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BASELINE RISK ASSESSMENT
BITTERROOT VALLEY, MONTANA
Prepared by (in alphabetical order by last name)
Kali Frost
MSES/MPA, May 2008
Mary Ruhter MSES, May 2008
Dayu Zhang
Department of Biology
For
Ms. Judy Hoy Wildlife Rehabilitator Stevensville, Montana
and
Dr. Diane Henshel
E560-Environmental Risk Analysis School of Public and Environmental Affairs
May 2008
Indiana University Bloomington, Indiana
TABLE OF CONTENTS
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ACRONYM DEFINITIONS�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…. v 1.0 INTRODUCTION�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�… 1
1.1 Overview�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�….. 1 1.1.1 The Bitterroot Valley �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�….. 1 1.1.2 General Problem at Site �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…. 2 1.1.3 General Site Objectives for Risk Assessment �…�…�…�…�…�…�…�… 2
1.2 Site Background �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�….. 2 1.2.1 Map of Site �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…. 2 1.2.2 General History of Site �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�… 2
1.3 Risk Assessment �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�….. 3 1.3.1 Rationale �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�… 3 1.3.2 Overview of Study Design �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…. 3
1.4 Organization of Risk Assessment Report �…�…�…�…�…�…�…�…�…�…�…�…�…�… 4 2.0 IDENTIFICATION OF CHEMICALS OF POTENTIAL CONCERN�…�…�…�…. 4
2.1 General Site-specific Data Collection Considerations �…�…�…�…�…�…�…�… 4 2.2 General Site-specific Data Evaluation Considerations and Uncertainties 4
2.2.1 Screening Procedure - Chemicals of Potential Concern Refinement Process �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�….
4
2.2.2 Uncertainties, Limitations and Data Gaps �…�…�…�…�…�…�…�…�…�… 5 2.3 Environmental Area �– The Bitterroot Valley �…�…�…�…�…�…�…�…�…�…�…�….. 5
2.3.1 Water �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�….. 5 2.3.2 Air �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�… 6 2.3.3 Biota �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…. 6
2.4 Summary of Chemicals of Potential Concern �…�…�…�…�…�…�…�…�…�…�…�… 7 2.4.1 Selected Chemicals of Potential Concern �…�…�…�…�…�…�…�…�…... 7
3.0 EXPOSURE ASSESSMENT �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�….. 8 3.1 Characterization of Exposure Setting �…�…�…�…�…�…�…�…�…�…�…�…�…�…�….. 8
3.1.1 Physical Setting �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…. 8 3.1.2 Potentially Exposed Populations �…�…�…�…�…�…�…�…�…�…�…�…�….. 10
3.2 Identification of Exposure Pathways �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�… 12 3.2.1 Sources and Receiving Media �…�…�…�…�…�…�…�…�…�…�…�…�…�…�… 12 3.2.2 Fate and Transport �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…... 12
3.2.2.1 Alachlor �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…... 12 3.2.2.2 Chlorothalonil �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�… 13 3.2.2.3 Diazinon �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�….. 14 3.2.2.4 DDE �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…. 15 3.2.2.5 Trifluralin �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�… 16
3.2.3 Exposure Points and Exposure Routes �…�…�…�…�…�…�…�…�…�…�… 16 3.2.4 Summary of Exposure Pathways to be Quantified in this
Assessment �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�… 17
3.3 Quantification of Exposure �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�… 17 3.4 Identification of Uncertainties �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�….. 18
3.4.1 Environmental Sampling and Analysis �…�…�…�…�…�…�…�…�…�…�… 18 3.4.2 Exposure Pathways Evaluated �…�…�…�…�…�…�…�…�…�…�…�…�…�….. 18 3.4.3 Fate and Transport Modeling �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…. 19
TABLE OF CONTENTS
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3.4.3.1 Justification for Long-Range Transport �…�…�…�…�…�…�…. 19 3.4.3.2 HYSPLIT Modeling �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�… 19
3.4.4 Parameter Values �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…. 21 3.5 Summary of Exposure Assessment �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�….. 21
4.0 TOXICITY ASSESSMENT �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�… 22 4.1 Toxicity Information for Non-carcinogenic Effects �…�…�…�…�…�…�…�…�….. 22
4.1.1 Alachlor �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…. 22 4.1.2 Chlorothalonil �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�….. 23 4.1.3 Diazinon �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…. 26 4.1.4 DDE �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…. 28 4.1.5 Trifluralin �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�….. 30
4.2 Toxicity Information for Carcinogenic Effects �…�…�…�…�…�…�…�…�…�…�….. 30 4.2.1 Alachlor �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�….. 30 4.2.2 Chlorothalonil �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�….. 31 4.2.3 Diazinon �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…. 32 4.2.4 DDE �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…. 32 4.2.5 Trifluralin �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�… 32
4.3 Uncertainties in Toxicity Information �…�…�…�…�…�…�…�…�…�…�…�…�…�…�….. 32 5.0 RISK CHARACTERIZATION�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�….. 33
5.1 Current and Future Land-use Conditions - Human Health Risk and Hazard �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…..
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5.2 Ecological Risk and Hazard �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�….. 36 5.2.1 Carcinogenic Risk to Terrestrial Organisms via Surface Water
Ingestion�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�… 36
5.2.1.1 Cumulative Risk to Terrestrial Organisms for Surface Water Ingestion �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…
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5.2.2 Dose-Modeling and Hazard Quotients �…�…�…�…�…�…�…�…�…�…�… 37 5.2.2.1 Oral (Terrestrial Species) �…�…�…�…�…�…�…�…�…�…�…�…�… 38
5.2.2.1.1 Cumulative Hazard to Terrestrial Organisms .. 41 5.2.2.2 Dermal (Aquatic Species)�…�…�…�…�…�…�…�…�…�…�…�…�… 41
5.2.2.2.1 Cumulative Hazard for Aquatic Organisms �….. 43 5.3 Uncertainties �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…. 43
5.3.1 Limited Sampling Data �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�… 43 5.3.2 Exposure Factors �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�….. 44 5.3.3 Modeling �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�… 44
5.4 Summary Discussion and Tabulation of the Risk Characterization �…�….. 44 5.4.1 Evaluating Persistence �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�… 45 5.4.2 Air Transport in the Region �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�… 45 5.4.3 Further Actions �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…. 45
6.0 SUMMARY & NEXT STEPS �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…. 46 6.1 Risk Management �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�… 46 6.2 Next Steps �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…. 47
7.0 REFERENCES �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�… 49 8.0 GLOSSARY �…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�…�….. 54
TABLE OF CONTENTS
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TABLES Table 2-1 COPC/COPEC Screening Table for Humans and Ecological Target
Organisms Table 3-1 Wildlife Exposure Factors, Quantified Exposure Pathways, and
Potential Daily Dose Calculations Table 3-2 Receiving Medium, Release Mechanisms, and Chemical Release
Sources Table 3-3 All Relevant Exposure Pathways for Human Health Table 3-4 All Relevant Exposure Pathways for Target Organisms Table 3-5 Qualitative Screening Table of Pesticides Used on Idaho Potato Fields
for Additional COPCs Table 3-6 Inputs to CALTOX Calculated from @Risk Monte Carlo Analysis of
HYSPLIT Model Outputs Table 4-1 Human Health Toxicity Values for Non-carcinogenic Effects Table 4-2 Toxicity of Alachlor for Different Exposure Pathways Table 4-3 Toxicity Values for Non-carcinogenic Effects to Target Organisms Table 4-4 Human Health Toxicity Values for Carcinogenic Effects Table 4-5 Toxicity Values for Carcinogenic Effects to Target Organisms
FIGURES Figure 1-1 Aerial Conceptual Site Model Figure 1-2 Idaho Conceptual Site Model Figure 1-3 Montana Conceptual Site Model Figure 2-1 Sampling Locations Figure 3-1 Montana Topography �– Missoula and Ravalli Counties Figure 3-2 Groundwater Hydrology of Study Site Figure 3-3 Locations of Wildlife Management Areas Figure 3-4 Primary Metabolites of Chlorothalonil Figure 3-5 Suggested Pathway for Soil Degradation of Chlorothalonil Figure 3-6 Chlorothalonil Metabolism Under Various Environmental Conditions Figure 3-7 Vertical Transport Over Complex Terrain Figure 3-8 HYSPLIT Concentration Output from Backward Trajectory for August 2007 Figure 4-1 Metabolic Pathways of Diazinon in Mammals Figure 4-2 Metabolism of DDE in Mammals
APPENDICES Appendix A Laboratory Case Narrative of Judy Hoy Samples Appendix B USGS Water Quality Pesticide Data Appendix C Selected Target Organisms �– Species Distributions Appendix D Technical Addendum �– Modeling Risk from Atmospheric Transport of
Pesticides Appendix E �“Pesticide Use in Idaho and Montana Wildlife Exposures�” by Mary Ruhter
and Kali Frost
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ACRONYMS
AChE Acetylcholinesterase ADDpot Potential average daily dose ARC Anticipated Residue Concentration ATSDR Agency for Toxic Substances and Disease Registry BAF Bioaccumulation Factor BCF Bioconcentration Factor ChE Cholinesterase COPC Chemicals of Potential Concern (human health) COPEC Chemicals of Potential Ecological Concern CSM Conceptual Site Model DDE Dichlorodiphenyldichloroethylene (a breakdown product of DDT) DEIQ diethyliminoquinone DNR Department of Natural Resources EEC Estimated environmental concentration ERfD Ecological reference dose ESL Ecological Screening Level (USEPA Region 5 ESL) GC/MS gas chromatography-mass spectroscopy HYSPLIT Hybrid Single Particle Lagrangian Intergrated Trajectory ID identification MDEQ Montana Department of Environmental Quality MDL method detection limit MOE margin of exposure MTD maximum tolerated dose NASS National Agricultural Statistics Survey NIR normalized ingestion rate NOAA National Oceanic and Atmospheric Administration OCP organochlorine pesticide OPIND organophosphate-induced delayed neurotoxicity OPP organophosphorus pesticide PAH Polynuclear aromatic hydrocarbon PQL practical quantitation limit PRG preliminary remediation goal (USEPA Region 9 PRG) PUF polyurethane foam QA quality assurance QC quality control RfD reference dose SF slope factor SOP standard operating procedures SVOC semi-volatile organic compound UF Uncertainty Factor USEPA United States Environmental Protection Agency USDA United States Department of Agriculture USGS United States Geological Survey VOC volatile organic compound WHO World Health Organization
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BASELINE RISK ASSESSMENT BITTERROOT VALLEY, MONTANA
1.0 INTRODUCTION 1.1 Overview
1.1.1 The Bitterroot Valley The Bitterroot Valley is located west of the Bitterroot Mountain Range and the Selway-Bitterroot Wilderness Area, and east of the Sapphire Mountains and Anaconda-Pintler Wilderness Area in southwestern Montana. This area has long been known for its historical significance and home to abundant wildlife. Ranching, agriculture, forestry and tourism are major industries in this region.
1.1.2 General Problem at Site It is suspected that pesticides from the region proximal to the valley are transporting to, and concentrating in, the Bitterroot Valley. The late potato blight that struck Idaho beginning in 1995 and continued to wreak havoc on Idaho potato crops into the late 1990s, led to a dramatic increase in the number of fungicides utilized for potato crops in Idaho. Several of the fungicides used in the control of late blight are known or suspected endocrine disruptors, causing reproductive and skeletal abnormalities for humans and wildlife.
Pesticides are used for pest control purposes in agricultural regions all over the United States. Although pesticides have been very successful in the control of plant diseases, weeds, and insects, there are severe environmental consequences associated with their use. Pesticides kill organisms including beneficial insects and may upset the ecological balance of the area where they are applied. Pesticides are harmful to human health, especially to children. In the Bitterroot Valley, pesticides have been detected in surface water and biota (USGS 2008).
Directly west of the valley is eastern Idaho. Idaho is the number one producer of potatoes in the United States. The major region of potato production is the eastern Idaho counties that border western Montana. Late blight is one of the most devastating diseases for potatoes and the chemical control of the disease is very intensive. The three major fungicides used in the control of late blight are maneb, mancozeb and chlorothalonil. It is estimated that 75% of potato growers in Idaho apply four or more applications of fungicide every year (University of Idaho 1998).
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The main crop produced within the Bitterroot Valley is hay (USDA, NASS). There does not appear to be an abundant use of chemicals on crops in the Bitterroot Valley. There may have been mint production in the valley in the recent past according Judy Hoy.
Around the same time of the increased use of these fungicides in Idaho, Judy Hoy, a wildlife rehabilitator in Montana, observed skeletal and reproductive malformations in various wildlife including white-tailed deer, mule deer, bison, goats and birds. Beginning in 1995, genital abnormalities were noticed in white-tailed deer. Many male fawns born exhibited ectopic testicles and/or undersized scrota (Hoy JA et al, 2002). "Pesticide Use in Idaho and Montana Wildlife Exposure" by Mary Ruhter and Kali Frost is provided in Appendix E of this report and provides more detailed information on Judy Hoy's observations. These malformations are consistent with the disruptive effects seen from endocrine dysfunction. Interestingly, incidence of abnormalities reduced after 2001, when use of extensive amounts of chlorothalonil on Idaho crops was curbed.
1.1.3 General Site Objectives for Risk Assessment There are three primary objectives of this risk assessment. First, this assessment evaluates the risk and hazard to humans and ecological organisms using current measured levels of pesticides in the Bitterroot Valley. Second, due to the limited amount of actual quantitative data for various site media in the valley, this project will model and characterize the risk associated with suspected transport of pesticides from Idaho potato crops to the Bitterroot Valley, Montana. The last primary objective is to see how concentrations from deposition affect site media.
1.2 Site Background
The site description (e.g., physical setting) is described in Section 3.0 in detail. Please refer to Section 3.0 for this information.
1.2.1 Map of Site
Figure 1-1 provides an aerial conceptual site model (CSM) for the site. The site boundary is depicted in green. Figures 1-2 and 1-3 depict CSMs for Idaho and Montana, respectively.
1.2.2 General History of Site
The Bitterroot Valley was historically known for its agricultural and timber production, although in recent years the valley has been economically driven
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by tourism. The population of the Bitterroot Valley rapidly increased by 40% from 1990 and 2000. The City of Missoula (the county seat of Missoula County) and the City of Hamilton (the county seat of Ravalli County) are located towards the northern and southern end of the Bitterroot Valley, respectively. Based on current available population estimates from the U.S. Census Bureau, the current population of the valley is approximately 101,300.
1.3 Risk Assessment
1.3.1 Rationale This risk assessment follows guidelines as documented in the USEPA Risk Assessment Guidance for Superfund, Volume I, Human Health Evaluation Manual, Part A, (RAGS) and Guidelines for Ecological Risk Assessment (USEPA 1989, 1998). In the U.S., pesticides are strictly reviewed by the Federal Government to ensure that these pesticides do not pose unreasonable risks. As part of that effort, the U.S. Environmental Protection Agency (hereafter referred to as USEPA or EPA) requires extensive test data for these pesticide products. Scientists and analysts at EPA set up RAGS to determine whether to register (license) a pesticide product or review the possible risk in the region of pesticide use and to decide whether specific restrictions are necessary (USEPA 1989). 1.3.2 Overview of Study Design Data and tools used in this study include regional pesticide use data, atmospheric transport data, soil and water sampling data, crop data, chemical transport into site media, and pesticide toxicology data. This risk assessment first integrates these data per RAGS Part A. These steps include Hazard Identification, Exposure Assessment, Toxicity Assessment and Risk Characterization. Mathematic modeling will be also used to predict the transport, distribution and bioaccumulation of pesticides in the region. Exhibit 1:
Hazard Identification: Site description Gather and analyze relevant site data Identify chemicals of potential concern
Exposure Assessment: Exposure setting Identify exposed populations and potential exposure pathways Exposure mechanisms by chemical of potential concern Quantification of exposure Identification of uncertainties to be addressed through modeling
Toxicity Assessment: Collect qualitative and quantitative toxicity information Determine appropriate toxicity values Toxicity values by chemical of potential concern for
noncarcinogenic and carcinogenic effects Uncertainty evaluation for exposure factors and modeling
Risk Characterization: Characterize potential for adverse health effects to occur
(e.g., cancer risks, noncancer hazard quotients) Evaluate uncertainty and summarize risk/hazard findings
Next Steps: Risk management conclusions Next steps to be taken to further
characterize risk/hazard
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1.4 Organization of Risk Assessment Report
Section 2.0 of the risk assessment will describe the chemicals of potential concern (COPCs) and outline our COPC refinement approach. Section 3.0 provides the exposure assessment, in which COPCs are further characterized for potential exposure pathways. Additionally, Section 3.0 will describe the use of atmospheric modeling (HYSPLIT) and multi-media chemical distribution (CalTOX) for use in calculating potential exposure. Section 4.0 reports the toxicological effects of each COPC. Section 5.0 is the risk characterization section. This section will describe the risk from the chosen COPCs to humans and wildlife and will describe the uncertainty associated with estimates of risk and hazard. Section 6.0 will summarize the baseline risk assessment and provide recommendations for risk management and actions for further research.
2.0 IDENTIFICATION OF CHEMICALS OF POTENTIAL CONCERN
2.1. General Site-specific Data Collection Considerations
Limited sampling data are available. While water and air sampling has been conducted, additional sampling of these media and others would be necessary to adequately characterize contamination in the Bitterroot Valley. A summary of available data used in this risk assessment is provided in Section 2.3.
Specifically, additional surface water and soil samples should be collected in Ravalli County to better characterize potential contamination in the southern portion of the Bitterroot Valley. Additional surface water and soil samples should be collected in Missoula County as well. The importance of additional sampling efforts will be discussed in later sections including Section 3.4, Identification of Uncertainties.
Samples collected from Ravalli County were collected by Judy Hoy, a Wildlife Rehabilitator living and working in Stevensville, Montana. Hoy collected samples in accordance with USEPA sampling guidelines to ensure quality assurance (QA)/quality control (QC). Samples collected in Missoula County were collected by the United States Geological Survey (USGS) and were sampled in accordance with USGS standard operating procedures (SOPs) to ensure QA/QC.
2.2 General Site-specific Data Evaluation Considerations and Uncertainties
2.2.1 Screening Procedure - Chemicals of Potential Concern Refinement Process
Sample results were screened against USEPA Region 9 Preliminary Remediation Goals (PRGs) as part of the human health evaluation. For the ecological risk assessment, sample results were screened against USEPA
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Region 5 Ecological Screening Levels (ESLs) or other ecological screening criteria where ESLs are not available.
Chemicals that exceed these screening criteria were carried forward in the risk assessment calculations as COPCs (human health) or chemicals of potential ecological concern (COPECs).
2.2.2 Uncertainties, Limitations and Data Gaps
Soil data are not available. Sediment data is available; however, all values are non-detections. Additional sampling of all site media for appropriate analytes (e.g., a full suite of organochlorine pesticides [OCPs] and organophosphorus pesticides [OPPs]) is needed to adequately characterize the site. Laboratory analytical methods should have detection limits low enough to detect compounds at levels below applicable screening criteria.
Sampling events vary significantly. For example, the USGS water quality sampling did not include chlorothalonil and other various OCPs in its sampling program. Also, air samples were not sampled for a full suite of OCPs or OPPs, and it appears that laboratory contamination occurred for these samples; therefore these are not reliable for risk assessment (Appendix A of this report, RAGS 1989).
2.3 Environmental Area �– The Bitterroot Valley
The study area is focused to the Bitterroot Valley which encompasses portions of Ravalli County to the south and Missoula County to the north. Figure 2-1 depicts sampling locations.
2.3.1 Water
Two rainwater samples were collected in March 1998 and July 1998, respectively. The March 1998 sample (Sample ID Number 98-23297) was analyzed for chlorothalonil only, while the July 1998 sample (Sample ID Number 98-42782) was also analyzed for other organochlorine pesticides (OCPs) by EPA Method 8081. These samples were collected from Judy Hoy�’s backyard in Stevensville, Montana and analyzed by Energy Laboratories, Inc.
Energy Laboratories, Inc. detected chlorothalonil at 0.031 J micrograms per liter ( g/l) in Sample 98-23297. The �“J�” qualifier indicates that the compound was detected below the laboratory�’s practical quantitation limit (PQL) but above its method detection limit (MDL). PQL is defined as the lowest level that can be reliably achieved during routine laboratory operations and MDL is defined as the minimum concentration of a compound that can be measured and reported with 99 percent confidence that the analyte concentration is greater than zero (Energy Laboratories, Inc. REF #). The
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data qualifier J indicates that results are estimated; present but less than the PQL.
No compounds were detected in Sample 98-42782. The full pesticide analyte list for Sample ID 98-42782 is provided in Appendix A. Appendix A contains all available laboratory reports.
Water quality sampling data was obtained from the United States Geological Survey (USGS). Pesticide sampling of the Bitterroot River near Missoula, Montana in Missoula County by the USGS was conducted from 1998 to 2000. The specific pesticides that were sampled vary by year. Sampling ID numbers and results are provided in Table B-1, Appendix B.
According to Johnson et al. (2002), a water sample contained alachlor at 0.53 g/l; however, no abnormal amphibians were recorded at the site. This
sampling result has been evaluated as part of this risk assessment. It was collected from Ravalli County just west of the Rocky Mountain Laboratory in Hamilton, Montana from the Bitterroot River.
2.3.2 Air
Eight air samples (Sample ID Numbers 02-2 through 02-9) including an unused glass/PUF filter and a sample called control. These samples were analyzed for the common suite of OCPs and chlorothalonil, as well as, 4-hydroxy-chlorothalonil, a metabolite of chlorothalonil. Samples were collected using PUF containers from Judy Hoy�’s backyard in Stevensville, Montana, and were analyzed by Dr. Piero Gardinal of Florida International University, Miami, Florida. MDLs could not be obtained from Dr. Gardinal for these samples. Hexachlorobenzene and 2,4-DDT were detected at 0.00026 J
g/m3 and 0.00031 J g/m3 in Sample 02-6, respectively. Hexachlorobenzene was also detected in Sample 02-9 at 0.00019 J g/m3. However, it appears from the laboratory case narrative that cross contamination may have occurred. Dr. Gardinal could not be reached to verify.
Detection limits are not provided in the laboratory case narrative for all analytes (Appendix A). Therefore, it cannot be determined whether detection limits exceed preliminary human health and ecological screening criteria for most compounds. However, the laboratory case narrative for Dr. Gardinal�’s analysis, provided in Appendix A, does indicate that the MDL for chlorothalonil is high.
2.3.3 Biota
The data obtained from USGS contains whole organism biota results for various pesticides. While most all pesticides were not detected, p,p�’-DDE was detected at 9 g/kg in sample p49372.
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Additionally, one deer fat sample was collected from a one and one-half year old female white-tailed deer that was hit by a vehicle one-half mile from Hoy�’s residence, and one sample of Ponderosa pine (Pinus ponderosa) needles was collected from Hoy�’s backyard (Stevensville, Montana). These samples were also analyzed by Dr. Gardinal. p,p�’-DDE was detected in the deer fat sample, while a possible chlorothalonil metabolite was detected both in the deer fat sample and the Ponderosa pine needles. Specific concentrations of these compounds were not provided in the available file material obtained from Judy Hoy, and Dr. Gardinal could not be reached for inquiry.
2.4 Summary of Chemicals of Potential Concern
Table 2-1 provides a list of selected COPC and COPECs and depicts the COPC/COPEC refinement process. This table provides all detected chemicals and screens these chemicals against applicable human health and ecological screening criteria.
2.4.1 Selected Chemicals of Potential Concern
Alachlor
Alachlor was detected at 0.53 g/l in a study conducted by Johnson et al. (2002). This concentration exceeds the human health screening criterion which has been adjusted for uncertainty (0.084 g/l). Alachlor, however, did not exceed its ecological screening criterion (1.1 g/l). Therefore, this compound will be evaluated as a human health COPC, but will not be evaluated as a COPEC. Please see Table 2-1, COPC/COPEC Screening Table for Humans and Ecological Target Organisms.
Chlorothalonil
Chlorothalonil was detected at 0.031 g/l in Sample 98-23297 (rain water). This concentration is well below the USEPA Region 9 PRG for Tap Water (6.1
g/l). Therefore, this compound is not considered a COPCs for humans. Alternatively, chlorothalonil was detected above the ecological screening criterion that has been adjusted for uncertainty (0.0018 g/l). Therefore, this compound will be evaluated as a COPEC in water. Please see Table 2.1, COPC/COPEC Screening Table for Humans and Ecological Target Organisms.
Diazinon
Diazinon was detected at 0.003 J g/l in USGS water quality sample p39572 collected in 2000. Since this concentration is below the USEPA Region 9 PRG for Tap Water (33 g/l), this compound will not be carried forward as a COPC for human health. However, the detected concentration exceeds the
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ecological screening criterion which has been adjusted for uncertainty (0.00043 g/l). Therefore, it will be evaluated as a COPEC in water. Please see Table 2.1, COPC/COPEC Screening Table for Humans and Ecological Target Organisms.
p,p-DDE
DDE was detected at 0.001 g/l in USGS water quality sample p34653 collected in 1999. This concentration is below the USEPA Region 9 PRG for Tap Water (0.2 g/l). Therefore, this DDE will not be considered as a COPC. This compound, however, will be considered a COPC in water since the detected concentration exceeds the USEPA Region 5 ESL for 4,4-DDE (4.51x10-9 g/l). p,p-DDE was also detected at 9 mg/kg in USGS tissue sample p49372 (whole organism) collected in 1998. Please see Table 2.1, COPC/COPEC Screening Table for Humans and Ecological Target Organisms.
Trifluralin
Trifluralin was detected at 0.004 J g/l in USGS water quality sample p82661 collected in 1999. This concentration is below the USEPA Region 9 PRG for Tap Water (8.7 g/l), therefore this compound will not be carried forward as a human health COPC. Alternatively, trifluralin was detected above its ecological screening criterion that has been adjusted for uncertainty (0.00063
g/l). Therefore, trifluralin will be carried forward as a COPEC. Please see Table 2.1, COPC/COPEC Screening Table for Humans and Ecological Target Organisms.
3.0 EXPOSURE ASSESSMENT 3.1 Characterization of Exposure Setting The Bitterroot Valley is located between the Bitterroot and Sapphire Ranges (part of the Northern Rocky Mountains) in southwest Montana near the Idaho-Montana border. The valley is a basin that is 52 miles long and 7 miles wide with a total of 2394 square miles of land area and 6 square miles of water. The Bitterroot Valley is bisected along its length by the Bitterroot River which flows north.
