A PROBABILISTIC ECOLOGICAL RISK ASSESSMENT OF … · A PROBABILISTIC ECOLOGICAL RISK ASSESSMENT OF...

A PROBABILISTIC ECOLOGICAL RISK ASSESSMENT OF CHLORPYRIFOS TO

AQUATIC ORGANISMS IN AN AGRICULTURAL DRAIN

JERRY D. JORDEN

A thesis submitted in partial fulfillment of

the requirements for the degree of

MASTER OF SCIENCE IN ENVIRONMENTAL SCIENCE

WASHINGTON STATE UNIVERSITY

School of Earth and Environmental Sciences

DECEMBER 2010

To the Faculty of Washington State University:

The members of the Committee appointed to examine the thesis of

JERRY D. JORDEN find it satisfactory and recommend that it be accepted.

___________________________________

Allan Felsot, Ph.D., Chair

___________________________________

John Strand, Ph.D.

___________________________________

Vincent Hebert, Ph.D.

ACKNOWLEDGEMENTS

Many people contributed to my education and completion of my master‘s degree.

Pat ―Grandma Science‖ and Jim Falco, my undergraduate advisors, set the stage for my

enrollment into graduate school. Words cannot capture my gratitude.

The Environmental Assessment Program at the Washington State Department of Ecology, Dan

Dugger, and Chris Burke are to thank for the internship at Ecology which lead to the topic of my

thesis. Thanks for believing in me.

Joe ―Buddy, Bud‖ Herrera and Cheryl Ermey for listening to me cry and whine when no one else

would (or wanted to). Thanks for believing in me, too.

Vince Hebret for providing data on Marion Drain and serving on my committee.

John Stand for inspiring a probabilistic risk assessment and serving on my committee.

And finally, Allan Felsot. Thank you, Allan. You chaired my committee and edited the hell out

of this paper. Your passion for science should be witnessed by everyone.

A PROBABILISTIC ECOLOGICAL RISK ASSESSMENT TO AQUATIC ORGANISMS IN

AN AGRICULTURAL DRAIN

Abstract

By Jerry D. Jorden, M.S.

Washington State University

December 2010

Chair: Allan Felsot

The Yakima River Basin of eastern Washington State is located mainly in Yakima

County. This watershed provides a valuable water resource to the areas' farmers and is federally

recognized as a critical habitat for middle Columbia Steelhead. A possible conflict could arise

between agriculture‘s crop protection needs and water quality when pesticides are carried to

streams from irrigation and ground water return canals that feed the Yakima River, potentially

endangering human health and the environment. Recent state and federal agency sampling

campaigns show detection of numerous pesticides in waters of this Basin. This assessment

focuses on water inputs into the Yakima River from the Marion Drain, which is a nineteen mile

highly canalized ground water return that runs through the cities of Harrah, Toppenish and

Granger. The organophosphorus insecticide chlorpyrifos has been detected in the lower Yakima

River Basin at levels exceeding state and federal water quality limits, thus placing the Marion

Drain on the Federal Clean Water Act 303(d) list. Marion Drain is listed as a category 5 water,

i.e., impaired, and it now requires determination of a TMDL (Total Maximum Daily Load). A

deterministic risk assessment by the Washington State Department of Ecology (ECY) indicates

several chlorpyrifos concentrations contributed to significant risk for aquatic organisms in

Marion Drain.

The purpose of this study was to assess chlorpyrifos residues in surface water and to

probabilistically characterize their risks to the aquatic animals. Chlorpyrifos 96-h lethal effects

on an assemblage of aquatic animals and lotic invertebrates were assessed. Exposure was

probabilistically determined using a Monte Carlo simulation of risk quotients and joint

probability analysis techniques. Risk criteria were taken from EPA procedures for determining

ambient water quality criteria as applied to pesticides like chlorpyrifos (Stephan et al. 1985;

Urban and Cook 1986).

The probabilistic analysis of exposure indicated that risk to an assemblage of aquatic

animals and lotic invertebrates were moderate to high between March and October. These

results agree with the NOAA (2008) and Burke et al (2006) deterministic assessments and

conflict with the Geisy et al. (1999) probabilistic risk assessment.

TABLE OF CONTENTS

Acknowledgments ………………………………………………………………...……………. iii

Abstract………………….………………………………………………………………………. iv

Table of Contents……………………….……………………………………...…………………vi

List of Tables…………………………………………………………………………………..…ix

List of Figures……………………………………………..……………………….……………..x

1.0 Introduction ………………..………………………………..………………..………............1

1.1 Background………..…………………………………………………………...……...1

1.2 Regulatory Concerns: Federal and State Laws ……………………………………….2

1.2.1 Clean Water Act……………………………………………………..………5

1.2.2 Endangered Species Act…………………………………………………….6

1.2.3 Federal Insecticide, Fungicide, and Rodenticide Act…………………..…..6

1.2.4 Water Pollution Control Act……………………………………….…….6

1.2.5 Washington State Water Quality Standards for Surface Waters …………..7

1.3 Overview………………………………………………………………………..…….7

2.0 Problem Formulation………………………………………………………...……………….8

2.1 Chlorpyrifos ………………………………………………………………...………..8

2.1.1 Mode of Action……………………………………………………...………9

2.1.2 Usage, Pests and Application Rates………………………………………..10

2.1.3 Environmental Fate and Transport…………………………...…………….10

2.2 Ecosystem Potentially at Risk………………………………………………………..12

2.2.1 Yakima County……………………………………………………...……..12

2.2.1.1 Marion Drain…………………………………………….……….13

2.2.2 Aquatic Organisms at Risk………………………………………………...15

2.2.2.1 Yakima River Basin Fish Species…………………………….…15

2.2.2.2 Yakima River Basin Invertebrate Species…………………….…17

3.0 Conceptual Model for Characterizing Chlorpyrifos Risk…………………………………....17

3.1 Exposure………………………………………………………….………………….17

3.2 Effects……………………………………………………………………..…………19

3.3 Risk Characterization………………………………………………………..………19

4.0 Assessment Endpoints, Measures of Effect and Risk Criteria……………………..………..20

5.0 Exposure Assessment…………………………………………………………….…………..22

5.1 United States Geological Survey at Marion Drain…………………………………..22

5.2 ECY Pesticide Sampling at Marion Drain………………………………….………..22

5.2.1 ECY 2007 Intensive Sampling at Marion Drain……………………...……24

5.2.1.1 Daily Grab Sample Results………………………………………26

5.2.1.2 POCIS and SPMD Results………………………….……………27

5.3 Chlorpyrifos Detections by Month Between 2003 and 2008……………………...…27

5.4 Exposure Modeling Analysis……………………………………………..…………28

6.0 Effects Assessment…………………………………………………………………………..30

6.1 Laboratory Toxicity Studies…………………………………………………………30

6.2 Microcosm and Mesocosm Studies………………………………………………….32

6.3 Chlorpyrifos and Atrazine Synergism……………………………………….………33

6.4 Effects Model Analysis………………………………………………………………34

7.0 Risk Characterization ………………………………………………………..………………39

7.1 Joint Probability Analysis…………………………………………….……………...39

7.1.1 Aquatic Animal Risk: March thru October……………………………..…40

7.1.2 Lotic Invertebrate Risk: March thru October ……………………….……..42

7.2 Monte Carlo Simulation…………………………………………..………………….44

7.2.1 Aquatic Animal Risk: March thru October………………………………..44

7.2.2 Lotic Invertebrate Risk: March thru October ……………………….……..45

7.3 Discussion……………………….…………………………………….……………..46

7.4 Uncertainties…………………………………………………………..……………..48

7.5 Conclusion……………………………………………………………………...……50

8.0 Literature Cited……………………………………………………………………..……….52

Appendices……………………………………………………………………………….………56

Appendix A. 1990 Aquatic invertebrate survey results for the Yakima River Basin

(Cuffney et al. 1997)………......……………………………………………….....57

Appendix B. Concentration data from the Washington State Department of Ecology‘s 2003-

2008 pesticide sampling campaigns and the 2006 FEQL study.…………………63

Appendix C. Lognormal concentration models for March thru April, May thru August and

September thru October……………………..……………………………….……70

Appendix D. Aquatic animal and lotic invertebrate 96-h LC50 lognormal models…………...73

Appendix E. 1D Monte Carlo simulation of the RQ probability distribution results……..…….75

Appendix F. Joint probability results table of results for the aquatic animal assemblage and

lotic invertebrates……………………………………..………………….………..79

LIST OF TABLES

Table 1. Water quality standards for acute and chronic ESLOC (Endangered Species Level of

Concern), WAC (Washington Administrative Code) and NRWQC (National

Recommended Water Quality Criteria) for Chlorpyrifos (µg/L) (Burke et al., 2006)…5

Table 2. Summary of physical and chemical properties of chlorpyrifos…………………………9

Table 3. 1990 Fish survey results for the Yakima River Basin (Cuffney et al. 1997)……….….16

Table 4: Risk criteria for the Monte Carlo simulation of the risk quotient and the joint

probability analysis…………………………………………….………………..……..22

Table 5. Summary of intensive sampling grab samples results……………………………...….26

Table 6. POCIS results (ng/POCIS)………………………………………………………..……27

Table 7. SPMD results (ng/L)………………………………………………………...…………27

Table 8. Model parameters and results for the lognormal and probit concentration models for all

three temporal time frames……………………………………………….……………30

Table 9. 96hr LC50 data set…………………………………………………………….……….35

Table 10. Model parameters and results for the 96-h LC50 lognormal and probit models……..39

Table 11. Joint probability acute toxicity risk median and 95th

percentile and the percent of

exposures affecting five percent of an assemblage of aquatic animals between March

and October in the Marion Drain…………………………………………..…………41

exposures affecting 10 and 50 percent of lotic invertebrates between March and

October in the Marion Drain……………………...………………………….……….43

Table 13. The median and 95th

percentile RQs for the aquatic animals and the percent of RQs

greater than 0.05 from March thru October…………………………………........…..45

percentile RQs for lotic invertebrates and the percent of RQs

greater than 0.10 and 0.50 from March thru October…………………………...……45

LIST OF FIGURES

Figure 1. Chlorpyrifos (O,O-DiethylO-(3,5,6-trichloro-2-pyridyl)-phosphorothioate) structural

formula…………………………………………………...………………………...…..8

Figure 2. Location of Marion Drain Watershed in the Lower Yakima Watershed

(Dugger et al. 2008)……………………………………………...………...…………14

Figure 3. Temporal distribution of chlorpyrifos exposure events between 2003 and 2008…….24

Figure 4. Passive sampler deployment in Marion Drain (Dugger et al. 2008)………………….25

Figure 5. Joint probability risk of chlorpyrifos acute toxicity predicted for an assemblage of

aquatic animals between March and October in the Marion Drain……………...……41

Figure 6. Joint probability risk of chlorpyrifos acute toxicity predicted for lotic invertebrates

between March and October in the Marion Drain…………………………………….43

1.0 Introduction

1.1 Background

Chlorpyrifos, [0,0-diethyl 0-(3,5,6-trichloro-2-pyridinyl)-phosphorothioate], is an

insecticide used to control hundreds of insects pests in food and feed crops, This substance falls

in the class of chlorinated organophosphates (U. S. EPA 2002) and was first registered in 1965.

In 2008, the National Oceanic Atmospheric Administration (NOAA) National Marine Fisheries

Service's (NMFS) final biological opinion (BioOP) was issued under the authority of section

7(a)(2) of the Endangered Species Act (ESA). This BioOP declared the use of three

organophosphate (OP) insecticides (chlorpyrifos, diazinon, and malathion) was likely to harm or

adversely modify critical habitat for 25 of 26 listed Pacific salmonids within designated critical

habitats. The survival and recovery of Middle Columbia River (MCR) steelhead was deemed

one of several critical habitats at high risk due to the use of the above organophosphate

insecticides.

