600R08111
United States
Environmental Protection
Agency
Results of the Lake Michigan
Mass Balance Project:
Atrazine Modeling Report
RESEARCH AND DEVELOPMENT
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EPA/600/R-08/111
September 2008
Results of the Lake Michigan Mass
Balance Project:
Atrazine
Modeling Report
Prepared for
United States Environmental Protection Agency
Great Lakes National Program Office
77 West Jackson Boulevard
Chicago, Illinois 60604
Prepared by
United States Environmental Protection Agency
Office of Research and Development
National Health and Environmental Effects Research Laboratory
Mid-Continent Ecology Division
Large Lakes and Rivers Forecasting Research Branch
Large Lakes Research Station
9311 Groh Road
Grosse lie, Michigan 48138
Kenneth R. Rygwelski, Editor
Recycled/Recyclable
Printed with vegetable-based ink on
paper that contains a minimum of
50% post-consumer fiber content
processed chlorine free.
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Notice
The information in this document has been obtained primarily through funding by the United States
Environmental Protection Agency (USEPA) under the auspices of the Office of Research and Development
(ORD) and by the Great Lakes National Program Office (GLNPO). The report has been subjected to the
Agency's peer and administrative review and it has been approved for publication as an USEPA document.
Mention of trade names or commercial products does not constitute endorsement or recommendation for use.
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Foreword
The Lake Michigan Mass Balance Project (LMMBP) was initiated by the United States Environmental
Protection Agency (USEPA), Great Lakes National Program Office (GLNPO) to determine strategies for
managing and remediating toxic chemicals in the lake basin. Within the ecosystem approach, the mass
balance framework is considered the best means of accomplishing this objective, and GLNPO requested the
assistance of the USEPA Office of Research and Development (ORD) to facilitate and produce mathematical
models that account for the sources, sinks, transport, fate, and food chain bioaccumulation of certain
chemicals. This approach has been used in the past and builds upon the modeling efforts that have occurred
in the Assessment and Remediation of Contaminated Sediments (ARCS) Program and the lower Fox
River/Green Bay Mass Balance Project. The feasibility of such studies and resultant alternative management
options for contaminants in large rivers and a large embayment were demonstrated, and a logical extension
to the entire Lake Michigan receiving water body and major tributaries was warranted. There were a large
number of cooperators in this project, and by focusing federal, state, local, private, and academic efforts and
resources on a common goal, much more was accomplished than if these entities acted independently.
The project was conducted in conjunction with the Enhanced Monitoring Program, and the approach required
that all monitoring and field research be coordinated and common methodologies used. Mathematical
modelers were consulted during planning for sample design, parameters, and temporal and spatial sampling
considerations. This yielded a consistent and reliable database of information that was accessible by project
participants and the public. Data for the LMMBP were collected during 1994 and 1995 and have been
compiled according to specified quality assurance/quality control (QA/QC) requirements, and other data
assessments have been made for modeling purposes.
The need to consider the environmental benefits and consequences of alternative remediation choices to
protect and improve our environment continues to intensify as: 1) environmental problems become more
complex; 2) the means to address and investigate problems become more technical, time-consuming, and
expensive; and 3) the actual cost to implement action strategies has escalated. The integrated atrazine mass
balance modeling results are presented in this document and can aid managers in establishing priorities for
both lake-wide and local improvements. Primary goals of the modeling effort were to determine the
persistence of atrazine and to forecast concentrations in Lake Michigan water. The capability of forecast
modeling presented here is a salient feature of this approach directed toward providing multiple alternatives,
which then can be examined through benefit-cost analyses.
This report presents the current status and results of the atrazine modeling effort through 2005, and it fulfills
documentation requirements as described in the Quality Assurance Plan for Modeling: The Lake Michigan
Mass Balance Project. Of course, a model and modeling applications are never complete, and it is expected
that further efforts will change some results, insights, and our understanding of Lake Michigan. These efforts
require an investment of resources and time, and improvements with additional model run executions are
measured in years. In the larger picture, the need for Agency modeling technologies continues to intensify,
and the requirement for reduced uncertainty will lead to future improved generations of models. We have
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placed great emphasis on following guidance provided by the USEPA and other agencies in assuring that the
scientific theory is implemented accurately and completely by model computer code and that best modeling
practices have been instituted. The fundamental principles driving the atrazine models presented in this report
have received scientific peer review using an interdisciplinary panel of scientists and experts. The purpose
of the reviews was to ensure that decisions based on the modeling efforts are reliable and scientifically
credible.
This document is not intended to include all of the details and background required to understand the entire
LMMBP. Rather, the reader should refer to the LMMBP Work Plan and other materials on the GLNPO web
site and the Lake Michigan Mass Balance Modeling Quality Assurance Plan on the ORD-Grosse lie web site
for further information.
IV
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Abstract
The Lake Michigan Mass Balance Project (LMMBP) was conducted to measure and model nutrients, atrazine,
polychlorinated biphenyls (PCBs), frans-nonachlor, and mercury to gain a better understanding of the sources,
sinks, transport, fate, and effects of these substances within the system and to aid managers in the
environmental decision-making process for the Lake Michigan basin. The United States Environmental
Protection Agency (USEPA) Office of Research and Development (ORD) was requested to conduct and
facilitate modeling in cooperation with the USEPA Great Lakes National Program Office (GLNPO); the USEPA
Region V; other federal agencies; the states of Michigan, Wisconsin, Illinois, and Indiana; the tribes; and the
public and private sectors.
This report focuses on the load sources and fate and transport modeling of atrazine only. In the Lake Michigan
basin, atrazine is used primarily as a herbicide on corn crops. With the recent increase in corn acreage in the
United States associated with biofuel (ethanol) production, increased loadings of atrazine to lakes and streams
are expected.
The atrazine modeling effort described in this report was supported by intensive sampling of the atmosphere,
major tributaries, and water column during the 1994-1995 field years as well as by extensive quality assurance
and database development. Using these data and historical data, loadings of atrazine to the lake were
estimated for the tributaries and atmosphere. Multimedia, mass balance modeling frameworks were applied
to examine primary source and loss categories and make various model forecasts for a variety of loading
scenarios. A literature search revealed that atrazine sorption to particles is negligible. Hence, atrazine
transport associated with settling, resuspension, and burial were determined to be negligible. This report
focuses on the modeling practices applied and results for atrazine from the MICHTOX screening-level model
and the higher-resolution LM2-Toxic and LM3-Atrazine models.
The results of the LM2-Toxic system mass balance model show that the largest atrazine load to the lake is
from the watershed. For the year 1994, it was estimated that 5,264 kg of atrazine were discharged to the lake
via the tributaries. The second major load to the lake was from atmospheric wet deposition with a loading
estimate of 2,493 kg. The greatest loss of atrazine from the lake was through transport to Lake Huron (2,546
kg) via the Straits of Mackinac. Loss due to internal decay (1,662 kg) was the second largest loss mechanism.
The total inventory of atrazine in the lake was determined to be 184,310 kg in 1994. In this large, cold northern
lake, the model suggests that in situ atrazine decay is very slow (0.009/year). This translates into an estimated
atrazine half-life of 77 years. Using the model to forecast alternative futures, a 35% load reduction, if
implemented in January 1, 2005, would have been needed in order to prevent atrazine concentrations from
increasing further in the lake. If loadings and boundary conditions are assumed to be constant in the future,
the model predicts that the lake will eventually reach a steady-state concentration of 66 ng/L in the year 2194.
Our high-resolution model, LM3-Atrazine, was primarily used to evaluate environmental exposure
concentrations of atrazine in 5 km x 5 km model cells receiving loadings from the major tributaries to the lake.
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The model segment receiving loads from the St. Joseph River, associated with the largest tributary load of
atrazine to the lake, ranged from winter concentrations of 37 ng/L to spring peaks of 100-350 ng/L. These
predicted exposure concentrations in the lake are all below selected toxicological endpoints, including the most
sensitive, phytoplankton primary production reduction.
This synthetic lake-wide perspective is anticipated to aid lake managers in moving forward on prevention,
remedial actions, and legislative priorities associated with Lake Michigan Lake-wide Management Plans. The
models developed provide an in-depth understanding of atrazine transport and fate processes in this valuable
freshwater resource. This abstract does not necessarily reflect USEPA policy.
VI
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Tables of Contents
Notice ii
Foreword iii
Abstract v
Table of Contents vii
List of Figures xii
List of Tables xvi
Abbreviations xviii
Acknowledgments xx
Executive Summary xxi
Part 1 Introduction 1
Chapter 1 Project Overview 1
1.1.1 Background 1
1.1.2 Description 2
1.1.3 Scope 3
1.1.3.1 Modeled Pollutants 3
1.1.3.1.1 PCBs 3
1.1.3.1.2 frans-Nonachlor 5
1.1.3.1.3 Atrazine 5
1.1.3.1.4 Mercury 5
1.1.3.2 Other Measured Parameters 6
1.1.3.3 Measured Compartments 7
1.1.4 Objectives 8
1.1.5 Design 8
1.1.5.1 Organization 8
1.1.5.2 Study Participants 8
1.1.5.3 Workgroups 9
1.1.5.4 Information Management 9
1.1.5.4.1 Data Reporting 9
1.1.5.4.2 Great Lakes Environmental Monitoring Database 10
1.1.5.4.3 Public Access to LMMBP Data 11
1.1.5.5 Quality Assurance Program 11
1.1.6 Project Documents and Products 13
VII
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Chapter 2 General Information on the Herbicide Atrazine and Its Degradation
Products 15
1.2.1 Background 15
1.2.2 Physical-Chemical Properties of Atrazine 16
1.2.3 Atrazine Degradation 17
1.2.3.1 Biotic Degradation in Surface Water 17
1.2.3.2 Abiotic Degradation in Surface Water 19
1.2.3.2.1 Hydrolysis 19
1.2.3.2.2 Photolysis 19
1.2.3.3 Atrazine Degradation in Soil 20
Chapter 3 Atrazine Field Data Observations 23
1.3.1 Background 23
1.3.2 Atmospheric Components 24
1.3.2.1 Sampling and Analytical Methodology 24
1.3.2.2 Results 25
1.3.2.2.1 Atrazine in the Gas Phase Fraction 25
1.3.2.2.2 Atrazine in the Particulate Fraction 25
1.3.2.2.3 Atrazine and Degradation Products in Wet Deposition 26
1.3.3 Atrazine in Tributaries 29
1.3.3.1 Sampling and Analytical Methodology 30
1.3.3.2 Results 30
1.3.4 Atrazine in Lake Water 31
1.3.4.1 Sampling and Analytical Methodology 31
1.3.4.2 Results 31
1.3.4.2.1 Spatial Variation 31
1.3.4.2.2 Seasonal Variation 32
Appendix 1.3.1 Information Management 35
A1.3.1.1 Overview of Information Management at the LLRS 35
A1.3.1.2 Summary 37
Chapter 4 Representativeness of the Lake Michigan Mass Balance Project (LMMBP)
Years Relative to Lake Michigan's Historic Record 46
1.4.1 Introduction 46
1.4.2 Ice Cover 46
1.4.3 Water and Air Temperatures 47
1.4.4 Lake Water Levels 50
1.4.5 Precipitation 50
1.4.5.1 Annual Comparisons 51
1.4.5.2 Monthly Comparisons 51
1.4.6 Tributary Flows 51
1.4.7 Summary 51
VIII
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Chapter 5 Atrazine Modeling Overview 55
1.5.1 Background 55
1.5.2 LMMBP Modeling Objectives 55
1.5.3 Historical Modeling 56
1.5.3.1 Completely-Mixed Lakes-ln-Series Model 57
1.5.3.2 MICHTOX 57
1.5.3.3 Green Bay Mass Balance Project 57
1.5.4 Resolution for the LMMBP Models 58
1.5.5 Models Developed and Applied 59
1.5.5.1 Lake Process Models 60
1.5.5.2 Hydrodynamics (POM) 60
1.5.6 Model Quality Assurance 60
1.5.7 Model Application and Computational Aspects 61
1.5.7.1 Annual Simulations 61
1.5.7.2 Long-Term Simulations 61
Part 2 Lake Michigan Mass Balance Project Atrazine Loadings to Lake Michigan 63
Chapter 1 Historical Atrazine Usage in the United States 63
2.1.1 Background 63
2.1.2 Total Annual Usage Estimates 64
2.1.3 Future Atrazine Use Estimates 64
Chapter 2 Estimation of Atrazine Tributary Loadings 69
2.2.1 Atrazine Tributary Load Estimates Utilizing County-Level Atrazine
Application Data 69
2.2.1.1 County-Level Atrazine Application Data 70
2.2.1.2 The Watershed Export Percentage 70
2.2.1.3 Calculating the Atrazine Tributary Load 71
2.2.2 Estimating Atrazine Tributary Loads for Years When County-Level Atrazine
Application Data Was Not Available 74
2.2.3 Atrazine Tributary Loads for MICHTOX and LM2-Atrazine 75
2.2.4 Atrazine Tributary Load Estimates for LM3-Atrazine 76
2.2.4.1 Tributary Sampling Program 76
2.2.4.2 Atrazine Load Estimation for Monitored Rivers Using the Stratified
Beale Ratio Estimator (SBRE) Method 77
2.2.4.3 Atrazine Load Estimation for Unmonitored Watersheds 78
2.2.5 Comments on Atrazine Tributary Loading Estimates 79
Chapter 3 Estimation of Atrazine Loads in Wet Deposition (Precipitation) 81
2.3.1 Atmospheric Components Considered in Modeling Atrazine in Lake Michigan 81
2.3.2 Atrazine Wet Deposition Load Estimates Based on Measured Fluxes in the
Basin 82
2.3.3 Atrazine Wet Deposition and Tributary Loads for MICHTOX and LM2-Atrazine .... 83
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Part 3 Lake Michigan Mass Balance Project Level 1 Model: MICHTOX-Atrazine 86
3.1 MICHTOX-Atrazine Executive Summary 86
3.2 MICHTOX-Atrazine Recommendations 86
3.3 Model Description 86
3.3.1 Model Overview 86
3.3.2 MICHTOX Model Segmentation and Circulation 87
3.4 MICHTOX Model Application to Lake Michigan 88
3.4.1 Screening Model Application 88
3.4.2 Enhanced Screening Model Application 89
3.4.2.1 Field Data 89
3.4.2.2 Model Assumptions and Calibration Procedures 89
3.4.2.3 Tributary Loadings 90
3.4.2.4 Atmospheric Loadings 90
3.4.2.5 Model Confirmation 90
3.4.2.6 Model Application (Scenarios) 90
3.4.2.7 Discussion of Results 91
Part 4 Lake Michigan Mass Balance Project Level 2 Model: LM2-Atrazine 95
4.1 LM2-Atrazine Executive Summary 95
4.2 LM2-Atrazine Recommendations 95
4.3 Model Description 95
4.3.1 Model Overview 95
4.3.2 LM2-Atrazine Model Segmentation and Circulation 96
4.4 LM2-Atrazine Model Application to Lake Michigan 98
4.4.1 Enhanced Screening Model Application 98
4.4.2 Field Data 98
4.4.3 Tributary Loadings 99
4.4.4 Atmospheric Loadings 99
4.4.5 Model Assumptions 99
4.4.6 Model Calibration and Application (Scenarios) 99
4.4.7 Model Confirmation 101
4.4.8 Discussion of Results 101
Part 5 Lake Michigan Mass Balance Project Level 3 Model: LM3-Atrazine 107
5.1 LM3-Atrazine Executive Summary 107
5.2 LM3-Atrazine Recommendations 108
5.3 LM3-Atrazine Transport and Fate Modeling 108
5.3.1 Purpose of High-Resolution Model 108
5.3.2 Model Description and Framework 109
5.3.2.1 POM Hydrodynamic Model 109
5.3.2.2 Model Framework 115
5.3.2.2.1 Water Quality Processes 115
5.3.2.2.2 Spatial Resolution 117
5.3.2.2.3 Temporal Resolution 118
5.3.2.2.4 Model Assumptions 118
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5.3.3 Description of Data Used 118
5.3.3.1 Field Data 118
5.3.3.2 Initial and Boundary Conditions 118
5.3.3.3 Loadings 119
5.3.3.3.1 Tributary 119
5.3.3.3.2 Atmospheric 120
5.3.4 Description of Model Simulations and Results 121
5.3.4.1 Mass Budgets 124
5.3.4.2 Selected Model Versus Observation Statistics 124
5.3.4.3 Comparison to Toxicological Endpoints 124
5.3.5 Model Uncertainty 128
Part 6 Review of Atrazine Models 131
6.1 LMMBP Atrazine Models 131
6.1.1 Peer Reviews of LMMBP Atrazine Models 131
6.1.2 Comparison of LMMBP Models 132
6.2 Comparison of LMMBP Models to Other Recent Atrazine Models Applied to
Lake Michigan 133
6.2.1 Schottler and Eisenreich (1997) 133
6.2.2 Tierney etal. (1999) 133
6.3 Atrazine Models Applied to Lake or Deep River Systems Outside the Lake
Michigan Basin 134
6.3.1 Swiss Lakes 134
6.3.2 St. Lawrence River 135
6.4 Atrazine Models Applied to Shallow Surface Water Systems in Agricultural
Areas 135
6.4.1 Saylorville Reservoir, Iowa 135
6.4.2 Other Small Surface Water Systems 136
6.5 Conclusions 136
Appendix 6.1 Peer Review of LMMBP Atrazine Models, September 27, 2000,
Romulus, Michigan 138
A6.1.1 Overview 138
A6.1.2 Comments on Technical Issues 139
XI
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List of Figures
1.1.1 Simplified mass balance approach 2
1.1.2 The LMMBP sampling locations 7
1.1.3 Flow of information in the LMMBP 10
1.2.1 Chemical structures of atrazine and its major degradation products 18
1.3.1 Monthly precipitation amounts at cities in two large corn-growing regions. Data are
from Peoria, Illinois and Omaha, Nebraska 28
1.3.2 Monthly average temperatures at cites in two large corn-growing regions. Data are
from Peoria, Illinois and Omaha, Nebraska 29
1.3.3 Atrazine concentrations in Lake Michigan, 1994 32
1.4.1 Location of the NOAA buoys in Lake Michigan 49
1.4.2 Monthly mean water temperatures in southern Lake Michigan 49
1.4.3 Monthly mean water temperatures in northern Lake Michigan 49
1.4.4 Mean June water temperatures in southern Lake Michigan 49
1.4.5 Mean June water temperatures in northern Lake Michigan 49
1.4.6 Monthly mean air temperatures in southern Lake Michigan 50
1.4.7 Monthly mean air temperatures in northern Lake Michigan 50
1.4.8 Mean June air temperatures in southern Lake Michigan 50
1.4.9 Mean June air temperatures in northern Lake Michigan 50
1.4.10 Record of mean monthly water levels for Lake Michigan 51
1.4.11 Annual precipitation to Lake Michigan between 1949 and 1998 52
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1.4.12 Comparison of 1982, 1983, 1994, and 1995 monthly mean precipitation to the mean
for the period of 1949 through 1998 52
1.4.13 Comparison of tributary flow for hydrodynamic model calibration (1982-1983)
to the historic means 53
1.4.14 Comparison of tributary flow for the study period (1994-1995) to the historic means 53
1.5.1 Surface water segmentation for alternative Lake Michigan mass balance
model levels 58
1.5.2 Model construct used for the LMMBP to model atrazine 59
2.1.1 Atrazine usage in the United States for 1991 65
2.1.2 Estimates of atrazine usage in the Lake Michigan basin for 1994 and 1995 66
2.1.3 Historical trend of total annual usage of atrazine in the United States with acreage
planted in corn, sorghum, and sugarcane 66
2.2.1 Soil textures typical for the Lake Michigan basin and part of the Lake Erie basin 72
2.2.2 WEP-based total atrazine tributary loading estimates to Lake Michigan 75
2.2.3 Tributary loadings to Lake Michigan MICHTOX model segments 76
2.2.4 WEP-based Lake Michigan tributary loadings, 1994 76
2.2.5 1995 USGS SBRE atrazine loadings and median concentrations relative to median
flow in Lake Michigan tributaries 78
2.3.1 Wet deposition (rain and snow) of atrazine for 1991 for Midwestern United States 82
2.3.2 Gradients of atrazine in wet deposition loadings over Lake Michigan for May 1994 83
2.3.3 Seasonality of atrazine wet deposition loadings to Lake Michigan for 1994-1995 83
2.3.4 Total atrazine tributary loading and wet deposition loading estimates to Lake Michigan .... 83
2.3.5 Tributary and wet deposition loadings to MICHTOX model segments for 1994 and
1995 84
2.3.6 Tributary and wet deposition loadings to LM2-Atrazine model segments for 1994 and
1995 84
3.1 MICHTOX model segmentation 87
3.2 Total annual estimated tributary and precipitation loadings of atrazine to Lake Michigan .... 88
XIII
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3.3 A comparison of MICHTOX - Predicted atrazine concentrations in Lake Michigan
to averaged Lake Michigan data for the years 1991, 1992, and 1995 are depicted 89
3.4 Lake Michigan (open-lake) forecast scenarios: 1 - upper estimate of boundary
condition, 2 - lower estimate of boundary condition, and 3 - estimate of average
boundary condition 92
3.5 Lake Michigan (open-lake) hindcast and scenario forecasts: 4 - virtual elimination
of all loadings and 0.0 ng/L atrazine at the Straits of Mackinac boundary, 5 - no tributary
loads, 6 - no wet deposition, 7 - no further degradation of lake water quality 92
4.1 Water column segmentation for LM2-Atrazine 97
4.2 LM2-Atrazine model results for Lake Michigan and Green Bay for the year 1994 101
4.3 LM2-Atrazine model runs of scenarios 102
4.4 Historical trends of United States corn acreage planted and harvested from 1986
to 2007 103
4.5 Model-predicted lake-wide averaged atrazine concentrations in water related to
increases in atrazine loadings resulting from corn crop acreage increases are depicted ... 104
5.1 Lake Michigan hydrodynamic model 5 km x 5 km computational grid 110
5.2 Simulated temperature (black) compared to measured temperature (gray) at two buoys
in Lake Michigan for 1982-1983 111
5.3a Time-series of simulated water temperature versus observed at 45007 for 1994-1995 112
5.3b Time-series of simulated surface water temperature versus observed at 45002 and
45010 for 1994-1995 113
5.4 Simulated mean temperature (°C) profile for 1982-1983 114
5.5 Temporal evolution of simulated versus observed temperature profiles, Station 18M 114
5.6 Watershed and mid-lake sampling stations for the LMMBP study 119
5.7 Atrazine loads for Lake Michigan tributaries, 1994-1995 121
5.8 Comparison of field data to predicted mid-lake surface concentrations for the 1994-1995
model simulation and two loading conditions 122
5.9 Model simulation results of surface concentrations for May 29, 1995 using long-term
WEP-based loads 122
5.10 Comparison of near-shore surface cell model results for the 1994-1995 model simulation
and two loading conditions 123
XIV
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5.11 Mid-lake surface concentration model results for 1994-2005 model simulation and two
loading conditions 125
5.12 Mass budget average annual results for the 1994-1995 model simulations 126
5.13 Comparison of model predictions, measured data, and selected toxicological endpoints ... 127
xv
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List of Tables
1.1.1 Characteristics of the LMMBP Modeled Pollutants 4
1.1.2 The LMMBP Parameters 6
1.2.1 Physical and Chemical Properties of Atrazine 17
1.3.1 Summary of Wet Deposition Annual Volume-Weighted Mean Deethylatrazine (DEA)
Concentrations, Atrazine Concentrations, and Deethylatrazine/Atrazine Ratios (DAR) for
All Stations in the Lake Michigan Basin 27
1.3.2 Annual Mean Precipitation Amounts Measured at Chicago, Illinois; Fort Wayne,
Indiana; South Bend, Indiana; Muskegon, Michigan; Grand Rapids, Michigan; and
Milwaukee, Wsconsin 29
1.3.3 Summary of Historical Atrazine, DEA, and DIA Concentrations in Lake Michigan 32
A1.3.1 List of Parameters Analyzed and Principal Investigators for the LMMBP Atrazine
Modeling 36
A1.3.2 Example of Data Verification Checklist Used for the LMMBP 38
A1.3.3 Printout of Information Stored in the LMMBP Tracking Database Related to Atrazine
Modeling 42
A1.3.4 Generalized Format for the LMMBP Water Data to be Analyzed With IDL Programs 43
1.4.1 Summary of Lake Michigan Ice Cover Based Upon Assel (2003) 48
2.1.1. U.S. Department of Agriculture Corn Crop Summaries of Atrazine Usage in the
United States for 1991, 1994, and 1995 65
2.1.2 Total Annual Usage of Atrazine in the United States 67
2.2.1 Sources of County-Level Atrazine Application Data for the Lake Michigan Basin 70
2.2.2 Atrazine Watershed Export Data Summarized From the Literature 72
2.2.3 Atrazine Watershed Export Data From Various Northern Sites 73
XVI
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5.1 1982-1983 Hydrodynamic Model Evaluations for Surface Temperature at NDBC
Buoys (45002 and 45007) and Subsurface Temperature at GLERL Current Meter
Moorings (28 Instruments) 113
5.2 1994-1995 Hydrodynamic Model Evaluations for Surface Temperature at NDBC
Buoys (45002, 45007, and 45010) and Subsurface Temperature at GLERL Current
Meter Moorings (10 Instruments) 113
5.3 Mass Budget Average Annual Results for 1994-1995 Model Simulations 125
6.1 Comparison of LM2-Atrazine Model to Other Models 134
XVII
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Abbreviations
AOCs Areas of Concern
AREAL Atmospheric Research and Exposure Assessment Laboratory
CMAQ Community Multiscale Air Quality
CMC Criterion maximum concentration
CO2 Carbon dioxide
DAR Deethylatrazine/atrazine ratio
DEA Deethylatrazine
DIA Deisopropylatrazine
DOC Dissolved organic carbon
DQOs Data quality objectives
EMPs Enhanced Monitoring Plans
ERS Economic Research Service
EU European Union
FIFRA Federal Insecticide, Fungicide, and Rodenticide Act
FQPA Food Quality Protection Act
GBMBP Green Bay Mass Balance Project
GIS Geographical Information System
GLENDA Great Lakes Environmental Monitoring Database
GLERL Great Lakes Environmental Research Laboratory
GLNPO Great Lakes National Program Office
GLWQA Great Lakes Water Quality Agreement
GWP Great Waters Program
HUC Hydrological Unit Code
IDLs Instrument detection limits
IJC International Joint Commission
IRED Interim Reregistration Eligibility Decision
LaMP Lake-wide Management Plan
LAPU Load as a percentage of use
LLRFRB Large Lakes and Rivers Forecasting Research Branch
LLRS Large Lakes Research Station
LMMBP Lake Michigan Mass Balance Project
MCL Maximum Contaminant Level
MDEQ Michigan Department of Environmental Quality
MDLs Method detection limits
MED Mid-Continent Ecology Division
MQOs Measurement quality objectives
NDBC National Data Buoy Center
NHEERL National Health and Environmental Effects Research Laboratory
NOAA National Oceanic and Atmospheric Administration
XVIII
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ORD Office of Research and Development
PCB Polychlorinated biphenyl
PEM Pesticide Emissions Model
Pis Principal Investigators
POM Princeton Ocean Model
QA Quality assurance
QAPPs Quality Assurance Project Plans
QC Quality control
RAP Remedial Action Plan
RDMQ Research Data Management and Quality Control System
RED Reregistration Eligibility Decision
RMSD Root mean square difference
RPD Relative percent difference
SAP Scientific Advisory Panel
SBRE Stratified Beale Ratio Estimator
SCFAH Standing Committee on the Food Chain and Animal Health
SDLs System detection limits
TiO2 Titanium dioxide
TMDL Total Maximum Daily Load
USDA United States Department of Agriculture
USDOI United States Department of Interior
USEPA United States Environmental Protection Agency
USFWS United States Fish and Wildlife Service
USGAO United States General Accounting Office
USGS United States Geological Survey
VWA Volume-weighted averages
WEP Watershed export percentage
XIX
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Acknowledgments
Special thanks to the United States Environmental Protection Agency, Great Lakes National Program Office
for leadership, support, and collaboration on the Lake Michigan Mass Balance Project. The multiple efforts
by the Principal Investigators for providing data, necessary for the modeling, are greatly appreciated. Thank
you to Ronald Rossmann, Timothy Feist, James Pauer, Xiaomi Zhang, and Amy Anstead for providing
valuable technical review comments. Thanks to Kay Morrison for the graphic renditions and figures and to
Debra L. Caudill for formatting and word processing. Finally, thanks to Paul Capel, Miriam Diamond, Kevin
Farley, Raymond Hoff, Robert Hudson, and Barry Lesht for serving on the peer-review panel.
xx
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Executive Summary
The Lake Michigan Mass Balance Project (LMMBP) provided an opportunity to improve our understanding of
atrazine transport and fate in a large freshwater lake, Lake Michigan. A rigorous, quality-assured large
supporting data set derived from samples collected in 1994-1995 was used to establish atmospheric and
tributary loads, estimate initial conditions, and perform model calibration and confirmation exercises. Historical
data collected outside of the LMMBP were also used to support the modeling effort.
Models developed at the United States Environmental Protection Agency's Large Lakes Research Station,
to assess atrazine transport and fate in Lake Michigan included MICHTOX, LM2-Toxic, and LM3-Atrazine.
Both LM2-Toxic and LM3-Atrazine utilized results from a hydrodynamic model to describe the lake's physics.
Results from air and tributary models were used to provide atrazine loadings to the lake.
Lake Michigan is acted upon by a number of physical parameters that impact the hydrology, chemistry, and
biology of the lake. For a lake the size of Lake Michigan, changes in these parameters can lead to significant
changes, especially when models are used in long-term predictions to predict the outcome of various
scenarios. The primary driving forces are wind, air temperature, and precipitation. These impact tributary
flows, lake levels, waves, water circulation, water temperature, and ice cover. For the period of record, these
driving forces vary from year-to-year. The period of 1982 to 1983 was used to calibrate the hydrodynamic
models. For this period of time, hydrodynamic conditions were not at any extreme. This is also true for the
period of 1994 and 1995 when the models were applied.
Temperature will impact contaminant modeling. Air temperature impacts how quickly the lake warms in any
one year. Water temperature impacts the volatilization of contaminants. There appears to be a four-year cycle
of quicker warming which exists within a trend of general warming of the lake. The trend of warming may be
part of a longer term, undocumented cycle, or may be related to climate change.
MICHTOX is a toxic chemical mass balance and food chain bioaccumulation model developed in the early
1990s. The model has nine water segments encompassing both Lake Michigan and Green Bay and is derived
from the general water quality model WASP4. Before the onset of the LMM BP, MICHTOX was applied to Lake
Michigan in a hindcast mode to gain an initial understanding of key atrazine processes in the lake and
controlling loads. Tributary loadings of atrazine to the lake were determined based on historical usage of the
chemical in the basin and a literature-derived Watershed Export Percentage (WEP) of 0.6%. The processes
modeled included advection, dispersion, and reaction (decay). MICHTOX was used to provide a screening-
level analysis of the potential future trends in atrazine concentrations in lake water under a variety of
contaminant load scenarios. MICHTOX was run for seven scenarios to help evaluate the impacts on atrazine
trends caused by various loading sources and boundary conditions. Results using the assumption of average
boundary conditions indicate that atrazine decays at a rate of approximately 0.01/yr. This represents a half-life
of atrazine in the lake due to decay of 69.3 years. MICHTOX modeling indicates that a total loading reduction
of approximately 37%, if implemented on January 1,2005, would be needed to keep concentrations in the lake
near steady-state.
XXI
-------
LM2-Toxic is a sophisticated and state-of-the-art toxic chemical fate and transport model for Lake Michigan.
LM2-Toxic is also a revision of the USEPA-supported WASP4 water quality modeling framework. The
processes modeled included advection, dispersion, decay, absorption, and volatilization. The transport fields
that were output from the 19-layered 5 km x 5 km gridded Princeton Ocean Model for the Great Lakes
(POMGL) were aggregated and used by LM2-Toxic. The results of the LM2-Toxic system mass balance
model show that the largest atrazine load to the lake is from the watershed. For the year 1994, it was
estimated that 5,264 kg of atrazine were discharged to the lake via the tributaries. The second major load to
the lake was from atmospheric wet deposition with a loading estimate of 2,493 kg. The greatest loss of
atrazine from the lake was through transport to Lake Huron (2,546 kg) via the Straits of Mackinac. Loss due
to internal decay (1,662 kg) was the second largest loss mechanism. The total inventory of atrazine in the lake
was determined to be 184,310 kg in 1994. In this large, cold northern lake, the model suggests that in situ
atrazine decay is very slow (0.009/year). This translates into an estimated atrazine half-life of 77 years. Using
the model to forecast alternative futures, a 35% load reduction, if implemented in January 1, 2005, would have
been needed in order to prevent atrazine concentrations from increasing further in the lake. If loadings and
boundary conditions are assumed to be constant in the future, the model predicts that the lake will eventually
reach a steady-state concentration of 66 ng/L in the year 2194.
LM3-Atrazine is a high-resolution (44,042 cells and 19 sigma layers) model that provides a better description
of areas such as near and offshore zones, bays, river confluences, and the thermocline. The transport fields
are provided by output from the Princeton Ocean hydrodynamics Model. Our high-resolution model, LM3-
Atrazine, was primarily used to evaluate environmental exposure concentrations of atrazine in 5km x 5km
model cells receiving loadings from the major tributaries to the lake. The modeled processes included
advection, dispersion, decay, absorption, and volatilization. The atrazine decay (0.009/year) used in LM3-
Atrazine was taken from the results derived from the hindcast run using LM2-Toxic.
The model segment receiving loads from the St. Joseph River, associated with the largest tributary load of
atrazine to the lake, ranged from winter concentrations of 37 ng/L to spring peaks of 100-350 ng/L. These
predicted exposure concentrations in the lake are all below selected toxicological endpoints, including the most
sensitive, phytoplankton primary production reduction.
In comparing the results from the three LMMBP atrazine models to other models in the literature, it is apparent
that atrazine decays very slowly in large lakes that stratify in the summer months. The literature suggests that
degradation of atrazine in small lakes and streams that are well-mixed can be significant. A hypothesis can
be formulated that the decay in surface water is likely to be dominated by photolytic processes, either directly
or indirectly. In lakes that stratify in the summer, atrazine in the hypolimnion is isolated from the intense solar
radiation during the peak time of the year. Hence, atrazine in this layer of the lake receives little degradation.
The LMMBP atrazine models differ from two other atrazine models recently applied to Lake Michigan. The
main reason for the differences appears to be based on how they estimated tributary loadings - both used
higher estimates of tributary loadings. Consequently, these other models predicted much faster in situ decay.
Since tributary loadings are the major source atrazine to the lake, detailed assessments of these loads is very
important.
XXII
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PART1
INTRODUCTION
Chapter 1. Project Overview
Harry B. McCarty, Ken Miller, Robert N. Brent, and
Judy Schofield
DynCorp (a CSC Company)
601 Stevenson Avene
Alexandria, Virginia 22304
and
Ronald Rossmann and Kenneth R. Rygwelski
United States Environmental Protection Agency
Office of Research and Development
National Health and Environmental Effects
Research Laboratory
Mid-Continent Ecology Division
Large Lakes and Rivers Forecasting Research
Branch
Large Lakes Research Station
9311 Groh Road
Grosse lie, Michigan 48138
The United States Environmental Protection
Agency's (USEPA) Great Lakes National Program
Office (GLNPO) instituted the Lake Michigan Mass
Balance Project (LMMBP) to measure and model the
concentrations of representative pollutants within
important compartments of the Lake Michigan
ecosystem. For the LMMBP, concentrations of
polychlorinated biphenyls (PCBs), frans-nonachlor,
and mercury were measured in tributaries, lake
water, sediments, food webs, and the atmosphere
surrounding Lake Michigan. Atrazine was measured
only in the tributaries, lake water, and atmospheric
components. This chapter provides an overview of
the entire LMMBP. It includes a summary of the
parameters measured and identifies the participants.
Some of the data handling procedures are covered,
as well as a listing of various project reports.
1.1.1 Background
The Great Lakes, which contain 20% of the world's
freshwater, are a globally important natural resource
currently threatened by multiple stressors. While
significant progress has been made to improve the
quality of the lakes, pollutant loads from point, non-
point, atmospheric, and legacy sources continue to
impair ecosystem functions and limit the attainability
of designated uses of these resources. Fish
consumption advisories and beach closings continue
to be issued, emphasizing the human health
concerns from lake contamination. Physical and
biological stressors, such as invasion of non-native
species and habitat loss, also continue to threaten
the biological integrity of the Great Lakes.
The United States and Canada have recognized the
significance and importance of the Great Lakes as a
natural resource and have taken steps to restore and
protect the lakes. In 1978, both countries signed the
Great Lakes Water Quality Agreement (GLWQA).
This Agreement calls for the restoration and
maintenance of the chemical, physical, and biological
integrity of the Great Lakes by developing plans to
monitor and limit pollutant flows into the lakes.
The GLWQA, as well as Section 118(c) of the Clean
Water Act, require the development of a Lake-wide
Management Plan (LaMP) for each Great Lake. The
purpose of these LaMPs is to document an approach
to reduce inputs of critical pollutants to the Great
Lakes and restore and maintain Great Lakes
-------
integrity. To assist in developing these LaMPs and
to monitor progress in pollutant reduction, federal,
state, tribal, and local entities have instituted
Enhanced Monitoring Plans (EMPs). Monitoring is
essential to the development of baseline conditions
for the Great Lakes and provides a sound scientific
base of information to guide future toxic load
reduction efforts.
The LMMBP is a part of the EMPs for Lake Michigan.
The LMMBP was a coordinated effort among federal,
state, and academic scientists to monitor tributary
and atmospheric pollutant loads, develop source
inventories of toxic substances, and evaluate the fate
and effects of these pollutants in Lake Michigan. A
mass balance modeling approach provides the
predictive ability to determine the environmental
benefits of specific load reduction scenarios for toxic
substances and the time required to realize those
benefits. This predictive ability will allow federal,
state, tribal, and local agencies to make more
informed load reduction decisions.
1.1.2 Description
The LMMBP used a mass balance approach to
evaluate the sources, transport, and fate of
contaminants in the Lake Michigan ecosystem. A
mass balance approach is based on the law of
conservation of mass, which states that the amount
of a pollutant entering a system is equal to the
amount of that pollutant leaving, trapped in, and
chemically changed in the system (Figure 1.1.1). In
the Lake Michigan system, pollutant inputs may
come from atmospheric sources, adjacent lakes, or
tributary loads.
Pollutants may leave the system through burial in
bottom sediments, volatilization to the atmosphere, or
discharge into Lake Huron through the Straits of
Simple Mass Budget for Conservative Substances
source
mass ;
in
water system
mass out ~ mass jn + ^sources
1 source
Mass Balance Modeling Approach
rr
air system ^
f !
ass in ^
^
! t
water system
1 i
1 1
sediment system
mass OU|
_ air sources
= mass jn + ^sources
^
± air-water exchange
± sediment-water exchange
± ^internal processes
Figure 1.1.1. Simplified mass balance approach.
-------
Mackinac. The relative magnitude of these loss
mechanisms is, in part, due to the physical and
chemical properties of the chemicals being modeled.
Pollutants within the system may be transformed
through degradation or stored in ecosystem
compartments such as the water column, sediments,
or biota.
For the LMMBP, contaminant concentrations in
various inputs and ecosystem compartments over
spatial and temporal scales were measured.
Mathematical models that track the transport and fate
of contaminants within Lake Michigan were
developed and calibrated using these field data. The
LMMBP models will serve as a basis for future mass
budget/mass balance efforts for the LMMBP
contaminants and other chemicals of interest.
1.1.3 Scope
1.1.3.1 Modeled Pollutants
When the USEPA published the Water Quality
Guidance for the Great Lakes System (58 FR
20802), the Agency established water quality criteria
for 29 pollutants. Those criteria were designed to
protect aquatic life, terrestrial wildlife, and human
health. PCBs, frans-nonachlor, and mercury are
included in the list of 29 pollutants. The water quality
criteria and values proposed in the guidance apply to
all of the ambient waters of the Great Lakes system,
regardless of the sources of pollutants in those
waters. The proposed criteria provide a uniform
basis for integrating federal, state, and tribal efforts
to protect and restore the Great Lakes ecosystem.
The number of pollutants that can be intensively
monitored and modeled in the Great Lakes system is
limited by the resources available to collect and
analyze thousands of samples, assure the quality of
the results, manage the data, and develop and
calibrate the necessary models. Therefore, the
LMMBP focused on constructing mass balance
models for a limited group of pollutants. PCBs, trans-
nonachlor, atrazine, and mercury were selected for
inclusion in the LMMBP because these pollutants
currently or potentially pose a risk to aquatic and
terrestrial organisms (including humans) in the Lake
Michigan ecosystem (Table 1.1.1). These pollutants
also were selected to cover a wide range of chemical
and physical properties and represent other classes
of compounds which pose current or potential
problems. Once a mass budget for selected
pollutants is established and a mass balance model
calibrated, additional contaminants can be modeled
with limited data and future resources can be
devoted to activities such as emission inventories
and dispersion modeling.
1.1.3.1.1 PCBs
Polychlorinated biphenyls (PCBs) are a class of man-
made, chlorinated, organic chemicals that include
209 congeners, or specific PCB compounds. The
highly stable, nonflammable, non-conductive
properties of these compounds made them useful in
a variety of products including electrical transformers
and capacitors, plastics, rubber, paints, adhesives,
and sealants. PCBs were produced for such
industrial uses in the form of complex mixtures under
the trade name "Aroclor" and were commercially
available from 1930 through 1977, when the USEPA
banned their production due to environmental and
public health concerns. PCBs also may be produced
by combustion processes, including incineration, and
can be found in stack emissions and ash from
incinerators.
Because they were found by the USEPA in the
effluents from one or more wastewater treatment
facilities, seven Aroclor formulations were included in
the Priority Pollutant List developed by the USEPA
Office of Water under the auspices of the Clean
Water Act. Aroclors may have entered the Great
Lakes through other means, including spills or
improperdisposal of transformerfluids, contaminated
soils washing into the watershed, or discharges from
ships. The PCBs produced by combustion
processes may be released to the atmosphere where
they are transported in both vapor and particulate
phases and enter the lakes through either dry
deposition or precipitation events (e.g., rain).
The stability and persistence of PCBs, which made
them useful in industrial applications, have also made
these compounds ubiquitous in the environment.
PCBs do not readily degrade and thus accumulate in
water bodies and aquatic sediments. PCBs also
bioaccumulate, or build up, in living tissues. Levels
of PCBs in some fish from Lake Michigan exceed
-------
Table 1.1.1. Characteristics of the LMMBP Modeled Pollutants
Pollutant
PCBs
frans-Non-
achlor3
Sources
• Waste incinerators
(unintentional
byproducts of
combustion)
• Industrial
dischargers
• Electrical power
• Application to crops
and gardens
Uses
• Electrical
transformers and
capacitors
• Carbonless copy
paper
• Plasticizers
• Hydraulic fluids
• Pesticide on corn
and citrus crops
• Pesticide on
lawns and
gardens
Toxic Effects
• Probable human
carcinogen
• Hearing and vision
impairment
• Liver function alterations
• Reproductive impairment
and deformities in fish and
wildlife
• Probable human
carcinogen
• Nervous system effects
• Blood system effects
• Liver, kidney, heart, lung,
spleen, and adrenal gland
damage
Biocon-
centration
Factor1
1,800 to
180,000
4, 000 to
40,000
USEPA
Regulatory
Standards2
MCL = 0.5 |jg/L
CCC= 14ng/L
HH = 0.17ng/L
MCL = 2 |jg/L
CMC = 2.4 |jg/L
CCC = 4.3 ng/L
HH = 2.1 ng/L
Atrazine
Mercury
Application to crops
Herbicide for corn
and sorghum
production
Waste disposal
Manufacturing
processes
Energy production
Ore processing
Municipal & medical
waste incinerators
Chloralkali factories
Fuel combustion
Battery cells
Barometers
Dental fillings
Thermometers
Switches
Fluorescent lamps
Weightless 2 to 100
Cardiovascular damage
Muscle and adrenal
degeneration
Congestion of heart,
lungs, and kidneys
Toxic to aquatic plants
Possible human 63,000 to
carcinogen 100,000
Damage to brain and
kidneys
Adverse affects on the
developing fetus, sperm,
and male reproductive
organs
MCL = 3|jg/L
CMC4 = 350
CCC4 = 12|jg/L
MCL = 2 |jg/L
CMC = 1.4|jg/L
CCC = 0.77 |jg/L
HH = 50 ng/L
FWA5 = 2.4 |jg/L
FWC5= 12 ng/L
Wildlife6 = 1.3
ng/L
1From: U.S. Environmental Protection Agency, 1995a, National Primary Drinking Water Regulations, Contaminant Specific
Fact Sheets, Inorganic Chemicals, Technical Version, EPA 811/F-95/002-T, USEPA, Office of Water, Washington, D.C.;
and U.S. Environmental Protection Agency, 1995b, National Primary Drinking Water Regulations, Contaminant Specific
Fact Sheets, Synthetic Organic Chemicals, Technical Version, EPA 811/F-95/003-T, USEPA, Office of Water,
Washington, D.C.
2MCL = Maximum Contaminant Level for drinking water. CMC = Criterion Maximum Concentration for protection of aquatic
life from acute toxicity. CCC = Criterion Continuous Concentration for protection of aquatic life from chronic toxicity. HH
= water quality criteria for protection of human health from water and fish consumption. Data from: U.S. Environmental
Protection Agency, 1999, National Recommended Water Quality Criteria-Correction, EPA 822/Z-99/001, USEPA, Office
of Water, Washington, D.C.
Characteristics presented are for chlordane. frans-Nonachlor is a principal component of the pesticide chlordane.
4Draft water quality criteria for protection of aquatic life. From: U.S. Environmental Protection Agency, 2001 b, Ambient
Aquatic Life Water Quality Criteria for Atrazine, USEPA, Office of Water, Washington, D.C.
5FWA = Freshwater acute water quality criterion. FWC = Freshwater chronic water quality criterion. From National Toxics
Rule (58 FR 60848).
6Wildlife criterion. From the Stay of Federal Water Quality Criteria for Metals (60 FR 22208), 40 CFR 131.36 and the
Water Quality Guidance for the Great Lakes System (40 CFR 132).
-------
the U.S. Food and Drug Administration tolerances,
prompting closure of some commercial fisheries and
issuance of fish consumption advisories. PCBs are
a probable human carcinogen, and human health
effects of PCB exposure include stomach, kidney,
and liver damage; liver and biliary tract cancer; and
reproductive effects, including effects on the fetus
after exposure of the mother.