3.1.1 Physical Setting Climate
The climate of Bitterroot Valley can be categorized as one of variable precipitation. The average annual precipitation in the valley is 13.61 inches with an average rainfall of less than one inch for most months. Average snowfall each year is 25.7 inches. The average max temperature is 58.9
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degrees F and minimum temperature is 33.3 degrees F. The growing period extends from May 30 to September 10 (103 days) (McNab and Avers, 1994). The Bitterroot Mountains receive about twice as much precipitation than the Sapphire Mountains, thus most of the drainage into the valley is from the west.
Topography The valley is dominated by two types of topographic features. The flood plain of the Bitterroot River is 1-2 miles wide and runs down the center of the entire length of the valley. Along both east and west sides of the valley run high �“benches�” which slope down into the valley on the west side from 5 degrees near the basin edges to 1 degree closer to the floodplain. The east side is more gently sloping. Elevations in the Bitterroot Valley range from 2500 to 6000 feet (Figure 3-1).
Vegetation
Vegetation is dominated by Douglas fir (Pseudotsuga menziesii) and Ponderosa Pine (Pinus ponderosa) forests, with western larch (Larix occidentalis) and subalpine fir (Abies lasiocarpa) also being commonly found. Bluebunch wheatgrass (Pseudoroegneria spicata), Idaho fescue (Festuca idahoensis), and Rough fescue (Festuca scabrella) are the most common herbaceous species (McNab and Avers, 1994).
Soil/Sediment Type
The river floodplain is dominated by alluvial soils consisting of sand and gravel. The soils in the mountains are thin and coarse with areas of exposed bedrock. Surface Hydrology
Precipitation, snowmelt, and irrigation feeding streams, lakes, and wetlands comprise the hydrology of this region. The major surface water body in the valley is the Bitterroot River. The river flows northward out of the valley until it hits Clark Fork. The river is mainly fed from the Bitterroot Mountains with the Sapphire Mountains only contributing about a quarter of the annual flow. The streamflow is the highest in the spring with 55% of the total annual streamflow release in May and June due to a combination of snowmelt and rainfall. The flow of the Bitterroot is diminished during the summer due to heavy use for irrigation and is only partially replenished by irrigation return flow (Kudray and Schemm, 2008).
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Ground-water Hydrology
Underground aquifers are recharged by streamflow infiltrant, irrigation, subsurface flow, precipitation and snowmelt. In general the direction of movement of the groundwater follows topography with steeper gradients in the more mountainous areas and shallower gradients along the flood plain of the Bitterroot River (Kudray and Schemm, 2008). See Figure 3-2, Groundwater Hydrology of the Study Site.
3.1.2 Potentially Exposed Populations Relative Locations of Populations With Respect to Site The population of the Bitterroot Valley rapidly increased 40% from 1990 to 2000. The City of Missoula (the county seat of Missoula County) and the City of Hamilton (the county seat of Ravalli County) are located towards the northern and southern end of the Bitterroot Valley, respectively. Based on current available population estimates from the U.S. Census Bureau, the current population of the valley is approximately 101,300. Population was estimated using the 2006 U.S. Census Bureau estimate of Ravalli County and the 2003 U.S. Census Bureau estimate for the City of the Missoula since all of Missoula County does not fall within the site. Potentially exposed populations include both human and wildlife populations occurring throughout the Bitterroot Valley (the study site). For example, wildlife occurring at the Lee Metcalf Wildlife Refuge or at any of the following wildlife management areas (WMA) may be at risk: Calf Creek WMA, Threemile WMA, and Mount Jumbo WMA. Figure 3-3 depicts the locations of the WMAs.
Current Land Use
The current land use is mixed residential and agriculture with forestry and recreation as the dominant remaining land use. As previously mentioned, wildlife management areas also occur within or intersect site boundaries. It is important to characterize activity patterns in terms of the current land use for the exposure assessment. While recreational use is common in the Bitterroot Valley, the primary land use with respect to human health exposure in the valley is residential. This human health risk assessment will evaluate a residential exposure scenario and will use a maximum daily exposure of 24 hours for residents.
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Potential Alternate Future Land Uses This region of Montana is one of the most rapidly growing areas in the state, with new subdivisions and housing developments continuing to be constructed. A permanent countywide zoning plan is not yet available for Ravalli County; however, land use is not anticipated to change. Ravalli County is currently working towards completing and implementing a countywide permanent zoning plan by November 2008. In regard to the northern portion of the site, the future land use for the City of Missoula is not anticipated to change based on the Missoula County Planning Board documents. Therefore, primary future land use for the Bitterroot Valley study site is not expected to change significantly.
Subpopulations of Potential Concern Subpopulations may be at increase risk from chemical exposures due to increased sensitivity, behavior patterns that may result in high exposure (e.g., children which are more likely to contact soil, wildlife that forage in the valley), and/or current or past exposures (e.g., accrued exposure in the elderly). Risk to both adults and children will be assessed as part of this risk assessment.
Several species of concern (i.e., threatened, endangered, or candidates for listing) occur at the site. Species of special concern occurring in Ravalli County include the Bald Eagle (Haliaeetus leucocephalus), the Gray Wolf (Canus lupus), Bull Trout (Salvelinus confluentus), and Yellow-bellied Cuckoo (Coccyzus americanus), while species of special concern occurring in Missoula County include the aforementioned species as well as Grizzly Bear (Ursus arctos horribilis), Canada Lynx (Lynx canadensis), and Water Howellia (Howellia aquatilis).
As introduced in Section 1.0, skeletal and reproductive effects have been observed and documented in avian species and ungulates at the site by Judy Hoy. This risk assessment will seek to determine risk to key food chain species by calculating risk for target organisms. The rationale for each selected target organism is provided below. Species distributions are provided in Appendix C.
Exposure factors for the following target organisms are provided and described in Table 3-1. The rationales for the use of these exposure factors are provided in the footnotes of the aforementioned table.
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White-tailed Deer (Odocoileus virginianus): White-tailed deer are a common herbivore occurring at the site. Columbia Spotted Frog (Rana luteiventris): Columbia spotted frogs are a common top predator aquatic organism at the site. Bull Trout (Salvelinus confluentus): Bull trout are a species of special concern in Montana and occur at the site (within the Bitterroot River). Gray Wolf (Canus lupus): Gray wolves are a top terrestrial carnivore occurring at the site and are a federally listed endangered species. Red-tailed Hawk (Buteo jamaicensis): Red-tailed hawks are common top end carnivores that occur at the site.
3.2 Identification of Exposure Pathways
3.2.1 Sources and Receiving Media Table 3-2 provides a summary of receiving medium and the associated release mechanism and release source. Also, refer to the CSMs for a depiction of sources and receiving media. 3.2.2 Fate and Transport Five COPCs were identified from available sampling data: alachlor, chlorothalonil, diazinon, DDE and trifluralin.
3.2.2.1 Alachlor
Alachlor is moderately persistent and highly mobile in the environment with Log Koc values in the range of 2.08 to 2.28 (USEPA 2006). The half-life of alachlor is approximately 15 days and is transformed to its metabolites primarily through biodegradation. The important routes of dissipation are aerobic soil metabolism and leaching (USEPA 1998). Alachlor has relatively high water solubility (242 mg/L) and does not readily adsorb to soil with high organic content, but groundwater
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contamination has been observed in areas with sand and/or gravel subsurface geology. In terms of surface water, photolysis and biodegradation are important for reducing concentrations of alachlor with photolysis becoming more significant as water depth decreases and clarity increases (USEPA 2006). Evaporation of alachlor from soil increases as moisture content and soil temperature increases. The estimated half-life of alachlor evaporation from soil ranges from 12 to 200 days. The Henry�’s Law constant of alachlor is 3.2x10-8 and with its low vapor pressure, alachlor does not readily volatize from water. The half-life in the atmosphere is 2.1 hours due to its reaction with the hydroxyl radical. Alachlor in the particulate phase is removed through dry and wet deposition (USEPA 2006). Alachlor is not expected to bioconcentrate or bioaccumulate based on its low octanol/water partitioning coefficient (Log Kow=3.52). In an experiment with fish, 81% and 98% of alachlor was eliminated after 24 hours and 14 days, respectively (USEPA 2006).
3.2.2.2 Chlorothalonil
Chlorothalonil has a low aqueous solubility (0.81 mg/L) with an aqueous photolysis half-life of 65 days, and an aqueous hydrolysis half-life ranging from 16-38 days (Syngenta Group 2003). It is considered relatively non-volatile with a Henry's Law constant of 2.6x10-7 atm-m3/mol (USEPA RED, 1997); however, due to low degradation rates in air, if chlorothalonil is taken up into the air via dust, it can be transported through long range air transport. Chlorothalonil has a half-life in field soil ranging between 10 and 60 days (Syngenta Group 2003). These physicochemical properties indicate that this compound is rather immobile in site media. Chlorothalonil is removed from aqueous media by strong adsorption on suspended matter due to its high Koc value of 1800. However, modeled data shows little or no partition to bottom sediment (World Health Organization [WHO] 1996).
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Biodegradation may occur in natural waters by way of enzyme processes. Chlorothalonil is rapidly degraded in soil with the production of the 4-hydroxy metabolite (4-hydroxy-2,5,6-trichloroisophthalonitrile) which is more mobile than the parent compound (Figure 3-4). In terms of soil breakdown products, Figure 3-5 lists the structure and identification code pertaining to metabolites identified in aerobic soil studies in the laboratory. Franzier et al. (1993) notes that this scheme is a suggested pathway and there is no direct evidence that any of the five soil metabolites are converted directly to �“bound�” residue. ISK Biosciences (1995) created a complete picture of all the known transformations which occur with chlorothalonil under various environmental conditions (Figure 3-6).
Chlorothalonil has a low potential for bioaccumulation with a low octanol water partition coefficient (log Kow=3.05). This chemical has a bioconcentration factor (BCF) of 264, though vary by species.
3.2.2.3 Diazinon
Diazinon is an organophosphate pesticide (OCP) and can be released into the atmosphere by volatilization from soil after application and movement in the area after spray applications. Its aqueous solubility is 40 mg/L and has Log Kow and Koc values of 3.81 and 1.602-2.635, respectively, and the Henry's Law Constant is 1.17x10-7 atm-m3/mol (ATSDR 2006). These values indicate that there is moderate mobility in soil and is not expected to bind strongly to soil. Volatilization is not a critical fate of diazinon; however, up to 25% of applied diazinon can return to the air from the surface where it was applied on dust or vapor reflection (ATSDR 2006).
In natural waters, diazinon has a half-life of 5-15 days (WHO 1998). This compound has a low persistence in soil with a half-life of 2-4 weeks. Typically, diazinon does not migrate into subsurface soil, although studies show that the potential for groundwater contamination exists (e.g., California, Canada) (Howard 1991, Wauchope et al. 1992).
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Uptake by aquatic organisms is rapid and toxic effect typically occurs within 30 hours of exposure or less. Diazinon is not bioaccumulative (ATSDR 2006). Low bioconcentration factors have been reported for aquatic organisms ranging from 3 (shrimp) to 152 (gudgeon) (WHO 1998).
3.2.2.4 DDE
DDE is a degradation product of DDT. DDT is volatilized into the atmosphere, and after wet or dry deposition, breaks down to DDE in aerobic soil conditions.
DDE has a Henry�’s Law constant of 4.16x10-5 atm-m3/mole and an atmospheric half-life of ~17 hours (summer), with a range of up to 6.1 days in winter (at 40 degrees latitude) (ATSDR, 2002). Although it has a seemingly short half-life, about 50% of atmospheric DDE is bound to particles, which can slow photolysis and reaction with hydroxyl radicals, effectively lengthening the half-life of DDE. DDE can be removed from air by wet and dry deposition, re-deposit on soil, and volatilize again later. This is how DDE and DDT have been spread globally (grasshopper effect) and move from warm climates to cool climates (IJC, 1997). DDE adsorbs to soil surfaces readily with Log Kocs of 4.70.
Low water solubility means limited mobility. Although DDE has a high Koc (Log Koc of 4.7) it will be volatilized out of wet soil (water-escaping) but not out of dry soil unless it is bound to dust particles (ATSDR, 2002).
Another reason DDE is so persistent in the environment is that it is stored in the lipids of biota. In a USGS study monitoring p,p DDE in whole fish, researchers found the 50th percentile concentration for agricultural, mixed, undeveloped and urban settings were 43.5, 42, 3.5, and 36 ug/kg, respectively. Due to this persistence in organisms, DDE bioconcentrates in organisms and biomagnifies up the food chain.
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3.2.2.5 Trifluralin
The half-life of trifluralin ranges from 25 (anaerobic) to 115 days (aerobic) in soil. It has an extremely high Koc (organic carbon partition coefficient) value, with studies ranging from 4000-13000, which indicates a very high affinity for soil/sediment organic matter and explains the lengthy half-life of this chemical. If trifluralin is not bound to soil or sediment organic matter, its photolysis half-life on the soil surface is 41 days and aqueous photolysis half-life is 8.9 hours. Due to the ability of trifluralin to bind to soil substrate and its corresponding low water solubility, trifluralin is rarely a groundwater contaminant; although surface run-off can cause trifluralin contamination in surface water. The major route of dissipation for trifluralin is through volatilization, with 25% loss of applied trifluralin to the atmosphere (Henry�’s Law constant is 1.6x10-4 atm-m3/mol). Although trifluralin is highly volatile it rapidly degrades in the atmosphere (3-4 hrs) (OSPAR, 2005). However, if trifluralin is bound to dust particles it does not photodegrade readily and can be transported long distances.
In a study using bluegill sunfish exposed to 0.0059 ppm of trifluralin the bioconcentration factors were 2041x, 9568x, and 5974x for edible, non-edible and whole fish tissues, respectively. Trifluralin is considered by USEPA to be moderately bioaccumulative (EPA, 1996).
3.2.3 Exposure Points and Exposure Routes
Exposure points are locations where humans or target organisms may come in contact with a contaminated medium. Exposure points for ecological receptors at the site include any areas where surface water (e.g., the Bitterroot River), sediment, soil or biota occur. In terms of exposure routes, the three routes of exposure are ingestion, inhalation, and dermal contact. Table 3-3 and Table 3-4 provide a matrix of potential exposure routes for both humans and target organisms, respectively.
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3.2.4 Summary of Exposure Pathways to be Quantified in this Assessment
Based on reviews of the available ecological information for the Bitterroot Valley, potential receptor categories were identified for aquatic and terrestrial biota. Potential receptor categories for aquatic exposure pathways are:
Aquatic vegetation (e.g., submerged aquatic vascular plants, phytoplankton, etc.) Pelagic invertebrates (e.g., zooplankton) Benthic macroinvertebrates Herptiles (e.g., amphibians and reptiles) Birds (e.g., waterfowl, piscivorous birds, raptors, etc.) Fish Aquatic feeding mammals (including mammals that may drink from the
Bitterroot River)
Potential receptor categories for terrestrial exposure pathways are: Vascular plants Soil invertebrates Soil microbes and microbial processes Mammals (herbivores, omnivores, and predators) Birds (omnivores and raptors)
Table 3-1 provides a summary of exposure pathways to be quantified in this assessment for ecological receptors.
3.3 Quantification of Exposure
Quantitative sampling concentrations obtained from the site will be used directly as exposure concentrations for soil, surface water, and air for this baseline risk assessment. Humans and target organisms may be exposed to pesticides through various routes of exposure. These have been summarized in Section 3.2.3. Table 3-1 provides the estimation of daily dose (chemical intake) and defines the exposure parameters evaluated in this Baseline Risk Assessment for ecological target organisms. Section 5.0, Risk Characterization, discusses the exposure intake parameters by exposure pathway for both human health and ecological target organisms. Wildlife exposure parameters for this risk assessment are based on conservative assumptions. For example, bioavailability of contaminants is assumed to be 100 percent. In terms of surface water, however, it was assumed that 100% of the drinking water obtained by terrestrial target organisms came from contaminated
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surface water. Other exposure factors and their rationale are provided in the aforementioned table. When pesticides are taken into an organism, it is clear that pesticides and their metabolites may be stored or accumulated, or be detoxified, or eliminated, which is based on the pesticides structure and ability of detoxification in an organism. Some pesticides such as DDT, Lindane, Heptachlor, Dieldrin and Endrin are very stable in the body of organism and can be accumulated. Although most of these pesticides have been banned, some organochlorine pesticides are still used. Organochlorine pesticides are more slowly eliminated and they can easily accumulate in humans and other organism compared with other pesticides. Bioaccumulation was not evaluated in this assessment. 3.4 Identification of Uncertainties
3.4.1 Environmental Sampling and Analysis Additional sampling is necessary to adequately determine the nature and extent of potential contamination at the site. Site media from the Bitterroot Valley have not been analyzed for most pesticides used in Idaho and Montana. Sampling of all site media (i.e., air, water, soil) is needed, and additional analytical parameters should be analyzed. In addition to pesticides, samples should be analyzed for a full analytical suite of volatile organic compounds (VOCs), semi-volatile organic compounds (SVOCs), heavy metals (including cyanide), organochlorine pesticides (OCPs), organophosphorus pesticides (OPPs), carbamate pesticides, and other pesticides.
This risk assessment uses all available pesticide sample results; however, quantitative results are limited at the site. Due to the uncertainties resulting directly from inadequate data, this risk assessment should not be considered conclusive of risk to human health and ecological target organisms at the site. Future sampling efforts for appropriate analytical parameters are needed.
3.4.2 Exposure pathways evaluated
Exposure pathways associated with sediment were not evaluated. Available sediment data (collected as part of USGS monitoring) indicates that pesticides were not detected. However, the USGS monitoring efforts did not analyze sediment samples for pesticides of interest. As mentioned in Section 3.4.1, additional sampling should be conducted. All other exposure pathways were evaluated.
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3.4.3 Fate and Transport Modeling
The HYSPLIT model was used to help determine whether Idaho pesticide use is impacting the Bitterroot Valley of Montana via long range air transport. Uncertainty with the HYSPLIT model output concentrations was further evaluated using the Monte Carlo approach using @Risk software. The Monte Carlo approach helps to avoid potential conflicts with overestimation by performing probabilistic analysis of the data (Ricci 2006). CalTOX was then used to model how these air concentrations affected other site media. Specifically, CalTOX evaluates the distribution of a chemical among different environmental compartments (e.g., air, soil, surface water, vegetation, etc.) and also calculates how much of the pesticide reaches the body using environmental concentration and contact factors (e.g., breathing rate) (EETD LBNL 2008).
3.4.3.1 Justification for Long-Range Transport
Although the concept of long-range pesticide transport over the Bitterroot Mountains has been called into question, several studies confirm that this type of transport does in fact occur (Cohn et. al, 2004, Dommen et. al, 2003, Kim & Stockewell, 2007). Kim & Stockwell (2007) explained that air flowing over complex terrain creates a vertical component to the air stream and forces pollutants up and over mountain peaks. As the air is forced over the peaks, horizontal winds dominate pollutant transport. The colder air slows the reaction rates in the atmosphere thereby increasing the half-lives of the pollutants and allowing horizontal transport over longer distances (Figure 3-7).
3.4.3.2 HYSPLIT modeling
HYSPLIT or Hybrid Single Particle Lagrangian Integrated Trajectory is a model created by NOAA which utilizes archived climatic data and mathematical modeling of particle and puff dispersion to create trajectories of the atmospheric transport of chemicals (Draxler and Rolph, 2003). To model the transport of the chemicals used on Idaho potato crops available pesticide use data was compiled. Using statistics from the National Agricultural Statistics Survey (NASS) under the US Department of Agriculture (USDA), data was obtained which describes the chemicals used on potato crops and the percentage of farmers that used each chemical. This data was combined with data for the number of acres of potatoes being farmed in each region of Idaho to determine how many pounds of active ingredient of each chemical was applied each year over the period from 1990-2005. This initial list of chemicals used on potato crops from Idaho will be referred
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to as �“modeled COPCs.�” By utilizing qualitative screening criteria, such as whether the chemical was used to treat late potato blight, whether it is an endocrine disruptor, and/or its usage trend over the mid to late 1990s, it was possible to further refine modeled COPCs. These nine modeled COPCs were then carried forward into the analysis. Table 3-5 depicts the qualitative analysis used to narrow the field of modeled COPCs. After these additional COPCs were determined, Henry�’s Law constants were used to calculate the amount of pesticide that would volatilize upon application. The application height assumed for the HYSPLIT emissions model was 5 meters above the surface. It should be noted that for the application heights of concern (i.e., 0.1 meters for surface application, 5 meters for overhead sprayer, and approximately10 meters for crop dusting), there was no difference in atmospheric modeling. This new value, adjusted based on the Henry�’s Law constant, was used as the initial �“emission�” concentration in HYSPLIT. HYSPLIT was then utilized to calculate projected pesticide concentrations over space and time. The output provided by HYSPLIT displayed air concentrations of chemicals averaged over 0 to 100 meters above ground. Figure 3-8 depicts the graphic output from the HYSPLIT model.
The HYSPLIT output for each primary agricultural region of Idaho was used as the input for the @Risk program. @Risk was used to conduct Monte Carlo analysis on the air modeling output due to the uncertainty associated with modeling chemical transport using HYSPLIT. The HYSPLIT output of each region was used as the mean concentration for the Monte Carlo analysis in @Risk. The standard deviation utilized to for this estimate was rather large (i.e., 10 times the mean) due to the uncertainty associated with atmospheric modeling. A normal distribution was used, with concentrations truncated at zero to avoid any negative concentration estimates. The output received from @Risk was then entered into CalTOX. Table 3-6 depicts the inputs used for the CALTOX program (concentrations from HYSPLIT model that have been modified for uncertainty in @Risk). CalTOX uses integrated chemical information to model transport from air to water, soil, and biota. CalTOX gives the distribution for each chemical into these compartments. The three chemicals chosen to be modeled in CALTOX were those with the highest modeled air concentrations. These three chemicals were chlorothalonil, trifluralin, and diazinon. The results from CalTOX were extremely low with exposure concentrations on the order of 1x10-20 or lower in the various
21
media. The CALTOX results depict negligible concentrations for the three remaining modeled COPCs.
Interestingly, the three COPCs (i.e., chlorothalonil, diazinon, and trifluralin) with the highest modeled air concentrations corresponded to 3 of the 5 measured COPCs determined from the USGS Water Quality Monitoring Data.
3.4.4 Parameter values
There is uncertainty associated with the HYSPLIT modeling of atmospheric transport of chemicals. The version of the model used for this analysis is not sophisticated enough to model specific chemicals in the atmosphere, but rather gives the user an idea of how a typical particle or puff would move based on measured meteorological conditions. The model has default assumptions for radioactive decay, dry deposition rate, and wet deposition rate. Although these vary for each chemical of the nine chemicals modeled, the purpose of the HYSPLIT modeling was to show that chemicals from Idaho may in fact be reaching the Bitterroot Valley, and to get a rough idea of their projected concentrations. Chemical specific information for each of the nine chemicals modeled would be required to fully and accurately characterize the chemical loading to the valley. Thus, this is an area of recognized uncertainty for this modeling technique. As discussed above, due to the uncertainty associated with HYSPLIT modeling, @Risk was used to model this uncertainty with assumptions of a large standard deviation around the mean.
For further discussion of fate and transport modeling and justification for modeling assumptions refer to Appendix D, Technical Addendum. 3.5 Summary of Exposure Assessment This exposure assessment described the exposure site as a valley flanked on each side by the Bitterroot and Sapphire Mountains. The Bitterroot Valley, once characterized by agriculture and forestry, is now mainly used for recreation. The population growth in this area is the fastest in the state, further increasing the number of individuals with potential exposure at the site; especially due to future residential development that is planned. This assessment went on to describe the vegetation and wildlife characteristics of this site, as well as the wildlife receptors chosen for the exposure analysis. The White-Tailed Deer, Columbia Spotted Frog, Bull Trout, Gray Wolf, and Red-tailed Hawk were chosen as the wildlife receptors at this site. The assessment then went on to identify the major routes of exposure by the receiving media. Due to the fact that
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the source of contamination is believed to be pesticide transport and subsequent deposition into water bodies, the major exposure pathways chosen were inhalation, ingestion of surface water, and dermal contact with surface water. This assessment went on to describe the parameters used to calculate exposure values and the uncertainty associated with these parameters. A discussion of the HYSPLIT, CalTOX, and @RISK models was utilized to describe the potential exposure from atmospheric transport of chemicals from Idaho potato fields. The output from the COPCs modeled in these three programs portrayed negligible levels of COPCs at the site.
4.0 TOXICITY ASSESSMENT
4.1 Toxicity Information for Non-carcinogenic Effects
4.1.1 Alachlor
As one of the most widely used chloroacetanilide herbicides in the United States, alachlor 2-chloro-N-(2,6-diethylphenyl)-N-(methoxymethyl)acetamide is used mainly on sweet corn, soybeans and cotton crops (Heydens WF, et al 2001). This chemical was detected in the Bitterroot Valley although there was no record of spraying found in that area. Alachlor was evaluated as a COPC for human health since its maximum detected concentration exceeded the adjusted human health criteria (USEPA Region 9 Preliminary Remediation Goals [PRGs] for Tap Water) (Table 2-1).
Alachlor is a slightly toxic herbicide. The acute toxicity in laboratory ratsshowed that the oral LD50 was between 930 mg/kg and 1350 mg/kg. In laboratory mice,, the LD50 is between 1910 and 2310 mg/kg (USEPA, Health Advisory, 1987). The LC50 for rat inhalation was 1.04 mg/L for 4 hours (USEPA, RED, 1998). Clinical signs such as ataxia, muscle tremors, hyperactivity, lethargy, dyspnea and convulsions and nasal irritation were observed (EPA RED, 1998). Alachlor is a skin sensitizer in guinea pigs and humans With skin irritation being slight to moderate. The dermal LD50 in rabbits is 13,300 mg/kg (Oregon State University, 1996).
In a sub-chronic feeding study in beagle dogs, toxicity was observed as liver weights increased at the dose of 5 mg/kg/day in males and at 25 mg/kg/day in females. The incidence of gross pathology was noted at 50 mg/kg/day for both sexes (USEPA RED, 1998). Alachlor is slightly toxic to fowl, though some studies indicate no toxicity (USEPA RED, 1998). Alachlor is moderately toxic to fish and studies indicate LC50 (96-hour) values of 2.4 mg/L, 4.3 mg/L, 6.5 mg/L, and 4.6 mg/L for rainbow trout, bluegill sunfish, catfish, and carp, respectively. Additionally, this compound is not toxic to bees and is only very slightly toxic to earthworms (USEPA RED, 1998).