Since 2003, the Washington State Department of Ecology (ECY) and the Washington

State Department of Agriculture (WSDA) have conducted weekly grab sample campaigns in

several watersheds throughout the state to characterize pesticide residues in salmonid bearing

streams (Anderson et al. 2004; Burke et al. 2006; Dugger et al. 2008; Sargeant et al. 2010). Of

the watersheds sampled, the lower Yakima River Basin, which lies in MCR steelhead critical

habitat, is an area of heavy and diverse agriculture and represented by three drainages.

Drainages regularly sampled in the lower Yakima River basin include Marion Drain

(Toppenish), Sulfur Creek (Sunnyside) and Spring Creek (Prosser).

In 2007, Marion Drain was chosen as the site to conduct a 22-day intensive sampling

campaign. Relative to other sites in ECY surface water monitoring sampling campaigns, Marion

Drain has had the most frequent and diverse range in pesticide detections (Burke et al. 2006).

Furthermore, Burke et al. (2006) has indicted that the frequency and magnitude of chlorpyrifos

detections in Marion Drain may affect directly and indirectly several life stages of summer

steelhead.

Running 19-miles, Marion Drain collects irrigation return flows and groundwater

exfiltration from Harrah Drain, Toppenish Creek, Wanity Slough and connects with the Yakima

river at mile 82.6. Marion Drain is a channelized drainage ditch that attracts Toppenish Creek

steelhead (Dugger et al. 2008). Marion Drain is considered a poor fish spawning habitat but still

provides habitat for fall chinook, summer steelhead and resident fishes. In 2008, Marion Drain

was added to the 303(d) list (ranked category 5) for several chlorpyrifos exceedences of the

Washington Administrative Code (WAC) chronic limit of 0.041 µg/L (List ID:

WA1201939463324_0.357). The standard has been exceeded more than once every three years,

and now Marion Drain is considered an impaired water body.

The risk assessment endpoints and risk criteria that the WAC adopts are based on

Stephan et al. (1985) and Urban et al. (1986). Regardless of the results of risk assessment,

Marion Drain has met the criteria for 303(d) category 5 listing and is an impaired watered body

that now requires a Total Maximum Daily Load (TMDL). A TMDL for Marion Drain has yet to

be implemented. Although category 5 means severely impacted, the magnitude of the ‗severely

impacted‘ has not been characterized. The risk assessment contained herein will characterize the

risk of chlorpyrifos to an assemblage of aquatic animals and lotic invertebrate populations that

reside in Marion Drain.

1.2 Regulatory Concerns: Federal and State Pesticide Laws

Based on the Code of Federal Regulations (CFR), a pesticide is defined as, ―Any

substance or mixture of substances intended for preventing, destroying, repelling, or mitigating

any pest, or intended for use as a plant regulator, defoliant, or desiccant....‖ (40 CFR 152.3).

Farmers rely on numerous formulated pesticide active ingredients as tools for pest management

on a diverse array of crops. The use of pesticides automatically triggers public concern,

prompting state and federal regulation. Due to their potential for negative effects on non-target

organisms, pesticide applications have prompted state and federal agencies to draft several laws

that protect human health and the environment. Federal laws that are relevant to Marion Drain

water quality are the Water Pollution Control Act amended by the Federal Water Pollution

Control Act (FWPCA) Amendments of 1972 (i.e., the Clean Water Act [CWA]), Federal

Insecticide, Fungicide, and Rodenticide Act (FIFRA), and due to the presence of threatened

salmonid species in the drain, the Endangered Species Act (ESA). WA State regulates water

quality under authority of the Water Pollution Control Act in the Revised Code of Washington

(RCW) 90.48 (010-906) and the Washington water quality standards for surface waters in

Chapter 173-201A of the Washington Administrative Code (WAC), sections 010-612.

The Endangered Species Level of Concern (ESLOC) and the National Recommended

Water Quality Criteria (NRWQC) include derived water quality standards for chlorpyrifos

(Table 1). The WAC adopts NRWQC standards in setting risk. The data set used to derive the

1986 water quality standard for chlorpyrifos was evaluated by U. S. EPA (1986). Stephan et al.

(1985) describes the criteria used in selecting toxicity endpoints from literature and the

calculation used to derive the final acute and chronic values for NRWQC. NRWQC derived

acute and chronic values for chlorpyrifos from 96-h LC50 (i.e. the concentration of the chemical

that kills 50% of the test animals) toxicity tests for seven fish and 11 invertebrate species (U. S.

EPA 1986). Different sizes of rainbow trout (Oncorhynchus mykiss), fathead minnow

(Pimephales promelas) and cutthroat trout (Oncorhynchus clarki) were used in the calculation.

A genus geometric mean LC50 was calculated from the toxicity data for multiple species. Next,

the organisms were ranked from lowest to highest susceptibility based on genus geometric

means, and their cumulative probabilities of occurrence were calculated from the ranked toxicity

values. The four organisms with that were closest to a cumulative probability of 0.05 were used

in the final calculation. In other words, biota chosen to derive the final criteria represented the

upper 5th percentile of susceptibility. The lowest four values for the data-set ranged in

cumulative probability between 6.25 and 25% of the community toxicity values. The four values

were used to calculate a toxicity value of 0.1669 µg/L, which was then divided by a safety factor

of two to generate an acute value of 0.083 µg/L. A safety factor of two was applied to protect

the organism(s) that are at the fifth percentile of susceptibility.

Few studies were available at the time of the NRWQC development to calculate a

chronic level for chlorpyrifos. As a result, an acute-to-chronic toxicity ratio, using saltwater

organisms, was calculated. The value of 0.1669 µg/L was divided by an acute-to-chronic

toxicity ratio of 4.064, thereby giving a final chronic value of 0.041 µg/L. Acute toxicity values

apply for exposures lasting for one-hour, and chronic values apply for exposures that that last

greater than four days. U. S. EPA (1986) states, ―Freshwater aquatic organisms and their uses

should not be affected unacceptably if the four-day average concentration of chlorpyrifos does

not exceed 0.041 µg/L more than once every three years on the average and if the one-hour

concentration does not exceed 0.083 µg/L more than once every three years on the average.‖

Survivors and immigrants are assumed to restore the ecosystem within three years after lethal

exposure (U. S. EPA 1986). Pertinently, ambient water quality values were not designed as rules

but as guidelines and States have the option of implementing these standards as laws. States are

still required, however, to have standards (Stephan et al. 1985).

Table 1. Water quality standards for acute and chronic ESLOC (Endangered Species Level of

Concern), WAC (Washington Administrative Code) and NRWQC (National Recommended

Water Quality Criteria) for Chlorpyrifos (µg/L) (Burke et al., 2006).

Population ESLOC WAC/NRWQC

Acute Chronic Acute Chronic

Fish 0.15 0.57 0.083 0.041

Invertebrates 0.05 0.04

1.2.1 Clean Water Act

The Clean Water Act (CWA) contains several water quality goals that directly apply to

Marion Drain. Section 102(a) of the CWA states, ―The Administrator shall, after careful

investigation, and in cooperation with other Federal agencies, State water pollution control

agencies, interstate agencies, and the municipalities and industries involved, prepare or develop

comprehensive programs for preventing, reducing, or eliminating the pollution of the navigable

waters and ground waters and improving the sanitary condition of surface and underground

waters. In the development of such comprehensive programs due regard shall be given to the

improvements which are necessary to conserve such waters for the protection and propagation of

fish and aquatic life and wildlife, recreational purposes, and the withdrawal of such waters for

public water supply, agricultural, industrial, and other purposes.‖ Section. 303(d)(4) requires

that any pollutant that has exceeded a State standard must have a Total Maximum Daily Load

(TMDL) calculated for the impaired water body. Section 304(a) of the Clean Water Act (CWA)

maintains that the administrator, U. S. EPA, will create a list of recommended water quality

standards for states and tribes to use. U. S. EPA has drafted a list of standards for approximately

150 pollutants for the protection of aquatic life and human health in surface water (U. S. EPA

2002b). Section 305(B), State Reports on Water Quality, mandate ―An analysis of the extent to

which all navigable waters of such State provide for the protection and propagation of a balanced

population of shellfish, fish, and wildlife, and allow recreational activities in and on the water.‖

1.2.2 Endangered Species Act

Title 50 Code of Federal Regulations (C.F.R) 17.11(h) lists MCR steelhead

(Oncorhynchus mykiss) as a threatened species. Title 50 (C.F.R) 226-212 (r)(3)(iii) designates

lower Toppenish Creek watershed (1703000304) as a critical habitat for the MCR steelhead.

Marion Drain is one of several water bodies that lie in the lower Toppenish Creek watershed.

However, 50 C.F.R 226-212(d) excludes Marion Drain as a critical habitat due to its location on

the Yakama Indian Reservation, a sovereign nation.

1.2.3 Federal Insecticide, Fungicide, and Rodenticide Act

Under FIFRA, pesticides intended for use in the United States must be registered by the

U. S. EPA before they may be sold or distributed in commerce. U. S. EPA will register a

pesticide if scientific data provided by the applicant show that, when used according to labeling

directions, it will not cause ―any unreasonable risk to man or the environment, taking into

account the economic, social and environmental costs and benefits of the use of any pesticide‖

(FIFRA section 2(bb)).

1.2.4 Water Pollution Control Act

The Water Pollution Control Act of the Revised Code of Washington (RCW 90.48.010-

906) states, ―It is declared to be the public policy of the state of Washington to maintain the

highest possible standards to insure the purity of all waters of the state consistent with public

health and public enjoyment thereof, the propagation and protection of wild life, birds, game,

fish and other aquatic life, and the industrial development of the state.‖

1.2.5 Washington State Water Quality Standards for Surface Waters

Washington water quality standards for surface waters in Chapter 173-201A of the WAC,

sections 010-612, is divided into six parts. Part II—Designated Uses and Criteria in Section 173-

201A-240—Toxic Substances lists the minimum fresh water levels of several selected

chemicals. Section 173-201A states, ―Toxic substances shall not be introduced above natural

background levels in waters of the state which have the potential either singularly or

cumulatively to adversely affect characteristic water uses, cause acute or chronic toxicity to the

most sensitive biota dependent upon those waters...to ensure that aquatic communities and the

existing and characteristic beneficial uses of waters are being fully protected.‖ U. S. EPA Quality

Criteria for Water, 1986, as revised, shall be used in the use and interpretation of the

values…Surface waters of the state include lakes, rivers, ponds, streams, inland waters,

saltwaters, wetlands, and all other surface waters and water courses within the jurisdiction of the

state of Washington.‖

1.3 Overview of Research Project Goals

Chlorpyrifos ecological risks are generically characterized by U. S. EPA during

registration decisions using worst case exposure estimates. A more habitat specific risk

assessment relevant to exposure conditions in the Pacific Northwest is lacking. Thus, the main

goal of this risk assessment was to use ECY pesticide monitoring together with Washington State

University Food and Environmental Quality Lab (FEQL) residue data for Marion Drain to

estimate the probability of an adverse effect to aquatic organisms. The risk assessment followed

a framework developed by the U.S. Environmental Protection Agency (U. S. EPA 1998) and

consisted of three phases: problem formulation, analysis of effects and exposure data, and risk

characterization.

Due to the presence of threatened Middle Columbia Steelhead (MCS), an individual

assessment of risk to MCS populations should in principle be included but the following

assessment more comprehensively focused on an assemblage of aquatic organisms therein. An

ideal goal would be contrasting the ecological risk versus the human benefit of chlorpyrifos

usage. Although there is data available for the economic contribution of agriculture to the

economy, no specific studies could be found to examine the economic benefit of chlorpyrifos

alternatives and/or limited use.

2.0 Problem Formulation

2.1 Chlorpyrifos

Figure 1. Chlorpyrifos (O,O-DiethylO-(3,5,6-trichloro-2-pyridyl)-phosphorothioate) structural

formula.