PCB congeners exhibit a wide range of physical and
chemical properties (e.g. vapor pressures,
solubilities, boiling points), are relatively resistant to
degradation, and are ubiquitous. These properties
make them ideal surrogates for a wide range of
organic compounds from anthropogenic sources.
1.1.3.1.2 trans-Nonachlor
frans-Nonachlor is a component of the pesticide
chlordane. Chlordane is a mixture of chlorinated
hydrocarbons that was manufactured and used as a
pesticide from 1948 to 1988. Prior to 1983,
approximately 3.6 million pounds of chlordane were
used annually in the United States. In 1988, the
USEPA banned all production and use of chlordane
in the United States.
Like PCBs, chlordane is relatively persistent and
bioaccumulative. frans-Nonachlor is the most
bioaccumulative of the chlordanes and is a probable
human carcinogen. Other human health effects
include neurological effects, blood dyscrasia,
hepatoxicity, immunotoxicity, and endocrine system
disruption.
Historically, frans-nonachlor may have entered the
Great Lakes through a variety of means related to
the application of chlordane, including improper or
indiscriminate application, improper cleaning and
disposal of pesticide application equipment, or
contaminated soil washing into the watershed. In the
LMMBP, frans-nonachlor served as a model for the
cyclodiene pesticides.
1.1.3.1.3 Atrazine
Atrazine is a triazine herbicide based on a ring
structure with three carbon atoms alternating with
three nitrogen atoms. Atrazine is the most widely
used herbicide in the United States for corn and
sorghum production. Atrazine has been used as an
agricultural herbicide since 1959, and 64 to 75 million
pounds of atrazine are used annually in the United
States. Atrazine is extensively used in the upper
Midwest, including the Lake Michigan watershed,
where it is primarily associated with corn crops.
Unlike PCBs and frans-nonachlor, atrazine is not
bioaccumulative. It can be persistent in water;
however, it is moderately susceptible to
biodegradation in soils with a half-life of about 60-150
days. Atrazine rarely exceeds the 3 ppb maximum
contaminant level (MCL) set by the USEPA as a
drinking water standard, but localized peak values
can exceed the MCL following rainfall events after
atrazine application.
On January 31, 2003, the USEPA issued an Interim
Reregistration Eligibility Decision (I RED) for atrazine.
In an October 2003 addendum to the I RED, the
Agency concluded that there is sufficient evidence to
formulate a hypothesis that atrazine exposure may
impact gonadal development in amphibians, but
there are currently insufficient data to either confirm
or refute the hypothesis. However, in an October
2007 report to the Federal Insecticide, Fungicide, and
Rodenticide Act (FIFRA) Scientific Board, the
Agency's review concluded that the weight-of-
evidence from a literature review does not show that
atrazine produces consistent, reproducible effects
across the range of exposure concentrations and
amphibian species tested. Based on available test
data, atrazine is not likely to be a human carcinogen.
The Agency does have concern in regards to the
potential hormonal effects observed in laboratory
animals exposed to atrazine. Above certain
concentration thresholds, atrazine is toxic to aquatic
plants. In the LMMBP, atrazine served as a model to
describe the transport and fate of a water soluble
pesticide in current use.
1.1.3.1.4 Mercury
Mercury is a naturally-occurring toxic metal. Mercury
is used in battery cells, barometers, thermometers,
switches, fluorescent lamps, and as a catalyst in the
oxidation of organic compounds. Global releases of
mercury in the environment are both natural and
anthropogenic (caused by human activity). It is
estimated that about 11,000 metric tons of mercury
-------
are released annually to the air, soil, and water from
anthropogenic sources. These sources include
combustion of various fuels such as coal; mining,
smelting, and manufacturing activities; wastewater;
and agricultural, animal, and food wastes.
As an elemental metal, mercury is extremely
persistent in all media. Mercury also bioaccumulates
with reported bioconcentration factors in fish tissues
in the range of 63,000 to 100,000. Mercury is a
possible human carcinogen and causes the following
human health effects: stomach, large intestine,
brain, lung, and kidney damage; blood pressure and
heart rate increase; and fetal damage. In the
LMMBP, mercury served as a model for
bioaccumulative metals.
1.1.3.2 Other Measured Parameters
In addition to the four chemicals modeled in the
LMMBP, many otherchemicals and parameters were
measured in the LMMBP as part of the EMPs. A
survey of these chemicals and parameters aids in the
understanding of the overall ecological integrity of
Lake Michigan. These additional parameters include
various biological indicators; meteorological
parameters; and organic, metal, and conventional
chemicals in Lake Michigan. Many of the parameters
included in this study are provided in Table 1.1.2.
Table 1.1.2. The LMMBP Parameters
Organics (Continued)
Organics
acenaphthene
acenaphthylene
aldrin
anthracene
atrazine
a-BHC
(3-BHC
5-BHC
Y-BHC
benzo[a]anthracene
benzo[g,/?,/]perylene
benzo[£>]fluoranthene
benzo[/(]fluoranthene
benzo[e]pyrene
benzo[a]pyrene
a-chlordane
p,p'-DDT
endosulfan sulfate
endosulfan I
endosulfan II
endrin
endrin aldehyde
endrin ketone
fluoranthene
fluorene
heptachlor
heptachlor epoxide
hexachlorobenzene (HCB)
indeno[1,2,3-cd]pyrene
mirex
frans-nonachlor
oxychlordane
benzo[a]pyrene
a-chlordane
y-chlordane
chrysene
coronene
p,p'-DDE
p,p'-DDD
frans-nonachlor
oxychlordane
PCBs congeners
phenanthrene
pyrene
retene
toxaphene
Metals
aluminum
arsenic
calcium
cadmium
chromium
cesium
copper
iron
mercury
potassium
magnesium
manganese
sodium
nickel
lead
selenium
thorium
titanium
vanadium
zinc
Conventionals
alkalinity
ammonia
bromine
chloride
chlorine
sulfate
conductivity
dissolved organic
carbon
dissolved oxygen
dissolved phosphorus
dissolved reactive silica
dry weight fraction
element carbon
nitrate
particulate organic carbon
percent moisture
PH
phosphorus
silica
silicon
temperature
total Kjeldahl nitrogen
total organic carbon
total phosphorus
total suspended
particulates
orffto-phosphorus
total hardness
turbidity
Biologicals
fish species
fish age
fish maturity
chlorophyll a
fish lipid amount
zooplankton
fish weight
fish length
fish taxonomy
fish diet analysis
primary productivity
Meteorological
air temperature
relative humidity
barometric pressure
weather conditions
wind direction
wind speed
visibility
wave height and direction
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1.1.3.3 Measured Compartments
In the LMMBP, contaminants were measured in the
following compartments:
• Open-Lake Water Column: The water column in
the open-lake was sampled and analyzed for the
modeled pollutants.
• Tributaries: Major tributaries were sampled and
analyzed for the modeled pollutants.
• Fish: Top predators and forage base species
were sampled and analyzed for diet analysis and
contaminant burden.
• Lower Pelagic Food Chain: Phytoplankton and
zooplankton were sampled and analyzed for
species diversity, taxonomy, and contaminant
burden.
• Sediments: Cores were collected and trap
devices were used to collect sediment for
determination of contaminants and sedimentation
rates.
• Atmosphere: Vapor, particulate, and precipitation
phase samples were collected and analyzed for
the modeled pollutants.
For the modeled pollutants, more than 20,000
samples were collected at more than 300 sampling
locations and analyzed, including more than 9,000
quality control (QC) samples (Figure 1.1.2). Field
data collection activities were initially envisioned as
a one-year effort. However, it became evident early
into the project that a longer collection period would
be necessary to provide a full year of concurrent
Manistique River,
uskegon River
Grand River
O water survey stations
, ;j=atmospheric monitoring
D tributary monitoring
stations
Figure 1.1.2. The LMMBP sampling locations.
7
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information on contaminant loads and ambient
concentrations for modeling purposes. Therefore,
field sampling occurred from April 1994 to October
1995.
1.1.4 Objectives
The goal of the LMMBP was to develop a sound,
scientific base of information to guide future toxic
load reduction efforts at the federal, state, tribal, and
local levels. To meet this goal, the four following
LMMBP objectives were developed:
>• Estimate pollutant loading rates: Environmental
sampling of major media will allow estimation of
relative loading rates of critical pollutants to the
Lake Michigan basin.
>• Establish baseline: Environmental sampling and
estimated loading rates will establish a baseline
against which future progress and contaminant
reductions can be gauged.
- Predict benefits associated with load
reductions: The completed mass balance model
will provide a predictive tool that environmental
decision-makers and managers may use to
evaluate the benefits of specific load reduction
scenarios.
>• Understand ecosystem dynamics: Information
from the extensive LMMBP monitoring and
modeling efforts will improve our scientific
understanding of the environmental processes
governing contaminant cycling and availability
within relatively closed ecosystems.
1.1.5 Design
1.1.5.1 Organization
The GLNPO proposed a mass balance approach to
provide coherent, ecosystem-based evaluation of
toxics in Lake Michigan. GLNPO served as the
program sponsor for the LMMBP. GLNPO formed
two committees to coordinate study planning, the
Program Steering Committee and the Technical
Coordinating Committee. These committees were
comprised of federal, state, and academic
laboratories as well as commercial laboratories (see
Section 1.1.5.2, Study Participants). The committees
administered a wide variety of tasks including:
planning the project, locating the funding, designing
the sample collection, coordinating sample collection
activities, locating qualified laboratories, coordinating
analytical activities, assembling the data, assuring
the quality of the data, assembling skilled modelers,
developing the models, and communicating interim
and final project results. The Mid-Continent Ecology
Division (MED) at Duluth, in cooperation with the
National Oceanic and Atmospheric Administration
(NOAA) Great Lakes Environmental Research
Laboratory (GLERL) and the Atmospheric Sciences
Modeling Division, supported the modeling
component of the mass balance study by developing
a suite of integrated mass balance models to
simulate the transport, fate, and bioaccumulation of
the study target analytes.
1.1.5.2 Study Participants
The LMMBP was a coordinated effort among federal,
state, and academic scientists; and commercial
laboratories. The following agencies and
organizations have all played roles in ensuring the
success of the LMMBP. Except for the three
organizations indicated with an asterisk (*), all of the
participants were members of the LMMBP Steering
Committee.
Federal and International
•• USEPA/GLNPO (Program Sponsor)
» USEPA/Region V Water Division (WD)
»• USEPA/Region V Air Division
- USEPA/ORD/NHEERL/MED/LLRFRB
>• ORD/National Exposure Research Laboratory
•• U.S. Department of the Interior (USDOI) U.S.
Geological Survey (USGS) Water Resources
Division (WRD)
•• USDOI/USGS Biological Resources DivisionGreat
Lakes Science Center (GLSC)
- U.S. Fish and Wildlife Service (USFWS)
>• U.S. Department of Energy
•• U.S. Department of Commerce NOAA/GLERL
•• USEPA/Office of Air and Radiation*
- USEPA/Office of Water*
>• Environment Canada*
•* U.S. Department of Energy Battelle NW
8
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State
* Illinois Department of Natural Resources
>• Illinois Water Survey
>• Indiana Department of Environmental
Management
>• Michigan Department of Natural Resources
>• Michigan Department of Environmental Quality
(MDEQ)
>• Wisconsin Department of Natural Resources
>• Wsconsin State Lab of Hygiene
Academic and Commercial
* Indiana University
>• Rutgers University
>• University of Maryland
>• University of Michigan
>• University of Minnesota
>• University of Wisconsin
>• Grace Analytical
1.1.5.3 Workgroups
Eleven workgroups were formed to provide oversight
and management of specific project elements. The
workgroups facilitated planning and implementation
of the study in a coordinated and systematic fashion.
The workgroups communicated regularly through
participation in monthly conference calls and annual
"all-hands" meetings. Workgroup chairs were
selected and were responsible for managing tasks
under the purview of the workgroup and
communicating the status of activities to other
workgroups. The workgroups and workgroup chairs
are listed below.
• Program Steering Committee - Paul Horvatin
(USEPA/GLNPO)
• Technical Coordinating Committee-Paul Horvatin
(USEPA/GLNPO)
• Modeling Workgroup - William Richardson
(USEPA/ORD/NHEERL/MED/LLRFRB)
• Air Monitoring Workgroup-Jackie Bode (USEPA/
GLNPO)
• Biota Workgroup - Paul Bertram (USEPA/
GLNPO) and John Gannon (USDOI/USGS/
GLSC)
• Chemistry Workgroup-David Anderson (USEPA/
GLNPO)
• Data Management Workgroup - Kenneth Klewin
and Philip Strobel (USEPA/GLNPO)
• Lake Monitoring Workgroup - Glenn Warren
(USEPA/GLNPO)
• Tributary Monitoring Workgroup - Gary Kohlhepp
(USEPA/Region V/WD) and Robert Day (MDEQ)
• Quality Assurance Workgroup - Louis Blume and
Michael Papp (USEPA/GLNPO)
• Sediment Monitoring Workgroup - Brian Eadie
(NOAA/GLERL)
1.1.5.4 Information Management
As program sponsor, GLNPO managed information
collected during the LMMBP. Principal Investigators
(Pis) participating in the study reported field and
analytical data to GLNPO. GLNPO developed a data
standard for reporting field and analytical data and a
database for storing and retrieving study data.
GLNPO was also responsible for conducting data
verification activities and releasing verified data to the
study modelers and the public. The flow of
information is illustrated in Figure 1.1.3.
1.1.5.4.1 Data Reporting
Over 20 organizations produced LMMBP data
through the collection and analysis of more than
20,000 samples. In the interest of standardization,
specific formats (i.e., file formats and codes to
represent certain data values) were established for
reporting the LMMBP data. Each format specified
the "rules" by which data were submitted, and, in
many cases, the allowable values by which they were
to be reported. The data reporting formats were
designed to minimize the number of data elements
reported from the field crews and laboratory analysis.
Data reporting formats and the resulting Great Lakes
Environmental Monitoring Database (GLENDA, see
Section! 1.5.4.2) were designed to be applicable to
projects outside the LMMBP as well.
Principal Investigators (Pis) (including sampling
crews and the analytical laboratories) supplied
sample collection and analysis data following the
standardized reporting formats, if possible. The
LMMBP data were then processed through an
automated SAS-based data verification system,
Research Data Management and Quality Control
System (RDMQ), for quality assurance (QA)/QC
-------
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I
Ripert Fltld and
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' ^~''*x -^~-^^__
•
QLENDA
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•^^
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Coixs uot Data
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and And^tfczl Data L^qg
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MM Flltuid Pravld* le
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Pr4d»»Fln*!Vii1hd
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J
Figure 1.1.3. Flow of information in the LMMBP.
checking. After verification and validation by the Pis,
the data sets were output in a form specific for
upload to GLENDA. Finally, these data sets were
uploaded to GLENDA.
1.1.5.4.2 Great Lakes Environmental Monitoring
Database
Central to the data management effort is a
computerized database system to house LMMBP
and other project results. That system, GLENDA,
was developed to provide data entry, storage,
access, and analysis capabilities to meet the needs
of mass balance modelers and other potential users
of Great Lakes data.
Development of GLENDA began in 1993 with a
logical model based on the modernized STORE!
concept and requirements analysis. GLENDA was
developed with the following guiding principles:
• True multi-media scope: Water, air, sediment,
taxonomy, fish tissue, fish diet, and meteorology
data can all be housed in the database.
• Data of documented quality: Data quality is
documented by including results of quality control
parameters.
• Extensive contextual indicators: Ensure data
longevity by including enough information to allow
future or secondary users to make use of the data.
10
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• Flexible and expandable: Database is able to
accept data from any Great Lakes monitoring
project.
• National compatibility: GLENDA is compatible
with STORE! and allows ease of transfer between
these large databases.
In an effort to reduce the data administration burden
and ensure consistency of data in this database,
GLNPO developed several key tools. Features
including standard data definitions, reference tables,
standard automated data entry applications, and
analytical tools are (or will soon be) available.
1.1.5.4.3 Public Access to LMMBP Data
All LMMBP data that have been verified (through the
QC process) and validated (accepted by the PI) are
available to the public. Currently, GLNPO requires
that written requests be made to obtain the LMMBP
data. The data sets are available in several formats
including WK1, DBF, and SD2. More information
about the data sets is available on the LMMBP web
site at: http://www.epa.gov/glnpo/lmmb/
database.html.
The primary reason for requiring an official request
form for the LMMBP data is to keep track of
requests. This allows GLNPO to know how many
requests have been made, who has requested data,
and what use they intend for the data. This
information assists GLNPO in managing and
providing public access to Great Lakes data and
conducting public outreach activities. As of
November 2000, 38 requests for the LMMBP data
have been made: eight from USEPA, five from other
federal agencies, five from state agencies, five from
universities, ten from consultants, three from
international agencies, and two from non-profit or
other groups. In the future, after all data are verified
and validated, GLNPO intends to make condensed
versions of the data sets available on the LMMBP
web site for downloading. This will allow easy public
access to the LMMBP data.
Further information on the information management
for the LMMBP can be found in The Lake Michigan
Mass Balance Study Quality Assurance Report (U.S.
Environmental Protection Agency, 2001 a).
1.1.5.5 Quality Assurance Program
At the outset of the LMMBP, managers recognized
that the data gathered and the models developed
from the study would be used extensively by
decision-makers responsible for making
environmental, economic, and policy decisions.
Environmental measurements are never true values
and always contain some level of uncertainty.
Decision-makers, therefore, must recognize and be
sufficiently comfortable with the uncertainty
associated with data on which their decisions are
based. In recognition of this requirement, the
LMMBP managers established a QA program goal of
ensuring that data produced under the LMMBP would
meet defined standards of quality with a specified
level of confidence.
The QA program prescribed minimum standards to
which all organizations collecting data were required
to adhere. Data quality was defined, controlled, and
assessed through activities implemented within
various parameter groups (e.g., organic, inorganic,
and biological parameters). QA activities included
the following:
>• QA Program: Prior to initiating data collection
activities, plans were developed, discussed, and
refined to ensure that study objectives were
adequately defined and to ensure that all QA
activities necessary to meet study objectives were
considered and implemented.
•• QA Workgroup: USEPA established a QA
Workgroup whose primary function was to ensure
that the overall QA goals of the study were met.
•• QA Project Plans (QAPPs): USEPA worked with
Pis to define program objectives, data quality
objectives (DQOs), and measurement quality
objectives (MQOs) for use in preparing Quality
Assurance Project Plans (QAPPs). Pis submitted
QAPPs to the USEPA for review and approval.
USEPA reviewed each QAPP for required QA
elements and soundness of planned QA activities.
>• Training: Before beginning data collection
activities, Pis conducted training sessions to
ensure that individuals working on the project were
capable of properly performing data collection
activities for the LMMBP.
11
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» Monthly Conference Calls and Annual
Meetings: USEPA, Pis, and support contractors
participated in monthly conference calls and
annual meetings to discuss project status and
objectives, QA issues, data reporting issues, and
project schedules.
>• Standardized Data Reporting Format: Pis were
required to submit all data in a standardized data
reporting format that was designed to ensure
consistency in reporting and facilitate data
verification, data validation, and database
development.
>• Intercomparison Studies: USEPA conducted
studies to compare performance among different
Pis analyzing similar samples. The studies were
used to evaluate the comparability and accuracy
of program data.
>• Technical Systems Audits: During the study,
USEPA formally audited each Pi's laboratory for
compliance with their QAPPs, the overall study
objectives, and pre-determined standards of good
laboratory practice.
>• Data Verification: Pis and the USEPA evaluated
project data against pre-determined MQOs and
DQOs to ensure that only data of acceptable
quality would be included in the program
database.
>• Statistical Assessments: USEPA made
statistical assessments of the LMMBP data to
estimate elements of precision, bias, and
uncertainty.
>• Data Validation: USEPA and modelers evaluated
the data against the model objectives.
Comparability of data among Pis participating in the
LMMBP was deemed to be important for successful
completion of the study. Therefore, MQOs for
several data attributes were developed by the Pis
and defined in the QAPPs. MQOs were designed to
control various phases of the measurement process
and to ensure that the total measurement uncertainty
was within the ranges prescribed by the DQOs.
MQOs were defined in terms of six attributes:
>• Sensitivity/Detectability: The determination of
the low-range critical value that a method-specific
procedure can reliably discern for a given
pollutant. Sensitivity measures included, among
others, method detection limits (MDLs) as defined
at 40 CFR Part 136, system detection limits
(SDLs), or instrument detection limits (IDLs).
>• Precision: A measure of the degree to which
data generated from replicate or repetitive
measurements differ from one another. Analysis
of duplicate samples was used to assess
precision.
>• Bias: The degree of agreement between a
measured and actual value. Bias was expressed
in terms of the recovery of an appropriate
standard reference material or spiked sample.
>• Completeness: The measure of the number of
samples successfully analyzed and reported
compared to the number that were scheduled to
be collected.
>• Comparability: The confidence with which one
data set can be compared to other data sets.
>• Representativeness: The degree to which data
accurately and precisely represent characteristics
of a population, parameter variations at a
sampling point, a process condition, or an
environmental condition.
The Pi-defined MQOs also were used as the basis
for the data verification process. GLNPO conducted
data verification through the LMMBP QA Workgroup.
The workgroup was chaired by GLNPO's QA
Manager and consisted of QC Coordinators that were
responsible for conducting review of specific data
sets. Data verification was performed by comparing
all field and QC sample results produced by each PI
with their MQOs and with overall LMMBP objectives.
If a result failed to meet predefined criteria, the QC
Coordinator contacted the PI to discuss the result,
verify that it was correctly reported, and determine if
corrective actions were feasible. If the result was
correctly reported and corrective actions were not
feasible, the results were flagged to inform data
users of the failure. These flags were not intended to
suggest that data were not useable; rather they were
intended to caution the user about an aspect of the
12
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data that did not meet the predefined criteria. Data
that met all predefined requirements were flagged to
indicate that the results had been verified and were
determined to meet applicable MQOs. In this way,
every data point was assigned one or more validity
flags based on the results of the QC checks. GLNPO
also derived data quality assessments for each
LMMBP data set for a subset of the attributes listed
above, specifically sensitivity, precision, and bias.
The LMMBP modelers and the LLRS Database
Manager also performed data quality assessments
prior to inputting data into study models. Such
activities included verifying the readability of
electronic files, identifying missing data, checking
units, and identifying outliers. A detailed description
of the QA program is included in The Lake Michigan
Mass Balance Project Quality Assurance Report
(U.S. Environmental Protection Agency, 2001 a). A
brief summary of quality implementation and
assessment is provided in each of the following parts.
1.1.6 Project Documents and Products
During project planning, LMMBP participants
developed study tools including work plans, a
methods compendium, QAPPs, and data reporting
standards. Through these tools, LMMBP participants
documented many aspects of the study including
information management and QA procedures. Many
of these documents are available on GLNPO's
website at http://www.epa.gov/glnpo/lmmb.
The LMMBP Work Plan
Designers of the LMMBP have documented their
approach in a report entitled Lake Michigan Mass
Budget/Mass Balance Work Plan (U.S.
Environmental Protection Agency, 1997a). The
essential elements of a mass balance study and the
approach used to measure and model these
elements in the Lake Michigan system are described
in the work plan. This document was developed
based upon the efforts of many federal and state
scientists and staff who participated in the initial
planning workshop, as well as Pis.
QA Program/Project Plans
The Lake Michigan Mass Balance Project: Quality
Assurance Plan for Mathematical Modeling, Version
3.0 (Richardson et a/., 2004) documents the QA
process for the development and application of
LMMBP models, including hydrodynamic, sediment
transport, eutrophication, transport chemicalfate, and
food chain bioaccumulation models.
The Enhanced Monitoring Program QA Program
Plan
The Enhanced Monitoring Program Quality
Assurance Program Plan (U.S. Environmental
Protection Agency, 1997b) was developed in 1993 to
ensure that data generated from the LMMBP
supported its intended use.
The LMMBP Methods Compendium
The Lake Michigan Mass Balance Project Methods
Compendium (U.S. Environmental Protection
Agency, 1997c, 1997d) describes the sampling and
analytical methods used in the LMMBP. The entire
three volumes are available on GLNPO's website
mentioned above.
The LMMBP Data Reporting Formats and Data
Administration Plan
Data management for the LMMBP was a focus from
the planning stage through data collection,
verification, validation, reporting, and archiving. The
goal of consistent and compatible data was a key to
the success of the project. The goal was met
primarily through the development of standard
formats for reporting environmental data. The data
management philosophy is outlined on the LMMBP
website mentioned above.
Lake Michigan LaMP
"Annex 2" of the 1972 Canadian-American Great
Lakes Water Quality Agreement (amended in 1978,
1983, and 1987) prompted development of a Lake-
wide Area Management Plan (LaMP) for each Great
Lake. The purpose of these LaMPs is to document
an approach to reducing input of critical pollutants to
the Great Lakes and restoring and maintaining Great
Lakes integrity. The Lake Michigan LaMP calls for
basin-wide management of toxic chemicals.
13
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GLENDA Database
Central to the data management effort is a
computerized data system to house LMMBP and
other project results. That system, the Great Lakes
Environmental Monitoring Database (GLENDA), was
developed to provide data entry, storage, access,
and analysis capabilities to meet the needs of mass
balance modelers and other potential users of Great
Lakes data.
References
Richardson, W.L., D.D. Endicott, R.G. Kreis, Jr., and
K.R. Rygwelski (Eds.). 2004. The Lake Michigan
Mass Balance Project Quality Assurance Plan for
Mathematical Modeling. Prepared by the
Modeling Workgroup. U.S. Environmental
Protection Agency, Office of Research and
Development, National Health and Environmental
Effects Research Laboratory, Mid-Continent
Ecology Division, Large Lakes Research Station,
Grosse lie, Michigan. EPA/600/R-04/018, 233
pp.
U.S. Environmental Protection Agency. 1995a.
National Primary Drinking Water Regulations,
Contaminant Specific Fact Sheets, Inorganic
Chemicals, Technical Version. U.S.
Environmental Protection Agency, Office of
Water, Washington, D.C. EPA/811/F-95/002-T.
U.S. Environmental Protection Agency. 1995b.
National Primary Drinking Water Regulations,
Contaminant Specific Fact Sheets, Synthetic
Organic Chemicals, Technical Version. U.S.
Environmental Protection Agency, Office of
Water, Washington, D.C. EPA/811/F-95/003-T.
U.S. Environmental Protection Agency. 1997a. Lake
Michigan Mass Budget/Mass Balance Work Plan.
U.S. Environmental Protection Agency, Great
Lakes National Program Office, Chicago, Illinois.
EPA/905/R-97/018, 155 pp.
U.S. Environmental Protection Agency. 1997b. The
Enhanced Monitoring Program Quality Assurance
Program Plan. U.S. Environmental Protection
Agency, Great Lakes National Program Office,
Chicago, Illinois. EPA/905/R-97/017, 61 pp.
U.S. Environmental Protection Agency. 1997c. Lake
Michigan Mass Balance Study (LMMB) Methods
Compendium, Volume 1: Sample Collection
Techniques. U.S. Environmental Protection
Agency, Great Lakes National Program Office,
Chicago, Illinois. EPA/905/R-97/012a, 1,440pp.
U.S. Environmental Protection Agency. 1997d. Lake
Michigan Mass Balance Study (LMMB) Methods
Compendium, Volume 2: Organic and Mercury
Sample Analysis Techniques. U.S.
Environmental Protection Agency, Great Lakes
National Program Office, Chicago, Illinois.
EPA/905/R-97/012b, 532 pp.
U.S. Environmental Protection Agency. 1999.
National Recommended Water Quality Criteria-
Correction. U.S. Environmental Protection
Agency, Office of Water, Washington, D.C.
EPA/822/Z-99/001, 25 pp.
U.S. Environmental Protection Agency. 2001a. The
Lake Michigan Mass Balance Study Quality
Assurance Report. U.S. Environmental
Protection Agency, Great Lakes National
Program, Chicago, Illinois. EPA/905/R-01/013.
U.S. Environmental Protection Agency. 2001 b.
Ambient Aquatic Life Water Quality for Atrazine.
U.S. Environmental Protection Agency, Office of
Water, Washington, D.C. EPA/822/D-01/002,
230 pp.
14
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PART1
INTRODUCTION
Chapter 2. General Information on the
Herbicide Atrazine and Its Degradation
Products
Kenneth R. Rygwelski
United States Environmental Protection Agency
Office of Research and Development
National Health and Environmental Effects
Research Laboratory
Mid-Continent Ecology Division
Large Lakes and Rivers Forecasting Research
Branch
Large Lakes Research Station
9311 Groh Road
Grosse lie, Michigan 48138
1.2.1 Background
Atrazine is a triazine herbicide registered to control
broadleaf weeds and some grassy weeds by
inhibiting photosynthesis. Its primary use in the Lake
Michigan basin is for the control of weeds in corn
crops. It is estimated to be the most heavily used
herbicide in the United States. Usage on corn
accounts for approximately 86% of total United
States domestic usage, followed by sorghum at 10%,
and sugarcane at 3% (all other uses make up the
remaining 1%). For corn crops, it is usually applied
in the spring prior to, during, or after planting a crop
or after crop emergence. The product is formulated
as an emulsifiable concentrate, flowable concentrate,
water dispersible granular (dry flowable), soluble
concentrate, wettable powder, granular, and as a
ready-to-use formulation. It may be applied to the
field with a groundboom sprayer, aircraft, or by
means of a tractor-drawn spreader (U.S.
Environmental Protection Agency, 2003a). In a
1990-1991 period, atrazine was the single highest-
use pesticide in the Great Lakes basin (United States
only) and represented 19.4% of all pesticides used
on agricultural crops (U.S. General Accounting
Office, 1993).
Atrazine was registered in 1958 (U.S. Environmental
Protection Agency, 2003a), and is currently
undergoing a reregistration review. Syngenta is the
primary atrazine registrant. Pesticides registered
prior to November 1984 are subject to the
reregistration process. On January 31, 2003, the
U.S. Environmental Protection Agency (USEPA)
issued an Interim Reregistration Eligibility Decision
(IRED) for atrazine (U.S. Environmental Protection
Agency, 2003b). In October 2003, the USEPA
issued an addendum that updates the January 31,
2003 IRED (U.S. Environmental Protection Agency,
2003c). The Agency expects the registrants to adopt
the risk management measures presented in the
IRED. Among other requirements, the IRED
mandates the monitoring of 40 representative
watersheds in the United States to determine if
specific atrazine levels of concern are exceeded, a
testing program to better evaluate potential risk to
amphibians, and measures to mitigate exposure risk
to applicators in both residential and agricultural
settings. Watersheds exceeding levels of concern
criteria will be subject to remedies under the
USEPA's Total Maximum Daily Load (TMDL)
program requirements. In the October 2003
addendum to the IRED, the Agency concluded that
there is sufficient evidence to formulate a hypothesis
that atrazine exposure may impact gonadal
15
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development in amphibians, but there are currently
insufficient data to confirm or refute the hypothesis.
On October 9-12, 2007, the Federal Insecticide,
Fungicide, and Rodenticide Act (FIFRA) Scientific
Advisory Panel (SAP) met with the Agency to
evaluate the potential for atrazine to affect the
development of amphibian species. However, in an
October 2007 report to the FIFRA Scientific Advisory
Panel, the Agency's review of the literature indicated
that studies do not show that atrazine produces
consistent, reproducible effects across the range of
exposure concentrations and amphibian species
tested. Based on available test data, atrazine is not
likely to be a human carcinogen. The Agency does
have concern in regards to the potential hormonal
effects observed in laboratory animals exposed to
atrazine. A Reregistration Eligibility Decision (RED)
was issued for atrazine, a triazine pesticide, in April
2006. In that RED, an evaluation was performed to
determine if the cumulative effect from the triazine
pesticides (atrazine, simazine, propazine, and their
chlorinated degradates) that share a common
mechanism of toxicity are below the Food Quality
Protection Act (FQPA) regulatory level - that the
risks associated with the pesticide residues pose a
reasonable certainty of no harm.
A comprehensive review of atrazine toxicity to
various freshwater trophic groups was conducted by
Solomon et al. (1996). A total of 85 species were
tested, and the order of sensitivity from most to least
sensitive trophic groups was as follows:
phytoplankton > aquatic macrophytes > benthos >
zooplankton > fish. Due to limited data, amphibians
were not included in this sensitivity review. Atrazine
was found to be more inhibitory to photosynthesis
than were its transformation products. Atrazine was
seven to 10 times more inhibitory to blue-green algae
and four to six times more inhibitory to green algae
than the most potent transformation product,
deethylatrazine (DEA). Young fish survival may be
at risk if the atrazine exposure concentrations are
significant enough to impact phytoplankton
populations and macrophytes. Zooplankton, an
important food source for juvenile fish, may be
depleted if the phytoplankton are reduced, and the
juvenile fish may become easier prey if they lose the
protective cover of macrophytes.
Atrazine is often found in surface water and is
regulated under the Safe Drinking Water Act. A
Maximum Contaminant Level (MCL) of 3 ppb was
established in 1991 by the USEPA's Office of Water
(U.S. Environmental Protection Agency, 1995).
Loadings associated with run-off from farm fields are
often seasonal with the spring and early summer-
time periods being the highest. For municipalities
dependent upon drinking water supplies from rivers,
potential exceedences of the MCL are most likely to
occur from mid-April through mid-July in the Lake
Michigan basin when atrazine concentrations are
most likely to be high. Using a variety of bench-scale
water treatment processes such as coagulation,
softening, ozonation, chlorination, and powdered
activated charcoal, researchers had difficulty
adequately removing atrazine from the water and
recommended that other removal processes should
be investigated (Westerhoff et al., 2005).
Atrazine has been banned in the European Union
(EU) since October 4, 2003 when the herbicide was
not granted re-registration. This decision was taken
by the Standing Committee on the Food Chain and
Animal Health (SCFAH), the EU regulatory body.
Additional background information on atrazine and
access to the documents cited in this section can be
downloaded at http://www.epa.gov/pesticides/.
1.2.2 Physical-Chemical Properties of
Atrazine
Physical and chemical properties of atrazine are
given in Table 1.2.1. Wth a low Henry's law
constant, atrazine volatilization from the lake is low.
Also, with a moderate solubility in water, run-off from
farm fields can occur, especially in the spring after
significant rainfall and when soil moisture content is
high. With a low octanol-water partition coefficient
(Kow), atrazine is not strongly sorbed to particles in
the water, and it is not bioaccumulated to any extent.
Frank et al. (1979) analyzed suspended solids from
12 streams (45 samples) in 1974 and 1976 flowing
into the Great Lakes from the Canadian side
(Ontario) and were unable to detect atrazine in these
particulates (detection limit of 0.05 ug/g). However,
of the 92 streams sampled in 1977, they detected
atrazine in the water approximately 80% of the time.
From that study, they concluded that atrazine was in
16
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Table 1.2.1. Physical and Chemical Properties of
Atrazine
Empirical Formula
Chemical Name
Chemical Family
Structural Formula:
(CH;
C8H14CIN5
2-chloro-4-ethylamino-6-
isopropylamino-1,3,5-triazine
Triazine
Cl
\
N
(CH3)— (CH)— (NH)— C C— (NH)— (CH2)— (CH3)
\\
Molecular Weight
Melting Point
Vapor Pressure
Solubility in Water
Density
Henry's Law
Constant
Physical state
log K,,w
N
215.7g/mol
173°Cto 175°C
40LiPaat20°C
33 PPM at25°C
0.35 g/ml
8.1 x 10"8 (dimensionless) at
25°C (U.S. Department of
Agriculture, 2001)
White crystalline solid
2.7645
the dissolved phase, rather than attached to
particles. Laboratory measurements of the partition
coefficients for atrazine, DEA, and
deisopropylatrazine (DIA) resulted in the following:
1.1, 0.4, and 0.3 (ng/g)/(ng/ml), respectively. The
particulate substrate was Eudora Silt Loam with a
1.0% carbon content. These results indicated that
the two degradation products are even more soluble
than the parent compound, atrazine (Mills and
Thurman, 1994). So, models often omit the
interaction of atrazine with solids (both suspended
solids and sediment) and do not include
bioaccumulation components. Because atrazine is
primarily transported in a dissolved phase,
groundwater is vulnerable to contamination as it can
receive a load associated with infiltration.
Other chemical compounds, such as cyanazine and
simazine, with the same triazine ring structure as
atrazine have been used in the Great Lakes
watershed. Cyanazine usage in the basin in the
early 1990s was about 40% that of atrazine, and
simazine was approximately 1%that of atrazine (U.S.
General Accounting Office, 1993). Both cyanazine
and simazine were used as herbicides.
Unless otherwise specified, the information in Table
1.2.1 was obtained from USEPA's Office of
Pesticides (January 2003a).
1.2.3 Atrazine Degradation
Atrazine is known to degrade in the environment
through either biotic or abiotic processes. The
specific bacteria strain and population, physical and
chemical conditions present, and media type all
contribute to determining the degradation fate of
atrazine in the environment.
1.2.3.1 Biotic Degradation in Surface Water
Bacterial processes are known to convert atrazine to
DEA and DIA; however, this degradation is not likely
occurring in the surface water. Abiotic processes
often convert atrazine to hydroxyatrazine. See
Figure 1.2.1 for the chemical structures of these
major degradation products. Biodegradation assays
of 14- to 32-days of unfiltered water from the River
Po, Italy, spiked with various concentrations of
atrazine, yielded no degradation products (Brambilla
et a/., 1993). Ingerslev and Nyholm (2000) tested
natural water samples from an unpolluted forest
stream using 14C-labeled atrazine. Microbial
degradation of atrazine was evaluated by measuring
the evolution of 14C in carbon dioxide (CO2). Testing
these samples with a wide range of atrazine
concentrations typically found in streams showed that
the natural population of microbes did not degrade
the labeled atrazine. Biodegradation of atrazine was
not found in two shallow impounded small lakes in
Nebraska that receive agricultural inputs of atrazine
from run-off (Spalding et a/., 1994). Half-lives of
atrazine in these lakes were estimated to range from
193 to 124 days. The biodegradation product, DEA,
was not increasing relative to atrazine in the lake,
therefore suggesting that the degradation observed
was not biotic. They surmised that degradation was
17
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(CH3)2CHHN
NHC2H5
NHC2H5
Deisopropylatrazine
Cl
(CH3)2CHHN
Deethylatrazine
N
(CH3)2CHHN
NHC2H5
Hydroxyatrazine
Figure 1.2.1. Chemical structures of atrazine and
its major degradation products.
due to abiotic processes. Evidence of
biodegradation was not found in a study of a lake in
Nebraska (Ma and Spalding, 1997). However, these
researchers did suggest that abiotic degradation was
the likely mechanism for degradation. A study of
atrazine degradation in an Iowa stream determined
that atrazine biodegradation was not occurring in the
river (Kolpin and Kalkhoff, 1993). Modeling analysis
of a small Swiss lake (hydraulic detention time of 1.2
years) found that atrazine is rather stable in the lake
water with removal primarily due to export with water
flowing out of the lake (Buser, 1990; Ulrich et a/.,
1994; Mulleref a/., 1997). Atrazine degradation via
biotic and/or abiotic processes in Lake Michigan was
found to be negligible using a mass balance model
(Rygwelski etal., 1999). Biodegradation products of
atrazine are commonly found in surface waters, but
their origin is likely from agricultural soils where
biodegradation is known to occur to a significant
extent.
There are various hypotheses why researchers
cannot find evidence of atrazine biodegradation in
surface water. In systems such as Lake Michigan,
this potential biotic "food" source (atrazine) is very
dilute, and therefore, it is hypothesized that bacteria
specific to atrazine degradation do not thrive. If
atrazine were to substantially partition to particles in
the water, then perhaps atrazine would be in a more
concentrated form that could sustain the specific
strain of atrazine-degrading bacteria. Using granular
activated charcoal to enhance atrazine adsorption
and the inclusion of atrazine-specific bacterial
degraders in a laboratory batch reactor, significant
reductions (45% to 86%) in atrazine concentrations
were achieved after a 15-day incubation period at
10°C (Feakin et a/., 1994). Also, if present in
sufficient quantities, more readily available sources
of nitrogen other than that provided by the 1,3,5-
triazine structure may be preferentially used by the
atrazine-degrading bacteria. Therefore, the atrazine
triazine structure would be left intact (Feakin et a/.,
1994). Typically, the first stage in the biodegradation
of the 1,3,5-triazines is deisopropylation and
deethylation leading to the removal of nitrogen from
positions four and six of the 1,3,5-triazine ring.
Feakin etal. (1994) also showed that degradation in
water without sufficient assimilable organic carbon
did not support biodegradation. They theorized that
the bacteria needed a certain minimum level of
carbon for maintenance energy and growth.
While atrazine biodegradation is not likely to occur
naturally in surface waters, efforts have been made
to find ways to create better conditions for biotic
degradation in water in laboratory operations, with
the intent of applying the methodology to water
treatment facilities. A pilot plant operation studying
the potential to degrade atrazine in water found that
18
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an atrazine-specific degrading bacterium,
Rhodococcus rhodochrous strain SL1, was effective
in degrading the herbicide after the atrazine was
adsorbed to granular activated carbon packed in
columns (Jones et al., 1998). However, periodic
reinoculation onto the columns was required to
maintain adequate numbers of SL1. Conventional
water treatment facilities are not effective in reducing
atrazine concentrations. Conventional activated
sludge wastewater treatment plants are also
ineffective at removing atrazine from the waste
stream (Monteith et a/., 1995).
1.2.3.2 Abiotic Degradation in Surface Water
1.2.3.2.1 Hydrolysis
Degradation by hydrolysis is likely in water if the
environmental conditions are favorable. Hydrolysis
was not found to occur at pH greater than 4 at 15°C
in buffered distilled water or natural river water
(Comber, 1999). Furthermore, the addition of iron
hydroxide and aluminum silicate did not promote
degradation via catalysis as some researchers have
hypothesized. The pH of Lake Michigan is relatively
high (8.2) and, therefore unlikely to support
hydrolysis. However, at temperatures of 35°C,
atrazine was found to slowly degrade via hydrolysis
at a range of pHs from 3 to 8 in distilled water (Lei et
a/., 2001). Hydrolysis rate constants were increased
(half-lives shortened) with the addition of humic acids
and nitrate ions. An evaluation of atrazine hydrolysis
in groundwater samples at a pH of 7.8 and
temperatures of 4°C and 30°C showed no significant
loss (Widmer et a/., 1993). Also, when hydrolysis
experiments were conducted at room temperature
and a pH of 6.5, dissolved organic carbon (DOC)
additions with and without nitrate did not cause any
degradation (Hapeman et a/., 1998). Spalding et al.
(1994) theorized that surface catalyzed hydrolysis
was a possible mechanism for degrading atrazine in
two small lakes located in Iowa. These shallow lakes
had high turbidity with high DOC (5.1 to 8.4 mg/l).
1.2.3.2.2 Photolysis
Photolysis is enhanced when nitrate ions are present
to facilitate indirect photolysis by acting as a catalyst.
It is hypothesized that in the presence of the nitrate
ion, hydroxy radicals are produced resulting in
oxidation and/or removal of the alkyl groups. In a
small stream in Iowa, isolated from groundwater
intrusion, Kolpin and Kalkhoff (1993) found that the
atrazine half-life had a significant inverse relationship
with sunlight, suggesting that photolysis was
responsible. This same inverse relationship was
noted in a reservoir in Iowa (Chung and Gu, 2003).
However, in both of these studies a correlation
between atrazine half-lives and concentrations of
nitrate ions was poor. The relationship between half-
lives and nitrate concentrations may be masked in
the natural environment because of the strong
seasonality of photodegradation with sunlight. Using
titanium dioxide (TiO2) as a photocatalyst and
simulated solar light in a laboratory setting,
researchers have found that atrazine can be
degraded very rapidly (Pelizzetti et al., 1990) with a
half-life estimated at 19 minutes (Konstantinou et al.,
2001a). Some DOC mimics can significantly
increase photodegradation of atrazine, while others
do not, leading researchers to believe that both the
structural properties and concentration of DOC in
water are important factors to consider when
assessing potential photodegradation impact
(Hapeman et al., 1998). Using natural light sources,
some studies have found that structural properties of
some types of natural DOC present in surface water
will actually reduce photodegradation rates
(Konstantinou et al., 2001 b). The degradation
products found in the Konstantinou study using
natural water samples were the hydroxy and
dealkylated derivatives of atrazine. It appears that
light energy at wavelengths less than 300 nm is
necessary to initiate direct photolysis where
photolysis occurs without the need of an intermediary
(Comber, 1999). However, natural sunlight provides
very little of this light energy. Direct
photodegradation produces primarily hydroxyatrazine
(Konstantinou etal., 2001 b).
Even though Lake Michigan has very low nitrate
(1994-1995 median 0.28 mg/L) and DOC
concentrations (1994-1995 median 1.5 mg/L), it is
possible that some degradation is occurring via
various photolysis processes. However, it is believed
that the impact on the lake is small because the
depth of the lake limits light penetration through the
water column and isolates the hypolimnion during the
high solar radiation period. Studies of atrazine
transport, atmospheric deposition, and fate in Isle
19
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Royale National Park have shown that the shallow
lakes have lower atrazine concentrations than the
deeper lakes on this island in Lake Superior
(Thurman and Cromwell, 2000). These island lakes
are in a pristine area and receive their atrazine input
from the atmosphere. If atrazine were highly
persistent in water, then one would expect that the
shallow lakes would have higher concentrations than
the deeper lakes because the shallow lakes have a
higher surface area to depth ratio. However,
Thurman and Cromwell's findings are just the
opposite, and a possible explanation for this is that
photolysis in the shallow lakes occurs throughout the
water column, but in the deeper lakes it may be
limited to the upper water column only.
1.2.3.3 Atrazine Degradation in Soil
The degradation of atrazine in soils is much faster
than in water. Durand and Barcelo (1992) presented
half-life values for atrazine in soil from six studies. All
of the studies found half-lives of 125 days or less.
Nair and Schnoor (1994) found that degradation
rates in soil depend strongly on soil environmental
conditions. Degradation increased with increasing
soil water and organic carbon content; however,
degradation rates decreased in low oxygenated soils.
Mirgain et al. (1993) found that bacteria degrade
atrazine in soils where the organic carbon content is
greater than 2%. Degradation increased with
increasing carbon content. They also noted that
repeated applications of atrazine on the same soil
sample results in the enhancement of degradation
with each successive application. They found that
the reason for this is that bacteria populations
specific to degrading atrazine increased with each
application and the number of bacteria strains
decreased. Compared to water, soil is better in
facilitating degradation of atrazine because the "food"
source (atrazine) is readily available to support
bacterial strains that are efficient in degrading the
herbicide.