In a 21-day dermal study in New Zealand white rabbit, skin damage was observed in a dose-related manner at a dose levels of 0, 50, 300 or 1000
23
mg/kg. An increase in polymorphonuclear leukocytes was noted at high doses. There was also a significant decrease in body weight and elevated white blood cell count, and platelet counts, and a decreased albumin/globulin ratio at the high dose (USEPA, RED 1998). Toxicity such as uveal degeneration and hepatotoxicity was observed from a two year feeding study on rats at levels equivalent to 14-126 mg/kg-day while there is no-effect level as 2.5 mg/kg-day (USEPA, RED 1998).
The chronic reference dose (RfD) was calculated to be 0.01 mg/kg/day (Table 4-1), which was based on a NOAEL of 1 mg/kg/day in a one year chronic dog study. An uncertainty factor (UF) of 100 was used to account for interspecies extrapolation and intraspecies variability (USEPA, RED, 1998).
EPA has assessed the chronic (non-carcinogenic) dietary (food and water) risk. The Anticipated Residue Concentration (ARC) is less than 1% of the chronic RfD for the overall U.S. population. That amount does not cause adverse effects if consumed daily over a 70-year lifetime and it is acceptable risk (USEPA RED, 1998).
Alachlor does not appear to cause reproductive effects. High oral doses (150 or 400 mg/kg/day) fed to rats during gestation results in maternal and fetal toxicity, but there was no indication that reproduction was affected. Alachlor also does not appear to be mutagenic (USEPA, health advisory 1987). The assay of a variety of microbial mutagenicity showed there was no mutagenic effects (Cornell University EXTOXNET, 1993).
The toxicological effects of alachlor can vary with different exposure durations and routes. The entire toxicity of alachlor determines which risk assessments are necessary based on the effects seen for different durations and routes of exposure (Table 4-2).
4.1.2 Chlorothalonil
Chlorothalonil was selected as a COPC for ecological target organisms. Table 4-3 provides toxicity values for non-carcinogenic effects.
Chlorothalonil is an organochlorine (OC) fungicide commonly sold under the trade names Daconil and Bravo, and has both agricultural and household uses. It is the second most widely used agricultural fungicide in terms of pounds per year with applications totaling 11 million pounds annually (Cox 1997). Primary crops protected by chlorothalonil include potatoes, peanuts, tomatoes, unions and celery. Chlorothalonil was used on potato crops in Idaho heavily from 1995 to 1999, during efforts to combat the Idaho late potato blight (USDA 2004). It continues to be used today in less quantity. Alternatively, it appears that chlorothalonil was not used in the Bitterroot
24
Valley yet sampling results indicate its presence in rainwater collected from the valley. It should be noted that hexachlorobenzene is a common contaminant in the manufacture of chlorothalonil (Vargyas 1995), and both compounds are classified as a probable human carcinogen (USEPA 1997). All use of hexachlorobenzene was discontinued in 1984 (USEPA 1997). Studies indicate that both compounds cause liver, kidney, and thyroid tumors in rats, mice, and hamsters, and non-carcinogenic adverse health effects include impaired immune system function, porphyria, kidney damage, effects on the thyroid, tremors, and reduced fertility (USEPA 1997). Hexachlorobenzene is persistent in the environment with a half-life between 3 and 6 years, and is bioaccumulative (US Dept. of Health and Human Services 1996, as cited in Cox 1997).
A breakdown product in soil is m-phthalodinitrile which has shown to cause confusion and loss of consciousness, but has otherwise not been investigated (Cox 1997). Chlorothalonil is metabolized via glutathione conjugation to allow for optimal polar excretory products, facilitated by the enzyme glutathione-S-transferase (WHO 1996). When glutathione-chlorothalonil derivatives form, they tie up all of the cells�’ available glutathione, leaving enzymes that are glutathione-dependent unable to function. This consequence directly affects cellular respiration, the process by which large molecules are broken down and provide the cell with energy (Cox 1997). Since several enzymes important for cellular respiration are glutathione-dependent, their inhibition leads to the toxic effects of chlorothalonil. The 4-hydroxy metabolite is found in soil, plants, and animals during the breakdown of chlorothalonil, and has a half-life in soil ranging between 6 and 43 days (Cox 1997) (WHO 1996). The metabolite is approximately 30 times more acutely toxic than chlorothalonil itself and is more persistent and mobile in soil (WHO 1996). Sub-chronic toxicity tests have shown that the 4-hydroxy metabolite causes a decrease in weight, anemia, and damage to bone marrow, spleen, liver and kidney (WHO 1996). In chronic toxicity tests, similar affects were observed for sub-chronic exposure, such as anemia, a buildup of fibrous proteins in the spleen, and mortality in rats (WHO 1996). Based on the nature of the site, this ecological evaluation primarily assesses chronic exposure of contaminants and metabolites to wildlife. Chronic studies of laboratory animals that were fed chlorothalonil-contaminated food show effects including increased kidney weights, kidney damage, and the increased cell division in the kidney tubules (Cox 1997). Treated animals also
25
exhibited inflammation of arteries and increased cell division in the parathyroid (Cox 1997). A two-year study with beagles found that oral ingestion of chlorothalonil-contaminated food caused mild anemia; an increase in thyroid, liver, and kidney weights; gastritis; and excessive growth of cells in the kidney tubules even at low doses (Cox 1997). In the liver, development of fibrous tissue in the liver�’s portal vein and increased cell division and inflammation of the bile duct occurred at all doses tested (Cox 1997). Studies indicated that chlorothalonil has adverse reproductive effects. These include an increase in the number of early embryo failure and subsequent absorption into the mother�’s body, an increase in the number of embryos that were lost following implantation into the wall of the uterus, and transgenerational weight loss (i.e., offspring of rats fed chlorothalonil over two generations weighed less than the offspring of unexposed rats) (USEPA 1995, Cox 1997). Chlorothalonil has also been shown to cause genetic damage in mammals, both in live animals and cell cultures (Cox 1997). One study in live animals showed that oxidative damage to the liver increased dramatically at the two lowest doses tested, but not at higher doses (Lodovici et al. 1994). In terms of effects on other organisms, chlorothalonil is highly toxic to fish and other aquatic organisms. Concentrations of less than 100 parts per billion (ppb) have shown to be fatal to fish, and that the LC50s for rainbow trout channel catfish varies between 10 and 76 ppb, and 52 and 90 ppb, respectively (Cox 1997, Caux et al. 1996). Sublethal effects of chlorothalonil to fish occur at significantly lower levels than the LC50. For example, concentrations of 2 ppb in rainbow trout reduced the diffusive capacity of the gills to about 40 percent less than fish not exposed to chlorothalonil (Cox 1997). Chlorothalonil has shown to bioconcentrate in fish tissues above levels present in the water in which they are living. After spraying of potato fields, fish kills and respiratory distress in fish at trout farms have been reported (Davies 1987, Cox 1997). Frog kills have also been reported after cranberry bogs were treated with chlorothalonil (Davies et al. 1994, Cox 1997). A field study of aquatic organisms that were exposed to chlorothalonil suggests that toxicity is less than that predicted from laboratory studies. Deaths were seen in some species exposed experimentally in the field, yet, there have been no reported incidents of kills in the environment (WHO 1996). Despite the short residence time of chlorothalonil in environmental media, kills would be expected to occur. However, linking kills to chlorothalonil would be difficult given that residues would not persist long enough for chlorothalonil to be identified.
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Other adverse effects have been observed in plant and animals. Earthworms reared in chlorothalonil-contaminated soil had their life spans reduced by 50% when compared to earthworms raised in untreated soil (WHO 1996). Adverse reproductive effects were observed in bobwhite quail at moderate and high doses, such as reduced survival of quail offspring. Also, a study with mallard ducks showed a reduction in egg production and hatching success at high dose (WHO 1996, Cox 1997). Chlorothalonil has also been shown to have adverse effects on plants including direct mortality, growth inhibition, reductions in yield of crop plants, effects on mycorrhizal fungi, and other impacts (Cox 1997). Dermal exposure to chlorothalonil has been shown to cause skin rashes and skin sensitivity in humans (Cox 1997). USEPA lists chlorothalonil as a suspected endocrine disruptor (Keith 1998). Exposure to endocrine disrupting compounds (EDCs) in the environment have resulted in abnormal thyroid function in birds and fish; decreased fertility in birds, fish, shellfish and mammals; decreased hatching success in fish, birds, and turtles; demasculinization and feminization of male fish, birds, and mammals; defeminization and masculinization of female fish, gastropods, and birds; and alteration of immune function in birds and mammals (Colborn et al 1993). The endocrine system, composed of a network of glands and hormones, regulates growth, development and maturation, and how various organs operate. The endocrine glands include the pituitary, thyroid, adrenal, thymus, parathyroid, pancreas, ovaries, and testes which release specific levels of hormones into the bloodstream that act as natural chemical messengers for life functions. The endocrine system provides the link between the nervous system and reproduction, immunity, metabolism, and behavior. Other COPCs have been identified as known or suspected endocrine disruptors, which will be discussed in relevant sections. 4.1.3 Diazinon Diazinon was selected as a COPC for ecological target organisms. Table 4-3 provides toxicity values for non-carcinogenic effects. Diazinon is an organophosphorus pesticide (OPP). It has been used in Idaho at relatively low quantities since 1999 and has also been use sporadically in the Bitterroot Valley (USDA 2007). Diazinon is an insecticide used to control pest insects in soil, on ornamental plants, and on fruit and vegetable crops, and is sold under trade names including Alfatox, Basudin, Gardentox, and Knoxout (ATSDR 2006).
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The principle toxicity of OPPs is based on disruption of the nervous system by inhibition of cholinesterase (ChE; acetylcholinesterase, EC 3.1.1.7, and a mixture of nonspecific esterases) activity in the central nervous system and at neuromuscular junctions with death generally attributed to acute respiratory failure (Hoffman et al., 2003). When OP binds to ChE, a relatively stable bond is formed and prevents the ChE from deactivating the neurotransmitter acetylcholine. As a result, acetylcholine builds up and overstimulation of the cholinergic nervous system occurs. In comparison with other OPPs, diazinon is considered to be of moderate toxicity. The main concern about OPPs is their acute lethal toxicity. Some of the nonspecific signs following acute OP exposure of small mammals and birds include lethargy, labored breathing, excessive bronchial secretion, vomiting, diarrhea, tremors, convulsions, and death. Two additional syndromes of single or very short-term OP exposure include, what is referred to as an "intermediate syndrome" and OP-induced delayed neurotoxicity (OPIDN). "Intermediate syndrome" is a potentially lethal paralytic condition of the neck, limbs, and respiratory muscles. The paralysis from muscle necrosis follows an acute OP exposure by 2 to 3 days and appears to be initiated by depressed ChE activity and calcium accumulation in the region of the motor end-plate. OPIDN is where a relatively small dosage (1/25 LD50) of OP causes degeneration in the myelin sheath of long peripheral nerves and the spinal cord (Hoffman et al., 2003). The main metabolic pathways of diazinon are cleavage of the ester bond of diazinon or diazinoxon leading to the hydroxypyrimidine derivatives; transformation of P-S moiety to the P-O derivative, leading to the acute metabolite, diazoxon; oxidation of isopropyl substituent leading to the corresponding tertiary and primary alcohol derivatives; oxidation of the methyl substituent leading to the corresponding alcohol; and glutathione-mediated cleavage of the ester bond leading to a glutathione conjugate (WHO 1998). Glutathione conjugation appears to be of small importance in the metabolism of diazinon. The hydrolytic and oxidative cleavage of the phosphorus ester bond, leading directly or via diazoxon to the pryrimidinyl derivative, play the most prominent role (WHO 1998). Figure 4-1 provides the general metabolic pathways of diazinon in mammals. The metabolites formed (e.g., diethylphosphoric acid, diethylthiphosphoric acid and the derivatives of pyrimidinyl ring) are eliminated mainly through the kidneys (WHO 1998). Only minimal quantities were detected in milk and eggs (WHO 1998). The metabolite diazoxon, is a strong enzyme inhibitor (ExToxNet 1992). Based on the environmental setting and study question, this risk assessment focuses on chronic exposure. According to WHO (1998), the daily
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administered NOAEL for humans is 0.025 mg/kg body weight per day based on inhibition of the enzyme AChE. In laboratory studies with rats and dogs, systemic toxicity occurred with decreases in body weight and body weight gains (USEPA 2000). In terms of affects on aquatic organisms, diazinon is highly toxic to fish. For example, the LC50 ranges between 2.6 to 3.2 mg/L for rainbow trout (Kidd and James 1991). However, studies indicate that diazinon does not bioconcentrate significantly in fish as bioconcentration ratios range from 200 in minnows to 17.5 for guppies (Howard 1991). Diazinon is highly toxic to birds. In fact, former use on golf courses and on other open areas resulted in avian die-offs in the 1980s and 1990s (Hoffman et al. 2003). Avian die-offs due to diazinon have been reported and documented across the US. The LD50 values range from 2.75 mg/kg to 40.8 mg/kg for birds (US Public Health Service 1995). Diazinon does not appear to have reproductive effects based on available studies (ATSDR 2006). Studies indicate that dermal LD50 values are very high, therefore, diazinon is considered to have very low toxicity via the dermal route of exposure (ATSDR 2006). Diazinon is not considered carcinogenic by the USEPA. Diazinon is not listed as a suspected endocrine disruptor by the USEPA. 4.1.4 DDE DDE was selected as a COPC for ecological target organisms. Table 4-3 provides toxicity values for non-carcinogenic effects. DDE is a first transformation product of the persistent organochlorine insecticide DDT.
DDE is preferentially absorbed by the intestinal lymphatic system (in rats) from ingestion (ATSDR, 2002). The distribution of DDE is the same regardless of route of exposure. Blood and lymph deliver DDE to body tissues (primarily in adipose tissue) with the amount of DDE storage dependent largely on lipid content of tissue. DDE stores in adipose tissue are eventually metabolized through conjugation in the liver. The proposed metabolism of DDE is depicted in Figure 4-2. The half-life of DDE in rats was measured to be 120 days with a large amount of the excretion in the feces (34% of parent compound) and a small amount (1%) excreted in the urine.
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The RfD for DDE was 0.0005 mg/kg/day. The NOAEL in this 27-week rat study was 0.05 mg/kg/day and the LOAEL was 1 mg/kg/day with liver lesions as the endpoint. The uncertainty factor of 100 was for interspecies conversion and protection of sensitive subpopulations. DDE has anti-androgenic effects through induction or repression of transcription from androgen responsive genes. This has been illustrated by the presence of various reproductive and developmental effects seen in male rats.
For non-cancer effects, DDE is a stronger anti-androgenic than it is as an estrogen-mimic. For example doses of 100 mg/kg/day during gestation in Sprague-Dawley rats cause a decrease in prostate weigh of males at 15 months of age. Long-Evans rats similarly showed decreased weights in penis, prostate, and epididymis and an increased number of retained nipples and reduction of anogenital distance at the same dose (ATSDR, 2002).
In Osborne-Mendel rats dosed at 30 mg/kg/day for males and 16 mg/kg/day researchers observed a decrease in body weight gain.
In hamsters decreased body weight gain was seen at 122 mg/kg/day for males and 114 mg/kg/day for females.
Cockerels experienced decreased testis weights and secondary sex characteristics at 6.25-50 mg/kg/day of DDT* for 47 weeks (ATSDR, 2002).
Embryonic survival was significantly decreased when Mallard ducks were fed 10 ppm DDE for over 1 year (ATSDR, 2002).
Japanese quail had a decrease in egg production when fed DDT* at rates of 10-100 ppm for up to 95 days (ATSDR, 2002).
American kestrels which died after exposure of 2.8 ppm DDE in their diet for 14-18 months had lost 30-35% body weight (ATSDR, 2002).
There was an increase in thyroid weights in homing pigeons in response to DDT* in the diet at 3-36mg/kg/day for up to 8 weeks (ATSDR, 2002).
Common frog (Rana temporaria) tadpole metamorphosis was significantly delayed compared to controls in tadpoles exposed for 28 days to 0.001 ppm DDT*.
*DDT is commonly used as a surrogate for DDE because of their close chemical relationship.
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4.1.5 Trifluralin Trifluralin is a dinitroaniline pesticide. Dinitroanilines are known for their ability to inhibit protein synthesis (Claasen, 2001).
In rats, trifluralin is not readily absorbed into the GI tract when exposed orally. Nearly all of the parent chemical is metabolized and eliminated within 3 days. Eighty percent is eliminated in feces and the rest is eliminated in the urine. The RfD for trifluralin is 0.0075 mg/kg/day. The study was done in dogs and the endpoint effects were increased liver weights and increase in methemoglobinemia. The NOEL was 0.75 mg/kg/day with safety factor of 100 for inter-species extrapolation and intra-species variability.
The developmental toxicity NOEL in rats was 475 mg/kg/day and the LOEL of 1000 mg/kg/day was observed as reduced mean fetal body weight (USEPA, 1996).
Teratogenicity was studied in rabbits with a maternal toxicity NOEL of 100 mg/kg/day due to anorexia. Decreased fetal weight and increased runts led to a developmental toxicity NOEL of 225 mg/kg/day (USEPA, 1996).
Another study conducted on beagle dogs established a NOEL of 2.4 mg/kg/day (USEPA, 1996).
The NOEC for trifluralin in the Northern Bobwhite was 452.3 ppm and the LOEC was 910.5 ppm with cracked eggs as the endpoint affected (USEPA, 1996).
Trifluralin did not display mutagenecity (Ames test) (USEPA, 1996).
Triflurlain is practically non-toxic in birds and mammals. However, it is highly toxic to aquatic organisms. The 96-hour LC50 is 0.02-0.06 mg/L for rainbow trout (USEPA, 1996).
A life-cycle study was done on the fathead minnow and researchers found a NOEC of 1.9 ppb and a LOEC of 5.1 ppb (USEPA, 1996).
4.2 Toxicity Information for Carcinogenic Effects
4.2.1 Alachlor
The margin of exposure (MOE) approach has been utilized for this risk assessment. This approach uses a non-linear assumption which reflects the fact that there is an exposure dose below which tumor formation is not likely to occur (threshold model). Q1 is another approach utilized in this risk
31
assessment, but unlike the MOE approach, the Q1 approach assumes that any exposure could result in tumor formation (non-threshold model). The Q1 was calculated to be 0.08(mg/kg/day)-1 as a carcinogenic slope factor for alachlor. For alachlor, it will be based on both the MOE approach and the Q1 approach for the evaluation of carcinogenic potential (USEPA, 1997).
Understanding the mechanisms of alachlor toxicity in mice and rats can be utilized to investigate potential toxicity to humans. Alachlor has been evaluated for carcinogenic activity in animal tests such as rats and mice. Malignant and combined benign/malignant multiple tumor types in rats as well as nasal tumors in humans are likely to occur at high doses of Alachlor. Alachlor also produced a significant increase in lung tumors in female rats. In mice, significant increases in thyroid and stomach tumors were observed at doses exceeding the maximum tolerated dose (MTD).
Alachlor is initially metabolized in the liver through the CYP450 pathway (Kale VM, 2007) and glutathione conjugation. Alachlor can be converted into a diethyliminoquinone metabolite (DEIQ). There are differences between rats and humans in their relative capacity to convert alachlor to DEIQ. Because of this difference between rats and humans, the tumors detected in the rat are not likely relevant to man and alachlor presents no significant cancer risk to humans according to threshold-based non-genotoxic mechanisms (Heydens et al. 1999).
Alachlor was classified as �“likely�” to be a human carcinogen at high doses, but �“not likely�” at low doses. The USEPA�’s Cancer Peer Review Committee concluded it was sufficient for a B2 classification, probable human carcinogen (EPA RED, 1998).
Nasal turbinate tumors in rats is a significant adverse effect caused by alachlor (California EPA 1997).
4.2.2 Chlorothalonil
Carcinogenic toxicity values for chlorothalonil are provided in Table 4-5. The USEPA categorizes chlorothalonil as a Class B2 "probably human carcinogen" with an oral cancer slope factor of 0.011 (mg/kg-day)-1. Tumors have been shown to develop in laboratory animals.
For example, 50 male and 50 female Osborne-Mendel rats were administered doses of 0, 5,063 ppm or 10,126 ppm (approximately 250 and 500 mg/kg/day) for two years. Renal tubular epithelial adenomas and carcinomas were found in treated animals after 80 weeks of dietary exposure (USEPA 1998). In another study, Charles River Fischer 344 rats received chlorothalonil in their diet at dose levels of 0, 2, 4, 15, and 175 mg/kg/day for 111 weeks (males) and 125 weeks (females). Carcinogenic potential was evident by statistically
32
significant trends and pair-wise increases in the incidence of kidney tubular adenomas, carcinomas, and adenomas/carcinomas combined in male and female rats at the 175 mg/kg/day dose level. Additionally, the incidence of forestomach papillomas and carcinomas was increased only at the 175 mg/kg/day in males and at the 15 and 175 mg/kg/day in females, however, at 15 mg/kg/day in male rates, there was a significant pair-wise difference in the incidence of kidney tubular adenomas and/or carcinomas combined (USEPA 1998).
4.2.3 Diazinon
Diazinon is not considered by the USEPA to be carcinogenic.
4.2.4 DDE
Carcinogenic toxicity values for DDE are provided in Table 4-5.
A study conducted by the National Cancer Institute showed that there was a dose-related increase in liver tumors in B6C3F1 mice at 51 mg/kg/day for males and at both 29 and 53 mg/kg/day in females (ATSDR, 2002).
DDE is structurally similar to DDT which is also a considered a probable carcinogen by EPA.
For cancer effects, DDE has been shown to cause �“stronger tumor[s]�” in female rats versus male rats. Additionally males more readily lost body weight, but females had an increased rate of mortality (ATSDR, 2002).
4.2.5 Trifluralin
Carcinogenic toxicity values for trifluralin are provided in Table 4-5.
In Fischer rats an increase in malignant and benign urinary bladder tumors in females and an increase in carcinomas of the renal pelvis and thyroid glands in males was seen at a dose of 325 mg/kg/day.
The EPA categorizes trifluralin as a Class C �“possible human carcinogen�” with a Q1 of 0.0077 mg/kg/day.
4.3 Uncertainties in Toxicity Information
A primary source of uncertainty involves the use of dose-response information, for non-carcinogenic effects or carcinogenic effects, from laboratory animals such as rats or mice to predict effects in humans.
In terms of wildlife toxicity, uncertainty factors were applied to NOAELs. Table 3.1 provides the uncertainty factors used for target organisms and the rationale for their use (see footnotes).
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5.0 RISK CHARACTERIZATION
5.1 Current and Future Land-use Conditions - Human Health Risk and Hazard In terms of human health, the current land-use conditions primarily pertain to residential users. Future land-use conditions are expected to remain residential. Alachlor was the only chemical selected as a COPC for human health based on the COPC refinement process previously discussed in Sections 2.0 and 3.0. Humans may be exposed to alachlor by various routes of exposure: ingestion, inhalation, and dermal contact. The following equations are used to calculate the exposure for adult and child (Exhibit 2). Exhibit 2: Quantified Exposure Pathways for Residents
Exposure Pathway Equation and Results Explanation
Residential exposure: Ingestion of chemicals in drinking water (and beverages made using drinking water)
Intake (mg/kg-day) = CW x IR x EF x ED / (BW x AT)
= 0.53/1000 x 1.4 x 365 x 70 / (70x 365 x 70) = 1.06 x 10-
6(for adult)
= 0.53/1000 x 1.0x 365 x 70 / (70x 365 x 70) = 0.76 x 10-6
(for child)
CW: Chemical concentration in water (mg/liter)
IR = Ingestion Rate (liters/day)
EF = Exposure Frequency (day/year)
ED = Exposure Duration (years)
BW = Body Weight (kg)
AT = Averaging time (period over which exposure is averaged �– days)
Residential exposure: Ingestion of chemicals in surface water while swimming
Intake (mg/kg-day)= CW x CR x EF x ED / (BW x AT)
=0.53/1000 x 50/1000 x 24 x7 x 70 / (70x 365 x 70) = 1.75 x 10-8 (for adult)
= 0.53/1000 x 50/1000x24 x
CW: Chemical concentration in water (mg/liter)
CR = Contact Rate (liters/hour)
EF = Exposure Frequency (day/year)
ED = Exposure Duration
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7 x 6 / (15x 365 x 6) = 8.1 x 10-8 (for child)
(years)
BW = Body Weight (kg)
AT = Averaging time (period over which exposure is averaged �– days)
Residential exposure: Dermal contact with chemicals in water
Absorbed dose (mg/kg-day)= CW x SA x PC x ET x EF x ED x CF/ (BW x AT)
= 0.53/1000 x 194 x (0.25/0.25 x 0.1) x 2.6 x 7 x 70x 1/1000 x 70 / (70x 365 x 70) = 0.5 x 10-6 (for adult)
= 0.53/1000 x 90 x 0.1 x2.6 x7 x6 x1/1000 x 6 / (15x 365 x 6) = 0.1x 10-6 (for child)
CW: Chemical concentration in water (mg/liter)
SA = Skin surface area available for contact (cm2)
PC = Chemical-specific dermal permeability constant (cm/hr)
ET = Exposure Time (hours/day)
EF = Exposure Frequency (days/year)
ED = Exposure Duration (years)
CF = Volumetric conversion factor for water (1 liter/1000 cm3)
BW = Body Weight (kg)
AT = Averaging time (period over which exposure is averaged �– days)
Residential exposure: Inhalation of airborne (vapor phase) chemicals
Intake (mg/kg-day) = CA x IR x ET x ED / (BW x AT)
= 106 / 1000 x 2.2x10-5 /760 x 20 x 7/60 x 24x 365 x 70 / (70 x 365 x 70) = 2.3 x 10-6 (for adult)
CA = Chemical concentration in air (mg/m3)
IR = Inhalation Rate (m3/hour)
EF = Exposure Frequency (days/year)
ED = Exposure Duration
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= 106 /1000 x 2.2x10-5 /760 x 10 x 7/60 x 24x 365 x 6 / (15 x 365 x 6) = 5 x 10-6 (for child)
(years)
BW = Body Weight (kg)
AT = Averaging time (period over which exposure is averaged �– days)
Carcinogenic risks and chronic hazard quotients for adults and children for different exposure pathways (ingestion of chemicals in drinking water, ingestion of chemicals in surface water while swimming, dermal contact with chemicals in water and inhalation of airborne chemicals) are provided in Exhibits 3 and 4. Exhibits 3 and 4 also include cumulative risk and hazard values. Exhibit 3: Carcinogenic Risk and Hazard Quotient for Adults (Risk= CDI xSF; HQ=E/RfD)
Exposure Pathway CDI Risk HQ
Ingestion of chemicals in drinking water
1.06x10-6 8.48x10-8 1.06x10-4
Ingestion of chemicals in surface water while swimming
1.75x10-8 1.4x10-9 1.75x10-4
Dermal contact with chemicals in water
5.0x10-7 4.0x10-8 5.0x10-5
Inhalation of airborne (vapor phase) chemicals
2.3x10-6 1.84x10-7 2.3x10-4
Cumulative risk = 3.1x10 -7 Hazard Index= 5.6x10-4
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Exhibit 4: Carcinogenic Risk and Hazard Quotient for Children (Risk= CDI xSF; HQ=E/RfD)
Exposure Pathway CDI Risk HQ Ingestion of chemicals in drinking water
7.6x10-7 6.1x10-8 7.6x10-5
Ingestion of chemicals in surface water while swimming
8.1x10-8 6.5x10-9 8.1x10-6
Dermal contact with chemicals in water 1.0x10-7 8.0x10-9 1.0x10-5
Inhalation of airborne (vapor phase) chemicals
5.0x10-6 4.0x10-8 5.0x10-4
Cumulative risk = 1.2x10-7 Hazard Index= 5.9x10-4
USEPA typically uses a risk range of 1x10-6 to 1x10-4 to determine site-specific levels of acceptable risk. Alternatively, the Montana Department of Environmental Quality (MDEQ) considers any risk levels exceeding 1x10-5 as unacceptable. The results of the human health evaluation, both for individual pathways and for cumulative risk, do not exceed either of these benchmarks for either children or adults. Therefore, risk levels would be considered acceptable. Non-carcinogenic hazard is also less than one for both individual pathways and cumulative hazard, therefore hazard is expected to be low. 5.2 Ecological Risk and Hazard
5.2.1 Carcinogenic Risk to Terrestrial Organisms via Surface Water Ingestion
The equation used to calculate risk is: Risk = (CDI x SF)
Where, CDI = chronic daily intake (mg/kg-day), or ERfD SF = oral cancer slope factor ((mg/kg-day)-1)
Chlorothalonil
The calculated risk values for white-tailed deer, gray wolf, and red-tailed hawk were 2.23x10-8, 2.90x10-8, and 2.01x10-8, respectively.