Registered in 1965, Chlorpyrifos, [0,0-diethyl 0-(3,5,6-trichloro-2-pyridinyl)-

phosphorothioate], (Figure 1) is an insecticide, acaricide and nematicide used to control a variety

of insect pests on a approved food and feed crops, falling in the class of chlorinated

organophosphates (U. S. EPA 1986). Table 2 summarizes some of the physical and chemical

properties of chlorpyrifos.

Table 2. Summary of physical and chemical properties of chlorpyrifos. Property Value*

C.A.S. number 2921-88-2

Chemcial name O ,O -DiethylO -(3,5,6-trichloro-2-pyridyl)-phosphorothioate

Molecular weight 350.6

Molecular formula C5H11NO3PSCl3

Melting point 41-44oC

Water solubility (distilled) 1.39 mg/L at 25oC

Vapor pressure 2.0 x 10-5

mm Hg at 25oC

Henry's law constant 6.64 x 10-3

atm-L mol-1

Log Kow 4.7-5.3

Koc 8500 mL/g

Hydrolysis (25oC) Below pH 7, average half life (t1/2) 77 days (d)

Above pH 7, t1/2 10-16 d

Aqueous phtolysis Average t1/2 30 d under midsummer sunlight at ~ 40oN latitude

Soil photolysis 0-17 d t1/2 on moist soil.

Aerobic soil metabolism (25oC) 30-60 d t1/2

Anerobic aquatic metabolism (25oC) 40-50 d t1/2 to 150 d

C.A.S. Chemical Abstract Service

*From Giesy et al (1999)

2.1.1 Mode of Action

Chlorpyrifos's primary mode of action is acetylcholineesteratase (AChE) inhibition (Carr

et al. 1997; U. S. EPA 1986). Chlorpyrifos also has an affinity for several other enzymes, but the

greatest affinity is for AChE (U. S. EPA 1986). Briefly, chlorpyrifos is first metabolized into the

more potent toxin chlorpyrifos-oxon. Chlorpyrifos-oxon then phosphorylates the active site of

AChE via a covalent modification that is difficult to reverse. Loss of AChE activity causes

acetylcholine to accumulate in the synapse between neurons thereby excessively stimulating

signal transduction. Eventually, the over-exposed organism would suffer paralysis and

ultimately death. The degradate 3,5,6-trichloro-2-pyridinol, is considerably less toxic than

chlorpyrifos (U. S. EPA 1986; Giesy et al. 1999). Bioconcentration factors for chlorpyrifos

range from 100 to 1715 based on tissue wet weight and on the life stage of the organism and

exposure concentration (U. S. EPA 1986; Montanes et al. 1995). Accumulated chlorpyrifos in

organisms is rapidly detoxified and eliminated (U. S. EPA 1986; Montanes et al. 1995), thus

lowering bioconcentration potential for a compound with such a high octanol/water partition

coefficient (Kow).

2.1.2 Usage, Pests and Application Rates

Top formulation brands for Chlorpyrifos are Dursban®, Lorsban®, Empire®, Equity®

and Whitmire PT 270® (Geisy et al. 1999). Chlorpyrifos is approved for usage on alfalfa,

almonds, apples, asparagus, broccoli, cabbage, carrots (grown for seed only), cauliflower,

cherries, citrus, corn, cotton, cranberries, figs, filberts, grapes, grass seed, mint, nectarines,

onions, pears, peaches, pecans, plums & prunes, radishes, snap beans (seed treatment), sorghum,

strawberries, sugarbeets, sunflowers, sweet potatoes, turnips, other vegetables, walnuts, wheat,

pulp wood, and Christmas trees. Chlorpyrifos is typically applied as a dormant spray for

hundreds of insect pests in several different formulations (Geisy et al. 1999). Application ranges

from one to three pound(s) active ingredient per acre and is applied from either ground blast

sprayers or boom sprayers once a year (Giesy et al. 1999; U. S. EPA 2002; Burke et al. 2006).

Total application amounts and frequency/timing of applications are not available for Yakima

County or for Washington State. Since 2002, chlorpyrifos was no longer registered for use in

residential outdoor nor indoor areas.

2.1.3 Environmental Fate and Transport

After application, chlorpyrifos's persistence can range from moderate to high in the

environment, contaminating surface water by spray drift and runoff. Most chlorpyrifos transport

may occur by eroding soil, tile drainage and drift (Carpri et al. 2005). During time of intense

irrigation and rain fall, water runoff may play a major transport process. Unpredictable

meteorological events during chlorpyrifos applications may severely influence movement of

chlorpyrifos for several weeks to months post-application. Chlorpyrifos has a halflife in soil of

16 days at pH 9 and is relatively stable a pH 7 and lower (Table 2). Cuppen et al. (2002)

calculated a half-life of 8.9 days. Chlorpyrifos degradation is rapid in alkaline soils (Baskaran et

al. 1999).

Aerobic and anaerobic metabolism are the major processes that degrade chlorpyrifos.

Under aerobic and anaerobic conditions, chlorpyrifos can be persistent, but the majority of the

chemical will partition to soil sediment. Awashti (1997) studied persistence of chlorpyrifos in

three different soil types under three different moisture regimes. The results showed that

submerged chlorpyrifos degraded slower than in air dry soil. The faster rate of air-dry soil was

attributed to clay catalyzed hydrolysis. Slower degradation rates of submerged chlorpyrifos were

assumed to be due to organic content, low pH and low clay content. The range of chlorpyrifos

degradation rates were 1.6 to 10.0 days under air-dry conditions, 5.2 to 22.0 days at field

saturation conditions and 8.7 to 25.1 days under submerged conditions.

Carpri et al. (2005) assessed chlorpyrifos concentration in surface water introduced

through spray drift in a vineyard in Northern Italy. A solution of chlorpyrifos (0.408g/L) was

sprayed on 11,100 m2 that encompassed two vineyard fields for two consecutive years. The

fields sloped slightly downward toward a drainage ditch that ran between both fields.

Applications were conducted in conditions that optimized drift (windy days), and the vegetation

near the drain was removed to insure maximum drift distance. The application rate for one field

was 248 g active ingredient (a.i.)/ hectare (ha) and the other was 540 g a.i./ha. Chromatographic

paper strips were placed along a 6.8 meter area between the fields and the ditch and then

collected after insecticide application. Three water samplers were placed at the beginning, 50

and 100 meter along the stream. Samples were collected 1, 2, 5, 12 hours after application and

daily for 90 days thereafter. The highest ground deposition sampled in the 6.8-m buffer zone

was 0.33 and 0.32 µg/cm2 for one year and 0.69 µg/cm

2 for the second. Canopy height and

environmental condition greatly influenced the movement of chlorpyrifos. Most water samples

had no detectable chlorpyrifos residues above the level of quantification (0.05 µg/L). There was

a single detect after 24 hrs that had a maximum concentration of 0.31 µg/L. A concentration

0.21µg/L was detected 32 hrs after application and after an intense rain fall. The authors

suggested that residues washed off of plants caused the spike in concentration. During both

years, the sediment had no detectable amount of chlorpyrifos. The study design employed by

Capri et al. (2005) may have facilitated transport of chlorpyrifos out of the area of measurement

and may have underestimated ground and water concentrations. Several authors have concluded

that volatilization from surfaces plays a significant role in dissipating chlorpyrifos post-

application (U. S. EPA 1986; Giddings et al. 1997; Cuppen et al. 2002; Van den Brink et al.

1995).

2.2 Ecosystem Potentially at Risk

2.2.1 Yakima County

Yakima County, Washington is ranked number one in the State for agricultural

production and is considered one of the most agriculturally intense areas in the United States

(Cuffney et al. 1997). In 2007, Yakima County has 3,540 farms which cover 1,649,281 acres.

Of the these farms, 2,711 have been used as cropland, comprising 344,505 acres. The market

value of agricultural products sold was $1,203,806,000 with an average of $340,058 per farm.

Crops, including nursery and greenhouse crops, generated $787,459,000. The total in farming

expenses for Yakima County was $857,111 (NASS 2009).

Approximately 192 classes of soil are reported in the Soil Survey of Yakima County

(Lenfesty et al. 1985). Most soils were created in alluvium, eolian sand, lake sediment, loess and

residuum derived from basalt and sandstone. Typically, the soils are well drained with some

exceptions. The textures of most of the soils are sandy to clay and vary in depth. The slope in

many irrigated areas varies between nearly level to strongly sloping. The soils of the Columbia

Basin are alkaline and have an average pH of 8.

The Pacific Ocean borders the west side of Washington State, and heavy precipitation

falls on the west side of Cascade Range due to orographic lift. The uplift removes much of the

moisture in the air, keeping the east side warmer and drier. Yakima County lies on the east side

of the Cascade Mountains, and the climate is characterized as semi-arid/arid.

The Yakima County growing season occurs between April and September. From 1971

to 2001, the average monthly temperatures between April and August for Yakima were 48.7,

56.2, 62.9, 69.1 and 68.3 o

F (NCDC/NESDIS/NOAA 2002). From 1971-2001, the average

monthly precipitation for Yakima from April through August was 0.53, 0.51, 0.62, 0.22 and 0.36

inches (NCDC/NESDIS/NOAA 2002). Evapotranspiration accounts for roughly 53 percent of

the precipitation loss (Jones et al. 2006). During the summer, 80-85% of the days are sunny.

Due to the difference in elevation across the Columbia Basin, average daily wind speeds can

vary from 12-25 mph with the lower velocities being most common.

2.2.1.1 Marion Drain

Located within the Yakima County, the Marion Drain watershed has an area of

approximately 85,786 acres and is 19 miles long (Figure 2). Fifty-nine percent of the land area is

cropland, dominated by apple, hops and corn (Dugger et al. 2008).

Figure 2. Location of Marion Drain watershed in the lower Yakima watershed (Dugger et al.

2008).

The break down of crops for the Marion Drain watershed are apples (8,076 acres, 9.41%

of total area), hops (7,581, 8.84%), corn (7,581, 8.84%), wheat (7,011, 8.17%), alfalfa, hay

(4,793, 5.59%), mint (3,849, 4.49%), grapes (3,500, 4.08%), fallow (2,318, 2.70%), asparagus

(995, 1.16%), potatoes (795, 0.93%), green beans (609, 0.71%), grass hay (547, 0.64%), and

pears (500, 0.58%). For the remainder of the crops, each comprises less than 400 acres (0.45%)

(Burke et al. 2006).

2.2.2 Aquatic Organisms at Risk

2.2.2.1 Yakima River Basin Fish Species

Nearly fifty species of fish have been identified in the Yakima sub-basin. Before

introduction of exotic species, primary native fish species present in the Yakima River basin

included northern pike minnow, sculpin, bull trout, rainbow trout, cutthroat trout and burbot.

Exotic species include brook trout, lake trout, brown trout, sunfish, perch, catfish and minnow

species. The number of salmon species has declined considerably over the last century and a

half. Fish declines have been attributed to irrigated agriculture and floodplain development that

compromise vital fish habitat (Turner 2003; Cuffney et al. 1997). A 1990 survey of aquatic

organisms in the Yakima River Basin (Table 3) demonstrated degraded fish communities at the

Yakima River sites of Parker, Toppenish, and Kiona (Cuffney et al. 1997). In addition, fish

captured during the study were noted with external anomalies. No fish surveys are available for

Marion Drain.

Table 3. 1990 fish survey results for the Yakima River Basin (Cuffney et al. 1997).

