" 600R08111

ft EA~United States ...... . Environmental Protection ,.. . Agency

Results of the L"ake Michigan

Mass Balance Project:

Atrazine Modeling Report

RES EAR C H AN 0 0 EVE LOP M E NT
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 Ile, 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.
ii

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.
iii

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
iv

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 Ile web site

for further information.
v

Abstract

The Lake Michigan Mass Balance Project (LMMBP) was conducted to measure and model nutrients, atrazine,

polychlorinated biphenyls (PCBs), trans-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.
vi

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.
vii

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 trans-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
viii

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
ix

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-In-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
x

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
xi

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 et al. (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
xii

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
xiii

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
xiv

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
xv

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
xvi

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, Wisconsin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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
xvii

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
xviii

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
xix

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
xx

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.
xxi

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 LMMBP, 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 screeninglevel 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. 
xxii

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.
1

PART 1

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 Ile, 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), trans-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, nonpoint, 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
2

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

Figure 1.1.1. Simplified mass balance approach.

Simple Mass Budget for Conservative Substances

source

mass in

water system

tsource

mass out=; mass in + [sources

Mass Balance Modeling Approach

air system * I- air sources

+- I

mass in I ., mass out = mass in + [sources water system

+I ± air-water exchange

I t

± sediment-water exchange sediment system

± Linternal processes
3

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, trans-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, transnonachlor, 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 manmade, 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

improper disposal of transformer fluids, 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
4

Table 1.1.1. Characteristics of the LMMBP Modeled Pollutants

Pollutant Sources Uses Toxic Effects

Bioconcentration 

Factor1

USEPA 

Regulatory 

Standards2

PCBs • Waste incinerators

(unintentional

byproducts of

combustion)

• Industrial

dischargers

• Electrical power

• Electrical

transformers and

capacitors

• Carbonless copy

paper

• Plasticizers

• Hydraulic fluids

• Probable human

carcinogen

• Hearing and vision

impairment

• Liver function alterations

• Reproductive impairment

and deformities in fish and

wildlife

1,800 to

180,000

MCL = 0.5 :g/L

CCC = 14 ng/L

HH = 0.17 ng/L

trans-Nonachlor3

• Application to crops

and gardens

• Pesticide on corn

and citrus crops

• Pesticide on

lawns and

gardens

• Probable human

carcinogen

• Nervous system effects

• Blood system effects

• Liver, kidney, heart, lung,

spleen, and adrenal gland

damage

4,000 to

40,000

MCL = 2 :g/L

CMC = 2.4 :g/L

CCC = 4.3 ng/L

HH = 2.1 ng/L

Atrazine • Application to crops • Herbicide for corn

and sorghum

production

• Weight loss

• Cardiovascular damage

• Muscle and adrenal

degeneration

• Congestion of heart,

lungs, and kidneys

• Toxic to aquatic plants 

2 to 100 MCL = 3 :g/L

CMC = 350 4

:g/L

CCC = 12 :g/L 4

Mercury • 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

• Possible human

carcinogen

• Damage to brain and

kidneys

• Adverse affects on the

developing fetus, sperm,

and male reproductive

organs

63,000 to

100,000

MCL = 2 :g/L

CMC = 1.4 :g/L

CCC = 0.77 :g/L

HH = 50 ng/L

FWA = 2.4 :g/L 5

FWC = 12 ng/L 5

Wildlife = 1.3 6

ng/L

From: U.S. Environmental Protection Agency, 1995a, National Primary Drinking Water Regulations, Contaminant Specific 1

 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.

MCL = Maximum Contaminant Level for drinking water. CMC = Criterion Maximum Concentration for protection of aquatic 2

 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. trans-Nonachlor is a principal component of the pesticide chlordane. 3

Draft water quality criteria for protection of aquatic life. From: U.S. Environmental Protection Agency, 2001b, Ambient 4

 Aquatic Life Water Quality Criteria for Atrazine, USEPA, Office of Water, Washington, D.C.

FWA = Freshwater acute water quality criterion. FWC = Freshwater chronic water quality criterion. From National Toxics 5

 Rule (58 FR 60848).

Wildlife criterion. From the Stay of Federal Water Quality Criteria for Metals (60 FR 22208), 40 CFR 131.36 and the 6

 Water Quality Guidance for the Great Lakes System (40 CFR 132).
5

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

trans-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. trans-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, trans-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, trans-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 trans-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 (IRED) for atrazine.

In an October 2003 addendum to the IRED, 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-ofevidence 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
6

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 other chemicals 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

acenaphthene

acenaphthylene

aldrin

anthracene

atrazine

"-BHC

$-BHC

*-BHC

(-BHC

benzo[a]anthracene

benzo[g,h,i]perylene

benzo[b]fluoranthene

benzo[k]fluoranthene

benzo[e]pyrene

benzo[a]pyrene

"-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

trans-nonachlor

oxychlordane

Organics (Continued)

benzo[a]pyrene

"-chlordane

(-chlordane

chrysene

coronene

p,p’-DDE

p,p’-DDD

trans-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

ortho-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
7

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

Figure 1.1.2. The LMMBP sampling locations.

o

o water survey stations

o

Pere Marquette River -N-J-~

o¢'d/1'>!Kalamazoo River

r----------,

~,,. .

o

Manistique Rive"I:J--~-J

Sheboygan

River

'Q.

o

Milwaukee

River ,<} A.-

"0 'v

o '¢'

Calum

'¢' 0

st. Joseph c{f atmospheric monitoring

RIver stations

o tributary monitoring

stations
8

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 Division Great

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
9

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

< Wisconsin 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

Section1.1.5.4.2) 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
10

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 STORET

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.

__P~nclptl(PI) ~~- OLNPOQA ~.- _noIPan1_ ......"ll....nt Workg,,,,,,

""...._-

-.......,

-

I -

"",." """on. co..""""..

(.box

...,..,.

.....

.......... , ....0- -.- Tm........... ..... ---\-....... ...._0__ -, I -,

I I ....""'".....- - ••m_~

! r .,_.n'" Yd••

--, "'.............." .""...1__ ."- - .........""'V..... ............_- In '""'""'~"""t>Io ,- .....Tm_••"" -_. ~.","...to""" •••• I "'E'''''' I I

R_."",,, I
11

• Flexible and expandable: Database is able to

accept data from any Great Lakes monitoring

project.

• National compatibility: GLENDA is compatible

with STORET 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, 2001a).

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.
12

< 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
13

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, 2001a). 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 al., 2004) documents the QA

process for the development and application of

LMMBP models, including hydrodynamic, sediment

transport, eutrophication, transport chemical fate, 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 Lakewide 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.
14

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 Ile, 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, 1,440 pp.

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 CriteriaCorrection. 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. 2001b.

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.
15

PART 1

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 Ile, 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 highestuse 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
16

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 summertime 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. With 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

ow (K ), 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 μg/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
17

Table 1.2.1. Physical and Chemical Properties of

Atrazine

 

Empirical Formula C H CIN 8 14 5

Chemical Name 2-chloro-4-ethylamino-6-

 isopropylamino-1,3,5-triazine

Chemical Family Triazine

Structural Formula:

 Cl

 I

 C

 // \

3

 (CH ) N N 

 | | ||

3 2 3

(CH )—(CH)—(NH)—C C—(NH)—(CH )—(CH )

 \\ /

 N

Molecular Weight 215.7 g/mol

Melting Point 173°C to 175°C

Vapor Pressure 40 :Pa at 20°C

Solubility in Water 33 PPM at 25°C

Density 0.35 g/ml

Henry's Law 8.1 x 10 (dimensionless) at -8

 Constant 25°C (U.S. Department of

 Agriculture, 2001)

Physical state White crystalline solid

ow log K 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 al., 1993). Ingerslev and Nyholm (2000) tested

natural water samples from an unpolluted forest

stream using C-labeled atrazine. Microbial 14

degradation of atrazine was evaluated by measuring

2

the evolution of C in carbon dioxide (CO ). Testing 14

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 al., 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
18

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 al.,

1994; Müller et al., 1997). Atrazine degradation via

biotic and/or abiotic processes in Lake Michigan was

found to be negligible using a mass balance model

(Rygwelski et al., 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 al., 1994). Also, if present in o

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 al.,

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 et al. (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

CI

NoAN

(CH3)2CHHN~N)l NHC H

Atrazine 2 5

CI

NoAN

(CH3)2CHHNAN H )l Deethylatrazine 2

N

OH

NoAN

(CH3)2CHHN~N)l NHC

Hydroxyatrazine 2

H

5
19

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 al., 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

al., 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 al., 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 al., 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 halflives and nitrate concentrations may be masked in

the natural environment because of the strong

seasonality of photodegradation with sunlight. Using

2

titanium dioxide (TiO ) 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., 2001b). 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 et al., 2001b). 

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
20

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.

References

Brambrilla, A., B. Rindone, S. Polesselo, S. Galassi,

and R. Balestrini. 1993. The Fate of Triazine

Pesticides in River Po Water. Sci. Total Environ.,

132(2/3):339-348.

Buser, H.-R. 1990. Atrazine and Other s-Triazine

Herbicides in Lakes and in Rain in Switzerland.

Environ. Sci. Technol., 24(7):1049-1058.

Chung, S. and R.R. Gu. 2003. Estimating TimeVariable Transformation Rate of Atrazine in a

Reservoir. Adv. Environ. Res., 7(4):933-947.

Comber, D.W. 1999. Abiotic Persistence of Atrazine

and Simazine in Water. Pest. Sci., 55(7):696-

702.

Durand, G. and D. Barcelo. 1992. Environmental

Degradation of Atrazine, Linuron, and

Fenitrothion in Soil Samples. Toxicol. Environ.

Chem., 36(3/4):225-234.

Feakin, S.J., E. Blackburn, and R.G. Burns. 1994.

Biodegradation of s-Triazine Herbicides at Low

Concentrations in Surface Waters. Water Res.,

28(11):2289-2296.

Frank, R., G.J. Sirons, R.L. Thomas, and K.

McMillan. 1979. Triazine Residues in

Suspended Solids (1974-1976) and Water (1977)

From the Mouths of Canadian Streams Flowing

Into the Great Lakes. J. Great Lakes Res.,

5(2):131-138.

Hapeman, C.J., S. Bilboulian, B.G. Anderson, and A.

Torrents. 1998. Structural Influences of LowMolecular-Weight Dissolved Organic Carbon

Mimics on the Photolytic Fate of Atrazine.

Environ. Toxicol. Chem., 17(6):975-981.

Ingerslev, F. and N. Nyholm. 2000. Shake-Flask

Test for Determination of Biodegradation Rates of

C-Labeled Chemicals at Low Concentrations of 14

Surface Water Systems. Ecotoxicol. Environ.

Safety, 45(3):274-283.