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and R. Balestrini. 1993. The Fate of Triazine
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Konstantinou, I.K., T.M. Sakellarides, V.A. Sakkas,
and T.A. Albanis. 2001 a. Photocatalytic
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22
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PART1
INTRODUCTION
Chapter 3.
Observations
Atrazine Field Data
Kenneth R. Rygwelski
United States Environmental Protection Agency
Office of Research and Development
National Health and Environmental Effects
Research Laboratory
Mid-Continent Ecology Division
Large Lakes and Rivers Forecasting Research
Branch
Large Lakes Research Station
9311 Groh Road
Grosse lie, Michigan 48138
and
Harry B. McCarty, Ken Miller, Robert N. Brent, and
Judy Schofield
DynCorp (a CSC Company)
601 Stevenson Avenue
Alexandria, Virginia 22304
1.3.1 Background
In this chapter, a summary of the Lake Michigan
Mass Balance Project (LMMBP) atrazine data and
historical data are presented along with a brief
description of sampling and analytical methodology.
A LMMBP atrazine data report by DynCorp Science
and Engineering Group was prepared that provides
more details regarding concentrations of atrazine and
its degradation products related to sampling
atmospheric components, tributaries, and the open-
lake water column (Brent eta/., 2001). The DynCorp
data report also provides an in-depth discussion on
data quality implementation and assessment. Also,
see Part 1, Chapter 1, Section 1.1.6 in this report for
references to additional documents, such as the
LLMBP Methods Compendium and quality assurance
plans, that provide additional details on the project.
Project data reside in a Great Lakes National
Program Office (GLNPO)-managed Great Lakes
Environmental Monitoring Database (GLENDA). The
data were collected for use in the mass balance
models.
For the LMMBP, measurements of atrazine, along
with two degradation products deisopropylatrazine
(DIA) and deethylatrazine (DEA), were attempted for
all media. However, for some media, the detection of
the degradation products was difficult because
atrazine concentrations were very low. Whenever
possible, Principal Investigators (Pis) were requested
to report analytical results as measured, even if the
value was lower than the method detection limit.
This modeling report focuses on modeling atrazine
and not the degradation products because of the
sparsity of degradation data for some media. Also,
triazines other than atrazine can degrade into DEA
and DIA (Thurman et a/., 1994). So, if all of the
parent compounds are not modeled, the degradation
products cannot be modeled. In a summary report
(U.S. General Accounting Office [USGAO], 1993) for
pesticide usage in the basin for 1990 and 1991, two
other triazines used as herbicides in the Lake
Michigan basin (simazine and cyanazine) can
degrade into DIA. Simazine usage in the basin was
extremely low compared to atrazine usage so
degradation products from simazine would be
insignificant. Cyanazine usage, however, was about
37% of the atrazine usage estimates. Propazine can
degrade into DEA, but this chemical was not listed in
the usage tables of the USGAO report. It has
23
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been estimated that atrazine is the major source for
DEA (98%) in the Corn Belt (Thurman et a/., 1994).
Hydroxylated atrazine degradation products are also
found in the environment (Lerch et a/., 1998) and are
formed by substitution of the chlorine atom with a
hydroxyl group in the parent atrazine compound. In
a survey of Midwestern streams, they found that
these hydroxylated atrazine degradation products
were less abundant than DEA and DIA.
Hydroxyatrazine is the primary hydroxylated atrazine
degradation product but was not measured in the
LMMBP.
1.3.2 Atmospheric Components
Atrazine enters the atmosphere by volatilization from
either agricultural land (soil and plant) or water, by
wind erosion from fields where the chemical is either
sorbed onto soil particles or as a pure pesticide
particle from plant or soil surfaces, and by physical
drift of spray during application (Banks and Tierney,
1993). Once the chemical is airborne, a variety of
physical and/or chemical processes can cause
degradation, and various physical processes can
cause deposition back to land or water. In addition to
the atrazine data report by DynCorp and the LMMBP
Methods Compendium, information on atmospheric
media sampled can be found in a master's thesis by
Sondra Miller (Miller, 1999).
1.3.2.1 Sampling and Analytical Methodology
Primary atmospheric sampling occurred at eight
shoreline stations. Sampling locations are identified
in Figure 1.1.2. Some limited atmospheric sampling
also occurred at selected open-lake stations aboard
the research vessel, Lake Guardian. Also, three
stations (Eagle Harbor, Michigan; Brule River,
Wisconsin on the southern shore of Lake Superior;
and Bondville, Illinois) were located outside of the
basin and were established to characterize air
masses from the southwest or northwest directions.
Vapor, particulate, and wet deposition were sampled
and analyzed. Atmospheric sampling occurred from
March 15, 1994 to October 20, 1995. A total of 294
vapor phase samples, 226 particulate samples, and
207 precipitation samples were collected. All
samples were analyzed for atrazine and primary
degradation products DEA and DIA except for the
Sleeping Bear Dunes site where only atrazine was
analyzed. From April 1994 through July 1994,
samples from the Sleeping Bear Dunes site were
collected and analyzed at the Illinois State Water
Survey (Clyde Sweet). For the remaining period at
the same site, August 1994 through October 1995,
atmospheric samples were both collected and
analyzed by Indiana University (Ronald Hites).
Samples from all other atmospheric stations were
collected and analyzed by the Illinois State Water
Survey.
Wet deposition composite samples were collected
over a 28-day period at the shore-based stations
using a Meteorological Instruments of Canada (MIC-
B) sampler modified with a heater for all-weather
sampling. Equipped with a precipitation sensor, the
samplerwas open to the atmosphere only during wet
events. Rain and snow that was collected flowed
through a 30 cm XAD-2 resin column that absorbed
the atrazine and degradation products from the
sample. Glass wool plugs, before and after the
column, prevented particles from entering the
column. After the required collection period, the
collection funnel was rinsed with water and wiped
with clean quartz fiber filter paper to remove any
adhering particles. Both the filter paper and the
rinsing became part of the sample. Five percent of
Illinois State Water Survey wet deposition samples
were field duplicates with a system precision of 115%
for samples above the method detection limit. The
mean laboratory matrix spike recovery was reported
at 82%. Indiana University analyzed only 14 routine
samples and 12 field duplicates. Their system
precision of the duplicates was 28.1% for samples
above the method detection limit. They achieved
laboratory matrix spike recovery of 110%.
Composite atmospheric vapor and particulate
samples were collected over a period of 24 hours
every 12 days using a high-volume air sampler. Air
was passed through a XAD-2 resin to collect the
atrazine and degradation products. Air flow was
maintained at approximately 34 m3/hour during
sampling. Resin traps were wrapped in aluminum foil
and sealed in tin cans and held at -18°C until
analysis. Particulate phase atmospheric samples
were collected on pre-fired quartz fiber filters. Filters
were wrapped in aluminum foil and sealed in tin cans
and stored at -18°C until analysis. Multiple 24-hour
24
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samples were often composited to yield a monthly
sample composite. At the Sleeping Bear Dunes site,
and occasionally some other sites, 24-hour samples
were analyzed individually and then mathematically
composited to yield a monthly average.
The XAD-2 resin or filter samples were extracted by
Soxhlet extraction with 300 ml of a 1:1 hexane and
acetone mixture. The extract was concentrated by
rotary evaporation and then cleaned-up with 3%
deactivated silica with a sodium sulfate cap to
remove non-target interfering compounds. Samples
were analyzed using gas chromatography coupled to
a mass spectrometer detector.
1.3.2.2 Results
1.3.2.2.1 Atrazine in the Gas Phase Fraction
Gas phase samples were extremely low; therefore,
quantifying over-the-lake concentrations used in the
volatilization and absorption mass balance algorithms
was difficult. Only 11 samples were above the
detection limits. And of these samples, four were
flagged by the analysts as possibly contaminated due
to field or laboratory blanks, and four others were
from a station outside the Lake Michigan basin
located in Bondville, Illinois. This leaves three
samples collected in the basin with measurements of
atrazine above the detection limit-one sample from
South Haven collected July 7, 1994 through July 8,
1994 at 70 pg/m3 and the rest at Sleeping Bear
Dunes collected November 16,1994 -November 17,
1994 at 22.1 pg/m3 and September 9, 1995-
September 14, 1995 at 31.5 pg/m3. The sampling
stations at both the Bondville and South Haven sites
are located where local agricultural influences on the
gas phase concentrations are likely. Therefore,
these concentrations may not be representative of
gas phase concentrations over-the-lake. Peck and
Hornbuckle (2005) measured gas phase
concentrations of atrazine in the intensively farmed
state of Iowa. They found that gas phase atrazine
concentrations showed a seasonal pattern with
highest concentrations evident during the spring and
early summer. In their study, the average
concentration of atrazine in the air was 1,200 pg/m3.
In the LMMBP study, DEA and DIA were not
detected in the gas phase.
Because gas phase measurements did not provide a
reliable over-the-lake estimate in the LMMBP, we
made assumptions about this value based on
detection limits. In Miller (1999), the method
detection limit (MDL) for atrazine for the shore-based
and open-water sites gas phase concentration was
21.3 ng. Knowing the average flow rate of air
through the sampler and assuming a 24-hour
collection period, Miller estimated a MDL of 9.26
pg/m3. Modeling scenarios presented in this report
utilized this method detection limit to place an upper
expected limit on this boundary condition.
1.3.2.2.2 Atrazine in the Particulate Fraction
Atrazine in the particulate fraction in air was low and
often difficult to detect. This finding is also supported
by other studies, such as in rural Iowa - a state with
the highest pesticide applications in the United States
and where 94% of the state is farmland and 60% of
that area is planted with corn (Nations and Hallberg,
1992). Only 23% of the particulate samples taken for
the LMMBP had atrazine concentrations above the
sample-specific detection limit. Also, the chemical
was primarily observed in the months of April, May,
June, and July. Only one particulate sample
collected from August through March contained
levels above the MDL. Maximum monthly average
atrazine concentrations ranged from a low of 160
pg/m3 at Sleeping Bear Dunes in northern Lake
Michigan to a high of 1,400 pg/m3 at the Bondville
site. The elevated concentration at the Bondville site
is most likely related to the fact that it is in the middle
of an intensive corn-growing region. A summary of
spring/summer atrazine concentrations measured in
the particulate phase can be found in the atrazine
data report (Brent et a/., 2001). Particle size
distribution analyses were not conducted on
particulates collected in the air samples. Sweet and
Harlin (1998) estimated that approximately 1% of the
total atrazine load associated with wet and dry
particle deposition to Lake Michigan is due to
atrazine associated with particulates.
Of the over-water sampling stations, only two
samples had detectable atrazine in the particulate
fraction, and both of these samples were collected
close to land in the southern part of the lake (near
Chicago and Indiana Dunes). An atrazine
concentration of 560 pg/m3 was measured at station
25
-------
1 in May 1994, and a concentration of 280 pg/m3 was
measured at station 5. Station 1 is shown on Figure
1.1.2 as the southern-most over-water atmospheric
monitoring station, and station 5 is located
immediately north and slightly west of station 1.
In the spring/summer of 1994, the LMMBP project
detected atrazine but not the degradation products at
the Eagle Harbor site, which is located in Michigan's
Upper Peninsula near Lake Superior. From a period
of early April to mid-September 1995, atrazine
sampled at Eagle Harbor was detected
approximately 34% of the time in the particulate
fraction but not in the vapor phase at this remote site
(Foreman et al., 2000). In addition, both DIA and
DEA were detected in the particulate phase. This
suggests that long range transport is possible for
both atrazine and the two degradation products via
particles.
Having a higher detection limit than the Foreman et
al. (2000) study may be one reason why atrazine was
difficult to detect over Lake Michigan in the LMMBP
study. Foreman's detection limit was 6 pg/m3. For
the LMMBP, the detection limit ranged from 3.0 to 68
pg/m3 (average of 17 pg/m3) for particulate phase
samples analyzed at the Illinois Water Survey, and
from 26.8 to 284 pg/m3 (average of 70.7 pg/m3) for
samples analyzed at Indiana University. Another
possible reason for the lack of particulate atrazine
data over-the-lake is that the type of particulate
matter carrying atrazine may not be transported very
far from the source. As a consequence of the low
number of detects at land-based collection sites, and
the lack of evidence of atrazine-associated
particulate fluxes over-the-lake, these fluxes were not
estimated for modeling purposes.
1.3.2.2.3 Atrazine and Degradation Products in
Wet Deposition
Atrazine in wet deposition was primarily detected in
the spring and summer months. This seasonality
was also reported by Nations and Hallberg (1992)
and Goolsby et al. (1993). All LMMBP samples
collected in April and May had detectable levels of
atrazine. Atrazine was not detected in samples from
November through February. DEA and DIA were
also primarily detected in the spring and summer
months. DEA was detected in samples collected
from March through August, and DIA was only
detected in samples collected from April through
June. DEA had a higher frequency of being detected
and also had a higher concentration on average than
DIA. Twenty-eight day maximum atrazine
concentrations measured over 1994 and 1995
ranged from 100 ng/L at Eagle Harbor to 2,800 ng/L
at the Indiana Dunes site. The high Indiana Dunes
value was associated with a low volume sample
collected over a 28-day sampling period and may
have been influenced by emissions from nearby
agricultural fields. During a rain event, atrazine
concentrations are often much higher at the
beginning of the event compared to concentrations
measured at the end of the event (Nations and
Hallberg, 1992; Goolsby et al., 1993). Nations and
Hallberg (1992) also found that a rain event closely
following an earlier rain event by a day or two had
much lower concentrations (and often non-
detectable) levels of atrazine in the wet deposition
sample. Presumably the first event scavenges the
available pesticide in the atmosphere. Without a
detailed record of the number and duration of rain
events in the Indiana Dunes sample, it is difficult to
conclude if any of the scavenging circumstances
occurring early in a rain event(s) comprised a major
volumetric proportion of the sample collected.
Nations and Hallberg (1992) also found that atrazine
concentrations in wet deposition tend to be higher in
regions that have higher usage of atrazine. They
found consistent, striking differences between two
stations only 11 km apart. One station located
adjacent to a row-cropped field had a much higher
reported value compared to a station located in a
forested region. Volume-weighted mean LMMBP
spring/summer atrazine levels for the two-year
sampling period (1994-1995) ranged from 19 ng/L at
Eagle Harbor to 120 ng/L at Indiana Dunes. Due to
the high variability of wet deposition concentrations
of atrazine at sites, stations around the lake were not
statistically different based on the Kruskal-Wallis test.
Sampling at over-water stations was limited. A
southern central lake station contained 7.5 ng/L on
August 20, 1994 and a station in Green Bay
contained 29 ng/L on April 12, 1995.
Concentrations of atrazine and DEA in wet deposition
in 1995 were much lower than observations in 1990,
1991, and 1994. The concentrations of atrazine
collected in the Lake Michigan basin, as reported in
26
-------
Table 1.3.1 for 1990 and 1991, compare very well to
other data collected by Goolsby et al. (1997) across
the Midwestern and Northeastern United States.
They found a range of 200 to 400 ng/L for 1990-
1991. In 1994, atrazine was found in LMMBP rain
samples collected between mid-March and mid-April,
even though corn planting had not yet begun in
southern Wisconsin. This suggests that atrazine was
being transported long range, originating from farm
fields in more southerly states that had been planted
earlier in the season. In 1995, however, the
occurrence of atrazine in wet deposition more closely
coincided with application in the region (Sweet and
Harlin, 1998). Further evidence that long range
transport of atrazine to Lake Michigan was minimal in
1995 is reflected in a low deethylatrazine/atrazine
ratio (DAR) (0.145) for 1995 (Table 1.3.1). The DAR
was calculated using the volume-weighted means for
DEA and atrazine. Generally, higher DAR ratios
represent higher levels of degradation of atrazine to
DEA. Long range transport allows more time for
degradation of atrazine to occur in the air mass.
DAR ratios were calculated for Isle Royale, a
wilderness national park in Lake Superior, and the
ratio at the park was calculated to be approximately
0.4 (Thurman and Cromwell, 2000) for the study
period 1991-1994. So in regards to DAR and except
for 1995, the two areas (Isle Royale and Lake
Michigan) appear to compare very well, suggesting
that under normal circumstances, transport of
atrazine from distant sources does occur in the wet
deposition phase.
A possible explanation of the low atrazine
concentrations for 1995 is that the spring of 1995
was cold and wet in major corn-growing areas south
and west of the Lake Michigan basin compared to
1991 and 1994. This may have limited long range
transport to the Lake Michigan basin. Omaha,
Nebraska and Peoria, Illinois were selected as being
representative of that area south and west because
they are located in geographic areas where the
Table 1.3.1. Summary of Wet Deposition Annual Volume-Weighted Mean Deethylatrazine (DEA)
Concentrations, Atrazine Concentrations, and Deethylatrazine/Atrazine Ratios (DAR) for All Stations
in the Lake Michigan Basin
v Deethylatrazine Atrazine Deethylatrazme/Atrazme _ ..
Year „ „ ' .. ,„.,-.» Samp ing
ng/L ng/L Ratios (DAR) K a
19901
19911
19942
19952
Mean
Mean (Year
1995 Excluded)
101.0
233.0
32.4
4.02
92.6
122
259.0
432.0
80.6
30.0
200
257
0.402
0.540
0.422
0.145
0.377
0.455
3/27/1990
4/2/1991 -
3/15/1994
3/14/1995
Data Range
-8/14/1990
7/9/1991
-7/5/1994
-8/31/1995
1Data from Goolsby et al., 1995. All data were used in calculating the volume-weighted mean concentrations.
Data reported with the detection limit were converted to half the detection limit. DAR represents only
situations where both the reported DEA and atrazine concentrations were above the detection limit.
2Data from the LMMBP. All data, including zeros, were used in calculating the volume-weighted mean
concentrations. DAR represents only situations where both reported DEA and atrazine concentrations
were above the detection limit.
27
-------
greatest spring atrazine emissions were estimated
(Scholtz et a/., 1997). Figure 1.3.1 shows the
monthly precipitation at these cities for the important
months when wet deposition fluxes are normally high
(National Climatic Data Center, 2000). For both
Peoria and Omaha, 1991 and 1994 were similar in
rainfall events; however, for 1995, the months of April
and May were wetter than the other two years.
Figure 1.3.2 shows the monthly average
temperatures for the same two cities. For both
Peoria and Omaha, 1991 and 1994 were similar in
average temperatures; however, for 1995 the months
of April and May were colder than the two other
years. Not only were the LMMBP atrazine
concentrations in precipitation low for 1995, but the
total atrazine deposition for 1995 was approximately
half of what it was in 1994. This cannot be explained
by very low precipitation in the Lake Michigan basin
for 1995. Table 1.3.2 displays the combined mean
precipitation amounts from Chicago, Illinois; South
Bend, Indiana; Muskegon, Michigan; Grand Rapids,
Michigan; Green Bay, Wisconsin; and Milwaukee,
Wisconsin for 1994 and 1995 (34.26 and 33.73
inches, respectively) and they are close to the 30-
year mean of all these sites (34.22 inches).
Comparisons to a 50-year mean for over-lake
precipitation to Lake Michigan can be found in
Figures 1.4.11 and 1.4.12 in Part 1, Chapter 4 of this
report and show similar results. Also, the
differences between 1994 and 1995 cannot be
explained by differences in amounts of atrazine
applied in the basin between the two years, because
these amounts are nearly the same (see the atrazine
loading chapter for more information). The
differences may be explained by the cold and wet
spring in the south and west corn-growing regions
relative to the Lake Michigan basin. In a cold and
wet spring, less atrazine emission would be expected
to occur because temperature is a driving force of
atrazine volatilization from the soil to the air. In the
wet spring of 1995, among both the Peoria and
Omaha stations, there was one rain event in April
over one inch and seven events in May where rainfall
was over one inch (and as high as 2.5 inches on May
8 at one of the stations). For spring 1994, there was
only one rainfall event among the two stations
12
10
CO
-------
Peoria, IL
Omaha, NE
April April April May May May June June June
1991 1994 1995 1991 1994 1995 1991 1994
Month and Year
Figure 1.3.2. Monthly average temperatures at cities in two large corn-growing regions. Data are from
Peoria, Illinois and Omaha, Nebraska.
Table 1.3.2. Annual Mean Precipitation Amounts Measured at Chicago, Illinois; Fort Wayne, Indiana;
South Bend, Indiana; Muskegon, Michigan; Grand Rapids, Michigan; and Milwaukee, Wisconsin
Time Period
30 Years
1994
1995
Total Inches (Mean at All Sites)
34.22
34.26
33.73
Standard Deviation
3.57
6.94
4.61
over one inch and that was on June 22, 1994.
Perhaps the frequent rainfall events in 1995 washed
significant quantities of atrazine from the fields into
streams, rivers, reservoirs, and groundwater via
infiltration which allowed less of the atrazine to
volatilize from the farm fields. Atrazine that is diluted
in reservoirs, lakes, and rivers would have a lower
volatilization flux than if it were in a concentrated
form on farm soil. Also, heavier rainfall in the corn-
growing region could increase scavenging of the
chemical from the atmosphere, thereby leaving less
available for long-range transport.
1.3.3 Atrazine in Tributaries
Eleven tributaries to Lake Michigan were sampled
from April 4, 1995 through October 31,1995. A total
of 108 filtered samples were collected. Most tributary
samples contained detectable levels of atrazine,
DEA, and DIA. The tributary samples were collected
for purposes of estimating loadings of atrazine to the
lake. However, the load estimates are believed to be
low, and consequently, alternative tributary loadings
were estimated based on watershed run-off
algorithms using the amount of atrazine applied and
a watershed export factor of 0.6% for the MICHTOX
29
-------
and LM2-Atrazine models. For LM3, the United
States Geological Survey (USGS) provided loadings
that they calculated from flow and concentration data.
However, for a 90-day period in the spring months,
loadings were enhanced to make up for a "lost" load
(please see section 5.3.3.3.1 in Part 5 for more
information). Because the concentration data were
not directly used in the models, only a brief
description of the data will be presented here. For a
more complete description of these data, please refer
to Brent et a/. (2001).
1.3.3.1 Sampling and Analytical Methodology
Samples were collected as near to river mouths as
possible without being subject to flow reversals
where lake water moves up the river. Composites
were collected using the USGS quarter-point
sampling procedure. In this procedure, the river is
visually divided into three equal flow areas. The
midpoint of each flow panel is sampled at 0.2 and 0.8
times the depth. All samples were pumped and
composited using a peristaltic pump through a 0.7
urn glass fiber filter. The filtrate was passed through
a 250 g, XAD-2 resin to trap the dissolved atrazine.
Chilled samples were then taken to the analytical lab.
Analyses were conducted using gas chromatography
coupled to a mass spectrometer. Full details of the
analytical methods have been published in the
Methods Compendium (U.S. Environmental
Protection Agency, 1997a; 1997b).
1.3.3.2 Results
Since tributary samples were only collected over a
seven-month period, full seasonal trends could not
be assessed. For the three tributaries with the
highest mean concentrations of atrazine (St. Joseph
River, Kalamazoo River, and the Grand River), peaks
in atrazine concentrations occurred in mid- to late-
May. Spring peaks were also observed for the
degradation products DIA and DEA.
Individual atrazine concentrations measured in the
streams ranged from a low of 0.5 ng/L in the Pere
Marquette River to 2,700 ng/L in the St. Joseph
River. Mean concentrations of atrazine in the
tributaries ranged from 3.7 in the Manistique River to
350 ng/L in the St. Joseph River. Per Brent et a/.
(2001), these concentrations are comparable to
concentrations measured elsewhere in the Great
Lakes region. Eighty-six percent of the tributary
samples contained less than 100 ng/L of atrazine,
and all samples above 100 ng/L were in the St.
Joseph, Kalamazoo, or Grand Rivers. Tributaries
with the lowest mean atrazine levels were located in
the northern portions of the lake, where land use is
less dominated by agriculture.
Atrazine degradation in the watershed can be
assessed by looking at the degradation products.
DEA and DIA concentrations correlated well with
atrazine concentrations in tributary water samples
(Brent et a/., 2001). As atrazine concentrations
increased, both the DEA and DIA increased. The
ratio of concentrations of [DEA]/[atrazine] or DAR is
often used to assess the extent of atrazine
degradation in a sample. Ratios on individual
measurement pairs ranged from 0.08 to 3.7, and the
median was 0.77. Mean DARs were above 1.0 at the
Pere Marquette, Sheboygan, and Milwaukee Rivers,
and were significantly higher than the mean ratios at
the Kalamazoo, Manistique, Grand, and St. Joseph
Rivers. For all samples, the mean DAR of 1.4
measured in October was significantly greater than
the mean ratios in April (0.75), May (0.63), and June
(0.87). It is common to find that the ratios increase
for a given tributary as the time since application of
atrazine increases. Thurman et a/. (1994) also found
an increase in DAR from <0.1 shortly after atrazine
application to 0.4 measured later in the year. As the
atrazine resides in the soil, processes (both biotic
and abiotic) are operative that degrade the chemical.
Run-off from these fields will reflect the composition
of DAR in the soil. Furthermore, during dry spells in
the late summer, groundwater can make up a
significant percentage of the total flow of a river.
Groundwater is often associated with high DARs. In
July-August 1991, Pereira and Hostettler (1993)
found that the DAR for Mississippi River water was
relatively constant at 0.2 for the entire river. This
suggests that during the travel time from Minneapolis
to New Orleans (45-65 days), the DAR showed no
evidence of degradation. However, in October-
November, 1991, they found that the DAR in the river
was 0.6 in the upper reaches of the river. The low
DAR is believed to be associated with more run-off in
July and August. During the fall period, the river was
near base flow in the upper river. During base flow,
most of the river flow is due to groundwater. DARs
30
-------
measured in groundwater impacted by infiltration
through an agricultural soil matrix are often high, and
exceed or are close to unity (Ma and Spalding,
1997).
1.3.4 Atrazine in Lake Water
1.3.4.1 Sampling and Analytical Methodology
Open-lake water column samples were collected
during six cruises from April 25, 1994 to April 17,
1995. Open-lake samples were collected from 35
sampling locations on Lake Michigan, two sampling
locations in Green Bay, and one sampling location on
Lake Huron (see Figure 1.1.2). The Lake Huron
samples were collected to characterize a model
boundary condition. Samples were collected at
depths ranging from 1 to 257 m. During stratification,
samples were collected at mid-epilimnion and mid-
hypolimnion, and master stations were sampled at
one meter below the surface and two meters off the
bottom. During non-stratification, samples were
collected at mid-water column depths, one meter
below the surface, and two meters off the bottom.
Water samples were collected using a General
Oceanics (Model 1015) rosette sampler on board the
Lake Guardian research vessel. Water was
transferred from individual rosette canisters to amber
one-liter bottles and stored at 4°C until processing at
the testing laboratory.
Atrazine, DEA, and DIA were isolated from filtered
water samples using 250 mg Carbopack (Supelco
Corporation) solid phase extraction (SPE) cartridges.
Analytes were eluted from the SPE using 7 ml of a
90% dichloromethane and 10% methanol solution
(vol:vol), followed by 5 ml of methanol. The eluent
was then passed through clean anhydrous sodium
sulfate to remove excess water. Extracts were
concentrated to <100 uL under a nitrogen gas
stream. Analysis of atrazine, DEA, and DIA was
conducted using gas chromatography coupled to a
mass spectrometer detector. Further details of the
analytical methods can be found in the methods
compendium (U.S. Environmental Protection Agency,
1997a; 1997b).
1.3.4.2 Results
1.3.4.2.1 Spatial Variation
A total of 234 samples (including Green Bay and the
northern Lake Huron boundary condition samples)
were collected and analyzed for atrazine, DEA, and
DIA. All lake samples contained levels of atrazine
and DEA above the MDL. All but 12 samples
contained DIA above the MDL for that parameter.
MDLs computed were 1.25 ng/L for atrazine, 2.46
ng/L for DEA, and 8.27 ng/L for DIA. Skewness
characterizes the degree of asymmetry of a
distribution. Positive skewness indicates a
distribution with an asymmetric tail extending towards
more positive values. In a normal distribution,
skewness is approximately zero. A statistical
analysis of all lake data indicated that atrazine
skewness equaled 0.145. To further evaluate the
skewness for atrazine, the following analysis was
performed (Tabachnick and Fidell, 1996).
Skewness values of two standard errors of skewness
(ses) or more (regardless of the sign) are probably
skewed to a significant degree. The ses for atrazine
can be estimated by:
ses =
= 0.144
where, n = total number of open Lake Michigan
values including duplicates and triplicates (excludes
Green Bay and the northern Lake Huron stations) =
288
2(ses) = 0.2886
Since the skewness for the atrazine lake data, 0. 1 45,
is less than 2 x ses, the distribution can be assumed
to be normal. The deviation from zero can be
assumed to be to chance fluctuation.
Wthin Lake Michigan (excluding Green Bay and
northern Lake Huron stations), lateral and vertical
atrazine concentrations were relatively consistent
during the LMMBP (Brent et a/., 2001). Individual
sample results ranged from 22.0 to 58.0 ng/L, and
sampling station mean atrazine concentrations only
ranged from 33.0 to 48.0 ng/L. Similar patterns of
consistency among sampling stations were observed
31
-------
for DEA and DIA concentrations. Atrazine
concentrations in southern Green Bay were
significantly higher than atrazine concentrations at 18
Lake Michigan sampling stations. Due to the spatial
consistency of atrazine, DEA, and DIA
concentrations within Lake Michigan, lake-wide mean
concentrations can be calculated to reliably represent
the lake. Schottlerand Eisenreich (1994) also found
Lake Michigan (excluding Green Bay) to lack vertical
and lateral gradients in the 1991 and 1992 data. It is
not surprising that no vertical gradients were found,
because most of the samples collected for the
LMMBP were collected during times of non-
stratification of the lake. Lake-wide concentrations
from the LMMBP study (April 1994-April 1995) and
previous studies are summarized in Table 1.3.3. A
graphical representation of concentrations observed
in 1994 is depicted in Figure 1.3.3.
1.3.4.2.2 Seasonal Variation
Open-lake atrazine concentrations were measured
during six sampling cruises. Brent et a/. (2001)
concluded that statistically significant mean open-
lake concentrations of atrazine, DEA, and DIA
increased during the one-year LMMBP sampling
campaign (1994-1995). Schottler and Eisenreich
(1997) found that 1992 atrazine concentrations in the
lake were statistically higher than the mean lake
concentration measured in 1991. Based on these
field measurements, it appears that the lake is
accumulating atrazine over time. More information
on this accumulation will be discussed in the
modeling chapters.
Table 1.3.3. Summary of Historical Atrazine,
DEA, and DIA Concentrations in Lake Michigan
Year
Atrazine
(ng/L)
35 (2.0)1*
37(1.8)1
38.1
DEA
(ng/L)
163
25.8
DIA (ng/L)
1991 35 (2.0)1* 163 Not Available
1992 37(1.8)1 243 Not Available
4/1994- 38.1 25.8 14.9
4/19952
1Schottler and Eisenreich, 1997
2Brentefa/.,2001
3Schottler and Eisenreich, 1994
*Values are means with the standard deviation in
parenthesis.
dissolved
atrazine in
Lake Michigan
water (ng/L)
1994
sample locations
(0-300 meters)
Figure 1.3.3. Atrazine concentrations in Lake
Michigan, 1994.
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1994. Formation and Transport of
Deethylatrazine and Deisopropylatrazine in
Surface Water. Environ. Sci. Technol.,
28(13):2267-2277.
Thurman, E.M. and E. Cromwell. 2000.
Atmospheric Transport, Deposition, and Fate of
Triazine Herbicides and Their Metabolites in
Pristine Areas at Isle Royale National Park.
Environ. Sci. Technol., 34(15):3079-3085.
U.S. Environmental Protection Agency. 1997a. Lake
Michigan Mass Balance Study (LMMB) Methods
Compendium, Volume 1: Sample Collection
Techniques. U.S. Environmental Protection
Agency, Great Lakes National Program Office,
Chicago, Illinois. EPA/905/R-97/012a, 1,440pp.
U.S. Environmental Protection Agency. 1997b. Lake
Michigan Mass Balance Study (LMMB) Methods
Compendium, Volume 2: Organic and Mercury
Sample Analysis Techniques. U.S.
Environmental Protection Agency, Great Lakes
National Program Office, Chicago, Illinois.
EPA/905/R-97/012b, 532 pp.
U.S. General Accounting Office. 1993. Reporttothe
Chairman, Subcommittee on Oversight of
Government Management, Committee on
Governmental Affairs, U.S. Senate: Pesticides -
Issues Concerning Pesticides Used in the Great
Lakes Watershed. U.S. General Accounting
Office, Washington, D.C. GAO/RCED-93-128,
39 pp.
34
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PART1
INTRODUCTION
Appendix 1.3.1 Information Management
David A. Griesmer
Computer Sciences Corporation
Large Lakes Research Station
9311 Groh Road
Grosse lie, Michigan 48138
and
Kenneth R. Rygwelski
United States Environmental Protection Agency
Office of Research and Development
National Health and Environmental Effects
Laboratory
Mid-Continent Ecology Division
Large Lakes and Rivers Forecasting Research
Branch
Large Lakes Research Station
9311 Groh Road
Grosse lie, Michigan 48138
To support the modeling efforts of the Lake Michigan
Mass Balance Project (LMMBP), samples were
collected and analyzed by the United States
Geological Survey (USGS) and several universities
(Table A1.3.1). The focus group acronyms in the
table provide a unique identifier of data sets. The
first two letters stand for the organization, the third
letter represents the media sampled (air, lake, or
tributary), and the fourth letter identifies the chemical
(atrazine) analyzed. Project data were sent to the
United States Environmental Protection Agency
(USEPA) Great Lakes National Program Office
(GLNPO) in Chicago, Illinois. GLNPO staff, under
the direction of Louis Blume, were responsible for
quality assurance (QA) assessment, organization,
and consolidation of all data. To facilitate the QA
assessment process, a SAS application, the
Research Data Management and Quality Assurance
System (RDMQ), developed by Syd Allen, a private
contractor, was used to automate the QA process
(Sukloff et a/., 1995). RDMQ is a menu-driven SAS
program. It has capabilities for loading data, applying
quality control (QC) checks, adding validity flags,
viewing and editing data, producing user-defined
tables and graphs, and exporting data in ASCII files.
These tasks are performed through a set of menu-
driven SAS programs and macros. Data which had
been put through the assessment process and
approved for release by both GLNPO and the
Principal Investigator (PI) were then sent to USEPA,
Office of Research and Development (ORD)/National
Health and Environmental Effects Research
Laboratory (NHEERL)/Large Lakes and Rivers
Forecasting Research Branch (LLRFRB)/Large
Lakes Research Station (LLRS) for use by the
modeling staff.
A1.3.1.1 Overview of Information
Management at the LLRS
Data received from GLNPO were usually in the form
of electronic media. Data were typically E-mailed,
but sometimes they were downloaded from GLNPO
databases or received on CD-ROM. Data were
reformatted by GLNPO into a form facilitating entry
into database programs at the LLRS. Upon arrival,
raw data were copied to the "Immb" folder on David
Griesmer's personal network space ("M:\" drive). In
addition, data were imported into one of several
Microsoft Access databases in the "\Access\lmmb"
folder on Mr. Griesmer's "M:\" drive. The "M:\" drive
was used to facilitate data security because this file
35
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Table A1.3.1
Modeling
List of Parameters Analyzed and Principal Investigators for the LMMBP Atrazine
Parameter
Focus
Group
Media
Notes
Principal Investigator
Atrazine, Deethylatrazine
(DEA),
Deisopropylatrazine
(DIA), Terbuthylazine
IUAA
Atmospheric Vapor
and Particulate
Phase, Precipitation
Atrazine, DEA, DIA,
d5-Atrazine
WSAA
Atmospheric Vapor
and Particulate
Phase, Precipitation
Atrazine
Atrazine
RULA
RUTA
Open-lake
Tributary
Flow
N/A
Tributary
Sleeping Bear
Dunes site only.
Keri
Hornbuckle, U.
of Iowa, used
these data to
calculate
loadings
All stations
except Sleeping
Bear Dunes
site. Keri
Hornbuckle, U.
of Iowa, used
these data to
calculate
loadings
David Hall,
USGS, used
these data to
calculate
loadings
Ronald Hites, Indiana
University
Clyde Sweet, Illinois
State Water Survey
Steven Eisenreich,
Rutgers University
Steven Eisenreich,
Rutgers University
David Hall, USGS
space is backed up regularly and is available only to
Mr. Griesmer. At some point in the future, the
location of these data may change; however, limited
access and backups of the data will be maintained.
Data were placed in the Microsoft Access databases
to facilitate data review/assessment and later
retrieval for the modeling team.
Prior to use, several reviews were done of the data
received to look for errors in the data sets. At the
LLRS, this review was broken up into two parts.
First, an initial review was made to check for
completeness of information; to lookfortranscription,
programming, and formatting errors; and to review
comments added by collection and analysis
personnel. Second, a review was done by the data
users to determine if the data made environmental
sense. This type of review was conducted for the
open-lake data. Tributary atrazine loadings and
atmospheric atrazine fluxes/loadings did not go
through this review process at the LLRS, but they
were assessed by study members assigned with
providing these loading values. Tributary atrazine,
deisopropylatrazine (DIA), and deethylatrazine (DEA)
loading assessments were done by David Hall,
(USGS). All atmospheric atrazine loading/-
36
-------
concentration data were assessed by Keri
Hornbuckle, University of Iowa.
Samples that GLNPO determined had failed the
RDMQ QA process were flagged with the value of
-9999 in the Grosse lie database. GLNPO preserved
all of the values in the data sets that were received
and flagged the analytical remark field for that
parameter. Flagging these values as -9999
facilitated processing by analytical software such as
IDL. In addition, parameter values with analytical
remark flags of "INV" (invalid data, as determined by
the GLNPO QA evaluation), and "NAI" (no result
reported - interference) were changed to -9999.
Samples with the analytical remark flag of "LAC" (no
results reported, laboratory accident) were removed.
Documentation associated with the data was
reviewed. RDMQ data warning fields
(RS_NMAND,RS_WARN, RS_UPDAT) were
checked to verify that there were no problems
flagged by RDMQ which were inadvertently included
in the database. Every routine field sample (RFS)
and field duplicate (FD#) was checked to verify that
a valid station name, sampling date, and depth
collection information were included. The value
ranges (minimum, maximum, average) for atrazine
and its degradation products (DEA and DIA) were
checked to look for any obvious errors. Data ranges
of all data were also checked for obvious errors.
Data were checked to verify units and to confirm
whether blank, dilution, or surrogate correction were
done. Sample QC and station comment fields
(RECSTAT, RECSTATF, and STNNOTES) were
checked for any comments associated with a sample.
All of this information was recorded on a Data
Verification Checklist (Table A1.3.2). If questions or
errors were found, they were referred back to
GLNPO for resolution.
Upon completion of this initial data check, readme
files were created to describe the data, and the raw
data set(s) and readme files were copied to a data
archive on the LLRS Unix systems. This archive is
located at \usr\lmmbdata on the Unix servers and is
available to modeling staff at the LLRS. Each study
has its own directory (LMI0001-LMI0028) within the
Immbdata archive. The directories related to the
atrazine modeling can be found in Table A1.3.3.
Information on other LMMBP directories can be
found in the LMMBP PCB report (Rossmann, 2006).
At the same time, information about data received
(metadata) was stored in a searchable Microsoft
Access database. The database is found on the
LLRS common drive "\\giord2\grlcommon", which is
also known as the "L:\" drive. This database is
named "Imtrack2000.mdb" and is found in the
L:\Public\Access\lmmb folder. This database is
available to all staff. This database can be searched
by library number (consecutive number assigned
when data are logged in, corresponds to LMI folder
name in Immbdata archive), PI, parameter, PI and
parameter, or library number and parameter (Table
A1.3.3).
After initial review of a data set was completed, data
were retrieved from the Microsoft Access databases
and exported into files (usually Microsoft Excel) for
assessment by the modeler who would be using the
data set. Atrazine data were assessed by William
Richardson. Initially, only routine field samples and
field duplicates were given to the data assessors. If
issues or problems were found, the person assessing
the data would then request additional QA data. If
questions/problems could not be resolved by looking
at QA data, they were referred back to GLNPO for
resolution. GLNPO was informed whenever we
rejected data.
After the assessment process was completed, files
were created which could be used in IDL, which is a
software package used for visualization and analysis
of LMMBP data. A standard format was developed
for water data (Table A1.3.4). All files were fixed
format ASCII text files. One of the principal uses of
IDL was to develop volume-weighted averages
(VWA) estimates of parameter concentrations for
each cell in the modeling grid. These VWA estimates
could then be compared to model results.
A1.3.1.2 Summary
The LMMBP data received at the LLRS were
carefully evaluated prior to use to ensure that the
field data being used by the modelers were as
accurate as possible. In addition, data were archived
and cataloged to protect these valuable data sets
37
-------
Table A1.3.2. Example of Data Verification Checklist Used for the LMMBP
Data Verification Checklist
FOCUS Version Number Date Received
Description:
1. Read any documentation which came with data files:
2. Make sure I understand field names in RDMQ files:
3. Check fields which according to RDMQ should not be flagged/or indicate some question, with data (e.g.
RS_NMAND, RS_WARN, RS_UPDAT).
RS_NMAND
RS_WARN
RS UPDAT
4. Make sure every RFS and field duplicate has station, date, depth collected information.
5. Check to make sure every sample has station name that is valid.
6. Check number of RFS and field duplicates for every analyte. Total Samples
Analyte RFS FDn
Analyte RFS FDn
Analyte RFS FDn
Analyte RFS FDn
Analyte RFS FDn
Analyte RFS FDn
38
-------
7. Analysis Results for RFS and field duplicates for every analyte.
Analyte Avg Min Max Count
Min Max Count
Min Max Count
Min Max Count
Min Max Count
Min Max Count
Min Max Count
Min Max Count
Min Max Count
Min Max Count
Min Max Count
Min Max Count
Min Max Count
Min Max Count
Min Max Count
Min Max Count
Min Max Count
Min Max Count
Min Max Count
Min Max Count
Min Max Count
Analyte
Analyte
Analyte
Analyte
Analyte
Analyte
Analyte
Analyte
Analyte
Analyte
Analyte
Analyte
Analyte
Analyte
Analyte
Analyte
Analyte
Analyte
Analyte
Analyte
Ava
Ava
Ava
Ava
Ava
Ava
Ava
Ava
Ava
Ava
Ava
Ava
Ava
Ava
Ava
Ava
Ava
Ava
Ava
Ava
39
-------
8. Check date ranges of data to see if they are reasonable.
Analyte Min Max
Analyte Min Max
Analyte Min Max
Analyte Min Max
Analyte Min Max
Analyte Min Max
Analyte Min Max
Analyte Min Max
Analyte Min Max
Analyte Min Max
Analyte Min Max
Analyte Min Max
9. Check to verify units information looks alright.
10. Number of significant digits for each analyte.
11. Number of negative values for each analyte.
12. Check flags on RFS and field duplicates.
40
-------
13. Core slice range (sediment)/species, age, length, weight (fish).
14. Check blank correction, dilution, and surrogate correction fields.
15. Questions about QC Coordinator remarks (RECSTAT). Check flags for whole record (RECSTATF).
Questions about Station Notes (STNNOTES), Field Remarks (FREMARK), and Sample Description
(SAMPDESC).
16. Additional Questions.
41
-------
Table A1.3.3. Printout of Information Stored in the LMMBP Tracking Database Related to Atrazine
Modeling (L:\Public\Access\lmmb\lmtrack2000.mdb)
LMMBP DATA ARCHIVE - QUICK REPORT. Note: All Data Archived on
superior.grl.epa.gov in /usr/lmmbdata.
Library No.
Description
Library No.
Description
Library No.
Description
Library No.
Description
Library No.
Description
Library No.
Description
Library No.
Description
Library No.
Description
Library No.
Description
LM10001
PI:
David Schwab
Hourly Lake Michigan wind, wave, and atmospheric data (5 km grid) for 1982, 1983, 1994, 1995.
Original data files were converted to SEDZL and POM formerly by M. Settles. Also, bathymetric data
for Lake Michigan.
LM10002
PI:
William Richardson
STORET conventional and general chemistry data for Lake Michigan, April 1962-August 1993. Note:
Date range varies by parameter, includes original file, reformatted spreadsheet, and MS Access file.
LM10003
PI:
David Schwab
Two-dimensional and three-dimensional GLERL hydrodynamics data forthe Lake Michigan 5 km grid.
2D data: January 1982-September 1983; 3D: covers January-July 1982. Program//llrssrv2
/~model/dev/PATRIC2D/RCS is for 2D processing, no three-dimensional programming yet.
LM10004
PI:
Steven Eisenreich
Open-lake (RULA) and tributary (RUTA), atrazine, DEA, DIA data forthe LMMBP. Open-lake 325
samples (1/17/94-4/17/95). Tributary: 126 samples (4/4/95-5/15/96). Revised version of data sent
2/19/98.
LM10005
PI:
Angela Bandemehr
Hourly meteorological data (airtemperature, solarradiation, relative humidity, wind speed and direction,
and precipitation) from 13 air sampling sites both in and outside of the Lake Michigan basin. 11/30/90-
12/31/96 (Dates vary by site).
LM10006
PI:
Glenn Warren
Sea bird water temperature data for seven LMMBP surveys, April 1994-October 1995. Data collected
at 0.5 m intervals. Does not include January 1994 survey. Note: Data received was extensively
revised from original version.
LM10007
PI:
David Hall
Tributary flow data for 11 tributaries to Lake Michigan (Fox, Grand, Indiana Harbor, Kalamazoo,
Manistique, Menominee, Milwaukee, Muskegon, Pere Marquette, Sheboygan, St. Joseph), 1/1/94-
12/31/95. Some data estimated.
LM10011
PI:
David Schwab
Lake Michigan final report, hourly circulation, meteorology, and wave data (5 km grid) for 1982, 1983,
1994, 1995. Includes intake, cruise, mooring, water level data. Also, HTML files and images, model
results (XDR format), Fortran and IDL programs.
LM10020
PI:
Keri Hornbuckle
Atmospheric atrazine and nutrient (NO3, total phosphorus, TKN) wet deposition loading data for Lake
Michigan 5 km grid cells used in hydrodynamic model. Atrazine wet deposition and particulate monthly
concentration data. Data for 10/94-10/95 (nutrient) and 5/94-10/95 (atrazine).
42
-------
Table A1.3.3. Printout of Information Stored in the LMMBP Tracking Database Related to Atrazine
Modeling (L:\Public\Access\lmmb\lmtrack2000.mdb) (Continued)
Library No.
Description
Library No.
Description
Library No.
Description
Library No.
Description
LM10022
PI:
David Hall
Atrazine, DEA, DIA tributary loading data for 11 monitored tributaries and atrazine data for unmonitored
tributaries to Lake Michigan. Data covers the time period: 1/1/94-12/31/95.