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Diazinon
IRIS does not have a published oral cancer slope factor for diazinon as this compound is not suspected to cause cancer according to the USEPA.
DDE The calculated risk values for white-tailed deer, gray wolf, and red-tailed hawk were 2.27x10-8, 2.89x10-8, and 1.94x10-8, respectively.
Trifluralin The calculated risk values for white-tailed deer, gray wolf, and red-tailed hawk were 2.02x10-9, 2.61x10-9, and 1.76x10-9, respectively.
5.2.1.1 Cumulative Risk to Terrestrial Organisms for Surface Water Ingestion
Cumulative risk for terrestrial organisms for the surface water ingestion pathway is 1x10-7, which is less than USEPA or MDEQ benchmarks. Therefore, carcinogenic risk via this pathway is acceptable.
Aquatic organisms were not evaluated for surface water ingestion pathway.
5.2.2 Dose-Modeling and Hazard Quotients
Hazard quotients (HQs) were calculated using the following equation from Ecological Risk Assessment Guidance for Superfund: Process for Designing and Conducting Ecological Risk Assessments - Interim Final, dated June 1997, EPA 540-R-97-006:
Where,
HQ = hazard quotient; Dose = estimated contaminant intake at the site (e.g., mg contaminant/kg body weight per day) EEC = estimated environmental concentration at the site in ug contaminant/L water AL = action limit, similar to an ERfD, value based on adjustment of NOAEL to address uncertainty factors, ug/L (e.g., sensitive life stages).
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For the purposes of assessing initial hazards associated with surface water, EECs were used in HQ calculations rather than dose. EECs are based on detected concentrations in water samples. It should be noted that limited sampling was conducted, and in all cases, EECs use the only available concentration data for each compound. The modified HQ equation is HQ= EEC/AL .
Chlorothalonil - HQ = 0.031 ug/L / 0.15 ug/L = 0.206 Diazinon - HQ = 0.003 ug/L / 0.2 ug/L = 0.015 DDE - HQ = 0.001 ug/L/.005 ug/L = 0.2 Trifluralin - HQ = 0.004 ug/L / .075 ug/L = .053 Hazard Index (HI) was calculated using the following equation: HI = HQ1 + HQ2 + HQ3 + HQ4 = 0.474
These HQs and HI are very low for the initial evaluation. Since all HQs were below one, additional calculations were performed to better quantify hazard.
5.2.2.1 Oral (Terrestrial Species)
Oral doses were calculated using the following equation for each target organism which estimates the potential daily dose for water intake from a single water body (e.g., Bitterroot River):
Where,
ADDpot = potential average daily dose (e.g., mg/kg-day) C = Average contaminant concentration in a single water source (e.g., in mg/L or in mg/kg, because 1 liter of water weighs 1 kg). FR = Fraction of total water ingestion from the contaminated water source (unitless). Assumption is 1. NIR = Normalized water ingestion rate (i.e., fraction of body weight consumed as water per unit time; e.g., in g/g-day)
Selected target organisms expected to ingest surface water include white-tailed deer, gray wolf, and red-tailed hawk. Water ingestion factors were not available for fish and amphibian target organisms; therefore, only the dermal absorption pathway was evaluated for these organisms. NIR differs for each target organism. Exposure factors for each target organism are provided in Table 3-1. Note from Table 3-1 that surrogate species are used for some species (e.g., red fox [Vulpes
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vulpes] for gray wolf). When HQs or the HI is less than one, hazard for non-carcinogenic affects are low. Exhibit 5: Carcinogenic Risk and Non-carcinogenic Hazard Quotient for the Surface Water Ingestion Exposure Pathway for Terrestrial Organisms (Risk= CDI x SF; HQ=E/RfD) Surface Water Ingestion Exposure Pathway
CDI Risk HQ
White-tailed Deer
Chlorothalonil 2.03x10-6 2.23x10-8 1.4x10-3
Diazinon* N/A N/A 1.0x10-2
DDE 6.50x10-8 2.23x10-8 1.3x10-2
Trifluralin 2.62x10-7 2.02x10-9 3.5x10-3
Gray Wolf
Chlorothalonil 2.64x10-6 2.90x10-8 1.8x10-3
Diazinon* N/A N/A 1.3x10-2
DDE 8.50x10-8 2.89x10-8 1.7x10-2
Trifluralin 3.40x10-7 2.62x10-9 4.5x10-3
Red-Tailed Hawk
Chlorothalonil 1.83x10-6 2.013x10-8 1.2x10-3
Diazinon* N/A N/A 9.0x10-3
DDE 5.70x10-8 1.94x10-8 1.14x10-2
Trifluralin 2.28x10-7 1.76x10-9 3.0x10-3
TOTAL for Ingestion Exposure Pathway
Cumulative risk =1.0x10-7
Hazard Index= 8.8x10-2
*IRIS does not have a published oral cancer slope factor for diazinon as this compound is not suspected to cause cancer according to the USEPA.
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Chlorothalonil
The calculated ADDpot values for white-tailed deer, gray wolf, and red-tailed hawk were 2.03x10-6 mg/kg-day, 2.64x10-6 mg/kg-day, and 1.83x10-6 mg/kg-day, respectively. The HQ was re-calculated based on ADDpot and ERfD values. The re-calculated HQ values for white-tailed deer, gray wolf, and red-tailed hawk were 0.0014, 0.0018, and 0.0012, respectively.
All of these HQs were less than one, thus hazard for non-carcinogenic effects are low.
Diazinon
The calculated ADDpot values for white-tailed deer, gray wolf, and red-tailed hawk were 2.0x10-7 mg/kg-day, 2.6x10-7 mg/kg-day, and 1.8x10-7 mg/kg-day, respectively. The HQ was re-calculated based on ADDpot and ERfD values. The re-calculated HQ values for white-tailed deer, gray wolf, and red-tailed hawk were 0.01, 0.013, and 0.009, respectively.
All of these HQs were less than one, thus hazard for non-carcinogenic affects are low. DDE
The calculated ADDpot values for white-tailed deer, gray wolf, and red-tailed hawk were 6.55 x10-8 mg/kg-day, 8.50 x10-8 mg/kg-day, and 5.70 x 10-8 mg/kg-day, respectively. The HQ was re-calculated based on ADDpot values. The HQ was re-calculated based on ADDpot and ERfD values. The re-calculated HQ values for white-tailed deer, gray wolf, and red-tailed hawk were 0.013, 0.017, and 0.0114, respectively.
All of these HQs were less than one, thus hazard for non-carcinogenic affects are low.
Trifluralin
The calculated ADDpot values for white-tailed deer, gray wolf, and red-tailed hawk were 2.62x10-7 mg/kg-day, 3.40x10-7 mg/kg-day, and 2.28x10-7 mg/kg-day, respectively. The HQ was re-calculated based on ADDpot and ERfD values. The re-calculated HQ values for white-tailed deer, gray wolf, and red-tailed hawk were 0.0035, 0.0045, and 0.003, respectively.
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All of these HQs were less than one, thus hazard for non-carcinogenic affects are low.
5.2.2.1.1 Cumulative Hazard for Terrestrial Organisms
Cumulative hazard to ecological organisms calculated by summing all HQs into a hazard index (HI) indicate that terrestrial hazards to ingestion of surface water is 0.088. Therefore, hazard for non-carcinogenic affects are low.
Due to limited data, as mentioned previously, only the ingestion of surface water pathway was evaluated in this risk assessment for terrestrial organisms.
5.2.2.2 Dermal (Aquatic Species)
Dermal exposure to surface water was evaluated for the bull trout and spotted frog. Exposure factors for each target organism are provided in Table 3-1. Note that the green frog (Rana clamitans) is used as a surrogate species for spotted frog. Absorbed dose calculations and equations are provided in Table 3-1. Dermal permeability is based on chemical-specific factors and differs by aquatic organism. Absorbed dose for the bull trout was calculated only using the gill surface area rather than the whole body skin surface area since this is the primary location of absorption. Dermal toxicity values (e.g., ERfD) are provided in Table 4-3. Please see Exhibit 6 (next page).
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Exhibit 6: Carcinogenic Risk and Non-carcinogenic Hazard Quotient for the Dermal Contact with Surface Water Exposure Pathway for Aquatic Organisms (HQ=E/RfD)
Dermal Contact to Surface Water Exposure Pathway HQ
Bull Trout
Chlorothalonil 3.1x10-4 Diazinon 2.4x10-5 DDE 4.608x10-4 Trifluralin 0.02707
Green Frog
Chlorothalonil 0.00173 Diazinon 9.0x10-5 DDE 1.19x10-4 Trifluralin 9.3x10-4
TOTAL for Dermal Exposure Pathway Hazard Index= 0.03073
Chlorothalonil
The absorbed doses for bull trout and spotted frog were 1.83x10-6 mg/kg-day and 1.035x10-5 mg/kg-day, respectively. The HQ was re-calculated based on absorbed doses. The re-calculated HQ values for bull trout and spotted frog were 3.1x10-4 and 0.00173, respectively.
These HQs were less than one, thus hazard for non-carcinogenic affects are low.
Diazinon
The absorbed doses for bull trout and spotted frog were 2.60x10-7 mg/kg-day and 1.01x10-6 mg/kg-day, respectively. The HQ was re-calculated based on absorbed doses. The re-calculated HQ values for bull trout and spotted frog were 2.40x10-5 and 9.0x10-5, respectively.
These HQs were less than one, thus hazard for non-carcinogenic affects are low.
DDE
The absorbed doses for bull trout and spotted frog were 4.608x10-6 mg/kg-day and 1.187x10-5 mg/kg-day, respectively. The HQ was re-
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calculated based on absorbed doses. The re-calculated HQ values for bull trout and spotted frog were 4.608x10-4 and 1.19x10-4, respectively.
These HQs were less than one, thus hazard for non-carcinogenic affects are low.
Trifluralin
The absorbed doses for bull trout and spotted frog were 5.414x10-6 mg/kg-day and 1.395x10-5 mg/kg-day, respectively. The HQ was re-calculated based on absorbed doses. The re-calculated HQ values for bull trout and spotted frog were 0.02707 and 9.3x10-4, respectively.
These HQs were less than one, thus hazard for non-carcinogenic affects are low.
5.2.2.2.1 Cumulative Hazard for Aquatic Organisms
The HI for dermal contact with surface water for aquatic target organisms is 0.0307338 which is less than one. Therefore, non-carcinogenic hazard is low.
5.3 Uncertainties
5.3.1 Limited Sampling Data
There are several sources of uncertainty in the baseline human health and ecological risk assessment that have been discussed at the end of each applicable section. One source of uncertainty deals with the initial selection of COPCs and COPECs based on the sampling data and available toxicity information. For example, limited sampling data is available and not all contaminants of interest (e.g., endocrine disrupting pesticides, etc.) were analyzed as part of the sampling events. Also, there are natural variation uncertainties. For example, ecosystems include highly variable abiotic (e.g., weather, soils) and biotic (e.g., population density) characteristics. Since only a fraction of the instances can be sampled and confirmed there is uncertainty concerning the true distribution of values. Available sampling results were then compared to applicable human health and ecological screening criteria. In terms of the CSM, the initial description of the ecological components at the site such as exposure pathways, COPECs, and target organisms require professional judgment and various assumptions that result in CSM uncertainties. Please see Section 3.4 for discussion of CSM uncertainties.
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5.3.2 Exposure Factors
In terms of risk characterization, applicable exposure factors are not published for all of the target organisms, and in these cases, a surrogate organism was selected (see Sections 4.0 and 5.0). Additional sources of uncertainty include estimates of toxicity to ecological receptors at the site based on limited data from the laboratory. Specimens were not collected for tissue analysis as part of this study; therefore, the specific toxicity values were obtained from applicable laboratory studies or published values. In terms of estimating risk and hazard, uncertainty factors were applied to NOAELs for the ecological assessment, and there are uncertainties as a result of simultaneous exposures to multiple COPECs. It is possible that synergistic affects could be occurring.
5.3.3 Modeling
Uncertainties due model output concentrations were evaluated by Monte Carlo analysis using @Risk software. HYSPLIT was used to model concentrations of pesticides that were transported from Idaho to the Bitterroot Valley, and CalTOX was used to determine deposition concentrations into site media. These concentrations were very low. The modeled concentrations from HYSPLIT were on the order of magnitude of 1x10-10, while the modeled CalTOX concentrations were on the order of 1x10-20.
Based on the modeled concentrations, risk and hazard would be negligible. However, there are other factors to consider. These factors should be addressed in the next steps of this project. Section 6.0 provides a summary of the baseline risk assessment and describes next steps that should be taken to further investigate whether humans and wildlife in the Bitterroot Valley of Montana are being exposed to pesticides at concentrations that would be harmful to human health and the environment.
5.4 Summary Discussion and Tabulation of the Risk Characterization
Based on the available sampling data, it appears that risk and hazard to humans and target organisms may be deemed acceptable. Cancer risk estimations fall below 1x10-6 and HQs fall well below 1.0 for surface water ingestion and dermal contact to humans and target organism. However, available data is limited and additional sampling events are necessary to collect adequate data for risk assessment purposes. Further sampling of site media, including surface water and soil, would be more representative of site conditions and allow for estimation of risk and hazard from other relevant exposure pathways. Tables 3-3 and 3-4 provides a summary of relevant exposure pathways. In terms of tabulation, the HI still falls below 1 for surface water ingestion and dermal contact.
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Other factors are important for characterization of risk and hazard (discussed below). Modeling was used in an attempt to address our study question of whether pesticides used in Idaho are being transported via long range air transport and to evaluate the inherent uncertainties.
5.4.1 Evaluating Persistence
Persistence is a significant factor when evaluating exposure potential. Persistent compounds degrade or dissipate slowly in air, water and soil. If a compound is persistent in the environment, they tend to be a greater threat to wildlife as they are taken in continuously over time. Once absorbed, they persist in the organism as well, thus persistent chemicals are also typically bioaccumulative. Bioaccumulation should be modeled for human and ecological site users. Additionally, the combination of more than one chemical can have the synergistic effect of increasing toxicity above that of a single chemical. Both co-exposures and persistence are important factors in evaluating toxicity. Since more than one pesticide is typically used in pest management and crop protection programs, synergistic effects must be evaluated. A good example of synergy is glyphosate, trade name Roundup®. Most herbicides containing glyphosate are either made or used with a surfactant (chemicals that help glyphosate to penetrate cells) (Cox 2000). Its surfactant is more acutely toxic than glyphosate and the combination of the two results in greater toxicity (Cox 2000).
5.4.2 Air Transport in the Region
Air transport modeling of the key pesticides will be done using HYSPLIT software from the National Oceanic and Atmospheric Administration. The HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectory) model is software that models air parcel trajectories. The HYSPLIT model is linked to several meteorological databases (can choose month and year) and combines this information with complex dispersion equations to create trajectories of particle transport. These trajectories can be calculated forward or backward from any point on the grid and the software creates easy to see color maps that is compatible with ArcView GIS (Draxler and Rolph 2003). These HYSPLIT modeled concentrations were very low (i.e., on the order of 1x10-10).
5.4.3 Further Actions
Section 6.0 provides conclusions of the baseline risk assessment and provides comprehensive recommendations for next steps.
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6.0 SUMMARY & NEXT STEPS Section 1.0 gave a brief summary of the scope of this project and the goals and organization of the baseline risk assessment. Section 2.0 identified the COPCs and outlined the approach for their selection. The five COPCs were alachlor, chlorothalonil, diazinon, DDE, and trifluralin. The exposure assessment (Section 3.0) evaluated exposure pathways for humans and ecological target organisms and discussed the air transport modeling of pesticides used on Idaho potato fields. Based on the qualitative screening process, additional pesticides were selected to be modeled (model COPCs). The modeling results did not indicate high concentrations of model COPCs; however, these results did confirm that three of the original COPCs (chlorothalonil, trifluralin, and diazinon) are likely being transported from Idaho potato fields. The toxicity assessment (Section 4.0) outlined the toxicity values for both cancer and non-cancer effects for the five original COPCs determined from available analytical data. The risk characterization section (Section 5.0) characterized risk and hazard for exposure to COPCs. The cumulative risk values for all five COPCs across all quantified pathways indicate no appreciable cancer or non-cancer risk from exposure to alachlor, chlorothalonil, trifluralin, DDE, or diazinon. In the exposure, toxicity, and risk characterization sections, the uncertainties associated with the various assumptions and calculations utilized throughout this assessment were discussed. 6.1 Risk Management Further characterization of the study site is recommended. Without additional environmental investigation, the risk and hazard to humans and target organism in the Bitterroot Valley cannot be fully characterized. A sampling plan should be prepared and reviewed by qualified scientists, and subsequently implemented in accordance with approved QA/QC procedures. While this assessment indicates there is acceptable risk or hazard to humans or wildlife in the valley from Idaho pesticides, uncertainties with this assessment (e.g., data gaps, model assumptions, etc.) should be thoroughly evaluated and addressed to the extent possible. As such, the findings of acceptable risk and hazard are not definitive. The air modeling findings, however, are of considerable importance. This assessment explicitly indicates that long-range atmospheric transport and deposition of Idaho pesticides into the valley are occurring. In addition to a review of the literature and the model outputs, pesticides were detected in the Bitterroot Valley which are not currently used, or have been used recently, on valley crops.
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6.2 Next Steps The following is a description of further research that needs to be conducted to help better characterize the site. Additional sampling is necessary to adequately determine the nature and extent of potential contamination in the valley. Site media from the Bitterroot Valley have not been analyzed for most pesticides used in Idaho and Montana. Sampling of all site media (i.e., air, water, soil) is needed, and additional analytical parameters should be analyzed. In addition to pesticides, samples should be analyzed for a full analytical suite of volatile organic compounds (VOCs), semi-volatile organic compounds (SVOCs) (including polynuclear aromatic hydrocarbons (PAHs), heavy metals (including cyanide), organochlorine pesticides (OCPs), organophosphorus pesticides (OPPs), carbamate pesticides, and other pesticides. A more accurate technique for modeling transport in HYSPLIT is required to better understand movement of pesticides from Idaho to western Montana. When modeling in HYSPLIT, only pesticides from Idaho potato crops were evaluated. This may be an underestimate of risk given that several insecticides and herbicides are used on wheat and barley crops in Idaho, plus the more pesticide-intensive mint and sugarbeet crops. The scope of this project was limited to pesticide transport from Idaho; however, pesticide transport from other areas in the northwest region is possible and should be modeled. Utilizing HYSPLIT, one could do backward trajectories over several days, predicting the several areas of the Northwestern US and Canada that may be contributing to deposition of chemicals in the Bitterroot Valley. The scope of this project was limited to pesticides only. Non-pesticide sources of organochlorines have not been determined for this site. This could be approached by looking at the TRI (toxic release inventory) from USEPA for the Northwest region and incorporating those values into the HYSPLIT model. Additionally, there is a toxic release of xylene at a rate of approximately 11,000 lbs/year from a surgical products manufacturer in Ravalli County (USEPA, 2008). This source was not analyzed for its potential contribution as a cosolvent in pesticide toxicity. Additionally, it is possible that synergist effects between pesticides or other chemicals may be occurring, however, these were not evaluated. The synergistic effects of multiple pesticides should be evaluated in subsequent assessments. Modeling the volatilization or �“emissions�” of pesticides from crops was conducted by using a chemical partitioning coefficient. Pesticide volatilization was represented in this simplified manner due to data limitations; however, in-depth modeling of pesticide emissions from potato crops should be conducted. This technique is further discussed in Appendix D.
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Atmospheric modeling with HYSPLIT was very general in nature and not coupled with any chemical-specific modeling. Coupling the atmospheric modeling with more chemical-specific information for each pesticide would provide a more accurate depiction of atmospheric transport of pesticides. In addition to chemical-specific photodegradation rates, wet/dry deposition rates would characterize the behavior of these pesticides more fully. Furthermore, the pesticides were modeled as if they were in the vapor phase and not sorbed to dust particles. This is not representative of site conditions as pesticides could be transported via dust in the atmosphere over long distances. To fully characterize the fate and transport of these chemicals, analyzing this relationship with other particles is essential. This is especially true because of the occurrence of forest fires in this area and their ability to release a lot of particles into the atmosphere in a given period of time. This project also did not attempt to analyze the bioaccumulation of COPCs. It is known that several of these chemicals (e.g., chlorothalonil, DDE, trifluralin) both bioaccumulate and biomagnify. This behavior may obviously increase the risk from a chemical due to additive effects over time. Bioaccumulation should be thoroughly evaluated in subsequent assessments. Finally, it is not anticipated that hydrogeologic units beneath Idaho and beneath Montana interact in the region of the Bitterroot Mountains. However, further study of the hydrogeology in this region should be conducted to determine whether there is any interaction between hydrogeologic units of eastern Idaho and western Montana. If so, the potentially complete exposure pathways associated with groundwater should be evaluated. This baseline risk assessment serves as the first baseline risk evaluation conducted for the Bitterroot Valley. Additional investigations are necessary to adequately characterize the nature and extent of possible contamination in the valley. Upon further investigation, a follow-up risk evaluation should be conducted.
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7.0 REFERENCES Agency for Toxic Substances and Disease Registry. 2002. Toxicological Profile for DDT, DDE, and DDD. Agency for Toxic Substances and Disease Registry. 2006. Toxicological Profile for Diazinon. Beyer et al. 1996. Environmental Contaminants in Wildlife: Interpreting Tissue Concentrations. CRC Press California EPA. 1997. Public Health Goal for Alachlor in Drinking Water. Last Accessed May 3, 2008: http://www.oehha.org/water/phg/pdf/alach_c.pdf Caux et al. 1996. Environmental fate and effects of chlorothalonil: a Canadian perspective. Crit. Rev. Environ. Sci. Technol. 26; 1 (1996), pp. 45�–93. Claasen, 2001. Toxicology: The Basic Science of Poisons, 6th edition. McGraw-Hill Medical Publishing Division. New York. 1236 p. Cohn, S.A. 2004. Flow in complex terrain: observations by radar wind profilers and anemometers near Juneau, Alaska. Journal of Applied Meterology 43: 437-448. Colborn T, vom Saal FS, Soto AM. 1993. Developmental effects of endocrine-disrupting chemicals in wildlife and humans. Environ Health Perspect 101:378-384. Cornell University EXTOXNET. 1993. Last Accessed April 30, 2008: http://pmep.cce.cornell.edu. Cox C. 1997. Fungicide Factsheet: Chlorothalonil. J. Pest Reform. V. 17, No. 4. Cox C. 2000. Herbicide Factsheet: Glyphosate. J Pest Reform, V. 103, No. 3 Davies, P.E. 1987. Physiological, anatomic and behavioral changes in the respiratory system of Salmo gairdneri on acute and chronic exposure to chlorothalonil. Comp. Biochem. Physiol. 87C ;1 (1987), pp. 113�–119. Davis, P.E. et al. 1994. Sublethal responses of pesticides in several species of Australian freshwater fish and crustaceans and rainbow trout. Environ. Toxicol. Chem. 13:1341-1354 Dommen, J., Prevot, A. Henne, S. Nyeki, S., Weingartner, E., Baltensperger, U., 2003. Mountain Venting: A potentially important process for the ozone budget of the lower free troposphere. European Transport of Particulates and Ozone by Long-
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Range Transport. EURO TRAC-2 Subproject Final Report. EUROTRAC-2 ISS, Munich, pp 61-64. Draxler, R.R. and Rolph, G.D. 2003. HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) Model acces via NOAA ARLO READY WEbsite (http:www.arl.noaa.gov/ready/hysplit4.html). NOAA Air Resources Laboratory, Silver Spring, MD. Environmental Energy Technologies Division, Lawrence Berkeley National Labs. 2008. CalTox. Last Accessed May 3, 2008: http://eetd.lbl.gov/ie/ERA/caltox/
Hayes, W.J. and Laws, E.R. 1991. Handbook of Pesticide Toxicology: Volume I General Principles. Academic Press Heydens WF, Wilson AG, Kier LD. et al. 1999. An evaluation of the carcinogenic potential of the herbicide alachlor to man. Hum. Exp. Toxicol. 18:363-91. Hoffman et al. 2003. Handbook of Ecotoxiocology. CRC Press. Howard, P. H., Ed. 1991. Handbook of Environmental Fate and Exposure Data for Organic Chemicals. Vol 3: Pesticides. Lewis Publishers, Chelsea, MI, 5-13 Hoy JA, Hoy R and Seba R et al. 2002. Genital abnormalities in white-tailed deer (Odocoileus virginianus) in west-central Montana: pesticide exposure as a possible cause. J. Environ. Biol. 23:189-197. Johnson, Pieter TJ, Lunde KB. et al. 2002. Parasite (Ribeiroia ondatrae) infection linked to amphibian malformations in the western United States. Ecological Monographs. 72:2 Kale VM, Miranda SR, Wilbanks MS. et al. 2008. Comparative cytotoxicity of Alachlor, Acetochlor, and metolachlor herbicides in isolated rat and cryopreserved human hepatocytes. J. Biochem. Molecular Toxicology. 22:41-49. Keith, L. H. 1998. Environmental Endocrine Disruptors - A Handbook of Properties, John Wiley & Sons,Inc., New York, NY Kim, D. and Stockewell, W.R. 2007. An online coupled meteorological and air quality modeling study of the effect of complex terrain on the regional tranport and transformation of air pollutants over the Western United States. Atmospheric Environment. 41: 2319-2334. Korach et al. 1998. Reproductive and Developmental Toxicology. Marcel Dekker, Inc.
