of Interior-USGS
Associated Earth Sciences
Incorporated
Research Triangle Institute
The Nature Conservancy
(WRO)
Tufts University
Health Dept. Hammond, IN
Address
Commander, US
Army Environmental
Center, ATTN Billy
Ray Scott, SFIM-
AEC-EDQ-C
Bldg. E-4435
P.O. Box C
215 Executive Ct.,
Suite A
3117 Plant Sciences
Bldg.
P.O. Box C
P.O. Box 427
3152 Paradise Dr.
5 190 Neil Road,
Suite 300
2221 Devine Street,
Suite 222
MS 178
Code 426, 110023rd
Ave.
5293 Ward Road
91 1 5th Ave.,
Suite 100
3040 Comwallis Rd.,
P.O. Box 121 94
2060 Broadway,
Suite 230
649 Conkey St.
City/State
Aberdeen
Proving Ground,
MD 21010
Warm Springs,
OR 97761
Yreka, CA
96097
Athens, GA
^0602
Warm Springs,
OR 97761
Trenton, NJ
08625-0427
Tiburon, CA
94920
Reno, NV
89502
Columbia, SC
29205
Reno, NV
895570088
Port Hueneme,
CA 93043-4370
Arvada, CO
80002
Kirkland, WA
98033
Research
Triangle Park,
NC 27709-21 94
Boulder, CO
80302
Medford, MA
02155
Hammond, IN
46324
Phone
(410)612-7073
(541)553-3462
(530)842-6123
(706) 542-9072
(541)553-3462
(415)435-6465
(702) 827-7900
734-9097
(702)784-1779
(805) 982-3590
(303)467-8101
(425) 827-7701
(919)541-6307
(303)541-0341
(219)853-6358
Fax
(410)671-1675
(541)553-1994
(530)842-1027
(541)553-1994
(415)435-3796
(702) 826-7925
(702)784-1709
(805) 982-4832
(303) 467-8240
(425) 827-5424
(919)541-8830
(219)853-6403
E-mail
brscott@aec.apagea.army.mil
seelbrede @ snowcrest.com
wsegars @ uga.cc. uga.edu
MikeS@njgs.dep .state. us
lserpa@ix.netcom.com
lisa® nbmg.unr.edu
bshin @ nfesc.navy.mil
mshockey @ usgs.gov
tshugrue@aesgeo.com
nsilk@tnc.org
-------
Name
Richard Simmers
Jerry Simmons
John Simons
Lynn Singleton
Kristen Sipes
Tom Sima, P.G.
Lynn Sisk
Daniel Smith
td
I
0 Donald Smith
Duane Smith
Derek Smithee
Jennifer Smout
Jennie Snyder
Terrence Sobecki
Stephen Sorenson
Patti Spindler
Title
Administrator
Principal Envir.
Engineer
Watershed
Research
Associate
Hydrgeologist
Engineer
National Water
Issues Team
Leader
Water Quality
Monitoring
Coordinator
Executive Director
Chief, Water
Quality Programs
Division
Chemist
Supt., Water
Quality
Management
Soil Scientist/
Landscape
Ecologist
Acting Assistant
Chief, Office of
Water Quality
Aquatic Biologist
Affiliation
Dept. of Natural Resources
BDM Petroleum Technologies
U.S. EPA
Dept. of Ecology
Coyote Creek Riparian
Station
Solid Waste Authority of Palm
Beach Co.
Alabama Dept. of
Environmental Mgt.
USDA Natural Resources
Conser. Service
Virginia Department of
Environmental Quality
Oklahoma Water Resources
Board
Oklahoma Water Resources
Board
U.S. Bureau of Reclamation
Imperial Irrigation District
USDA-NRCS
USGS
ADEQ
Address
3575 Forest Lake
Dr., Suite 150
P.O. Box 2543
401 M. St. SW-Mail
Code 7506c
Employee Services,
Department Of
Ecology, P.O. Box
47600
P.O. Box 1027
7501 N. Jog Rd.
1751 W.L. Dickson
Drive
P.O. Box 2890,
Rm. 6032 South Bldg
P.O. Box 10009
3800 N. Classen
Blvd.
3800 N. Classen
Blvd.
1 1 SON. Curtis Rd.,
S-100
333 E. Barioni Blvd.,
Bldg. J-15
1400 Independence
Ave., SW
Room 6808S
12201 Sunrise Valley
Drive, MS 41 2
3033 N Central Ave.,
#319
City/State
Uniontown, OH
44685
Bartlesville, OK
74005
Washington, DC
20460
Olympia, WA
98503
Alviso, CA
95002
West Palm
Beach, R.