LM10026
PI:
Nathan Hawley
Current velocity, water transparency, temperature from three stations, 10/31/94-10/11/95. In situ
sediment resuspension from sediment flume experiments (8/12/95-9/23/98). Also profile data -
temperature, dissolved oxygen, conductivity, BAT, pH, fluorescence, TSM data from six stations in
Lake Michigan (1/4/95-11/29/95).
LM10027
PI:
Barry Lesht
Current velocity and direction, bottom wave orbital velocity, temperature, beam attenuation, and TSM
data collected from Tripod Station 98 (latitude 42 52.18, longitude 87 42.41), during the EEGLE project,
4/2/98-12/1/98. Data collected every 30 minutes.
LM10028
PI:
Michael Settles
NEMA and NOAA wind speed and direction, wave height and period data for six stations in Lake
Michigan, retrieved from USACOE Web Site (http://bigfoot.wes.army.mil/c300.html). 1980-1998 (not
all stations cover entire date range). NEMO-Daily data, NOAA-Hourly data.
Table A1.3.4. Generalized Format for the LMMBP Water Data to be Analyzed With IDL Programs
Beginning -
Ending Columns
1 -7
8-8
9- 14
15- 15
16-22
23-23
24-35
36 -36
37-44
45 -45
Variable Description
Cruise Name
Blank Space
Latitude (ddd.ddd)
Blank Space
Longitude (-ddd.ddd)
Blank Space
Station Name
Blank Space
Depth Sampled
Blank Space
Format (A = Alpha, F
= Floating Point No., I
= Integer, X = Skip)
A7
1X
F6.3
1X
F7.3
1X
A12
1X
F8.0
1X
Sort Order (A =
Ascending, D =
Descending,
Blank = None)
A
N/A
N/A
N/A
A
N/A
A
N/A
Missing Data
Code
Blank
N/A
Blank
N/A
Blank
N/A
Blank
N/A
Blank
N/A
43
-------
Table A1.3.4. Generalized Format for the LMMBP Water Data to be Analyzed With IDL Programs
(Continued)
Beginning -
Ending Columns
46-53
54-54
55-58
59 -59
60 -67
68 -68
69-72
73 -73
74-75
76 -76
77-79
80 -80
81 -88
89- 103
104-111
112-126
Variable Description
Sampling Start Date
(mm/dd/yy)
Blank Space
Sampling Start Time (24
hour clock)
Blank Space
Sampling End Date
(mm/dd/yy)
Blank Space
Sampling End Time (24
hour clock)
Blank Space
Filter Fraction
Blank Space
Sample Type
Blank Space
Value Parameter 1
Parameter 1 Flags
Value Parameter 2
Parameter 1 Flags
1
f
Value Parameter n
Parameter n Flags
Format (A = Alpha, F
= Floating Point No., 1
= Integer, X = Skip)
A8
1X
A4
1X
A8
1X
A4
1X
A2
1X
A3
1X
F8.0
A15
F8.0
A15
F8.0
A15
Sort Order (A =
Ascending, D =
Descending,
Blank = None)
A
N/A
N/A
A
N/A
N/A
A
N/A
D
N/A
Missing Data
Code
Blank
N/A
Blank
N/A
Blank
N/A
Blank
N/A
Blank
N/A
Blank
N/A
-9999
Blank
-9999
Blank
-9999
Blank
44
-------
and make it easier for users to find the information. Sukloff, W.B., S. Allan, and K. Ward. 1995. RDMQ
Incorporation of this information into LLRS Microsoft User Manual. Environment Canada, Atmospheric
Access databases has given us flexibility in retrieving Environment Service, North York, Ontario,
the information needed by the modeling staff at the Canada. 91 pp.
LLRS.
References
Rossmann, R. (Editor). 2006. Results of the Lake
Michigan Mass Balance Project: Polychlorinated
Biphenyl Modeling Report. U.S. Environmental
Protection Agency, Office of Research and
Development, National Health and Environmental
Effects Research Laboratory, Mid-Continent
Ecology Division-Duluth, Large Lakes Research
Station, Grosse lie, Michigan. EPA/600/R-
04/167, 579 pp.
45
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PART1
INTRODUCTION
Chapter 4. Representativeness of the
Lake Michigan Mass Balance Project
(LMMBP) Years Relative to Lake
Michigan's Historic Record
Ronald Rossmann, Kenneth R. Rygwelski, and
Russell G. Kreis, Jr.
United States Environmental Protection Agency
Office of Research and Development
National Health and Environmental Effects
Research Laboratory
Mid-Continent Ecology Division
Large Lakes and Rivers Forecasting Research
Branch
Large Lakes Research Station
9311 Groh Road
Grosse lie, Michigan 48138
and
Gregory J. Gerstner, Xiaomi Zhang, and Brent
Burman
Welso Federal Services, LLC
Large Lakes Research Station
9311 Groh Road
Grosse lie, Michigan 48138
1.4.1 Introduction
A major concern related to modeling contaminants in
the lake was the representativeness of the years of
sampling (1994-1995) relative to the historical record.
This was particularly important when using the
models to predict future conditions in the lake. The
LM2-Atrazine and LM3-Atrazine models used
hydrodynamic model output from 1994-1995 in their
construct (see Parts 4 and 5 of this report,
respectively, for more information). In addition,
atrazine loading estimates for any given year can be
affected by various meteorological conditions (see
Part 2, Chapters 2 and 3 of this report). If these data
did not represent something close to average
conditions, the resulting predictions could be biased.
Parameters considered most important to the
performance of the atrazine models included ice
cover, airtemperature, water temperature, lake water
levels, precipitation, tributary flows, wind, and waves.
Potential impacts on the various models are
discussed below. Each of these were investigated
for the representativeness of the 1994-1995 project
data relative to the available historical data record.
1.4.2 Ice Cover
Ice cover impacts the volatilization, absorption, and
physical mixing of the lake during the winter months.
In locations where there is ice cover, gas exchange
between the water and atmosphere is prevented by
the physical barrier. Physical mixing includes not
only the mixing of the water column, but also the
interaction of waves with the lake bottom to
resuspend sediments. Winters having extensive ice
cover yield a more poorly mixed water column, and
a large region of the lake becomes depositional due
to the lack of wave resuspension of sediments. Once
ice retreats in the spring, sediments accumulated
during ice cover will be resuspended as a pulse. Ice
cover can cause significant changes in winter
circulation patterns in a large lake (Campbell et a/.,
1987). The years of interest were 1982, 1983, 1994,
and 1995. The hydrodynamic modeling included
three-dimensional lake circulation, surface flux for
atmospheric input, and wind-wave models (Schwab
46
-------
and Beletsky, 1998). These were calibrated for the
period of 1982-1983 using temperature, current,
water level, and wind-wave measurements. The
calibrated model was applied to 1994-1995 and
verified. There was no ice modeling component for
the version of the hydrodynamic model applied.
Thus ice cover was important for understanding any
potential weaknesses associated with the
hydrodynamic results as well as the dynamics of
exchanges between the water and the atmosphere.
Ice cover data were available from the National
Oceanic and Atmospheric Administration (NOAA)/
Great Lakes Environmental Research Laboratory
(GLERL) (Assel, 2003). This data set is partially
described in Assel et a/. (2002). Tabular information
presented in Assel (2003) were summarized in a
manner that seemed appropriate for this discussion
(Table 1.4.1). For the period when ice was recorded
on Lake Michigan, the mean and median daily ice
cover were 16.7% and 14.7%, respectively. An ice
year began with the first ice. For example, 1982 may
include December of 1981. Both 1982 and 1994
were greater than the mean and median; whereas
1983 and 1995 were less than the mean and median.
None of the four years represented an extreme of
mean daily ice cover. The lowest mean daily ice
cover was observed in 2002, and the highest was
observed in 1977. Results for each winter's
maximum daily ice cover were similar to mean daily
ice cover. Ice cover is extremely variable from year-
to-year. The impact upon hydrodynamics as
modeled was believed to be minimal with respect to
1983 and 1995 when ice cover was quite low.
Though high ice cover occurred during the winters of
1982 and 1994, these periods were not a part of the
hydrodynamic model period. Using the
hydrodynamic model information for models used to
predict future conditions could lead to potential
errors. Modeled circulation patterns could be in error
and impact a high bias to modeled current velocities
during the winters of high ice cover years due to the
lack of an ice model within the hydrodynamics model.
1.4.3 Water and Air Temperatures
Water and air temperature data were retrieved from
the National Data Buoy Center (U.S. Department of
Commerce, 2002). Data from buoy numbers 45002
(north buoy) and 45007 (south buoy) were reviewed
(Figure 1.4.1). Water temperature sensors were
located 1 m below the water surface, and air
temperature sensors were located 4 m above the
surface. Water and air temperature data were
available 1979 through 2002 for the north buoy and
1981 through 2002 for the south buoy.
Water temperature is highly variable from year-to-
year. The data had been stratified in two ways for
presentation. First, monthly mean temperatures
were calculated and plotted for the south (Figure
1.4.2) and north (Figure 1.4.3) buoys. Years of
importance to the hydrodynamic model were
highlighted. It was interesting to note that 1983 and
1995 had higher monthly mean temperatures than
1982 or 1994. Both 1983 and 1995 had above
normal maximum mean monthly temperature;
whereas, 1982 had atypical maximum and 1995 had
a very low maximum. This was reflected in the
previously discussed ice cover for the four years.
Water temperatures tended to be higher at the
southern buoy than at the northern buoy, reflecting its
more southerly latitude.
One way to identify the relative lake warming rate
among years was to look at the mean June water
temperature for the period of observation available
from the NOAA buoys. Mean June temperatures at
the south (Figure 1.4.4) and north (Figure 1.4.5)
showed similar patterns that were quite interesting.
Beginning in 1983, relatively high mean June
temperatures were observed every four years (1983,
1987,1991, 1995,1999). This cycling, as well as the
apparent increasing mean June water temperature
for the period of record, should be further
investigated. Both of these trends can impact long-
term model forecasts. The years of the Lake
Michigan Mass Balance Project (LMMBP) (1994 and
1995) represented a fairly average mean June
temperature and one of the relatively high means,
respectively.
The exchange of atrazine between the air and water
are dependent on both water and air temperatures.
Air temperature varied from year-to-year at the south
and north buoys (Figures 1.4.6 and 1.4.7). Because
air temperature drives observed water temperatures,
it was not surprising that patterns observed and
47
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Table 1.4.1. Summary of Lake Michigan Ice Cover Based Upon Assel (2003)
Year
1973
1974
1975
1976
1977
1978
1979
1980
1981
|~ 1982
!_ 1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
|~ 1994
!_ 1995
1996
1997
1998
1999
2000
2001
2002
Mean
Median
Minimum
Maximum
Mean Daily
Percent Ice Cover
During Ice Period
13.3
16.9
13.9
15.5
46.5
26.6
35.2
18.2
24.6
24.0
8.2
15.6
20.1
25.3
9.1
16.6
13.1
17.5
10.0
8.3
11.0
27.3
7.2
19.4
13.4
6.1
8.7
9.2
13.4
6.0
16.7
14.7
6.0
46.5
Days of
Observed
Ice
104
122
113
119
132
132
132
106
112
135
118
127
119
126
100
104
140
132
120
149
126
134
120
161
156
109
111
103
134
116
124
121
100
161
Maximum
Daily Percent Ice
Cover
33.0
39.4
28.1
29.5
93.1
66.6
92.3
38.6
53.8
60.2 |
23.6 !
43.3
41.3
66.8
19.3
32.7
30.9
32.4
21.5
32.8
32.2
82.7 |
21.6 !
75.0
37.8
15.1
23.0
27.2
29.5
12.4
41.2
32.8
12.4
93.1
48
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Figure 1.4.1. Location of the NOAA buoys in
Lake Michigan.
ij
Figure 1.4.2. Monthly mean water temperatures
in southern Lake Michigan.
20-
1980
1985
1990
19§S
2000
2005
Figure 1.4.3. Monthly mean water temperatures
in northern Lake Michigan.
20
o
I 12
4
0 ' •
1980
south buoy
June
1985
1990
1995
2000
2005
Figure 1.4.4. Mean June water temperatures in
southern Lake Michigan.
o
a>
| 12
Q.
S 8
4 '
0
1980
north buoy
June
1985
1990
1995
2000
2005
Figure 1.4.5. Mean June water temperatures in
northern Lake Michigan.
49
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25
20:
015:
,i=
<0
south buoy
_«_ data years for Lake Michigan
hydrodynamic modeling
20
-5-
1980
1986
1990
1995
2000
2005
Figure 1.4.6. Monthly mean air temperatures in
southern Lake Michigan.
25:
20:
-15-
north buoy
_•- data years for Lake Michigan
hydrodynamic modeling
1980
1985
1990
1995
2000
2005
Figure 1.4.7. Monthly mean air temperatures in
northern Lake Michigan.
conclusions made for water temperature are the
same for air temperature. The cyclic pattern of June
mean water temperatures was also found for the air
temperatures (Figure 1.4.8 and 1.4.9). As additional
data become available, future modeling efforts will
need to address these cyclic patterns and long-term
temperature trends for water and air temperatures.
1.4.4 Lake Water Levels
Lake levels can affect model geometry. If segment
volume deviates significantly from the volumes used
at the time of calibration, model results can be
impacted. On a percentage basis, the impact will be
16-
,12-
8
4
south buoy
June
1980
1985 1990 1995 2000 2005
Figure 1.4.8. Mean June air temperatures in
southern Lake Michigan.
1980
1985 1990 1995 2000 2005
Figure 1.4.9. Mean June air temperatures in
northern Lake Michigan.
most noticeable for shallow water segments and
predictions from the hydrodynamic model and
surface water model could be affected. Monthly
mean lake water levels varied between 175.5 and
177.5 m for the period of record (1918-1997). Lake
levels during 1994 and 1995 were near the average
for the period of record (Figure 1.4.10).
1.4.5 Precipitation
Precipitation influences the flux of airborne
contaminants to the lake, impacts tributary loading
rates, and controls water levels. The 1982 and 1983
hydrodynamic years, and the 1994 and 1995 project
years were compared to the previous 50 years of
data (Croley and Hunter, 1994).
50
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177,5 ---.-
Lake Michigan and Lake Huron water levels (1918-1997)
date; US Army Corps of Engineers, Detroit District
.............. monthly mean water levels (meters - IGLD 1985)
all-time, record monthly high and low water levels
175,0-
i I I i i I I I i f I I I ; [ I I I i j I I I i i I i i j i i I i i i I I i i I I I • i I I I i i I I i i i I I i i i I I i i i i i i i i i i : M I I ! M
QG3 0>O3iJ)Q5GJ05ffi0>0>O'JO5
Figure 1.4.10. Record of mean monthly water levels for Lake Michigan.
1.4.5.1 Annual Comparisons
Precipitation to Lake Michigan for 1982,1983, 1994,
and 1995 were close to the 50-year mean for the lake
(Figure 1.4.11). 1982 and 1983 were slightly above
the mean and 1994 and 1995 were slightly below the
mean. 1995 total annual precipitation was very close
to the 50-year mean for over-lake precipitation. No
visual trend was apparent in the total annual amounts
of precipitation over the 50-year period.
1.4.5.2 Monthly Comparisons
The monthly mean precipitation for 1982, 1983,
1994, and 1995 were compared to the 50-year mean
for the period of 1949 through 1998 (Figure 1.4.12).
For the years of interest, January, July, November,
and December of 1982; May of 1983; and October of
1995 had relatively high amounts of precipitation,
exceeding one standard deviation of the 50-year
mean. For the four years of interest, February of
1982; June of 1983; March, May, and December of
1994; and June of 1995 had relatively low amounts
of precipitation. This illustrates that, in any one year,
precipitation varies from month-to-month while the
precipitation for the year can be at or near the
average expected.
1.4.6 Tributary Flows
Tributary flows impact the delivery of materials to the
lake, including nutrients and contaminants. During
high flow events triggered by spring snow melt or rain
events, tributary flows increase and materials can be
carried from the watersheds to the tributaries. Within
thetributary, sediments containing contaminants may
resuspend. Thus the fluxes of solids, nutrients, and
contaminants to the lake have the potential to
increase during high flow events. Tributary flows
were obtained from the United States Geological
Survey (USGS) website (www.usgs.gov). A historical
average and median daily flow were calculated for
each tributary for the period of record, as well as for
the 1994-1995 and 1982-1983 time periods. During
1982 and 1983, tributary flows were approximately
20% greater than the average flow (Figure 1.4.13).
The 1994-1995 time period had relatively ordinary
tributary flows (Figure 1.4.14).
1.4.7 Summary
Lake Michigan is acted upon by a number of physical
parameters that impact the physics, chemistry, and
biology of the lake. For a lake the size of Lake
Michigan, changes in these parameters can be
51
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12000
10000-
80QQ-
o
I
I 6000 -
4000-
2000-
Figure 1.4.11. Annual precipitation to Lake Michigan between 1949 and 1998.
1400 -i-
1200
1000
£
"o
2 800 -
600
400 -
200 -
Monthly mean precipitation
-*— 1949-1998 mean
-•- - 1949-1998 mean + 1 std.dev.
-t—1949-1998 mean -1 std.dev,
o 1982
A 1983
D 1994
•o 1995
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Figure 1.4.12. Comparison of 1982,1983,1994, and 1995 monthly mean precipitation to the mean for
the period of 1949 through 1998.
52
-------
5000-
4000-
-g 3000-
2000-
1000-
Ftn
II
6
o
K
.5
1
e-
TO
o
•5
1
fO
a.
IS
O 15
o
ui
Figure 1.4.13. Comparison of tributary flow for hydrodynamic model calibration (1982-1983) to the
historic means.
6000
5000-
4QQQ-
3000
2000-
1000-
f%-n
=
a
S
ef
=
1
i 2
o
»
1 I
Figure 1.4.14. Comparison of tributary flow for the study period (1994-1995) to the historic means.
53
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significant, especially when models are used in long-
term predictions to predict the outcome of various
scenarios. The primary driving forces are wind, air
temperature, and precipitation. These impact
tributary flows, lake levels, waves, water circulation,
water temperature, and ice cover. For the period of
record, these driving forces varied from year-to-year.
The period of 1982 to 1983 was used to calibrate the
hydrodynamic models. Fortunately for the period of
time the models were calibrated, conditions were not
at any extreme. This was also true for the period of
1994 and 1995 when the models were applied.
However, the impact of ice cover remains a concern
and will have to be dealt with in the future.
Temperature can impact atrazine contaminant
modeling. Air temperature impacts how quickly the
lake warms in any one year. Water temperature
impacts the volatilization of atrazine. There appears
to be a four-year cycle of quicker warming which
exists within a trend of general warming of the lake.
The trend of warming may be part of a longer-term
undocumented cycle or may be related to climate
change. For future modeling, these cycles and
trends will have to be considered to improve long-
term predictions.
Precipitation will impact both lake levels and tributary
flows. In wet years, more atrazine may be delivered
to the lake (see Part 2, Chapter 2). Precipitation was
within the normal range for all years of modeling
interest, resulting in lake levels and tributary flows
that were within normal bounds. Changes in lake
levels as well as the response of tributaries to
precipitation events will need to be considered for
future modeling used to predict changes of
contaminants within the lake.
References
Assel, R.A., D.C. Norton, and K.C. Cronk. 2002. A
Great Lakes Digital Ice Cover Data Base for
Winters 1973-2000. National Oceanic and
Atmospheric Administration, Great Lakes
Environmental Research Laboratory, Ann Arbor,
Michigan. NOAA Technical Memorandum ERL
GLERL-121,46pp.
Assel, R.A. 2003. NOAA Great Lakes Ice Atlas. An
Electronic Atlas of Great Lake Ice Cover.
National Oceanic and Atmospheric
Administration, Great Lakes Environmental
Research Laboratory, Ann Arbor, Michigan.
Campbell, J.E., A.M. Clites, and G.M. Green. 1987.
Measurements of Ice Motion in Lake Erie Using
Satellite-Tracked Drifter Buoys. National Oceanic
and Atmospheric Administration, Great Lakes
Environmental Research Laboratory, Ann Arbor,
Michigan. NOAA Technical Memorandum ERL
GLERL-30, 22 pp.
Croley, T.E., II and T.S. Hunter. 1994. Great Lakes
Monthly Hydrologic Data. National Oceanic and
Atmospheric Administration, Great Lakes
Environmental Research Laboratory, Ann Arbor,
Michigan. NOAA Technical Memorandum ERL
GLERL-83, 13 pp.
Schwab, D.J. and D. Beletsky. 1998. Lake Michigan
Mass Balance Study: Hydrodynamic Modeling
Project. National Oceanic and Atmospheric
Administration, Great Lakes Environmental
Research Laboratory, Ann Arbor, Michigan.
NOAA Technical Memorandum ERLGLERL-108,
55 pp.
U.S. Department of Commerce. 2002. National
Data Buoys. National Weather Service, National
Oceanic and Atmospheric Administration, Ann
Arbor, Michigan. Available from National Data
Buoy Center at http://www.ndbc.noaa.gov.
54
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PART1
INTRODUCTION
Chapters. Atrazine Modeling Overview
Douglas D. Endicott
Great Lakes Environmental Center
Traverse City, Michigan
and
William R. Richardson (Retired), Ronald Rossmann,
and Kenneth R. Rygwelski
United States Environmental Protection Agency
Office of Research and Development
National Health and Environmental Effects
Laboratory
Mid-Continent Ecology Division
Large Lakes and Rivers Forecasting Research
Branch
Large Lakes Research Station
9311 Groh Road
Grosse lie, Michigan 48138
1.5.1 Background
The atrazine mass balance project was based upon
the Enhanced Monitoring Program (EMP), a
comprehensive, two-year synoptic survey for
selected toxic chemicals in the Lake Michigan
ecosystem. The atrazine EMP included tributary load
and atmospheric deposition monitoring; ambient
water column; and additional measurements to
define and confirm transport and fate processes.
The project was led by the United States
Environmental Protection Agency (USEPA)/Great
Lakes National Program Office (GLNPO). Modeling
support to the project was provided by the
USEPA/Mid-Continent Ecology Division (MED)ADffice
of Research and Development (ORD)/Large Lakes
Research Station (LLRS) in cooperation with the
Atmospheric Research and Exposure Assessment
Laboratory (AREAL); the National Oceanic and
Atmospheric Administration (NOAA)/Great Lakes
Environmental Research Laboratory (GLERL); and
other cooperators. The research developed a suite
of integrated mass balance models to simulate the
transport and fate of atrazine in Lake Michigan.
The project directly supports the development of a
Lake-wide Management Plan (LaMP) for Lake
Michigan that is mandated under Section 118 of the
1992 Clean Water Act. Atrazine and degradation
products are on the Lake Michigan LaMP 2008
Watch List. Chemicals on the Watch List include
those chemicals that have the potential to impact the
Lake Michigan ecosystem; is present in the Lake
Michigan watershed; and has the potential for
bioaccumulation, persistence in water or sediment, or
toxicity singly or through synergistic effects. In a
June 1993 response to an inquiry by U.S. Senator
Carl Levin, the United States General Accounting
Office (now called the United States Government
Accountability Office [USGAO]) recommended that
the USEPA assess the persistence of pesticides,
such as atrazine, in the Great Lakes and to report
their findings to the pesticides reregistration program
(U.S. General Accounting Office, 1993). The results
of the Lake Michigan Mass Balance Project (LMMBP)
atrazine modeling have been forwarded to the
reregistration program for consideration.
1.5.2 LMMBP Modeling Objectives
Development of effective strategies for atrazine
management requires a quantitative understanding
of the relationships between sources, inventories,
55
-------
concentrations, and effects of atrazine in the
ecosystem. This approach integrated load
estimation, ambient monitoring, and research efforts
within a modeling framework that was compatible
with both scientific as well as ecosystem
management objectives. The mass balance
approach estimated the magnitude of mass fluxes
that constitute the pathways for atrazine transport
into and out of the lake and processes that distribute
toxics within the lake water column. Based upon
these estimates, the mass balance was used to
determine the rate of change in concentrations and
inventories of atrazine as inputs such as atmospheric
and tributary loadings changes. Thus the mass
balance can serve as a useful tool to estimate or
predict the outcome of alternatives under
consideration for toxics management.
In general, atrazine modeling efforts associated with
the LMMBP had the following objectives:
1. Provide a consistent framework for integrating
load estimates, ambient monitoring data, and
research efforts, leading to a better
understanding of atrazine chemical sources,
transport, and fate in Lake Michigan.
2. Using flow and concentration measurements,
estimate the loading of atrazine from all major
tributaries to Lake Michigan for the duration of
the study.
3. Based on county-level usage of atrazine within
the basin, make independent estimates of
atrazine loading to the lake via tributaries.
4. Estimate the atmospheric deposition and air-
water exchange of atrazine, including spatial and
temporal variability over Lake Michigan.
5. Calibrate and confirm mass balance models for
atrazine using project data based upon models
for hydrodynamic and atrazine transport and fate.
6. Based upon the mass balance models, evaluate
the magnitude and variability of toxic chemical
fluxes within and between lake compartments,
especially between the water column and the
atmosphere.
7. Apply the calibrated mass balance models to
forecast atrazine concentrations in water
throughout Lake Michigan based upon
meteorological forcing functions and future
loadings based upon load reduction alternatives.
8. Predict the water concentration of atrazine in
lake model cells receiving loads from tributaries
contributing a relatively high percentage of the
total tributary load to the lake. Compare these
predictions to water quality standards.
9. Estimate (quantify) the uncertainty associated
with estimates of tributary and atmospheric
loads of atrazine and model predictions of
contaminant concentrations.
10. Identify and prioritize further monitoring,
modeling, and research efforts to (1) further
reduce uncertainty and improve accuracy of
predictions; (2) establish additional cause-effect
linkages, such as ecological risk endpoints and
feedbacks; and (3) evaluate additional source
categories, such as non-point sources in the
watershed.
Unlike the other LMMBP-modeled toxics
(polychlorinated biphenyls (PCBs), mercury, and
frans-nonachlor), atrazine does not sorb to solids to
any great extent, and it does not bioaccumulate. It
is soluble in water and can migrate from farm fields
to Lake Michigan via run-off events. The herbicide is
also transported to Lake Michigan via atmospheric
pathways.
1.5.3 Historical Modeling
The modeling design and approach for the LMMBP
reflects a progression of prior modeling efforts in
Lake Michigan and throughout the Great Lakes.
These include eutrophication and toxic substance
mass balance models, food web bioaccumulation
models, and predictive hydrodynamic and sediment
transport models. Although not a comprehensive
review, several of these prior modeling efforts are
discussed below.
56
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1.5.3.1 Completely-Mixed Lakes-ln-Series Model
A lakes-in-series model for conservative substances
was developed by Sonzogni et al. (1983) and applied
to forecast chloride concentrations in each of the
Great Lakes as a function of expected future
loadings. This model demonstrated that
concentrations of non-reactive substances would
substantially "lag" the history of their input. This was
especially the case for Lake Michigan, where
maximum chloride concentrations were not predicted
to occur until the 22nd Century despite declining
loads after the 1970s. Similarly strong, non-steady-
state behavior may be expected for other chemicals
which are non-reactive and weakly associated to
particles.
1.5.3.2 MICHTOX
MICHTOX was adapted from the general model,
WASP4 (Ambrose et al., 1988), and has served well
as a screening-level model for Lake Michigan over
the past several decades. An integrated mass
balance and bioaccumulation model for PCBs
(modeled as two homologs) and 10 other toxic
chemicals was developed as a planning tool for the
LMMBP(Endicottefa/.,2005). The MICHTOX mass
balance model was calibrated to suspended solids
and plutonium data for the southern lake basin, while
the bioaccumulation model combined Thomann and
Connolly's (1984) effort with chemical-specific
parameterization from Lake Ontario. MICHTOX
demonstrated that reasonable predictions of PCB
concentration trends in water, sediment, and biota
could be developed although significant uncertainties
regarding sediment-water and air-water contaminant
transport remained. These are the most significant
transport fluxes for PCBs and presumably other
hydrophobic contaminants. Major data gaps for other
priority toxics allowed only order-of-magnitude
estimates of load-concentration relationships. When
this model was developed and run, available
monitoring data for toxic chemical concentration in
tributaries, air, lake water, sediment, and biota were
not adequate to define loading trends or to relate the
distribution of loadings to contaminant gradients
observed for sediment and biota. Credible model
predictions of toxic chemical transport, fate, and
bioaccumulation would depend upon developing a
comprehensive data set quantifying loadings,
sediment inventories, concentrations, and transport
fluxes on a spatially-resolved basis and localized
descriptions of food web structures.
MICHTOX was also applied to model atrazine in Lake
Michigan and Green Bay. It was first applied prior to
the release of LMMBP data using only historical data
(Rygwelski et al., 1999), and it was also applied
again after LMMBP data became available.
MICHTOX served as a low-resolution model and the
application is discussed in this report.
1.5.3.3 Green Bay Mass Balance Project
The Green Bay Mass Balance Project (GBMBP)
demonstrated the feasibility of applying mass
balance principles to manage toxic chemicals in the
Great Lakes ecosystem. A two-year (1989-1990)
synoptic sampling program was designed to collect
appropriate and complete data for the mass balance
study. A suite of integrated mass balance and
bioaccumulation models were developed which,
together, provided an ecosystem-level simulation of
sources, transport, fate, and bioaccumulation of
PCBs throughout the Fox River and Green Bay.
These mass balance models were also based on the
general WASP4 model construct. This study
advanced the state-of-the-art of mass balance
modeling, particularly the ability to construct a fairly
complete and accurate description of contaminant
mass transport.
Several aspects of the Green Bay modeling effort
were noteworthy. Particle transport and sorption
processes were found to be of fundamental
importance as bases for contaminant modeling.
Resuspension of contaminated sediments in the Fox
River constituted the major source of PCBs to the
river as well as the bay. In the bay, particle sorbent
dynamics were strongly affected by phytoplankton
production and decay. The relative significance of
hydraulic transport, sediment transport, burial,
volatilization, and open-lake boundary exchange
processes upon the PCBs mass balance varied
considerably with location in Green Bay.
Radionuclide tracers were again essential for
calibration of particle fluxes and confirmation of long-
term contaminant transport predictions. The
significance of contaminant accumulation at the base
of the food web and fish movement in relation to
57
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exposure gradients were demonstrated in the
bioaccumulation model. The LMMBP demonstrated
the linked submodel approach to ecosystem model
development and application, and the feasibility of
using such a model for assessing the effectiveness
of toxics management control alternatives.
The GBMBP models were a precursor to our LM2-
Atrazine model. LM2-Atrazine served as our mid-
level spatial resolution atrazine model, and the
application is discussed in this report.
1.5.4 Resolution for the LMMBP Models
Model resolution is the spatial and temporal scale of
predictions, as well as the definitions of model state
variables. While factors such as data availability,
model sophistication, and computer resources
constrain resolution to a degree, different levels of
model resolution are possible and are, in fact,
necessary. Three "levels" of spatial resolution,
indicated by the segmentation grid of the lake
surface, are illustrated in Figure 1.5.1. Level 1 was
resolved at the scale of lake basins (characteristic
length, L = 150 km) with an associated seasonal
temporal resolution. This was a screening-level
model resolution used in MICHTOX. Level 2 was
resolved at a regional scale defined by food webs (L
= 40 km); temporal resolution was weekly-to-monthly.
This resolution was roughly comparable to that
achieved by models developed in the GBMBP. Level
3 was a hydrodynamic scale resolution (L = 5 km),
with associated daily temporal resolution. Both near-
shore and offshore regions can be distinguished with
the Level 3 resolution. Level 3 was scaled to resolve
to predict hydrodynamic transport.
LEVEL 1 - MICHTOX
(Screening)
6 surface segments
9 water segments
LEVEL2-LM-2
10 surface segments
41 water segments
LEVEL 3 - LM-3
(High resolution 5km X 5km grid)
2318 surface segments
44.042 water segments
19 sigma layers
Figure 1.5.1. Surface water segmentation for alternative Lake Michigan mass balance model levels.
58
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Although the LaMP and the Great Waters Program
(GWP) objectives are "lake-wide," both of these
emphasize biotic impairments occurring primarily in
localized, near-shore regions. LaMP objectives also
require that the transport of contaminants from
tributaries and other near-shore sources to the open-
lake be resolved. Therefore, the Level 1 model was
not adequate for the study objectives. Level 2
resolution was adequate for most modeling
objectives but not for resolution of significant
hydrodynamic impact or near-shore influence of
atrazine from tributaries. Level 3 resolution was
required for accurate hydrodynamic modeling and
was desirable for predicting near-shore gradients,
especially those formed by transients such as
thermal bars and upwelling; as well as more
persistent features such as tributary plumes and
thermal stratification. Level 3 transport resolution
also has the potential in relating toxics loading from
the 10 Areas of Concern (AOCs) adjoining Lake
Michigan which must be addressed by the Remedial
Action Plan (RAP) process to the LaMP via the
LMMBP.
The modeling design for the LMMBP was based on
the development of a number of models at three
levels of resolution. For the atrazine contaminant
transport and fate models, MICHTOX was resolved
at Level 1; LM2-Atrazine was resolved at Level 2;
and LM3-Atrazine was resolved at Level 3. The
Princeton Ocean Model (POM) and atmospheric
loading models were resolved at Level 3. Results of
the hydrodynamic model were spatially and
temporally averaged prior to coupling to the Level 2
model. The rationale for specifying different
resolutions was the hydrodynamic models require a
Level 3 resolution to offer the best capability for
transport simulation and forecasting. A lower
resolution was specified for LM2-Atrazine because
this model had been demonstrated at this resolution.
1.5.5 Models Developed and Applied
The transport and fate atrazine models developed,
refined, and applied by the Large Lakes and Rivers
Forecasting Research Branch (LLRFRB) included
MICHTOX, LM2-Atrazine, and LM3-Atrazine (Figure
1.5.2). Models developed and run elsewhere
included a hydrodynamics model (POM) (Schwab
and Beletsky, 1998), an atmospheric loading model
based on local observations (Green et a/., 2000;
Miller et a/., 2001), a tributary loading model (Hall
and Robertson, 1998), and the Community Multiscale
Air Quality (CMAQ) model. CMAQ was adapted to
simulate the regional atmospheric fate and transport
of atrazine (Cooter et a/., 2002; Cooter and Hutzell,
Hydrodynamic
and
load models
Contaminant
transport
and fate
models
transport
aggregated
to level 2
LM2-Atrazine
level 2
model
POM
hydrodynamic
model
advective/
dispersive
transport
and bottom
shear stress
LM3-Atrazine
level 3
model
environmental exposure
concentration
Figure 1.5.2. Model construct used for the LMMBP to model atrazine.
59
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2002). This atmospheric model utilized atrazine
emissions from agricultural soils provided by the
Pesticide Emissions Model (PEM) (Scholtz et a/.,
1999; and Scholtz et a/., 2002). The CMAQ
predictions of atrazine in air and rainfall compared
well with some field observations taken along the
Lake Michigan shoreline in 1995. Although the
results from the CMAQ were not used directly in the
any of the LMMBP atrazine models, the model
demonstrated a potential for this purpose in future
modeling efforts. Only the models developed,
refined, and applied at LLRFRB will be discussed in
detail within this document.
1.5.5.1 Lake Process Models
The mass balance models for atrazine in Lake
Michigan were comprised of linked hydrodynamic
(POM) with LM2-Atrazine and LM3-Atrazine. The
hydrodynamic model-predicted water movements
necessary to describe the three-dimensional
transport of dissolved constituents in the water
column, and these transport parameters were
incorporated into the water quality models. The
benefit of using hydrodynamic model output in this
way relieves the modeler from having to use a tracer
in the water, such as chloride, to calibrate advective
and dispersive transport functions. More discussion
can be found on this topic in Part 4 (LM2-Atrazine)
and Part 5 (LM3-Atrazine).
MICHTOX was not linked in any way with the POM
hydrodynamic model. In MICHTOX, circulation is
specified as advective and dispersive transport
functions. This approach suffers the disadvantages
in that calibration of the transport functions requires
extensive tracer data (chloride), circulation is not
predicted by meteorologic forcing functions, and the
model loses resolution because of the difficulty in
measuring/calibrating fine-scale transport variability.
In Green Bay, chloride data was used to calibrate the
transport functions. However, in the main lake, the
chloride gradients were not evident, and therefore,
were of no value for the purpose of calibrating the
transport functions. MICHTOX vertical and horizontal
exchange coefficients were obtained from previous
Great Lakes modeling studies. See Section 3.3.2 in
the MICHTOX chapter for more discussion on this
topic.
The models described the contaminant transport and
fate within the water column, mass transfer between
media (air and water), and atrazine degradation via
total kinetic decay processes. Together, these
models formed an integrated description of atrazine
chemical cycling in the aquatic ecosystem with which
to predict the relationship between loadings and
concentrations of atrazine in the lake.
1.5.5.2 Hydrodynamics (POM)
The Princeton Ocean Model (POM) (Blumberg and
Mellor, 1980, 1987) was used to compute three-
dimensional current fields in the lake. The POM
simulated large- and medium-scale (km) circulation
patterns, vertical stratification, velocity distribution,
seiche, and surface waves. This model was also
used to simulate a thermal balance for the lake. The
POM is a primitive equation, numerical hydrodynamic
circulation model that predicts three-dimensional
water column transport in response to wind stress,
temperature, barometric pressure, and Coriolis force.
The POM has been demonstrated to accurately
simulate the predominant physics of large water
bodies (Blumberg and Mellor, 1983,1985; Blumberg
and Goodrich, 1990). This model was used to
develop year-long simulations on a 5 km horizontal
grid with 19 sigma-coordinate vertical layers at one-
hour intervals for Lake Michigan (Schwab and
Beletsky, 1998). Observed and simulated
meteorological data were used to define model
forcing functions. Extensive measurements of
temperature and current distributions collected in
Lake Michigan during 1982-1983 were used to
provide the necessary data for model calibration;
measurements of water temperature and current
distributions were used to confirm hydrodynamic
simulations for 1994-1995.
1.5.6 Model Quality Assurance
A Quality Assurance Project Plan (QAPP) was
prepared and implemented for the atrazine modeling
(Richardson et a/., 2004). The QAPP specified
procedures for code development; testing;
modification; documentation; as well as methods and
measures applied in model calibration, confirmation,
and uncertainty analysis.
60
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1.5.7 Model Application and
Computational Aspects
1.5.7.1 Annual Simulations
Annual forecast simulations were run with the LM3-
Atrazine model. Model input reflected seasonal,
regional, and lake-wide contaminant loads. Model
output was analyzed within the high-resolution of
spatial and temporal gradients of contaminant
concentrations.
1.5.7.2 Long-Term Simulations
MICHTOX and LM2-Atrazine long-term simulations
were used to forecast the lake-wide impact of various
management scenarios. Forecasts were performed
to determine time to near steady-state conditions for
both continuing and discontinued loads. Forecasts
were also run to evaluate reductions in exposure
concentrations resulting from elimination of tributary
and/or atmospheric loading.
References
Ambrose, R.B., T.A. Wool, J.P. Connolly, and R.W.
Shanz. 1988. WASP4, A Hydrodynamic and
Water Quality Model - Model Theory, User's
Manual, and Programmer's Guide. U.S.
Environmental Protection Agency, Office of
Research and Development, Environmental
Research Laboratory, Athens, Georgia.
EPA/600/3-87-039, 297 pp.
Blumberg, A.F. and D.M. Goodrich. 1990. Modeling
of Wind-Induced Destratification in Chesapeake
Bay. Estuaries, 13(3): 1236-1249.
Blumberg, A.F. and G.L Mellor. 1980. A Coastal
Ocean Numerical Model. In: J. Sunderman and
K.P. Holtz (Eds.), Mathematical Modeling of
Estuarine Physics, pp. 203-214, Proceedings of
the International Symposium, Hamburg,
Germany, August 1978.
Blumberg, A.F. and G.L. Mellor. 1983. Diagnostic
and Prognostic Numerical Circulation Studies of
the South Atlantic Bight. J. Geophys. Res.,
88(C8):4579-4592.
Blumberg, A.F. and G.L. Mellor. 1985. A Simulation
of the Circulation in the Gulf of Mexico. Israel J.
Earth Sci., 34:122-144.
Blumberg, A.F. and G.L. Mellor. 1987. A Description
of a Three-Dimensional Coastal Ocean
Circulation Model. In: N.S. Heaps (Ed.), Three-
Dimensional Coastal Ocean Models, Coastal and
Estuarine Sciences, pp. 1-16. American
Geophysical Union, Washington, D.C.
Cooter, E.J. and W.T. Hutzell. 2002. A Regional
Atmospheric Fate and Transport Model for
Atrazine. 1. Development and Implementation.
Environ. Sci. Technol., 36(19):4091-4098.
Cooter, E.J., W.T. Hutzell, W.T. Foreman, and M.S.
Majewski. 2002. A Regional Atmospheric Fate
and Transport Model for Atrazine. 2. Evaluation.
Environ. Sci. Technol., 36(21):4593-4599.
Endicott, D.D., W.L. Richardson, and D.J. Kandt.
2005. 1992 MICHTOX: A Mass Balance and
Bioaccumulation Model for Toxic Chemicals in
Lake Michigan. In: R. Rossmann (Ed.),
MICHTOX: A Mass Balance and
Bioaccumulation Model for Toxic Chemicals in
Lake Michigan, Part 1. U.S. Environmental
Protection Agency, Office of Research and
Development, National Health and Environmental
Effects Research Laboratory, Mid-Continent
Ecology Division-Duluth, Large Lakes Research
Station, Grosse lie, Michigan. EPA/600/R-
05/158, 140 pp.
Green, M.L, J.V. DePinto, C.W. Sweet, and K.C.
Hornbuckle, 2000. Regional Spatial and
Temporal Interpolation of Atmospheric PCBs:
Interpretation of Lake Michigan Mass Balance
Data. Environ. Sci. Technol., 34(9):1833-1841.
Hall, D. and D. Robertson. 1998. Estimation of
Contaminant Loading from Monitored and
Unmonitored Tributaries to Lake Michigan forthe
USEPA Lake Michigan Mass Balance Study.
Quality Systems and Implementation Plan.
Submitted October 23,1998. U.S. Environmental
Protection Agency, Great Lakes National
Program Office, Chicago, Illinois. 19 pp.
61
-------
Miller, S.M., M.L. Green, J.V. DePinto, and K.C.
Hornbuckle. 2001. Results from the Lake
Michigan Mass Balance Study: Concentrations
and Fluxes of Atmospheric Polychlorinated
Biphenyls and frans-Nonachlor. Environ. Sci.
Technol., 35(2):278-285.
Richardson, W.L, D.D. Endicott, R.G. Kreis, Jr., and
K.R. Rygwelski(Eds.). 2004. The Lake Michigan
Mass Balance Project Quality Assurance Plan for
Mathematical Modeling. Prepared by the
Modeling Workgroup. U.S. Environmental
Protection Agency, Office of Research and
Development, National Health and Environmental
Effects Research Laboratory, Mid-Continent
Ecology Division-Duluth, Large Lakes Research
Station, Grosse lie, Michigan. EPA/600/R-
047018,233pp.
Rygwelski, K.R., W.L. Richardson, and D.D. Endicott.
1999. A Screening-Level Model Evaluation of
Atrazine in the Lake Michigan Basin. J. Great
Lakes Res., 25(1):94-106.
Scholtz, M.T., B.J. Van Heyst, and A. Ivanhoff.
1999. Documentation for the Gridded Hourly
Atrazine Emissions Data Set for the Lake
Michigan Mass Balance Study. U.S.
Environmental Protection Agency, Office of
Research and Development, National Exposure
Research Laboratory, Research Triangle Park,
North Carolina. EPA/600/R-99/067, 61 pp.
Scholtz, M.T., E. Voldner, A.C. McMillan, and B.J.
Van Heyst. 2002. A Pesticide Emission Model
(PEM) Part 1: Model Development. Atmos.
Environ., 36(32):5005-5013.
Schwab, D. and D. Beletsky. 1998. Lake Michigan
Mass Balance Study: Hydrodynamic Modeling
Project. National Oceanic and Atmospheric
Administration, Great Lake Environmental
Research Laboratory, Ann Arbor, Michigan.
NOAA Technical Memorandum ERL GLERL-108,
55 pp.
Sonzogni, W.C., W. Richardson, P. Rodgers, and
T.J. Monteith. 1983. Chloride Pollution of the
Great Lakes. Water Pollut. Contr. Fed. J.,
55(5) :513-521.
Thomann, R.V. and J.P. Connolly. 1984. An Age
Dependent Model of PCB in a Lake Michigan
Food Chain. U.S. Environmental Protection
Agency, Office of Research and Development,
Environmental Research Laboratory-Duluth,
Large Lakes Research Station, Grosse lie,
Michigan. EPA/600/S3-84/026, 3 pp.
U.S. General Accounting Office. 1993. Reporttothe
Chairman, Subcommittee on Oversight of
Government Management, Committee on
Governmental Affairs, U.S. Senate: Pesticides-
Issues Concerning Pesticides Used in the Great
Lakes Watershed. U.S. General Accounting
Office, Washington, D.C. GAO/RCED-93-128,
39 pp.
62
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PART 2
LAKE MICHIGAN MASS BALANCE PROJECT ATRAZINE
LOADINGS TO LAKE MICHIGAN
Kenneth R. Rygwelski
United States Environmental Protection Agency
Office of Research and Development
National Health and Environmental Effects Research Laboratory
Mid-Continent Ecology Division
Large Lakes and Rivers Forecasting Research Branch
Large Lakes Research Station
9311 Groh Road
Grosse lie, Michigan 48138
Chapter 1. Historical Atrazine Usage in
the United States
2.1.1 Background
The company, J.R. GeigyA.G., of Basel Switzerland
applied for a patent with the United States Patent
Office on January 12, 1955 that described a method
for making atrazine and listed various mixtures with
the active ingredient that could be used to kill weeds
(U.S. Patent Office, 1959). Atrazine was registered
with the United States Department of Agriculture
(USDA) in 1958 (U.S. Environmental Protection
Agency, 2003). On June 23, 1959, the atrazine
patent (Number 2891855) was issued. By the mid-
1960s, widespread use of atrazine was observed
(Duke, Ciba Geigy Patent Office, personal
communication, 1994; Kells, Michigan State
University, personal communication, 1994).
In the Lake Michigan basin, atrazine is primarily used
on corn crops to control broadleaf and some grassy
weeds by inhibiting photosynthesis. For corn crops,
it is usually applied to the fields in the spring, prior to,
during, or after planting a crop or after emergence of
the corn seedlings. Atrazine is usually mixed in a
water solution along with other herbicides. Estimates
by Nako and Keitt (1994) indicate that atrazine is
relatively inexpensive compared to other herbicides.