Species Scientific Name Species Common Name Number of Individuals

Rhinichthys osculus Speckled dace 1587

Oncorhynchus mykiss Rainbow trout 694

Cott Beldingi Paiute sculpin 567

Cottus rhotheus Torrent sculpin 459

Richardsonius balteatus Redside shiner 392

Catostomus marocheilus Largescale sucker 356

Ptychocheilus oregonis Northern squawfish 337

Prosopium williamsoni Mountain whitefish 228

Oncorhynchus clarki Cutthroat trout 195

Acrocheilus alutaceus Chiselmouth 190

Catostomus columbianus Bridgelip sucker 178

Oncorhynchus tshawytsca Chinook salmon 156

Cottus spp. Unidentified sculpins 132

Rhinichthys cataractae Longnose dace 76

Cottus confusus Shorthead sculpin 64

Cottus cognatus Slimy sculpin 53

Oncorhynchus kisutch Coho salmon 48

Salvelinus fontinalis Brook trout 39

Catostomus platyrhynchus Mountain sucker 38

Cyprinus carpio Common carp 32

Rhinichthys falcatus Leopard dace 9

Gasterosteus aculeatus Three-spine stickleback 5

Lepomis gibbosus Pumpkinseed 4

Lampetra richardsoni Western brook lamprey 3

Lepomis macrochirus Bluegill 2

Micropterus salmoides Largemouth bass 2

Cyprinidae sp. unidentified minnow 1

Lampetra ayresi River lamprey 1

Lamptera sp. unidentified lamprey 1

Lepomis spp. unidetified sunfish 1

Micropterus dolomieu Smallmouth bass 1

Rhinichthys sp. unidentified dace 1

Salvelinus malma Dolly Varden 1

2.2.2.2 Yakima River Basin Invertebrate Species

Aquatic invertebrate surveys in the Yakima River Basin identified a total of 193 species

(see Appendix A for the complete list of aquatic invertebrates identified from the Cuffney et al.

(1997) survey). One hundred twenty-four taxa were identified in the Columbia Basin region of

the Yakima River Basin. The survey concluded that the Columbia Basin region‘s aquatic

invertebrate populations had been impaired due to pesticides, agricultural development and

municipal waste water discharges.

3.0 Conceptual Model for Characterizing Chlorpyrifos Risk

The results of the problem formulation indicate that aquatic animals, fish and lotic

invertebrates, are the organisms that are at potential risk from exposure to chlorpyrifos residues.

Thus, the objective of this assessment is to characterize the probability and associated

uncertainty of risk of lethal effects on an assemblage of aquatic animals exposed to chlorpyrifos

residues in the Marion Drain. Aquatic invertebrates are much more sensitive to chlorpyrifos

exposures than are fish and therefore will be addressed separately. Due to the low phytotoxicity

of chlorpyrifos, assessment of macrophytes is not addressed (U. S. EPA 1986; Geisy et al. 1999).

Chlorpyrifos residue data from ECY and others will be used to evaluate exposure, and toxicity

data will be used to asses effects to the aquatic animals.

3.1 Exposure

The following risk assessment will characterize chlorpyrifos exposure from existing

residue data bases in three temporal time frames based on exposure patterns, which are more

fully discussed in the exposure assessment. Chlorpyrifos concentrations occurring from March

until April, May thru August, and September thru October were assessed. Samples were not

collected for November thru February, and therefore the risk during these months will be

uncertain. Combining all the data over an entire year would skew the results and underestimate

risk. The highest and most frequent exposure of aquatic organism in Marion Drain to

chlorpyrifos residues occurs during September and October (Burke et al. 2006; Sargeant et al.

2010). The exposure assessment should consider all potential sources of chlorpyrifos residues,

including air, water, soil, and biota. However, aquatic organisms are predominantly exposed to

pesticides through water (Solomon et al. 2000, Carpri et al. 2005). No sediment samples have

been collected from Marion Drain. However, Geisy et al. (1999) concluded that water column

concentrations are comparable to interstitial pore water concentrations. Based on U. S. EPA

analysis (U. S. EPA 1986) and the estimated chlorpyrifos concentrations, no significant

bioconcentration of chlorpyrifos is anticipated.

Samples from Marion Drain were gathered weekly by ECY staff and the interval between

samples varied between one and nine days. It is unlikely that exposure to residues was

continuous. It is more likely that the exposures pulsed and fluctuated between sampling events.

Naddy et al. (2001) demonstrated that C. dubia can recover from lethal chlorpyrifos exposures

when the exposure was pulsed. However, this assumption cannot be proven by the available

data. Rather than assume continuous chlorpyrifos exposure and thus overestimate risk, a more

realistic assumption would be a series of pulsed exposures. However, toxicological tests are not

designed in this manner. The compound being tested is maintained at a uniform concentration

for a certain period of time. Therefore, measured chlorpyrifos water concentrations in Marion

Drain was assumed to have lasted 96 h at a uniform level.

3.2 Effects

Single-species laboratory LC50 bioassays, microcosm and mesocosm studies were used

to evaluate the effects of chlorpyrifos residues on fish and lotic invertebrates. Probabilistic

analysis of risk used the mean 96-h LC50 data from U. S. EPA (1986) and amended with data

from U. S. EPA‘s ECOTOX (2007) database. Because one objective of this assessment was to

characterize risk probabilistically for comparison to U. S. EPA risk criteria that are based on

acute toxicity, sublethal chlorpyrifos concentrations were not assessed. The organisms used in

U. S. EPA (1986) and amended data served as surrogates species in the probabilistic analysis

because there are no aquatic surveys available for Marion Drain. The results of the Cuffney et

al. (1997) study indicate what organisms may reside in Marion Drain, but there is no

toxicological data for many of the identified species.

When chlorpyrifos was detected in water samples, additional chemicals were present that

may augment toxicity either in an additive or synergistic manner. For example, many samples

that contained chlorpyrifos residues also contained terbacil, 2,4-D and atrazine. No studies have

examined the effect of these pesticides on the toxicity of chlorpyrifos in a mixture or pre/post-

chlorpyrifos exposure.

3.3 Risk Characterization

The risk characterization phase utilized probabilistic techniques. Probabilistic risk

assessments are considered superior to traditional deterministic risk assessments. Deterministic

risk assessments provide a single point estimate of risk, whereas probabilistic risk assessment

methods generate a distribution of values from probability distribution functions. Two different

probabilistic methods were implemented for risk characterization. Assessing the risk using two

different methods provides additional confidence to the risk assessment and allows for the

application of different risk criteria. The first method, which is referred to as a joint probability

analysis or exceedance probability, compared the overlap of the probability distribution of

environmental concentrations with the probability distribution of species response data as

reported from laboratory single-species toxicity tests. The overlap of these distributions is a

measure of the risk to aquatic animals. Numerous authors (Giesy et al. 1999; Solomon et al.

2000; Poletika et al. 2002; Brain et al. 2006) have utilized joint probability curves or exceedence

probabilities to assess risk.

The second method of analysis involved a Monte Carlo simulation of a Risk Quotient

(RQ). A RQ is a ratio of chemical exposure to defined toxicological endpoint such as LC50 or

NOAEC. RQs are typically used in deterministic risk assessments but can be used in

probabilistic or semi-probabilistic risk assessments. The Monte Carlo simulation of RQs is

similar to the joint probability analysis in that it compares the exposures to toxicological

endpoints. But instead of characterizing the overlap of exposure probability distributions with

toxicological endpoint probability distributions, the Monte Carlo simulation of a risk quotient

involves randomly sampling from the probability distributions of environmental concentrations

and toxicological endpoints and inputting the values in the risk quotient equation, thereby

providing a distribution of risk quotients.

4.0 Assessment Endpoints, Measures of Effect and Risk Criteria

Assessment endpoints reflect the goals set forth in the risk management process,

integrating two elements, a valued ecological unit and its associated characteristic to be

protected. The U. S. EPA (1998) defines assessment end points as ―explicit expressions of the

actual environmental value that is to be protected, operationally defined by an ecological entity

and its attributes.‖ In the following assessment, the valued ecological unit is aquatic animals

(fish and lotic invertebrates). The associated characteristic to be protected and effects measures

used in the assessment are population sustainability (survival and recruitment) assessed using

data from laboratory LC50 values, mescosom and microcosm studies, and field studies. Table 4

identifies the risk assessment criteria used to evaluate the extent of risk to aquatic animals and

lotic invertebrate populations in the probabilistic analysis.

Criteria for the evaluation of the Monte Carlo simulation of the Risk Quotient (RQ) and

the joint probability analysis were selected from Urban et al. (1986) and from Stephan et al.

(1985). For the Monte Carlo simulation of the RQ, a RQ greater than 0.05 for the aquatic

animals was considered high risk. For the lotic invertebrate population, RQ levels range from

less than 0.10 for low risk, between 0.10 and 0.50 for moderate risk and greater than 0.5 for high

risk. The U. S. EPA uses a risk quotient of 0.5 for non-endangered invertebrates that are food for

endangered species and 0.1 for a restricted use pesticide. Lotic fish were not assessed separately

due to their relatively high LC50 values and low levels of chlorpyrifos detections in Marion

Drain and are assumed to be at low risk (U. S. EPA 1986; Burke et al. 2006; Sargeant et al.

2010). However, a life stage risk assessment to fish may indicate a higher level of risk.

Table 4. Risk criteria used for evaluating the Monte Carlo simulation of the risk quotient and

the joint probability analysis.

Monte Carlo Simulation of the Risk Quotient

Parameter Low Risk Moderate Risk High Risk

Aquatic animals (fish and lotic

invertebrates) RQ<0.05 N/A RQ>0.05

Lotic invertebrates RQ<0.1 0.1<RQ>0.5 RQ>0.5

Joint Probability Analysis

Parameter Low Risk Moderate Risk High Risk

Aquatic animals (fish and lotic

invertebrates) PE<5% N/A PE>5%

Lotic invertebrates PE<10% 10%<PE>50% PE>50%

PE=Probability Exceedance

RQ=Risk Quotient

5.0 Exposure Assessment

5.1 United States Geological Survey at Marion Drain

The United States Geological Survey (USGS) conducted some of the first measurements

of pesticides, nutrients and physical parameters (i.e. discharge, temperature, specific

conductance, dissolved oxygen, pH, turbidity, suspended sediment, total and dissolved organic

carbon, nitrogen, total and dissolved phosphorus) in the Marion Drain watershed. Marion Drain

at Indian Church Road in Granger, was sampled twice, once in July of 1988 and June of 1989.

Also, Toppenish Creek at Indian Church Road in Granger was sampled seven times between

May 1988 and June 1989. No detectable water concentrations were observed for chlorpyrifos

(Rinella et al. 1992).

5.2 Pesticide Sampling at Marion Drain

A total of 205 grab samples (not including replicates) were collected by ECY from

Marion Drain at Indian Church Road between 2003 and 2008 between the months of February

and October by ECY (Burke et al. 2006; Sargeant et al. 2010). Additionally in 2006, the

Washington State University‘s Food and Environmental Quality Lab (FEQL) conducted a small

study (collecting four samples) to assess chlorpyrifos water concentrations in the Marion Drain

at Indian Church Road, Granger (FEQL 2006). Appendix B lists the sample date, concentration

(µg/L) and data qualifiers from the ECY sampling campaigns and the FEQL study.

For the ECY data sets, the Average lower Limit of Quantification (ALOQ) for 2003 was

0.027 µg/L (Anderson et al. 2004). The ALOQ for 2004 was 0.025 µg/L, 0.026 µg/L for 2005

and 0.032 µg/L for 2006, 2007, and 2008 (Burke et al. 2006; Sargeant et al. 2010). The lower

practical quantification limit for the FEQL (2006) study was 0.02 µg/L. The 2003 and the 2008

ECY samples had the lowest number of quantifiable chlorpyrifos water concentrations while

2004, 2006 and 2007 samples had several frequent and quantifiable water residue concentrations.

Of the 209 samples, 109 (52.2%) were positive for chlorpyrifos. However, only 25 (12.0%)

samples were above the lower limit of quantification. Between 2004 and 2007, 17 samples

(8.1%) exceeded the WAC chronic limit of 0.041 µg/L, and six samples (2.9%) exceeded the

WAC chlorpyrifos acute criteria of 0.083 µg/L, once each year in September and October. Data

from the ECY from 2003-2008 and from the FEQL 2006 study were analyzed for temporal

distribution of exposure events (Figure 3).