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Krieger et al. 2001. Handbook of Pesticide Toxicology: Volume 2 Agents. 2nd ed. Academic Press Kudray, G.M. and Schemm, T. 2008. Wetlands of the Bitterroot Valley: Change and Ecological Functions. Montana Natural Heritage Program. Lodovici, M. et al. 1994. Effect of a mixture of 15 commonly used pesticides on DNA levels of 8-hydroxy-2-deoxyguanosine and xenobiotic metabolizing enzymes in rat liver. J. Environ. Pathol. Toxicol. Oncol. 13(3):163-168. McNab W.H. and Avers, P.E. 1994. Ecological Subregions of the United States: Chapter 44. Milne, G.W. 1995. CRC Handbook of Pesticides. CRC Press Missoula, City of. 2008. Consolidated Planning Board. Last Accessed April 12, 2008: http://www.ci.missoula.mt.us/mayor/PlanningBoard.htm Montana Department of Natural Resources. 2006. Threatened, Endangered or Candidates for Listing for Montana-by- county. Last Accessed April 7, 2008: http://fwp.mt.gov/wildthings/tande/default.html Montana Department of Natural Resources. 2008. Bull Trout Life History. Last Accessed March 29, 2008: http://fwp.mt.gov/wildthings/tande/bulltrout.html Oregon State University. 1996. http://extoxnet.orst.edu/pips/alchlor.htm. Pesticide Action Network Pesticides Database. Http://www.pesticideinfo.org/Index.html. OSPAR Commission. 2005. Hazardous Substance Series: Trifluralin Update. Ravalli County. 2005. Countywide Zoning Project. Last Accessed April 12, 2008: http://www.ravallicounty.mt.gov/planning/CountywideZoning.htm Ricci, P. 2006. Environmental and Health Risk Assessment and Management. Springer Rolph, G.D., 2003. Real-time Environmental Applications and Display system (READY) Website (http://www.arl.noaa.gov/ready/hysplit4.html). NOAA Air Resources Laboratory, Silver Spring, MD. Smith, L.N. 2006. Altitude of the Bedrock Surface in the Bitterroot Valley: Missoula and Ravalli Counties, Montana. Mountain Bureau of Mines and Geology.
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Syngenta Group, Inc. 2003. Envirofacts: Chlorothalonil. Last Accessed May 1, 2008: http://www.syngentacropprotection-us.com/enviro/futuretopics/Bravo%20July17.pdf University of Idaho. Ihaho Plant Disease Reporter. Last Accessed April 30, 2008: Http://www.uidaho.edu/ag/plantdisease/Ibhome.htm. US Census Bureau. 2000. City of Missoula, Montana. Last Accessed February 24, 2008: http://quickfacts.census.gov/qfd/states/30/3050200.html US Census Bureau. 2006. Ravalli County, Montana. Last Accessed February 24, 2008: http://quickfacts.census.gov/qfd/states/30/30081.html USEPA. 1987. Health Advisory. Office of Drinking Water. Washington, DC. 10-61. USEPA. 1989. Risk Assessment Guidance for Superfund, Volume I, Human Health Evaluation Manual (Part A). EPA/540/1-89/002 USEPA. 1996. Reregistration Eligibility Decision (RED) Trifluralin. EPA 738-R-95-040. USEPA. 1998. Registration eligibility decision (RED). Alachlor. Washington, DC: U.S. Environmental Protection Agency, Office of Pesticide Program; EPA-738-R-98-020; 1998. USEPA. 1998. Carcinogenicity of Chlorothalonil: Data in Support of a Non-Linear Mechanism for Carcinogenicity. Last Accessed April 30, 2008: http://www.epa.gov/scipoly/sap/meetings/1998/july/session4.pdf USEPA. 1998. Guidelines for Ecological Risk Assessment. April 1998. EPA/630/R-95/002F USEPA. 1999. Reregistration Eligibility Chlorothalonil. Prevention, Pesticides and Toxic Substances. April 1999. Last Accessed May 1, 2008: http://www.epa.gov/oppsrrd1/REDs/0097red.pdf USEPA. 2003. Region 5 Ecological Screening Levels, August 2003 USEPA. 2004. Region 9 Preliminary Remediation Goal for Tap Water, October 2004 USEPA. 2004. Report of the Food Quality Protection Act (FQPA) Tolerance Reassessment Progress and Risk Management Decision (TRED) for Trifluralin.
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USEPA. 2006. Technical Fact Sheet: Alachlor, Groundwater and Drinking Water. Last Accessed May 6, 2008: http://www.epa.gov/ogwdw000/dwh/t-soc/alachlor.html USEPA Toxic Release Inventory. 2006. State Fact Sheet for Montana. Last Accessed May 6, 2008: www.epa.gov US Forest Service. Chapter 44 - Ecological Subregions of the United States. Bitterroot Valley. Last Accessed February 20, 2008: http://www.fs.fed.us/land/pubs/ecoregions/ch44.html#M332B. USGS. 2008. Water Quality Monitoring Data for Pesticides, 1999-2000, Missoula, Montana Water Collection Location. Retrieved 2008 from USGS. See Appendix B of this report. Vargyas, L.D. et al. 1995. Simultaneous determination of chlorothalonil and hexachlorobenzene in technical and formulated materials by capillary gas chromatography. J. AOAC Intern. 78:604-609 Wauchope, R. D., Buttler, T. M., Hornsby A. G., Augustijn-Beckers, P. W. M. and Burt, J. P. 1992. SCS/ARS/CES Pesticide properties database for environmental decisionmaking. Rev. Environ. Contam. Toxicol. 123: 1-157. World Health Organization. 1996. Alachlor. Last Accessed May 3, 2008: http://www.inchem.org/documents/pds/pds/pest86_e.htm World Health Organization. 1996. Chlorothalonil. Last Accessed May 3, 2008: http://www.inchem.org/documents/ehc/ehc/ehc183.htm World Health Organization. 1998. Diazinon. Last Accessed May 3, 2008: http://www.inchem.org/documents/ehc/ehc/ehc198.htm#SectionNumber:1.3
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8.0 GLOSSARY
Chemicals of Potential Concern
Chemicals of Potential Concern. Chemicals that are potentially site-related and whose data are of sufficient quality for use in the quantitative risk assessment.
Chronic Daily Intake (CDI)
Exposure expressed as mass of a substance contacted per unit body weight per unit time. averaged over a long period of time.
Chronic Reference Dose
An estimate (with uncertainty spanning perhaps an order of magnitude or greater) of a daily exposure level for the human population, including sensitive subpopulations, that is likely to be without an appreciate risk of deleterious effects during a lifetime. Chronic RfDs are specifically developed to be protective for long-term exposure to a compound.
Detection Limit (DL) Detection Limit (DL). The lowest amount that can be distinguished from the normal "noise" of an analytical instrument or method.
Endocrine Disruptor
Endocrine disruptors (sometimes also referred to as hormonally active agents) are exogenous substances that act like hormones in the endocrine system and disrupt the physiologic function of endogenous hormones.
Exposure Assessment Exposure Assessment. The determination or estimation (qualitative or quantitative) of the magnitude, frequency, duration, and route of exposure.
Exposure Pathway
The course a chemical or physical agent takes from a source to an exposed organism. An exposure pathway describes a unique mechanism by which an individual or population is exposed to chemicals or physical agents at or originating from a site.
Hazard Identification
The process of determining whether exposure to an agent can cause an increase in the incidence of a particular adverse health effect and whether the adverse health effect is likely to occur in humans.
Hazard Index
Hazard Index (HI). The sum of more than one hazard quotient for multiple substances and/or multiple exposure pathways. The HI is calculated separately for chronic, sub-chronic, and shorter-duration exposures.
55
Hazard Quotient Hazard Quotient. The ratio of a single substance exposure level over a specified time period (e.g., sub-chronic) to a reference dose for that substance derived from a similar exposure period.
Lowest-Observed-Adverse-Effect-Level (LOAEL)
In dose-response experiments, the lowest exposure level at which there are statistically or biologically significant increases in frequency or severity of adverse effects between the exposed population and its appropriate control group.
Maximum Tolerated Dose
The highest daily dose of a chemical that does not cause overt toxicity in a 90-day study using laboratory rats or mice
No-Observed-Adverse-Effect-Level (NOAEL)
In dose-response experiments, an exposure level at which there are no statistically or biologically significant increases in the frequency or severity of adverse effects between the exposed population and its appropriate control.
No-Observed-Effect-Level (NOEL)
In dose-response experiments, an exposure level at which there are no statistically or biologically significant increases in the frequency or severity of any effect between the exposed population and its appropriate
Pesticide
A pesticide is a substance or mixture of substances used for preventing, controlling, or lessening the damage caused by a pest. A pesticide may be a chemical substance, biological agent (such as a virus or bacteria), antimicrobial, disinfectant or device used against any pest. Pests include insects, plant pathogens, weeds, mollusks, birds, mammals, fish, nematodes (roundworms) and microbes that compete with humans for food, destroy property, spread or are a vector for disease or cause a nuisance.
Reference Dose (RfD) Reference Dose (RfD). The Agency's preferred toxicity value for evaluating noncarcinogenic effects resulting from exposures at Superfund sites.
Slope Factor
Slope Factor. A plausible upper-bound estimate of the probability of a response per unit intake of a chemical over a lifetime. The slope factor is used to estimate an upper-bound probability of an individual developing cancer as a result of a lifetime of exposure to a particular level of a potential carcinogen.
56
Sub-chronic Daily Intake (SDI)
Sub-chronic Daily Intake (SDI). Exposure expressed as mass of a substance contacted per unit body weight per unit time, averaged over a portion of a lifetime (as Superfund program guideline, two weeks to seven years).
Toxicity Value
Toxicity Value. A numerical expression of a substance's dose-response relationship that is used in risk assessments. The most common toxicity values used in Superfund program risk assessments are reference doses (for noncarcinogenic effects) and slope factors (for carcinogenic effects)
Weight of Evidence Classification
Weight of Evidence Classification. An EPA classification system for characterizing the extent to which the available data indicate that an agent is a human carcinogen. Recently, EPA has developed weight-of-evidence classification systems for some other kinds of toxic effects, such as developmental effects.
TABLES
Page 1 of 2
Page 2 of 2
Table 2-1 (continued)
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Table 3-2. Receiving Medium, Release Mechanisms, and Chemical Release Sources
Receiving Medium Release Mechanism Release Source Air Volatilization
Fugitive dust generation
Idaho fields where pesticides have been applied Air transport of pesticides following aerial application
Surface Water Surface water runoff Wet Deposition Groundwater seepage into surface water bodies
Soil containing pesticides Deposition of pesticides following air transport via mountain passes Groundwater contaminated with pesticides following leaching in the Bitterroot Valley
Groundwater Leaching after deposition has occurred in the Bitterroot Valley
Surface soil containing pesticides
Soil Leaching Fugitive dust generation/deposition Surface water runoff
Surface soil containing pesticides Air transport of pesticide dust Soil containing pesticides
Sediment Surface runoff Groundwater seepage
Air transport prior to deposition at the site Groundwater contaminated with pesticides following leaching in the Bitterroot Valley
Biota Uptake via direct dermal contact, ingestion, and inhalation
Contaminated soil, surface water, sediment, air, residue present on vegetation
Page 1 of 1
Table 3-3. All Relevant Exposure Pathways for Human Health
Exposure Medium/Exposure Route
Residential Population
Recreational Population
Groundwater Ingestion L - Dermal Contact L - Surface Water Ingestion L L,C Dermal Contact L L,C Sediment Incidental Ingestion C C Dermal Contact C L,C Air Inhalation of Particulates Indoors - - Outdoors L L Soil/Dust Incidental Ingestion L,C L,C Dermal Contact L,C L,C Food Ingestion Fish and Shellfish L L Meat and Game L L Dairy L,C L Eggs L L Vegetables L L
L=lifetime exposure C=exposure in children may be significantly greater than adults Only residential exposure is evaluated in the Baseline Risk Assessment.
Page 1 of 1
Table 3-4. All Relevant Exposure Pathways for Target Organisms
Exposure Medium/Exposure Route
White-tailed Deer (Odocoileus virginianus)
Gray Wolf (Canus lupus)
Red-tailed Hawk (Buteo jamaicensis)
Bull Trout (Salvelinus confluentus)
Spotted Frog (Rana luteiventris)
Groundwater Ingestion Dermal Contact Surface Water Ingestion L L L L L Dermal Contact L L L L L Sediment Ingestion L L L L L Dermal Contact L L L L L Air Inhalation of Particulates Outdoors L L L L Soil/Dust Ingestion L L L Dermal Contact L L L Food Ingestion Aquatic Animals/Insects L L Aquatic Plants L J Terrestrial Animals/Insects L L Terrestrial Plants L
L=lifetime exposure J=juvenile exposure
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Table 4-1. Human Health Toxicity Values for Non-carcinogenic Effects (USEPA IRIS)
Chronic RfD
(mg/kg-day)
Confidence Level
Critical Effect
RfD Basis/RfD Source
Uncertainty/Modifying Factors
0.01 Point of departure= 1 mg/kg-day (NOAEL)
High
Hemosiderosis, hemolytic anemia
Water/ IRIS 1993
UF �— The uncertainty factor of 100 reflects 10 for interspecies extrapolation and 10 for intraspecies-variability.
MF �— None
Page 1 of 1
Table 4-2. Toxicity of Alachlor for Different Exposure Pathways (USEPA 1998)
Assessment Exposure Route NOAEL for Use in Estimating Risk
Acute
Dietary (food and water)
Not required �– no evidence of significant toxicity from a one day or single event exposure by the oral route
Chronic (non-carcinogenic)
Dietary (food and water)
RfD=0.01mg/kg-day
Short-term occupational
Dermal + Inhalation NOAEL=150mg/kg-day
Use of dermal absorption factor required
Intermediate-term
Dermal + Inhalation NOAEL=50mg/kg-day
Residential
Dermal + Inhalation Not appropriate - The Agency has not identified any alachlor products that are intended for home use, or uses in/around schools, parks or other public areas.
Note: In terms of human health, this Baseline Risk Assessment only evaluates risk and hazard for chronic residential exposure from water ingestion.
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Table 4-4. Human Health Toxicity Values for Carcinogenic Effects
Slope Factor (mg/kg-day)
Weight-of-Evidence Classification
Type of Cancer RfD Basis/RfD Source
Q1 approach carcinogenic -- Q1=0.08 (mg/kg-day)
B2 carcinogen (probable human carcinogen)
Malignant and combined benign/malignant multiple tumor; nasal tumor
Food and water/IRIS
Page 1 of 1
FIGURES
Figure 1-1. Aerial Conceptual Site Model
Figure 1-2. Idaho Conceptual Site Model
Figure 1-3. Bitterroot Valley, Montana Conceptual Site Model
Figure 2-1. Sampling Locations
Figure 3-1. Montana Topography �– Missoula and Ravalli Counties (Source: Montana DNR)
Figure 3-2. Surface Water Hydrology of the Bitterroot Valley
Figure 3-3. Wildlife Management Areas in/within the Bitterroot Valley (Source: Montana DNR)
Figure 3-4. Primary Metabolites of Chlorothalonil (Source: Cox 1997)
Figure 3-5: Suggested Pathway for Soil Degradation of Chlorothalonil (Frazier, 1993)
Figure 3-6. A Summary of Chlorothalonil Metabolism Under Various Environmental Conditions (Source: ISK Biosciences, 1995)
Figure 3-7. Vertical Transport Over Complex Terrain (Kim & Stockwell, 2007)
Figure 3-8. HYSPLIT Concentration Output from Backward Trajectory for August 2007. (HYSPLIT access: www.arl.noaa.gov/ready)
Figure 4-1. Metabolic Pathways of Diazinon in Mammals (WHO 1998)
Figure 4-2. Metabolism of DDE in Mammals
(Bergman et al. 1994, Letcher et al. 1998, Weistrand and Noren 1997, USEPA 2006)
APPENDICES
APPENDIX A
APPENDIX B
Table B-1. USGS Water Quality Pesticide Data (Source: USGS Surface Water Quality Monitoring Raw Data File)
Page 1 of 15
agency_cd site_no sample_dt sample_tm sample_end_dt sample_end_tm sample_start_time_datum_cd tm_datum_rlbty_cd coll_ent_cd medium_cd tu_id
5s 15s 10d 4d 10d 4d 1s 1s 8s 1s 11s
USGS 12352500 8/10/1998 17:00 MDT T USGS-WRD C 163896
USGS 12352500 8/10/1998 17:20 MDT T USGS-WRD H
USGS 12352500 3/4/1999 9:30 MST T USGS-WRD 9
USGS 12352500 3/24/1999 13:00 MST T USGS-WRD 9
USGS 12352500 5/13/1999 9:15 MDT T USGS-WRD 9
USGS 12352500 6/7/1999 14:45 MDT T USGS-WRD 9
USGS 12352500 6/19/1999 15:30 MDT T USGS-WRD 9
USGS 12352500 7/21/1999 15:00 MDT T USGS-WRD 9
USGS 12352500 8/19/1999 14:30 MDT T USGS-WRD 9
USGS 12352500 10/20/1999 16:00 MDT T USGS-WRD 9
USGS 12352500 3/8/2000 15:45 MST T USGS-WRD 9
USGS 12352500 5/23/2000 11:30 MDT T USGS-WRD 9
USGS 12352500 6/20/2000 14:30 MDT T USGS-WRD 9
agency_cd site_no sample_dt p38501 p38538 p38548 p38711 p38746 p38811 p38866 p38933 p39341 p39381
5s 15s 10d 12s 12s 12s 12s 12s 12s 12s 12s 12s 12s
USGS 12352500 8/10/1998 USGS 12352500 8/10/1998 USGS 12352500 3/4/1999 < 0.004 < 0.004 < 0.001 USGS 12352500 3/24/1999 < 0.004 < 0.004 < 0.001 USGS 12352500 5/13/1999 < 0.004 < 0.004 < 0.001 USGS 12352500 6/7/1999 < 0.004 < 0.004 < 0.001 USGS 12352500 6/19/1999 < 0.004 < 0.004 < 0.001 USGS 12352500 7/21/1999 < 0.004 < 0.004 < 0.001 USGS 12352500 8/19/1999 < 0.004 < 0.004 < 0.001 USGS 12352500 10/20/1999 < 0.004 < 0.004 < 0.001 USGS 12352500 3/8/2000 < 0.004 < 0.004 < 0.001 USGS 12352500 5/23/2000 < 0.08 < 0.06 < 0.09 < 0.02 < 0.05 < 0.06 < 0.02 < 0.004 < 0.004 < 0.001 USGS 12352500 6/20/2000 < 0.08 < 0.06 < 0.09 < 0.02 < 0.05 < 0.06 < 0.02 < 0.004 < 0.004 < 0.001
Table B-1. USGS Water Quality Pesticide Data (Source: USGS Surface Water Quality Monitoring Raw Data File)
Page 2 of 15
agency_cd site_no sample_dt p04039 p04040 p04041 p04095 p34253 p34653 p38442 p38478 p38482 p38487
5s 15s 10d 12s 12s 12s 12s 12s 12s 12s 12s 12s 12s
USGS 12352500 8/10/1998 USGS 12352500 8/10/1998 USGS 12352500 3/4/1999 < 0.002 < 0.004 < 0.003 < 0.002 < 0.006 USGS 12352500 3/24/1999 < 0.002 < 0.004 < 0.003 < 0.002 < 0.006 USGS 12352500 5/13/1999 < 0.002 < 0.004 < 0.003 < 0.002 < 0.006 USGS 12352500 6/7/1999 < 0.002 < 0.004 < 0.003 < 0.002 < 0.006 USGS 12352500 6/19/1999 < 0.002 < 0.004 < 0.003 < 0.002 < 0.006 USGS 12352500 7/21/1999 < 0.002 < 0.004 < 0.003 < 0.002 < 0.006 USGS 12352500 8/19/1999 < 0.002 < 0.004 < 0.003 < 0.002 < 0.006 USGS 12352500 10/20/1999 < 0.002 < 0.004 < 0.003 < 0.002 E 0.001 USGS 12352500 3/8/2000 < 0.002 < 0.004 < 0.003 < 0.002 < 0.006 USGS 12352500 5/23/2000 < 0.06 < 0.002 < 0.004 < 0.003 < 0.002 < 0.006 < 0.10 < 0.07 < 0.06 < 0.06 USGS 12352500 6/20/2000 < 0.06 < 0.002 < 0.004 < 0.003 < 0.002 < 0.006 < 0.10 < 0.07 < 0.06 < 0.06
agency_cd site_no sample_dt p38501 p38538 p38548 p38711 p38746 p38811 p38866 p38933 p39341 p39381
5s 15s 10d 12s 12s 12s 12s 12s 12s 12s 12s 12s 12s
USGS 12352500 8/10/1998 USGS 12352500 8/10/1998 USGS 12352500 3/4/1999 < 0.004 < 0.004 < 0.001 USGS 12352500 3/24/1999 < 0.004 < 0.004 < 0.001 USGS 12352500 5/13/1999 < 0.004 < 0.004 < 0.001 USGS 12352500 6/7/1999 < 0.004 < 0.004 < 0.001 USGS 12352500 6/19/1999 < 0.004 < 0.004 < 0.001 USGS 12352500 7/21/1999 < 0.004 < 0.004 < 0.001 USGS 12352500 8/19/1999 < 0.004 < 0.004 < 0.001 USGS 12352500 10/20/1999 < 0.004 < 0.004 < 0.001 USGS 12352500 3/8/2000 < 0.004 < 0.004 < 0.001 USGS 12352500 5/23/2000 < 0.08 < 0.06 < 0.09 < 0.02 < 0.05 < 0.06 < 0.02 < 0.004 < 0.004 < 0.001 USGS 12352500 6/20/2000 < 0.08 < 0.06 < 0.09 < 0.02 < 0.05 < 0.06 < 0.02 < 0.004 < 0.004 < 0.001
Table B-1. USGS Water Quality Pesticide Data (Source: USGS Surface Water Quality Monitoring Raw Data File)
Page 3 of 15
agency_cd site_no sample_dt p39415 p39532 p39542 p39572 p39632 p39732 p46342 p49235 p49236 p49261
5s 15s 10d 12s 12s 12s 12s 12s 12s 12s 12s 12s 12s
USGS 12352500 8/10/1998 90 USGS 12352500 8/10/1998 USGS 12352500 3/4/1999 < 0.002 < 0.005 < 0.004 < 0.002 < 0.001 < 0.002 USGS 12352500 3/24/1999 < 0.002 < 0.005 < 0.004 < 0.002 < 0.001 < 0.002 USGS 12352500 5/13/1999 < 0.002 < 0.005 < 0.004 < 0.002 < 0.001 < 0.002 USGS 12352500 6/7/1999 < 0.002 < 0.005 < 0.004 < 0.002 < 0.001 < 0.002 USGS 12352500 6/19/1999 < 0.002 < 0.005 < 0.004 < 0.002 < 0.001 < 0.002 USGS 12352500 7/21/1999 < 0.002 < 0.005 < 0.004 < 0.002 < 0.001 < 0.002 USGS 12352500 8/19/1999 < 0.002 < 0.005 < 0.004 < 0.002 0.006 < 0.002 USGS 12352500 10/20/1999 < 0.002 < 0.005 < 0.004 < 0.002 < 0.001 < 0.002 USGS 12352500 3/8/2000 < 0.002 < 0.005 < 0.004 < 0.002 < 0.001 < 0.002 USGS 12352500 5/23/2000 < 0.002 < 0.005 < 0.004 < 0.002 < 0.006 < 0.08 < 0.002 < 0.1 < 0.07 USGS 12352500 6/20/2000 < 0.002 < 0.005 < 0.004 E 0.003 < 0.001 < 0.08 < 0.002 < 0.1 < 0.07
agency_cd site_no sample_dt p49275 p49291 p49292 p49293 p49294 p49296 p49297 p49300 p49301 p49302
5s 15s 10d 12s 12s 12s 12s 12s 12s 12s 12s 12s 12s
USGS 12352500 8/10/1998 USGS 12352500 8/10/1998 71 USGS 12352500 3/4/1999 USGS 12352500 3/24/1999 USGS 12352500 5/13/1999 USGS 12352500 6/7/1999 USGS 12352500 6/19/1999 USGS 12352500 7/21/1999 USGS 12352500 8/19/1999 USGS 12352500 10/20/1999 USGS 12352500 3/8/2000 USGS 12352500 5/23/2000 < 0.07 < 0.07 < 0.08 < 0.07 < 0.08 < 0.07 < 0.08 < 0.04 < 0.05 USGS 12352500 6/20/2000 < 0.07 < 0.07 < 0.08 < 0.07 < 0.08 < 0.07 < 0.08 < 0.04 < 0.05
Table B-1. USGS Water Quality Pesticide Data (Source: USGS Surface Water Quality Monitoring Raw Data File)
Page 4 of 15
agency_cd site_no sample_dt p49304 p49305 p49306 p49308 p49309 p49310 p49311 p49312 p49313 p49314
5s 15s 10d 12s 12s 12s 12s 12s 12s 12s 12s 12s 12s
USGS 12352500 8/10/1998 USGS 12352500 8/10/1998 USGS 12352500 3/4/1999 USGS 12352500 3/24/1999 USGS 12352500 5/13/1999 USGS 12352500 6/7/1999 USGS 12352500 6/19/1999 USGS 12352500 7/21/1999 USGS 12352500 8/19/1999 USGS 12352500 10/20/1999 USGS 12352500 3/8/2000 USGS 12352500 5/23/2000 < 0.07 < 0.04 < 0.05 < 0.06 < 0.06 < 0.06 < 0.06 < 0.08 < 0.2 < 0.03 USGS 12352500 6/20/2000 < 0.07 < 0.04 < 0.05 < 0.06 < 0.06 < 0.06 < 0.06 < 0.08 < 0.2 < 0.03
agency_cd site_no sample_dt p49315 p49316 p49317 p49318 p49319 p49320 p49321 p49322 p49324 p49325
5s 15s 10d 12s 12s 12s 12s 12s 12s 12s 12s 12s 12s
USGS 12352500 8/10/1998 USGS 12352500 8/10/1998 < 1 < 1 < 1 < 1 < 1 < 1 < 5 < 5 < 1 USGS 12352500 3/4/1999 USGS 12352500 3/24/1999 USGS 12352500 5/13/1999 USGS 12352500 6/7/1999 USGS 12352500 6/19/1999 USGS 12352500 7/21/1999 USGS 12352500 8/19/1999 USGS 12352500 10/20/1999 USGS 12352500 3/8/2000 USGS 12352500 5/23/2000 < 0.06 USGS 12352500 6/20/2000 < 0.06
Table B-1. USGS Water Quality Pesticide Data (Source: USGS Surface Water Quality Monitoring Raw Data File)
Page 5 of 15
agency_cd site_no sample_dt p49326 p49327 p49328 p49329 p49330 p49331 p49332 p49335 p49338 p49339
5s 15s 10d 12s 12s 12s 12s 12s 12s 12s 12s 12s 12s
USGS 12352500 8/10/1998 USGS 12352500 8/10/1998 < 1 < 1 < 1 < 2 < 2 < 1 < 1 < 2 < 1 < 1 USGS 12352500 3/4/1999 USGS 12352500 3/24/1999 USGS 12352500 5/13/1999 USGS 12352500 6/7/1999 USGS 12352500 6/19/1999 USGS 12352500 7/21/1999 USGS 12352500 8/19/1999 USGS 12352500 10/20/1999 USGS 12352500 3/8/2000 USGS 12352500 5/23/2000 USGS 12352500 6/20/2000
agency_cd site_no sample_dt p49341 p49342 p49343 p49344 p49345 p49346 p49347 p49348 p49349 p49350
5s 15s 10d 12s 12s 12s 12s 12s 12s 12s 12s 12s 12s
USGS 12352500 8/10/1998 USGS 12352500 8/10/1998 < 1 < 1 < 1 < 1 < 1 < 5 < 5 < 1 < 5 < 5 USGS 12352500 3/4/1999 USGS 12352500 3/24/1999 USGS 12352500 5/13/1999 USGS 12352500 6/7/1999 USGS 12352500 6/19/1999 USGS 12352500 7/21/1999 USGS 12352500 8/19/1999 USGS 12352500 10/20/1999 USGS 12352500 3/8/2000 USGS 12352500 5/23/2000 USGS 12352500 6/20/2000
Table B-1. USGS Water Quality Pesticide Data (Source: USGS Surface Water Quality Monitoring Raw Data File)