Cost for chemically treating one acre in 1992 was
about three dollars (estimate does not include costs
for fuel and labor). During 1994-1995, atrazine use
as a percentage of total pesticide use in the basin
was 13.8% (Brody et a/., 1998). During the same
time frame, corn represented 38.8% of planted
acreage. For the period 1964 through 1993, atrazine
was the leading herbicide used in the United States
(U.S. Department of Agriculture, 1994; Lin et a/.,
1995). Atrazine trade names/synonyms include:
Aatrex, Actinite PK, Akticon, Argezin, Atazinax,
Atranex, Atrataf, Atred, Candex, Cekuzina-T,
Chromozin, Crisatrina, Cyazin, Fenamin, Fenatrol,
Gesaprim, Griffex, Hungazin, Inakor, Pitezin,
Primatol, Radazin, Strazine, Vectal, Weedex A,
Wonuk, and Zeapos (U.S. Environmental Protection
Agency, 2006).
63
-------
Application rates of atrazine to farm fields have
decreased over time. In 1990, a label change
initiated by the manufacturers of atrazine set the
maximum recommended application rate for atrazine
to three pounds active ingredient per acre. Prior to
this, four pounds active ingredient per acre was
recommended (Scribner et al., 2000). In 1992, the
manufacturers again voluntarily reduced the
maximum recommended application rate of atrazine
on corn and sorghum to a range of 1.6-2.5 pounds
active ingredient per acre depending on soil organic
residue and erosion potential. The 1992 label
changes also included atrazine mixing/loading and
application setbacks to protect various water sources
including wells, streams, lakes, and reservoirs
(Pearson and Giles, 1993). The lower 1992
application rate was recommended for fields with less
than 30% plant residues on the surface. The label
changes reduced recommended application rates by
nearly 50%, however, actual application rates used
by farmers decreased by only about 11% from an
average 1.1 pounds per acre in 1991 to 0.97-0.98
pounds per acre in 1994-1995. (U.S. Department of
Agriculture, 2006). Evidently, farmers were satisfied
with the results from lower than recommended
application rates set by the manufacturers of the
herbicide. The reduced application rates in 1994-
1995 and 1998 compared to 1989-1990 were
reflected in reduced concentrations observed in
several Midwestern streams during post-application
run-off (Scribner et a/., 2000).
2.1.2 Total Annual Usage Estimates
Usage of atrazine is predominant in the eastern half
of the United States (see Figure 2.1.1). As depicted
in the figure, usage is heavy south of the Lake
Michigan basin in the states of Illinois and Indiana.
However, except for the northwestern part of Indiana,
most of the drainage and associated load from these
two states discharge into the Mississippi River
watershed. But, the proximity of these high-use
areas to Lake Michigan does impact the atmospheric
loading of atrazine to the southern area of the lake.
In Table 2.1.1, some statistics are presented on the
usage of atrazine on corn crops in the United States
for crop years 1991, 1994, and 1995 (U.S.
Department of Agriculture, 2006). For all three years,
atrazine was the most used herbicide on corn crops.
In the survey year 1994, the ranking of the top 10
states in order of highest corn acreage to lowest was
Iowa, Illinois, Nebraska, Minnesota, Indiana, South
Dakota, Wisconsin, Ohio, Michigan, and Montana.
Figure 2.1.2 depicts county usage of atrazine during
the Lake Michigan Mass Balance Project (LMMBP).
Note the highest use region is in the southwestern
part of Michigan and northern Indiana. Little atrazine
is used in the northern parts of the basin. The data
for 1994 were provided by Kirschner (International
Joint Commission, personal communication, 1997)
and the data from 1995 were provided by Macarus
(U.S. Environmental Protection Agency, personal
communication, 1999).
Historical total annual atrazine usage estimates in the
United States are depicted in Figure 2.1.3 for years
where data were available. The data used in the
graphic are presented in Table 2.1.2. The atrazine
data (zero usage) for 1963 (Duke, Ciba Geigy Patent
Office, personal communication, 1994) matches
estimates made by Scribner et al. (2000). Robert
Torla's (United States Environmental Protection
Agency (USEPA), personal communication, 1994)
data (1964, 1966, and 1971) are from
USDA/Economic Research Service (ERS) published
estimates (U.S. Department of Agriculture, 2003),
and the rest of the data are from Aspelin and Nako
(USEPA, personal communication, 1997). The data
represent total annual usage (both agricultural and
non-agricultural). However in the 1990s, it was
estimated that approximately 95% was used for
agricultural purposes. For some of the years (such
as 1993 and 1995), a range of values was reported.
When this occurred, a mean of the range was used.
Also plotted on Figure 2.1.3 are historical (1964-
2002) total United States acreage for corn and
another for the sum acreage of corn, sorghum, and
sugarcane - all crops that use atrazine to suppress
weeds. Notice that the pattern of atrazine use,
except for the earliest years, follows the pattern of
corn acreage planted in the United States. The low
corn acreage in 1983 and 1988 were due to drought
conditions (Shapouri et al., 1995).
2.1.3 Future Atrazine Use Estimates
Atrazine currently holds its large market share
because it is a pre-emergent herbicide active against
most of the serious broadleaf weeds in corn, and it is
64
-------
Kilograms per
Square Kilometer
I Missing or 0
Less than 0.5
0.5 to 2,5
2.6 to 10.0
10 1 to 25.0
more than 25.0
0 300 Miles
I I II
III II I
0 500 Kilometers
Graphic by William Battaglin
U.S. Geological Survey
Figure 2.1.1. Atrazine usage in the United States for 1991.
Table 2.1.1. U.S. Department of Agriculture Corn Crop Summaries of Atrazine Usage in the United
States for 1991, 1994, and 1995
Number of
Year States
Surveyed
1991
1994
1995
17
10
15
% of Total
Corn Crop
Surveyed
90
79
90
% of Corn
Crop Treated
With
Herbicides
94
98
96
% of Corn
Crop Treated
With
Atrazine
66
68
65
Average
Application
Rate of
Atrazine
(Ibs/acre)
1.1
0.97
0.98
Total Amount
of Atrazine
Applied
(millions of
kg)
23.61
20.59
20.74
65
-------
0-12000
•112001-24000
•124001-36000
tm 36001-48000
• 48001-60000
Kilograms of Atrazine Applied
Figure 2.1.2. Estimates of atrazine usage in the Lake Michigan basin for 1994 and 1995.
— corn + sorghum + sugarcane
1 ... rnrn
£45-
55
o>40
o
^35-
o
« 30-
o
|2S-
0
01
s15-
•510
3
i 5
s
< 0
-A^VJ
---*'*'_."'
•* *
^
t
•x
1 total annual atrazine usage
—
-•
r
i *
> *
V
\
y
*-''
*—***
:
A
r
—\
*^
f
r120
'
-100
"en"
•80 .0
' I
-60 ~
w
2
•40 §
•
•20
-0
_ 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000
Year
Figure 2.1.3. Historical trend of total annual usage of atrazine in the United States with acreage
planted in corn, sorghum, and sugarcane.
66
-------
Table 2.1.2. Total Annual Usage of Atrazine in the United States (Aspelin and Nako, U.S. Environmental
Protection Agency, Personal Communication, 1997; Torla, U.S. Environmental Protection Agency,
Personal Communication, 1994)
Year
Millions of kg Atrazine Used in the United States
1963
1964
1966
1971
1974
1976
1978
1980
1982
1984
1986
1989
1991
1993
1995
0.0
6.3
12.0
25.8
31.8
36.3
39.9
38.6
35.4
39.9
35.8
34.9
34.0
34.7
33.8
inexpensive (Nako and Keitt, 1994). Any
replacements must be equally as effective in
controlling weeds and matching or beating costs.
Due to repeated annual usage, some weeds, such as
pigweed, are showing resistance to triazine
herbicides. Blending other herbicides with atrazine
may help to eliminate some of these resistant plants.
If the resistant plants do not have an efficient seed
dispersal mechanism, then these problem plants
become a local problem. However, if the resistant
plant shows resistance to other herbicides and has
an effective seed dispersal mechanism, then usage
of atrazine may decline. Another factor to consider
in projecting future usage is possible regulatory
action that could restrict usage in some manner.
With growing ethanol demand and strong export
sales of corn, U.S. farmers planted 92.9 million acres
of corn in 2007. This exceeded the 2006 acreage by
19 percent (U.S. Department of Agriculture, 2007).
The actual planted acreage is the highest since 1944.
References
Brody, T.M., B.A. Furio, and DP. Macarus. 1998.
Agricultural Pesticide Use in the Great Lakes
Basin: Estimates of Major Active Ingredients
Applied During 1994-1995 for the Lake Erie,
Michigan, and Superior Basins. U.S.
Environmental Protection Agency, Region 5,
Chicago, Illinois. 15 pp.
Lin, B., M. Padgitt, L Bull, H. Delvo, D. Shank, and
T. Harold. 1995. Pesticide and Fertilizer Use
and Trends in the U.S. Agriculture. U.S.
Department of Agriculture, Economic Research
Service, Washington, D.C. Document Number
717, 56pp.
Nako, S. and G. Keitt. 1994. Use of Triazines and
Other Herbicides for Broadleaf Control on Corn.
U.S. Environmental Protection Agency, Office of
Pesticide Programs, Biological and Economic
Analysis Division, Washington, D.C. 7 pp.
67
-------
Pearson, D. and E. Giles. 1993. Atrazine Label
Changes. Resource Update One, Illinois Food
and Agriculture Council, Urbana, Illinois.
Scribner, E.A., W.A. Battaglin, D.A. Goolsby, and
E.M. Thurman. 2000. Changes in Herbicide
Concentrations in Midwestern Streams in
Relation to Changes in Use, 1989-1998. Sci.
Total Environ., 248(2/3):255-263.
Shapouri, H., J.A. Duffield, and M.S. Graboski.
1995. Estimating the Net Energy Balance of
Corn Ethanol. U.S. Department of Agriculture,
Economic Research Service, Office of Energy
and New Uses, Washington, D.C. Agricultural
Economic Report Number 721, 24 pp.
U.S. Department of Agriculture. 1994. Agricultural
Resources and Environmental Indicators. U.S.
Department of Agriculture, Economic Research
Service, National Resources and Environment
Division, Washington, D.C. 216 pp.
U.S. Department of Agriculture. 2003. Historical
Track Records - National Agricultural Statistics
Service. Available from U.S. Department of
Agriculture at http://usda.mannlib. Cornell.
edu/usda/nass/96120/trackrec2003.txt
U.S. Department of Agriculture. 2006. Agricultural
Chemical Usage-1991, 1994, 1995 Field Crops
Summary. National Agricultural Statistics
Service, Washington, D.C. Available from U.S.
Department of Agriculture at http://usda/
mannlib.cornell.edu/data-sets/inputs/9x171.
U.S. Department of Agriculture. 2007. National
Agricultural Statistics Service. U.S. Department
of Agriculture, Washington, D.C. Available from
U.S. Department of Agriculture at http://www.
nass.usda.gov.
U.S. Environmental Protection Agency. 2003.
Pesticides: Topical and Chemical Fact Sheets -
Atrazine Background. U.S. Environmental
Protection Agency, Office of Pesticide Programs,
Washington, D.C. Available from U.S.
Environmental Protection Agency at http://www.
epa.gov/pesticides/factsheets/atrazine_
background.
U.S. Environmental Protection Agency. 2006.
Consumer Factsheet on: Atrazine. U.S.
Environmental Protection Agency, Ground Water
and Drinking Water, Washington, D.C. Available
from U.S. Environmental Protection Agency at
http:/www.epa.gov/safewater/dwh/csoc/atrazine.
U.S. Patent Office. 1959. Compositions and
Methods for Influencing the Growth of Plants.
Assignors: Hans Gysin and Enrico Knusli, J.R.
Geigy A.G., Basel, Switzerland. Patent Number:
2891855; Serial Number 481474.
68
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PART 2
LAKE MICHIGAN MASS BALANCE PROJECT ATRAZINE
LOADINGS TO LAKE MICHIGAN
Chapter 2. Estimation
Tributary Loadings
of Atrazine
Tributary loadings for the Lake Michigan Mass
Balance Project (LMMBP) atrazine models were
estimated using an approach based on watershed
export of the applied chemical from farm fields to the
lake and another approach that utilized LMMBP
measurements of atrazine concentration and flow in
the 11 monitored streams within the construct of the
Stratified Beale Ratio Estimator (SBRE) to calculate
loadings. Watersheds that drained into a monitored
tributary were identified as a monitored watershed.
The other watersheds in the Lake Michigan basin
were identified as unmonitored. Both methods made
estimates of watershed loadings of atrazine to Lake
Michigan for both the monitored and unmonitored
watersheds. The MICHTOX and LM2-Atrazine
models solely utilized estimates based on watershed
export. The LM3-Atrazine model utilized load
estimates based on the SBRE and on a hybrid of the
two load estimates, whereby the SBRE loads were
enhanced with additional loadings based on the
annual watershed export estimates (see Part 5,
Chapter 3, Section 5.3.3.3.1). Both the MICHTOX
and LM2-Atrazine models utilized annualized
loadings only and are useful for long-term
simulations. LM3-Atrazine loadings were calculated
on a daily basis so as to capture seasonal variations
on a finer time and spatial resolution that were not
available in either of the other two models.
Watershed loading estimates were made for all of the
Lake Michigan sub-basins. A sub-basin may have an
identifiable tributary that discharges this loading into
the lake, or it may not. However they were
calculated, both were collectively referred to as
tributary loadings.
2.2.1 Atrazine Tributary Load Estimates
Utilizing County-Level Atrazine
Application Data
Literature values for estimates of the percentage of
the amount of atrazine applied in a watershed that is
delivered to a receiving body of water were used in
the loading estimates. This percentage is identified
as the Watershed Export Percentage (WEP), but it is
also referred to in the literature as Load as a
Percentage of Use (LAPU). The calculation of
atrazine tributary loads (mass/time) to a MICHTOX or
LM2-Atrazine segment for a given year when
application rates and corn acreage are known were
calculated as follows:
WMershetl Export Load or Tributary Load =
where
j = a county within a Hydrological Unit Code
(HUC) draining into a given water segment
n = total number of counties in a HUC
k = the load from a given HUC in a sub-basin
delivered to a model segment
69
-------
m = total number of HUC loads in a sub-basin
delivered to a model segment
A = atrazine application rate (mass/acre/time) for
corn
T = fraction of corn acreage treated with atrazine
C = corn acreage in a given county
F = fraction of county within the HUC
L = (Watershed Export Percentage)/! 00
2.2.1.1 County-Level Atrazine Application Data
In this project, we received atrazine data in the form
of county-level application estimates for a given year
or the product of variables A, T, and C in Equation
2.2.1. Sources of these data are identified in Table
2.2.1. As is evident from the table, data were only
available for six years. Additional data beyond 2002
are likely but were not included in this analysis. The
atrazine data were reported as an active ingredient,
so no conversion was required before model loadings
were estimated.
The area fraction of a given county that lies within a
HUC in the Lake Michigan basin was determined by
Geographical Information System (GlS)-defined HUC
boundaries and county boundaries. Within the basin,
there are a number of HUCs that collectively form
sub-basins. Some of these sub-basins defined
watersheds of the 11 LMMBP major tributaries in the
Lake Michigan basin. Other sub-basins were not
readily identifiable with tributaries; however, load
estimates, identified as unmonitored tributary loads,
were made for these sub-basins and the discharge
into the lake was associated with a model segment.
The GIS was used to calculate what fraction of a
county fell into a given HUC. Note that more than
one county may fall within a given HUC.
2.2.1.2 The Watershed Export Percentage
The atrazine WEP (variable L in Equation 2.2.1) is
known to be a function of soil type, population of
atrazine-degrading bacteria in the soil, field
topography, timing and amount of rainfall after
application, and other explanatory variables.
Seventy-six reservoir drainage basins in the
Midwestern United States were studied using multiple
linear regression and logistic regressions to
Table 2.2.1. Sources of County-Level Atrazine Application Data for the Lake Michigan Basin
Application
Year
Data Source
Affiliation
Date Received
1989
1992
1993
1994
1995
1998
W.A. Battaglin and D.A.
Goolsby (see reference)
U.S. Geological Survey
B. Kirschner and R. Baksh International Joint Commission
(personal communication) (UC), Windsor, Ontario, Canada
R. Baksh (personal
communication)
B. Kirschner (personal
communication)
D. Macarus (personal
communication)
D. Macarus (personal
communication)
UC
UC
USEPA/Region V, Chicago,
Illinois
USEPA/Region V
1995 (Publication Date)
1994
1995
1997
1999
2000
70
-------
determine the significance of the explanatory
variables in predicting concentrations of atrazine in
the reservoirs (Battaglin and Goolsby, 1996). Both of
the statistical tests used in the analysis found soil
hydrologic group values to be a significant
explanatory variable. This same conclusion was
drawn from studies performed by Blanchard and
Lerch (2000) in northern Missouri. Small hydrologic
group values (1.75) are associated with well-drained
soil (sand and gravel), whereas larger values (>3.25)
are associated with poorly-drained soil (clays,
wetlands, urban). Soil textures in Michigan,
Wisconsin, Illinois, Indiana, and Ohio are shown in
Figure 2.2.1. These data were obtained from the
State Soil Geographic (STATSGO) database
provided by the United States Department of
Agriculture (USDA), Soil Conservation Service
(SCS). A review of 1992 and 1993 atrazine field
application data revealed that approximately 80% of
the total atrazine application in the Lake Michigan
basin is applied to crops in the sub-basins that drain
into the southeastern part of the lake. These sub-
basins include the southwestern quarter of the lower
peninsula of Michigan including a small portion of
northern Indiana that also resides in the Lake
Michigan basin. Soils in that part of the basin can be
identified as moderate to fine textures and have
hydrologic group values ranging from 2.51 to 3.25.
At the start of a rain event, the rate of rainfall may
equal the rate of infiltration into the soil. However,
after some time, the infiltration rate will start to
decrease asymptotically and reach some constant,
but lower, infiltration rate. Run-off begins at the point
the rainfall rate exceeds the infiltration rate.
A literature review of atrazine watershed export
percentages is summarized in Table 2.2.2. The raw
data used in this summary are presented in Table
2.2.3. Watershed export percentages for various
watersheds are grouped by soil type. The data are
from northern watershed systems (Ontario, Canada;
northern Ohio; northern Iowa; and southern
Minnesota). In addition, the data reflect watershed
export percentages that were calculated for an entire
year. Many published studies of WEP fall short of a
full year of monitoring and this causes the estimate to
be biased low for the annual estimate. Based on this
literature review, a watershed export percentage of
0.6% was selected for the Lake Michigan and Green
Bay watersheds to represent the predominant
moderate texture soil hydrologic group in this area.
Note that WEPs for clay soils (1.4) are much higher
than for sandy soils (0.2). WEP differences between
clay and sandy soils will yield large differences on
loading estimates as Equation 2.2.1 indicates. In a
rain event, run-off will occur sooner on non-saturated
clay soils than non-saturated sandy soils, because
sandy soils have a higher infiltration rate. So, it is
important to carefully assess this parameter when
estimating watershed export of atrazine. Climatic
conditions for the 12 annual studies used in deriving
this watershed export percentage for loam/fine-
textured soils included a balance of five wet and five
dry years. For the other two years, one was
considered average in precipitation and conditions for
the other year were not reported. Including both wet
and dry years should help minimize bias in the
estimate, since atrazine-associated run-off in drought
years has been observed to be lower compared to
wet years (Richards et a/. 1996). A plot of WEPs
versus watershed size indicated that there was no
relationship, and this was also the conclusion by
Capel and Larsen (2001). They evaluated data from
408 observations of WEPs across numerous types of
soil textures. Their median WEP was calculated to
be 0.66% for watersheds less than 100,000 ha.
Although not calculated, a more rigorous derivation
of the average watershed export percentage could be
achieved if a detailed accounting of soil types was
performed for corn croplands within the basin. With
that detailed soil type information, a weighted-
average WEP could be calculated for each sub-
basin.
2.2.1.3 Calculating the Atrazine Tributary Load
County-level application data for a given year were
multiplied by the fractional area of the county in a
HUC (Equation 2.2.1). This load was further divided
if a monitored river basin occupied a portion of that
county. In that case, the atrazine load was further
divided and apportioned by area to a monitored river
load and the rest to unmonitored tributary load. This
procedure was repeated for all counties that had
overlap in any given HUC within the Lake Michigan
basin. These fractional application loads (monitored
and unmonitored) were summed separately for each
HUC. The point of discharge of the monitored
tributary into the lake was associated with a model
segment and likewise for unmonitored tributary loads.
Only those whole counties or fractional counties that
71
-------
300 kilometers
soil types
| sands and gravels
^^ moderate textures
^| fine textures
^| clays, impervious
(from STATSGO database)
Figure 2.2.1. Soil textures typical for the Lake Michigan basin and part of the Lake Erie basin.
Table 2.2.2. Atrazine Watershed Export Data Summarized From the Literature. Raw Data Used to
Create This Table Can be Found in Table 2.2.3
Soil Type
Clay
Loam/Fine Textured
Watershed Export Standard 95% Confidence
Percentage Deviation Level
1.4
0.61
0.61
0.38
0.94-1.85
0.37-0.85
Range
0.11-2.5
0.21-1.5
Number of
Studies
9
12
72
-------
Table 2.2.3. Atrazine Watershed Export Data From Various Northern Sites
73
Water-
shed
Size (sq
km)
23.83
30.25
30.25
50.8
50.8
62
62
6790
16395
18.6
18.6
30
30
54.72
54.72
45.04
45.04
3998
17820
38585
38585
19.9
19.9
56.45
79.13
79.13
Location
Ontario
Ontario
Ontario
Ontario
Ontario
Ontario
Ontario
Ontario
Ohio
Ontario
Ontario
Ontario
Ontario
Ontario
Ontario
Ontario
Ontario
Ontario
N. Iowa
S.
Minnesota
S.
Minnesota
Ontario
Ontario
Ontario
Ontario
Ontario
Watershed
Soil Type Export Year(s)
Percentage
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay/Loam/Sand
Clay
Loam
Loam
Loam
Loam
Loam
Loam
Loam
Loam
Sandy/Loam
Fine Textured
Fine Textured
Fine Textured
Sandy
Sandy
Sandy
Sandy
Sandy
0.11
1.47
1.35
2.51
1.40
1.28
1.31
1.49
1.50
0.32
0.54
0.21
0.26
0.50
0.35
1.09
0.80
0.65
1.50
0.33
0.62
0.15
0.20
0.17
0.29
0.18
1976
1975
1976
1975
1976
1975
1976
1981,82,83,84,85
NA
1975
1976
1975
1976
1975
1976
1975
1976
1981,82,83,84,85
84
90
90
1975
1976
1975
1975
1976
Watershed
Number R.
Twenty Mile Cr
Twenty Mile Cr
Thames R.
Thames R.
Au Sable R.
Au Sable R.
Grand R.
Maumee R.
Grand R.
Grand R.
Thames R.
Thames R.
Maitland R.
Maitland R.
Saugeen R.
Saugeen R.
Saugeen R.
Cedar R. Basin
Minn. R. Basin
Minn. R. Basin
Hillman Cr
Hillman Cr
Shelter Val. Cr
Big Creek
Big Creek
Annual
Precipita-
tion
Dry
Wet
Dry
Wet
Dry
Wet
Dry
Average
Average
Wet
Dry
Wet
Dry
Wet
Dry
Wet
Dry
Average
NA
Dry
Wet
Wet
Dry
Wet
Wet
Dry
Adjusted
% Loss to
Reference Represent
Atrazine
Only
Franks Sirons, 1979
Franks Sirons, 1979
Franks Sirons, 1979
Franks Sirons, 1979
Franks Sirons, 1979
Franks Sirons, 1979
Franks Sirons, 1979
Franks Logan, 1988
Richards etal., 1996
Franks Sirons, 1979
Franks Sirons, 1979
Franks Sirons, 1979
Franks Sirons, 1979
Franks Sirons, 1979
Franks Sirons, 1979
Franks Sirons, 1979
Franks Sirons, 1979
Franks Logan, 1988
SquillaceSThuman, 92
Schottleref a/., 1994
Schottlerefa/., 1994
Franks Sirons, 1979
Franks Sirons, 1979
Franks Sirons, 1979
Franks Sirons, 1979
Franks Sirons, 1979
Y
Y
Y
Y
Y
Y
Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
N
N
N
Y
Y
Y
Y
Y
-------
lie within the Lake Michigan basin boundaries were
considered for tributary load estimation. When taking
fractional areas of a county, we assumed that
atrazine usage within the county was uniform.
Tributary loading estimates were made for each of
the years in Table 2.2.1 using this method.
2.2.2 Estimating Atrazine Tributary Loads
for Years When County-Level Atrazine
Application Data Was Not Available
For the six years where county-level application data
were available, tributary loads were estimated using
the approach identified in the previous section, 2.2.1;
however, to make estimates for additional years, an
approach was selected that utilized some of the
results from Section 2.2.1 and estimates of total
annual atrazine usage in the United States. The
approach was to calculate a Tributary Load Ratio
(TLR) of known application rates for a given year and
divide this number by the total annual United States
usage amount of atrazine for that same year. For
years when application data are missing but total
annual usage is known, the ratio multiplied by the
total annual usage yields an estimate of tributary
load. Seventeen years of total annual United States
usage data are displayed in Figure 2.2.2. This
approach was used for both MICHTOX and LM2-
Atrazine model runs.
Tributary Load Ratio = {Tributary Load to Model
Segment) k Total Annual USA Atrazine Usage)
(2.2.2)
For any year (y), where only total annual United
States usage is known, a tributary load was
calculated utilizing a tributary load ratio:
Tributary Load = [Tributary Load Ratio) x
(Total Annual USA Usage for Any Year jy)j
(2.2.3)
Due to label changes that lowered application
amounts and established planting setbacks from
water bodies in 1990 and 1992, a decision was made
to use two TLRs in order to address atrazine
application practices for pre- and post-label changes.
For the pre-label change period, tributary load
estimates for years 1964 through 1986 used the TLR
calculated for 1989. We used 1989 because this was
the only year during that pre-label change period
where we had both county-level application data and
total annual United States usage estimates. An
average atrazine application rate of 1.54 pounds/acre
on corn from a 1982 survey of 16 states with more
than one million acres of corn compares well to an
average application rate for Michigan and Wisconsin
of 1.5 pounds/acre for the same year (Gianessi and
Puffer, 1988). So for at least that year, the atrazine
usage rate per acre in the Lake Michigan basin
matches usage rates in the rest of the major United
States corn-growing regions. For comparison
purposes, atrazine tributary load estimates to
MICHTOX segment 1 were made for the year 1984
using the TLR method based on total annual United
States usage estimates for 1984 and also by using
available atrazine use data (Gianessi and Puffer,
1988) that included application rate data by state,
total corn crop acreage by state, and fraction of corn
crop that was treated with atrazine. The TLR method
yielded a total tributary load estimate of 15.4 kg/day
of atrazine to segment one. The tributary load
estimate based on Equation 2.2.1 and data from
Gianessi and Puffer yielded a result of 17.7 kg/day.
For this latter estimate, data on the percent of corn
acreage treated with atrazine was from 1984
(Michigan), 1982 (Illinois), and early 1980s
(Wisconsin and Indiana). Also, the application rate
data are from 1984 for Michigan, Indiana, and
Wisconsin and from 1982 for Illinois. Data on the
percent of corn acreage treated with atrazine for
Wisconsin and Indiana were based on expert opinion
of the U.S. Department of Agriculture/Economics
Research Service, rather than survey data. Total
corn acreage in each county within the sub-basin
draining into segment 1 was based on actual survey
data for 1984 (Kevin Pautler, U.S. Department of
Agriculture, personal communication, 1997). Given
the uncertainties of both methods, the two numbers
are reasonably close.
For the post-label change period, an average of the
tributary ratios for 1992 and 1993 was used to
calculate tributary loadings for 1991. For the rest of
the post-label change years 1992,1993,1994,1995,
and 1998, loads were calculated based on county-
level application data using Equation 2.2.1. In
74
-------
C/5
2 45-1
O)
i 40"
o 35-
CO
o 30-
I 25-
c
o 20-
O)
co 15-
3
TO 10-
3
c
S 5-
5 0-
i
r
r
]
n total annual usage
• historical tributary loa
r
d
r
r
!
i
1
r
1
•1-9000
-8000
-7000 J
O)
-6000 ^
-5000 "i
o
-4000 j*
ro
-3000 -5
JD
-2000 B
-1000
--0
1964 1968 1972 1976 1980 1984 1988 1992 1996 2000
year
Figure 2.2.2. WEP-based total atrazine tributary loading estimates to Lake Michigan.
comparing tributary loading ratios for pre- and post-
label change years 1989 and 1995, the TLR for 1995
(MICHTOX segment 1), which carries most of the
atrazine tributary loading to Lake Michigan, was 26%
lower than what it was in 1989. A similar trend was
noted for the other segments.
Yet, total annual United States usage only dropped
three percent from 1989 through 1995, and total corn
crop acreage fell just 1.16 percent (Good and Irwin,
2007). This indicates that usage in the Lake
Michigan basin dropped more relative to the rest of
the United States during that period. The Lake
Michigan basin has a number of rivers and lakes.
Perhaps the label changes requiring setbacks from
these water bodies reduced the corn acreage and
hence usage dropped. The state of Wisconsin
mandated atrazine usage changes that went beyond
those related to the 1990 and 1992 label changes
and includes establishment of statewide usage rates,
application timing, record keeping (Wisconsin
Register, 2004), and prohibited use areas (Wisconsin
Register, 2005). Efforts to reduce atrazine usage in
the state have been ongoing since 1991. Other
possible reasons for the use reduction in the Lake
Michigan basin include substitution of different
herbicides, cropland taken out of corn production,
changes in farming practices, increase of corn
acreage outside the Lake Michigan basin that
replaced lost acreage within the basin, etc.
Regardless of the reason for the pre- and post-label
changes reflected in the TLRs, use of two ratios
seems to be warranted.
2.2.3 Atrazine Tributary
MICHTOX and LM2-Atrazine
Loads for
Based on methods described above, total tributary
loadings to Lake Michigan are depicted in Figures
2.2.3 and 2.2.4. Since the mid-1980s, atrazine
tributary loadings have been declining in the Lake
Michigan basin; however, total annual usage in the
United States has not changed much since
approximately 1986. Tributary loadings for
MICHTOX segments are shown in Figure 2.2.3. Note
that the watershed delivering atrazine to model
segment 1 delivers the highest load to the lake. See
Figure 3.1 in Part 3 for a graphic showing MICHTOX
segments. This watershed drains the southwestern
part of the state of Michigan and a section of
75
-------
40UU'
Annn .
3500 •
3000
900
700
600
400
300
200
100
_
• 1994 loads
D1995 loads
•n
04 2
^^_^
— -I J] __,
34567
MICHTOX Segment
Figure 2.2.3. Tributary loadings to Lake Michigan
MICHTOX model segments.
monitored
tributary
loads: 4305
Figure 2.2.4. WEP-based Lake Michigan tributary
loadings, 1994.
Northeastern Indiana. For the whole-lake, the total
annual tributary load estimate is the same for both
models. However, LM2-Atrazine had 10 receiving
surface water segments and MICHTOX had seven
(six primary segments and a small segment
representing the lower Fox River). Each surface-
water segment sharing a boundary with a sub-basin
received that sub-basin load.
For years where atrazine application data or total
annual USA usage are unknown, both MICHTOX and
LM2-Atrazine calculate loads by assuming that the
loads between the two years bracketing the missed
annual loads are linear.
2.2.4 Atrazine Tributary Load Estimates
for LM3-Atrazine
The LM3-Atrazine model was not used to conduct a
hindcast simulation. This model used United States
Geological Survey (USGS) loading estimates that
were based on actual measurements of river flow
and atrazine concentration. Because these load
estimates were low compared to the WEP-based
load estimates discussed in the previous section, the
USGS loadings were adjusted upward in the
spring/early summer period so that the total annual
load was equal to the WEP-based annual loading.
See Section 5.3.3.3.1 in the LM3-Atrazine modeling
chapter for more information on LM3 tributary
loadings.
2.2.4.1 Tributary Sampling Program
As part of the LMMBP, the USGS calculated loads
for 11 monitored tributaries in the Lake Michigan
basin (Hall et a/., 1998). Based on these load
calculations and land use information, estimates of
loadings from unmonitored areas were made.
Loadings were calculated for atrazine,
deethylatrazine (DEA), and deisopropylatrazine
(DIA). Tributary data used in the load estimates were
gathered from samples collected from April 4, 1995
through October 30, 1995 (U.S. Geological Survey
and Eisenreich, 1997). Samples were collected far
enough upstream to minimize mixing of lake and
tributary water. The Grand Calumet, Kalamazoo,
and Pere Marquette Rivers were generally well-mixed
throughout the sampling period. The Sheboygan,
Menominee, Manistique, Muskegon, Grand, and St.
76
-------
Joseph Rivers were generally well-mixed during the
winter months and stratified with respect to
temperature and conductance in summer months.
The Milwaukee River, and to a lesser extent the Fox
River, were found to be poorly mixed at irregular
intervals throughout the sampling period. The
location and identification of the USGS stations
sampled can be found in Hall et a/. (1998). Sampling
was conducted by the USGS in cooperation with the
Wisconsin and Michigan Departments of Natural
Resources, the Wisconsin State Laboratory of
Hygiene, and the University of Wisconsin Water
Chemistry Program. The primary objective of the
contaminant-loading data was to provide a detailed
space and time tributary loading history for input into
the LMMBP LM3-Atrazine model.
To reduce errors associated with the load
calculations, sampling was deliberately biased toward
high-flow conditions where more than 20% of
samples were collected at times of discharge above
the 20% exceedance, (Dolan etal., 1981; Hall etal.,
1998). The assumption is that during the high-flow
periods, most of the load is transported. Sampling
for atrazine was delayed for one year due to
uncertainty in selection of methods and laboratory.
As a consequence of having only seven months of
load data to quantify atrazine loadings, the USGS
believed that the atrazine load estimates based on
actual concentration and flow measurements were
not as good as estimates for the other mass balance
contaminants of interest that were based on 19
months of measurements. Furthermore, load error
estimates for atrazine were especially poor, again
due to the short sampling period.
Three to four sampling crews in three states were on
call to capture storm-induced flow events (Hall, U.S.
Geological Survey, personal communication, 2001).
Weather was monitored 24 hours per day.
Equipment was used to trigger pagers upon the
onset of rising hydrographs. Sampling occurred
during rising, peak, and falling hydrographs. Except
for the shallow Pere Marquette and Kalamazoo
Rivers, rivers were sampled at 0.2 and 0.8 of the total
depth. These samples were taken at the midpoints
of river panels that divided the total river flow into
three visually estimated equal flow panels that were
determined during discharge calibration
measurements (Hall et a/., 1998). These six samples
were composited into one sample. For the Pere
Marquette and Kalamazoo Rivers, only three
samples (one in each flow panel) were composited
(Hall etal., 1998). A total of 405 samples (including
quality control samples) were collected.
River discharge was measured either by stage and
discharge techniques for the Manistique, Pere
Marquette, and Kalamazoo Rivers in Michigan or
acoustic velocity meters for the Muskegon, Grand,
and St. Joseph Rivers in Michigan; Grand Calumet
River in Indiana; and Milwaukee, Sheboygan, Fox,
and Menominee Rivers in Wisconsin (Hall et a/.
1998).
2.2.4.2 Atrazine Load Estimation for Monitored
Rivers Using the Stratified Beale Ratio Estimator
(SBRE) Method
Concentration data are usually limited due to cost
constraints; however, flow data are usually readily
available at short-time intervals. Sampling for the
LMMBP was focused on high-flow, high-
concentration events. However, if the mean
concentration from these limited samples were
multiplied by the total annual discharge, the load
estimate would be biased high. The reason it would
be high is that the mean concentration observed
would be disproportionately distorted by the number
of high-flow, high-concentration samples.
The SBRE method is nearly bias-free when the data
are sufficient to give acceptable precision to the load
estimate. The SBRE method used by the USGS for
the LMMBP can be found in Richards (1994).
Another factor in the selection of the SBRE is that the
method is robust over a range of data distributions.
The method has been the method preferred by the
International Joint Commission (IJC) for a number of
years. The SBRE was used for the period April 4,
1995 through October 30, 1995 when atrazine was
sampled.
For the unmonitored period, January 1,1994 through
April 3, 1995 and October 31, 1995 through
December 31, 1995, a combination of Beale-derived
daily loads and regression loads from the monitored
period were used to adjust regression-produced daily
loads from the unmonitored period (Hall, 2004). The
Beale method does not provide an algorithm to
77
-------
extend the loadings derived from the monitored
period to an unmonitored period. An adjustment
coefficient was computed by dividing the sum of
Beale-model daily loads from the monitored period by
the sum of the Estimator Regression Model loads for
the same period. The adjustment coefficient was
then multiplied by each daily load produced by the
selected regression model for each of the two
unmonitored periods to produce "corrected" daily
loads. For example, if the Beale model was
producing a sum of daily loads greater than the sum
of the regression model daily loads for the monitored
period, the adjustment coefficient would be greater
than one and the adjustment multiplication would
linearly increase each regression-daily load in each
of the two unmonitored periods.
The 1995 USGS SBRE tributary loadings are
depicted in Figure 2.2.5. Median river flows and
median atrazine concentrations are also shown. The
rivers are ordered based on the highest load on the
left to the lowest load on the right. Note that although
the Grand Calumet had the lowest atrazine load, it
did have the fourth highest median atrazine
concentration.
2.2.4.3 Atrazine Load Estimation for
Unmonitored Watersheds
Hall (2004) presents material on the method used to
estimate daily loading from watersheds in the Lake
Michigan basin where no samples were taken for the
analytes of interest. Loading estimates derived from
the 11 monitored tributaries were used to predict
loadings from the additional 25 unmonitored
tributaries larger than 325 km2. Unit area yields from
the monitored basin were calculated as follows:
Unit Area Yield = !,
where
(2.2.4)
/, = load estimate for any given day
A = area of the watershed for a monitored tributary
)— 90
1600-?8Q
- 6
-1200-| 601
€1000-| 50
o o
7v 800-5 40
£ 600-£ 30-
™ 400-2 20-
"5
200— 10
0— 0
D atrazine loading (kg/yr)
B atrazine concentration (ng/L)
• flow (cfs)
.C T3 X O
Q. C O O
O3 c crash
3 03 I-L 3 >< 'F '-= ,*^ ^
ec ^ cr O t tn CD-=
•^ ^n t_ r^ /-^ '^ "5
1995 monitored tributaries
Figure 2.2.5. 1995 USGS SBRE atrazine loadings and median concentrations relative to median flow
in Lake Michigan tributaries.
78
-------
The USGS used Unit Area Yields from monitored
watersheds that best matched unmonitored
watersheds in terms of land use and nature of
surficial land deposits. A GIS was used to help in the
watershed classification. Once this classification was
done, the areas of the 25 unmonitored watersheds
were expanded to encompass smaller adjacent
basins that were poorly defined in terms of land use,
discharge location, and other properties. The sum of
all monitored and unmonitored watershed loads were
designed to represent the total loading to Lake
Michigan from the entire Lake Michigan watershed.
2.2.5 Comments on Atrazine Tributary
Loading Estimates
Estimates of atrazine tributary loadings to Lake
Michigan for years 1994 and 1995 were made
independent of the USGS estimates. These
independent estimates were based on actual
application of atrazine to the basin and using a
literature-derived WEP of 0.6%. The following are
the results:
1994 USGS: 1163kg
1995 USGS: 1426kg
1994 WEP-Based: 5263 kg
1995WEP-Based: 4916kg
The ratio of WEP-based to USGS load for 1994 is
4.5, and the ratio for 1995 is 3.4.
For a discussion on possible reasons for the
discrepancy between the two load estimation
techniques, see Section 5.3.3.3.1 in this report.
References
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, Atlanta, Georgia. Water
Resources Investigations Report 94-4176,87 pp.
Available from U.S. Geological Survey at
http://pubs.usgs.gov/wri/wri944176/SHDRZ.
Battaglin, W.A. and D.A. Goolsby. 1996. Using GIS
and Regression to Estimate Annual Herbicide
Concentrations in Outflow From Reservoirs in the
Midwestern USA, 1992-93. In: Proceedings of
the American Water Resource Association
Annual Symposium on GIS and Water
Resources, pp. 89-98. American Water
Resources Association, Middleburg, Virginia.
Blanchard, P.E. and R.N. Lerch. 2000. Watershed
Vulnerability to Losses of Agricultural Chemicals:
Interactions of Chemistry, Hydrology, and Land-
Use. Environ. Sci. Technol., 34(16):3315-3322.
Capel, P.O. and S.J. Larson. 2001. Effect of Scale
on the Behavior of Atrazine in Surface Waters.
Environ. Sci. Technol., 35(4):648:657.
Dolan, D.M., A.K. Yui, and R.D. Geist. 1981.
Evaluation of River Load Estimation Methods for
Total Phosphorus. J. Great Lakes Res., 7(3):
207-214.
Frank, R. and G.J. Sirons. 1979. Atrazine: Its Use
in Corn Production and Its Loss to Stream
Waters in Southern Ontario, 1975-1977. Sci.
Total Environ., 12(3):223-239.
Frank, R. and L. Logan. 1988. Pesticide and
Industrial Chemical Residues at the Mouth of the
Grand, Saugeen and Thames Rivers, Ontario,
Canada, 1981-85. Arch. Environ. Contam.
Toxicol., 17(6):741-754.
Gianessi, L. P. and C.M. Puffer. 1988. Use of
Selected Pesticides for Agricultural Crop
Production in the United States, 1982-1985. U.S.
Department of Commerce, National Technical
Information Service, Springfield, Virginia.
Document Number PB89-191100, 490 pp.
Good, D. and S. Irwin. 2007. Marketing and Outlook
Briefs-2007 U.S. Corn Production Risks: What
Does History Teach Us? U.S. Department of
Agricultural and Consumer Economics, University
of Illinois at Urbana Champaign. May 2007
lssue/MOBR01-07.
79
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Hall. D.W., I.E. Behrendt, and P.E. Hughes. 1998.
Temperature, pH, Conductance, and Dissolved
Oxygen in Cross Sections of 11 Lake Michigan
Tributaries, 1994-95. U.S. Geological Survey,
Middleton, Wisconsin. Open File Report 98-567,
85pp.
Hall, D.W. 2004. Quality Systems and
Implementation Plan (QSIP) in the Quality
Assurance Project Plan for the LMM BP Modeling.
In: W.L. Richardson, D.D. Endicott, R.G. Kreis,
Jr., and K.R. Rygwelski (Eds.), The Lake
Michigan Mass Balance Project Quality
Assurance Plan for Mathematical Modeling,
Appendix G, pp. 233. U.S. Environmental
Protection Agency, Office of Research and
Development, National Health and Environmental
Effects Research Laboratory, Mid-Continent
Ecology Division-Duluth, Large Lakes Research
Station, Grosse lie, Michigan. EPA/600/R-
047018,233pp.
Richards, R.P. 1994. Tributary Loading Estimates
for Selected Herbicides in Lake Erie Tributaries of
Michigan and Ohio. U.S. Environmental
Protection Agency, Great Lakes National
Program Office, Chicago, Illinois.
Richards, R.P., D.B. Baker, J.W. Kramer, and D.E.
Ewing. 1996. Annual Loads of Herbicides in
Lake Erie Tributaries of Michigan and Ohio. J.
Great Lakes Res., 22(2):414-428.
Schottler, S.P., S.J. Eisenreich, and P.O. Capel.
1994. Atrazine, Alachlor, and Cyanazine in a
Large Agricultural River System. Environ. Sci.
Technol, 28(6):1079-1089.
Squillace, P.J. and E.M. Thurman. 1992. Herbicide
Transport in Rivers: Importance of Hydrology
and Geochemistry in Nonpoint Source
Contamination. Environ. Sci. Technol.,
26(3):538-545.
U.S. Geological Survey and S. Eisenreich. 1997.
USGS Field Operation Plan: Tributary
Monitoring, Version 1. In: L. Blume (Ed.), Lake
Michigan Mass Balance Study (LMMB) Methods
Compendium, Volume 1: Sample Collection
Techniques, pp. 215-219. U.S. Environmental
Protection Agency, Great Lakes National
Program Office, Chicago, Illinois. EPA/905/R-
97/012a, 403 pp.
Wisconsin Register. 2004. Pesticide Product
Restrictions. State of Wisconsin, Madison,
Wisconsin. Document Number 586:1244-147.
Wisconsin Register. 2005. Atrazine Prohibition
Areas, Appendix A. State of Wisconsin, Madison,
Wisconsin. Document Number 591:149-251.
80
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PART 2
LAKE MICHIGAN MASS BALANCE PROJECT ATRAZINE
LOADINGS TO LAKE MICHIGAN
Chapter 3. Estimation of Atrazine Loads
in Wet Deposition (Precipitation)
2.3.1 Atmospheric Components
Considered in Modeling Atrazine in Lake
Michigan
Both the MICHTOX and LM2-Atrazine models utilize
annualized wet deposition loadings for long-term
simulations. However, LM3-Atrazine wet deposition
loadings were calculated on a daily basis to capture
seasonal loading variations. LM3-Atrazine was used
to make predictions in lake segments on short-time
scales in a fine-grid framework as a function of
seasonally varying loads - both wet deposition and
tributary.
Particulate deposition was not considered in the
MICHTOX, LM2-Atrazine, and LM3-Atrazine models
because studies have shown that atrazine deposition
associated with atmospheric particulates represents
a minor fraction of the total deposition of atrazine
(Nations and Hallberg 1992; Siebers etal. 1994). In
the Lake Michigan Mass Balance Project (LMMBP)
(Section 1.3.2.2.2), the detection limit for atrazine
associated with atmospheric particulates was
relatively high. As a consequence, there was a low
number of detects at land-based collection sites
positioned around the lake. Attempts to measure
atrazine-associatedparticulatesover-the-lakeyielded
only two detects, and both of them were in the
southernmost part of the lake near major atrazine
sources. To make an estimate of atrazine deposition
fluxes associated with particulates, one needs both
reliable measurements of atrazine concentration on
the particles and an estimate of the deposition rate of
the particles. To calculate a rate of deposition,
particle sizes are needed. Particle size fractionation
was not part of the LMMBP analysis.
Some researchers have attempted to make
estimates of atmospheric, particulate-associated
atrazine fluxes to Lake Michigan using some
assumptions about the particle sizes. Miller et al.