Temporal Distribution of Chlorpyrifos Detection

0.000.010.020.030.040.050.060.070.080.090.100.110.120.130.140.15

Figure 3. Temporal distribution of chlorpyrifos exposure events between 2003 and 2008.

Temporal distribution of the data showed that detections occurred throughout the

agricultural season. Elevated detections were at the beginning and end of the agricultural season.

The highest, most frequent detections occurred in September and October months.

5.2.1 ECY 2007 Intensive Sampling at Marion Drain

The ECY conducted a 22 day sampling campaign at Marion Drain at Indian Church Road

in Granger from April 24 until May 15, 2007 (Dugger et al. 2008). Samples were collected

approximately 100 meters down stream of the bridge at Indian Church Road. Once in the field,

stream pH, conductivity, dissolved oxygen, and water temperature was measured. Both grab and

passive samples were gathered. Grab samples for pesticides, total suspended solids, and total

and dissolved organic carbon samples were collected in a one-liter transfer container. Two types

of passive samplers were deployed: Semipermeable Membrane Device (SPMD) and Polar

Organic Chemical Integrative Sampler (POCIS) (Burke et al. 2007; Dugger et al 2008).

Sampler protective canisters were submerged and attached to a cable in a slow moving

eddy as shown in (Figure 4). Each passive sampler unit was a composite of either five SPMDs or

six POCISs in the same canister. The samplers were deployed in duplicate. The SPMD and

POCIS were monitored daily and any sediment or algae build up was removed.

Figure 4. Passive sampler deployment in Marion Drain (Dugger et al. 2008).

5.2.1.1 Daily Grab Sample Results

Daily grab samples detected a total of 16 pesticides, 13 herbicides and three insecticides

(Table 5) during the course of the 22 day study. One malathion residue detection of 0.082 µg/L

approached the WAC standard of 0.1 µg/L for chronic exposure. Benzaton (38% of samples),

bromoxynil (67%), chlorpyrifos (77%), dicamba I (57%), diuron (14%), simazine (50%) were

detected at or below the lower limit of quantification. Carbaryl was detected once above and

three times below (19%) the lower limit of quantification. Several detections of 2,4-D (67%),

Eptam (41%), MCPA (57%), atrazine (68%) and trifluralin (82%) were detected at or above the

lower limit of quantification. Low levels of the herbicides terbacil (100%) and pendimethalin

(95%) were frequently detected above the lower limit of quantification. A few sporadic residues

were detected in the daily samples: clopyralid (5%) and malathion (5%).

Table 5. Summary of intensive sampling grab samples results.

Chemical Detection Frequency Median (ng/L) Maximum (ng/L)

Terbacil 100% 200 490

Pendimethalin 95% 54 98

Trifluralin 82% 25 40

Chlorpyrifos 77% 14 22

Atrazine 68% 22 36

2,4-D 67% 47 500

Bromoxynil 67% 36 63

Dicamba I 57% 15 61

MCPA 57% 60 130

Simazine 50% 23 36

Eptam 41% 30 71

Bentazon 38% 25 53

Carbaryl 19% 13 35

Diuron 14% 28 47

Clopyralid 5% 27 27

Malathion 5% 82 82

5.2.1.2 POCIS and SPMD Results

All POCIS results were considered qualitative due to poor laboratory control sample

(LCS) and surrogate recovery and inconsistency between replicates. Excluding the blank

contamination, the POCIS detected seven pesticides, and one degradate (Table 6). Most pesticide

concentrations in the POCIS extracts were near the lower limit of quantification. Bromoxynil,

MCPA, 2,4-D, atrazine, bentazon, and terbacil were present by both the grab and POCIS

samples. SPMD samplers detected 8 pesticides and 3 degradates (Table 6 and 7). Five of these

compounds were not found by daily grab sampling (4,4‘DDT, 4,4‘DDD, 4,4‘DDE, endosulfan I,

and trans-nonachlor) due to higher detection limits. Pendimethalin, trifluralin, and chlorpyrifos

were the only compounds detected in both SPMDs and grab samples. Calculated water

concentrations were much lower in the SPMDs than the average of the daily grab samples.

Table 6. POCIS results (ng/POCIS). Table 7. SPMD results (ng/L).

Chemical Mean Chemical Mean

Atrazine 0.15 4,4'-DDT 0.015

Bentazon 0.101 4,4'-DDE 0.052

Eptam 0.099 4,4'-DDD 0.016

MCPA 0.115 Endosulfan I 2

2,4-D 0.27 Trans-Nonachlor 0.003

Bromoxynil 0.205 Trifluralin 0.034

Methomyl 0.044 Pendimethalin 0.9

Oxamyl 0.01 Chlorpyrifos 1.3

5.3 Chlorpyrifos Detections by Month Between 2003 and 2008

In 2007, three samples were taken from Marion Drain during February, all of which had

no pesticide residues above the lower limit of quantification. No additional samples were taken

from Marion Drain in February during other campaigns. Between 2004 and 2008, 13 samples

were taken from Marion Drain in March, and five of them were the below the lower limit of

quantification for chlorpyrifos. Two samples (March 23, 2005—0.054 µg/L; March 27, 2007—

0.044 µg/L) samples exceeded the WAC chronic limit of 0.041 µg/L. On March 14, 2007, a

sample was quantified at 0.250 µg/L and was not considered a positive detect due to operator

error. In April months between 2003 and 2008, 31 grab samples were acquired with 19

detections. All samples were below the lower limit of quantification, and their values estimated.

The highest estimated concentration was April 8, 2004 at 0.083 µg/L followed by 0.038 µg/L

April 4, 2007, and the 18 additional estimate concentrations ranged from 0.002 to 0.024 µg/L.

From 2003 to 2008, the month of May was sampled 38 times and had 24 positive detections but

none above the lower limit of quantification. May estimated concentrations ranged from 0.002

to 0.030 µg/L. June had 11 positive detects out of 26 samples, but none were above the lower

limit of quantification. Estimated concentrations for May ranged from 0.003 µg/L to 0.028 µg/L.

July had four positive detects out of 23 samples, and two were at or above the detection limit:

July 18 and July 30, 2007 at 0.039 and 0.032 µg/L, respectively. Estimated concentrations for

July ranged from 0.003 µg/L to 0.028 µg/L. August had seven detects in 2007 and 2008 out of

24 samples. One sample was above the lower limit of quantification (August 27, 2007), and the

other six detects were estimated between 0.009 to 0.017 µg/L. September had the most frequent

and highest concentrations relative to other months. Out of 27 grab samples taken from Marion

Drain, 24 were positive for chlorpyrifos. Seventeen of the 24 positive detects were above the

lower limit of quantification. The highest concentration detected was 0.400 µg/L on September

21, 2005. Estimated concentrations ranged from 0.004 to 0.120 µg/L.

5.4 Exposure Modeling Analysis

The results from ECY sampling campaigns (2003-2008) and the FEQL (2006) study were

modeled to lognormal probability distributions and probit models, based on exposure patterns.

The data were grouped and modeled in three time frames: March and April, May thru August

and September and October. The month of February was only sampled three times in 2003 and

was not modeled. No samples were available from November thru January. The ECY applies

data qualifiers to the analysis results (Burke et al. 2006). Exposure data qualified as blank, J or

NJ were considered positive detects, and the given values used in the models. J and NJ values

were below the lower limit of quantification, and their values were estimated. Data qualified as

U or UJ were considered as non-detects and assigned half the lower limit of quantification.

Replicate samples were averaged. SPMD and POCIS values were not included in the statistical

models but served as a qualitative measure of chlorpyrifos presence. Table 8 lists the arithmetic

mean and standard deviation for the lognormal concentration models and the slope and intercept

for the probit concentration models for all three temporal time frames as well as values at the 50th

and 95th

percentiles and the percent of samples exceeding the WAC 0.041 µg/L criteria WAC for

chlorpyrifos. Appendix C contains the graphical representation of the lognormal concentration

models for March thru October.

Table 8. Model parameters and results for the lognormal and probit concentration models for all

three temporal time frames.

6.0 Effects Assessment

6.1 Laboratory Toxicity Studies

In aquatic vertebrates and invertebrates, the major route of chlorpyrifos exposure is

through water, either by integumental absorption, diffusion across gills or other respiratory

structures, or oral uptake via diet. At relatively high concentration, such as those resulting from

an accidental spill, organophosphate insecticides induce hyperactivity, muscle twitching, loss of

equilibrium, and ultimately death. At sublethal concentrations, organophosphates impair several

important physiological and behavioral processes including swimming performance, swimming

stamina, prey capture, predator detection, predator avoidance, migration, learning, and

conspecific social interactions (Sandahl et al. 2005). Acute 96-h LC50 values for chlorpyrifos

for fish range from 2.4 µg/L for bluegill sunfish to 806 µg/L for channel catfish (U. S. EPA

Concentration model parameters and results

Lognormal model

March thru

May thru

August

September

thru October

Arithmetic mean (µg/L) 0.021 0.015 0.04

Arithmetic standard deviation 0.024 0.007 0.043

Concentration at the 50th percentile 0.013 0.013 0.027

Percent of samples exceeding the

WAC 0.041 µg/L criteria 14.4 1.9 32.7

Probit model

Slope 2.189 4.27 2.414

Intercept 9.098 12.999 8.797

Percent of samples exceeding the

WAC 0.041 µg/L criteria 11.5 0.9 31.5

1986). Acute 96-h LC50 values for chlorpyrifos to lotic invertebrates range from 0.053 µg/L for

Ceriodaphnia dubia to 10 µg/L for the stonefly Pteronarcys californica (U. S. EPA 2007).

Amphipods and daphnids are the most sensitive taxa (Table 8). Toxicity of chlorpyrifos appears

to correlate inversely with an organism‘s surface area (U. S EPA 1986).

Since aquatic invertebrates are more sensitive to chlorpyrifos residues than fish, the

majority of the effects assessment will focus on the effects of chlorpyrifos residues on aquatic

invertebrates. Biotic and abiotic factors affect the toxicity of chlorpyrifos to invertebrates.

Buchwalter et al. (2003) demonstrated that chlorpyrifos uptake rates for air-breathing and gill-

bearing aquatic insects increases with temperature. Uptake by gill-bearing insects was 2.75-fold

higher than by air-breathing aquatic insects.

Naddy et al. (2001) studied the effect of pulsed chlorpyrifos concentration on Daphnia

magna. The study examined concentration, duration, interval and frequency of exposure. In

standard 48-hour exposure tests with D. magna chlorpyrifos had an LC50 between 0.1-0.5 µg/L .

After 6.5 and 12.2 hours at a concentration of 0.8 and 0.4 µg/L, respectively, half of the D.

magna population died. D. magna were exposed to either 12 hours at 0.5 µg/L or 6 hours at 1.0

µg/L from 0-4 days at 25oC. Two consecutive six hour pulses at 0.5 µg/L stunned 50% of the

population, but individuals recovered after seven days. A minimum of 72 hours were required

for D. magna to recover from a 0.5 µg/L exposure. Pulsed exposure experiments suggested that

D. magna can recover from acutely toxic exposures to chlorpyrifos given adequate time intervals

between pulses

Zalizniak et al. (2006) investigated the effect of lethal and sublethal concentrations of

chlorpyrifos on several generation of D. carinata. Organisms were exposed to 0.005 to 0.5 µg/L

over twenty-days. The following two generations were also tested. Measured endpoints

included survival, fecundity, time to first brood, and number of offspring per female. The first

generation results showed that at 0.5 LC50 (0.25µg/L) all organisms died after 13 days, and at an

exposure equivalent to the LC50 ( 0.5µg/L), all died after seven days. There was a reduction in

the number of offspring per female and body weight for the 0.01, 0.05 and 0.1 µg/L treatments.

The second generation was tested at the same concentration as their parents. Results for the

second generation generally showed statistically insignificant effects in all surviving groups.

There was a linear response in mortality for the 0.5 LC50 group and an increase in the number of

offspring from the 0.1 LC50 (0.05 µg/L). Third generation offspring showed little change in

toxicity relative to the first generation.