Jones, L.R., S.A. Owen, P. Horrell, and R.G. Burns.

1998. Bacterial Inoculation of Granular Activated

Carbon Filters for the Removal of Atrazine From

Surface Water. Water Res., 32(8):2542-2549.

Kolpin, D.W. and S.J. Kalkhoff. 1993. Atrazine

Degradation in a Small Stream in Iowa. Environ.

Sci. Technol., 27(1):134-139.
21

Konstantinou, I.K., T.M. Sakellarides, V.A. Sakkas,

and T.A. Albanis. 2001a. Photocatalytic

Degradation of Selected s-Triazine Herbicides

and Organophosphorus Insecticides Over

Aqueous TiO Suspensions. Environ. Sci. 2

Technol., 35(2):398-405.

Konstantinou, I.K., A.K. Zarkadis, and T.A. Albanis.

2001b. Photodegradation of Selected Herbicides

in Various Natural Waters and Soils Under

Environmental Conditions. J. Environ. Quality,

30(1):121-130.

Lei, Z., C. Ye, and X. Wang. 2001. Hydrolysis

Kinetics of Atrazine and Influence Factors. J.

Environ. Sci., 13(1):99-103.

Ma, L. and R.F. Spalding. 1997. Herbicide

Persistence and Mobility in Recharge Lake

Watershed in York, Nebraska. J. Environ. Qual.,

26(1):115-125.

Mills, M.S. and E.M. Thurman. 1994. Reduction of

Nonpoint Source Contamination of Surface Water

and Groundwater by Starch Encapsulation of

Herbicides. Environ. Sci. Technol., 28(1):73-79.

Mirgain, I., G.A. Green, and H. Monteil. 1993.

Degradation of Atrazine in Laboratory

Microcosms: Isolation and Identification of the

Biodegrading Bacteria. Environ. Toxicol. Chem.,

12(9):1627-1634.

Monteith, H.D., W.J. Parker, J.P. Bell, and H.

Melcher. 1995. Modeling the Fate of Pesticides

in Municipal Wastewater Treatment. Water

Environ. Res., 67(6):964-970.

Müller, 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.

Nair, D.R. and J.L. Schnoor. 1994. Effect of Soil

Conditions on Model Parameters and Atrazine

Mineralization Rates. Water Res., 28(5):1199-

1205.

Pelizzetti, E., V. Maurino, C. Minero, V. Carlin, E.

Pramauro, and O. Zerbinati. 1990.

Photocatalytic Degradation of Atrazine and Other

s-Triazine Herbicides. Environ. Sci. Technol.,

24(10):1559-1565.

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.

Solomon, K.R., D.B. Baker, R.P. Richards, K.R.

Dixon, S.J. Klaine, T.W. LaPoint, R.J. Kendall,

C.P. Weisskopf, J.M. Giddings, J.P. Giesy, L.W.

Hall, Jr., and W.M. Williams. 1996. Ecological

Risk Assessment of Atrazine in North American

Surface Waters. Environ. Toxicol. Chem.,

15(1):31-76.

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.

Thurman, E.M. and A.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.

Ulrich, M.M., S.R. Müller, 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.

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. Environmental Protection Agency. 1995.

National Primary Drinking Water Regulations,

Contaminant Specific Fact Sheets, Synthetic

Organic Chemicals, Consumer Version.

EPA/811/F-95/003-T. 
22

U.S. Environmental Protection Agency. 2003a.

Pesticides: Topical and Chemical Fact Sheets -

Atrazine Background. U.S. Environmental

Protection Agency, Office of Pesticides Program,

Washington, D.C.. Available from U.S.

Environmental Protection Agency at http://www.

epa.gov/pesticides/factsheets/atrazine_

background.html.

U.S. Environmental Protection Agency. 2003b.

Interim Reregistration Eligibility Decision (IRED)

for Atrazine. U.S. Environmental Protection

Agency, Office of Pesticides Program,

Washington, D.C. Case Number 0062, 285 pp.

U.S. Environmental Protection Agency. 2003c.

October 31, 2003 Addendum to the January 31,

2003 IRED. U.S. Environmental Protection

Agency, Office of Pesticides Program,

Washington, D.C. 16 pp.

U.S. General Accounting Office. 1993. Report to the

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.

Westerhoff, P., Y. Yoon, S. Snyder, and E. Wert.

2005. Fate of Endocrine-Disruption,

Pharmaceutical, and Personal Care Product

Chemicals During Simulated Drinking Water

Treatment Processes. Environ. Sci. Technol.,

39(17):6649-6663.

Widmer, S.K., J.M. Olson, and W.C. Koskinen.

1993. Kinetics of Atrazine Hydrolysis in Water.

J. Environ. Sci. Health, 28(1):19-28.
23

PART 1

INTRODUCTION

Chapter 3. Atrazine Field Data

Observations

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 Ile, 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 openlake water column (Brent et al., 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 al., 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
24

been estimated that atrazine is the major source for

DEA (98%) in the Corn Belt (Thurman et al., 1994).

Hydroxylated atrazine degradation products are also

found in the environment (Lerch et al., 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 (MICB) sampler modified with a heater for all-weather

sampling. Equipped with a precipitation sensor, the

sampler was 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 m /hour during 3

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
25

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/m and the rest at Sleeping Bear 3

Dunes collected November 16, 1994 - November 17,

1994 at 22.1 pg/m and September 9, 1995- 3

September 14, 1995 at 31.5 pg/m . The sampling 3

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/m .3

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/m . Modeling scenarios presented in this report 3

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/m at Sleeping Bear Dunes in northern Lake 3

Michigan to a high of 1,400 pg/m at the Bondville 3

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 al., 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/m was measured at station 3
26

1 in May 1994, and a concentration of 280 pg/m was 3

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/m . For 3

the LMMBP, the detection limit ranged from 3.0 to 68

pg/m (average of 17 pg/m ) for particulate phase 3 3

samples analyzed at the Illinois Water Survey, and

from 26.8 to 284 pg/m (average of 70.7 pg/m ) for 3 3

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 nondetectable) 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
27

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

Year Deethylatrazine

ng/L

Atrazine

ng/L

Deethylatrazine/Atrazine

Ratios (DAR) Sampling Data Range

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 - 8/14/1990

4/2/1991 - 7/9/1991

3/15/1994 - 7/5/1994

3/14/1995 - 8/31/1995

Data from Goolsby et al., 1995. All data were used in calculating the volume-weighted mean concentrations. 1

 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.

Data from the LMMBP. All data, including zeros, were used in calculating the volume-weighted mean 2

 concentrations. DAR represents only situations where both reported DEA and atrazine concentrations

 were above the detection limit.
28

greatest spring atrazine emissions were estimated

(Scholtz et al., 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

Figure 1.3.1. Monthly precipitation amounts at cities in two large corn-growing regions. Data are from

Peoria, Illinois and Omaha, Nebraska. 

12 -r--------------------..,

D Peoria, IL

• Omaha, NE

10

(f)

(j)

"58+-----------------1

c

c

a

.,,6+----------------1

ro

0=

Cl. .(3

4 +-------1

Cl.

2

o

April April April May May May June June June

1991 1994 1995 1991 1994 1995 1991 1994 1995

Month and Year
29

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 Total Inches (Mean at All Sites) Standard Deviation

30 Years

1994

1995

34.22

34.26

33.73

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 corngrowing 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

80

o

o Peoria,IL

• Omaha, NE

I--

I-- I--

I-- I-- I--

I-- I-- I--

I- - l- I-- I--

I-- - I- - lI- - l- I-- I--

T T

April April April May May May June June June

1991 1994 1995 1991 1994 1995 1991 1994 1995

Month and Year
30

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 al. (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

μm 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 lateMay. 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 al.

(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 al., 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 al. (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 OctoberNovember, 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
31

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 midhypolimnion, 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 μL 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:

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, 0.145,

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.

Within Lake Michigan (excluding Green Bay and

northern Lake Huron stations), lateral and vertical

atrazine concentrations were relatively consistent

during the LMMBP (Brent et al., 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
32

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. Schottler and 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 nonstratification 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 al. (2001)

concluded that statistically significant mean openlake 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)

DEA

(ng/L) DIA (ng/L)

1991

1992

4/1994-

 4/19952

35 (2.0)1*

37 (1.8)1

38.1

163

243

25.8

Not Available

Not Available

14.9

Schottler and Eisenreich, 1997 1

Brent et al., 2001 2

Schottler and Eisenreich, 1994 3

*Values are means with the standard deviation in

parenthesis.

Figure 1.3.3. Atrazine concentrations in Lake

Michigan, 1994.

References

Banks, P.A. and D. Tierney. 1993. Biological

Assessment of Atrazine and Metolachlor in

Rainfall. Ciba Plant Protection Department, CibaGeigy Corporation, Greensboro, North Carolina.

Technical Paper 1-1993, 16 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, 92 pp.

56

52

48

44

40

36

32

28

dissolved

atrazine in

Lake Michigan

water (ng/L)

1994

• sample locations

(0-300 meters)
33

Foreman, W.T., M.S. Majewski, D.A. Goolsby, F.W.

Wiebe, and R.H. Coupe. 2000. Pesticides in the

Atmosphere of the Mississippi River Valley, Part

II - Air. Sci. Total Environ., 248(2):213-216.

Goolsby, D.A., E.M. Thurman, M.L. Pomes, and

W.A. Battaglin. 1993. Occurrence, Deposition,

and Long Range Transport of Herbicides in

Precipitation in the Midwest and Northeast United

States. In: D.A. Goolsby, L.L. Boyer, and G.E.

Mallard (Eds.), Selected Papers in Agricultural

Chemicals in Water Resources of the

Midcontinental United States, pp. 75-89. U.S.

Geological Survey, Denver, Colorado. Open File

Report 93-418, 89 pp.

Goolsby, D.A., E.A. Scribner, E.M. Thurman, M.L.

Pomes, and M.T. Meyer. 1995. Data on

Selected Herbicides and Two Triazine

Metabolites in Precipitation of the Midwestern

and Northeastern United States, 1990-1991.

U.S. Geological Survey, Lawrence, Kansas.

Open File Report 95-469, 341 pp.

Goolsby, D.A., E.M. Thurman, M.L. Pomes, M.T.

Meyer, and W.A. Battaglin. 1997. Herbicides

and Their Metabolites in Rainfall: Origin,

Transport, and Deposition Patterns Across the

Midwestern and Northeastern United States,

1990 - 1991. Environ. Sci. Technol., 31(5):1325-

1333.

Lerch, R.N., P.E. Blanchard, and E.M. Thurman.

1998. Contribution of Hydroxylated Atrazine

Degradation Products to the Total Atrazine Load

in Midwestern Streams. Environ. Sci. Technol.,

32(1):40-48.