(2000) roughly estimated that the load from particles
for the high-loading spring months (April through
June, 1994-1995), could range from 230 to 1000
kg/yr. Schottlerand Eisenreich (1997) estimated that
the atrazine-associated particulate load to the lake
for the period 1991 to 1994 was approximately 160
kg/yr. Sweet and Harlin (1998) estimated that the
1994-1995 atrazine-associated particulate load to the
lake using data from April through July to be about
220 kg/yr. Using these estimates, as well as wet
deposition and tributary loadings for 1994, the
relative contribution of dry particulate deposition to
the total load of atrazine to the lake
(wet+dry+tributary) is 2.8% to 11.4% (Miller et al.,
2000), 2.0% (Schottler and Eisenreich, 1997), and
2.7% for Sweet and Harlin (1998). Note that these
estimates were based on particulates collected at
land-based stations around the lake. However, other
than the two atrazine-associated particulate detects
in the southernmost part of the lake, we have no
evidence that these loadings are occurring over-the-
lake.
Vapor phase concentrations of atrazine were used in
the models as a boundary condition; please see
81
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Parts 4 (LM2-Atrazine) or 5 (LM3-Atrazine) for
details.
2.3.2 Atrazine Wet Deposition Load
Estimates Based on Measured Fluxes in
the Basin
Over-the-lake wet deposition of atrazine for 1991
(Figure 2.3.1) was based on data collected from
shore-based samples (Goolsby et al. 1993).
Goolsby's study area included Midwestern and
Northeastern states in a geographic rectangle
defined by the states North Dakota, Kansas, Virginia,
and Maine. It is interesting to note that the total
amount of wet-deposited atrazine in this region is
calculated to be 0.6% of the amount applied in the
0 200 Miles
I I
1 T
300 Kilometers
Estimated atrazine
deposition in micrograms
per square meter
per year -1991
1 Less than 10
10 to 25
26 to 50
51 to 100
I more than 100
Figure 2.3.1. Wet deposition (rain and snow) of
atrazine for 1991 for Midwestern United States
(Figure by W.A. Battaglin, U.S. Geological Survey,
1997).
region. This is the same percentage used to
estimate the atrazine tributary load export from the
Lake Michigan watershed. Higher fluxes of atrazine
to Lake Michigan are noted in the southern part of
the lake compared to the northern part. This gradient
is the result of higher use of the chemical in the
states south and west of the lake and wind patterns.
Wet deposition data for 1994 and 1995 associated
with the LMMBP were received from Hornbuckle
(University of Iowa, personal communication, 1999).
These over-the-lake wet deposition estimates were
used in all three models. Figure 2.3.2 depicts wet
deposition for the month of May 1994, and again the
southern region depicts higher atrazine fluxes. There
is a strong seasonal trend of wet deposition loadings
to the lake (Figure 2.3.3) - high loadings in the spring
and early summer and very little loading during the
rest of the year. Translating Hornbuckle's loadings
into wet deposition fluxes over Lake Michigan and
Green Bay yielded a value of 30.8 ug/m2/yr for 1994
and 1995. A similar calculation of flux for 1991
(Figure 2.3.1) yielded a value of 45 ug/m2/yr.
Wet deposition to the lake other than 1991, 1994,
and 1995 was estimated from total annual usage
estimates in a similar manner as described for
historical tributary loadings. However, instead of a
"Tributary Load Ratio," a "Precipitation Load Ratio"
was defined. Precipitation ratios were calculated as
an average for years 1991, 1994, and 1995 as
follows:
Precipitation Load Ratio = I Precipitation
Load to a Mode! Segment \j\ Total Annual
USA Atrazine Usage i (2.3.1)
For any year (y), where only total annual United
States usage is known, a segment load was
calculated utilizing the precipitation ratio:
Precipitation Load =
! Precipitation Load Ratio \ x
I Total Annual USA Usage Year i y 11 (2.3.2)
Along with total annual usage estimates, annual
atrazine wet deposition and tributary loadings for
Lake Michigan and Green Bay are depicted in Figure
82
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wet deposition
atrazine load
(kg/month)
May 1994
Figure 2.3.2. Gradients of atrazine in wet
deposition loadings over Lake Michigan for May
1994.
1200-
£1000-
f. 80°"
g BOO-
a
§ 400-
200-
n-
n _
Mar. May Jul. Sep. Nov. Jan. Mar. May Jul. Sep.
1994 1994 1994 1994 1994 1995 1995 1995 1995 1995
Figure 2.3.3. Seasonality of atrazine wet
deposition loadings to Lake Michigan for 1994-
1995.
2.3.4. The wet deposition load calculated for 1995
was very low compared to 1994 (Figures 2.3.3 and
2.3.4). It is believed that a cold and wet spring in the
major corn-growing regions of the United States may
explain this low estimate (see Section 1.3.2.2.3).
n
£
5 45
o
^ 40-
"5
| 35-
1 30-
I25'
Si 20-
(0
U> 4 r-
3 1 3-
ISA annual
a on o
J_
n total annual usage
• historical tributary loa
D wet deposition
f—i
r~i
J
>-
"&
ygooo e
•8000 ra
O
•7000 c
o
•6000 =
•5000 |
T>
•4000 -
s
•3000 S
•n
•2000 i
•1000 jo
••0 -Q
1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 -
year
Figure 2.3.4. Total atrazine tributary loading and
wet deposition loading estimates to Lake
Michigan.
2.3.3 Atrazine Wet Deposition and
Tributary Loads for MICHTOX and LM2-
Atrazine
Both tributary and precipitation loadings for the
MICHTOX and LM2-Atrazine models' surface water
segments are shown in Figures 2.3.5 and 2.3.6,
respectively. In MICHTOX, the southern third of the
lake is identified as segment 1, the central lake
region is segment 2, and the northernmost part of the
lake is segment 3 (see Part 3, Figure 3.1). Note that
total loadings are greater in the southern region of
the lake compared to the northern region. In LM2-
Atrazine, the southern third of the lake is represented
by segments 1 and 2; central lake, 3 and 4; and the
northern lake, 5 and 6. Segments 2, 4, and 6 are
located on the eastern side of the lake. The rest of
the segments are located in Green Bay. See Figure
4.1 in Part 4 for a graphic identifying segments for
LM2-Atrazine. The highest load to LM2-Atrazine is in
segment 2. Both MICHTOX and LM2-Atrazine
perform a linear interpolation to estimate missing
loads between dates that have known loads. For the
whole lake, the total annual load estimates were the
83
-------
4500
4000 -
3500 -
3000 -
2500 -
• 1994tributary loads
D1995 tributary loads
• 1994wet deposition
n 1995 wet deposition
234567
MICHTOX Segment
Figure 2.3.5. Tributary and wet deposition loadings to MICHTOX model segments for 1994 and 1995.
t DU U
3000
_._._
-^ ZDUU
1 cnn
1 UUU
•^nn -
n -
[Til
-
=
• 199 4 tributary loads
D1995 tributary loads
D1995 wet deposition
, ^
11 , DTl , HTI , •-• i •-•, — — , PTL
4567
LM2-Atraiine Segment
10
Figure 2.3.6. Tributary and wet deposition loadings to LM2-Atrazine model segments for 1994 and
1995.
84
-------
same for MICHTOX and LM2-Atrazine; however,
MICHTOX had seven receiving surface water
segments and LM2-Atrazine had 10. See Part 5 for
information on tributary loads and wet deposition
estimates used in LM3-Atrazine.
References
Goolsby, D.A., E.M. Thurman, M.L. Pomes, M.
Meyer, and W.A. Battaglin. 1993. Occurrence,
Deposition, and Long Range Transport of
Herbicides in Precipitation in the Midwestern and
Northeastern United States. In: D.A. Goolsby,
LL. Boyer, and G.E. Mallard (Eds.), Selected
Papers on Agricultural Chemicals in the Water
Resources of the Midcontinental United States,
pp. 75-89. U.S. Geological Survey, Denver,
Colorado. Document Number 93-418, 89 pp.
Miller, S.M., C.W. Sweet, J.V. DePinto, and K,C.
Hornbuckle. 2000. Atrazine and Nutrients in
Precipitation: Results From the Lake Michigan
Mass Balance Study. Environ. Sci. Technol.,
34(1):55-61.
Nations, B.K. and G.R. Hallberg. 1992. Pesticides
in Iowa Precipitation. J. Environ. Qual.,
21(3):486-492.
Siebers, J., D. Gottschild, and H.G. Nolting. 1994.
Pesticides in Precipitation in Northern Germany.
Chemosphere, 28(8):1559-1570.
Schottler, S.P. and S.J. Eisenreich. 1997. Mass
Balance Model to Quantify Atrazine Sources,
Transformation Rates, and Trends in the Great
Lakes. Environ. Sci. Technol., 31 (9):2616-2625.
Sweet, C.W. and K.S. Harlin. 1998. Atmospheric
Deposition of Atrazine to Lake Michigan.
Presented at the Air and Waste Management
Association's 91st Annual Meeting and
Exhibition, June 14-18, 1998, San Diego,
California. Illinois State Water Survey,
Champaign, Illinois. ReportNumber98-TA37.02.
85
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PARTS
LAKE MICHIGAN MASS BALANCE PROJECT
LEVEL 1 MODEL: MICHTOX-ATRAZINE
Kenneth R. Rygwelski
United States Environmental Protection Agency
Office of Research and Development
National Health and Environmental Effects Research Laboratory
Mid-Continent Ecology Division
Large Lakes and Rivers Forecasting Research Branch
Large Lakes Research Station
9311 Groh Road
Grosse lie, Michigan 48138
3.1 MICHTOX-Atrazine Executive
Summary
Our coarse-segmented model, MICHTOX, was run in
a hindcast and forecast mode under various load
modification scenarios. A calibration run based on
average boundary conditions using historical loadings
of atrazine to Lake Michigan suggests that
approximately 1% of the atrazine in the lake decays
each year. In the forecasts of alternate futures,
eliminating all loadings to the lake resulted in the
largest decline in model predictions. A total loading
reduction of approximately 37%, if implemented on
January 1, 2005, would have been needed in order
to prevent atrazine concentrations from increasing
further than above those that were observed in the
lake on January 1, 2005.
3.2 MICHTOX-Atrazine Recommendations
For long-term forecasts, future modeling efforts
should utilize LM2-Atrazine as a model because it is
more highly resolved and has advective and
dispersive components that were derived from a
hydrodynamic model (see Part 4). The
hydrodynamic model components can be considered
to best represent "average" lake conditions because
the various hydrodynamic forcing functions were
considered to be average (see Part 1, Chapter 4).
3.3 Model Description
3.3.1 Model Overview
For calibration purposes, the MICHTOX mass
balance model (Endicott et a/., 2005) was used in a
hindcast mode to simulate atrazine concentrations in
Lake Michigan and Green Bay in response to mass
loadings to these systems from the time of
introduction in the early 1960s to 1995. The
calibrated model was then used in a forecast mode
to predict lake-wide concentrations in Lake Michigan
as a function of various loading scenarios.
MICHTOX was adapted from the general water
quality model WASP4 (Ambrose et a/., 1988). The
model solves mass balance equations based on a
finite volume spatial discretization (Thomann and
86
-------
Mueller, 1987) and Euler time integration. The
MICHTOX model framework is capable of
incorporating a full range of transport and fate
processes such as advection, dispersion, particle
settling, sediment resuspension, sediment burial,
transport in sediment pore water, partitioning to
particles, chemical reaction, volatilization, and
absorption.
3.3.2 MICHTOX Model Segmentation and
Circulation
The segmentation schematic for Lake Michigan and
Green Bay is depicted in Figure 3.1. Lake Michigan
and Green Bay have nine water segments. Surface
segments 1 (southern lake), 2 (central lake), and 3
(northern lake) cover the entire main lake. Segments
5 (southern bay), 6 (central bay), and 7 (northern
bay) coverGreen Bay. Hypolimnetic water segments
in the main lake are numbered 8 (southern lake), 9
(central lake), and 10 (northern lake). Segment 4 is
a very small segment located in the lower Fox River.
During a period of approximately 100 days in the
summer, flow and exchange across the Straits of
Mackinac occurs in two discrete layers between the
surface water (segment 3) and Lake Huron and
between the deep hypolimnetic water (segment 10)
and water that primarily originates from Lake
Superior (Quinn, 1977) mixed with water from Lake
Huron. During this period of stratification, surface
layer flow (segment 3) is from Lake Michigan to Lake
Huron, and a deeper return flow to Lake Michigan is
observed. It has been observed that Lake Superior
water discharging from the St. Marys River travels in
a persistent westerly direction during stratification
and constitutes a significant component of the return
flow to Lake Michigan (Ayers et a/., 1956; Schelske
et a/. 1976; Saylor, J., National Oceanographic and
Atmospheric Administration, personal
communication, 1998). During the unstratified
period, all of the flow is from Lake Michigan to Lake
Huron.
Two-layered flow has been observed at the mouth of
Green Bay during thermal stratification (Martin et a/.,
1995); however, this structured flow process was not
incorporated in the MICHTOX model framework.
MICHTOX incorporates the flows between Green
Bay and Lake Michigan as net flows.
fir
|m
__I t~>. • J
L— I wSter column
Figure 3.1. MICHTOX model segmentation.
The MICHTOX model has two water column layers
for Lake Michigan to simulate the effects of summer
stratification of the lake. Also, the model consists of
just three horizontal compartments in the main lake.
This low spatial resolution was considered adequate
to address open-lake concentrations. Water column
concentration profiles of atrazine at 10 stations,
representing fourto 10 depths per station, showed no
vertical gradients during lake stratification for the
years 1991 and 1992 (Schottler and Eisenreich,
1997) and 1994-1995 (Brent et a/., 2001).
Furthermore, they reported that analysis of data from
their 10 lake stations that covered a central north-
south axis and an east-west axis showed no
horizontal gradients in atrazine concentrations in the
lake.
87
-------
MICHTOX exchange coefficients were taken from the
literature. Vertical exchange coefficients, which
quantify the extent of mixing between epilimnetic and
hypolimnetic segments in the main lake, were taken
from the Lake Michigan WASP eutrophication model,
MICH1 (Rodgers and Salisbury, 1981). Horizontal
exchange coefficients in Green Bay were calibrated
to reproduce chloride gradients. In the main lake,
however, horizontal exchange coefficients were
taken from work by Thomann et a/. (1979) on Lake
Ontario.
Flows in the lake were based on the whole-lake water
balance by Quinn (1977), which provided monthly
average changes in storage, tributary flow, outflow,
diversion, precipitation, and evaporation. The
hydraulic residence time (volume/outflow) for the
main lake was estimated to be 62 years (Quinn,
1992).
3.4 MICHTOX Model Application to Lake
Michigan
3.4.1 Screening Model Application
A screening-level model of MICHTOX was applied
before Lake Michigan Mass Balance Project
(LMMBP) loadings were available (Rygwelski et a/.,
1999). This early MICHTOX application assumed
that volatilization was negligible due to a very small
Henry's law constant of 8.1 x 10"8 (U.S. Department
of Agriculture, 2001) and that the chemical could be
modeled as a conservative substance.
For this screening model, tributary loads were
estimated based on atrazine applications to the basin
in 1992 and 1993 using algorithms identified in
Equation 2.2.1. The watershed export percentage
(WEP) used was 0.6% (see Table 2.2.2). Inorderto
predict loadings for years when application data were
not available, the loads estimated for 1992 and 1993
were divided by estimates of total annual United
States usage of atrazine using Equation 2.2.2 (no
annual United States usage estimate was available
for 1992, so an estimate for that year was calculated
as a mean of United States usage reported for 1991
and 1993). A mean of these two ratios was assumed
to be constant over the entire historical record of
atrazine usage in the basin. For years where only
total annual usage was available, an estimate of
loadings could be determined by multiplying the
mean load ratio by total annual usage.
Loadings of wet deposition to the lake were obtained
for 1991 (Goolsby et a/., 1993). These wet
deposition loads were based on actual
measurements of atrazine in rain and snow. In a
similar manner as was calculated for tributary loads,
the load from Goolsby was divided by a mean of the
total annual United States usage of atrazine for the
years 1992 and 1993. Usage in the United States
between 1989 through 1995 was relatively constant
so errors in substituting a mean of 1992 and 1993
usage for 1991 were believed to be small. In a
manner similar to the mean Tributary Load Ratio, a
mean atmospheric load ratio was used to estimate
historical wet deposition to the lake. See Figure 3.2
for both tributary and precipitation atrazine loads.
7000
6000-
"35000 -
g>4000
§ 3000
2000-
1000-
0
Precipitation
1964 1968 1972 1976 1980 1984 1988 1992 1996
year
Figure 3.2. Total annual estimated tributary and
precipitation loadings of atrazine to Lake
Michigan.
Using the load history and assuming that atrazine
decay is zero with negligible volatilization, a model
hindcast run starting in 1964 yielded a good fit with
lake data (see Figure 3.3). The results shown in the
figure are from the main lake only and does not
include Green Bay. The initial conditions in the lake
model were set to an atrazine concentration of zero.
No calibration of the model was needed. Also
depicted are the effects of using the upper and lower
95% confidence intervals on the 0.6% WEP reported
in the literature for moderate textured soils (see Part
2, Chapter 2). As a sensitivity test, a hypothetical
0.05 per year overall decay constant was
incorporated into the model. The model is very
88
-------
^— Mean watershed export percentage 0.60
decay constant 0.0/year
Upper 95% confidence interval
watershed export percentage 0.84
decay constant 0.0/year
— — Lower 95% confidence interval
watershed export percentage 0.36
decay constant 0.0/year
Mean watershed export percentage 0.60
decay constant 0.05/year
Field Data ±1 standard deviation
1964 1968 1972 1976 1980 1984 1988
year
1992 1996
Figure 3.3. A comparison of MICHTOX -
Predicted atrazine concentrations in Lake
Michigan to averaged Lake Michigan data for the
years 1991, 1992, and 1995 are depicted. Field
data for 1991 and 1992 were obtained from the
literature (Schottler and Eisenreich, 1997) and
data for 1995 are LMMBP data.
sensitive to this decay as shown in Figure 3.3.
"Decay" as used in this paper is internal decay likely
due to the combined effects of abiotic and biotic
transformation of atrazine to degradation products.
Considering that the model required no calibration
and relied mostly on data from the literature, it
performed remarkably well.
3.4.2 Enhanced Screening Model
Application
As additional county-level atrazine application data in
the basin and total United States usage estimates
became available, MICHTOX modeling in Lake
Michigan continued to develop. In the earlier
screening model application, only two years of
county-level atrazine application data were available.
For the enhanced screening-level model, seven
years of application data were available and used.
Also, due to label changes that lowered application
amounts and established planting setbacks from
water bodies in 1990 and 1992, a decision was made
to use two Tributary Load Ratios in order to address
atrazine application practices for pre- and post-label
changes. New data from the LMMBP also became
available to modelers. With these additional data,
loading ratios for both the tributaries and wet
deposition were updated (see Part 2, Chapters 2 and
3). The model was calibrated by determining a total
decay that would yield a best fit of the model to
observations in the lake. Also, several forecasting
scenarios were run with the model. The efforts of
this additional modeling are described in the following
sections of this part.
3.4.2.1 Field Data
See Part 1, Chapter 3 for atrazine data obtained from
lake, tributaries, and atmospheric components
samples.
3.4.2.2 Model Assumptions and Calibration
Procedures
Due to atrazine's physical and chemical properties
(Part 1, Chapters 1 and 3), processes modeled
included only advection, dispersion, and reaction
(decay).
Model processes involving sediments and
particulates in the water column were not included in
the MICHTOX model runs because atrazine is
primarily in the dissolved state in surface waters;
therefore, any processes that involve sediment or
suspended particle interactions are of minor
significance (Section 1.2.2).
A literature review of atrazine degradation processes
in surface freshwater presented in Part 1, Chapter 2
suggests that degradation is hindered in freshwater
such as in Lake Michigan where the water is cold,
has low solids concentrations with low dissolved
organic carbon, has a high pH, and has low
concentrations of nitrate ions. Degradation of
atrazine is known to occur through either biotic or
abiotic processes in some environmental
compartments. Given the lack of any Lake Michigan-
specific kinetic information on any of these
processes, the approach taken in MICHTOX was to
estimate the loading history of atrazine to the lake
and find an overall first-order loss rate constant to fit
the model to observations of atrazine in the lake
89
-------
water. Loadings were not part of the calibration
procedure. Considerable effort was expended to
ensure that loadings were fairly represented in the
model (Part 2, Chapters 2 and 3).
Due to a very small Henry's law constant,
volatilization and absorption were not simulated.
3.4.2.3 Tributary Loadings
It was assumed that a WEP of 0.6% derived from the
literature for fine/moderate textured soils adequately
described the overall WEP of the Lake Michigan
watershed. This WEP, along with historical annual
atrazine usage in the United States was used to
calculate atrazine loadings from the tributaries to the
lake. For a complete discussion on the WEP method
used to estimate MICHTOX loadings, please see
Part 2, Chapter 2.
Utilizing flow and concentration data, the Stratified
Beale Ratio Estimator (SBRE) method was used to
estimate tributary loads in the 11 monitored
tributaries during the LMMBP. Also, estimates of
loads from the unmonitored watersheds were made.
However, loads were apparently missed and
therefore MICHTOX tributary loads were only based
on WEP, county-level application data, and total
United States annual usage records. See Section
5.3.3.3.1 for a discussion of this topic.
3.4.2.4 Atmospheric Loadings
Loading estimates of wet deposition to Lake
Michigan and Green Bay were made for each of the
top surface water segments. These loadings were
estimated for MICHTOX per discussion in Part 2,
Chapter 3.
3.4.2.5 Model Confirmation
In 2005, atrazine water samples were collected in
Lake Michigan for the purpose of confirming the
model predictions. However, as of this printing,
these analyses were not available.
3.4.2.6 Model Application (Scenarios)
The calibration of the model was undertaken using
three scenarios (1, 2, and 3) that included lower
boundary conditions, upper boundary conditions, and
a most likely or average boundary condition scenario,
respectively. All of these model runs started on
January 1, 1963. The model was calibrated by
finding an appropriate internal decay until the model
output best matched the observed atrazine
concentration in the lake for samples taken in 1991,
1992, 1994, and 1995.
The scenarios 4 through 7 are referred to as load
reduction scenarios. These are not necessarily
management scenarios but can give managers
insight as to which loads are important in the model
and environment for the purpose of predicting
concentrations of atrazine in the lake. It is believed
that they bound the entire range of potential loads
and provide some specific load scenarios within the
range. Scenario 3 was used to simulate conditions
from January 1,1996 through December 31,2004 for
scenarios 4 through 7 described below. When
December 31, 2004 is reached, each of the load
reduction scenarios 4 through 7 began on January 1,
2005 and were run for a period of 50 years.
The Lake Superior boundary condition was assumed
linear during the period modeled (0 ng/L at the
beginning of year 1963 and 3.5 ng/L at 1994) and
likewise for the Lake Huron boundary condition (0
ng/L at the beginning of year 1963 and 23 ng/L at
1992). The boundary conditions were assumed to be
zero in 1963 because this was the year when the
herbicide was first introduced to the basin. Lake
Superior and Lake Huron atrazine concentrations for
the years 1993 and 1992, respectively, were based
on measurements of atrazine in these lakes
(Schottler and Eisenreich, 1994). While the Lake
Superior flow component of the return flow to Lake
Michigan is primarily characteristic of concentrations
of atrazine in Lake Superior, the actual concentration
is probably somewhere between Lake Superior and
Lake Michigan due to some mixing (see section
3.3.2).
1. Calibration Based on Upper Estimate of
Boundary Conditions - The summer inflow
concentration at the Straits of Mackinac was
assumed to be 100% Lake Huron water. Lake
Huron water started at 0 ng/L and was assumed
to linearly rise to 23 ng/L as observed in 1995
(Station 54) and then held constant at that level
for the remainder of the simulation. Tributary
loading projections were set equal to an
90
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averageof loadings for 1995 and 1998, but prior
to that time, the historical loading estimates were
used. Wet deposition projections were set equal
to an average of loads for 1978 through 1998.
Wet deposition loadings before that were based
on historical load estimates. The model was
calibrated by adjusting the overall internal decay
to best match whole-lake volume-weighted
average concentration.
2. Calibration Based on Lower Estimate of
Boundary Conditions - The summer inflow
concentration at the Straits of Mackinac was
assumed to be 100% Lake Superior water. Lake
Superior was assumed to begin with an atrazine
concentration of 0 ng/L, was then assumed to
linearly rise to 3.5 ng/L as observed in 1994
(Schottler and Eisenreich, 1997), and was then
held constant at that level for the remainder of
the simulation. Tributary loading projections were
set equal to an average of loadings for 1995 and
1998, but prior to that time, the loading history
estimates were used. Wet deposition projections
were set equal to an average of loads for 1978
through 1998. Wet deposition loadings before
that time were based on historical load estimates.
The model was calibrated by adjusting the overall
internal decay to best match whole-lake volume-
weighted average concentration.
3. Calibration Based on "Average" Boundary
Conditions - The inflow concentration at the
Straits of Mackinac was assumed to be 50%
Lake Superior and 50% Lake Huron water. This
mix of water was assumed to begin with an
atrazine concentration of 0 ng/L, was assumed to
linearly rise to 13.25or[(3.5+23)/2] ng/L in 1995,
and was then held constant at that level for the
remainder of the simulation. Tributary loading
projections were set equal to an average of
loadings for 1995 and 1998, but prior to that time,
loading history estimates were used. Wet
deposition projections were set equal to an
average of loads for 1978 through 1998. Wet
deposition loadings before that were based on
historical load estimates. The model was
calibrated by adjusting the overall internal decay
to best match whole-lake volume-weighted
average atrazine concentration.
4. Virtual Elimination (Lower Bound on Model
Prediction) - This scenario simulated a 100%
reduction of tributary and atmospheric loads. For
the projections, the Lake Huron/Superior
boundary conditions were set equal to zero. This
scenario was run using scenario 3 for predictions
leading up to the date when the virtual elimination
scenario was to take place.
5. No Tributary Loads - This scenario simulated a
100% reduction of tributary loadings. Wet
deposition loads were set equal to an average of
loads for 1978 through 1998. This scenario was
run using scenario 3 for predictions leading up to
the date when the 100% tributary load reduction
scenario began.
6. No Wet Atmospheric Deposition Loadings -
Tributary loads were set equal to an average of
loadings for 1995 and 1998. Atmospheric wet
deposition loadings were decreased by 100%.
This scenario was run using scenario 3 for
predictions leading up to the date when the 100%
atmospheric load reduction scenario began.
7. No Further Degradation of Lake Water Quality
- A total load (tributary and wet deposition) was
determined such that no further increase in lake-
wide volume-weighted concentration was
observed starting in January 1,2005. Up through
December 31, 2004, scenario 3 was used.
3.4.2.7 Discussion of Results
Total internal degradation of atrazine in the water (kj
determined by model calibration was low in all
scenarios where evaluated (see Figures 3.4and 3.5).
These rates of decay for scenarios 1, 2, and 3 were
0.0125/yr, 0.008/yr, and 0.01/yr, respectively. For
the calibration based on average boundary
conditions, MICHTOXpredictsthat approximately 1%
of the atrazine in the lake decays each year due to
some combination of abiotic and biotic decay in the
lake.
Decay can be related to the half-life of the chemical
in the lake by the following:
Half- Life = f1/2 = (In 2)/kd
(3.1)
91
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Field Data +/-1 standard deviation
Scenario 3; kinetic decay 1%/yr
Scenario 2: kinetic decay 0.8%/yr
Scenario 1; kinetic decay 1.25%/yr
Sept. 9
1976
May19
1990
Jan. 26
2004
Oct. 4
2017
June 13
2031
Feb. 19
2045
Oct. 29
2058
date
Figure 3.4. Lake Michigan (open-lake) forecast scenarios: 1 -upper estimate of boundary condition,
2 - lower estimate of boundary condition, and 3 - estimate of average boundary condition.
Field Data +/-1 standard deviation
Scenario 4: virtual elimination
Scenario 5; no tributary loads
Scenario 6; no wet deposition
Scenario 7: 37% total load reduction
Jan. 26 Oct. 4
2004 2017
date
June 13
2031
Feb. 19
2045
Oct. 29
2058
Figure 3.5. Lake Michigan (open-lake) hindcast and scenario forecasts: 4-virtual elimination of all
loadings and 0.0 ng/L atrazine at the Straits of Mackinac boundary, 5 - no tributary loads, 6 - no wet
deposition, 7 - no further degradation of lake water quality.
92
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Assuming that scenario 3 captures typical conditions,
then the 1% internal decay associated with this
scenario represents a half-life of the chemical in the
lake of 69.3 years. In scenario 3, the water at the
Straits of Mackinac was assumed to be half Lake
Superior water and half Lake Huron water and is
believed to be a fair assessment of the conditions
during summer stratification. It is intuitive that the
decay rate associated with scenario 1 that has the
highestboundary condition concentrations of atrazine
(assumed to be all Lake Huron water) is the one with
the highest decay rate because higher boundary
concentrations will mean that more atrazine is
transported into the lake at the Straits of Mackinac.
This higher loading will result in a higher decay
needed in the modeling calibration exercise in order
for model output to match observations. The
opposite argument is true for the scenario where the
boundary condition at the Straits of Mackinac is
based solely on the lower concentrations of atrazine
from Lake Superior.
In the forecasts of alternate futures (Figure 3.5),
eliminating all loadings to the lake resulted in the
largest atrazine decline in model predictions. A total
loading reduction of approximately 37%, if
implemented on January 1, 2005, would be needed
in order to prevent atrazine concentrations from
increasing higher than what was estimated in the
lake on January 1, 2005. If only the atmospheric
loadings ceased (scenario 6), concentrations would
continue to increase. However, if only the tributary
loadings ceased (scenario 5), concentrations in the
lake would decline relative to scenario 3 predictions.
References
Ambrose, R.B., T.A. Wool, J.P. Connolly, and R.W.
Schanz. 1988. WASP4, A Hydrodynamic and
Water Quality Model - Model Theory, User's
Manual, and Programmer's Guide. U.S.
Environmental Protection Agency, Office of
Research and Development, Environmental
Research Laboratory, Athens, Georgia.
EPA/600/3-87/039, 297 pp.
Ayers, J.C., D.V. Anderson, D.C. Chandler, and G.H.
Lauff. 1956. Currents and Water Masses of
Lake Huron (1954 Synoptic Surveys). The
University of Michigan, Great Lakes Research
Institute, Ann Arbor, Michigan. Technical Paper
Number 1, 101 pp.
Brent, R.N., J. Schofield, and K. Miller. 2001.
Results of the Lake Michigan Mass Balance
Study: Atrazine Data Report. U.S.
Environmental Protection Agency, Great Lakes
National Program Office, Chicago, Illinois. EPA/
905/R-01/010, 92pp.
Endicott D.D., W.L. Richardson, and D.J. Kandt.
2005. 1992 MICHTOX: A Mass Balance and
Bioaccumulation Model for Toxic Chemicals in
Lake Michigan, Part 1. In: R. Rossmann (Ed.),
MICHTOX: A Mass Balance and Bioaccumulation
Model for Toxic Chemicals in Lake Michigan.
U.S. Environmental Protection Agency, Office of
Research and Development, National Health and
Environmental Effects Research Laboratory, Mid-
Continent Ecology Division, Large Lakes
Research Station, Grosse lie, Michigan.
EPA/600/R-05/158, 140 pp.
Goolsby, D.A., E.M. Thurman, M.L Pomes, M.
Meyer, and W.A. Battaglin. 1993. Occurrence,
Deposition, and Long Range Transport of
Herbicides in Precipitation in the Midwestern and
Northeastern United States. In: D.A. Goolsby,
LL. Boyer, and G.E. Mallard (Eds.), Selected
Papers on Agricultural Chemicals in the Water
Resources of the Midcontinental United States,
pp. 75-89. U.S. Geological Survey, Denver,
Colorado. Document Number: 93-418, 89 pp.
Martin, S.C., S.C. Hinz, P.W. Rodgers, V.J. Bierman,
Jr., J.V. DePinto, and T.C. Young. 1995.
Calibration of a Hydraulic Transport Model for
Green Bay, Lake Michigan. J. Great Lakes Res.,
21(4):599-609.
Quinn, F.H. 1977. Annual and Seasonal Flow
Variations Through the Straits of Mackinac.
Water Resources Res., 13(1):137-144.
Quinn, F.H. 1992. Hydraulic Residence Times for
the Laurentian Great Lakes. J. Great Lakes
Res., 18(1):22-28.
93
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Rodgers, P.W. and O.K. Salisbury. 1981. Water
Quality Modeling of Lake Michigan and
Consideration of the Anomolous Ice Cover of
1976-1977. J. Great Lakes Res., 7(4):467-480.
Rygwelski, K.R., W.L. Richardson, and D.D. Endicott.
1999. A Screening-Level Model Evaluation of
Atrazine in the Lake Michigan Basin. J. Great
Lakes Res. 25(1):94-106.
Schelske, C.L., E.F. Stoermer, J.E. Gannon, and
M.S.Simmons. 1976. Biological, Chemical, and
Physical Relationships in the Straits of Mackinac.
U.S. Environmental Protection Agency, Office of
Research and Development, Environmental
Research Laboratory-Duluth, Large Lakes
Research Station, Grosse lie, Michigan.
EPA/600/3-76/095, 267 pp.
Schottler, S.P. and S.J. Eisenreich. 1997. Mass
Balance Model to Quantify Atrazine Sources,
Transformation Rates, and Trends in the Great
Lakes. Environ. Sci. Technol., 31(9):2616-2625.
Schottler, S.P. and S.J. Eisenreich. 1994.
Herbicides in the Great Lakes. Environ. Sci.
Technol., 28(12):2228-2232.
Thomann, R.V. and J.A. Mueller. 1987. Principlesof
Surface Water Quality Modeling and Control.
HarperCollins Publishers, Inc., New York, New
York.
Thomann, R.V., R.P. Wnfield.and J.J. Segna. 1979.
Verification Analysis of Lake Ontario and
Rochester Embayment Three-Dimensional
Eutrophication Models. U.S. Environmental
Protection Agency, Office of Research and
Development, Environmental Research
Laboratory-Duluth, Large Lakes Research
Station, Grosse lie, Michigan. EPA/600/3-79-
094, 136pp.
U.S. Department of Agriculture. 2001. Agriculture
Research Service Pesticide Properties. Available
from U.S. Department of Agriculture at
http://www.ars.usda.gov.
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PART 4
LAKE MICHIGAN MASS BALANCE PROJECT
LEVEL 2 MODEL: LM2-ATRAZINE
Kenneth R. Rygwelski
United States Environmental Protection Agency
Office of Research and Development
National Health and Environmental Effects Research Laboratory
Mid-Continent Ecology Division
Large Lakes and Rivers Forecasting Research Branch
and
Xiaomi Zhang
Z-Tech, an ICF International Company
Large Lakes Research Station
9311 Groh Road
Grosse lie, Michigan 48138
4.1 LM2-Atrazine Executive Summary
LM-2 Atrazine was run in a hindcast and forecast
mode under various load and modified boundary
condition scenarios. A calibration run, based on
average boundary conditions using historical loadings
of atrazine to Lake Michigan, suggests that only 0.9%
of the atrazine in the lake decays each year. Net
volatilization of atrazine is negligible. Tributaries,
transporting the atrazine run-off load from farm fields,
contribute most of the atrazine load to Lake
Michigan. In the forecasts of alternate futures,
eliminating all loadings to the lake resulted in the
largest decline in model predictions. A total load
reduction of approximately 35%, if implemented on
January 1, 2005, would have been needed in order
to prevent atrazine concentrations from increasing
above those that were estimated in the lake on
January 1, 2005.
4.2 LM2-Atrazine Recommendations
Due to its fast run-time speed, LM2-Atrazine can be
used to perform long-term model forecasts of lake
concentrations. As additional loading data become
available, the updated loading history can easily be
added to existing model input files. For additional
model confirmation purposes, it is recommended that
the model predictions be compared to data from lake
samples that were collected in 2005 when these data
become available.
4.3 Model Description
4.3.1 Model Overview
As one of the models in the Lake Michigan Mass
Balance Project (LMMBP), LM2-Toxic was
specifically developed to simulate the transport and
fate of hydrophobic toxic chemicals, such as
polychlorinated biphenyl (PCB) congeners, in both
95
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the water and sediment of Lake Michigan (Zhang,
2006). LM2-Toxic is a descendant of the WASP4
water quality modeling framework (Ambrose et al.,
1988). The model solves mass balance equations
based on a finite volume spatial discretization
(Thomann and Mueller, 1987) and Euler time
integration. Compared to WASP4, LM2-Toxichasan
updated air-water exchange formulation that includes
a Henry's law temperature-corrected coefficient as
described by Bamford et al. (1999); water phase
mass transfer coefficient per Wanninkhoff et al.
(1991); and the air phase mass transfer coefficient by
Schwarzenbach et al. (1993). The LM2-Toxic model
is capable of incorporating a full range of transport
and fate processes such as advection, dispersion,
particle settling, sediment resuspension, sediment
burial, transport in sediment pore water, partitioning
to particles in the water column and sediment,
reaction, volatilization, and gas absorption.
The LM2-Atrazine model is identical to LM2-Toxic
except for differences in the volatilization algorithms.
In LM2-Atrazine, the algorithm for calculating the
temperature-dependent Henry's law coefficient
follows that of Scholtzef al. (1999) and Miller (1999).
The dimensionless value for the Henry's law constant
was set to 8.1 x10"8(U.S. Department of Agriculture,
2001). The water and air phase mass transfer
coefficients were that of the O'Connor "long form"
and O'Connor, respectively (O'Connor, 1983). The
volatilization algorithm differences between LM2-
Toxic and LM2-Atrazine would not be expected to
have a significant impact on atrazine model
predictions because of the low value of the Henry's
law constant for atrazine. As a non-hydrophobic
chemical, atrazine was not associated with
particulates in LM2-Atrazine. Therefore, processes
such as resuspension, settling, burial in sediment,
transport in sediment pore water, and partitioning to
solids in the water column and sediment were not
operative. Processes such as advection, dispersion,
reaction, volatilization, and gas absorption were
active. For information on the physical and chemical
properties of atrazine, see Part 1, Chapter 2.
In a manner similar to MICHTOX (see Part 3), LM2-
Atrazine was used in a hindcast mode to simulate
atrazine concentrations in Lake Michigan and Green
Bay in response to mass loadings to those systems
from the time of introduction in 1964 up to 1995. The
calibrated model was then used in a forecast mode
to predict lake-wide atrazine concentrations in Lake
Michigan as a function of various loading scenarios.
4.3.2 LM2-Atrazine Model Segmentation
and Circulation
Compared to MICHTOX (Level 1 contaminant
transport and fate model developed for Lake
Michigan) segmentation (Figure 1.5.1), the LM2-
Atrazine model has a finer resolution (Figure 4.1).
Most water column segments in the LM2-Atrazine
model segmentation schematic share the same or
portions of the segment boundaries used in the
MICHTOX atrazine model. The spatial segmentation
for the LM2-Atrazine model was developed from
digitized bathymetric (5 km x 5 km grid) and shoreline
data for Lake Michigan provided by Dr. David
Schwab, National Oceanic and Atmospheric
Administration (NOAA) (Schwab and Beletsky, 1998).
The lake, including Green Bay, was divided into 10
horizontal columns, five water column layers, and
one surficial sediment layer. A detailed spatial and
cross sectional display of the water segments for
LM2-Atrazine is illustrated in Figure 4.1. There are
41 segments in total. Segments 1-10 are surface
water segments with an interface with the
atmosphere. The rest of the segments lie below
these surface segments.
Water balance is one of the major components in a
traditional water quality modeling framework. Water
movement directly controls the transport of solids and
chemicals in dissolved and particulate phases in a
water system. In terms of LM2-Atrazine model
inputs, the data in the transport fields such as
advective flows and dispersive exchanges, or mixing,
were used to describe the water balance in the
model. The components and their sources used in
LM2-Atrazine model transport fields are listed below:
1. Bi-direction horizontal advective flows (provided
by David Schwab, NOAA; originally based on
Schwab and Beletsky (1998).
2. Net vertical advective flows (provided by David
Schwab, NOAA; originally based on Schwab and
Beletsky (1998).
3. Tributary flows and bi-directional flows across the
Straits of Mackinac (Endicott et al., 2005; Quinn,
1977).
96
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21
ID
18
48
l\
/v
CO
>eg
turn
rater
umn
merit
bers
1
11
20
29
36
10
10
10
20
100
average
segment
thickness
in meters
Figure 4.1. Water column segmentation for LM2-
Atrazine.
4. Water balancing flows.
5. Vertical dispersion coefficients.
Components such as precipitation, evaporation, and
groundwater infiltration were not considered in the
water transport fields used in the LM2-Atrazine
model.
Correct water circulation is essential for the accuracy
of outputs from the LM2-Atrazine model. The
Princeton Ocean Model (POM) has been
demonstrated to accurately simulate water
movement for a given large water body (Schwab and
Beletsky, 1997; Blumberg and Mellor, 1987). Using
an extensively tested version of POM for the Great
Lakes (POMGL), transport fields were generated for
Lake Michigan at different spatial and temporal
resolutions for use in a series of mass balance
models adapted for LMMBP (Schwab and Beletsky,
1998). The hydrodynamic model for Lake Michigan
had 20 vertical layers and a uniform horizontal grid
size of 5 km x 5 km (Schwab and Beletsky, 1998).
Because the LM2-Atrazine model segmentation was
constructed based on the 5 x 5 km2 grid used in the
POMGL for Lake Michigan, the hydrodynamic model
results were relatively easily aggregated to the
resolution used in LM2-Atrazine (Schwab and
Beletsky, 1998). The aggregated horizontal bi-
direction flows at each interface provided a good
approximation of horizontal advective and dispersive
transport components at the interface. The
advantage of using bi-directional flows at an interface
was that it bypassed the tedious and necessary
horizontal dispersion coefficient calibration
procedure required when only net flow is available at
the interface.
The vertical transport field was calculated in the form
of net vertical flow [provided by David Schwab,
NOAA and originally based on Schwab and Beletsky
(1998)]. Therefore, vertical exchange coefficients
were calculated and calibrated to define the vertical
mixing process between vertically adjacent
segments. A summer period of strong stratification
and a non-stratified period of intense vertical mixing
are important limnological features of the Great
Lakes (Chapra and Reckhow, 1983; Thomann and
Mueller, 1987). Therefore, determining the dynamics
of vertical mixing was considered an important model
development task for the LMMBP.
A thermal balance model was constructed to
calibrate the vertical exchange coefficients at the
interfaces (Zhang et a/., 1998, 2000). The
coefficients were calibrated using 250 observed
vertical temperature profiles collected at 40 stations
in Lake Michigan during the 1994-1995 LMMBP
period (Zhang, 2006).
97
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Water balancing flow was another advective
component added into the water transport field for
LM2-Atrazine. The aggregated advective flows
provided by NOAA were not balanced in individual
segments over the two-year LMMBP period.
However, the total water mass was perfectly
balanced on a whole - lake basis. Over the two-year
LMMBP period, some segments lost or gained a
certain amount of water. This problem could be very
significant for long-term simulations for the LM2-
Atrazine model because the model simulation stops
once the volume of a segment reaches zero. To
counter the amount lost or gained in each segment,
a water balancing flow was introduced to keep the
volume of water unchanged in each segment at any
time during the simulation. The balancing flows were
generated based on the aggregated advective flows
[provided by David Schwab, NOAA, and originally
based on Schwab and Beletsky (1998)], original
volume of each segment, and the general water
circulation patterns during the LMMBP period.
Tributary flows and flows through the Straits of
Mackinac were based on MICHTOX model inputs
(Endicott et a/., 2005), the literature (Quinn, 1977),
and water circulation patterns during the LMMBP
period [provided by David Schwab, NOAA, and
originally based on Schwab and Beletsky (1998)].
During a period of approximately 100 days in the
summer, flow and exchange across the Straits of
Mackinac occurs in two discrete layers formed by the
surface water and deep, cold, hypolimnetic water.
During this period of stratification, surface layer flow
is from Lake Michigan to Lake Huron, and a deeper
return flow to Lake Michigan is observed. It has been
observed that Lake Superior water discharging from
the St. Marys River travels in a persistent westerly
direction during stratification and constitutes a
significant component of the return flow to Lake
Michigan (Ayersef a/., 1956; Schelskeefa/., 1976; J.
Saylor, NOAA, personal communication, 1998). The
remainder of this return flow to Lake Michigan is Lake
Huron water.
Hydraulic residence times (volume/outflow) for the
main lake has been estimated to be 62 years (Quinn,
1992).
After vertical exchange coefficients were calibrated,
a conservative constituent, chloride, was simulated
using the LM2 model configuration to verify that the
water transport components described above were a
good representation of the overall water transport
field for atrazine. The chloride model was run just
once without adjusting any parameters or
coefficients. The model results agreed very well with
the observations during the LMMBP period (Zhang,
2006).
Water column concentration profiles of atrazine at 10
open-lake stations representing four to 10 depths per
station showed no vertical gradients during lake
stratification for the years 1991-1992 (Schottler and
Eisenreich, 1997) and 1994-1995 (Brent et a/., 2001).
Furthermore, Schottler and Eisenreich reported that
analysis of data from their 10 lake stations that
covered a central north-south axis and an east-west
axis showed no horizontal gradients of atrazine
concentrations in the lake.
4.4 LM2-Atrazine Model Application to
Lake Michigan
4.4.1 Enhanced Screening
Application
Model
For the LM2-Atrazine model runs, seven years of
atrazine application data were available and used.
Also, due to label changes that lowered application
amounts and established planting setbacks from
water bodies in 1990 and 1992, a decision was made
to use two tributary load ratios in order to address
atrazine application practices for pre- and post-label
changes. New data from the LMMBP also became
available to modelers. Wth these additional data,
loading ratios for both the tributaries and wet
deposition were updated (see Part 2, Chapters 2 and
3). The model was calibrated by determining a total
decay that would yield a best fit of the model to
observations in the lake. Also, several forecasting
scenarios were run with the model. The efforts of
this additional modeling are described in the following
sections of this part.
4.4.2 Field Data
See Part 1, Chapter 3 for atrazine field data from the
lake, tributaries, and atmospheric components.
98
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4.4.3 Tributary Loadings
It was assumed that a Watershed Export Percentage
(WEP) of 0.6% derived from the literature for
fine/moderate textured soils adequately described
the overall WEP of the Lake Michigan watershed.
This WEP, along with historical annual atrazine
usage in the United States, was used to calculate
atrazine loadings from the tributaries to the lake. For
a complete discussion on the WEP method used to
estimate LM2-Atrazine loadings, please see Part 2,
Chapter 2.
The Stratified Beale Ratio Estimator (SBRE) method
was used to estimate tributary loads in the 11
monitored tributaries during the LMMBP utilizing
tributary flow and concentration data. Also,
estimates of loads from the unmonitored watersheds
were made. However, loads were apparently
missed, and therefore, LM2-Atrazine tributary loads
were based only on WEP, county-level application
data and total United States annual usage records.