Page 6 of 15
agency_cd site_no sample_dt p49351 p49353 p49355 p49356 p49357 p49358 p49359 p49360 p49361 p49362
5s 15s 10d 12s 12s 12s 12s 12s 12s 12s 12s 12s 12s
USGS 12352500 8/10/1998 < 5 < 200 < 5 < 5 < 5 < 5 < 5 < 5 < 5 USGS 12352500 8/10/1998 < 200 USGS 12352500 3/4/1999 USGS 12352500 3/24/1999 USGS 12352500 5/13/1999 USGS 12352500 6/7/1999 USGS 12352500 6/19/1999 USGS 12352500 7/21/1999 USGS 12352500 8/19/1999 USGS 12352500 10/20/1999 USGS 12352500 3/8/2000 USGS 12352500 5/23/2000 USGS 12352500 6/20/2000
agency_cd site_no sample_dt p49363 p49364 p49365 p49366 p49367 p49368 p49369 p49370 p49371 p49372
5s 15s 10d 12s 12s 12s 12s 12s 12s 12s 12s 12s 12s
USGS 12352500 8/10/1998 < 5 < 5 < 5 < 5 < 5 < 5 < 5 < 17 < 5 9 USGS 12352500 8/10/1998 USGS 12352500 3/4/1999 USGS 12352500 3/24/1999 USGS 12352500 5/13/1999 USGS 12352500 6/7/1999 USGS 12352500 6/19/1999 USGS 12352500 7/21/1999 USGS 12352500 8/19/1999
USGS 12352500 10/20/1999 USGS 12352500 3/8/2000 USGS 12352500 5/23/2000 USGS 12352500 6/20/2000
Table B-1. USGS Water Quality Pesticide Data (Source: USGS Surface Water Quality Monitoring Raw Data File)
Page 7 of 15
agency_cd site_no sample_dt p49373 p49374 p49375 p49376 p49377 p49378 p49379 p49380 p50295 p50299
5s 15s 10d 12s 12s 12s 12s 12s 12s 12s 12s 12s 12s
USGS 12352500 8/10/1998 < 5 < 5 < 5 < 5 < 5 < 5 < 5 < 5 USGS 12352500 8/10/1998 USGS 12352500 3/4/1999 USGS 12352500 3/24/1999 USGS 12352500 5/13/1999 USGS 12352500 6/7/1999 USGS 12352500 6/19/1999 USGS 12352500 7/21/1999 USGS 12352500 8/19/1999
USGS 12352500 10/20/1999 USGS 12352500 3/8/2000 USGS 12352500 5/23/2000 < 0.07 < 0.06 USGS 12352500 6/20/2000 < 0.07 < 0.06
agency_cd site_no sample_dt p50300 p50306 p50337 p50355 p50356 p50359 p50364 p50407 p50470 p50471
5s 15s 10d 12s 12s 12s 12s 12s 12s 12s 12s 12s 12s
USGS 12352500 8/10/1998 USGS 12352500 8/10/1998 USGS 12352500 3/4/1999 USGS 12352500 3/24/1999 USGS 12352500 5/13/1999 USGS 12352500 6/7/1999 USGS 12352500 6/19/1999 USGS 12352500 7/21/1999 USGS 12352500 8/19/1999
USGS 12352500 10/20/1999 USGS 12352500 3/8/2000 USGS 12352500 5/23/2000 < 0.02 < 0.04 < 0.04 < 0.2 < 0.1 < 0.06 < 0.07 < 0.09 < 0.09 < 0.06 USGS 12352500 6/20/2000 < 0.02 < 0.04 < 0.04 < 0.2 < 0.1 < 0.06 < 0.07 < 0.09 < 0.09 < 0.06
Table B-1. USGS Water Quality Pesticide Data (Source: USGS Surface Water Quality Monitoring Raw Data File)
Page 8 of 15
agency_cd site_no sample_dt p61159 p61188 p61692 p61693 p61694 p61695 p61697 p82630 p82660 p82661
5s 15s 10d 12s 12s 12s 12s 12s 12s 12s 12s 12s 12s
USGS 12352500 8/10/1998 USGS 12352500 8/10/1998 USGS 12352500 3/4/1999 < 0.004 < 0.003 < 0.002 USGS 12352500 3/24/1999 < 0.004 < 0.003 < 0.002 USGS 12352500 5/13/1999 < 0.004 < 0.003 < 0.002 USGS 12352500 6/7/1999 < 0.004 < 0.003 < 0.002 USGS 12352500 6/19/1999 < 0.004 < 0.003 < 0.002 USGS 12352500 7/21/1999 < 0.004 < 0.003 < 0.002 USGS 12352500 8/19/1999 < 0.004 < 0.003 < 0.002
USGS 12352500 10/20/1999 < 0.004 < 0.003 E 0.004 USGS 12352500 3/8/2000 < 0.004 < 0.003 < 0.002 USGS 12352500 5/23/2000 < 0.07 < 0.1 < 0.09 < 0.05 < 0.09 < 0.1 < 0.1 < 0.004 < 0.003 < 0.002 USGS 12352500 6/20/2000 < 0.07 < 0.1 < 0.09 < 0.05 < 0.09 < 0.1 < 0.1 < 0.004 < 0.003 < 0.002
agency_cd site_no sample_dt p82663 p82664 p82665 p82666 p82667 p82668 p82669 p82670 p82671 p82672
5s 15s 10d 12s 12s 12s 12s 12s 12s 12s 12s 12s 12s
USGS 12352500 8/10/1998 USGS 12352500 8/10/1998 USGS 12352500 3/4/1999 < 0.004 < 0.002 < 0.007 < 0.002 < 0.006 < 0.002 < 0.004 < 0.01 < 0.004 < 0.003 USGS 12352500 3/24/1999 < 0.004 < 0.002 < 0.007 < 0.002 < 0.006 < 0.002 < 0.004 < 0.01 < 0.004 < 0.003 USGS 12352500 5/13/1999 < 0.004 < 0.002 < 0.007 < 0.002 < 0.006 < 0.002 < 0.004 < 0.01 < 0.004 < 0.003 USGS 12352500 6/7/1999 < 0.004 < 0.002 < 0.007 < 0.002 < 0.006 < 0.002 < 0.004 < 0.01 < 0.004 < 0.003 USGS 12352500 6/19/1999 < 0.004 < 0.002 < 0.007 < 0.002 < 0.006 < 0.002 < 0.004 < 0.01 < 0.004 < 0.003 USGS 12352500 7/21/1999 < 0.004 < 0.002 < 0.007 < 0.002 < 0.006 < 0.002 < 0.004 < 0.01 < 0.004 < 0.003 USGS 12352500 8/19/1999 < 0.004 < 0.002 < 0.007 < 0.002 < 0.006 < 0.002 < 0.004 < 0.01 < 0.004 < 0.003
USGS 12352500 10/20/1999 < 0.004 < 0.002 < 0.007 < 0.002 < 0.006 < 0.002 < 0.004 < 0.01 < 0.004 < 0.003 USGS 12352500 3/8/2000 < 0.004 < 0.002 < 0.007 < 0.002 < 0.006 < 0.002 < 0.004 < 0.01 < 0.004 < 0.003 USGS 12352500 5/23/2000 < 0.004 < 0.002 < 0.007 < 0.002 < 0.006 < 0.002 < 0.004 < 0.01 < 0.004 < 0.003 USGS 12352500 6/20/2000 < 0.004 < 0.002 < 0.007 < 0.002 < 0.006 < 0.002 < 0.004 < 0.01 < 0.004 < 0.003
Table B-1. USGS Water Quality Pesticide Data (Source: USGS Surface Water Quality Monitoring Raw Data File)
Page 9 of 15
agency_cd site_no sample_dt p82673 p82674 p82675 p82676 p82677 p82678 p82679 p82680 p82681 p82682
5s 15s 10d 12s 12s 12s 12s 12s 12s 12s 12s 12s 12s
USGS 12352500 8/10/1998 USGS 12352500 8/10/1998 USGS 12352500 3/4/1999 < 0.002 < 0.003 < 0.01 < 0.003 < 0.02 < 0.001 < 0.004 < 0.003 < 0.002 < 0.002 USGS 12352500 3/24/1999 < 0.002 < 0.003 < 0.01 < 0.003 < 0.02 < 0.001 < 0.004 < 0.003 < 0.002 < 0.002 USGS 12352500 5/13/1999 < 0.002 < 0.003 < 0.01 < 0.003 < 0.02 < 0.001 < 0.004 < 0.003 < 0.002 < 0.002 USGS 12352500 6/7/1999 < 0.002 < 0.003 < 0.01 < 0.003 < 0.02 < 0.001 < 0.004 < 0.003 < 0.002 < 0.002 USGS 12352500 6/19/1999 < 0.002 < 0.003 < 0.01 < 0.003 < 0.02 < 0.001 < 0.004 < 0.003 < 0.002 < 0.002 USGS 12352500 7/21/1999 < 0.002 < 0.003 < 0.01 < 0.003 < 0.02 < 0.001 < 0.004 < 0.003 < 0.002 < 0.002 USGS 12352500 8/19/1999 < 0.002 < 0.003 < 0.01 < 0.003 < 0.02 < 0.001 < 0.004 < 0.003 < 0.002 < 0.002
USGS 12352500 10/20/1999 < 0.002 < 0.003 < 0.01 < 0.003 < 0.02 E 0.002 < 0.004 < 0.003 < 0.002 < 0.002 USGS 12352500 3/8/2000 < 0.002 < 0.003 < 0.01 < 0.003 < 0.02 < 0.001 < 0.004 < 0.003 < 0.002 < 0.002 USGS 12352500 5/23/2000 < 0.002 < 0.003 < 0.01 < 0.003 < 0.02 < 0.001 < 0.004 < 0.003 < 0.002 < 0.002 USGS 12352500 6/20/2000 < 0.002 < 0.003 < 0.01 < 0.003 < 0.02 < 0.001 < 0.004 < 0.003 < 0.002 < 0.002
agency_cd site_no sample_dt p82683 p82684 p82685 p82686 p82687 p90854
5s 15s 10d 12s 12s 12s 12s 12s 12s
USGS 12352500 8/10/1998 9 USGS 12352500 8/10/1998 USGS 12352500 3/4/1999 < 0.004 < 0.003 < 0.01 < 0.001 < 0.005 USGS 12352500 3/24/1999 < 0.004 < 0.003 < 0.01 < 0.001 < 0.005 USGS 12352500 5/13/1999 < 0.004 < 0.003 < 0.01 < 0.001 < 0.005 USGS 12352500 6/7/1999 < 0.004 < 0.003 < 0.01 < 0.001 < 0.005 USGS 12352500 6/19/1999 < 0.004 < 0.003 < 0.01 < 0.001 < 0.005 USGS 12352500 7/21/1999 < 0.004 < 0.003 < 0.01 < 0.001 < 0.005 USGS 12352500 8/19/1999 < 0.004 < 0.003 < 0.01 < 0.001 < 0.005
USGS 12352500 10/20/1999 < 0.004 < 0.003 < 0.09 < 0.001 < 0.005 USGS 12352500 3/8/2000 < 0.004 < 0.003 < 0.01 < 0.001 < 0.005 USGS 12352500 5/23/2000 < 0.004 < 0.003 < 0.01 < 0.001 < 0.005 USGS 12352500 6/20/2000 < 0.004 < 0.003 < 0.01 < 0.001 < 0.005
Table B-1. USGS Water Quality Pesticide Data (Source: USGS Surface Water Quality Monitoring Raw Data File)
Page 10 of 15
Sample Identification Description and Summary: Missoula County, Montana
Latitude 46°49'55", Longitude 114°03'11" NAD27
Gage datum 3,110 feet above sea level NGVD29
Site Type: Stream/River
#
# File created on 2008-01-29 17:10:32 EST
#
# U.S. Geological Survey
#
# This file contains selected water-quality data for stations in the National Water Information
# System water-quality database. Explanation of codes found in this file are followed by
# the retrieved data.
#
# The data you have secured from the USGS NWISWeb database may include data that have
# not received Director's approval and as such are provisional and subject to revision.
# The data are released on the condition that neither the USGS nor the United States
# Government may be held liable for any damages resulting from its authorized or
# unauthorized use.
#
# To view additional data-quality attributes, output the results using these options:
# one result per row, expanded attributes. Additional precautions are at:
# http://waterdata.usgs.gov/nwis/qwdata?help#Data_retrievals_precautions.
#
# agency_cd - Agency Code
# site_no - Station number
# sample_dt - Begin date
# sample_tm - Begin time
# sample_end_dt - End date
# sample_end_tm - End time
# sample_start_time_datum_cd - Time datum
# tm_datum_rlbty_cd - Time datum reliability code
# coll_ent_cd - Agency Collecting Sample Code
# medium_cd - Medium code
# tu_id - Taxonomic unit code
# body_part_id - Body part code
# P04024 - Propachlor, water, filtered, recoverable, micrograms per liter
# P04028 - Butylate, water, filtered, recoverable, micrograms per liter
Table B-1. USGS Water Quality Pesticide Data (Source: USGS Surface Water Quality Monitoring Raw Data File)
Page 11 of 15
# P04029 - Bromacil, water, filtered, recoverable, micrograms per liter
# P04031 - Cycloate, water, filtered, recoverable, micrograms per liter
# P04032 - Terbacil, water, filtered, recoverable, micrograms per liter
# P04033 - Diphenamid, water, filtered, recoverable, micrograms per liter
# P04035 - Simazine, water, filtered, recoverable, micrograms per liter
# P04037 - Prometon, water, filtered, recoverable, micrograms per liter
# P04038 - 2-Chloro-6-ethylamino-4-amino-s-triazine, water, filtered, recoverable, micrograms per liter
# P04039 - Chlorodiamino-s-triazine, water, filtered, recoverable, micrograms per liter
# P04040 - 2-Chloro-4-isopropylamino-6-amino-s-triazine, water, filtered, recoverable, micrograms per liter
# P04041 - Cyanazine, water, filtered, recoverable, micrograms per liter
# P04064 - Thallium, bed sediment smaller than 62.5 microns, dry sieved, total digestion, dry weight, micrograms per gram
# P04095 - Fonofos, water, filtered, recoverable, micrograms per liter
# P34253 - alpha-HCH, water, filtered, recoverable, micrograms per liter
# P34653 - p,p'-DDE, water, filtered, recoverable, micrograms per liter
# P38442 - Dicamba, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P38478 - Linuron, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P38482 - MCPA, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P38487 - MCPB, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P38501 - Methiocarb, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P38538 - Propoxur, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P38548 - Siduron, water, filtered, recoverable, micrograms per liter
# P38711 - Bentazon, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P38746 - 2,4-DB, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P38811 - Fluometuron, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P38866 - Oxamyl, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P38933 - Chlorpyrifos, water, filtered, recoverable, micrograms per liter
# P39341 - Lindane, water, filtered, recoverable, micrograms per liter
# P39381 - Dieldrin, water, filtered, recoverable, micrograms per liter
# P39415 - Metolachlor, water, filtered, recoverable, micrograms per liter
# P39532 - Malathion, water, filtered, recoverable, micrograms per liter
# P39542 - Parathion, water, filtered, recoverable, micrograms per liter
# P39572 - Diazinon, water, filtered, recoverable, micrograms per liter
# P39632 - Atrazine, water, filtered, recoverable, micrograms per liter
# P39732 - 2,4-D, water, filtered, recoverable, micrograms per liter
# P46342 - Alachlor, water, filtered, recoverable, micrograms per liter
# P49235 - Triclopyr, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P49236 - Propham, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P49260 - Acetochlor, water, filtered, recoverable, micrograms per liter
# P49261 - alpha-HCH-d6, surrogate, biota, whole organism, percent recovery
Table B-1. USGS Water Quality Pesticide Data (Source: USGS Surface Water Quality Monitoring Raw Data File)
Page 12 of 15
# P49291 - Picloram, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P49292 - Oryzalin, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P49293 - Norflurazon, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P49294 - Neburon, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P49296 - Methomyl, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P49297 - Fenuron, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P49300 - Diuron, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P49301 - Dinoseb, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P49302 - Dichlorprop, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P49304 - Dacthal monoacid, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P49305 - Clopyralid, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P49306 - Chlorothalonil, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P49308 - 3-Hydroxy carbofuran, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P49309 - Carbofuran, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P49310 - Carbaryl, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P49311 - Bromoxynil, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P49312 - Aldicarb, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P49313 - Aldicarb sulfone, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P49314 - Aldicarb sulfoxide, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P49315 - Acifluorfen, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P49316 - cis-Nonachlor, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49317 - trans-Nonachlor, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49318 - Oxychlordane, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49319 - Aldrin, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49320 - cis-Chlordane, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49321 - trans-Chlordane, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49322 - Chloroneb, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49324 - DCPA, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49325 - o,p'-DDD, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49326 - p,p'-DDD, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49327 - o,p'-DDE, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49328 - p,p'-DDE, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49329 - o,p'-DDT, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49330 - p,p'-DDT, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49331 - Dieldrin, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49332 - alpha-Endosulfan, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49335 - Endrin, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49338 - alpha-HCH, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49339 - beta-HCH, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
Table B-1. USGS Water Quality Pesticide Data (Source: USGS Surface Water Quality Monitoring Raw Data File)
Page 13 of 15
# P49341 - Heptachlor, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49342 - Heptachlor epoxide, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49343 - Hexachlorobenzene, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49344 - Isodrin, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49345 - Lindane, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49346 - p,p'-Methoxychlor, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49347 - o,p'-Methoxychlor, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49348 - Mirex, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49349 - cis-Permethrin, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49350 - trans-Permethrin, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49351 - Toxaphene, bed sediment smaller than 2 millimeters, wet sieved (native water), field, recoverable, dry weight, micrograms per kilogram
# P49353 - Aldrin, biota, whole organism, recoverable, wet weight, micrograms per kilogram
# P49355 - Toxaphene, biota, whole organism, recoverable, wet weight, micrograms per kilogram
# P49356 - Pentachloroanisole, biota, whole organism, recoverable, wet weight, micrograms per kilogram
# P49357 - Oxychlordane, biota, whole organism, recoverable, wet weight, micrograms per kilogram
# P49358 - trans-Nonachlor, biota, whole organism, recoverable, wet weight, micrograms per kilogram
# P49359 - cis-Nonachlor, biota, whole organism, recoverable, wet weight, micrograms per kilogram
# P49360 - Mirex, biota, whole organism, recoverable, wet weight, micrograms per kilogram
# P49361 - p,p'-Methoxychlor, biota, whole organism, recoverable, wet weight, micrograms per kilogram
# P49362 - o,p'-Methoxychlor, biota, whole organism, recoverable, wet weight, micrograms per kilogram
# P49363 - Lindane, biota, whole organism, recoverable, wet weight, micrograms per kilogram
# P49364 - delta-HCH, biota, whole organism, recoverable, wet weight, micrograms per kilogram
# P49365 - beta-HCH, biota, whole organism, recoverable, wet weight, micrograms per kilogram
# P49366 - alpha-HCH, biota, whole organism, recoverable, wet weight, micrograms per kilogram
# P49367 - Hexachlorobenzene, biota, whole organism, recoverable, wet weight, micrograms per kilogram
# P49368 - Heptachlor epoxide, biota, whole organism, recoverable, wet weight, micrograms per kilogram
# P49369 - Heptachlor, biota, whole organism, recoverable, wet weight, micrograms per kilogram
# P49370 - Endrin, biota, whole organism, recoverable, wet weight, micrograms per kilogram
# P49371 - Dieldrin, biota, whole organism, recoverable, wet weight, micrograms per kilogram
# P49372 - p,p'-DDE, biota, whole organism, recoverable, wet weight, micrograms per kilogram
# P49373 - o,p'-DDE, biota, whole organism, recoverable, wet weight, micrograms per kilogram
# P49374 - o,p'-DDD, biota, whole organism, recoverable, wet weight, micrograms per kilogram
# P49375 - p,p'-DDD, biota, whole organism, recoverable, wet weight, micrograms per kilogram
# P49376 - p,p'-DDT, biota, whole organism, recoverable, wet weight, micrograms per kilogram
# P49377 - o,p'-DDT, biota, whole organism, recoverable, wet weight, micrograms per kilogram
# P49378 - DCPA, biota, whole organism, recoverable, wet weight, micrograms per kilogram
# P49379 - trans-Chlordane, biota, whole organism, recoverable, wet weight, micrograms per kilogram
# P49380 - cis-Chlordane, biota, whole organism, recoverable, wet weight, micrograms per kilogram
# P50299 - Bendiocarb, water, filtered, recoverable, micrograms per liter
Table B-1. USGS Water Quality Pesticide Data (Source: USGS Surface Water Quality Monitoring Raw Data File)
Page 14 of 15
# P50300 - Benomyl, water, filtered, recoverable, micrograms per liter
# P50306 - Chlorimuron, water, filtered, recoverable, micrograms per liter
# P50337 - Sulfometuron, water, filtered, recoverable, micrograms per liter
# P50356 - Imazaquin, water, filtered, recoverable, micrograms per liter
# P50359 - Metalaxyl, water, filtered, recoverable, micrograms per liter
# P50364 - Nicosulfuron, water, filtered, recoverable, micrograms per liter
# P61159 - Tribenuron, water, filtered, recoverable, micrograms per liter
# P61188 - Chloramben methyl ester, water, filtered, recoverable, micrograms per liter
# P61692 - N-(4-Chlorophenyl)-N'-methylurea, water, filtered, recoverable, micrograms per liter
# P61693 - Bensulfuron-methyl, water, filtered, recoverable, micrograms per liter
# P61694 - Flumetsulam, water, filtered, recoverable, micrograms per liter
# P61695 - Imidacloprid, water, filtered, recoverable, micrograms per liter
# P61697 - Metsulfuron, water, filtered, recoverable, micrograms per liter
# P82661 - Trifluralin, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P82663 - Ethalfluralin, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P82664 - Phorate, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P82665 - Terbacil, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P82666 - Linuron, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P82667 - Methyl parathion, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P82668 - EPTC, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P82669 - Pebulate, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P82670 - Tebuthiuron, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P82671 - Molinate, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P82672 - Ethoprop, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P82673 - Benfluralin, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P82674 - Carbofuran, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P82675 - Terbufos, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P82676 - Propyzamide, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P82677 - Disulfoton, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P82678 - Triallate, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P82679 - Propanil, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P82680 - Carbaryl, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P82681 - Thiobencarb, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P82682 - DCPA, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P82683 - Pendimethalin, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P82684 - Napropamide, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P82685 - Propargite, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P82686 - Azinphos-methyl, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
# P82687 - cis-Permethrin, water, filtered (0.7 micron glass fiber filter), recoverable, micrograms per liter
Table B-1. USGS Water Quality Pesticide Data (Source: USGS Surface Water Quality Monitoring Raw Data File)
Page 15 of 15
# P90854 - DDT plus degradates, biota, whole organism, wet weight, calculated, dry weight, micrograms per kilogram
#
# Description of sample_start_time_datum_cd:
# MST - Mountain Standard Time
# MDT - Mountain Daylight Time
#
# Description of tm_datum_rlbty_cd:
# K - Known
# T - Transferred
#
# Description of coll_ent_cd:
# USGS - U.S. Geological Survey
# USGS-WRD - U.S. Geological Survey-Water Resources Discipline
#
# Description of medium_cd:
# 1 - Suspended sediment
# 9 - Surface water
# C - Animal tissue
# D - Plant tissue
# H - Bottom material
#
# Description of tu_id:
# http://www.itis.gov/
#
# Description of body_part_id:
# 59 - Organism, whole
# 6 - Liver
# 86 - Fillet
# 94 - Unknown
#
# Description of remark_cd:
# < - Less than.
# E - Estimated.
# M - Presence verified but not quantified.
# U - Analyzed for but not detected.
#
# Data for the following sites are included:
# USGS 12352500 Bitterroot River near Missoula MT
APPENDIX C
Page 1 of 4
Figure C-1. Red-tailed Hawk - Species Distribution (Source: Montana DNR)
Figure C-2. Gray Wolf - Species Distribution (Source: Montana DNR)
Page 2 of 4
Figure C-3. White-tailed Deer - Species Distribution (Source: Montana DNR)
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Figure C-4. Bull Trout Restoration Areas and Species Distribution (Source: Montana DNR)
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Figure C-5. Columbia Spotted Frog - Species Distribution (Source: Montana DNR)
APPENDIX D
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APPENDIX D: TECHNICAL ADDENDUM
MODELING OF ATMOSPHERIC TRANPSORT OF PESTICIDES
Source Receptor Relationships
According to Van Dijk and Guicherit (1999) and Cohen (2001), the easiest way to determine the origin of pesticides that have been detected in water or air is to determine the nearest agricultural area where the pesticides of interest are being appliedin substantial quantities and then subsequently, determine the distance the pesticide must travel from its source. Modern pesticides have been engineered so that their persistence in the atmosphere is reduced, thus more local and regional episodic effects may be observed (Van Dijk & Guicherit, 1999). Episodic effects refer to any intermittent acute exposure.