Ma, L. and R.F. Spalding. 1997. Herbicide

Persistence and Mobility in Recharge Lake

Watershed in York, Nebraska. J. Environ. Qual.,

26(1):115-125.

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.

National Climatic Data Center. 2000. Archive of

Climate Data. Available from the National

Oceanic and Atmospheric Administration at

http://www.ncdc.noaa.gov.

Nations, B.K. and G.R. Hallberg. 1992. Pesticides

in Iowa Precipitation. J. Environ. Qual.,

21(3):486-492.

Peck, A.M. and K.C. Hornbuckle. 2005. Gas-Phase

Concentrations of Current-Use Pesticides in

Iowa. Environ. Sci. Technol., 39(9):2952-2959.

Pereira, W.E. and F.D. Hostettler. 1993. Nonpoint

Source Contamination of the Mississippi River

and Its Tributaries by Herbicides. Environ. Sci.

Technol., 27(8):1542-1552.

Scholtz, M.T., A.C. McMillan, C. Slama, Y. Li, N.

Ting, and K. Davidson. 1997. Pesticide

Emissions Modeling–Development of a North

American Pesticide Emissions Inventory.

Canadian Global Emissions Interpretation Centre,

Ortech Corporation, Mississauga, Ontario,

Canada. Final Report #CGEIC-1997-1, 242 pp.

Schottler, S.P. and S.J. Eisenreich. 1994.

Herbicides in the Great Lakes. Environ. Sci.

Technol., 28(13):2228-2232.

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. Report Number 98-TA37.02.

Tabachnick, B.G. and L.S. Fidell. 1996. Using

Multivariate Statistics, Third Edition. Harper

Collin Publishers, Incorporated, New York, New

York.
34

Thurman, E.M., M.T. Meyer, M.S. Mills, L.R.

Zimmerman, C.A. Perry, and D. A. Goolsby.

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, 1,440 pp.

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. Report to the

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.
35

PART 1

INTRODUCTION

Appendix 1.3.1 Information Management

David A. Griesmer

Computer Sciences Corporation

Large Lakes Research Station

9311 Groh Road

Grosse Ile, 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 Ile, 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 al., 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 menudriven 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 “lmmb” 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
36

Table A1.3.1 List of Parameters Analyzed and Principal Investigators for the LMMBP Atrazine

Modeling

Parameter

Focus

Group Media Notes Principal Investigator

Atrazine, Deethylatrazine

(DEA),

Deisopropylatrazine

(DIA), Terbuthylazine

IUAA Atmospheric Vapor

and Particulate

Phase, Precipitation

Sleeping Bear

Dunes site only. 

Keri

Hornbuckle, U.

of Iowa, used

these data to

calculate

loadings

Ronald Hites, Indiana

University

Atrazine, DEA, DIA,

d5-Atrazine

WSAA Atmospheric Vapor

and Particulate

Phase, Precipitation

All stations

except Sleeping

Bear Dunes

site. Keri

Hornbuckle, U.

of Iowa, used

these data to

calculate

loadings

Clyde Sweet, Illinois

State Water Survey

Atrazine RULA Open-lake Steven Eisenreich,

Rutgers University

Atrazine RUTA Tributary David Hall,

USGS, used

these data to

calculate

loadings

Steven Eisenreich,

Rutgers University 

Flow N/A Tributary 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 look for transcription,

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/-
37

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 Ile 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

lmmbdata 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 “lmtrack2000.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 lmmbdata 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 
38

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 
39

7. Analysis Results for RFS and field duplicates for every analyte.

Analyte Avg Min Max Count 

Analyte Avg Min Max Count 

Analyte Avg Min Max Count 

Analyte Avg Min Max Count 

Analyte Avg Min Max Count 

Analyte Avg Min Max Count 

Analyte Avg Min Max Count 

Analyte Avg Min Max Count 

Analyte Avg Min Max Count 

Analyte Avg Min Max Count 

Analyte Avg Min Max Count 

Analyte Avg Min Max Count 

Analyte Avg Min Max Count 

Analyte Avg Min Max Count 

Analyte Avg Min Max Count 

Analyte Avg Min Max Count 

Analyte Avg Min Max Count 

Analyte Avg Min Max Count 

Analyte Avg Min Max Count 

Analyte Avg Min Max Count 

Analyte Avg Min Max Count 
40

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.
41

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.
42

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. LM10001 PI: David Schwab

Description

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.

Library No. LM10002 PI: William Richardson

Description 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.

Library No. LM10003 PI: David Schwab

Description Two-dimensional and three-dimensional GLERL hydrodynamics data for the 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. 

Library No. LM10004 PI: Steven Eisenreich

Description Open-lake (RULA) and tributary (RUTA), atrazine, DEA, DIA data for the 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.

Library No. LM10005 PI: Angela Bandemehr

Description Hourly meteorological data (air temperature, solar radiation, 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).

Library No. LM10006 PI: Glenn Warren

Description Seabird 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.

Library No. LM10007 PI: David Hall

Description 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.

Library No. LM10011 PI: David Schwab

Description 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.

Library No. LM10020 PI: Keri Hornbuckle

Description Atmospheric atrazine and nutrient (NO , total phosphorus, TKN) wet deposition loading data for Lake 3

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).
43

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. LM10022 PI: David Hall

Description 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.

Library No. LM10026 PI: Nathan Hawley

Description 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). 

Library No. LM10027 PI: Barry Lesht

Description 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.

Library No. LM10028 PI: Michael Settles

Description 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 Variable Description

Format (A = Alpha, F

= Floating Point No., I

= Integer, X = Skip)

Sort Order (A =

Ascending, D =

Descending,

Blank = None)

Missing Data

Code

1 - 7 Cruise Name A7 A Blank

8 - 8 Blank Space 1X N/A N/A

9 - 14 Latitude (ddd.ddd) F6.3 Blank

15 - 15 Blank Space 1X N/A N/A

16 - 22 Longitude (-ddd.ddd) F7.3 Blank

23 - 23 Blank Space 1X N/A N/A

24 - 35 Station Name A12 A Blank

36 - 36 Blank Space 1X N/A N/A

37 - 44 Depth Sampled F8.0 A Blank

45 - 45 Blank Space 1X N/A N/A
44

Table A1.3.4. Generalized Format for the LMMBP Water Data to be Analyzed With IDL Programs

(Continued)

Beginning -

Ending Columns Variable Description

Format (A = Alpha, F

= Floating Point No., I

= Integer, X = Skip)

Sort Order (A =

Ascending, D =

Descending,

Blank = None)

Missing Data

Code

46 - 53 Sampling Start Date

(mm/dd/yy)

A8 A Blank

54 - 54 Blank Space 1X N/A N/A

55 - 58 Sampling Start Time (24

hour clock)

A4 Blank

59 - 59 Blank Space 1X N/A N/A

60 - 67 Sampling End Date

(mm/dd/yy)

A8 A Blank

68 - 68 Blank Space 1X N/A N/A

69 - 72 Sampling End Time (24

hour clock)

A4 Blank

73 - 73 Blank Space 1X N/A N/A

74 - 75 Filter Fraction A2 A Blank

76 - 76 Blank Space 1X N/A N/A

77 - 79 Sample Type A3 D Blank

80 - 80 Blank Space 1X N/A N/A

81 - 88 Value Parameter 1 F8.0 -9999

89 - 103 Parameter 1 Flags A15 Blank

104 - 111 Value Parameter 2 F8.0 -9999

112 - 126 Parameter 1 Flags A15 Blank

Value Parameter n F8.0 -9999

Parameter n Flags A15 Blank
45

and make it easier for users to find the information.

Incorporation of this information into LLRS Microsoft

Access databases has given us flexibility in retrieving

the information needed by the modeling staff at the

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 Ile, Michigan. EPA/600/R04/167, 579 pp.

Sukloff, W.B., S. Allan, and K. Ward. 1995. RDMQ

User Manual. Environment Canada, Atmospheric

Environment Service, North York, Ontario,

Canada. 91 pp.
46

PART 1

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 Ile, Michigan 48138

and

Gregory J. Gerstner, Xiaomi Zhang, and Brent

Burman

Welso Federal Services, LLC

Large Lakes Research Station

9311 Groh Road

Grosse Ile, 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, air temperature, 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 al.,

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
47

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 al. (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 yearto-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-toyear. 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 a typical 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 longterm 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
48

Table 1.4.1. Summary of Lake Michigan Ice Cover Based Upon Assel (2003)

Year

Mean Daily

Percent Ice Cover

During Ice Period

Days of

Observed

Ice

Maximum

Daily Percent Ice

Cover

1973 13.3 104 33.0 

1974 16.9 122 39.4 

1975 13.9 113 28.1 

1976 15.5 119 29.5 

1977 46.5 132 93.1 

1978 26.6 132 66.6 

1979 35.2 132 92.3 

1980 18.2 106 38.6 

1981 24.6 112 53.8 

1982 24.0 135 60.2 

1983 8.2 118 23.6 

1984 15.6 127 43.3 

1985 20.1 119 41.3 

1986 25.3 126 66.8 

1987 9.1 100 19.3 

1988 16.6 104 32.7 

1989 13.1 140 30.9 

1990 17.5 132 32.4 

1991 10.0 120 21.5 

1992 8.3 149 32.8 

1993 11.0 126 32.2 

1994 27.3 134 82.7 

1995 7.2 120 21.6 

1996 19.4 161 75.0 

1997 13.4 156 37.8 

1998 6.1 109 15.1 

1999 8.7 111 23.0 

2000 9.2 103 27.2 

2001 13.4 134 29.5 

2002 6.0 116 12.4 

Mean 16.7 124 41.2 

Median 14.7 121 32.8

Minimum 6.0 100 12.4 

Maximum 46.5 161 93.1 
49

Figure 1.4.1. Location of the NOAA buoys in

Lake Michigan.

Figure 1.4.2. Monthly mean water temperatures

in southern Lake Michigan.

Figure 1.4.3. Monthly mean water temperatures

in northern Lake Michigan.

Figure 1.4.4. Mean June water temperatures in

southern Lake Michigan.

Figure 1.4.5. Mean June water temperatures in

northern Lake Michigan.

2005

2005

2000

south buoy

June

2000

1995

1995

1990

1990

1985

1985

Q-+-,,---,---r--r-r-r---r---,-,..-,-,----.--,-,......,r-r-.--.--.,.......,-,--.---.-4

1980

1980

2O-r------------------------,

20

north bUoy

__ data yea.,; for

2S --.----------1 Lake Michigan 1-----------,

hydrodynarric

modeling

" •

9' • 45002

•

45007

25...-------------=--1 20..,-----------------------,

20

l'

15

ill.

E

S 10 g

'"

~16

:!?

12

Q;

a.