See Section 5.3.3.3.1 for a discussion of this topic.
4.4.4 Atmospheric Loadings
Loading estimates of wet deposition to Lake
Michigan and Green Bay were made for each of the
top surface water segments. These loadings were
estimated for LM2-Atrazine per the discussion in Part
2, Chapter 3.
4.4.5 Model Assumptions
Model processes involving sediments and
particulates in the water column were not included in
the LM2-Atrazine model runs because atrazine is
primarily in the dissolved state in surface waters;
therefore, any processes that involve sediment or
suspended particle interactions were concluded to be
of minor significance (Section 1.2.2).
A literature review of atrazine degradation processes
in surface freshwater presented in Part 1, Chapter 2
suggests that degradation is hindered in freshwaters
such as in Lake Michigan where the water is cold,
has low solids concentrations, low dissolved organic
carbon, a high pH, and low concentration of nitrate
ions. Degradation of atrazine is known to occur
through either biotic or abiotic processes in some
environmental compartments. Given the lack of any
Lake Michigan-specific kinetic information on any of
these processes, the approach taken in LM2-Atrazine
was to estimate the loading history of atrazine to the
lake and then find an overall first-order loss rate
constant to fit the model to observations of atrazine
in the lake water.
Therefore, due to atrazine's physical and chemical
properties (Part 1, Chapters 2 and 3), processes
modeled included only advection, dispersion,
volatilization, absorption, and reaction (atrazine
decay).
4.4.6 Model Calibration and Application
(Scenarios)
The calibration of the model was undertaken using
three scenarios (1, 2, and 3) that included lower
boundary condition, upper boundary condition, and a
most likely or average boundary condition scenario,
respectively. All of these model runs started on
January 1, 1963 with a zero load. The model was
calibrated by finding an appropriate internal decay
until the model output best matched the observed
atrazine concentration in the lake for samples taken
in 1991, 1992, 1994, and 1995.
The scenarios 4 through 8 are referred to as load
reduction scenarios. These are not necessarily
management scenarios, but they can give managers
insight as to which loads are important in the model
and environment for the purpose of predicting
concentrations of atrazine in the lake. It is believed
that they provide bounds on the entire range of
potential loads. Scenario 3 was used to simulate
conditions from January 1, 1996 through December
31, 2004. Then on January 1, 2005, the load
reduction scenarios 4 through 8 began and ran for a
period of 50 years.
Scenario 1 - Calibration Based on an Upper
Estimate of Boundary Conditions: In this
scenario, the initial vapor phase concentration was 0
ng/m3 and increased linearly until December 31,
1977. Starting on January 1, 1978, the vapor phase
concentration was held constant at the atrazine
detection limit of 0.00926 ng/m3 (Miller, 1999)
throughout the remainder of the simulation period.
The summer inflow concentration at the Straits of
Mackinac was assumed to be 100% Lake Huron
water. Lake Huron water was initially set at 0 ng/L
99
-------
and was assumed to rise linearly to 23 ng/L observed
in 1995 (Station 54), and then remained constant for
the remainder of the simulation. Tributary loading
projections were set equal to an average of loadings
for 1995 and 1998, but prior to that time, the
historical loading estimates were used. Wet
deposition loads beyond 1998 were set equal to an
average of loads 1978 through 1998. Wet deposition
loadings before that were based on historical load
estimates. Volatilization, absorption, and other
processes were active in the model. An internal
decay was then selected for the model run that
yielded a best fit to whole-lake volume-weighted
average concentrations.
Scenario 2 - Calibration Based on a Lower
Estimate of Boundary Conditions: In this
scenario, the vapor phase concentration was initially
set at 0 ng/m3 and remained at that concentration for
the entire simulation period. The summer inflow
concentration at the Straits of Mackinac was
assumed to be 100% Lake Superior water. Lake
Superior water was initially set at 0 ng/L, and was
assumed to rise linearly to 3.5 ng/L observed in
1994, and then held constant at that level for the
remainder of the simulation. Tributary loading
projections were set equal to an average of loadings
for 1995 and 1998, but prior to that time, the
historical loading estimates were used. Wet
deposition projections beyond 1998 were set equal to
an average of loads for 1978 through 1998. Wet
deposition loadings before that were based on
historical load estimates. Volatilization, absorption,
and other processes were active in the model. An
internal decay was then selected for the model run
that yielded a best fit to whole-lake volume-weighted
average concentrations.
Scenario 3 - Calibration Based on "Average"
Boundary Conditions: In this scenario, the vapor
phase concentration was initially set at 0 ng/m3 and
then increased linearly up to 0.00463 ng/m3 (one-half
detection limit) until December 31,1977. Starting on
January 1,1978, this vapor phase concentration was
held constant at 0.00463 ng/m3 throughout the
remainder of the simulation period. The inflow
concentration at the Straits of Mackinac was
assumed to be 50% Lake Superior and 50% Lake
Huron water. This mix of water started out at 0 ng/L
and was assumed to linearly rise to 13.25 ng/L or (1/4
x (3.5+23)) ng/L as observed in 1995 in Lake
Superior and Lake Huron, respectively, and then held
constant at that level for the remainder of the
simulation. Tributary loading projections were set
equal to an average of loadings for 1995 and 1998,
but prior to that time the variable loading estimates
were used. Wet deposition projections beyond 1998
were set equal to an average of loads for 1978
through 1998. Wet deposition loading before that
were based on historical load estimates.
Volatilization, absorption, and other processes were
active in the model. An internal decay was then
selected for the model run that yielded a best fit to
whole-lakevolume-weighted average concentrations.
Scenario 4-Virtual Elimination (Lower Bound on
Model Predictions): In this scenario, tributary and
atmospheric loads were reduced by 100%. For the
projections, vapor phase concentrations and the
Lake Huron/Superior boundary conditions were set to
zero. All modeling processes were active. This
scenario was run using scenario 3 for predictions
leading up to the date when the virtual elimination
scenario began (January 1, 2005).
Scenario 5 - No Tributary Loads: In this scenario,
the tributary loadings were reduced by 100%. Wet
deposition loads were set equal to an average of
loads for 1978 through 1998. This scenario was run
using scenario 3 for predictions leading up to the
date when the 100% tributary load reduction scenario
was began (January 1, 2005). All other modeling
processes were active.
Scenario 6 - No Wet Atmospheric Deposition
Loadings: Tributary loads were set equal to an
average of loadings for 1995 and 1998. Atmospheric
wet deposition loadings were decreased by 100%.
This scenario was run using scenario 3 for
predictions leading up to the date when the 100%
atmospheric load reduction scenario began (January
1, 2005). All other modeling processes were active.
Scenario 7 - Zero Vapor Phase Concentration:
Tributary loads were set equal to an average of
loadings for 1995 and 1998. Wet deposition loads
were set equal to an average of loads for 1978
through 1998. Vapor phase concentration were set
equal to zero. This scenario was run using scenario
3 for predictions leading up to the date when the
zero vapor phase concentration scenario began
100
-------
(January 1, 2005).
were active.
All other modeling processes
Scenario 8 - No Further Degradation: A total load
(tributary and wet deposition) was determined using
the model such that no further increase in lake-wide
volume-weighted concentration would be observed
after January 1, 2005. Up through December 31,
2004, scenario 3 was used.
4.4.7 Model Confirmation
In 2005, atrazine water samples were collected in
Lake Michigan for the purposes of confirming the
model predictions. However, as of this printing,
these analyses were not available.
4.4.8 Discussion of Results
In terms of mass flow rates, LM2-Atrazine results
from scenario 3 are depicted in Figure 4.2 for 1994.
As shown, the highest load to the lake is from the
tributaries followed by the load from the atmosphere
in the form of wet deposition. The greatest loss of
atrazine from the system is via export through the
Straits of Mackinac. Loss due to internal decay is the
second highest loss mechanism in the lake.
Volatilization and gas absorption are minor processes
in terms of mass flow gain and loss.
Total internal degradation of atrazine in the water (kd)
determined by model calibration was low in all
scenarios evaluated. These rates of decay for
scenarios 1, 2, and 3 were 0.012/yr, 0.004/yr, and
0.009/yr, respectively. For the calibration based on
average boundary conditions (scenario 3), LM2-
Atrazine predicts that approximately 0.9% of the
atrazine in the lake decays each year due to some
combination of abiotic and biotic decay in the lake.
Decay can be related to the half-life of the chemical
in the lake by the following:
Half-Life = t1/2 = (In 2)/k,
(4.1)
absorption
231 kg/yr
volatilization
51 kg/yr
export via
Chicago
Diversion
145 kg/yr
atmospheric
wet deposition
2493 kg/yr
input from
Lake Huron
472 kg/yr
loss to decay: 1648 kg/yr
watershed
loading
5264 kg/yr
export to
Lake Huron
2531 kg/yr
Atrazine Inventory
182,779 kg
Dry deposition, settling, sediment resuspension
and net burial are negligible
Figure 4.2. LM2-Atrazine model results for Lake Michigan and Green Bay for the year 1994.
101
-------
Assuming that scenario 3 captures typical conditions,
then the 0.9% internal decay associated with this
scenario represents a half-life of the chemical in the
lake of 77 years. In scenario 3, the water at the
Straits of Mackinac is assumed to be half Lake
Superior water and half Lake Huron water. It is
intuitive that the decay rate associated with scenario
1 that has the highest boundary concentrations of
atrazine (assumed to be all Lake Huron water) is the
one with the highest decay rate because higher
boundary concentrations will mean that more
atrazine is transported into the lake at the Straits of
Mackinac. Furthermore, the increased vapor phase
concentration in scenario 1 will also contribute slightly
more to gas absorption than the other scenarios.
This cumulative higher mass flow will result in a
higher decay needed in the modeling calibration
exercise in order for model output to match lake
concentration observations. The opposite argument
is true for scenario 2 where the boundary condition at
the Straits of Mackinac is based solely on the lower
concentrations of atrazine from Lake Superior, and
the vapor phase concentration of atrazine is
assumed to be equal to zero throughout the entire
simulation.
In the forecasts of alternate futures (Figure 4.3),
constant conditions scenario 3 results in lake
concentrations increasing until a value of
approximately 66 ng/L is attained. Scenario 3 is
based on average boundary conditions, and the
forecasts using this scenario are based on constant
loadings that were observed in the mid to late 1990's.
Eliminating all loadings to the lake (scenario 4)
resulted in the largest decline in model predictions.
A total loading reduction of approximately 35%
(scenario 8), if implemented on January 1, 2005,
would be needed in order to prevent atrazine
concentrations from increasing further than what was
estimated in the lake on January 1, 2005. If only the
atmospheric loadings ceased (scenario 6), then
concentrations in the lake would not be expected to
change much after January 1, 2005, and the model-
predicted concentrations in the lake would be
expected to be only slightly higher than that predicted
by scenario 8. However, if only the tributary loadings
ceased (scenario 5), then atrazine concentrations in
the lake would decline relative to scenario 3
predictions. Maintaining the vapor phase
concentration at 0 ng/l (scenario 7) has very little
effect compared to the constant condition scenario 3.
This is intuitive because scenario 3 vapor phase
concentrations are set to one-half the detection limit
of atrazine in the vapor phase.
Ol
c
c
o
o
c
o
O
'N
1963 1993
2203 2233 2263
Figure 4.3. LM2-Atrazine model runs of scenarios.
102
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Related to the production of ethanol for motor
vehicles in this country, the demand for corn
increased the United States corn acreage planted in
2007 to 93.6 million acres, exceeding the acreage
planted in 2006 by 19.5 % (U.S. Department of
Agriculture, 2007). This also represents an increase
of 24.5% of the average acreage planted during the
project period, 1994-1995 (see Figure 4.4 for corn
acreage in the United States from 1986 to 2007).
This was the largest amount of corn planted in the
United States since 1944 when farmers planted 95.5
million acres. It can be assumed that this increase in
corn acreage has resulted in an increase in the use
of atrazine in the Lake Michigan watershed. To
estimate the potential impact on this increased usage
of atrazine in the Lake Michigan basin, both
atmospheric and tributary loadings were increased by
15% and 30% starting in 2007 in scenario 3 (see
Figure 4.5). For these increases, the lake reaches
steady-state at approximately 75.2 ng/L and 84.2
ng/L, respectively. At the time of this printing, data on
the actual usage amounts of atrazine applied to the
Lake Michigan basin were not available. Thus the
range of percent increases for the basin is probably
the best current estimate of the potential impact of
increased loadings to the lake.
In conclusion, the net volatilization of atrazine is
negligible in Lake Michigan. Furthermore, model
calibration over a hindcast suggests that very little of
the atrazine inventory in the lake decays each year.
The chemical almost behaves as a conservative
substance in the cold, deep waters of Lake Michigan.
If loadings stay that same or increase over what was
observed in the 1990s, then the lake concentration of
atrazine is expected to increase.
Million Acres
100
U.S. Corn Acres
1986 1989 1992 1995 1998 2001
Planted
Harvested
2004 2007
USDA-NASS
10-12-07
Figure 4.4. Historical trends of United States corn acreage planted and harvested from 1986 to 2007
(U.S. Department of Agriculture, 2007).
103
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90
80-
ra 70 H
I 6°H
2 50-1
o 40 -
o
O 30
-------
Endicott, D.D., W.L Richardson, and D.J. Kandt.
2005. 1992 MICHTOX: A Mass Balance and
Bioaccumulation Model for Toxic Chemicals in
Lake Michigan. In: R. Rossmann (Ed.),
MICHTOX: A Mass Balance and
Bioaccumulation Model for Toxic Chemicals in
Lake Michigan, Part 1. U.S. Environmental
Protection Agency, Office of Research and
Development, National Health and
Environmental Effects Research Laboratory,
Mid-Continent Ecology Division-Duluth, Large
Lakes Research Station, Grosse lie, Michigan.
EPA/600/R-05/158, 140 pp.
Miller, S.M. 1999. Spatial and Temporal Variability
of Organic and Nutrient Compounds in
Atmospheric Media Collected During the Lake
Michigan Mass Balance Study. M.S. Thesis,
Department of Civil, Structural, and
Environmental Engineering, State University of
New York, Buffalo, New York. 181 pp.
O'Connor, D.J. 1983. Wind Effects on Gas-Liquid
Transfer Coefficients. J. Environ. Engin.,
109(3):731-752.
Quinn, F.H. 1977. Annual and Seasonal Flow
Variations Through the Straits of Mackinac.
Water Resources Res., 13(1):137-144.
Quinn, F.H. 1992. Hydraulic Residence Times for
the Laurentian Great Lakes. J. Great Lakes
Res., 18(1):22-28.
Schelske, C.L., E.F. Stoermer, J.E. Gannon, and
M.S.Simmons. 1976. Biological, Chemical, and
Physical Relationships in the Straits of Mackinac.
U.S. Environmental Protection Agency, Office of
Research and Development, Environmental
Research Laboratory-Duluth, Large Lakes
Research Station, Grosse lie, Michigan.
EPA/600/3-76/095, 267 pp.
Scholtz, M.T., B.J. Van Heyst, and A. Ivanhoff.
1999. Documentation for the Gridded Hourly
Atrazine Emissions Data Set for the Lake
Michigan Mass Balance Study. U.S.
Environmental Protection Agency, Office of
Research and Development, National Exposure
Research Laboratory, Research Triangle Park,
North Carolina. EPA/600/R-99/067, 61 pp.
Schottler, S.P. and S.J. Eisenreich. 1997. Mass
Balance Model to Quantify Atrazine Sources,
Transformation Rates, and Trends in the Great
Lakes. Environ. Sci. Technol., 31 (9):2616-2625.
Schwab, D.J. and D. Beletsky. 1997. Modeling
Thermal Structure and Circulation in Lake
Michigan. In: Estuarine and Coastal Modeling,
pp. 511-522. Proceedings of the 5th International
Conference of the American Society of Civil
Engineers, Alexandria, Virginia. October 22-24,
1997.
Schwab, D.J. and D. Beletsky. 1998. Lake Michigan
Mass Balance Study: Hydrodynamic Modeling
Project. National Oceanic and Atmospheric
Administration, Great Lakes Environmental
Research Laboratory, Ann Arbor, Michigan.
NOAATechnical Memorandum ERLGLERL-108,
55 pp.
Schwarzenbach, R.P., P.M. Gschwend, and D.M.
Imboden. 1993. Environmental Organic
Chemistry. John Wiley and Sons, Incorporated,
New York, New York. 681 pp.
Thomann, R.V. and J.A. Mueller. 1987. Principles of
Surface Water Quality Modeling and Control.
HarperCollins Publishers, Inc., New York, New
York.
U.S. Department of Agriculture. 2001. Agriculture
Research Service Pesticide Properties Database.
Available from U.S. Department of Agriculture at
http://www.ars.usda.gov.
U.S. Department of Agriculture. 2007. National
Agricultural Statistics Service. U.S. Department
of Agriculture, Washington, D.C. Available from
U.S. Department of Agriculture at
http://www.nass.usda.gov.
Wanninkhoff, R., J.R. Ledwell, and J. Crusius. 1991.
Gas Transfer Velocities on Lakes Measured with
Sulfur Hexafluoride. In: S.C. Wlhelm and J.S.
Culliver(Eds.), Air-Water Mass Transfer, pp. 441-
458. American Society of Civil Engineers, New
York, New York.
105
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Zhang, X., D. Endicott, and W. Richardson. 1998.
Transport Calibration Model With Level 2 Model
Segmentation Scheme. First Lake Michigan
Mass Balance Project Science Panel Review,
Southgate, Michigan. June 23, 1998. 12pp.
Zhang, X., W. Richardson, and K. Rygwelski. 2000.
Preparation and Verification Transport Field for
LMMBP Level 2 Contaminant: Transport and
Fate Models. Second Lake Michigan Mass
Balance Project Science Panel Review,
Southgate, Michigan. September 27, 2000. 15
pp.
Zhang, X. 2006. LM-2 Toxic. In: R. Rossmann
(Ed.), Results of the Lake Michigan Mass
Balance Project: Polychlorinated Biphenyls
Modeling Report, pp. 216-452. U.S.
Environmental Protection Agency, Office of
Research and Development, National Health and
Environmental Effects Research Laboratory, Mid-
Continent Ecology Division-Duluth, Large Lakes
Research Station, Grosse lie, Michigan.
EPA/600/R-04/167, 579 pp.
106
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PARTS
LAKE MICHIGAN MASS BALANCE PROJECT
LEVEL 3 MODEL: LM3-ATRAZINE
Timothy J. Feist
Xiaomi Zhang
Z-Tech, an ICF International Company
Large Lakes Research Station
and
Kenneth R. Rygwelski
William L. Richardson (Retired)
Russell G. Kreis, Jr.
United States Environmental Protection Agency
Office of Research and Development
National Health and Environmental Effects Research Laboratory
Mid-Continent Ecology Division
Large Lakes and Rivers Forecasting Research Branch
Large Lakes Research Station
9311 Groh Road
Grosse lie, Michigan 48138
5.1 LM3-Atrazine Executive Summary
Most previous Great Lakes water quality models
were developed using large spatial scales. These
models were capable of predictions on a lake-wide or
regional scale but were not suitable for evaluating
differences on smaller spatial scales, such as
between off-shore and near-shore concentrations.
The LM3-Atrazine model is a high-resolution model
that is suitable for evaluating fine-scale spatial and
temporal changes in water quality.
The LM3-Atrazine model was based upon the same
framework as the United States Environmental
Protection Agency's (USEPA) other LM3 models.
The hydrodynamic transport was provided by the
National Oceanic and Atmospheric Administration's
(NOAA) Great Lakes Princeton Ocean Model (POM).
The water quality framework was the same as used
by the LM3 chloride and eutrophication models.
Water quality components for the atrazine model
were developed at the USEPA Large Lakes
Research Station (LLRS) and included a small first-
order decay rate and volatilization. The model spatial
resolution consisted of a 5 km x 5 km horizontal grid
with 19 vertical layers, for a total of 44,042 model
cells. The model was run using a time step of three
hours.
Tributary loads, atmospheric loads, and boundary
conditions for the model were estimated as part of
the Lake Michigan Mass Balance Project (LMMBP).
Model simulations were conducted using tributary
loads for the 1994-1995 LMMBP period estimated by
107
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the United States Geological Survey (USGS) and
alternative loads with a spring seasonal adjustment
based upon long-term historical loading trends.
The LM3-Atrazine model could not be fully calibrated
because only one year of in-lake data and one year
of tributary loading data were collected during the
LMMBP. However, confidence in model results was
provided by the favorable comparison of model
results to available data without adjustment of kinetic
parameters, by successful calibration of the
hydrodynamic model, by successful calibration of the
high-resolution model transport (in the form of a
chloride model), and by the long-term hindcast
calibrations of coarser segmented atrazine models
using the same water quality kinetics.
The high-resolution model was useful in
demonstrating the effects of tributary loadings on
near-shore waterquality. Predicted mid-lake atrazine
concentrations varied annually less than 5 ng/L
during the two-year simulations. In comparison, the
model segment receiving loads from the largest
tributary, the St. Joseph River, ranged from winter
concentrations of 37 ng/L to spring peaks of 100-350
ng/L depending upon whether 1994-1995 or long-
term tributary loads were used in the simulation.
The USEPA collected atrazine samples from Lake
Michigan during the 2005 field season; however, the
results were not available at the time the atrazine
modeling was conducted. To estimate potential
expected concentrations, the LM3-Atrazine model
was run for the period 1994-2005. Loads were
estimated by repeating the 1994-1995 loading time-
series over the 12-year period. The model was run
using both the USGS-estimated loads from 1994-
1995 and loads based upon long-term trends. Mid-
lake concentrations were predicted to increase from
the 1994 concentration of 37 ng/L to between 38 ng/L
and 46 ng/L in 2005.
Inflows and outflows of atrazine from the Lake
Michigan system were tracked during the 1994-1995
model simulations. Outflow through the Straits of
Mackinac and decay losses were approximately
equal and were the largest loss terms. Tributary
inputs and atmospheric wet deposition were the
largest sources of atrazine. Atmospheric exchange
was minimal.
Model results and measured data were compared to
toxicological endpoints to examine possible
ecological effects of atrazine concentrations in Lake
Michigan. Most model forecast and data
concentrations were below the toxicological
endpoints of concern at the spatial scales used in
these modeling analyses.
5.2 LM3-Atrazine Recommendations
Because of its high-resolution (5 km x 5 km model
cells), LM3-Atrazine is useful to determine seasonal
effects of loadings to various cells. Of particular
interest may be the effects of high run-off in the
spring after application to cells at the mouths of major
tributaries. Within these cells, dramatic changes in
atrazine concentrations may be noted over relatively
short periods of time. Some of the highest
concentrations in the lake would most likely be found
at these sites. The lower-resolution models,
MICHTOX and LM2-Toxic, have coarse
segmentation and would not respond like the high-
resolution model to these spring/early summer high
loading events. In the coarse segmented model, the
load is instantaneously dispersed uniformly into the
much larger model segment volume receiving the
river load. Hence, a concentration spike would be
low compared to a high-resolution segment receiving
the equivalent load.
5.3 LM3-Atrazine Transport and Fate
Modeling
5.3.1 Purpose of High-Resolution Model
Historically, waterquality models for the Great Lakes
have been developed using large spatial scales. The
first eutrophication model for Lake Ontario (Thomann
and Di Toro, 1975; Thomann et a/., 1979) was
configured with only two vertical segments
(epilimnion and hypolimnion). Similar scale models
were also developed for Lake Erie (Di Toro and
Connolly, 1980), Lake Huron (Di Toro and Matystik,
1980), and Lake Michigan (Rodgers and Salisbury,
1981). Even a more recent model of Green Bay was
developed on a relatively coarse-grid scale (DePinto
et a/., 1993). These models were capable of
adequately simulating average water quality over
large spatial segments and projecting future
concentrations. However, they were not capable of
108
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simulating spatial concentration gradients very well,
if at all. Also, there have been questions on whether
limnological processes could adequately be
represented on such a large spatial scale, particularly
sediment transport. During the design phase of the
LMMBP, modelers were determined to construct a
higher-resolution model to overcome these
deficiencies.
The LM3 level models include linked high-resolution
hydrodynamic and water quality components. The
hydrodynamic component of the models was
developed by modelers at the NOAA's Great Lakes
Environmental Research Laboratory (GLERL). The
transport framework was based on the U.S. Army
Corps of Engineers' CE-QUAL-ICM model (Cerco
and Cole, 1994). The water quality components were
developed at the USEPA's LLRS. Completed water
quality components included a eutrophication model
(Pauer et a/., 2006), the atrazine model described
below, and, subsequent to the LMMBP, an
ecosystem model (Miller et a/., 2007).
Although the LM3 level models have many scientific
and technological advantages, there are major
challenges. First, the LM3 models required a much
greater degree of computer resources to develop and
operate. Second, they required more computer
programming support to develop completely new
programs. Third, because there are over 40,000
water segments for which concentrations are being
simulated, there is much more computer output to
manage and evaluate. This has presented disk
storage issues and has required additional effort to
develop computer programs to analyze and display
model output.
The following sections describe the LM3-Atrazine
model, the assumptions used in developing the
model, the loading data and lake concentrations used
for model confirmation, and the results of model
simulations for the 1994-1995 LMMBP period and for
forecasts.
5.3.2 Model Description and Framework
LM3-Atrazine, as with most mass balance models,
incorporates segment geometry, advective and
dispersive transport, boundary concentrations for
state variables, point and diffuse source loads, kinetic
parameters, constants and time functions, and initial
conditions. These input data, together with the
general mass balance equations and the specific
chemical kinetics equations, uniquely define a special
set of water quality equations. These equations are
numerically integrated as the simulation proceeds in
time. At user-specified print intervals, values of
selected state variables are saved for subsequent
evaluation in visualization and statistical post-
processor programs.
In the Great Lakes environment, atrazine has the
chemical properties of a mostly conservative
substance. The important functions of the LM3-
Atrazine model consist of hydrodynamic transport,
external loads, atmospheric exchange, and a small
first-order decay rate.
This section contains a description of the
hydrodynamic model, the kinetic processes of the
atrazine model, and the spatial and temporal
configuration of the atrazine model.
5.3.2.1 POM Hydrodynamic Model
The basis of the LM3 water quality model is water
movement and material transport. Hydrodynamic
simulations were conducted by Schwab and Beletsky
(1998) who applied the POM. Portions of the
following section are excerpted from their report.
Subsequent to the preparation of the report, Schwab
included annual average tributary flows and average
Straits of Mackinac outflow in the final submission of
model results to USEPA for use in mass balance
models. In addition, computational modifications
were made that eliminated a minor problem with
water balance [for a technical discussion of the
details, see Appendix A in Melendez et a/. (2008)].
The primary goal was to provide three-dimensional
fields of currents, temperature, and wind-wave
characteristics for the study period (1994-1995) for
direct input to the LM3 water quality model. The
model was applied to Lake Michigan using a 5 km x
5 km grid (Figure 5.1). The output of POM
simulations was provided to the water quality
modeling team at the USEPA/LLRS for further
translation for the water quality models.
109
-------
Figure 5.1. Lake Michigan hydrodynamic model
5 km x 5 km computational grid.
During the development of the POM for Lake
Michigan, the model was applied for two periods:
1982-1983 and 1994-1995. The first period was
chosen for model calibration because of an extensive
set of observational data including surface
temperature observations at two National Data Buoy
Center (NDBC) weather buoys and current and
temperature observations during June 1982-July
1983 at several depths from 15 subsurface moorings.
Results were output to files containing values for
each of the 5 km x 5 km cells at specified time
intervals. To compare model simulations with data,
model results were averaged over various time
periods depending on the data period. For example,
the simulated temperature time-series for the 1982-
1983 period are shown in Figure 5.2 and for the
1994-1995 period in Figures 5.3a and 5.3b.
Statistics of temperature field validation are
presented in Table 5.1 for 1982-1983 and Table 5.2
for 1994-1995. RMSD is the root mean square
difference (error) between observed and computed
temperatures. Maximum Error is the maximum
temperature difference. Average is the arithmetic
mean. The correlation coefficient provides a
statistical indication of the strength of the linear
relationship between computed and observed
variables.
The model was able to reproduce all of the basic
features of the thermal structure of Lake Michigan
during the 600 day period of study: spring thermal
bar, full stratification, deepening of the thermocline
during the fall cooling, and the overturn in the late fall
(Figure 5.4).
Another model validation was made by comparing
observed temperature profiles acquired during the
seven Great Lakes National Program Office
(GLNPO) water quality surveys during 1994-1995 to
simulated temperature profiles at 20 locations.
Figure 5.5 depicts one of these locations, Station
18M. In addition, the USGS conducted several near-
shore transect surveys and compared simulated and
observed temperatures.
Schwab and Beletsky (1998) provided additional
information on model development and validation.
The basic conclusion was that, overall, the models
simulated the large scale thermal structure,
circulation, and waves quite realistically on the 5 km
x 5 km grid. There were some qualifications,
however. First, lack of an ice model will be a serious
problem if the model is applied during a year with
normal or severe ice conditions. It will cause both
significant violations of the lake's heat balance and
errors in calculating transfer of momentum from air-
to-water because of the difference in surface
roughness of ice and water and momentum
absorption by the ice. The 1994-1995 POM
simulation assumed a constant uniform water
temperature of 2°C for the period January 1 to
March 31, 1994. Because no hydrodynamic data
110
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JULIAN DAYS, 1982-83 JULIAN DAYS, 1982-83
Figure 5.2. Simulated temperature (black) compared to measured temperature (gray) at two buoys in
Lake Michigan for 1982-1983 (Schwab and Beletsky, 1998).
111
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Surface Temperature at 45007
o
a?
3
as
30
25
20
15
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5
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100 200 300 400 500 600
Temperature at 42 m
100 200 300 400 500 600
3
30
25
20
15
10
Temperature at 102 m
5
0
100 200 300 400 500 600
JULIAN DAYS, 1994-95
Figure 5.3a. Time-series of simulated watertemperature versus observed at 45007 for 1994-1995. Gray
line is observation; black line is model simulation (Schwab and Beletsky, 1998).
112
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o
5
CO
***
-------
Simulated Average Temperature of L. Michigan
100
100 200 300 400 500 600
JULIAN DAYS, 1982-83
Figure 5.4. Simulated mean temperature (°C) profile for 1982-1983 (Schwab and Beletsky, 1998).
Cruise 1 Day 126 Year 94
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Cruise 3 Day 234 Year 94
Cruise 4 Day 303 Year 94
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Temperature (C)
Cruise 6 Day 225 Year 95
50
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150
200
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Temperature (C)
Cruise 7 Day 281 Year 95
50
£100
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0 5 10 15 20 25 30 0 5 10 15 20 25 30
Temperature (C) Temperature (C)
Figure 5.5. Temporal evolution of simulated versus observed temperature profiles, Station 18M
(Schwab and Beletsky, 1998). Black line is model simulation; gray line is observation.
114
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were available after December 21, 1995, the LM3-
Atrazine model used the corresponding 1994 data for
the last 10 days in 1995.
Second, the model did not perform as well in the
thermocline area as it did near the surface. The
simulated thermocline was too diffuse. Although this
problem might be overcome by development of a
higher-resolution model, this problem is probably not
significant for the mass balance study in comparison
to other uncertainties with data and chemical and
biological processes.
Lastly, while the MlCHTOXand LM2-Atrazine models
have bidirectional flow through the Straits of
Mackinac, the present configuration of LM3-Atrazine
only uses a net, annual average outflow. In reality,
there is a return flow to Lake Michigan at the Straits
during stratification for a period of approximately 100
days in the summer. However, to include this
process within POM would have required significant
additional resources including the running of a
simultaneous Lake Huron hydrodynamic model. The
absence of bi-directional flow at the Straits was not
expected to have a significant impact on circulation
predictions in the main portion of the lake.
5.3.2.2 Model Framework
The LM3-Atrazine model uses the same computer
code and spatial resolution as other LM3 models
(LM3-Eutro and LM3-Eco). Detailed documentation
of the LM3 models has been provided by Melendez
etal. (2008). The documentation provides a history
of the models' development and a complete
description of the model framework, equations, and
use. Documentation of the LM3-Eutro application is
included in Pauerefa/. (2006). Version 3.2.15 of the
LM3 model code was used for the LM3-Atrazine
model analyses.
The transport model incorporated within the LM3
framework was based on the ULTIMATE QUICKEST
transport scheme, originally developed by Leonard
(1991) and subsequently augmented for use with
variable grid sizes by Chapman et a/. (1997). The
transport algorithm was coded in Fortran and
previously applied to the Chesapeake Bay model
(CE-QUAL-ICM) (Cerco and Cole, 1994,1995). The
transport model calculation performed numerical
integration of spatially varying concentrations using
quadratic interpolations of the concentration to infer
its value at flow faces and analytic integration over
space- and time-variables to account for changes in
the concentration at the cell wall during the course of
the time step. Further details of the dimensional
derivation of the ULTIMATE QUICKEST transport
method can be found in Melendez et a/. (2008).
Because atrazine is relatively stable in Lake
Michigan, only a subset of the model's kinetic
processes were used: hydrodynamic transport,
atmospheric exchange, and degradation. These
processes, and the spatial and temporal resolution
used in the simulations, are described below.
5.3.2.2.1 Water Quality Processes
The LM3 models are mass balance models based on
the principle of conservation of mass. They use the
same finite segment modeling approach used in the
USEPA-supported WASP4 and the CE-QUAL-ICM
modeling framework. The models describe where
and how a mass of constituent is transported and
transformed. The mass of a chemical or solid in
each water segment is controlled by water movement
between adjacent segments, solids and chemical
dynamics within the system, internal and external
loads, and boundary concentrations.
For LM3-Atrazine, external loads, hydrodynamic
outflow, and chemical transformation are the most
significant processes affecting atrazine
concentrations in Lake Michigan. Atmospheric
exchange (volatilization and absorption at the
water/air interface) was also included in the model
kinetic process, although the mass involved is
considerably smaller than that involved with outflow
or chemical transformation. Atrazine does not
partition onto solids. Thus the settling and sediment
interaction portions of the LM3 water quality model
were not utilized.
Mass balance equations representing the above
processes were used in the model to compute the
change of mass of atrazine in each segment at a
certain time. A general time-dependent finite
differential equation in a given segment can be
written to describe the change of mass for a state-
variable at a certain time. The change in mass of
atrazine in the LM3-Atrazine model for a given water
column segment is described as:
115
-------
where
n
R
(5.3.1)
Vj = volume of segment] (L3)
Cj = concentration of water quality constituent in
segment] (M/L3)
C, = concentration of water quality constituent in
segment I (M/L3)
C,j = concentration of water quality constituent at
the interface between segment I and j
(M/L3)
QIJ = net flow across the interface between
segment I and j (defined as positive when
entering segment j and negative when
leaving segment]) (L3/T)
= number of adjacent segments
, bulk dispersion/diffusion
coefficient (L3/T)
= mixing (dispersion/diffusion) coefficient
= interfacial area between segment I and j
(L2)
= characteristic mixing length between
segments I and j (L)
= external loading rate of segment] (M/T)
= mass change rate due to air-water
exchange process between segment] and
air directly above segment] (M/T)
= mass change rate due to sum of kinetic
transformation processes within segment]
(M/T), positive is source, negative is sink
Note: L = length; M = mass; T = time.
The mass change due to kinetic transformation
processes, SkJ, is represented in the atrazine model
by a single first-order decay rate. The decay
coefficient was determined during the long-term
hindcast simulations using the MICHTOX and LM2-
Atrazine models (Parts 3 and 4 of this report) and
was set at 0.009 year1 (2.854 x 10'10 s'1).
Gas exchange (volatilization and absorption) of
atrazine between the lake and the atmosphere is a
potential source or loss of atrazine to Lake Michigan.
Computing the atrazine mass transfer across the
water-air interface was necessary to satisfy the
overall atrazine inventory and mass budget in the
Lake Michigan system for the LMMBP period. The
mass change rate term (SawJ) for atrazine due to air-
water exchange processes was calculated in
Equation 5.3.2 as a product of the overall net mass
exchange flux and surface area of the water
segment].
(5.3.2)
where
kol = the overall mass exchange rate coefficient
(L/T)
CHail = dissolved atrazine concentration in water
'dwj
(M/L3)
CaJ = atmospheric atrazine concentration over
segment] (M/L3)
H' = temperature-dependent Henry's law
constant (dimensionless)
Aj = surface area of the water segment j (L2)
The overall mass exchange rate coefficient (/c0/) was
calculated using the Whitman two-film theory
formulation (Whitman, 1923) given as:
(5.3.3)
kt kg*Hl
116
-------
where
k, = the liquid film mass transfer rate coefficient
(L/T)
kg = the gas film mass transfer rate coefficient
(L/T)
The parameters H', k, and kg were calculated at
every time step for each LM3 segment. The
Wanninkhoff (1992) formulation for water mass
transfer resistance and the Schwarzenbach
(Schwarzenbach et a/., 1993) formulation for gas
mass transfer resistance were used for modeling the
air-water exchange of atrazine in Lake Michigan.
The Wanninkhoff equation for k,, with correction for
atrazine molecular diffusivity in reference to carbon
dioxide (CO2) molecular diffusivity across the air-
water interface, is given as:
(5.3.4)
where
u
10
= chemical molecular diffusivity in water
= CO2 molecular diffusivity in water (L2/T)
= wind velocity measured at 10 m above
water surface (L/T)
The Schwarzenbach formulation for kg with correction
of atrazine molecular diffusivity in reference to water
vapor molecular diffusivity across the air-water
interface is given as:
(5.3.5)
where
Da = chemical molecular diffusivity in air (L2/T)
Dg_H20 = water vapor molecular diffusivity in gas
phase (L2/T)
The atrazine model calculated a temperature-
corrected dimensionless Henry's law coefficient using
equations derived from Scholtz et a/. (1999) and
Miller (1999).
2.3Q3FI
where
(5.3.6)
H' = temperature-dependent Henry's law
constant (dimensionless)
HTref = Henry's law constant at the reference
temperature
AHH = the enthalpy of phase change (kJ/mol)
R = the ideal gas constant, 8.315 x 10"3
kJ/(mol)(°K)
T = interfacial temperature (°K)
' ref
= reference temperature of 298.16 K (25°
C)
The value for the dimensionless Henry's law constant
at 25°C was set to 8.1 x 10'8 (U.S. Department of
Agriculture, 2001). The enthalpy of phase change
was set to 50 kJ/mol (Scholtz et a/., 1999; Miller,
1999).
5.3.2.2.2 Spatial Resolution
Developing the high-resolution grid for the LM3
models required compromises between different
spatial configurations and the difficulties in translating
the 5 km x 5 km grid hydrodynamic output. The best
approach was to develop the fine-grid model at the
same 5 km scale as the POM (Figure 5.1). The high-
resolution LM3 grid consisted of 2,318
horizontal segments with 19 vertical "sigma" layers,
resulting in a total of 44,042 water column cells.
A linkage between POM and the LM3 model was
developed by Chapman et al. (1997). The linkage
mapped POM cell numbers with ULTIMATE
QUICKEST flow face numbers and the relationship
between horizontal and vertical components. LM3-
Atrazine inputs included POM output for water
temperature, horizontal and vertical dispersion, and
horizontal and vertical currents for each segment in
the water column.
117
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5.3.2.2.3 Temporal Resolution
The LM3-Atrazine model simulated the period from
January 1, 1994 through December 31, 1995 for the
LM M BP study period. To forecast the possible range
of atrazine concentrations expected in Lake Michigan
during the 2005 sampling surveys, the model was
also run for the 12-year period January 1, 1994-
December31, 2005.
The LM3-Atrazine model was run using a variable
time step based upon model stability. Over the
course of the 1994-1995 simulation, the average
value of the time step was approximately three hours.
Output from the POM hydrodynamic model was
averaged over three-hour intervals for input to the
LM3-Atrazine model. LM3-Atrazine model results
were output at a daily interval for two-year model
runs. Atrazine almost behaves as a conservative
constituent in Lake Michigan (has an extremely slow
chemical transformation), and daily behavior
provided sufficient resolution for interpretation of
simulation results. Results from some of the 12-year
forecast model runs were output at a six-day
frequency to maintain reasonable output file sizes for
long-term output animations.
5.3.2.2.4 Model Assumptions
The conceptualization of processes in the LM3-
Atrazine model was based upon literature review
(Part 1, Chapter 2) and previous LLRS atrazine
modeling efforts (Part 3 and Part 4). Atrazine
essentially behaves as a conservative substance in
Lake Michigan. Previous LLRS modeling
demonstrated that external loading and outflow from
the Straits of Mackinac were the most important
processes effecting atrazine concentrations in the
lake (Part 3; Part 4; Rygwelski et a/., 1999).
Although it occurs slowly, degradation of the
chemical is also an important process because of the
slow rate of export. Exchange between the water
surface and atmosphere was modeled, although it
only had a small effect on lake concentrations.
Atmospheric loads were assumed to be primarily
through wet deposition. Dry deposition was not
found to be significant based upon LMMBP sampling
reports (Brent et a/., 2001; Miller, 1999). Sections
1.3.2.2.2 and 1.3.2.2.3 of this report summarize the
atmospheric deposition sampling. Later papers have
suggested that dry deposition may be significant
(Miller et a/., 2000) but only provided a range of
possible loads and no spatial or temporal resolution
consistent with the LM3 models. The range of
possible dry deposition loads was taken into
consideration when estimating loads for long-term
forecasts.
Atrazine is primarily found in the dissolved state in
Lake Michigan, and sediment interactions with
atrazine are minor (Part 1, Chapter 2; Rygwelski et
a/., 1999). Sediment processes were assumed to be
negligible and were not included in the LM3-Atrazine
model kinetics.
5.3.3 Description of Data Used
The data used for the LM3-Atrazine modeling was
collected during the 1994-1995 LMMBP studies. The
data were reviewed in Brent et al. (2001) and
summarized in Part 1, Chapter 3 and Part 2 of this
report.
5.3.3.1 Field Data
Model simulation results were compared to data
collected during the LMMBP field surveys. Lake
water samples were collected for atrazine analysis
during six cruises from April 1994 through April 1995.
Data from mid-lake stations were selected for
comparison purposes because these stations were
sampled during most cruises. While data were also
collected from near-shore stations, these stations
were not routinely sampled.
5.3.3.2 Initial and Boundary Conditions
The Lake Michigan atrazine model initial
concentrations were estimated based upon the
LMMBP field survey data. A uniform concentration of
37 ng/L was set for all main lake and northern Green
Bay model cell initial concentrations based upon the
average concentrations measured during the spring
1994 sampling cruise. Southern Green Bay cells
nearest to the Fox River were assigned an initial
concentration of 50 ng/L based upon the limited
Green Bay sampling from the fall 1994 and spring
1995 cruises.
While the MICHTOX and LM2-Atrazine models have
bidirectional flow through the Straits of Mackinac, the
118
-------
present configuration of LM3-Atrazine only uses a net
outflow. For this assumed configuration, Lake Huron
boundary conditions are not necessary because
there is no flow to Lake Michigan at the Straits of
Mackinac.
Atmospheric atrazine vapor samples were collected
from March 1994 through October 1995.
Atmospheric sampling did not detect vapor phase
atrazine concentrations in 86% of the samples (Brent
et a/., 2001). For modeling purposes, the
atmospheric concentration for all locations and times
was set to a single value equal to one-half the
average method detection limit (MDL) of the
samples, 4.63 pg/m3 (Miller 1999).
5.3.3.3 Loadings
5.3.3.3.1 Tributary
Watershed atrazine loadings to Lake Michigan were
estimated by Hall and Robertson (1998). Loads were
calculated for 11 tributaries that were sampled as
part of the LMMBP field program and for 18
unmonitored watersheds (Figure 5.6). For the
monitored tributaries, event and base flow samples
were collected from April 1995 through October
1995. The Stratified Beale Ratio Estimator (SBRE)
was used to calculate loads for 1995 with these
sample data and the USGS flow data. Because
tributary samples were not collected in 1994, loads
for 1994 were estimated using USGS regression
methods and the 1995 data (Hall and Robertson,
1998). Loads for the unmonitored watersheds were
estimated using load to watershed area ratios from
monitored watersheds with similar soils and land
uses. Part 2, Chapter 2 discusses the tributary loads
in more depth.
The USGS estimated loads for 1994-1995 were
substantially smaller than what would have been
expected based upon long-term loading patterns.
Rygwelski et a/. (1999) reviewed previous studies
and found that, for soils similar to those in the corn-
producing watersheds of Lake Michigan, 0.6% of the
atrazine applied to the watershed reached Lake
Michigan. This amount is also referred to as the
Watershed Export Percentage (WEP). Rygwelski et
a/. (1999) also conducted long-term hindcast atrazine
modeling that confirmed the appropriateness of the
0.6% WEP (see Parts 3 and 4). The 1994-1995
Lake Michigan
watersheds
monitored
tributary
basins
unmonitored
tributary
basins
sampling
locations
Figure 5.6. Watershed and mid-lake sampling
stations for the LMMBP study.
USGS estimated tributary loads were only 30% of the
load estimated using the long-term WEP and 1994-
1995 atrazine application data for the Lake Michigan
watershed. Using the USGS-estimated loads for
1995, a WEP of 0.12% was calculated. Using this
WEP derived from the USGS load and no atrazine
decay in a MICHTOX hindcast, the model-predicted
less than one-half of the measured concentration in
the lake as observed in the mid-1990s.
The 1994-1995 loads may have been substantially
lower because of a number of possible factors: loads
were lower than normal due to the WEP possibly
decreasing over time because of improved
agricultural management practices, significant peaks
in tributary loads may have been missed because the
weekly storm event sampling was discontinued too
early, or atmospheric dry deposition may be higher
than expected. It is known that dry years can
depress atrazine watershed loadings. However,
119
-------
precipitation to the lake was near long-term averages
(see Sections 1.3.2.2.3, 1.4.5.1, 1.4.5.2, and 2.2.1.2
for information on rainfall and impact on WEPs).
Other potential meteorological forcing functions were
also near average conditions during 1994-1995 (see
Part 1, Chapter 4). Therefore, weather conditions
are an unlikely cause of the low USGS export
estimates.
The USGS loadings were based on an average of 14
atrazine samples per year per tributary (range:
seven to 20). When compared to other similar
atrazine load estimation studies (Schottler et al.,
1994; Richards et a/., 1996; Capel and Larson,
2001) this represents a very low number of samples
collected and thus could have contributed to
underestimation of loads. A study by Leu et al.