33412
Montgomery,
AL36130
Washington, DC
20250
Richmond, VA
23240
Oklahoma City,
OK 731 18
Oklahoma City,
OK 731 20
Boise, ID
83706-1234
Imperial, CA
92251
Washington, DC
20250
Reston, VA
20192
Phoenix, AZ
85012
Phone
(330)896-0616
(918)338-4488
(703) 305-6460
(408) 262-9204
(561) 640-4000
(334)271-7827
(202) 720-3524
(804) 698-4429
(405) 530-6800
(405) 530-8800
(208) 334-1540
(760) 339-9382
(202)690-3719
(703) 648-6864
(602) 207-4543
Fax
(330)896-1849
(918)338-4543
(703) 308-3259
(561)640-3400
(334) 279-3051
(202) 720-4265
(804)698-4136
(405) 530-8900
(405) 530-8900
(208)334-1858
(760) 339-9009
(202) 690-3266
(703) 648-5722
(602) 207-4528
E-mail
northreg @ akron.infl.net
simons.john @ epamail.epa.gov
tsima@swa.org
dan.smith@usda.gov
dhsmith@deq. state, va.us
dasmith@owrb.state.ok.us
drsmithee@owrb.state.us
jsmout@pn.usbr.gov
terry.sobecki @ usda.gov
sorenson@usgs.gov
-------
Name
Jean Spooner
Charles Spooner
Kirk Stoddard
Jeffrey Stoner
James Stribling
J.D. Strong
Ann Strong
Kevin Summers
Bruce Sutherland
h-i
to Steve Sutherland
Steven Swanson
Chris Sweeny
Marc Sylvester
Mike Talbot
Lynn Taylor
Tim Tear
Patti TenBrook
David Terry
Sue Terry
Anthony Tesoriero
Title
Co-Chair NWQMC
Environmental
Scientist
Hydrologist
Director of
Environmental
Affairs
Science Advisor
Environmental
Engineer
Sales Manager
Assistant Regional
Hydrologist for
NAWQA
Fisheries Manager
Aquatic
Toxicologist
Director, Div. of
Water Supply
Affiliation
NCSU Water Quality Group
U.S. EPA
Stanford Linear Accelerator
Center
USGS
Tetra Tech Inc.
State of Oklahoma
US Army Engineer
Waterways Experiment
Station
U.S. EPA
Lower Columbia River
Estuary Program
The Nature Conservancy
3uget Sound Naval Shipyard
Hydrolab Corp.
USGS, WRD
Wisconsin DNR
USGS
The Nature Conservancy
East Bay Municipal Utility
District
Massachusetts Department of
Environmental Protection
USGS
Address
vlorth Carolina State
University, Box 7637
401 M Street
Mail Stop 77,
2575 Sand Hill Road
Bldg53DFC,Ms415
Box 25046
10045 Red Run
Blvd., Suite 110
3800 N. Classen
Boulevard
3909 Halls Ferry
Road
1 Sabine Island Dr.
811 SW6th
1400 Farragat Ave.,
Code 106.3
12921 BumetRd.
345 Middlefield Rd,
MS 470
P.O. Box 7921
333 W. Nyelane
416 Mainstreet,
Suite 11 12
P.O. Box 24055,
MS #59
One Winter St.,
9th Floor
1201 Pacific Ave.,
Suite 600
City/State
Raleigh, NC
27695-7637
Washington, DC
20460
Menlo Park, CA
94025
Lakewood, CO
80225
Owings Mills,
MD 21117
Oklahoma City,
OK 731 18
Vicksburg, MS
39180-6199
Gulf Breeze, FL
32561
Portland, OR
97225
Bremerton, WA
98314
Austin, TX
78727
Menlo Park, CA
94025
Madison, Wl
53707
Carson City, NV
89706
Peoria, IL
61602
Oakland, CA
94623
Boston, MA
02108
Boston, MA
Tacoma, WA
98401
Phone
(202)260-1314
(650) 926-3801
(303)236-2101
(413)568-8993
(405) 530-8995
(503) 229-5995
(360) 476-2630
(512)255-8841
(650)329-4415
(608) 266-0832
(702) 887-7600
(309) 673-6689
(510)287-1427
(617) 292-5529
(253) 428-3600
Fax
(202)260-1977
(650)926-3175
(303)236-4912
(410)356-9005
(405) 530-8999
(503)229-5214
(360) 496-5295
(512)255-3106
(650) 329-4463
(608) 267-7857
(703) 648-6684
(309) 673-8986
(510)465-5462
(617)292-5696
(253)428-3614
E-mail
jean_spooner@ncsu.edu
spooner.charles @ epamail.epa.gov
stoddard@slac.stanford.edu
stoner@usgs.gov
jdstrong@owrb.state.ok.us
Summers. Kevin @ epamail.epa.gov
sutherland.bruce@deq.state.or.us
swansons@psns.navy.mil
Sylvest@usgs.gov
ttear@cyberdesic.com
ptenbroo@ebmud.com
dterry ©state. ma.us
tesorier@usgs.gov
-------
Name
Tom Toole
Jesus Tupaz
Robert Tyzzer
Bob Unnasch
Fred Van Alstyne
Christopher
Victoria
Joseph Von Stein
Eric Vowinkel
Richard Wagner
Tony Wagner
td Chris Walsh
to
Guang-yu Wang
Ping Wang
David Wangsness
Janice Ward
Robert Ward
Robert Ward
Dan Watson
Richard Webb
Steve Weisberg
Title
Environmental
Scientist
Director
Environmental
Engineer
Chief, Ground
Water Section
Reclamation
Specialist
Hydrologist
QW Specialist
Director, Water
Programs
Sr Associate
Geoscientist
Supervisory
Hydrologist
Acting Chief, Office
of Water Quality
Director/Professor
Aquatic
Toxicologist
Executive Director
Affiliation
State of Utah/DEQ
Mississippi-Alabama Sea
Grant Consortium
Naval Facilities Engineering
Service Center
The Nature Conservancy
Division of Water Resources
TetraTech Inc.