To determine if a measured pesticide has come from a local or regional source, sampling must be conducted at several sites at least 50 km apart. If the pesticides come from local sources, the pesticide levels in the air at the different sites will widely vary. If the pesticides occur due to regional transport then the sampling sites should show comparative levels of pesticides from air samples at different sites. If the pesticides origniate from even further distances (thousands of kilometers away) then the air samples should remain constant over space and time (i.e. pesticides will be detected during times of the year when no pesticides are being emitted) (Van Dijk & Guicherit, 1999).
For the Bitterroot Valley, due to prevailing winds, the closest location where 3 of the 5 pesticides detected (chlorothalonil, trifluralin, and diazinon) are used in heavy quantities is on Idaho potato fields. Alachlor and DDE, the two remaining pesticides which are not used on Idaho potato fields, may have been used historically in the valley or transported from longer distances.
In a meta-analysis of pesticide transport it was shown that alachlor, diazinon, chlorothalonil, and trifluralin may travel from 50 km up to thousands of kilometers away from their source (Van Dijk & Guichert, 1999). Additionally, another meta-study conducted by Van Dijk & Guichert (1999) found that these four chemicals were in both air and precipitation samples analyzed in several European countries, indicating that not only are these chemicals volatilizing into the atmosphere, but they are being deposited through precipitation events.
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Table D-1, Minimum Distance Travelled for Alachlor, Chlorothalonil, Diazinon, and Trifluralin
Chemical Minimum Distance Travelled (km)
Alachlor 200
Chlorothalonil 100-1000
Diazinon 50
Trifluralin 100-1000
Modeling Emissions Characteristics of Pesticides
This risk assessment attempted to calculate emissions from potato crops. The approach entailed multiplying the quantity of pesticide applied to the crops by the Henry�’s Law constant (KH) for each chemical. The Henry�’s Law constant is a partition coefficient that indicates the chemical�’s potential to go into the gas phase, rather than stay in the aqueous phase. For this calculation, the KH was modified for summer temperatures by utilizing the average daily temperature in July in Idaho. Due to the fact that degradation of the pesticide after application was ignored, this emissions modeling is based on a worst-case scenario (Guicherit et al., 1999).
EQUATION FOR CALCULATING POUNDS (lbs) OF PESTICIDES APPLIED PER YEAR
The equation used for calculating the pounds of pesticides applied per year for this evaluation was:
A x I = P
Where:
A = Acres of potato crops in region X in year Y
I = Average amount of pesticide active ingredient (AI) applied for potato crops in Idaho in year Y
P = Average amount of pesticide AI applied on region X�’s potato crops in year Y.
See Table D-2 and D-3 for crop and pesticide application data.
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MODIFYING KH FOR TEMPERATURE
The Henry�’s Law constants were modified for temperature using the following equation:
Where:
H = Henry�’s Law constant at modified temperature
H = Enthalpy of vaporization at the average daily temperature
TS = Average daily temperature
TR = Henry�’s law constant reference temperature
HR = Henry�’s law constant at the reference temperature
R = Gas constant
(EPA Factsheet, Correcting the Henry�’s Law Constant for Soil Temperature, 2001)
CALCULATION FOR PESTICIDES EMITTED
The equation used for calculating pesticide emission was:
P x KH
Where:
P = Average amount of pesticide AI (lbs) on region X�’s crops in year Y
KH = dimensionless Henry�’s Law constant modified for average July temperature
The ideal approach for modeling pesticide volatilization would combine soil, crop, meteorological conditions, and physico-chemical properties of the pesticide. This would be conducted using a flux equation that measures the vertical flux density of a chemical through the atmosphere and then multiplied by some measure of chemical partitioning (such as KH)(Majewski, 1999). Van den Berg et al (1999) found that it is common for up to 50% of the pesticide applied to volatilize upon application.
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Modeling Transformation of Pesticides
The approach used in this risk assessment ignored transformation of pesticides in the vapor phase or attached to particles. There is still much uncertainty and lack of measurements with regards to transformation reactions of pesticides.
The ideal approach for modeling transformation of pesticides would include modeling the chemical�’s reaction with hydroxyl (OH) radicals, the single most important atmospheric reaction for these chemicals. Based on the reactions of similarly structured pesticides with OH radicals, it was determined that the modeled COPCs in this risk assessment would have atmospheric lifetime long enough to transport the chemicals over the distance from Idaho to the Bitterroot Valley, Montana (Cohen, 2001). Chemical specific OH reaction rates are important to consider because if pesticides have a rapid first-order reaction rate then the first transformation products may be chemicals of potential concern and should be considered for risk assessment as well (Guicherit et al, 1999). Thus, not representing transformation reactions of these pesticides assumes that the chemical is present as the parent compound only.
In a meta-analysis conducted by Van Dijk & Guichert, they described several studies which found diazinonoxon (first transformation product of diazinon) in air, fog, and rain samples. One study in California found that the oxon concentration was higher in the non-agricultural area than in the nearby agricultural area, indicating transport of these transformation products (Schomburg et al, 1991). Additionally, diethylaniline (first transformation product of alachlor) was found in 20% of air samples taken in a study conducted along the Mississippi River (Majewski et al, 1998).
There may also be important reactions with NO3 radicals, ozone, and photolytic cleavage that should be considered. In general, additional research is needed to determine the transformation reactions and rates of these reactions for these chemicals in both the aerosol form and particulate form.
Modeling Pesticide Transport and Deposition
Pesticides behave like most other chemicals in the atmosphere as far as horizontal and vertical dispersion is concerned. The HYSPLIT Ready model utilizes both puff and plume modeling to model pesticide dispersion. This is ideal because puff modeling does a better job over longer distances (>50 km) and particle trajectories may be more appropriate for distances of less than 50 km. According to the
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Atmospheric Studies Group (CALPUFF), when modeling areas with complex terrain the assumptions of the plume modeling (straight-line and steady-state) may not be valid past 3-4 kilometers (TRC, 2008). In these instances the puff model is preferred; however, in general, particle modeling can be modeled more accurately. HYSPLIT combines these two modeling techniques; utilizing puff modeling along the horizontal axis and particle (plume) modeling along the vertical axis (Rolph, 2003). Guicherit et al. found that in general the uncertainty associated with atmospheric modeling of chemicals may range from 30-50% (1999).
Atmospheric transport should also consider dry and wet deposition rates. Van Jaarsveld & Van Pul (1999) found that a deposition rate of 0.1 cm/s (the rate used for this assessment and the default assumption in HYSPLIT) is characteristic of most pesticides. The wet deposition rates are dependent upon Henry�’s Law constants and were integrated into the CalTOX portion of this assessment.
Additionally, one should consider the re-emission of deposited pesticides as well. This risk assessment did not consider re-emissions of current-use pesticides or pesticides used in the Bitterroot Valley historically.
Modeling Uncertainty
As discussed in 3.4.3.2 of the baseline risk assessment, the HYSPLIT output for pesticide emissions and transport from the three primary potato growing regions of Idaho was used as the input for the @Risk program. The @Risk program was used to conduct Monte Carlo analysis on the air modeling output because potential error in the air modeling seemed to be the largest contributor to uncertainty in the overall modeling scheme. The HYSPLIT output of each region was used as the mean concentration for the Monte Carlo analysis in @Risk. The standard deviation input for @Risk was estimated by using 10 times the mean. This assumption was supported by a paper by Guicherit et al (1999) which estimated an uncertainty of 10 for estimating exposure from atmospheric transport of chemicals. A normal distribution was used, with concentrations truncated at zero to avoid any negative concentration estimates.
Modeling Multimedia Transport
The output received from @Risk was then entered into the model CalTOX. The CalTOX model modeled the transport and transformation of atmospheric concentrations of chemicals at ground level. This model used landscape (soil, hydrological, and meteorological data) and residential exposure (lifespan, food crops, animals, etc) parameters specific to Montana along with chemical specific
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physico-chemical properties (i.e. Kd, Koc, and vapor pressure) to model movements of the chemicals in the receiving environment.
The CalTOX model assumes no pre-existing contamination of soil and constructs an exposure model based on an initial exposure to the soil contaminant via continuous off-site emissions. The CalTOX output displays the concentrations of chemicals in each environmental compartment as well as the exposure to humans and wildlife from these media. CalTOX calculates exposure by integrating the soil concentration over time and summing dose over all exposure routes, media, and exposure pathways (McKone & Enoch, 2002).
CalTOX may not adequately predict risk to sensitive species. Although a standard uncertainty factor is integrated into the model, it may ignore particularly sensitive species that exist at the soil-air, plant-air interface, where pesticide residues tend to accumulate. Additionally, species that are especially long-lived or slow growing tend to be at a disadvantage when dealing with the type of persistent chemicals that are capable of long-range transport (Van Straalen & Van Gestel, 1999).
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Table D-2. Acres Planted for Potato Crops in Idaho.
Planted AcresIdaho 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005CountyBonner 200 200Boundary 100 200Kootenai 100 100D10 North 400 500 400 400 400 400 300 300 300 200 200 200 200 300 300 200Ada 100 500 600 500 500 500 1000 1100 900 900 800 500Canyon 6000 6000 6700 8100 9500 11000 10000 8300 8000 7500 8700 8200 9000 8800 9300 7600Elmore 10000 10000 9000 9000 10000 9000 9800 10700 9800 9400 11100 8100 10700 8900 8900 8700Owyhee 2000 2900 3500 4100 4500 3400 4500 4800 7000 6000 4600 3400 3600 3900 3600 2700Payette 1000 1900 1700 1400 1700 2000 2000 1500 1500 1000 1800 1900 2000 2000 1800 1300Valley 100 100 - 500 700 800 - - 500 - - - - - -Washington 800 600 600 500 - - - 800 600 600 - 600 - - -Southwest 20000 22000 22000 24000 27000 27000 28000 27000 28000 26000 28000 23000 27000 25000 25000 21000Blaine 500 500 700 2000 1700 2000 1500 1500 2000 2000 2000 1300 1400 1900 1800 1200Cassia 33000 31800 30700 33000 34000 33000 35500 32500 34000 34000 35500 30000 30400 28800 31000 30200Gooding 12000 11000 12000 8400 11400 12000 13000 14000 14000 8200 8800 4900 5800 5800 7000 6300Jerome 27000 15000 19000 16400 20900 19600 21100 18700 16000 15000 15700 11300 10800 8700 10900 9900Lincoln 5000 4000 5100 4500 5000 5000 4300 4200 5000 6000 6000 1500 1800 2000 2100 1800Minidoka 17000 27000 23000 25400 25700 25000 24000 24000 25500 28000 30400 30500 31900 27200 28500 21300Twin Falls 12000 10700 9500 12500 14300 15400 19500 19000 19500 16200 19000 14500 15900 16600 14700 11300South Central 106,500 100000 100000 102200 113000 112000 118900 114000 116000 109400 117400 94000 98000 91000 96000 82000Bannock 3100 3500 3300 3500 3500 4000 4500 4000 4500 4500 5200 3800 3600 3900 3500 3500Bingham 69200 73500 64000 66900 66000 63500 64500 63500 63800 64000 67500 55500 59900 60500 56400 52500Bonneville 48300 39000 41100 38000 38000 38000 38500 32000 32000 32000 30000 29000 31500 30000 30100 26800Butte 3200 3000 3500 3100 3500 4000 3000 2500 2000 2500 2800 1500 1600 2700 2000 700Caribou 6300 6500 5500 5600 6300 5500 6000 6000 6000 6000 7800 7800 7500 7500 6600 6900Clark 13600 13500 13000 13000 13000 13500 13500 10000 - - - - - - - -Custer 200 500 - - 700 500 700 - 900 500 - - 600 - - -Franklin 0 100 - - - - - - - - - - 500 - -Fremont 28600 25000 25000 27000 29000 27500 31200 31200 33000 33000 35000 28000 28500 28800 29500 27000Jefferson 26500 31000 26000 24500 27000 27000 27000 30000 33000 30000 31000 29700 36900 32200 24300 24500Lemhi 500 - -Madison 37800 35500 35000 36800 37700 35500 38100 40000 40000 38500 35800 30000 32000 31500 30300 29200Oneida 400 400 - - 600 - 500 - - - - - 500 500 600 -Power 32500 32500 32000 35800 36000 32500 33000 31500 35000 34000 36900 33900 36900 36800 39000 40500Teton 8400 8500 8400 8200 8200 8500 7000 7200 7200 7000 8700 7500 7500 7000 6800 6300East 278100 272500 257600 263400 269600 260600 267800 258700 265700 259400 269400 232800 249800 243700 233700 221800
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Table D-3. Pesticide Application Rates on Potato Crops in Idaho from 1990-2003.
AI =total lbs of active ingredient per year per acreIdaho Potato Crop Year % Avg AcresChemical Name Type of Pest 1990 1990 AI 1991 1991 AI 1992 1992 AI 1993 1993 AI 1994 1994 AI 1995 1995 AI 1996 1996 AI 1997 1997 AI 1999 2001 2001 AI 2003 2003 AIaldicarb Insecticide 11 2.62 6 7 2.96 7 2.49azinphos, methyl 10 0.26 2 0.28azoxystrobin Fungicide 10 26 0.15 28 0.21carbaryl Insecticide 5 1.02 6carbofuran Insecticide 2 1.35 4 0.6 11 1.97 16 1.68 16 0.99 17 1.06 11 25 2.23 5 1.21chlorothalonil Fungicide 20 0.93 23 1.37 23 1.13 32 1.45 42 1.64 77 2.12 85 2.89 66 44 1.72 29 1.52copper ammonium 4 0.56 4 0.5 1 1.12 3 0.26copper hydroxide 6 0.66 11 0.63 13 1.04 28 0.85 14 1.45 24 1.12 9 7 0.9 6 0.96copper sulfate 3 0.99cyfluthrin 9 0.04 22 0.04cymoxanil Fungicide 39 0.2 6diazinon 9 2.83 3 3.23dichloropropene Fumigant 3 182.34 3 170.93 5 170.24 9 166.31 5 5 178.02 2 139.52 2 188.43dimethoatedimethomorph Fungicide 3 0.25disulfoton Insecticide 3 3.35 5 4 3.06 1diquat Dessicant 4 0.5 3 0.44 3 0.29 6 0.4 9 0.42 6 0.39 16 0.43 12 9 0.41 7 0.45endosulfan Insecticide 4 1.06 5 0.8 5 0.72 6 1.17 6 1.06 13 1.47 17 7 0.98 6 0.58EPTC Herbicide 47 3.3 50 3.42 44 3.3 46 3.32 49 3.37 54 3.55 55 3.9 50 3.57 41 33 3.62 31 3.27esfenvalerate 15 0.03 31 0.03 13 0.03 8 0.03 5 0.04 10 0.04 5 0.05 6 19 0.05 10 0.07ethoprop Insecticide 24 5.02 19 4.36 16 3.72 14 4.2 12 4.13 10 4.08 5 4.51 5 4.49 9 4 4.71 3 3.42fluazinam 19 0.33flutolanil 7 0.28fonofos 5 2.2 4 1.9 4 1.73 5 2.38 8 2.29 7 2.58 1glyphosate Herbicide 3 0.53 3imidacloprid Insecticide 8 0.21 8 12 0.15 34 0.13iprodione 4 0.99 4 0.75 6 0.98 4 1 2 0.93 3maleic hydrazide Herbicide 3 2.89 2 2.56 3 3 0.76 3 1.84mancozeb Fungicide 6 0.95 9 1.49 9 1.49 13 1.45 14 1.64 22 1.81 25 2.44 79 3.05 64 30 2.38 43 1.88maneb Fungicide 6 1.67 5 1.28 5 2.21 4 1.45 3 1.44 3 1.99 7 1.54 16 1.46 4mefenoxam 13 11 0.19 16 0.3metalaxyl Fungicide 8 0.66 19 0.29 16 0.27 21 0.24 7 7 0.29 8 0.25metam sodium Fumigant 3 165.49 3 164.96 10 167.8 15 155.04 15 140.5 10 124.16 20 121.92 24 20 122.83 33 77.58metiram 4 1.21metolachlor Herbicide 5 3 1.92 9 1.52 8 2.47 7 2.47 9 1.29 11 9 1.77methamidophos Insecticide 3 0.73 7 0.91 6 1.06 7 0.95 6 0.79 6 1.1 12 26 1.25 28 5 0.9metribuzin Herbicide 86 0.58 85 0.47 89 0.49 85 0.51 81 0.52 86 0.48 78 75 0.45 82 66 0.47 78 0.48oxamyl 10 0.87 5 1.07paraquat HerbicidePCNB 3 1.07pendimethalin Herbicide 17 0.89 20 0.97 19 0.91 19 0.98 26 0.95 29 0.91 15 0.7 30 0.65 25 27 0.79 35 0.77permethrin Insecticide 6 0.12 11 0.12 7 0.1 11 0.13 18 0.14 9 0.24 4 0.07 5 13 0.13 0.19phorate Insecticide 43 3.18 46 3.22 45 2.88 45 2.83 46 2.75 36 2.66 26 2.66 41 2.69 42 26 2.83 22 2.8propamocarb hydFungicide 7 0.91pymetrozine 11 0.11 13 0.09pyrasclostrobin 13 0.13rimsulfuron Herbicide 15 0.02 16 0.02 16 15 0.02 25 0.02S, metolachlor 14 1.59sulfur 5 3.02sulfuric acid Dessicant 15 285.39 8 234.52 7 251.95 11 264.81 16 286.13 15 286.27 16 340.73 28 277.12 36 34 287.83 26 224.91thiamethoxam 3 0.08trifluralin 6 0.7 5 0.44 3 0.55 4 0.5 4 0.46 4 0.41 3 0.46 6 0.45 5triphenyltin hydroxide 33 0.18 7
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REFERENCES
Cohen, M. 2001. Atmospheric Processes: Transport of Air Toxics, Source-Receptor Relationships. NOAA Air Resources Laboratory. Presentation at National Park Service�’s Air Toxics Workshop. Seattle, WA.
Guicherit, R. Bakker, D.J., Voogt, P.D., Van Den, F., Berg, H, Van Dijk, F.G., and Van Pul, W.A.J. 1999. Environmental Risk Assessment for Pesticides in the Atmosphere; The Results of an International Workshop. Water, Air, and Soil Pollution. 115: 5-9.
Majewski, M.S. 1999. Micrometeorologic Methods for Measuring the Post-Application Volatilization of Pesticides. Water, Air, and Soil Pollution. 115: 83-113.
Majewski, M.S., Foreman, W.T., Goolsby, D.A. and Nakagaki, N. 1998. Environmental Science and Technology. 32 3689-3698.
McKone, T.E. and K.G. Enoch. 2002. CalTOX, A Multimedia Total Exposure Model Spreadsheet User�’s Guide Version 4.0 (Beta). National Exposure Research Laboratory, Berkeley, CA. Rolph, G.D., 2003. Real-time Environmental Applications and Display system (READY) Website (http://www.arl.noaa.gov/ready/hysplit4.html). NOAA Air Resources Laboratory, Silver Spring, MD. Schomburg, C.J. Glotfelty, D.E. and Seiber, J.N. 1991. Environmental Science and Technology. 25: 155-160.
Van Dijk, H.F. and Guicherit, R. 1999. Atmospheric Dispersion of Current-Use Pesticides: A Review of the Evidence from Monitoring Studies. Water, Air, and Soil Pollution. 115: 21-70.
Van Straalen & Van Gestel, 1999. Ecotoxicological Risk Assessment of Pesticides Subject to Long-Range Transport. Water, Air, and Soil Pollution. 115: 71-81
APPENDIX E
Contents Background . . . . . . . . . . . . . . . . . . . . 1
Endocrine Disrupting Chemicals . . 5
Agriculture in the Region . . . . . . . . . 5
Crop Production and Pesticide Use in Eastern Idaho . . . . . . . . 5 Crop Production and Pesticide Use in Western Montana . . . . . 6 Structure of Agrochemicals . . . . . . . . 9 Potential Routes of Exposure and Factors Affecting Toxicity . . . . . . . . . . 9
Air Transport in the Region . . . 9 Toxicity Considerations . . . . . . 11
Incidence Data Findings . . . . . . . . . . . 12 Methods . . . . . . . . . . . . . . . . . . . 12
Findings . . . . . . . . . . . . . . . . . . . 16
Conclusions and Next Steps . . . . . . . . 18
References . . . . . . . . . . . . . . . . . . . . . . 19
PESTICIDE USE IN IDAHO AND MONTANA WILDLIFE EXPOSURES
by Mary Ruhter MSES May 2008 Candidate
Kali Frost MSES/MPA May 2008 Candidate
Indiana University - Bloomington
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PESTICIDE USE IN IDAHO AND MONTANA WILDLIFE EXPOSURES
BACKGROUND The Bitterroot Mountains have long been known for their remoteness and historical significance. In September 1805, the Lewis and Clark Expedition hiked the Lost Trail Pass through the steep Bitterroot Mountains on their journey toward the Pacific Coast. Home to abundant wildlife, the Bitterroot Valley extends over a hundred miles from Horse Creek Pass north to a point near Missoula, Montana, and is located west of the Bitterroot Mountain Range and the Selway-Bitterroot Wilderness Area, and east of the Sapphire Mountains and the Anaconda-Pintler Wilderness Area. Figure 1 depicts the location of the Bitterroot Valley.
Figure 1: Location of Bitterroot Valley, Montana (Google Earth) Troubling to some, however, are the noxious weeds that run rampant in the Bitterroot Valley. To combat noxious weeds, officials in the Bitterroot Valley implement a strong pest management program which includes the use of herbicides (Montana Department of Agriculture 2007). Pesticides are also used in the Bitterroot Valley in crop protection programs and a heavy regiment of insecticides and fungicides are used on potato crops in southern Idaho just south and west of the southern part of the valley, across the Bitterroot Mountains. Numerous potato fields in south-central and southwestern Idaho were bombarded with heavy fungicide starting in 1995 to combat an extremely aggressive and resistant genotype of late potato blight that consumed numerous potato fields that same year and years after (Univ. of Idaho 1999).
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Beginning in 1995, Judy Hoy, a wildlife rehabilitor who has lived and worked in the Bitterroot Valley for the past 35 years, also began noticing genital abnormalities in white-tailed deer born that year, and in 1996, she began recording incidences of these abnormalities. Hoy�’s Bitterroot Wildlife Rehabilitation Center in Stevensville, Montana opened in 1980 and has received many of the deer killed or injured along valley roads from the local game warden. Hoy to feed carnivores undergoing rehabilitation at the center and has also received orphaned or injured fawns. Many male fawns born in 1995 brought into the center exhibited malformed and undersized scrota, often with ectopic testes positioned against the body wall just dorsal to the scrotum (Hoy 2002). From 1996 to 2006, the condition of the external genitalia of all male deer brought to the center was described and recorded, and many were noted to have genital malformations. FIGURE 2 through 9: Photographs of observed reproductive and skeletal malformations taken from 1998 to 2007 FIGURE 10: Normal Jaw of White-tailed Deer Fawn
Figure 2: Underdeveloped scrotum of bull bison in Lamar Valley, Yellowstone National Park, in 2007. Photo by J. Hoy
Figure 3: Bank Swallow hatchling with underdeveloped upper face and bill (maxillary brachynathia) in Bitterroot Valley in 2002. Photo by Wayne Tree
Figure 4: Prognathism on adult female goat in Bitterroot Valley in 2007. Photo by Dr. Diane Henshel
Figure 5: Short penis sheath and ectopic testicle of white-tailed deer fawn born in 2004 in Bitterroot Valley. Photo by J. Hoy
Figure 6: Deformed maxilla of a golden eagle fledged in Bitterroot Valley in 2001. Photo by J. Hoy
Figure 7: Prognathism of a pronghorn antelope killed by hunter in eastern Montana in 2005. Photo by J. Hoy
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Two main genital malformations have been documented in ungulates since 1996. It has been documented that the left testicular lymph node is formed further forward than the right testicular lymph node early in fetal development, and as a result, the left testis descends directly forward of the right testis to form a misaligned hemiscrota, with the left hemiscrota located directly forward of the right hemiscrota (Hoy et al. 2002). Further, a number of ungulates also exhibited a short penis sheath, with the root of the penis sheath moved far forward to the umbilicus (Hoy et al. 2002). Other observed genital malformations include misplaced mammae and udders, undescended testicles, no scrotal sac, and significant difference in right/left testicular size (Hoy et al. 2002). Scrotal malformations have also been documented for elk, bison, pronghorn antelope, bighorn sheep, and domestic sheep (Hoy Unpublished). Figures 2 through 9 depict examples of various malformations, while Figure 10 depicts a normal lower mandible of a white-tailed deer fawn. Figure 11 provides a comparison of three scrotal sacs (two abnormal and one normal) of mature white-tailed deer. Incidence of prognathism in ungulates, resulting from the underdevelopment of the skull, maxilla and premaxillary pad, also began to be observed in and around the Bitterroot Valley in 1995 (Hoy 2002). The incidence trends of skeletal and genital malformations appear to correlate with increased usage of fungicides on Idaho crops. Interestingly, incidence of abnormalities reduced after 2001 when use of extensive amounts of chlorothalonil on Idaho potato crops also reduced. In addition to observed malformations, the sex ratio was found skewed toward males (Hoy 2002). This study strives to determine whether pesticides used on crops and roadways in the region may be causing reproductive and skeletal deformities in wildlife. Figure 12 depicts the percent of chlorothalonil use from 1991 to 2003. Notice the increase use during the 1990's when skeletal and reproductive malformations were observed.