E

2 8

Q;

iii

4 north buoy

June

1985 1990 1995 2000 2005

Q+-.---r-;----.---.-,---.---r---,---.----.-.,.......,r-;----.--.-,---.---r---,---.----.-.---rl

1980

1980 1985 1990 1995 2000 2005
50

Figure 1.4.6. Monthly mean air temperatures in

southern Lake Michigan.

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

Figure 1.4.8. Mean June air temperatures in

southern Lake Michigan.

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).

2006

SOlAh buoy JOM

1990 1995 2000

o+-,~~~,~~~~~~~~~~~-rl

1980 1985

20-,-------------,

4

16

Q

12

I

§ 8

"iii

1986 1990 1995 2000 2006

south buoy

_ data years for Lake M;chigan

hydrodynamic modeling

J

I

I

-5

1980

"iij

20

o

25

Q: 15

•,

'§ 10

[

§ 5

25y--------------,

north buoy

June

1986 1990 1995 2000 2006

20

Q

1985 1990 1995 2000 2006

north buoy

r-

_ data years for Lake Michigan

hydrodynamic modeling

MMt Ii 1\

-10 \

-15

1980

25

20

15

U

-; 10

5

0

2

"iij -5
51

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

the tributary, 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

177.5

177.0

,

! 176,5

,

0

> 1760

175,5

La~e Michigan and La~e Huron water le~els (1918-1997)

data: US Army COI?S of Engineers, Detroit District

_ monthly mean water levels (meters -IGLD 1985)

____ all.-time, record monthly high and low water le~els

175,0 -+rTTITTl-rTrTTTTTTTITTlTTlrTTrTTTTTTTITTl-rTrTTrTTTTTTTITTl-rTrTTTTTTTITTl-rTrTTTTTm
52

Figure 1.4.11. Annual precipitation to Lake Michigan between 1949 and 1998.

Figure 1.4.12. Comparison of 1982, 1983, 1994, and 1995 monthly mean precipitation to the mean for

the period of 1949 through 1998.

12000

10000

8000

6000

4000

2000

,

I

III ro \III ... ,

JM tI -

I' I\I "

1\1

I W

\,/

50 year annual mean

___ total annual preciprtalion

Monlhly mean predpilalion

___ 1949·1998 mean

"00 - ...... -1949-1998 mean. 1 Sld,<!ev.

- ...... -1949-1998 mean -1 std.deY,

• -----<>- 19t12 , , , , ,

"00 _6_1983 ,•, _0_1994 ,

, -0------.-1995 ,

,000 • E

E

• '00 £

0

Ii0

:~ "" •0

'00

•

.' '00

,

V

"" '" ." '" ." '"" '"' "0 '" '" "" '"
53

Figure 1.4.13. Comparison of tributary flow for hydrodynamic model calibration (1982-1983) to the

historic means.

Figure 1.4.14. Comparison of tributary flow for the study period (1994-1995) to the historic means.

6000,-----------------------------,

.000

Tributary ftow

Q hislorical mean

• 19S2-e3 me,,"

o1982mean

C1983mean

,

..

I

i

,

f

]

"

j

•

j

i

•

i

'000

,

'000

• 3000

f

6000,-------------------------,

'000

• 3000

f

'000

'000

Tributary ft"",

oJ historical mean

• , 994-95 mean

(] 1994 mean

[11995 mean

J

"

J

1

"

j

•
54

significant, especially when models are used in longterm 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 longterm 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, 46 pp.

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.H. 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 ERL GLERL-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.
55

PART 1

INTRODUCTION

Chapter 5. 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 Ile, 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)/Office

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,
56

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 airwater 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

trans-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.
57

1.5.3.1 Completely-Mixed Lakes-In-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-steadystate 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 (Endicott et al., 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 longterm contaminant transport predictions. The

significance of contaminant accumulation at the base

of the food web and fish movement in relation to
58

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 midlevel 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 nearshore and offshore regions can be distinguished with

the Level 3 resolution. Level 3 was scaled to resolve

to predict hydrodynamic transport.

Figure 1.5.1. Surface water segmentation for alternative Lake Michigan mass balance model levels.

LEVEL 1 - MICHTOX

(Screen ing)

6 5urtace segments

9 water segments

LEVEL 2 - LM-2

10 suriace segments

41 water segments

LEVEL 3 - LM-3

(High resolution 5km X 5km grid)

2318 surface segments

44.042 water segments

19 sigma layers
59

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 openlake 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 al., 2000;

Miller et al., 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 al., 2002; Cooter and Hutzell,

Figure 1.5.2. Model construct used for the LMMBP to model atrazine.

LM3-Atrazine

level 3

model

POM

hydrodynamic

model Hydrodynamic

and

load models

Contaminant

transport

and fate

models

MICHTOX

level 1

model

atmospheric

loads

tributary

loads

LM2-Atrazine

level 2

model

transport

aggregated

to level 2

advective/

dispersive

transport

and bottom

shear stress

environmental exposure

concentration
60

2002). This atmospheric model utilized atrazine

emissions from agricultural soils provided by the

Pesticide Emissions Model (PEM) (Scholtz et al.,

1999; and Scholtz et al., 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 threedimensional 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 onehour 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 al., 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.
61

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.), ThreeDimensional 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 Ile, Michigan. EPA/600/R05/158, 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 for the

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.
62

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 trans-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 Ile, Michigan. EPA/600/R04/018, 233 pp.

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 Ile,

Michigan. EPA/600/S3-84/026, 3 pp.

U.S. General Accounting Office. 1993. Report to the

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.
63

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 Ile, Michigan 48138

Chapter 1. Historical Atrazine Usage in

the United States

2.1.1 Background

The company, J.R. Geigy A.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 mid1960s, 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 al., 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 al.,

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).
64

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 al., 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
65

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

Year

Number of

States

Surveyed

% of Total

Corn Crop

Surveyed

% of Corn

Crop Treated

With

Herbicides

% of Corn

Crop Treated

With

Atrazine

Average

Application

Rate of

Atrazine

(lbs/acre)

Total Amount

of Atrazine

Applied

(millions of

kg)

1991

1994

1995

17

10

15

90

79

90

94

98

96

66

68

65

1.1

0.97

0.98

23.61

20.59

20.74

Kilograms per

Square Kilometer

Missing orO

Less than 0.5

0.5to 2.5

2.6 to 10.0

10.1 to 25.0

more than 25.0

o

I I II I I

o

300 Miles

I

I I

500 Kilometers Graphic by Wliam Battaglin

u.s. Geological Survey
66

Figure 2.1.2. Estimates of atrazine usage in the Lake Michigan basin for 1994 and 1995.

Figure 2.1.3. Historical trend of total annual usage of atrazine in the United States with acreage

planted in corn, sorghum, and sugarcane.

0-12000

_12001-24000

_ 24001-36000

_ 36001-48000

_ 48001-60000

Kilograms of Atrazine Applied

120

100

- - sog

E 60-

4>

" 40~

20

0

" .'

- corn + sorghum + sugarcane

45,--------1"·· corn

'" I_I:....:IO;.:I=;"',.:":::n:.:n":.:":.:'.:"::.tr":.:z:::'n.=e:..:";.:s=",,ge:.......J lsJ40 .-

i:~,~--- o " . ~.v. ~\_f '.

;: 25 __ ....._.. ,. ',: '-" .~ . -20

"

en

15

Cij 10

5 II (Jiojl,........~.,I,-....h.I,,..,I,.......,I,-.....................,I,-....................1,..,I1,..l.

::> 1964196819721976198019841988 1992 19962000

Year
67

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 D.P. 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, 56 pp.

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.
68

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 Knüsli, J.R.

Geigy A.G., Basel, Switzerland. Patent Number:

2891855; Serial Number 481474. 
69

PART 2

LAKE MICHIGAN MASS BALANCE PROJECT ATRAZINE

LOADINGS TO LAKE MICHIGAN

Chapter 2. Estimation of Atrazine

Tributary Loadings

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:

(2.2.1)

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

Watershed Export Load or Tributary load =

,,- [",n [AXTXCXFxL}_l La.. L..,_, J
70

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)/100

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 (GIS)-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)

B. Kirschner and R. Baksh 

(personal communication)

R. Baksh (personal

communication)

B. Kirschner (personal

communication)

D. Macarus (personal

communication)

D. Macarus (personal

communication)

U.S. Geological Survey

International Joint Commission

(IJC), Windsor, Ontario, Canada

IJC

IJC

USEPA/Region V, Chicago,

Illinois

USEPA/Region V

1995 (Publication Date)

1994

1995

1997

1999

2000
71

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 subbasins 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/finetextured 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 al. 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 weightedaverage WEP could be calculated for each subbasin.

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
72

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

Watershed Export

Percentage

Standard

Deviation

95% Confidence

Level Range

Number of

Studies

Clay

Loam/Fine Textured

1.4

0.61

0.61

0.38

0.94-1.85

0.37-0.85

0.11-2.5

0.21-1.5

9

12

o 200 miles

1

---.------'--,------,'1I

o 300 kilometers

soil types

D sands and gravels

D moderate textures

• fine textures

• clays, impervious

(from STATSGO database)
73

Table 2.2.3. Atrazine Watershed Export Data From Various Northern Sites

Watershed

Size (sq

km)

Location Soil Type

Watershed

Export

Percentage

Year(s) Watershed

Annual

PrecipitationReference

Adjusted

% Loss to

Represent

Atrazine

Only

23.83

30.25

30.25

50.8

50.8

62

62

6790

16395

Ontario

Ontario

Ontario

Ontario

Ontario

Ontario

Ontario

Ontario

Ohio

Clay

Clay

Clay

Clay

Clay

Clay

Clay

Clay/Loam/Sand

Clay

0.11

1.47

1.35

2.51

1.40

1.28

1.31

1.49

1.50

1976

1975

1976

1975

1976

1975

1976

1981,82,83,84,85

NA

Humber R.

Twenty Mile Cr

Twenty Mile Cr

Thames R.

Thames R.

Au Sable R.

Au Sable R.

Grand R.

Maumee R.

Dry

Wet

Dry

Wet

Dry

Wet

Dry

Average

Average

Frank & Sirons, 1979

Frank & Sirons, 1979

Frank & Sirons, 1979

Frank & Sirons, 1979

Frank & Sirons, 1979

Frank & Sirons, 1979

Frank & Sirons, 1979

Frank & Logan, 1988

Richards et al., 1996

Y

Y

Y

Y

Y

Y

Y

Y

N

18.6

18.6

30

30

54.72

54.72

45.04

45.04

3998

17820

38585

38585

Ontario

Ontario

Ontario

Ontario

Ontario

Ontario

Ontario

Ontario

Ontario

N. Iowa

S.

Minnesota

S.