Anderson et al. (2003) employed several bioassays using Ceriodahnia dubia and Hyalella

azteca on sediment toxicity from samples taken from the bank and mid-stream at the confluence

of an agricultural drain and Salinas River. The results showed that sediment on the bank one

meter from the confluence of the drain and in the drain produced 100% mortality with every

chlorpyrifos positive sample. Diazinon was also present in the sample but C. dubia appeared not

to be affected. Chlorpyrifos concentrations ranged from 0.048 to 2.524 µg/L. H. azteca was also

sensitive for sediment samples containing chlorpyrifos. Invertebrate community samples

downstream of the agricultural drain showed significant losses in species abundance. Affected

taxa included Ephemeroptera and Chironomidae.

6.2 Microcosm and Mesocosm Studies

Mesocosm and microcosm studies have demonstrated that cladocerans are the most

sensitive organisms to chlorpyrifos exposures followed by copepods and rotifers (Van der Brink

et al. 1996; Kersting et al. 1997; Gidding et al. 1997; Van Wijngaarden et al. 2005; Daam et al.

2007; Lopez-Manicisidor et al. 2008). Van Wijngaarden et al. (2005) observed that treatments

greater than 0.005 µg/L eradicated cladoceran taxa. Lopez-Manicisidor et al. (2008) calculated

NOEC of 0.012 µg/L for arthropod populations. Following chlopyrifos exposure, cladocera

populations decline while rotifer populations increase, due to loss of competition for food (van

der Brink et al. 1996; Kersting et al. 1997; Gidding et al. 1997; Van Wijngaarden et al. 2005;

Daam et al. 2007; Lopez-Manicisidor et al. 2008). Several mesocosm studies have suggested a

community invertebrate NOAEC of 0.1 µg/L (van der Brink et al. 1996; Kersting et al. 1997;

Gidding et al. 1997; Van Wijngaarden et al. 2005; Lopez-Manicisidor et al. 2008). One

microcosm study calculated a community NOEC between 0.005-0.05 µg/L (Daam et al. 2007).

Invertebrate communities recovered over a period of 12-24 weeks following chlopyrifos

exposures ranging from 0.033 to 44 µg/L (van der Brink et al. 1996; Lopez-Manicisidor et al.

2008). Longer term impacts of chlorpyrifos exposure greater than a year have not been studied.

6.3 Chlorpyrifos and Atrazine Synergism

Belden et al. (2000) showed that atrazine potentiates chlorpyrifos toxicity. Jin-Clark et

al. (2002) discovered a 1.8-fold increase in toxicity when 200 µg/L of atrazine was mixed with

0.25 µg/L chlorpyrifos. Some effects were seen at concentrations of 10 µg/L atrazine and 0.25

µg/L chlorpyrifos. Mehler et al. (2008) calculated a chlorpyrifos 96-h LC50 of 5.3, >338 and

0.72 µg/L for L. macrochirus, Pimephales promelas and Chironomus tentans, respectively.

When the organisms were pre-exposed to 1000 µg/L of atrazine, chlorpyrifos 96-h LC50 for L.

marcochirus and P. promelas was unaffected, but the 96-h LC50 for C. tentans dropped to 0.4

µg/L.

Concentrations of atrazine and chlorpyrifos found in Marion Drains are much lower that

those used in the reviewed mixture studies, and thus, synergism is not expected. However,

additional pesticides were detected along with chlorpyrifos. Terbacil is commonly detected in

the drain as was well as 2,4-D and pendamethalin, and the effect these mixtures would have on

toxicity has not been studied. However, given their primary mode of action and the low

concentrations detected, their effect on chlorpyrifos toxicity is assumed to be negligible.

6.4 Effects Model Analysis

The 96-h LC50 aquatic animal data set (Table 9) was modeled to generate a Species

Sensitivity Distribution (SSD) for an assemblage of fish and lotic invertebrates. For multiple

values for a species or genus, a geometric mean was calculated and used in the model.

The LC50s for the assemblage of aquatic animals and lotic invertebrates (Table 8) were

also fit to a lognormal distribution and a probit model. A graphical representation of the

lognormal models can be found in Appendix D. Table 10 lists the arithmetic mean and standard

deviation used in the lognormal 96-h LC50 models, the slope and intercept of the probit 96-h

LC50 models, the concentrations affecting 5, 10 and 50% of the organisms, and the percentage

of organisms affected by the WAC chronic criteria for chlorpyrifos. A single value for a mollusk

(Alexa hypnorum) and a worm (Branchiura sowerbyi) was excluded from the lotic invertebrate

lognormal and probit models because they would have greatly skewed the final model.

Table 9. 96-h LC50 data set.

Species Scientific

Species Common

(µg/L) Reference

Amphipoda Scud Order 0.11 Mayer 1974

Ceriodaphnia dubia Water Flea 0.06 Bailey et al. 1996

Ceriodaphnia dubia+ Water Flea 0.06

Daphnia longispina Water Flea 0.3 Van Wijngaarden et al. 1993

Diaptomus forbesi Calanoid Copepod 3.6 Amma et al. 1996

Gammarus fasciatus Scud 0.32 Sanders 1972

Gammarus lacustris Scud 0.11 Mayer et al. 1986

Gammarus

pseudolimnaeus Scud 0.18 Siefert 1987

Gammarus pulex Scud 0.07 Van Wijngaarden et al. 1993

Gammarus sp.+ Scud 0.15

Hyalella azteca Scud 0.0651 Trimble et al. 2006

Hyalella azteca Scud 0.0427 Anderson et al. 2002

Hyalella azteca Scud 0.14 Siefert 1987

Hyalella azteca Scud 0.04 Ankley et al. 1995

Hyalella azteca+ Scud 0.07

Orconectes immunis Crayfish 6 Phipps et al. 1985

Simocephalus vetulus Water Flea 0.5 Van Wijngaarden et al. 1993

Chaoborus obscuripes Midge 6.6 Van Wijngaarden et al. 1993

Chironomus dilutus Midge 0.459 Harwood et al. 2009

Chironomus dilutus Midge 0.825 Harwood et al. 2009

Chironomus riparius Midge 0.09 Hooftman et al. 1993

Chironomus riparius Midge 0.34 Hooftman et al. 1993

Chironomus tentans Midge 0.47 Ankley et al. 1995

Chironomus tentans+ Midge 0.35

Claassenia sabulosa Stonefly 0.57 Mayer et al. 1986

Cloeon dipterum Mayfly 0.3 Van Wijngaarden et al. 1993

Corixa punctata Water Boatman 2 Van Wijngaarden et al. 1993

Corixa punctata Water Boatman 1.94 Van Wijngaarden et al. 1993

Corixa punctata+ Water Boatman 2

Leptoceridae Longhorn Caddisfly 0.77 Siefert 1987

Neoplea striola Pygmy Backswimmer 1.22 Siefert 1987

Neoplea striola Pygmy Backswimmer 1.56 Siefert 1987

Neoplea striola+ Pygmy Backswimmer 1.38

Peltodytes sp. Beetle 0.8 Federle et al. 1976

Plecoptera Stonefly Order 10 Mayer 1974

Pteronarcella badia Stonefly 0.38 Sanders et al. 1968

Pteronarcys californica Stonefly 10 Mayer et al. 1986

Aplexa hypnorum Snail 806 Phipps et al. 1985

Branchiura sowerbyi Oligochaete 66 Amma et al. 1996

Anguilla anguilla Common Eel 540 Ferrando et al. 1991

Bidyanus bidyanus Silver Perch 17 Patra et al. 2007

Carassius auratus Goldfish 806 Phipps et al. 1985

Catla catla Catla 300 Tilak et al. 2004

Catla catla Catla 1660 Hossain et al. 2000

Catla catla Catla 350 Tilak et al. 2004

Catla catla+ Catla 559

Channa punctata Snake-Head Catfish 365 Jaroli et al. 2005

Cirrhinus mrigala Carp, Hawk Fish 2350 Hossain et al. 2000

Cirrhinus mrigala Carp, Hawk Fish 550 Tilak et al. 2004

Cirrhinus mrigala Carp, Hawk Fish 650 Tilak et al. 2004

Cirrhinus mrigala+ Carp, Hawk Fish 944

Cyprinus carpio Common Carp 8 De Mel et al. 2005

Gambusia affinis Western Mosquitofish 297.63 Rao et al. 2005

Gambusia affinis Western Mosquitofish 280 Carter, F.L., and J.B. Graves

Gambusia yucatana Yucatan Gambusia 11 Rendon-von Osten et al. 2005

Gambusia sp+ 97

Gasterosteus aculeatus Threespine Stickleback 8.5 Van Wijngaarden et al. 1993

Heteropneustes fossilis Indian Catfish 2200 Srivastava et al. 1995

Heteropneustes fossilis Indian Catfish 2200 Srivastava et al. 1997

Heteropneustes

fossilis+ Indian Catfish 2200

Ictalurus punctatus Channel Catfish 806 Phipps et al. 1985

Ictalurus punctatus Channel Catfish 2077 Dalvi et al. 1998

Ictalurus punctatus Channel Catfish 280 Mayer et al. 1986

Ictalurus punctatus+ Channel Catfish 777

Labeo rohita Rohu 2350 Hossain et al. 2000

Labeo rohita Rohu 300 Tilak et al. 2004

Labeo rohita Rohu 470 Tilak et al. 2004

Labeo rohita+ Rohu 692

Lepomis macrochirus Bluegill 10 Phipps et al. 1985

Lepomis macrochirus Bluegill 5.8

Office of Pesticide Programs

Lepomis macrochirus Bluegill 2.6 Mayer 1974

Lepomis macrochirus Bluegill 1.7 Mayer et al. 1986

Lepomis macrochirus Bluegill 1.3

Lepomis macrochirus Bluegill 30 Carter, F.L., and J.B. Graves

Lepomis macrochirus+ Bluegill 6

Morone saxatilis Striped Bass 1000

Oncorhynchus clarki Cutthroat Trout 13.4 Mayer et al. 1986

Oncorhynchus clarki Cutthroat Trout 26 Mayer et al. 1986

Oncorhynchus clarki+ Cutthroat Trout 14

Oncorhynchus mykiss Rainbow Trout 8 Holcombe et al. 1982

Oncorhynchus mykiss Rainbow Trout 9 Phipps et al. 1985

Oncorhynchus mykiss Rainbow Trout 27

Oncorhynchus mykiss Rainbow Trout 8

Oncorhynchus mykiss Rainbow Trout 2000 Kikuchi et al. 1996

Oncorhynchus mykiss Rainbow Trout 11 Mayer 1974

Oncorhynchus mykiss Rainbow Trout 1 Mayer et al. 1986

Oncorhynchus mykiss Rainbow Trout 7.1 Mayer et al. 1986

Oncorhynchus mykiss Rainbow Trout 13.5

Oncorhynchus mykiss+ Rainbow Trout 19

Oreochromis

mossambicus Mozambique Tilapia 25.97 Rao et al. 2003

Oreochromis

mossambicus Mozambique Tilapia 4.8 Moorthy et al. 1982

Oreochromis

mossambicus Mozambique Tilapia 25.78 Rao 2008

Oreochromis

mossambicus Mozambique Tilapia 52 Amma et al. 1996

Oreochromis

mossambicus+ Mozambique Tilapia 20

Pimephales promelas Fathead Minnow 200 Geiger et al. 188

Pimephales promelas Fathead Minnow 203 Holcombe et al. 1982

Pimephales promelas Fathead Minnow 506 Geiger et al. 188

Pimephales promelas Fathead Minnow 542 Phipps et al. 1985

Pimephales promelas Fathead Minnow 120 Jarvinen et al. 1982

Pimephales promelas Fathead Minnow 140

Pimephales promelas Fathead Minnow 122.2 Jarvinen et al. 1988

Pimephales promelas+ Fathead Minnow 186

Poecilia reticulata Guppy 7.17 De Silva et al. 2002

Poecilia reticulata Guppy 7.17 De Silva et al. 2005

Poecilia reticulata+ Guppy 7.17

Pungitius pungitius Ninespine Stickleback 4.7 Van Wijngaarden et al. 1993

Salvelinus namaycush Lake Trout, Siscowet 244 Mayer et al. 1986

Salvelinus namaycush+ Lake Trout, Siscowet 91

Tilapia nilotica Nile Tilapia 117 Pathiratne et al. 1998

Tilapia zillii Tilapia 22.74 Golow et al. 1994

Tilapia zillii Tilapia 240 Shereif 1989

Tilapia sp.+ 86

(Note: Data was retrieved from ECOTOX database (2007). Data without references were the

geometric species mean and denoted with a +.)