(2004) found that a single run-off event that occurred
on day 23 after application of atrazine exported 70%
of the total cumulative load measured during a one-
to 67-day period after application. On a fine-loamy
field in Ohio, a rainstorm occurred just two days after
atrazine application to a no-till field. That rain event
accounted for only 3% of the yearly rainfall and 6% of
the yearly run-off; yet it produced 78% of the yearly
atrazine loss (Shipitalo and Owens, 2003). The
Shipitalo and Owens' study also concluded that the
timing of rainfall and run-off relative to atrazine
application can have a much greater effect on yearly
losses than agronomic management practices (till
versus no-till). So, a lack of adequate sampling
during an event shortly after atrazine application
could cause significant underestimation of the total
annual loading from a watershed. Also, Schottler et
al. (1994) and Williams etal. (1995) have noted that
the spring atrazine concentration often peaks in
streams just before the maximum flows are
achieved. One possible theory suggests that a
fraction of atrazine on the soil immediately following
application is readily available for transport by run-off
during a precipitation event. However, later in the
season, the peak concentration may actually lag the
peak flow suggesting that export from the fields is
associated with water that has infiltrated the soil and
carried via shallow saturated zones or surface
drainage tile networks to receiving tributaries. High
frequency sampling just before, during, and after a
flow event are important in order to fully capture
atrazine loading events.
To evaluate the possible range of loads occurring in
the Lake Michigan system, additional model runs
were conducted with annual loads set equal to those
expected based upon the long-term WEP of 0.6%.
The USGS loads were adjusted by multiplying loads
from each tributary during a 90-day period from April
15 to July 13 by a factor that resulted in the loads for
that tributary being equal to the expected WEP-
based load. Only the spring period was multiplied
since this is the period when the majority of atrazine
loads enter the lake and this is the period when
tributary and atmospheric loads have the largest
uncertainty. Load multiplication factors were
calculated as the multiplier for the specified time
period loads that set the total 1994 and 1995 USGS-
calculated loads for each tributary equal to the
combined WEP loads for both years for that tributary.
Computer code in the LM3-Atrazine model conducted
the multiplication during the model simulation by
reading inputs for the scaling time periods for each
year and the multiplication factors for each tributary.
WEP-based loads and USGS-estimated loads are
listed in Section 2.2.5. Figure 5.7 displays the USGS
and WEP-based loading time-series for the three
largest tributary loadings of atrazine to Lake
Michigan.
A loading series for the 12-year model runs was
developed by repeating the loads for the 1994-1995
period six times, using USGS-calculated or WEP-
adjusted loads as appropriate. The LM3-Atrazine
model did this automatically by looping over the two-
year loading input deck and applying the load
multiplier factors as needed.
In Figures 5.7-5.10 and for the remainder of this part,
the "long-term WEP loads" in the legends refer to
USGS loads that were adjusted as described in the
preceding paragraphs and "USGS estimated loads"
refer to the loads as received from the USGS.
5.3.3.3.2 Atmospheric
Atmospheric deposition samples were collected from
March 1994 through October 1995. Wet deposition
was the dominant atmospheric source of atrazine to
Lake Michigan. The monthly average 1994 and 1995
wet deposition loading time-series data were
provided by Hornbuckle (University of Iowa, personal
communication, 2002; Miller et al., 2000). Dry
deposition was not included in the atmospheric loads.
120
-------
O
0
-------
40-
I 30-
«
0 -
Station 27M
A JL A
01 ^
30-
USGS estimated loads
A field data (0-1 Oml
Station 47M
Z
A *
Jan July Jan July Jan Jan-1994 JuIy-1994 Jan July Jan
1 994 1 994 1 995 1 995 1 996 1 995 1 995 1 996
40-
1» 30-
g
c
g 20-
Station 18M
40-
0130-
i
5 20-
A Stations 40M/41
A
Jan July Jan July Jan Jan July Jan July Jan
1994 1994 1995 1995 1996 1994 1994 1995 1995 1996
Figure 5.8. Comparison of field data to predicted mid-lake surface concentrations for the 1994-1995
model simulation and two loading conditions. Station locations are shown in Figure 5.6.
May 29. 1995
600 •
1 Manistique ^_V ~~-\
42
41
40
39
w r
Menominee ^HP? /s \
^Br/ f^f^m
Oconto^BW/ f \j/
Fox* /
Manitowac ;, ji /
ff M^Pere Marquette
Sheboygan«f /
/ K^Muskegon
Milwaukee *« W Grand
Root <• m '
/f Kalamazoo
38
Calumet*
St. Joseph
37
Atrazine
(ng/L)
Figure 5.9. Model simulation results of surface concentrations for May 29,1995 using long-term WEP-
based loads. Selected tributary input locations are labeled.
122
-------
-250-
c
liso-
"100-
50-
0-
• long term WEP loads
""""""i USGS estimated loads
.1
segment181 near J||
St. Joseph River mouth |lj
SCr 1 ft
o* Mk
F
Fox' i
Rirer /
Jan
1994
July
1994
Jan
1895
July
1995
Jan
1998
SI. Joseph River
Figure 5.10. Comparison of near-shore surface cell model results for the 1994-1995 model simulation
and two loading conditions.
While the model is useful for demonstrating near-
shore impacts, it does not simulate concentrations in
a river plume entering the lake or in the tributary
itself. During model simulations, at each time step,
any tributary load is immediately mixed throughout
the 5 km by 5 km model cell near the tributary mouth,
and thus predicted near-shore concentrations are a
function of the size of the model cells and not
representative of concentrations in river plumes in
the lake. Furthermore, the LM3-Atrazine model was
not designed to make predictions of atrazine
concentrations in any of the tributaries.
The model results reasonably fit the available data,
and no adjustments were made to the initial model
kinetic parameters. A better model fit to data
probably could have been obtained by using different
initial concentrations in different regions of the main
lake rather than a uniform, lake-wide initial
concentration. However, since the January 1994
initial concentrations were estimated from spring
1994 data, it was believed that the sampling data
were not sufficient to justify that change.
A longer-term data set would be required to fully
calibrate the LM3-Atrazine model. However,
confidence in model predictions was provided in two
ways. First, atrazine in Lake Michigan acts as a
mostly conservative chemical, and the model was
previously calibrated to the conservative chemical
chloride in Lake Michigan (Richardson et a/., 2001).
Thus there is confidence that the transport of
substances, one of the primary loss processes of
atrazine, was being correctly simulated. The high-
resolution transport is the primary difference between
LM3-Atrazine and the MICHTOX and LM2-Atrazine
models. Second, an acceptable simulation of
atrazine concentrations was obtained using model
parameters derived from literature and previous
modeling studies, providing confidence in the kinetic
formulations and kinetic parameterization of the
model (Rygwelski et a/., 1999; Part 3; Part 4).
As part of the 2005 Lake Michigan sampling effort,
the USEPA collected atrazine samples at multiple
stations during multiple cruises. These data were not
available at the time of this report, but, when
available, they will provide a comparison of atrazine
concentrations to those measured in 1994-1995 and
an estimate of the change in atrazine mass in the
system over that time period. To estimate potential
changes in Lake Michigan atrazine concentrations
during the 1994-2005 time period, additional LM3-
Atrazine model simulations were conducted. Two
123
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model runs were conducted: one with the 1994-1995
USGS-estimated loads and one with the WEP-based
loads. The 12-year loading time-series for these runs
was developed by repeating the appropriate 1994-
1995 loading time-series and hydrodynamics six
times. Assuming that atrazine usage in the
watershed did not change significantly from that
during 1994-1995, results from these model
simulations will likely bracket the concentrations from
the 2005 sampling period. Predicted mid-lake
concentrations for 2005 ranged from 38 ng/L for the
USGS tributary loading time-series to 46 ng/L for the
WEP-based tributary loading time-series (Figure
5.11). Tributary loads were not sampled during the
2005 surveys, but by calculating the change in the in-
lake atrazine inventory and comparing it to the load
scenarios used for model runs, the actual magnitude
of present watershed loads will be able to be
estimated.
5.3.4.1 Mass Budgets
Inflows and outflows of atrazine to the Lake Michigan
system were tabulated during the 1994-1995 model
simulation runs (Table 5.3, Figure 5.12). For the
model run using the USGS-estimated tributary loads
for 1994-1995, the largest source of atrazine was wet
deposition from the atmosphere. The percentage of
loads from tributary sources was only slightly less.
For the model run using the long-term WEP-based
1994-1995 tributary loads, tributary loads dominated
and were almost three times higher than wet
deposition. Absorption from the atmosphere
(volatilization in) was minimal for both cases.
Losses from the Lake Michigan system were similar
for both loading scenarios. The largest losses of
atrazine were from decay and outflow through the
Straits of Mackinac, though the mass lost through
these processes is relatively small compared to the
total atrazine inventory in the lake. Outflow through
the Chicago Ship and Sanitary Canal was a small
percentage of total mass lost, and volatilization from
water to the air was negligible.
The annual net gain of atrazine to the system for the
model run using the USGS-estimated loads was 380
kg/year, or 11 % of the measured 1994-1995 loads to
the system. For the model run using long-term WEP-
based load estimates the net gain increased to 3,842
kg/year, equal to 55% of incoming sources.
5.3.4.2 Selected Model Versus Observation
Statistics
The variability in the field data made any comparison
with model results difficult. There was as much
variation between atrazine field duplicate samples as
there was seasonal variation predicted by the model.
Fifty-seven field duplicate and two field triplicate
samples were collected as part of the LMMBP
atrazine sampling. The median absolute difference
between field duplicate samples was 1.8 ng/L, with
the average relative percent difference (RPD) equal
to 6%. Maximum seasonal variation in model results
from representative mid-lake stations was 1.5 ng/L
for the model run using USGS-estimated loads and
6.6 ng/L for the model run using WEP-based loads.
There were also no significant spatial or temporal
trends in the Lake Michigan data (Brent et a/., 2001)
that would have assisted in evaluating model
prediction capabilities. This may have been due to
an actual lack of trends or because there was no
near-shore sampling during the late spring and early
summer period when the lake concentrations were
predicted to be most affected by seasonal
atmospheric and tributary loadings.
5.3.4.3 Comparison to Toxicological Endpoints
Model simulation and forecast results were plotted
with measured data against toxicological endpoints to
examine potential ecological effects of predicted
atrazine concentrations in Lake Michigan (Figure
5.13). Most forecast and data concentrations were
below the selected toxicological endpoints of concern
at the spatial scale used in these modeling analyses.
The toxicological endpoints selected for Figure 5.13
were developed as part of a review of toxicity studies
used for determining the eligibility of atrazine for
reregistration as an herbicide (U.S. Environmental
Protection Agency, 2003a). Endpoints for important
ecological components of the Lake Michigan system
included fish, zooplankton, other invertebrates, and
phytoplankton. Mortality endpoints correspond to
acute, or short-term, toxicity studies. Growth or
population reduction endpoints correspond to
chronic, or long-term, toxicity studies.
124
-------
^r 40-
1
a 20
10-
Station 27 M
,_^_
V '
Jan Jan Jan
1994 1997 2000
SO -
j40-
« 30-
JE
l»-
Station ISM
— ^OJ^,.J^.-*"' • ' '
I
fl
1
1
7i
Jan Jan
2003 2006
long term WEP loads
— USGS esfcmated loads
- '
Jan Jan Jan
1994 1997 2000
i
Jan Jan
2003 2006
•40-
)
' 30 -
20-
Station 47M
_„ «^—-~— ~~
Jan Jan Jan Jan Ja
1994 1997 2000 2003 2
-------
wet
deposition
1784
volatilization
out
13
volatilization
in
58
Chicago
River
outflow
110
Mackinac
outflow
1302
USGS loads
Atrazine Inventory
water column = 179,459 kg
mass change = + 380 kg/yr
1578
monitored and unmonitored
tributary loading
(Lake Michigan watershed)
wet
deposition
1784
volatilization
out
14
volatilization
in
58
decay
1647
Mackinac
outflow
1313
Long-term WEP loads
Atrazine Inventory
water column = 182,979 kg
mass change =+ 3842 kg/yr
Chicago
River
outflow
112
5086
monitoted and unmonitored
tributary loading
{Lake Michigan watershed)
Figure 5.12. Mass budget average annual results for the 1994-1995 model simulations. All mass
flow rates are in kg/yr.
126
-------
10,000=1
1.000 -=
o> 100 —
10 •=
1 •=
0,1 -=
0.01
fish mortality (5300)
draft acute toxicity criteria CMC (1500)
invertebrate mortality (720)
invertebrate population reduction (62)
fish population reduction (62)
phytoplankton acute toxicity (32)
zooplankton population reduction (10)
human drinking water MCL (3)
o measured data
model results
endpoint/criteria
maximum measured 1995 tributary concentration
in St. Joseph River (2.7)
phytoplankton primary production reduction (2.3)
highest Lake Michigan predicted concentration,
USGS 1994-1995 loads (0.10) -St. Joseph River mouth
Lake Michigan 2263 MICHTOX forecast concentration (0.066)
Lake Michigan 2005 LM3-Atrazine forecast range (0.038 - 0.046)
Lake Michigan 1994 average concentration (0.037)
Figure 5.13. Comparison of model predictions, measured data, and selected toxicological
endpoints.
Regulatory endpoints were also included in Figure
5.13. These endpoints included proposed criteria for
environmental protection and established limits for
human health protection. Water quality criteria for
the protection of aquatic ecosystems have been
proposed foratrazine (U.S. Environmental Protection
Agency, 2003b) but were not finalized at the time of
this report. The draft acute toxicity Criterion
Maximum Concentration (CMC) was included in
Figure 5.13. While a draft chronic criteria was also
published, it was not included in the figure because
it was not based upon a single concentration. The
draft chronic criteria were based upon modeling
ecological community changes in aquatic plants
using both exposure concentration and duration. The
human drinking water Maximum Contaminant Limit
(MCL) is also included in the graph.
Measured atrazine data collected during the LMMBP
were below endpoints of toxicological concern except
for one tributary sample from the St. Joseph River in
May 1995. This measurement, 2.7 ug/L, exceeded
the endpoint of 2.3 ug/L at which reductions in
primary production of phytoplankton were estimated
to occur. The St. Joseph River sample was also
close to the human drinking water MCL. Detailed
information on determining compliance with the MCL
for atrazine can be found in 40 CFR 141.24(h). The
second highest measured tributary concentration,
0.55 ug/L, was a sample from the Grand River in May
1996 and was below all selected toxicological
endpoints. The 1994 Lake Michigan annual average
atrazine concentration of 0.037 ug/L was well below
the selected toxicological endpoints.
Model forecasts were below all selected endpoints.
The MICHTOX long-term steady-state forecast
concentration of 0.066 ug/L was well below
toxicological endpoints. The LM3-Atrazine 12-year
(2005) forecast lake-wide concentration range was
lower than the MICHTOX steady-state forecast
concentration. The highest simulated single model
cell concentration from the high-resolution LM3-
Atrazine model was also below selected endpoints.
127
-------
The highest simulated concentration, using the
USGS loading time-series, was 0.10 ug/L near the
mouth of the St. Joseph River. It must be
remembered that this concentration represents an
average prediction from a volume representing a 5
km by 5 km area of the lake which provides
significant dilution to tributary event loads. The
WEP-based loading time-series was not used in this
analysis because the distribution of the long-term
annual loads among seasons and short-term loading
events was somewhat subjective. Thus, presenting
a concentration prediction based upon this loading
time-series at a single location and point in time
would have a large amount of uncertainty.
5.3.5 Model Uncertainty
While the LM3-Atrazine could not be fully calibrated
because of insufficient data, the basis upon which the
model was developed provided confidence that
model results were reasonable. The hydrodynamic
model was successfully compared to two separate
datasets (Schwab and Beletsky, 1998) and model
transport of a conservative substance, chloride, was
also calibrated (Richardson et a/., 2001). The only
additions to the chloride model for the LM3-Atrazine
model were volatilization and kinetic decay terms.
Volatilization was a minor effect on the fate of
atrazine in the lake, and the decay term was based
upon long-term hindcast calibrations with the
MICHTOX and LM2-Atrazine models. Furthermore,
the model provided reasonable fits to data without
changing model kinetic parameters from the initial
values based upon literature studies and previous
atrazine model calibration studies. There may be
some uncertainty about the decay term because the
LM2-Atrazine model used to calibrate the term
incorporated bi-directional flow at the Straits of
Mackinac outflow while the LM3-Atrazine model used
a net outflow from the Straits to Lake Huron.
However, this would only have had a minor effect on
the atrazine mass in the lake for the time periods
modeled with the LM3-Atrazine model.
There was probably more uncertainty from the
loading data used in the model and the field data
than from the model kinetic processes. The
estimated 1994-1995 tributary loads were
significantly less than those expected based upon
previous long-term modeling studies, and it was not
known if 1995 was a year of low atrazine loading, if
storm events were missed during the tributary
sampling, or if there were additional significant
sources of loads such as dry deposition that were not
measured.
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Capel, P.O. andS.J. Larson. 2001. Effect of Scale
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130
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PART 6
REVIEW OF ATRAZINE MODELS
Kenneth R. Rygwelski
United States Environmental Protection Agency
Office of Research and Development
National Health and Environmental Effects Research Laboratory
Mid-Continent Ecology Division
Large Lakes and Rivers Forecasting Research Branch
and
Timothy J. Feist and Xiaomi Zhang
Z-Tech, an ICF International Company
Large Lakes Research Station
9311 Groh Road
Grosse lie, Michigan 48138
6.1 LMMBP Atrazine Models
6.1.1 Peer Reviews of LMMBP Atrazine
Models
Two modeling science peer reviews were conducted
on the Lake Michigan Mass Balance Project
(LMMBP) atrazine modeling products. These
reviews were conducted near the beginning and final
phases of the atrazine modeling work. The first
review was general in nature and was conducted on
June 23-25, 1998 in Southgate, Michigan and
covered all components of the LMMBP modeling
effort including project design and organization;
project management, including an evaluation of
resources; model linkages; sediment transport;
loadings; hydrodynamics; model construct; atrazine;
polychlorinated biphenyls (PCBs); eutrophication;
and mercury. The second review was conducted on
September27,2000 in Romulus, Michigan and solely
focused on atrazine modeling.
In the first review, panel members recommended that
atrazine modeling advance to a level 2 type model
(LM2-Atrazine) with more resolution than MICHTOX.
Also, they recommended thatmanagementscenarios
for the prediction of alternative futures include model
sensitivity runs that include both zero atrazine
concentrations in the vapor phase and non-zero
concentrations, because measurements of the vapor
phase concentrations in the basin were difficult to
detect (see Part 4 for LM2-Atrazine modeling
results). Reviewers encouraged the development
and application of the high-resolution model, LM3-
Atrazine (see Part 5 for results of LM3-Atrazine, a 5
km x 5 km gridded model). The reviewers included
United States Environmental Protection Agency
(USEPA), Great Lakes National Program Office
(GLNPO); Dr. Paul Capel, United States Geological
Survey (USGS); Dr. Miriam Diamond, University of
Toronto; Dr. Kevin Farley, Manhattan College; Dr.
Raymond Hoff, Environment Canada; Dr. Robert
Hudson, University of Illinois - Urbana Champaign;
and Dr. Barry Lesht, Argonne National Laboratory.
131
-------
Review comments from the second peer review
appear in Appendix 6.1 of this Part. In general,
comments received on atrazine modeling for the
LMMBP were very favorable. Reviewers included
USEPA/GLNPO; Dr. Paul Capel, USGS; and Dr.
Robert Hudson, University of Illinois.
The reviewers acknowledged that although the
modified Stratified Beale Ratio Estimator (SBRE)
method and the USGS ESTIMATOR used in the
LMMBP are standard and reliable methods to
estimate loadings, the length of the data record (one
year) for the LMMBP was perhaps too short, and the
number of samples taken from the tributaries to
estimate loads was limited. Typically, multi-year
records are used. There was follow-up discussion
and evaluation of another load estimation procedure
by Dr. Robert Hudson after the formal peer review
comments were submitted. All of the necessary files
were provided to Dr. Hudson to make loading
assessments using rating curves similar to what is
used in ESTIMATOR, but also to look at all the sites
together rather than individually. He consulted
USGS, who performed the LMMBP load estimates,
before performing his analysis. The new attempt was
not successful. The reviewers concluded that the
load estimates made for the LMMBP using the
watershed export percentage (WEP) approach were
most likely the best estimates available for the
project.
It was recommended that further literature research
be conducted to determine what type of degradation
mechanisms may be operative in Lake Michigan.
This was done and the results were reported in Part
1, Chapter 2.
The reviewers concluded that there are a number of
combinations of watershed export percentages
(WEPs) and in situ decay rates that could achieve a
model "fit" to the data. It is true that if the WEP were
increased, the decay rate would have to increase. A
concern was raised by the reviewers that these
variables are somewhat unconstrained. However,
the WEP was constrained by focusing only on
northern freshwater drainage basins with soil texture
similar to that of the Lake Michigan basin. Also,
since rainfall can have an effect on measured WEPs,
a balance of both wet and dry years were included in
our long-term model runs. Using the mean WEP
from these studies reported in the literature was the
best estimate of the WEP's central tendency in the
Lake Michigan basin. Indeed, one of the reviewers,
Paul Capel, looked at WEPs from 408 observations
across numerous types of soil textures after the peer
review and calculated a mean WEP of 0.66%, which
was close to our mean of 0.6% (Capel and Larsen,
2001).
Other comments included a recommendation for a
follow-up atrazine sampling of Lake Michigan water
to help confirm short-term model predictions. This
sampling was done in 2005; however, the results
were not yet available at the time of this printing.
Also, the reviewers suggested that a model
sensitivity analysis be conducted. Sensitivity
analyses were performed using both the MICHTOX
model and the LM2-Atrazine models and are
reported in Parts 3 and 4 of this report.
The reviewers also were very pleased with the
progress made with the LM3-Atrazine application and
suggested that this high-resolution model would be
very useful for making local environmental
management decisions. The modelers agree with
this assessment and have demonstrated local
applications in the vicinity of the St. Joseph River,
Fox River, Grand River, and the Kalamazoo River
mouths. Some of the details of the St. Joseph
application are discussed in Part 5.
6.1.2 Comparison of LMMBP Models
The LMMBP models are those discussed in this
report: MICHTOX, LM2-Atrazine, and LM3-Atrazine
(Part 3, 4, and 5, respectively). The differences in
the model construct among these models has been
discussed. Total annual atrazine loadings for all
three models were the same and were based on an
estimate of the 0.6% WEP. Both MICHTOX and
LM2-Atrazine were calibrated using historical loading
estimates and comparing model output to available
lake data. Calibration consisted of selecting an
appropriate in situ total decay so that model output
matched lake data. For Scenario 3, based on
average conditions and the most likely scenario,
MICHTOX yielded a half-life of atrazine in the lake of
69.3 years (kinetic decay of 0.01/year). LM2-Toxic
predicted a similar half-life of 77 years (kinetic decay
of 0.009/year). LM3-Atrazine model used the
0.009/yr decay derived from calibration of decay in
the LM2-Atrazine model.
132
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6.2 Comparison of LMMBP Models to
Other Recent Atrazine Models Applied to
Lake Michigan
Within the last decade, three Lake Michigan atrazine
modeling papers have been published. All three
models were based on the principles of mass
balance. However, the three models yielded very
different estimates of in situ atrazine decay.
Tributary loads carry the most atrazine to the lake
compared to other sources. Therefore, any
significant differences in the amount of atrazine
delivered among the models will result in a range of
internal decay estimates. There are many
differences among these models, but the analysis
here will specifically focus on the main reasons why
these models differ.
6.2.1 Schottler and Eisenreich (1997)
Schottlerand Eisenreich (1997) predicted an internal,
overall, 14-year half-life for atrazine in Lake Michigan
using a mass balance model called Stella. They
used an atrazine WEP of 1% obtained from studies
on basins outside of the Lake Michigan basin.
However, their selection of WEP's did not appear to
be based on soil textures that match those of the
Lake Michigan basin. Also, it was not clear if the
WEP they used reflected wet or dry years (or a
combination of both). These considerations could
have an impact on selecting a representative WEP
for the Lake Michigan basin (See Part 2, Chapter 2).
The watersheds were from both northern and
southern regions. The higher WEP used by Schottler
and Eisenreich will yield higher atrazine tributary
loads to be delivered to the lake (approximately 67%
more mass loading from tributaries than the LMMBP
models delivered) and therefore more internal decay
was required in the lake to achieve a model fit to the
lake data. Their model predicted that atrazine
concentrations in the lake were at a steady-state
concentration of 34 ng/L in 1994, but the model
predicted that the lake concentration was close to
this value since the late 1980s. The LMMBP models
suggest that the lake, under constant 1995 loadings
into the future, will reach a steady-state concentration
of 66 ng/L in the year approximately 2194.
6.2.2 Tierney et a/. (1999)
Tierney et a/. (1999) predicted that the half-life of
atrazine in Lake Michigan is about two years. The
authors used atrazine run-off concentration data
derived from the Lake Erie basin (Richards and
Baker, 1993), and from Bodo (1991), who studied
watersheds in Southwestern Ontario to make
estimates of atrazine loading in the Lake Michigan
basin. The soils in the Lake Erie basin have much
more clay (Richards and Baker, 1993) than the soils
in the Lake Michigan basin and run-off (WEP) of
atrazine in the Lake Erie basin would likely approach
percentages over 1% (see Table 2.2.2 in Part 2 of
this report). The Lake Michigan basin has moderate
textured soils, and the run-off WEP would be closer
to 0.6%. Using atrazine concentration data from
Lake Erie tributaries with high WEPs and applying
them to characterize tributaries in the Lake Michigan
basin would result in more atrazine loadings to Lake
Michigan than what is likely, and therefore, in situ
decay will need to be high in their model in order for
the model to match observed lake data. High decay
is associated with the short half-life that they report.
Run-off loads of atrazine also is a strong function of
the amount of atrazine applied to corn in the
watershed. Predicted run-off concentrations in the
Lake Michigan basin by Tierney etal. (1999) did not
appear to be based on relating corn crop acreage in
Lake Erie basin and Lake Michigan basin. They
related flow-weighted concentrations in tributaries to
% total agricultural land use and then applied them to
the Lake Michigan basin. Total agricultural land use
would be a poor predictor of atrazine
usage/discharge if corn crop acreage per acre
agricultural land varies within or between the Lake
Erie and the Lake Michigan basins. The reason is
that atrazine is used almost exclusively on corn crops
in the Great Lakes basin. There is no indication in
the paper that an analysis of corn crop acreage
variation within agricultural lands was performed. To
further complicate this issue, the amount of atrazine
applied to corn acreage can vary from state-to-state.
Loadings in their model (both from watershed run-off
and precipitation) appear to be fixed to levels
observed in the early 1990's and applied for the
entire historical usage period of the chemical. This
would have overestimated loads from the period
leading up to approximately 1978. This
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overestimation of loads in those early years would
require that they include a significant non-zero
atrazine decay term in their mass balance.
The Tierney model predicted that Lake Michigan
reached steady-state atrazine concentrations in the
mid- to late-1970s with a concentration of 33 ng/L.
In contrast, Richardson and Endicott (1994) and
Rygwelski et al. (1999) and the modeling work in this
paper organized WEPs from the literature and used
a WEP based on moderate textured soils typical of
the Lake Michigan basin of 0.6%. Furthermore,
Rygwelski et al. (1999) and this paper selected
WEPs only from northern watersheds only and
included a mix of both wet and dry years (see Part 2,
Chapter 2). Also, only corn crops grown in the Lake
Michigan basin were included in this analysis to
determine atrazine loadings on a county-by-county
basis.
The results of the three atrazine models applied to
Lake Michigan are displayed in Table 6.1. A WEP of
approximately 5.6% was calculated for the Tierney
model, based on their estimates of loads to the lake
and amount of atrazine applied to the Lake Michigan
watershed. It is clear from the table, that higher
WEPs are associated with shorter atrazine half-lives.
6.3 Atrazine Models Applied to Lake or
Deep River Systems Outside the Lake
Michigan Basin
Other atrazine models have been applied to large
freshwater lakes and rivers. Consistent with the
results of the LMMBP models, these models have
shown that little to no atrazine decays in these lakes
and that loss via outflow from the lakes or rivers is
the primary atrazine removal mechanism.
6.3.1 Swiss Lakes
Ulrich et al. (1994) modeled atrazine in an eutrophic
lake, Greifensee, in Switzerland. The lake has a
maximum depth of 32 m with a mean of 17.8 m.
They found that, except for a short time in July and
August, atrazine showed a somewhat conservative
behavior. Within the overall mass balance, in situ
decay accounted for only 5% of total annual loss of
atrazine from the lake. Ninety-five percent of the loss
from the lake was attributed to outflow. The authors
Table 6.1 Comparison of LM2-Atrazine Model to Other Models
Model
WEP
Half-Life
Watershed Load
Methods Used
Estimated Year to
Reach Steady-
State
Atrazine Steady-
State Concentration
ng/L
Rygwelski and
Zhang, 2007
(Part 4 of this
report)
Schottler and
Eisenreich,
1997
0.6%
1.0%
77 yrs. County
Application and
WEP
14 yrs. County
Application and
WEP
2194
1994 But
Approached Near
Steady-State
66
34
Concentration in
the Late-1980s.
Tierney et al.,
1999
Not Used Directly 2 yrs.
(Approx. 5.6%)
Run-off Flow and
0.23 |jg/L
Forested; 1.6 |jg/L
Agricultural (Flow-
Weighted)
Mid-to Late-1970s
33
134
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noted that decay in the epilimnion layer of 0.003 per
day was needed only in July and August to get the
model to fit observations. They also noted that
during that time, nitrate levels in the lake increased.
High nitrate concentrations and high solar energy
have been associated with indirect photolytic
degradation of atrazine in water (see Section 1.2.3.2
of this report). Since the lake stratified in the warm
months of the year, water in the hypolimnion would
be somewhat more isolated from photolytic decay
than the epilimnion. During the rest of the year,
atrazine was modeled without decay. Modeled
processes such as volatilization and sedimentation
were negligible.
Buser (1990) modeled atrazine in Lake Zurich,
Switzerland. The maximum depth of the lake is 136
m with an average depth of 50 m. His results also
showed atrazine to be rather stable and its removal
primarily via outflowing waters compared to other
loss processes such as sedimentation, degradation,
and volatilization. This lake also stratified during the
warm months of the year.
Muller et a/. (1997) modeled atrazine in three Swiss
lakes: Greifensee, Murtensee, and Sempachersee.
The maximum/mean depths for the Murtensee and
Sempachersee are 45.5 m / 23.3 m and 87 m / 44 m,
respectively. Except for the July and August period
when they used an in situ decay of 0.003 per day in
the epilimnion, atrazine was modeled as a
conservative substance. Good agreement was
achieved between model output and measured
concentrations of atrazine in the lakes.
6.3.2 St. Lawrence River
Over an 18 month period in 1995 and 1996, Pham et
a/. (2000) measured the inputs and outputs of
loadings of atrazine to a reach of the St. Lawrence
River. The atrazine load was measured in both the
upper part of the river near Cornwall, Ontario,
Canada and at the outflow to the estuary, near
Quebec City, Quebec, Canada. Taking into account
loadings from the watershed, their measurements
indicated that atrazine does not degrade during the
three day transit in the river. This large river has a
mean discharge of approximately 12,000 m3/s at
Quebec City. At Cornwall, the depth is about 8.2 m
and at Quebec City the depth is approximately 11m.
6.4 Atrazine Models Applied to Shallow
Surface Water Systems in Agricultural
Areas
Atrazine degradation seems to be occurring in
shallow surface water systems in agricultural areas.
A hypothesis is that in these shallow systems, light
energy penetrates a greater percentage of the water
column than in lakes that show thermal stratification
in the summer. Compared to these lakes, shallow
rivers have fast mixing due to turbulence. This brings
a fresh supply of atrazine close to the surface where
photolysis can more easily degrade it. Rivers also
generally have higher solids concentrations that
could act as catalysts for hydrolysis. In deep lakes,
summer stratification isolates water from photolysis
in the hypolimnion and solids concentrations tend to
be lower than that found in rivers. See Part 1,
Chapter 2 for more discussion on this topic.
6.4.1 Saylorville Reservoir, Iowa
The Saylorville Reservoir is located on the upper Des
Moines River basin in Northern Iowa near the city of
Des Moines. Seventy-nine percent of the basin is
cropland, mostly corn and soybeans. The reservoir
is shallow, with a mean depth of only 4.3 m. Chung
and Gu (2003) modeled atrazine transport and fate in
1997. During the study period, the reservoir showed
very weak thermal stratification in the summer
months, which allowed them to assume well-mixed
conditions. The authors found a strong inverse
relationship between half-life and daily hours of
sunlight. This supports the notion that photolysis was
probably operative as a loss mechanism. In this
system, approximately 60% of the atrazine that
entered the reservoir was released through
discharge. Approximately 40% of atrazine in the
reservoir was transformed via kinetic loss
mechanism(s) such as photolysis, hydrolysis, etc.
The half-life of atrazine in the reservoir varied from
two to 58 days. Their analysis found that the half-life
of atrazine did not correlate well with nitrate
concentrations, suggesting that photolysis was not
nitrate-mediated indirect photolysis. Rather, they
indicated that direct photolysis, aided by the high
concentrations of dissolved organic carbon (DOC),
was probably operative.
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6.4.2 Other Small Surface Water Systems
Other modeling studies in small lakes and a shallow
creek in agricultural regions have shown similar,
relatively short half-lives of atrazine.
Spalding et a/. (1994) estimated the atrazine half-
lives in two very small lakes in Northeastern
Nebraska ranged from 124 to 193 days. Spalding
suggested that hydrolysis may have been
responsible for degradation of atrazine in these
lakes. However, these lakes had relatively high pH's
averaging 8.1 for one lake and 8.2 for the other, and
some researchers have found that hydrolysis above
pH 4 was difficult to achieve in the laboratory. The
authors did not rule out photolytic decay. These
small lakes were very turbid where average Secchi
readings were less than 1 m. None of the other
atrazine modeling papers reviewed suggested
hydrolysis as a possible explanation of atrazine
decay.
In a small creek in Iowa, Kolpin and Kalkhoff (1993)
found that atrazine half-lives had a significant inverse
relationship with sunlight, therefore suggesting
photolysis was responsible. To rule out temperature
as a confounding variable, they found that comparing
atrazine half-lives to water temperature did not yield
a significant correlation. The degradation in Roberts
Creek was very rapid. Half-lives ranged from 168
hours (November 1990) to 35.6 hours (July 1990).
The stream discharge ranged from 0.264 to 0.086
m3/s during the sampling period, April 11, 1990 to
December 2, 1990.
6.5 Conclusions
A likely reason for the discrepancies (see Table 6.1)
between the three Lake Michigan atrazine modeling
efforts has to do with the wide range of estimates
made for tributary loadings of atrazine to the lake.
Since tributary loadings are the major source of
atrazine to the lake, rigorous detailed efforts need to
be taken to ensure that these loads are as accurate
as possible.
Atrazine decay in large surface water systems (lakes
and rivers) appears to be much lower than decay
found in shallow water systems. In larger systems,
decay is very slow with half-lives estimated in years.
In shallow, small systems with a high degree of
mixing, atrazine decay can be rapid with half-lives
estimated in days or even hours.
Research suggests that decay in surface water may
be linked to photolysis, either direct of indirect.
Modeling studies in deeper lakes suggest that this
happens in the summertime when solar energy is
high. Photolysis is limited in lakes that are stratified
or deep rivers, because the exposure of light energy
to the inventory of atrazine in these systems is
limited. Systems that are well-mixed further facilitate
photodegradation, because a fresh supply of atrazine
is constantly being brought to the water surface
where light energy would be the greatest. Atrazine in
a hypolimnion layer would be less available for
photolysis because it is somewhat isolated from the
mixed epilimnion layer due to the thermocline.
In regards to Lake Michigan, can other degradation
processes besides photodegradation explain the in
situ decay? Per Part 1, Chapter 2, Section 1.2.3.1,
biodegradation in surface waters is not likely.
Hydrolysis in Lake Michigan is not likely because of
the high pH of 8.4, low solids, and low DOC (see Part
1, Chapter 2, Section 1.2.3.2.1).
References
Bodo, B.A. 1991. Trend Analysis and Mass-
Discharge Estimation of Atrazine in Southwestern
Ontario Great Lakes Tributaries: 1981-1989.
Environ. Toxicol. Chem., 10(9):1105-1121.
Buser, H.-R. 1990. Atrazine and Other s-Triazine
Herbicides in Lakes and in Rain in Switzerland.
Environ. Sci. Technol., 24(7): 1049-1058.
Capel, P.O. and S.J. Larson. 2001. Effect of Scale
on the Behavior of Atrazine in Surface Waters.
Environ. Sci. Technol., 35(4):648:657.
Chung, S. and R.R. Gu. 2003. Estimating Time-
Variable Transformation Rate of Atrazine in a
Reservoir. Adv. Environ. Res., 7(4):933-947.
Kolpin, D.W. and S.J. Kalkhoff. 1993. Atrazine
Degradation in a Small Stream in Iowa. Environ.
Sci. Technol.,27(1):134-139.
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Muller, S.R., M. Berg, M.M. Ulrich, and R.P.
Schwarzenbach. 1997. Atrazine and Its Primary
Metabolites in Swiss Lakes: Input Characteristics
and Long-Term Behavior in the Water Column.
Environ. Sci. Technol., 31(7):2104-2113.
Pham, T.-T., B. Rondeau, H. Sabik, S. Prouix, and D.
Cossa. 2000. Lake Ontario: The Predominant
Source of Triazine Herbicides in the St. Lawrence
River. Can. J. Fisher. Aquat. Sci., 57(Suppl.
1):78-85.
Richards, R.P. and D.B. Baker. 1993. Pesticide
Concentration Patterns in Agricultural Drainage
Networks in the Lake Erie Basin. Environ.
Toxicol. Chem., 12(1):13-26.
Richardson, W.L and D.D. Endicott. 1994. A
Screening Model for Establishing Load-Response
Relationships for Toxic Chemicals in Lake
Michigan. Presented at the Fifteenth Annual
Meeting of the Society of Environmental
Toxicology and Chemistry (SETAC), Denver,
Colorado, October 30 - November 3, 1994.
Rygwelski, K.R.,W.L Richardson, and D.D. Endicott.
1999. A Screening-Level Model Evaluation of
Atrazine in the Lake Michigan Basin. J. Great
Lakes Res., 25(1):94-106.
Schottler, S.P. and S.J. Eisenreich. 1997. Mass
Balance Model to Quantify Atrazine Sources,
Transformation Rates, and Trends in the Great
Lakes. Environ. Sci. Technol., 31 (9):2616-2625.
Spalding, R.F., D.D. Snow, D.A. Cassada, and M.E.
Burbach. 1994. Study of Pesticide Occurrence
in Two Closely Spaced Lakes in Northeastern
Nebraska. J. Environ. Qual., 23(3):571-578.
Tierney, D.P., P.A. Nelson, B.R. Christensen, and
S.M. Kloibery Watson. 1999. Predicted Atrazine
Concentrations in the Great Lakes: Implications
for Biological Effects. J. Great Lakes Res.,
25(3):455-467.
Ulrich, M.M., S.R. Muller, H.P. Singer, D.M.
Imboden, and R.P. Schwarzenbach. 1994. Input
and Dynamic Behavior of the Organic Pollutants
Tetrachloroethylene, Atrazine, and NTA in a
Lake: A Study Combining Mathematical
Modeling and Field Measurements. Environ. Sci.
Technol.,28(9):1674-1685.
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PART 6
REVIEW OF ATRAZINE MODELS
Appendix 6.1 Peer Review of LMMBP
Atrazine Models, September 27, 2000,
Romulus, Michigan
Report of the Second Review Panel Meeting
Submitted to:
Dr. Glenn Warren
United States Environmental Protection Agency
Great Lakes National Program Office
77 W. Jackson Boulevard
Chicago, Illinois 60606-3590
by
Paul Capel
United States Geological Survey
Water Science Center of Minnesota
Mounds View, Minnesota 55112
and
Robert Hudson
University of Illinois
S-518 Turner Hall, MC-047
1102 South Goodwin Avenue
Urbana, Illinois 61801
A.6.1.1 Overview
The second review meeting was focused solely on
the work that the Lake Michigan Mass Balance
Program (LMMBP) has completed on atrazine.
Presentations were made on the following subjects:
data quality assurance; summary statistics
measurements in air, rain, tributaries, and lake water;
tributary load calculations; modeling atmospheric
transport and deposition; atmospheric deposition
calculation results; hydrodynamic transport in the 41
segment model; hind/forecasting using MICHTOX;
and, simulation results from 41-segment and high-
resolution models.
The review team feels that the LMMBP has generally
met its goals for modeling atrazine loading to and
fate and transport within Lake Michigan. The
following aspects of the work were notably strong:
A. The data management system and data quality
assurance program were excellent. A great deal
of work was expended to develop the platforms
and communication that was needed to make
such a large data set useful. This work had
recently undergone an independent review.
B. The atmospheric modeling (from volatilization to
deposition) is an important contribution, both to
the LMMBP and to the scientific community. This
is the first attempt at a regional model for a semi-
volatile chemical. Although the work is still
underway, the planned attempts to compare
model predictions with the field measurements is
commended.
C. The hydrodynamic components of the 41-
segment model appear to be complete and well-
calibrated, based on the results for temperature
and chloride. These components of the model
will be further tested when the focus shifts from
atrazine, which is largely dissolved, to the
particle-associated chemicals (mercury and
PCBs).
D. The simulations of atrazine fate within the lake
based on the MICHTOX, 41-segment, and high-
resolution models agreed well with each other
138
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and with the measured data. The fact that the
measured atrazine concentrations were relatively
homogeneous throughout the lake (22 to 58
|jg/L) made the comparison of measured and
modeled results a "relatively" straightforward,
albeit necessary test of the model.
E. TheuseofMICHTOX(Rygwelskiefa/., 1999) to
simulate the evolution of atrazine levels in the
lake since atrazine use began (hindcasting) and
to forecast future levels was an excellent way to
tackle the issue of the rate of atrazine decay
within the lake. It also plays an important role in
testing the consistency of the loading and decay
estimates.
F. The high-resolution modeling has significance far
beyond the potential improvements in scientific
understanding of atrazine fate it may bring. The
model should be of great use in making local
environmental management decisions. In
addition, the animations produced from the daily
simulations should serve as an excellent
communications tool for environmental managers
to reach the public with. This work should
continue to be strongly encouraged.
A.6.1.2. Comments on Technical Issues
A. Tributary Loads of Atrazine - The LMMBP
work to date indicates that about 2/3 of the
atrazine load to Lake Michigan is borne by rivers.
The tributary loads were estimated using various
statistical approaches, such as the modified
Beale method and the USGS ESTIMATOR
software, to derive loads from a limited number of
dissolved atrazine measurements in water from
the rivers in the Lake Michigan basin. Although
determining "true" loads is impossible, these
estimation methods have proved reliable and are
considered standard where non-point source
loads need to be quantified. In this case,
however, the length of the data record for each
tributary (one-year) is short with a limited number
of measurements in comparison to multi-year
records that are typically used. Therefore, the
reviewers suggest exploring other statistical
approaches that can be used on the existing data
set. Appendix 1 (in preparation) presents a brief
description of one such approach that could be
considered. [Note to readers: Appendix 1 was
not completed by the review panel. However, Dr.
Hudson did make some loading estimates using
rating curves similar to what is used in
ESTIMATOR, but looked at all of the sites
together, rather than individually. He consulted
USGS, who performed the LMMBP load
estimates, before performing his analysis. The
new attempt was not successful.]
The tributary loads were also estimated using the
"watershed export percentage" (WEP) approach
and the estimated annual use of atrazine in each
watershed of rivers flowing into the lake. This
approximate method serves as a good check on
the tributary load calculations and has the benefit
of allowing the tributary loads to easily be
estimated each year for the hind/forecasting.
B. Atmospheric Deposition - The magnitude of
atmospheric deposition was estimated through
field measurements (for rain) and simple models
(for dry deposition). It appears that inputs via
rain are dominant. A single, typical value for the
particle depositional velocity was chosen and all
of the estimates based on this single value. The
reviewers suggest that the model sensitivity to
this approximation should be examined by
choosing an appropriate range of particle
depositional velocities. Large particles, coming
from Chicago, have been shown to have much
higher depositional velocities than the "typical"
value used, although it is unknown how much
atrazine is on these larger particles. [Note to
readers: Only wet deposition was estimated for
the Lake Michigan atrazine models, because dry
deposition was negligible. See Part 1, Chapters
for more information.]
C. Atrazine Decay Processes - Atrazine was
initially selected for study in the LMMBP as a
model of a reactive, biodegradable compound
(see Section 1.1 of Statistical Assessment of QA
Data documents). A half-life of 14 years was
estimated by Schottler and Eisenreich (1997)
based on the assumption that atrazine should be
approximately at steady-state within the lake.
Rygwelski et al. (1999) showed that current
atrazine levels within the lake could be predicted
from plausible historical loading estimates
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assuming no decay of atrazine within the lake.
This approach leads to predictions of very large,
continued increases in lake atrazine levels.
Although atrazine levels are not likely to exceed
current drinking water standards, this scenario is
obviously of greater concern than the steady-
state assumption.
Further literature review of mechanisms of
atrazine decomposition is warranted to help
determine which mechanisms are most likely to
be significant in the lake. Given the current state
of knowledge, it may be difficult to resolve this
issue. However, the full range of processes -
biodegradation, photochemical decomposition,
and chemical hydrolysis should be considered.
The possibility of more significant decay within
the lake needs to be kept open.
D. Summary - Both the data and modeling results
suggest that atrazine may not be as reactive
within the lake as originally anticipated. This
question is probably best resolved by continued
monitoring of atrazine levels in lake water. The
model results can be used to ensure that
sampling locations are not unduly affected by
tributary inputs. Further modeling work in this
area should combine the historical approach with
parameter sensitivity analyses. The results
presented in the review meeting showed that the
measured data can be correctly modeled by
different combinations of WEP and atrazine's
degradation rate in Lake Michigan. At this time,
neither parameter is well-constrained. It is
suggested by the panel that the LMMBP
investigate the relationship between values of
WEP and degradation rate that yield accurate
estimates of current atrazine levels from historical
loading rates. Presumably, an inverse
relationship between the two will result, with an
acceptable range for each.
The above discussion concerns an example of
variables in the models that are constrained at
this time only to a range of values, rather than a
single correct value. The LMMBP might wish to
consider other model variables to evaluate the
model's sensitivity to the appropriate ranges of
these values and to the relationships between
parameters.
References
Rygwelski, K.R., W.L. Richardson, and D.D. Endicott.
1999. A Screening-Level Model Evaluation of
Atrazine in the Lake Michigan Basin. J. Great
Lakes Res., 25(1):94-106.
Schottler, S.P. and S.J. Eisenreich. 1997. Mass
Balance Model to Quantify Atrazine Sources,
Transformation Rates, and Trends in the Great
Lakes. Environ. Sci. Technol., 31(9):2616-2625.
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