Minnesota

Loam

Loam

Loam

Loam

Loam

Loam

Loam

Loam

Sandy/Loam

Fine Textured

Fine Textured

Fine Textured

0.32

0.54

0.21

0.26

0.50

0.35

1.09

0.80

0.65

1.50

0.33

0.62

1975

1976

1975

1976

1975

1976

1975

1976

1981,82,83,84,85

84

90

90

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

Wet

Dry

Wet

Dry

Wet

Dry

Wet

Dry

Average

NA

Dry

Wet

Frank & Sirons, 1979

Frank & Sirons, 1979

Frank & Sirons, 1979

Frank & Sirons, 1979

Frank & Sirons, 1979

Frank & Sirons, 1979

Frank & Sirons, 1979

Frank & Sirons, 1979

Frank & Logan, 1988

Squillace&Thuman, 92

Schottler et al., 1994

Schottler et al., 1994

Y

Y

Y

Y

Y

Y

Y

Y

Y

N

N

N

19.9

19.9

56.45

79.13

79.13

Ontario

Ontario

Ontario

Ontario

Ontario

Sandy

Sandy

Sandy

Sandy

Sandy

0.15

0.20

0.17

0.29

0.18

1975

1976

1975

1975

1976

Hillman Cr

Hillman Cr

Shelter Val. Cr

Big Creek

Big Creek

Wet

Dry

Wet

Wet

Dry

Frank & Sirons, 1979

Frank & Sirons, 1979

Frank & Sirons, 1979

Frank & Sirons, 1979

Frank & Sirons, 1979

Y

Y

Y

Y

Y
74

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.

(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:

(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 countylevel application data using Equation 2.2.1. In

Tributiuy LoiJd Rilfio,. (TributiJry LOiJd to Model

segment)/(TotiJl AnnuiJl USA AtrllZine USiJge)

TributiJ'Y LoiJd,. (TributiJ'Y LoiJd Rilfio) x

(TotiJl AnnuiJl USA USiJge for Any YeiJr (yJ)
75

Figure 2.2.2. WEP-based total atrazine tributary loading estimates to Lake Michigan.

comparing tributary loading ratios for pre- and postlabel 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 Loads for

MICHTOX and LM2-Atrazine

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

9000

8000

o total annual usage

• historical tributary load f-

<Jl

45

,~ 40

-'"

'0 35

<Jl ,Q 30

'E 25

7000 >-

-- 6000 g

5000 -g

c 0

'~W ~OO>

15 3000 -5

Ctl 10 2000 :E

§ 5 1000

Ctl

0 0

::> 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000

year
76

Figure 2.2.3. Tributary loadings to Lake Michigan

MICHTOX model segments.

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 surfacewater 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 al., 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.

1994 loads

l==: .- 01995 loads

iI-

-- I-

- .. .1 -

4500

4000

3500

3000

1000

'" 900

800

700

600

500

400

300

200

100

o

2 345

MICHTOX Segment

6 7

o calculated

atrazine

loads (kg/year)

...monitored

'P: tributary

loads: 4305

unmonitored

'-J tributary

loads: 959
77

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 al. (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 et al., 1981; Hall et al.,

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 al., 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 et al., 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 al.

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, highconcentration 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
78

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 km . Unit area yields from 2

the monitored basin were calculated as follows:

(2.2.4)

where

i

I = load estimate for any given day

A = area of the watershed for a monitored tributary

Figure 2.2.5. 1995 USGS SBRE atrazine loadings and median concentrations relative to median flow

in Lake Michigan tributaries.

Unit Area Yield = Ii fA

1800- 90

1600-~ 80 Cl

~1400-..s 70 Cl c

~1200-2 60 c:

'01000- C 50

m <I.J

o u

800- § 40 c: u

'N 600- <I.J 30

.s .... N

m 400- 20

ro

200- 10

0- 0

o atrazine loading (kg/yr) fo atrazine concentration (ng/L)

• flow (cfs)

In lin 1n.·1 r .rl J ..c

a. 1:lc: 0

X 00 ID

Q) o

c: IDID

..... :t:: c:

Cll ID

Q)

Q)

::J 1:lc: .....

•

Q)

Q) u.. N Ol Q) Q) rn c rr mE en (!) m ::J <I.J CL::J >- E ..... ::J

0 E m 0 (9-

"' l: en .c 0 III

::J III Q) C C

ro U

en m :2 ..c <I.J

(J) :2 :2

1995 monitored tributaries

9000

8000

7000

6000 __

.!!2

5000

3:

4000 .g

3000

2000

1000

o
79

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: 1163 kg

1995 USGS: 1426 kg

1994 WEP-Based: 5263 kg

1995 WEP-Based: 4916 kg

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/#HDRZ.

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 LandUse. Environ. Sci. Technol., 34(16):3315-3322.

Capel, P.D. 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

Issue/MOBR01-07.
80

Hall. D.W., T.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,

85 pp.

Hall, D.W. 2004. Quality Systems and

Implementation Plan (QSIP) in the Quality

Assurance Project Plan for the LMMBP 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 Ile, Michigan. EPA/600/R04/018, 233 pp.

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.D. 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/R97/012a, 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.
81

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 et al. 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-associated particulates over-the-lake yielded

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. Schottler and 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-thelake. 

Vapor phase concentrations of atrazine were used in

the models as a boundary condition; please see
82

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

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 μg/m /yr for 1994 2

and 1995. A similar calculation of flux for 1991

(Figure 2.3.1) yielded a value of 45 μg/m /yr. 2

 

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:

(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:

(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

Precipitation Load Ratio = Precipitation

Load to a Model Segment I Total Annual

USA Atrazine Usage

Precipitation Load =

Precipitation Load Ratio x

Total Annual USA Usage Year y'

Estimated atrazine

deposition in micrograms

per square meter

per year - 1991

Less than 10

10 to 25

26 to 50

51 to 100

more than 100

200 Miles

I

I

300 Kilometers

o

I

o
83

Figure 2.3.2. Gradients of atrazine in wet

deposition loadings over Lake Michigan for May

1994.

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). 

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

-total annual usag. _ historical tributary load

o wet deposition

I I II I IJUt 1

I

E

5, 45 9000

40 8000

35 7000 c

g 30 6000·~

E 25 5000 a. £

20 4000 i

:::> 15 3000 .

.

:::> 10 2000 ffi

§

1lI 5 000 (;j

;f 0 0 " => 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 .:::

year

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

o

wet deposition

atrazine load

(kg/month)

May 1994

1400

1200

£1000

0

two ..

,

• wo

0 400 •

200

• •
84

Figure 2.3.5. Tributary and wet deposition loadings to MICHTOX model segments for 1994 and 1995.

Figure 2.3.6. Tributary and wet deposition loadings to LM2-Atrazine model segments for 1994 and

1995.

';00

<COO

%00

"00

0

, 0;00 <

0

,

"

, 0000

';00

'"00

;00

c

_.",..hi""'.". ~.,.

"""" hi""''''' ~. d.

._....,.."..~"

0.,.", .... , ••" ..~"

J l k

,

MICHTOXS<gmert

,

,~c

'000

o~c

0000

g ,~c

,

,

" '000

, ,~o

, 000

~o

0

.,"4trib",,,I",.

-l "'''' ,rib",,,I",•

•,".... '.,'~tio"

01'" ... ,. ,,,;t~"

1= -

J,Jl uh . •

,

'"

,

Atr"ine Se~ment
85

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,

L.L. 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. Report Number 98-TA37.02.
86

PART 3

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 Ile, Michigan 48138

3.1 MICHTOX-Atrazine Executiv e

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 al., 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 al., 1988). The

model solves mass balance equations based on a

finite volume spatial discretization (Thomann and
87

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) cover Green 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 al., 1956; Schelske

et al. 1976; Saylor, J., National Oceanographic and

Atmospheric Administr ation, 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 al.,

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.

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 four to 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 al., 2001).

Furthermore, they reported that analysis of data from

their 10 lake stations that covered a central northsouth axis and an east-west axis showed no

horizontal gradients in atrazine concentrations in the

lake.
88

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 al. (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 al.,

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). In order to

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 al., 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. 

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

7000,-------------------,

Precipitation

6000

en SOOO

~4000

'0

3000

2000

1000

....... ..... •••• -0' •••.••••••••••• .... .' .' " .' .' .' " . O+-,..~~~~~~~~~~~~~~._M

1964 1968 1972 1976 1980 1984 1988 1992 1996

year
89

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 Mode l

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 Michiganspecific 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

70

60

::;

0,

.s 50 c

.2

"§ 40

co

Q)

g 30 o

u

20

.!!l

10

-- Mean watershed export percentage 0.60

decay constant D.O/year

Upper 95% confidence interval

watershed export percentage 0.84

decay constant D.O/year

Lower 95% confidence interval

watershed export percentage 0.36

decay constant D.O/year

-- Mean watershed export percentage 0.60

decay constant D.OS/year

o Field Data ±1 standard deviation L- -----'•••••

.' .'

1980 1984 1988 1992 1996

year
90

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
91

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 volumeweighted 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 13.25 or [(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 lakewide 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 (kd

)

determined by model calibration was low in all

scenarios where evaluated (see Figures 3.4 and 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, MICHTOX predicts that 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:

(3.1)
92

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.

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.

70.....-----------------------------------,

Scenario 1; kinetic decay 1.25%/yr

Scenario 2; kinetic decay 0.8%/yr

• Field Data +/- 1 standard deviation

- Scenario 3; kinetic decay 1%/yr

20 +---------,..-------__j

10+---_F-----------j

6°1----------------=~~~~~

c

o

C

Q)

U

C

o

U :::::J 50 -t-------------------::::;orlS;.oo"<:..:..-------------j ~c,

oN ..s

c

co

Q) .- 40 -t---------------JWI~------------------___l

g':§

Q; :2: > Q) (0.><

"0 (0 30+---------,11"--------1 Q)...J

:E .£

.Ql

Q)

3:

W

E

='

o

>

Oct. 29

2058

Feb. 19

2045

June 13

2031

Oct. 4

2017

Jan. 26

2004

May19

1990

Sept. 9

1976

O~F_--___,----__r_---___,,._---__r_---___,,._---__r_---__j

Jan. 1

1963

date

50 ...-------------------------------,

• 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 5 -I-------.r---------i

40+---------- 1llfE-----"Wk"'.,,-----------------j

6

45 -t------------=~~=::::::::===~=l 7

15 -t-------.4J---j

10 +-------,j..------j

c

o

c

Q)

U

C

o

u:::::J

0, 35 +--------,.~-----~Ir_--::!!111 ...=__--------___l

'N -S

c 1ii 30 -I-----------.'----------------"I~--------"""' ...,------j

Q) .-

g':§

Q; :2: 25 -I--------*----------------''''Wo.::---------__j > Q) (0.><

al 5 20 +-----.------------------~~:__----___l :E .~ en 'iii

Q)

E

='

o

>

Oct. 29

2058

Feb. 19

2045

June 13

2031

Oct. 4

2017

Jan. 26

2004

May19

1990

Sept. 9

1976

O....~----.----__,_---__,_---__r---__r---~---___l

Jan. 1

1963

date
93

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

highest boundary 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, 92 pp.