Montana Salinity Control
Assoc.
USGS
USGS
Chemical Manufacturers
Assn.
McLaren/Hart
Santa Monica Bay
Restoration Project
MD DNR/CBPO
USGS
USGS
Colorado Water Resources
Research Institute
Colorado State University
City of San Jose
USGS
Southern California Coastal
Research Project
Address
P.O. Box 144870
P.O. Box 7000
Code 422, 110023rd
Avenue
50 Wolf Rd.,
Room 308
14440 Cherry Lane
Ct., Suite 101
P.O. Box 909
810 Bear Tavern Rd.,
Suite 206
1201 Pacific Avenue,
Suite 600
1300 Wilson Blvd.
1135 Atlantic Ave.
101 Centre Plaza Dr.
410 Severn Ave.,
Suite 109
3039 Amwiler Rd
Peachtree Bus. Ctr.
12201 Sunrise Valley
Drive, MS 41 2
410 North University
Services Building,
Colorado State
University
410 Univ. Services
Center
4245 Zanker Rd.
651 Federal Dr.,
Suite 400-1 5
7171 FenwickLane
City/State
Salt Lake City,
UT 841 14-4870
Ocean Springs,
MS 39566-7000
Port Hueneme,
CA 93043-4370
Albany, NY
12233-3502
Laurel, MD
20707
Conrad, MT
59425
West Trenton,
NJ 08628
Tacoma, WA
98402
Arlington, VA
22209
Alameda, CA
94501
Monterey Park,
CA91754
Annapolis, MD
21403
Atlanta, GA
30360
Reston, VA
20192
Ft Collins, CO
80523
Fort Collins, CO
80523
San Jose, CA
95134
Guaynabo,
Puerto Rico,
00965
Westminster,
CA 92268
Phone
(801)538-6045
(228) 868-6599
(805)982-1354
(518)457-0893
(301)953-9740
(406) 278-3071
(609)771-3931
(253) 428-3600
(703)741-5248
(510)521-5200
(323) 266-7568
(410)267-5744
(770)903-9156
(703) 648-6871
(970)491-6308
(408) 945-3739
(787) 749-4346
(714) 894-2222
Fax
(801)538-6016
(228) 875-0528
(805) 982-4244
(518)485-7786
(301)953-9743
(406) 278-3071
(609)771-3915
(253)428-3614
(703)741-6248
(510)521-1547
(323) 266-7626
(410)267-5777
(770)903-9199
(703) 648-5722
(970)491-2293
(408) 934-0476
(787) 749-4346
(714)894-9699
E-mail
wmaxell.deq.state.ut.us
jtupaz@seahorse.ims.usm.edu
rtyzzer@nfesc.navy.mil
cj_victoria@ hotmail.com
MSCA@3Rivers.net
rjwagner® usgs.gov
tony_wagner@mail. omaha.com
chris_walsh@ mclaren-hart.com
gwang @ rb4.swcb.ca.gov
wang.ping @ epamail.epa.gov
wangsnes@usgs.gov
jward@usgs.gov
rcw@lamar.colostate.edu
dan.watson@ci.sj.ca.us
rmwebb @ usgs.gov
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Name
Molly Welker
Clement Welsh,
PhD.