Figure 8: Head of full-term beef calf born in Bitterroot Valley in 2005, rounded forehead, dish face, maxillary brachynathia, malformed incisors, small eyes, rolled ears and almost no body hair. Photo by J. Hoy
Figure 9: Maxillary brachynathia on a male northern flicker in northern Bitterroot Valley near Lolo, Montana in 2006. Photo by Dale Dufour
Figure 10: Normal bite on a white-tailed deer fawn born in Bitterroot Valley in 2006. Photo by J. Hoy
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Figure 11: Right to left, mature normal scrotal sac, skewed vertical and split scrotal sac, and skewed vertical scrotal sac. White-tailed deer, same age group (2-3 years old). Photo taken in March 2007 in Bitterroot Valley, Stevensville, Montana by Dr. Diane Henshel.
Figure 12: Percentage of Potato Farmers Using Chlorothalonil from 1990 to 2003
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ENDOCRINE DISRUPTING CHEMICALS
Since World War II, numerous endocrine disruptors have been released into the environment in large quantities (Colborn et al. 1993). Endocrine disruptors are primarily man-made synthetic chemicals, but also include natural phytoestrogens that affect the endocrine system by mimicking or blocking hormones thus disrupting the body�’s normal functions. Endocrine disrupting chemicals (EDCs) have a wide range of molecular size, volume, and potency, with potency depending on the target organ or cell and specific end point. Chemicals including dioxin, furans, polychlorinated-biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), and various pesticides have been shown to have endocrine disrupting effects. Major endocrine effects on both an individual level and at the population level have been documented for exposure to EDCs (Colborn et al. 1993). Exposure to EDCs in the environment have resulted in abnormal thyroid function in birds and fish; decreased fertility in birds, fish, shellfish and mammals; decreased hatching success in fish, birds, and turtles; demasculinization and feminization of male fish, birds, and mammals; defeminization and masculinization of female fish, gastropods, and birds; and alteration of immune function in birds and mammals (Colborn et al 1993). The endocrine system, composed of a network of glands and hormones, regulates growth, development and maturation, and how various organs operate. The endocrine glands include the pituitary, thyroid, adrenal, thymus, parathyroid, pancreas, ovaries, and testes which release specific levels of hormones into the bloodstream that act as natural chemical messengers for life functions. The endocrine system provides the link between the nervous system and reproduction, immunity, metabolism, and behavior.
AGRICULTURE IN THE REGION
Crop Production and Pesticide Use in Eastern Idaho Idaho is the number one producer of potatoes in the United States, producing 29% of the total potato crop. The major region of potato production is the eastern counties that border Montana (65% of potato acreage, ~222,000 acres in production, [USDA, 2000]). This paper will focus on this region due to its proximity to the Bitterroot Valley, MT. Potatoes are grown from April to mid-June (USDA, 2000). Late blight is the most important disease for potatoes and can be very damaging to potatoes in the latter portion of their growth. Late blight can spread very quickly, devastating a once healthy field in a matter of days The fungus associated with late blight attacks the stems, leaves, and tubers of the potato plant. Late blight made it to western potato growers in 1995. The strains found in Idaho and Montana are very aggressive, thus, because of the potentially devastating effects of this disease, the chemical control of the disease is very intensive (Table 1, Pesticides Used on Idaho Potato Crops). It is estimated that 75% of potato growers in Idaho apply 4 or more applications of fungicide (within 7-10 days of each other) to control late blight every year (University of Idaho, 1998). The three major fungicides used in the control of late blight are maneb, mancozeb, and chlorothalonil.
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Maneb- This is used on 16% of potato crops for control of late blight. This is applied on average 1.4 times per year at an application rate of 1.05 lbs active ingredient (AI)/acre Mancozeb- This is used on average on 79% of potato acres. It is applied 2.6 times a year at a rate of 1.18 lbs AI/acre. Chlorothalonil- Chlorothalonil is used on ~55% of potato fields. It is applied anywhere from 1-5 times a year at 1.25 lbs AI/acre (USDA, 2000) Two other pesticides of interest which are used on potatoes, but not for late blight, are iprodione and endosulfan (post-harvest fungicide and insecticide, respectively). They are not used at nearly the volume of the other three chemicals, but have been definitively established to have anti-androgenic effects. Iprodione- This is used on 4% of acres. It is applied 1.07 times per year at 0.9 lbs AI/acre (National Agricultural Statistics Survey [NASS], 2002) Endosulfan- This is used on 13% of acres. It is applied at 1.7 applications a year at 0.86 lbs AI/acre. Crop Production and Pesticide Use in Western Montana The main crops produced within or in proximity to the Bitterroot Valley are hay, mint, potatoes, and small grains (mostly Durham wheat, [USDA, 2000]). Potato crops are treated similarly to those in Eastern Idaho with heavy use of chlorothalonil, maneb, and mancozeb and to a lesser extent, iprodione and endosulfan. Wheat production utilizes 2,4 D, 2,4 DB, chlorpyrifos, maneb, and endosulfan. Mint growers utilize 2,4 DB. Hay growers utilize chlorpyrifos (Montana State University, 2004). We still need to do more research on the amount used in these areas to see how pervasive these chemicals are in the Valley. Primary fungicides used on potato crops have included chlorothalonil, mancozeb, maneb, iprodione, and to a lesser extent, vinclozolin. By far, chlorothalonil and mancozeb have been used to the greatest extend, both by volume and area (USDA, 2000). Mancozeb and maneb are in a subclass of carbamate pesticides known as dithiocarbamates. Mancozeb is maneb with zinc added. Mancozeb is a cholinesterase inhibitor which can affect the nervous system. The primary metabolite of mancozeb and maneb is ethylene thiourea (ETU), which has been shown to cause thyroid and carcinogenic effects (PAN 2007). Mancozeb and its primary metabolite have also been shown to cross the placental barrier and produce DNA damage and initiate tumors in fetal cells (Shukla and Arora 2001). Mancozeb is also listed as a possible EDC by Illinois Environmental Protection Agency, and is listed in the U.S. EPA Toxic Release Inventory (TRI) as a reproductive and developmental toxin. Similarly, maneb has been identified as an EDC (Colborn et al. 1993). Maneb is also listed in the U.S. EPA TRI as a developmental toxin. Iprodione and vinclozolin are members of the imide group of the dicarboximide class of fungicide and both appear to be antiandrogenic (USEPA 2000). These fungicides reduce testosterone levels in the body. Iprodione disrupts the endocrine
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system by inhibiting androgen synthesis rather than competing for the androgen receptor, while vinclozolin reduces testosterone levels by binding and competing for the androgen receptor (USEPA 2000). A metabolite of iprodione is 3-(3,5-dichlorophenyl)-2,4,-dioxo-1-imidazolidinecarboxamide, and the two primary metabolites of vinclozolin are M1, (2-[[3,5-dichlorophenyl)-carbamoyl]oxy]-2-methyl-3-butenoic acid), and M2, (3`,5`-dichloro-2-hydroxy-2-methylbut-3-enanilide) (USEPA 1999, Wong et al. 1995). Chlorothalonil is a substituted benzene fungicide and is used widely in the region (PAN 2007, USGS 1997). The primary metabolite of chlorothalonil is 4-hydroxy-2,5,6-trichloroisophthalonitrile and is 30 times more acutely toxic than chlorothalonil itself and is more persistent and mobile in soil (Cox 1997). This metabolite has been shown in subchronic toxicity tests to cause a decrease in weight, anemia, and damage to bone marrow, spleen, liver and kidney (Cox 1997). A breakdown product in soil is m-phthalodinitrile which has shown to cause confusion and loss of consciousness, but has otherwise not been investigated (Cox 1997). While reproductive problems have been shown in rats, chlorothalonil is not currently listed as an EDC. However, chlorothalonil has been shown to cause carcinogenic effects (PAN 2007). Additionally, it should be noted that hexachlorobenzene is a common contaminant of chlorothalonil and that hexachlorobenzene is a known EDC (Colborn et al. 1993). Endosulfan is a chlorinated hydrocarbon which is listed as a class I �“highly toxic�” restricted use pesticide (RUP) by the EPA (ExToxNet). It is a high molecular weight compound, with low solubility and it is moderately persistent in the environment with a half of 35-150 days (depending on alpha or beta isomer) in soil environments. Soto et al. (1994) found endosulfan to be estrogenic at concentrations of 10-25 M. Endosulfan is highly toxic to fish and birds, and has been found to bioaccumulate in mussels at concentrations of 600 times that of surrounding water. The organs affected by endosulfan in animal studies were the kidneys, liver, blood, and the parathyroid gland (Oregon State). Endosulfan has also been shown to have anti-androgenic effects (Kobayashi, 2004). Chlorpyrifos, used on hay and wheat in Western Montana has been linked to anti-androgenic effects (Kobayashi, 2004). It is an organophosphate insecticide that is moderately toxic and moderately persistent (60-120 days) in soil (ExToxNet). Table 1 (next page) provides a comprehensive list of the pesticides and their application rates for potato crops in Idaho.
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Table 1 AI =total lbs of active ingredient per year per acre of potatoes in Idaho% Acres = Percentage of Potato Acres on Which the Pesticides Are Applied
Idaho Potato Crop
Chemical NameType of
Pesticide 1990 1990 AI 1991 1991 AI 1992 1992 AI 1993 1993 AI 1994 1994 AI 1995 1995 AI 1996 1996 AI 1997 1997 AI 1999 2001 2001 AI 2003 2003 AI% Acres % Acres % Acres % Acres % Acres % Acres % Acres % Acres % Acres % Acres % Acres
aldicarb Insecticide 11 2.62 6 7 2.96 7 2.49
azinphos, methyl 10 0.26 2 0.28azoxystrobin Fungicide 10 26 0.15 28 0.21carbaryl Insecticide 5 1.02 6carbofuran Insecticide 2 1.35 4 0.6 11 1.97 16 1.68 16 0.99 17 1.06 11 25 2.23 5 1.21chlorothalonil Fungicide 20 0.93 23 1.37 23 1.13 32 1.45 42 1.64 77 2.12 85 2.89 66 44 1.72 29 1.52copper ammonium 4 0.56 4 0.5 1 1.12 3 0.26
copper hydroxide 6 0.66 11 0.63 13 1.04 28 0.85 14 1.45 24 1.12 9 7 0.9 6 0.96copper sulfate 3 0.99cyfluthrin 9 0.04 22 0.04cymoxanil Fungicide 39 0.2 6diazinon 9 2.83 3 3.23dichloropropene Fumigant 3 182.34 3 170.93 5 170.24 9 166.31 5 5 178.02 2 139.52 2 188.43dimethoate
dimethomorph Fungicide 3 0.25disulfoton Insecticide 3 3.35 5 4 3.06 1diquat Dessicant 4 0.5 3 0.44 3 0.29 6 0.4 9 0.42 6 0.39 16 0.43 12 9 0.41 7 0.45endosulfan Insecticide 4 1.06 5 0.8 5 0.72 6 1.17 6 1.06 13 1.47 17 7 0.98 6 0.58EPTC Herbicide 47 3.3 50 3.42 44 3.3 46 3.32 49 3.37 54 3.55 55 3.9 50 3.57 41 33 3.62 31 3.27esfenvalerate 15 0.03 31 0.03 13 0.03 8 0.03 5 0.04 10 0.04 5 0.05 6 19 0.05 10 0.07ethoprop Insecticide 24 5.02 19 4.36 16 3.72 14 4.2 12 4.13 10 4.08 5 4.51 5 4.49 9 4 4.71 3 3.42fluazinam 19 0.33flutolanil 7 0.28fonofos 5 2.2 4 1.9 4 1.73 5 2.38 8 2.29 7 2.58 1
glyphosate Herbicide 3 0.53 3
imidacloprid Insecticide 8 0.21 8 12 0.15 34 0.13iprodione 4 0.99 4 0.75 6 0.98 4 1 2 0.93 3
maleic hydrazide Herbicide 3 2.89 2 2.56 3 3 0.76 3 1.84mancozeb Fungicide 6 0.95 9 1.49 9 1.49 13 1.45 14 1.64 22 1.81 25 2.44 79 3.05 64 30 2.38 43 1.88maneb Fungicide 6 1.67 5 1.28 5 2.21 4 1.45 3 1.44 3 1.99 7 1.54 16 1.46 4mefenoxam 13 11 0.19 16 0.3metalaxyl Fungicide 8 0.66 19 0.29 16 0.27 21 0.24 7 7 0.29 8 0.25
metam sodium Fumigant 3 165.49 3 164.96 10 167.8 15 155.04 15 140.5 10 124.16 20 121.92 24 20 122.83 33 77.58metiram 4 1.21
metolachlor Herbicide 5 3 1.92 9 1.52 8 2.47 7 2.47 9 1.29 11 9 1.77methamidophos Insecticide 3 0.73 7 0.91 6 1.06 7 0.95 6 0.79 6 1.1 12 26 1.25 28 5 0.9metribuzin Herbicide 86 0.58 85 0.47 89 0.49 85 0.51 81 0.52 86 0.48 78 75 0.45 82 66 0.47 78 0.48oxamyl 10 0.87 5 1.07paraquat HerbicidePCNB 3 1.07pendimethalin Herbicide 17 0.89 20 0.97 19 0.91 19 0.98 26 0.95 29 0.91 15 0.7 30 0.65 25 27 0.79 35 0.77
permethrin Insecticide 6 0.12 11 0.12 7 0.1 11 0.13 18 0.14 9 0.24 4 0.07 5 13 0.13 0.19phorate Insecticide 43 3.18 46 3.22 45 2.88 45 2.83 46 2.75 36 2.66 26 2.66 41 2.69 42 26 2.83 22 2.8propamocarb hydrochloride Fungicide 7 0.91
pymetrozine 11 0.11 13 0.09pyrasclostrobin 13 0.13rimsulfuron Herbicide 15 0.02 16 0.02 16 15 0.02 25 0.02
S, metolachlor 14 1.59sulfur 5 3.02
sulfuric acid Dessicant 15 285.39 8 234.52 7 251.95 11 264.81 16 286.13 15 286.27 16 340.73 28 277.12 36 34 287.83 26 224.91thiamethoxam 3 0.08trifluralin 6 0.7 5 0.44 3 0.55 4 0.5 4 0.46 4 0.41 3 0.46 6 0.45 5triphenyltin hydroxide 33 0.18 7
Year
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STRUCTURE OF AGROCHEMICALS
While we have yet to determine all the chemicals in this area that may cause endocrine disruption this figure from the EPA�’s Chemical Field Guide will help direct the search (Figure 13).
Figure 13: Diagnostic tool for identifying endocrine disrupting chemicals based on their chemical properties (USEPA, 2006).
POTENTIAL ROUTES OF EXPOSURE AND FACTORS AFFECTING TOXICITY
Air Transport in the Region Air transport modeling of the key pesticides will be done using HYSPLIT software from the National Oceanic and Atmospheric Administration. The HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectory) model is software that models air parcel trajectories. The HYSPLIT model is linked to several meteorological databases (can choose month and year) and combines this information with complex dispersion equations to create trajectories of particle transport. These trajectories can be calculated forward or backward from any point on the grid and the software creates easy to see color maps that is compatible with ArcView GIS (Draxler and Rolph, 2003). Figure 14 below depicts a backward trajectory from a point in Ravalli County (Bitterroot Valley).
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Figure 14: Backwards trajectory over 24 hour period with elevation graphed over time. (Draxler & Rolph, 2003).
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Toxicity Considerations Persistence is a significant factor when evaluating exposure potential. Persistent compounds degrade or dissipate slowly in air, water and soil. If a compound is persistent in the environment, they tend to be a greater threat to wildlife as they are taken in continuously over time. Once absorbed, they persist in the organism as well, thus persistent chemicals are also typically bioaccumulative. Additionally, the combination of more than one chemical can have the synergistic effect of increasing toxicity above that of a single chemical. Both co-exposures and persistence are important factors in evaluating toxicity. Since more than one pesticide is typically used in pest management and crop protection programs, synergistic effects must be evaluated. A good example of synergy is glyphosate, trade name Roundup®. Most herbicides containing glyphosate are either made or used with a surfactant (chemicals that help glyphosate to penetrate cells) (Cox 2000). Its surfactant is more acutely toxic than glyphosate and the combination of the two results in greater toxicity (Cox 2000). Contaminants can enter the body through three routes of exposure, ingestion, inhalation and dermal contact. The physiochemical properties then govern how these compounds will move throughout the body and how they will be metabolized and excreted. For example, factors that affect absorptions include size, physical state (e.g., gas, liquid, or solid), solubility and structure. Very small particles (less than 1 um) if inhaled can move down into the alveoli of the lung. Larger particles (larger than 2 um) will either be deposited onto mucous membranes and be transported back into the mouth to be swallowed, or, will not reach the bronchial tree of the lungs. When surface area of the particles is smaller, absorption is easier across the skin. Also, liquids and gases are absorbed into the skin more readily than solids. In terms of solubility, lipophilic compounds (i.e., fat soluble) will move easily across biological membranes including the skin. The chemical structure of the contaminant also dictates how readily it will be metabolized, by what pathways, and to what compound that will later be excreted. For example, the chemical structure determines how the contaminant will bind to receptors that control cellular actions. Factors that affect toxicity include, but are not limited to, genetic makeup, age, sex, life stage (e.g., pregnancy, elderly), health, size of individual or animal, hormonal status, and nutritional status. There are also environmental factors that can affect toxicity. These include, but are not limited to, temperature, air pressure, and co-exposures. For example, temperature dictates how readily a contaminant may volatize. As temperature increases, the potential for the contaminant to volatize increases, though this is strongly based on the Henry's Law Constant of the contaminant. Toxicity may also be affected by exposure with co-solvents. The Toxic Release Inventory for Ravalli County, MT indicates that there is a release of xylene (mixed isomers) from a surgical products manufacturer located in the valley at a rate of ~11,000 lbs/year (EPA, 2008). Additionally, xylene is often found as an "inert" ingredient in many pesticide formulations.
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INCIDENCE DATA FINDINGS
Judy Hoy recorded various malformations for both male and female ungulates, fawns and adults. This paper, however, focuses on malformations recorded for male white-tailed deer fawns including scrotal sac length, penis sheath length and distance from penis sheath to umbilicus, and placement of hemiscrota. Methods Hoy measured scrotal sac length, and other measurements, using a plastic metric ruler. The end of the ruler was placed against the body on the left side of the scrotal sac and the measurement was taken from the body to the most ventral point on the scrotal sac. The most ventral point was the lowest point of the scrotal sac when the deer was standing. Testes were also measured on animals that Hoy dissected. In most cases, the testes were symmetrical and the same length no matter where the testes were located (e.g., whether ectopic or in the hemiscrota, or whether the testes were horizontal, tipped or vertical). For comparison, a six month old fawn has testes that average between 4.7 cm and 5.9 cm in length (Hoy 2007). If the scrotal sac or two hemiscrota that make up the scrotal sac measure significantly less than the testicle in length, that indicates that the testicle was ectopic or partially ectopic. Therefore, if a male fawn 6 months of age has a scrotal sac that is 2.5 cm or less, only half of the testis or testes will fit into the scrotal sac. Penis sheath length was measured by measuring the distance between the tip of the penis to the body. Hoy also measured the distance from the penis sheath to the umbilicus (i.e., navel). The distance between the penis sheath and the umbilicus should be seven to eight centimeters or more (Hoy 2007). The placement of hemiscrota was also recorded. Three observations were recorded: bilateral hemiscrota (i.e., normal), left hemiscrota forward of right hemiscrota, and no hemiscrota present. Age was recorded in months and distance in centimeters. All male deer that Hoy received at her Wildlife Rehabilitation Center were measured. Fawns were considered to be all deer less than 12 months of age. Tables 2, 3, 4, & 5 provide the incidence data for the aforementioned malformations.
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Table 2: Male Fawn Scrotal Sac Length (in centimeters) (Source: Authors manipulation of Hoy data)
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Table 3: Male Fawn Penis Sheath Length and Penis Sheath to Umbilicus (in centimeters) (Source: Authors manipulation of Hoy data)
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Table 4: Male Fawn Hemiscrota Placement (Source: Authors manipulation of Hoy data)
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Findings An analysis of the mean scrotal sac lengths of white-tailed deer fawns from 1996 to 2006 showed a clear decreasing trend in length, with especially short scrotal sacs being recorded in 1999 to 2001 (Figure 15).
Figure 15: Mean Scrotal Sac Length of White-tailed Deer Fawns from 1996 to 2006 The data also indicates that the penis sheath length was relatively similar across years, however, a reduced length was observed from 1997 to 1999, and the again in 2005. More importantly, however, distance between the penis sheath and the umbilicus was shortest between 1997 and 2001 (Figure 16).
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Figure 16: Mean Penis Sheath Length and Length from Penis Sheath to Umbilicus, 1996 to 2005 Finally, the data also indicates that the percentage of abnormal hemiscrota in white-tailed deer fawns has a general upward trend, with steady increases from 1997 to 2001 (Figure 17). Figure 17: Percentage of Abnormal Hemiscrota in White-tailed Deer Fawns from 1995 to 2005
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CONCLUSIONS AND NEXT STEPS
Based on the physicochemical properties and chemical structures of the pesticides used in Idaho, these compounds have the potential to bioaccumulate and cause endocrine disruption. Endocrine disruption can result in both skeletal and reproductive malformations. There appears to be a strong correlation with malformation incidence and pesticide use in Idaho. Additionally, based on HYSPLIT modeling, it appears that air transport of long range pesticides used in Idaho potato crops may occur. Therefore, the following recommendations are made:
Conduct a data gathering effort to compile available pesticide sampling data in the Bitterroot Valley
Conduct a baseline risk assessment using measured data and evaluate uncertainties through models
Specifically, air modeling should be conducted to determine whether pesticides used in Idaho may be transported to the Bitterroot Valley of Montana at concentrations that would be adversely impact human health and the environment
Determine how contamination may compartmentalize within site media Perform bioaccumulation modeling and a thorough evaluation of
synergistic affects that may occur Further study into potential co-solvent exposure Further investigation of regional wildlife exposure including wildlife that
inhabit Yellowstone National Park and Glacier National Park Re-evaluate study status for additional next steps
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REFERENCES
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Cox C. 1997. Fungicide Factsheet: Chlorothalonil. J Pest Reform, V. 17, No. 4.
Cox C. 2000. Herbicide Factsheet: Glyphosate. J Pest Reform, V. 103, No. 3
Draxler, R.R. and Rolph, G.D., 2003. HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectory) Model access via NOAA ARL READY Website (http://www.arl.noaa.gov/ready/hysplit4.html). NOAA Air Resources Laboratory, Silver Spring, MD.
Extension Toxicology Network. Pesticide Information Profiles: Chlorpyrifos. http://extoxnet.orst.edu/pips/chlorpyr.htm, June 1996. Last Accessed March 2007.
Extension Toxicology Network. Pesticide Information Profiles: Endosulfan. http://extoxnet.orst.edu/pips/endosulf.htm, June 1996. Last Accessed March 2007.
Henshel, Diane. E520- Class Notes, Spring 2007
Hoy JA. 2002. Clouds of Death: Catastrophic Effects of Wind-Drift Chemicals and Locally Sprayed Pesticides on Western Montana Fauna. Wise Traditions in Food, Farming and the Healing Arts. Vol 3.
Hoy JA, Hoy R, Seba D, Kerstetter TH. 2002. Genital abnormalities in white-tailed deer (Odocoileus virginianus) in west-central Montana: pesticide exposure as a possible cause. J Environ Biol 23:189-197.
Hoy JA, Hoy RD, Tweedale T. 2007. Mechanisms likely involved in misplacement of mamma on both females and males and in misplacement and malformation of specific organs of the male genitalia in North American deer species in Western United States, including Alaska. Unpublished.
Kobayashi, Kunihiko. 2004. Screening for estrogen and androgen receptor activities in 200 pesticides by in vitro reporter gene assays using Chinese hamster ovary cells. Environmental Health Perspectives. Vol. 112.
Montana Department of Agriculture. Conversation with Ms. Amy Bamber, Groundwater Program Manager. March 5, 2007. 4PM (EST).
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Pesticide Action Network North America. PAN Pesticides Database. Last Accessed March 2007. http://www.pesticideinfo.org/Index.html
Shukla Y, Arora A. 2001. Transplacental carcinogenic potential of the carbamate fungicide mancozeb. Environ Pathol Toxicol Oncol. 20(2):127-31. Soto, AM, Chung, KL, Sonnenschein, C. 1994. The Pesticides Endosulfan, Toxaphene, and Dieldrin Have Estrogenic Effects on Human Estrogen-Sensitive Cells. Environmental Health Perspectives. Vol. 102, Number 4. University of Idaho. Idaho Plant Disease Reporter, Late Potato Blight. http://www.uidaho.edu/ag/plantdisease/lbhome.htm, September 2004. Last Accessed March 2007.
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USEPA. 2000. Vinclozolin: Common Mechanism of Toxicity of Dicarboximide Fungicide. Memorandum. USGS. 1997 Pesticide Use Map. Last Accessed March 2007. http://ca.water.usgs.gov/pnsp/pesticide_use_maps/compound_listing.php?year=97 Wong C, Kelce W, Sar M, Wilson E. 1995. Androgen receptor antagonist versus agonist activities of the fungicide vinclozolin relative to hydroxyflutamide. American Society for Biochemistry and Molecular Biology. 270(34):19998-20003.