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, MidContinent Ecology Division, Large Lakes

Research Station, Grosse Ile, 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,

L.L. 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.
94

Rodgers, P.W. and D.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 Ile, 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. Principles of

Surface Water Quality Modeling and Control.

Harper Collins Publishers, Inc., New York, New

York.

Thomann, R.V., R.P. Winfield, 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 Ile, Michigan. EPA/600/3-79-

094, 136 pp.

U.S. Department of Agriculture. 2001. Agriculture

Research Service Pesticide Properties. Available

from U.S. Department of Agriculture at

http://www.ars.usda.gov.
95

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 Ile, 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
96

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-Toxic has an

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 Scholtz et al. (1999) and Miller (1999).

The dimensionless value for the Henry’s law constant

was set to 8.1 x 10 (U.S. Department of Agriculture, -8

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).
97

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 km grid used in the 2

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 bidirection 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 al., 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).

5 10

15 8

24 9

r:r 10 ;JJ 10 2 10

lQ10 10

10 18

18

1 10 average 3642

1'- 11 10 segment 3748 20 10 thickness

water

920 in meters

column

segment

numbers 36100
98

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 al., 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 (Ayers et al., 1956; Schelske et al., 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 al., 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 Model

Application

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. 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.

4.4.2 Field Data

See Part 1, Chapter 3 for atrazine field data from the

lake, tributaries, and atmospheric components.
99

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/m and increased linearly until December 31, 3

1977. Starting on January 1, 1978, the vapor phase

concentration was held constant at the atrazine

detection limit of 0.00926 ng/m (Miller, 1999) 3

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
100

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/m and remained at that concentration for 3

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/m and 3

then increased linearly up to 0.00463 ng/m (one-half 3

detection limit) until December 31, 1977. Starting on

January 1, 1978, this vapor phase concentration was

held constant at 0.00463 ng/m throughout the 3

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 (1⁄2

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-lake volume-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
101

(January 1, 2005). All other modeling processes

were active.

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 (k )d

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 = t = (ln 2)/k 1/2 d(4.1)

Figure 4.2. LM2-Atrazine model results for Lake Michigan and Green Bay for the year 1994.

export to

lake Huron

2531 kg/yr

Atr.,ine Inventory

182,779 kg

input from

..",.,L.J--..,.....,_.--. lake Huron

472 kg/yr

loss to decay: 1648 kg/yr

Dry deposition, settling, sediment resuspension

and net burial are negligible

watershed

~Ioading /""'---- 5264 kg/v'

atmospheric

wet deposition

2493 kg/v,.

volatilization

51 kg/v'f

export via

fII1IIIIL ..; Chicago

..",,~..... Diversion

145 kg/yr
102

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 modelpredicted 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.

Figure 4.3. LM2-Atrazine model runs of scenarios.

I Field dala (+1- 1 standard deviation)

- Constant loads (scenario 3)

- - - Zero vapor phase concentration (scenario 7)

- No wet deposition (scenario 6)

r- -- 35% Reduction of tolalload (scenario 8)

""" 100(//0 Reduction of tributary load (scenario 5)

-- 100% Reduction of tolalload (scenario 4)

66 ng/b,....

~--- ~--_.- ------------------------

2005...... .-L- ------ ------ '.'."........................................................................

90

80

60

50

40

30

20

10

o

1963 1993 2023 2053 2083 2113 2143 2173 2203 2233 2263

Year
103

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.

Figure 4.4. Historical trends of United States corn acreage planted and harvested from 1986 to 2007

(U.S. Department of Agriculture, 2007).

u.s. Corn Acres

Million Acres

1989 1992 1995 1998 2001 2004 2007

100 ,------------------------,

95

90

85

80

75

70

65

60

55

50 L-L.....l-----L----'-----'---'------'-----'------'-----'-----'------..L.....l-----L----'-----'---'------'-----'------'-----'-----'

1986

I'" Planted -Ill- Harvested 1 USDA-NASS

10-12-07
104

Figure 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. The actual Lake

Michigan response is believed to be bracketed by the 15% to 30% total atrazine load increase to the

lake related to the corn-to-ethanol biofuels program.

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.

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.

Bamford, H.A., J.H. Offenberg, R.K. Larsen, F.C. Ko,

and J.E. Baker. 1999. Diffusive Exchange of

Polycyclic Aromatic Hydrocarbons Across the AirWater Interface of the Patapsco River, An

Urbanized Subestuary of the Chesapeake Bay.

Environ. Sci. Technol., 33(13):2138-2144.

Blumberg, A.F. and G.L. Mellor. 1987. A Description

of a Three-Dimensional Coastal Ocean

Circulation Model. In: N.S. Heaps (Ed.), ThreeDimensional Coastal Ocean Models, Coastal and

Estuarine Sciences, pp. 1-16. American

Geophysical Union, Washington, D.C.

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, 92 pp.

Chapra, S.C. and K.H. Reckhow (Eds.). 1983.

Engineering Approaches for Lake Management,

Volume 2: Mechanistic Modeling. Ann Arbor

Science Publishers, Ann Arbor, Michigan. 492

pp.

90

80

::J

-OJ 70 c

c 60

.9

«i 50

C

'"

u 40 c

0

() 30

'"

c

·N 20

;;;: 10

0

1963

I Field data (+/- 1 standard deviation)

- 30% total load increase

- 15% total load increase

- Constant loads (scenario 3)

1993 2023 2053 2083 2113 2143 2173 2203 2233 2263

Year
105

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 Ile, 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 Ile, 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.

NOAA Technical Memorandum ERL GLERL-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.

Harper Collins 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. Wilhelm and J.S.

Culliver (Eds.), Air-Water Mass Transfer, pp. 441-

458. American Society of Civil Engineers, New

York, New York.
106

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. 12 pp.

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, MidContinent Ecology Division-Duluth, Large Lakes

Research Station, Grosse Ile, Michigan.

EPA/600/R-04/167, 579 pp.
107

PART 5

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 Ile, 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 firstorder 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
108

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 water quality. 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 longterm 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 timeseries 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. Midlake 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 highresolution 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, water quality 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 al., 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 al., 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
109

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 al., 2006), the atrazine model described

below, and, subsequent to the LMMBP, an

ecosystem model (Miller et al., 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 postprocessor 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 al. (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.
110

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 nearshore 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 airto-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
111

Figure 5.2. Simulated temperature (black) compared to measured temperature (gray) at two buoys in

Lake Michigan for 1982-1983 (Schwab and Beletsky, 1998).

Surface Temperature at 45002

30

<3 25

20 o

1§ 15

1l. 10 E

5

0""- --'

100 200 300 400 500 600

Surface Temperature at 45007

30

<3 25

20 o

15

1l. 10 E

0'- 5 --'i

100 200 300 400 500 600

Temperature at 15 m 30 p-....;,.;....;;;.,;..,.;.;.;,....;,.;..;."....;,........"'l

2: 25

20 o

(ij 15

10 E

0'- 5 -'

100 200 300 400 500 600

Temperature at 15 m

30 F"~="""="":"';""""";"-"'l

2: 25

20 o

(ij 15

10 E

50'""- -'

100 200 300 400 500 600

....

300 400 500 600

Temperature at 50 m 30 rn....;,.;....;;;.,;..,.;.;.;,....;,.;;,;.,;....;,........"'l

<:) 25

- 20

o

15

1l. 10 E

5

0'-'"

100 200

Temperature at 50 m

30 F"~="""="":"';";';"";"";"",

<:) 25

- 20

o

15

1l. 10 E

5

o ----- 100 200 300 400 500 600

Temperature at 148 m

30 .........c.;c===..;;..::.:...:..:.::......;,.,

- 25 8

20

o

15

1l. 10 E

5

0"""

100 200 300 400 500 600

JULIAN DAYS, 1982-83

Temperature at 154 m

30 .........c.;c=..;;..:=..;;..::.:...:..::..c..;;.:..,

- 25 8

20

o

T§ 15

1l. 10 E

5

0..---

100 200 300 400 500 600

JULIAN DAYS, 1982-83
112

Figure 5.3a. Time-series of simulated water temperature versus observed at 45007 for 1994-1995. Gray

line is observation; black line is model simulation (Schwab and Beletsky, 1998).

Surface Temperature at 45007

30

- 25 S

20

;; 15

10 \

5

o

100 200 300 400 500 600

Temperature at 12 m 30 ,....-..:..::.;;.;;:.=:.:;;.:...:;;;...:.::..~...,

25 S

20

15 11;

Co 10

5

~~00:o--.:2~00~3O=0-4~0~0~50~0~60~0-'

Temperature at 42 m

30,....----'------..,

6 25

- 20

15 11;

Co 10

5

0t......~~_~~~......J

100 200 300 400 500 600

Temperature at 102 m

30,....---'-------..,

Q: 25

20

B

15

g- 10

5 ..

0h......~~_~~~......J

100 200 300 400 500 600

JULIAN DAYS, 1994-95
113

Figure 5.3b. Time-series of simulated surface water temperature versus observed at 45002 and 45010

for 1994-1995. Gray line is observation; black is model simulation (Schwab and Beletsky, 1998).

Table 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)

(Schwab and Beletsky, 1998)

RMSD

Maximum

Error

Average 

Observed

Average

Computed

Correlation

Coefficient

Surface

Subsurface

 Epilimnion

 Hypolimnion

1.2

2.5

0.7

6.6

10.6

3.3

12.1

7.1

4.2

12.1

6.4

4.3

0.99

0.87

0.78

Table 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) (Schwab and Beletsky, 1998)

RMSD

Maximum

Error

Average

Observed

Average

Computed

Correlation

Coefficient

Surface

Subsurface

 Epilimnion

 Hypolimnion

1.5

2.4

1.3

6.1

9.2

5.2

13.1

7.3

4.5

13.3

7.7

5.3

0.96

0.93

0.87

Surface Temperature at 45010

30

G 25

20 :>

10 15

g10

5

0'-- '-'- -'

100 200 300 400 500 600

JULIAN DAYS, 1994-95

Surface Temperature at 45002

30

G 25

20 :>

10 15

g10

5

Obl..:. • -'

100 200 300 400 500 600

JULIAN DAYS, 1994-95
114

Figure 5.4. Simulated mean temperature (°C) profile for 1982-1983 (Schwab and Beletsky, 1998).

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.

Simulated Average Temperature of L. Michigan

200 300 400 500 600

JULIAN DAYS, 1982-83

0

CIl 20

Ul 2 4 -Ul 40 E

.c 60 -c.