Table 10. Model parameters and results for the 96-h LC50 lognormal and probit models.

96-h LC50 model parameters and results

Lognormal model

Aquatic

Animals Lotic Invertebrates

Arithmetic Mean 1250.7 2.9

Arithmetic Standard Deviation 135573.8 10.3

Concentration affecting 5% of organisms (µg/L) 0.048 N/A

Concentration affecting 10% of organisms (µg/L) N/A 0.070

Percent of organisms affected by WAC 0.041 µg/L

criteria 4.5 5.8

Probit model

Slope 0.69204 1.23767

Intercept 4.26496 5.14758

Concentration affecting 5% of organisms (µg/L) 0.075 N/A

Percent of organisms affected by WAC 0.041 µg/L

criteria 3.6 3.3

7.0 Risk Characterization

A joint probability curve analysis and a Monte Carlo simulation were used to

probabilistically characterize the 96-h lethal effects of chlorpyrifos to an assemblage of aquatic

organisms and lotic invertebrates that served as surrogates for putative organisms inhabiting the

Marion Drain. Both methods are based on a probability distribution but differ in their approach.

7.1 Joint Probability Analysis

Exposure concentrations and 96-h LC50 data for selected species and chlorpyrifos detect

concentrations were log transformed, ranked and transformed to probits. Probits and log

transformed concentration data were linearly regressed. The slope and the x-intercept were used

to calculate the concentration affecting a selected percentage of species and the percentage of

observations (i.e., water samples containing chlorpyrifos) exceeding the selected percentage

(Solomon et al. 2000). Calculations were performed using a programmed Microsoft Excel

spreadsheet obtained courtesy of Keith Solomon (University of Guelph, Ontario, Canada), who

had reported previously on methods for conducting a joint probability analysis (Solomon et al.

2000). The results of the joint probability analysis were compared to the risk criteria in Table 4.

Appendix F contains tabular delineations of exceedance data generated by the joint probability

analysis.

7.1.1 Aquatic Animal Risk: March thru October

Figure 5 depicts a joint probability analysis that modeled the distribution of the

percentage of water samples with chlorpyrifos concentrations exceeding any species LC50

relative to the percent of the aquatic assemblage affected during the months of March through

October.

The results for March and April indicated that 11.1% of the water samples could

adversely affect 5% of the aquatic assemblage. At the 50th

and 95th

percentile of exposure, 2.1

and 6.5% of the aquatic assemblage would be affected, respectively. The results for May thru

August showed that 5% of the aquatic assemblage could be affected by 0.9% of the water

samples. At the 50th

and 95th

percentile of exposure, 2.1 and 3.9% of the aquatic assemblage

would be affected, respectively.

September and October suggest the highest probability of risk, relative to the other two

temporal time frames. The results for September and October indicated that 26.7% of the water

samples could adversely affect 5% of the aquatic assemblage. At the 50th

and 95th

percentile of

exposure, 3.4 and 8.8% of the aquatic assemblage would be affected, respectively. Table 11

summarizes the results of the joint probability risk analysis to the assemblage of aquatic animals.

0 2.5 5 7.5 10

Percent of Species Affected

March and April May thru August September and October

Figure 5. Joint probability risk of chlorpyrifos acute toxicity predicted for an assemblage of

aquatic animals between March and October in the Marion Drain.

exposures affecting five percent of an assemblage of aquatic animals between March and

October in the Marion Drain.

Aquatic animal joint probability acute toxicity risk: March thru October

March thru

May thru

August

September thru

October

Percent affected at the 50th percentile of

exposure 2.1 2.1 3.4

Percent affected at the 95th percentile of

exposure 6.5 3.9 8.8

Percent of observations affecting 5% of

the organisms 11.1 0.9 26.7

7.1.2 Lotic Invertebrate Risk: March thru October

Figure 6 shows the distribution of the percentage of concentrations exceeding any species

LC50 versus the percent of the lotic invertebrate affected by that exceedance. In March and

April months, the results indicated that 5.8 and 0.006% of the samples could affect 10 and 50%

of the lotic invertebrates, respectively. At the 50th

and 95th

percentile of exposure, 1.5 and 10.8%

of the aquatic assemblage could be affected, respectively. In May thru August months, the

results indicated that 0.107 and 3.5E-12% of the samples would affect 10 and 50% of the lotic

invertebrates, respectively. At the 50th

and 95th

percentile of exposure, 1.5 and 4.5% of the

aquatic assemblage would be affected, respectively. In September thru October months, the

results indicated that 15.6 and 0.022% of the samples would affect 10 and 50% of the lotic

invertebrates, respectively. At the 50th

and 95th

percentile of exposure, 3.6 and 17.0% of the

aquatic assemblage would be affected, respectively. Table 12 summarizes the results of the joint

probability risk analysis to the lotic invertebrates and a complete table of results can be found in

Appendix F.

Percent Species Affected

March and April May thru August September and October

Figure 6. Joint probability risk of chlorpyrifos acute toxicity predicted for lotic invertebrates

between March and October in the Marion Drain.

exposures affecting 10 and 50 percent of lotic invertebrates between March and October in the

Marion Drain.

Lotic invertebate joint probability acute toxicity risk: March thru October

March thru

May thru

August

September thru

October

Percent affect at the at the 50th percentile

of exposure 1.5 1.5 3.6

Percent affect at the at the 95th percentile

of exposure 10.8 4.5 17.0

the organisms 5.8 0.107 15.6

the organisms 0.006 3.5E-12 0.022

7.2 Monte Carlo Simulation

One-dimensional (1D) Monte Carlo analysis utilizing Crystal Ball software (Crystal

Ball® 7.3.1; Decisioneering, Denver, CO.) simulated the RQ from probabilistic exposures of

aquatic animals to chlorpyrifos residues. As mentioned in the problem formulation, the 1D

Monte Carlo simulation of the RQ characterized the risk of an adverse effect by randomly

sampling from the environmental concentration lognormal probability distribution and the 96-h

LC50 probability lognormal distribution and inputting selected values into Equation 1. The 1D

Monte Carlo simulation was implemented using 10,000 trials. The results of each simulation

were then compiled into a distribution of RQs. The median and 95th

percentile RQ and the

exceedance above the risk criteria for endangered species were evaluated (Table 13).

Risk Quotient (RQ) = Environmental Concentration Probability Distribution / LC50

Probability Distribution Equation 1

7.2.1 Aquatic Animal Risk: March thru October

Table 13 lists the median and 95th

percentile RQs and the percent of RQs less than 0.05

for each of the three temporal time frames. Graphical representation of the results can be found

in Appendix E. The results of the Monte Carlo simulation indicate that the median RQ for

March through October is less than 0.002, which falls well below the 0.05 risk criteria.

However, at the 95th

percentile, the RQ ranges from 0.204 to 0.396, which is four to eight times

the 0.05 risk criteria and thus would be interpreted by regulators as a high level of risk. Between

12 and 17 percent of the RQs exceed the 0.05 criterion for the three temporal time frames.

percentile RQs for the aquatic animals and the percent of RQs

greater than 0.05 from March thru October.

Aquatic animal risk quotient results: March thru October

March thru April May thru August

September thru

October

Median RQ 0.001 0.001 0.002

95th Percentile RQ 0.234 0.204 0.396

Percent of RQs>0.05 12.5 11.5 16.6

7.2.2 Lotic Invertebrate Risk: March thru October

Table 14 lists the median and 95th

percentile RQs and the percent of RQs less than 0.10

and 0.50 for each of the three temporal time frames. Graphical representation of the results can

be found in Appendix E. The results of the Monte Carlo simulation indicate that the median RQ

from March thru October ranges from 0.017 to 0.035, which falls below the 0.10 risk criterion.

At the 95th

percentile, the RQs between March thru August range from 0.271 to 0.364, indicating

a moderate amount of risk to the lotic invertebrates. Between September and October the 95th

percentile RQ is 0.731, which indicates that the lotic invertebrates are at high risk. Furthermore,

between September and October, approximately 29% of the RQs exceeded the 0.10 moderate

risk criterion and 7.5% of the RQs exceeded the 0.5 RQ risk criteria for aquatic invertebrates.

percentile RQs for lotic invertebrates and the percent of RQs

greater than 0.10 and 0.50 from March thru October.

Lotic invertebrate risk quotient results: March thru October

March thru April May thru August September thru October

Median RQ 0.017 0.017 0.035

95th Percentile RQ 0.364 0.271 0.731

Percent of RQs>0.1 17.2 14.9 28.9

Percent of RQs>0.5 3.7 2.5 7.5

7.3 Discussion

Single species laboratory studies have demonstrated that chlorpyrifos is highly toxic to

invertebrates, and microcosm and mesocosm studies have reported adverse effects from low dose

chlorpyrifos exposures that invertebrate populations require months to recover from. Based on

EPA criteria delineated in Urban and Cook (1986) and Stephen et al. (1985), both the joint

probability analysis and the Monte Carlo simulation of RQs demonstrated high risk to the aquatic

animals and moderate to high risk to lotic invertebrates at upper ends of the exposure

distributions. September and October showed the highest amount of risk to the aquatic animals

and the lotic invertebrates relative to the other two temporal time intervals, and the impact occurs

just before invertebrates become dormant, allowing for a shorter period of recovery. However,

there is only a small probability of exceeding the high risk criterion. In addition, chlorpyrifos is

only used as a dormant spray during March and April, and the higher risk in September and

October months probably results from late season non-orchard crop use.

The results of this assessment agrees with the Burke et al. (2006) suggestion of

unacceptable risk at Marion Drain and the NOAA (2008) designation of impacted habitat from

chlorpyrifos exposures. However, the magnitude of risk in this assessment is much less then

those suggested by Burke et al. (2006) and NOAA (2008) due to several different aspects in the

risk characterization. In the Burke et al. (2006) assessment, risk to aquatic invertebrates was

assessed using the RQ method with D. magna 96-h LC50 values of 0.1 µg/L. According to

criteria discussed in Urban and Cook (1986), RQs for invertebrates that are important for

protecting productivity of endangered species should be less than 0.5. Thus, for every

chlorpyrifos concentration above 0.05 µg/L, the RQ would have been exceeded. About five

chlorpyrifos concentrations in Marion Drain out of over 25 detections (i.e., at least 20%) would

have exceeded the RQ of 0.5. In contrast, the Monte Carlo analysis suggested that less than 5%

of the March-August detections and less than 10% of the September-October detections would

have exceeded the 95th

percentile RQ. Coincidently, the 96-hr LC50 value of 0.1 µg/L for D.

magna is the same value calculated for the mesocosm NOEC for invertebrate communities (van

der Brink et al. 1996; Kersting et al. 1997; Gidding et al. 1997; Van Wijngaarden et al. 2005;

Lopez-Manicisidor et al. 2008). The NOAA (2008) risk assessment, which did not take a

probabilistic approach to risk, deemed monitoring data gathered from ECY unreliable for

characterizing risk and ultimately decided to simulate exposure events using the 60-day average

residues generated by the PRZM/EXAMS model,. Based on meteorological data from Christmas

tree plantation scenarios in Oregon, PRZM/EXAMS model results were 0.84 µg/L for 60-day

chronic exposures. PRZM/EXAMS is a ―farm pond‖ based model and does not calculate

concentrations in flowing water, but NOAA (2008) believes that PRZM/EXAMS does represent

first order streams. The NOAA (2008) assessment considered these concentrations to not be

―worst case scenarios‖ based on monitoring data and may have actually underrepresented actual

concentrations. Regardless of the climate and agricultural differences and between Washington

State and Oregon, only a single detection by the ECY was greater than 0.4 µg/L between 2003

and 2008 (Burke et al. 2006; Sargeant et al. 2010). Weekly ECY sampling campaigns have

clearly demonstrated that the PRZM/EXAMS model results of 0.84 µg/L for 60-day chronic

exposures used in NOAA (2008) were unrealistic. In addition, the NOAA assessment also

assumed that chlorpyrifos exposures occurred with other organophosphate insecticide residue

exposures, and thus additive toxicity could increase hazard over that suggested by the

chlorpyrifos residues alone. However, chlorpyrifos residues rarely occurred in a water sample

simultaneously with other organophosphate residues (Burke et al. 2006; Sargeant et al. 2010).