Jody White
Robert White
Robert Wigington
Bill Wilber
Scott Wilber
Franceska Wilde
Llew Williams
Roland Williams
td Thomas Wilton
to
Stephen Winkler
Nathan Wiser
Paul Woods
Mike Woodside
Lloyd Woosley
Karen Worcester
Kimberly Yandora
Dean Yashan
Steven Yergeau
Chris Yoder
Title
Scientist
Research Scientist
Director, Aquatic
Monitoring Project
Unit Leader,
Montana Coop.
Fishery Res. Unit
Nat'l Systhesis
Coordinator
Sr Scientist
Environmental
Specialist
Environmental
Specialist
Geologist
Limnologist
Hydrologist
Assistant District
Chief, Texas
District
Environmental
Scientist
Monitoring
Technician
Senior Water
Quality Analyst
Director of
Research
Affiliation
University of Wyoming
California Dept. Health
Services
The XERCES Society
Fish and Wildlife Mgt.
Program
The Nature Conservancy
USGS
The Nature Conservancy
USGS
U.S. EPA
ADEQ
Iowa Dept. of Natural
Resources
St. Johns River Water
Management District
U.S. EPA
USGS
USGS
USGS
Central Coast Regional Water
Quality Control Bd.
City of Greensboro
Idaho Division of
Environmental Quality
Save the Sound, Inc.
Ohio EPA
Address
WWRC,
P.O. Box 3067
5900 Hollis St.,
Suite E
4828 SE Hawthorne
Boulevard
P.O. Box 173460,
Montana State Univ.
413 National Center
412 National Center
944 E Harmon Ave
3033 North Central
Ave.
Wallace State Office
Building
P.O. Box 1429
77 W. Jackson Blvd.
230 Collins Rd.
640 Grassmire Park,
Suite 100
801 1 Cameron Rd.,
Bldgl
81 Higuera St.,
Suite 200
401 Patton Ave.
141 ON. Hilton,
2nd Floor
185 Magee Avenue
1685 WestbeltDr
City/State
Laramie, WY
82071
Emeryville, CA
94608
Portland, OR
97215
Bozeman, MT
597173460
Reston, VA
22092
Reston, VA
20192
Las Vegas, NV
89119
Phoenix, AZ
85012
Des Moines, IA
50319
Palatka, FL
32178-1429
Chicago, IL
60604
Boise, ID 83702
Nashville, TN
37211
Austin, TX
78754-3898
San Luis
Obispo, CA
93401
Greensboro, NC
27406
Boise, ID
83706-1255
Stamford, CT
06902
Columbus, OH
43228
Phone
(307) 766-2740
(510)450-3818
(541)754-8110
(406) 994-3491
(703) 648-6878
(702)451-2290
(602) 207-4506
(515)281-8867
(904) 329-4543
(312)353-9569
(208)387-1353
(615)837-4706
(512)873-3043
(805) 549-3333
(336) 373-2741
(208) 373-0502
(203) 327-9786
Fax
(307) 766-2744
(510)450-3770
(406) 994-7479
(703) 648-6693
(702) 798-2892
(602) 207-4528
(515)281-8895
(904) 329-4329
(312)886-4235
(208)387-1372
(615)837-4799
(512)873-3090
(805) 543-0397
(336) 373-2988
(208) 373-0576
(203) 967-2677
(614) 728-3300
E-mail
molly@uwyo.edu
cdhsclem @ earthlink.net
jwhite @ peak.org
ubirw@montana.edu
wgwilber® usgs.gov
twilton@max.state.ia.us
steve_winkler@district.sjrwmd.state.fl.us
wiser.nathan @ epa.gov
pfwoods@usgs.gov
mdwoodsi@usgs.gov
lwoosley@usgs.gov
yandora@gte.net
dyashan@deq.state.id.us
Savethesound@snet.net
-------
Name
Tamim Younos
Michael Yurewicz
Carl Zipper
Juerg Zobrist
Donna Zoeller
Douglas Zollner
Title
Director
Assistant Regional
Hydrologist/
NAWQA
Assistant
Professor, Crop &
Soil Environmental
Sci.
Senior Scientist
Affiliation
Virginia Water Resources
Research Center
USGS
Virginia Tech
EAWAG
U.S. Army Corps of Eng.
The Nature Conservancy
Address
10 Sandy Hall,
Virginia Polytechnic
Institute and State
University
433 National Center
364 Symth Hall
CH-8600
Fret of Arsonal St.
City/State
Blacksburg, VA
24061-0444
Restin, VA
20192
Blacksburg, VA
24061
Duebendorf,
Switzerland
St. Louis, MO
63118
Phone
(703) 648-581 1
(540)231-9782
114118235
(314)263-4008
Fax
(703) 648-4850
(540)231-7630
823-5028
E-mail
tyounos@vt.edu
mcyurewl @ usgs.gov
czip@vt.edu
zobrist@eawag.ch
CO
I
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