CD

0 80

100

100

4 4

Cruise 1 Day 126 Year 94 Cruise 3 Day 234 Year 94 Cruise 4 Day 303 Year 94

0 0 0

J ;r

r

SO 50 50 rr

E E E

,5 tOO ,5 100 -a 1OO

" "

ISO 150 150

200 200 200

0 5 10 15 20 25 30 0 5 10 15

'" 25 30 0 5 10 15'" 25 30

Temperature (C) Temperature (e) Temperature (e)

Cruise 6 Dav 225 Year 95 Cruise 7 Dav 281 Year 95

0 0 J[

50 (( 50 r;

E E

,6 100 :5 100

" "

station

IBM 150 150

•

200 200

0 5 10 15

'" 25 30 0 5 10 15'" 25 30

Temperature (e) Temperature (e)
115

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 MICHTOX and 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

et al. (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 Pauer et al. (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 al. (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 al. (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 statevariable at a certain time. The change in mass of

atrazine in the LM3-Atrazine model for a given water

column segment is described as:
116

(5.3.1)

where

Vj = volume of segment j (L )3

Cj = concentration of water quality constituent in

segment j (M/L )3

Ci = concentration of water quality constituent in

segment I (M/L )3

Cij = concentration of water quality constituent at

the interface between segment I and j

(M/L )3

Qij = net flow across the interface between

segment I and j (defined as positive when

entering segment j and negative when

leaving segment j) (L /T) 3

n = number of adjacent segments

Rij = (E A / ij ij ij )X ), bulk dispersion/diffusion

coefficient (L /T) 3

Eij = mixing (dispersion/diffusion) coefficient

(L /T) 2

Aij = interfacial area between segment I and j

(L )2

)Xij = characteristic mixing length between

segments I and j (L)

Wj = external loading rate of segment j (M/T)

Saw,j = mass change rate due to air-water

exchange process between segment j and

air directly above segment j (M/T)

Sk,j = mass change rate due to sum of kinetic

transformation processes within segment j

(M/T), positive is source, negative is sink

Note: L = length; M = mass; T = time.

The mass change due to kinetic transformation

processes, Sk,j, 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 year (2.854 x 10 s ). -1 -10 -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 (Saw,j) for atrazine due to airwater 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 j.

(5.3.2)

where

kol = the overall mass exchange rate coefficient

(L/T)

Cdw,j = dissolved atrazine concentration in water

(M/L )3

Ca,j = atmospheric atrazine concentration over

segment j (M/L )3

H’ = temperature-dependent Henry’s law

constant (dimensionless)

Aj = surface area of the water segment j (L )2

The overall mass exchange rate coefficient (kol) was

calculated using the Whitman two-film theory

formulation (Whitman, 1923) given as:

(5.3.3)

,

k cI = "';+-;:='-':;;;

~k k. H'

,"
117

where

kl = the liquid film mass transfer rate coefficient

(L/T)

kg = the gas film mass transfer rate coefficient

(L/T)

The parameters H’ l g , k and k were calculated at

every time step for each LM3 segment. The

Wanninkhoff (1992) formulation for water mass

transfer resistance and the Schwarzenbach

(Schwarzenbach et al., 1993) formulation for gas

mass transfer resistance were used for modeling the

air-water exchange of atrazine in Lake Michigan.

The Wanninkhoff equation for kl, with correction for

atrazine molecular diffusivity in reference to carbon

dioxide (CO ) molecular diffusivity across the air- 2

water interface, is given as:

(5.3.4)

where

Dw = chemical molecular diffusivity in water

(L /T) 2

Dl_CO2 = CO molecular diffusivity in water (L /T) 2

2

u10 = 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

D" = chemical molecular diffusivity in air (L /T) 2

Dg_H O2 = water vapor molecular diffusivity in gas

phase (L /T) 2

The atrazine model calculated a temperaturecorrected dimensionless Henry’s law coefficient using

equations derived from Scholtz et al. (1999) and

Miller (1999).

(5.3.6)

where

H’ = temperature-dependent Henry’s law

constant (dimensionless)

HTref = Henry’s law constant at the reference

temperature

)HH = the enthalpy of phase change (kJ/mol)

R = the ideal gas constant, 8.315 x 10-3

kJ/(mol)(°K)

T = interfacial temperature (°K)

Tref = 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 (U.S. Department of -8

Agriculture, 2001). The enthalpy of phase change

was set to 50 kJ/mol (Scholtz et al., 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 highresolution 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.

(1/T-1/T.. l

log H' - log I H".. I- tJ.H,. 12.303R)
118

5.3.2.2.3 Temporal Resolution

The LM3-Atrazine model simulated the period from

January 1, 1994 through December 31, 1995 for the

LMMBP 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-

December 31, 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 al., 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 al., 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 al., 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

al., 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
119

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 al., 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/m (Miller 1999). 3 

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 al. (1999) reviewed previous studies

and found that, for soils similar to those in the cornproducing 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

al. (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

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,

lake Michigan

watersheds

•

monrtored

tributary

basins

"""""""- unmollitored

tributary

basins

• sampling

locations
120

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 al., 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 oneto 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 et al. (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 WEPbased 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 USGScalculated 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 WEPadjusted loads as appropriate. The LM3-Atrazine

model did this automatically by looping over the twoyear 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.
121

Figure 5.7. Atrazine loads for Lake Michigan tributaries, 1994-1995.

Estimates from the LMMBP study found it to be small

(208 kg/year) (Miller, 1999). However, later

estimates placed the range of dry deposition loads

from 230-1000 kg/year (Miller et al., 2000). For

additional discussion on atrazine in the particulate

fraction, see Part 1, Chapter 3.

5.3.4 Description of Model Simulations

and Results

Model simulations were conducted for different load

scenarios and time periods. Simulations were

conducted with tributary loads based upon the 1994-

1995 USGS estimates and with loads based upon

the long-term WEP loads. Atmospheric loads were

kept constant for all model runs. Time periods

modeled included the two-year period of the LMMBP

(1994-1995) and the 12-year period from 1994 to

2005. 

For the 1994-1995 simulation using USGS tributary

loads, mid-lake model results generally matched field

data (Figure 5.8). Mid-lake model predictions varied

little over the course of a year. The 1994-1995

simulation using the long-term WEP loads (Figure

5.8) was run to represent long-term trends of

loadings. These higher loads resulted in greater

seasonal increases in mid-lake concentrations

compared to the USGS-estimated loads, but annual

variation was still relatively small. 

The high-resolution LM3 model was especially useful

in demonstrating near-shore impacts resulting from

various watershed loads. Higher concentrations

were predicted in the areas near the mouths of major

tributaries during the spring and early summer period

when loadings were highest (Figure 5.9). The highresolution model was also useful for demonstrating

the impact of different loading scenarios on small

spatial areas. Model results from the 5 km x 5 km

model segments directly off the mouths of the major

tributaries showed the largest variations of atrazine

in the lake (Figure 5.10). Predicted concentrations

directly off the mouth of the St. Joseph River ranged

as high as 102 ng/L using the USGS-estimated loads

and as high as 350 ng/L using the long-term WEPbased loads model runs. 

Grand River

.. J"

1996

July

1995

J"

1995 J'" 1994

r'<._/ st. Jc>seph River

45

40

! ~~25

20

";::; 15

10

5

o

Jan Jan

1996 1994

J"

1996

- long-term WEP loads

- USGS estimated loads

July

1995

July

1995

J"

1995

J"

1995

Fox River

St. Joseph River

July

1994

July

1994

18

16

'g; 14

<'. 12

10 • 8 •

•

'N 6

• 4

•

2

0

J"

1994

60

50

<'. 40

• 30

•

'N• 20

•

• 10

0

J"

1994
122

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.

Figure 5.9. Model simulation results of surface concentrations for May 29, 1995 using long-term WEPbased loads. Selected tributary input locations are labeled.

Jan

1996

July

1995

Jan

1995

July-1994

Station 47M

20 -+-------------------1

30 -+-------------------1

5O-r-------------------,

10 -1------------------j

O+-................-T"'""T-.T"'""T-...,.,........-.......-T"'""T..-...-i

Jan Jan-1994

1996

July

1995

Jan

1995

July

1994

Station 27M

A

• ..

<::

N

l!

t • iii

- long term VVEP loads

-- USGS estimated loads

field data (0-10 m)

o

Jan

1994

50

40

::;-

'ij, 30

.s

..

<::

;;;; 20

co

10

10

50

40

::;-

'ij, 30 .s

..

l:

;;;; 20 l!

iii

10

Station IBM

'" '" .-.Ao..

A • A A -

50

40

A Stations 40M/41

... A •

o

Jan

1994

July

1994

Jan

1995

July

1995

Jan

1996

o

Jan

1994

July

1994

Jan

1995

July

1995

Jan

1996

May 29,1995

41

40

39

38

37

Atrazine

(ng/L)

Sheboygan~

Milwaukee *

Calumet~

jGrand

~Kalamazoo
123

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 nearshore 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 al., 2001).

Thus there is confidence that the transport of

substances, one of the primary loss processes of

atrazine, was being correctly simulated. The highresolution 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 al., 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

~500

~~

•

~300

.~

'00

sltil"",nl 1397 nur Fox River moulh

h -- \.. '"".

'00

sltil"",ntl1lnur

Grand Rlv.. moulh

• I

.\Il

o

••

'00'

'00

"" ~250

.~

o

11 150

ii 100

- long term MP loadsIs I -- USGS u~mated loads

, sltil"",nt1al nUr

SI. J05eph River mouth

Jo...

,-••
124

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 inlake 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 WEPbased 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 al., 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.
125

Figure 5.11. Mid-lake surface concentration model results for the 1994-2005 model simulation and two

loading conditions.

Table 5.3. Mass Budget Average Annual Results for 1994-1995 Model Simulations. All are in kg/year.

USGS Loads Long-Term WEP-Loads

Mass Change

Inflows

 Loads

 Tributary

 Wet Deposition

 Volatilization In

Outflows

 Decay

 Total Outflow

 Chicago Outflow

 Mackinac Outflow

 Volatilization Out

Totals

 Net Volatilization

 Total In

 Total Out

Average Mass Inventory (kg)

380

3362

1578

1784

58

1615

1412

110

1302

13

-45

3420

3040

179,459

98%

(46%)

(52%)

2%

53%

46%

(4%)

(43%)

0%

3842

6870

5086

1784

58

1647

1425

112

1313

14

-44

6928

3086

182,979

99%

(73%)

(26%)

1%

53%

46%

(4%)

(43%)

0%

"

60

Station 27M

I I I

,,===-------------. Station 47M

,,'"6Ot=~~

"i 3O , t-------------------1

~2Ot----------------__1

,

",0

""

,

",0

,,,..

- long term V\o€p lo~ds , I

Station IBM - USGS estim3led lo~ds

•

"

•

••

"

"

60

~40

30

20

"0"" "0"""

,,+-----------------1

,

",0 ",0 ",0 ",0 ",0 ,,,.. "" '''''' "'" """

"

Stations 40MI41

60

'" .

30

'"

"

,

"0 ",0 "0 "0 "0 ,,,.. "" '''''' """ """
126

Figure 5.12. Mass budget average annual results for the 1994-1995 model simulations. All mass

flow rates are in kg/yr.

Chicago

River

outflow

110

volatilization

out

13

volatilization

~~r

wet

deposition

1784~.

decay

1615

1578

monito d and unmonitored

tributary loading

(Lake Michigan watershed)

USGS loads

Atrazine Inventory

water column = 179,459 kg

mass change = + 380 kg/yr

Long-term WEP loads

Atrazine Inventory

water column = 182,979 kg

mass change = + 3842 kg/yr

._,....

5086

monito d and unmonitored

tributary loading

(Lake Michigan watershed)

wet

deposition

1784-f"""'"

decay

1647

volatilization

out

14

volatilization

~~r

Chicago

River

outflow

112
127

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 for atrazine (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 μg/L, exceeded

the endpoint of 2.3 μg/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 μg/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 μg/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 μg/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.

model results

• endpoint f criteria

o Imeasured data I

•

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 2005lM3-Atrazme forecast range (0.038·0.046)

• _ draft acute toxicity criteria CMC (1500)

• _ invertebrate mortality (720)

• invertebrate population reduction (62)

fish population reduction (62)

• phytoplankton acute toxicity (32)

• _ fish mortality (5300)

• _ zooplankton population reduction (10)

___ human drinking water MCl (3)

§ _ maximum measured 1995 tributary concentration

in 51. Joseph River (2.7)

.............. phytoplankton primary production reduction (2.3)

0.1 /

:---

~-

0.01 -

~L_-------- __--=IL='=k::'::M=;'=h=;9='::"::'=9=9=4::,=,::.=,,=g=.~,=0="=,=.::"::"::'::';::0::"::'::0::.0::'::'::' ====='J

1,000

10,000

100

c

0

c

c '0 0

"

•

c

.""

"
128

The highest simulated concentration, using the

USGS loading time-series, was 0.10 μg/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 al., 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. 

References

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, 92 pp.

Capel, P.D. and S.J. Larson. 2001. Effect of Scale

on the Behavior of Atrazine in Surface Waters.

Environ. Sci. Technol., 35(4):648:657.

Cerco, C. and T. Cole. 1994. Three-Dimensional

Eutrophication Model of Chesapeake Bay. U.S.

Army Corps of Engineers, U.S. Army Engineer

Waterways Experiment Station, Vicksburg,

Mississippi. Technical Report EL-94-4, 658 pp.

Cerco, C. and T. Cole. 1995. User’s Guide to the

CE-QUAL-ICM Three-Dimensional

Eutrophication Model. U.S. Army Corps of

Engineers, U.S. Army Engineer Waterways

Experiment Station, Vicksburg, Mississippi.

Technical Report EL-95-15, 2,420 pp.

Chapman, R.S., T.M. Cole, and T.K. Gerald. 1997.

Development of Hydrodynamic/Water Quality

(POM-IPXMT) Linkage for the Lake Michigan

Mass Balance Project. Final Report. U.S.

Environmental Protection Agency, Office of

Research and Development, Environmental

Research Laboratory-Duluth, Large Lakes

Research Station, Grosse Ile, Michigan. 63 pp.

DePinto, J.V., R. Raghunathan, P. Sierzenga, X.

Zhang, V.J. Bierman, Jr., P.W. Rodgers, and T.C.

Young. 1993. Recalibration of GBTOX: An

Integrated Exposure Model for Toxic Chemicals

in Green Bay, Lake Michigan. Final Report. U.S.

Environmental Protection Agency, Office of

Research and Development, Environmental

Research Laboratory-Duluth, Large Lakes

Research Station, Grosse Ile, Michigan. 132 pp.
129

Di Toro, D.M. and J.P. Connolly. 1980.

Mathematical Models of Water Quality in Large

Lakes, Part 2: Lake Erie. U.S. Environmental

Protection Agency, Office of Research and

Development, Environmental Research

Laboratory-Duluth, Large Lakes Research

Station, Grosse Ile, Michigan.

EPA/600/3-80/065, 97 pp.

Di Toro, D.M. and W.F. Matystik, Jr. 1980.

Mathematical Models of Water Quality in Large

Lakes, Part 1: Lake Huron and Saginaw Bay.

U.S. Environmental Protection Agency, Office of

Research and Development, Environmental

Research Laboratory-Duluth, Large Lakes

Research Station, Grosse Ile, Michigan.

EPA/600/3-80/056, 180 pp.

Hall, D. and D. Robertson. 1998. Estimation of

Contaminant Loading from Monitored and

Unmonitored Tributaries to Lake Michigan for the

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.

Leonard, B. 1991. The ULTIMATE Conservative

Difference Scheme Applied to Unsteady OneDimensional Advection. Comp. Methods Appl.

Mechan. Engin., 88(1):17-74.

Leu, C., H. Singer, C. Stamm, S.R. Müller, and R.P.

Schwarzenbach. 2004. Simultaneous

Assessment of Sources, Processes, and Factors

Influencing Herbicide Losses to Surface Waters

in a Small Agricultural Catchment. Environ. Sci.

Technol., 38(14):3827-3834.

Melendez, W., M. Settles, J. Pauer. 2008. LM3: A

High-Resolution Lake Michigan Mass Balance

Water Quality Model. 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 Ile, Michigan. 285 pp.

Miller, D.H., R.G. Kreis, Jr., W. Huang, and X. Xia.

2007. Application of an Ecosystem Modeling

Approach for Investigating Population Dynamics

of the Invasive Species Bythotrephes longimanus

in Lake Michigan. International Association for

Great Lakes Research Annual Meeting, State

College, Pennsylvania, May 28-June 1, 2007.

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.

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.

Pauer, J.J., K.W. Taunt, and W. Melendez. 2006.

LM3-Eutro. In: R. Rossmann (Ed.), Results of

the Lake Michigan Mass Balance Project:

Polychlorinated Biphenyls Modeling Report, pp.

120-182. U.S. Environmental Protection Agency,

Office of Research and Development, National

Health and Environmental Effects Research

Laboratory, Mid-Continent Ecology DivisionDuluth, Large Lakes Research Station, Grosse

Ile, Michigan. EPA/600R-04/167, 579 pp. 

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. 

Richardson, W.L., K.R. Rygwelski, X. Zhang, J.J.

Pauer, and W. Melendez. 2001. Models of

Chloride and Atrazine in Lake Michigan at Two

Spatial Resolutions. Presented at the 44th

Conference on Great Lakes Research,

International Association for Great Lakes

Research, University of Wisconsin, Green Bay,

Wisconsin, June 10-14, 2001.
130

Rodgers, P.W. and D. Salisbury. 1981. Water

Quality Modeling of Lake Michigan and

Consideration of the Anomalous 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.

Scholtz, M.T., B.J. Van Heyst, and A. Ivanoff. 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., S.J. Eisenreich, and P.D. Capel.

1994. Atrazine, Alachlor, and Cyanazine in a

Large Agricultural River System. Environ. Sci.

Technol., 28(6):1079-1089.

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 ERL GLERL108, 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.

Shipitalo, M.J. and L.B. Owens. 2003. Atrazine,

Deethylatrazine, and Deisopropylatrazine in

Surface Runoff from Conservation Tilled

Watersheds. Environ. Sci. Technol., 37(5):944-

950.

Thomann, R.V. and D.M. Di Toro. 1975.

Mathematical Modeling of Phytoplankton in Lake

Ontario, Part 1 - Model Development and

Verification. U.S. Environmental Protection

Agency, Office of Research and Development,

ERL-Corvallis, Large Lakes Research Station,

Grosse Ile, Michigan. EPA/600/3-75/005, 178

pp.

Thomann, R.V., R.P. Winfield, and 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, Minnesota.

EPA/600/3-79-094, 136 pp.

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. Environmental Protection Agency. 2003a.

Interim Reregistration Eligibility Decision (IRED)

for Atrazine. U.S. Environmental Protection

Agency, Office of Pesticides Program,

Washington, D.C. Case Number 0062, 285 pp.

U.S. Environmental Protection Agency. 2003b.

Ambient Aquatic Life Water Quality for Atrazine -

Revised Draft. U.S. Environmental Protection

Agency, Office of Water, Washington, D.C.

EPA/822\R-03\023,171 pp.

Wanninkhoff, R.J. 1992. Relationship Between Gas

Exchange and Wind Speed Over the Ocean. J.

Geophys. Res., 97:7373-7381.

Whitman, W.G. 1923. A Preliminary Experimental

Confirmation of the Two-Film Theory of Gas

Absorption. Chem. Metall. Eng., 29:146-148.

Williams, R.J., D.N. Brooke, P. Matthiessen, M. Mills,

A. Turnbull, and R.M. Harrison. 1995. Pesticide

Transport to Surface Waters Within an

Agricultural Catchment. J. Inst. Water Environ.

Manag., 9(1):72-81.
131

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 Ile, 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

September 27, 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 that management scenarios

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.
132

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. 
133

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)

Schottler and 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 al. (1999)

Tierney et al. (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 et al. (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
134

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 SteadyState

Atrazine SteadyState Concentration

ng/L

Rygwelski and

Zhang, 2007

(Part 4 of this

report)

Schottler and

Eisenreich,

1997

Tierney et al.,

1999

0.6%

1.0%

Not Used Directly

(Approx. 5.6%)

77 yrs.

14 yrs.

2 yrs.

County

Application and

WEP

County

Application and

WEP

Run-off Flow and

0.23 :g/L

Forested; 1.6 :g/L

Agricultural (FlowWeighted)

2194

1994 But

Approached Near

Steady-State

Concentration in

the Late-1980s.

Mid- to Late-1970s

66

34

33
135

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.

Müller et al. (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

al. (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 m /s at 3

Quebec City. At Cornwall, the depth is about 8.2 m

and at Quebec City the depth is approximately 11 m.

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. 
136

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 al. (1994) estimated the atrazine halflives 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

m /s during the sampling period, April 11, 1990 to 3

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 MassDischarge 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.D. 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 TimeVariable 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.
137

Müller, 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. Müller, 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.
138

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 highresolution 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 semivolatile 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 wellcalibrated, 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 highresolution models agreed well with each other
139

and with the measured data. The fact that the

measured atrazine concentrations were relatively

homogeneous throughout the lake (22 to 58

:g/L) made the comparison of measured and

modeled results a “relatively” straightforward,

albeit necessary test of the model.

E. The use of MICHTOX (Rygwelski et al., 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

communicationstool 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, Chapter 3

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
140

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 steadystate 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.