The results of this risk assessment conflict with the Geisy et al. (1999) risk assessment of

chlorpyrifos risk to North American aquatic habitats in which joint probability analysis indicated

that risk was low except to aquatic invertebrates. Geisy et al. (1999) and this assessments

calculations and perspective of risk differed in many aspects. The Geisy et al. (1999) assessment

used a 48-h LC50 data set derived from a 96-h LC50 data set. This assessment used 96-h LC50

data in the calculation of risk. Geisy et al. (1999) assumed low risk if less than 10% of the

community was affected, but Stephen et al (1985) assumed low risk if less than 5% of the

community is affected. Also, the aquatic animals used in this assessment differed from those

used in Geisy (1999).

Since Marion Drain is located in a critical habitat, a site specific risk assessment to

threatened and endangered species is warranted. A lifestage or a sublethal risk assessment to the

community may reveal elevated risk at early life stages. Also, a cumulative risk assessment of

Marion Drain may indicate a higher level of risk but may be difficult to conduct with the scant

literature available on the toxicity of pesticide mixtures and effect of environmental stressors on

the toxicology of the pesticide mixtures, especially those identified in Marion Drain. Additional

sampling campaigns in Marion Drain could further characterize chlorpyrifos residues and

provide a better indicator of risk. An important recommendation is that risk management must

be communicated to local users so that they can avoid practices that enhance chlorpyrifos

transport and exceed WAC limits.

7.4 Uncertainties

Variability is the heterogeneity of exposure or response. Uncertainty in a risk assessment

is defined by the lack of understanding in the variability. The exposure and effects assessment

has several areas of uncertainty. In the exposure assessment, a source of uncertainty is sample

design. In the environment, the actual concentration fluctuates and pulses. Agrochemical

concentration and transport is affected by factors such as physical properties, irrigation practices,

field geometry, soil composition, runoff, spray drift, application times, crop type, dissolved

organic concentration, redox potential, pH, climate and weather (Naddy et al. 2001; Huang et al.

2001). Concentration is also affected the by physiography and hydrological parameters of the

receiving body of water.

ECY pesticide monitoring was not designed to characterize the dynamics of chlorpyrifos

residues in Marion Drain in association with specific application events but rather to monitor

pesticide concentrations in grab samples. Only one sample per week was gathered from Marion

Drain during ECY sampling campaigns, expect for the Dugger et al. (2008) study which sampled

daily for 22 days. Samples were not taken throughout the drain to adequately characterize the

residues. ECY sampling design is geared more toward detection of multiple pesticides. Thus,

owing to limited sampling intervals, ECY data potentially missed high pulsed exposure

concentrations.

Another source of uncertainty is exposure for benthic invertebrates. Geisy et al. (1999)

concluded that chlorpyrifos pore water concentrations were comparable to water column

concentrations. However, owing to a lack of information about chlorpyrifos sediment Kd

(soil/water partition coefficients) in Marion Drain, exposure of benthic invertebrates remains

uncertain. Furthermore, some research suggests that suspended particles can reduce toxicity to

invertebrates and/or fish, presumably by reducing bioavailability as compounds become sorbed

(Sturm et al. 2007).

Analytical error is another source of uncertainty. Matrix spike recovery from the 2004-

2007 ECY sampling campaigns for chlorpyrifos was 84% with a maximum of 140% and a

minimum of 42%. An 11% recovery was considered an outlier. Replicate samples had a mean

relative standard deviation of 4.3 in 2003-2005 and 7.7 in 2006-2008 (Burke et al. 2006;

Sargeant et al. 2010).

Several sources of uncertainty are present in the effects assessment. Because residue

concentrations can readily vary, it is difficult to compare standard toxicology tests to

environmental exposures. Measured concentrations were assumed to be constant and

homogeneous to allow for comparison and utilization of the toxicity data. For the present

assessment, single detections were assumed to last for 96 hours. Standard toxicology tests do

not take into consideration pulsed exposures and true heterogeneity of the environment. Also,

the values used in the species sensitivity distributions were calculated mean 96-h LC50s. Use of

the lower 95th

percentile toxicity data (i.e., biasing toward the more susceptible organisms)

would have made the assessment more conservative but replicated tests were not available for all

species. The effect of the mixture of pesticides in Marion Drain is unknown. There are no

aquatic biotic surveys available for Marion Drain, and its community composition is unknown.

Many of the animals used in the 96-h LC50 data set served as surrogate species. Without

qualification of actual species present in Marion Drain, it is impossible to determine how well

these surrogates represented the species that reside in Marion Drain.

7.5 Conclusion

Chlorpyrifos is highly toxic to aquatic invertebrates. Chlorpyrifos concentrations

detected in Marion Drain may have put aquatic animals and lotic invertebrates at high risk based

on Stephen et al. (1985) and Urban et al. (1986) definitions of risk at the more susceptible end of

the LC50 distribution in combination with the high end of the residue distribution. Risk is

greatest in September and October compared to other months. The joint probability analysis and

the Monte Carlo simulation of RQ produce a similar perspective of risk. The NRWQC for

chlorpyrifos (1986) of 0.041 µg/L ensures that the aquatic animals are well protected. Future

ECY sampling campaigns will provide more data to better characterize risk. An aquatic survey

of the Marion Drain is needed to identify aquatic species present and to ascertain the ecosystem

status. Also, additional studies are needed to determine the effect of the mixture of pesticides

detected in Marion Drain.

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Appendices

Appendix A.

1990 Aquatic invertebrate survey results for the Yakima River Basin (Cuffney et al. 1997).

Species Scientific Name Species Group Species Common Name

Turbellaria Class Platyhelminthes Flat Worm

Lumbriculidae Family Annelida Segmented worms

Naididae Family Annelida Segmented worms

Aulodrilus americanus Annelida Segmented worms

Aulodrilus pluriseta Annelida Segmented worms

Rhyacodrilus coccineus Annelida Segmented worms

Spirosperma nikolskyi Annelida Segmented worms

Telmatodrilus vejdovskyi Annelida Segmented worms

Dina anoculata Hirudinea Leech

Mooreobdella fervida Hirudinea Leech

Ferrissia Genus Molluscs Snail

Amnicola Genus Molluscs Snail

Fossaria Genus Molluscs Snail

Lanx Genus Molluscs Snail

Physella Genus Molluscs Snail

Gyraulus Genus Molluscs Snail

Planorbella Genus Molluscs Snail

Vorticifex Genus Molluscs Snail

Juga Genus Molluscs Snail

Corbicula fluminea Molluscs Clam

Sphaerium Genus Molluscs Clam

Hydracarina Suborder Arthropoda Water Mite

Gammarus Genus Crustacean Scud

Hyalella azteca Crustacean Scud

Pacifastacus leniusculus Crustacean Signal Crayfish

Caecidotea Genus Crustacean Isopod

Podocopa Subclass Crustacean None

Ameletus Genus Insect Mayfly

Acentrella insignificans Insect Mayfly

Baetis bicaudatus Insect Mayfly

Baetis tricaudatus Insect Mayfly

Camelobaetidius Genus Insect Mayfly

Centroptilum Genus Insect Mayfly

Fallceon quilleri Insect Mayfly

Procloeon Insect Mayfly

Pseudocloeon Genus Insect Mayfly

Caenis Genus Insect Mayfly

Attenella Genus Insect Mayfly

Caudatella Genus Insect Mayfly

Drunella doddsi Insect Mayfly

Drunella grandis Insect Mayfly

Drunella spinifera Insect Mayfly

Ephemerella Genus Insect Mayfly

Eurylophella Genus Insect Mayfly

Serratella Genus Insect Mayfly

Cinygmula Genus Insect Mayfly

Epeorus Genus Insect Mayfly

Heptagenia Genus Insect Mayfly

lronodes Genus Insect Mayfly

Rhithrogena Genus Insect Mayfly

Stenonema Genus Insect Mayfly

Paraleptophlebia Genus Insect Mayfly

Tricorythodes Genus Insect Mayfly

Calopteryx Genus Insect Jewelwings (Damselfly)

Argia Genus Insect Dancers (Damselfly)

Enallagma Genus Insect American Bluets (Damselfly)

Ophiogomphus Genus Insect Snaketails (Dragonfly)

Eucapnopsis brevicauda Insect Shorttailed Snowfly

Alloperla Genus Insect Green Stonefly

Kathroperla perdita Insect Longhead Sallfly

Paraperla frontalis Insect Hyporheic Sallfly

Sweltsa Genus Insect Green Stonefly

Despaxia augusta Insect Smooth Needlefly

Moselia infuscata Insect Hairy Needlefly

Perlomyia Genus Insect Needlefly

Zapada Genus Insect Forestfly

Yoraperla Genus Insect Roachfly

Calineuria californica Insect Western Stonefly

Claassenia sabulosa Insect Shortwing Stone

Doroneuria Genus Insect Stonefly

Hesperoperla pacifica Insect Large Stonefly

Frisonia picticeps Insect Painted Springfly

lsoperla Genus Insect Green-winged Stoneflies

Kogotus Genus Insect Stonefly

Megarcys Genus Insect Springfly

Perlinodes aurea (Smith) Insect Longgill Springfly

Skwala Genus Insect Springfly

Setvena Genus Insect Springfly

Pteronarcys Genus Insect Salmonfly

Taenionema Genus Insect Willowfly

Taeniopteryx Genus Insect Willowfly

Belostoma Genus Insect Giant Water Bug

Corisella Genus Insect Water Boatman

Hesperocorixa Genus Insect Water Boatman

Sialis Genus Insect Nearctic Alderflies

Brachycentrus Genus Insect Caddisfly

Micrasema Genus Insect Caddisfly

Agapetus Genus Insect Caddisfly

Anagapetus Genus Insect Caddisfly

Glossosoma Genus Insect Caddisfly

Protoptila Genus Insect Caddisfly

Helicopsyche borealis Insect Caddisfly

Agraylea Genus Insect Caddisfly

Hydroptila Genus Insect Caddisfly

Leucotrichia Genus Insect Caddisfly

Ochrotrichia Genus Insect Caddisfly

Arctopsyche grandis Insect Caddisfly

Cheumatopsyche Genus Insect Caddisfly

Hydropsyche Genus Insect Caddisfly

Parapsyche Genus Insect Caddisfly

Lepidostoma Genus Insect Caddisfly

Apatania Genus Insect Caddisfly

Moselyana comosa Insect Caddisfly

Pedomoecus sierra Insect Caddisfly

Neophylax Genus Insect Caddisfly

Oligophlebodes Genus Insect Caddisfly

Ceraclea Genus Insect Caddisfly

Nectopsyche Genus Insect Caddisfly

Dolophilodes Genus Insect Caddisfly

Tinodes Genus Insect Caddisfly

Rhyacophila angelita Insect Caddisfly

Rhyacophila brunnea Insect Caddisfly

Rhyacophila narvae Insect Caddisfly

Rhyacophila vaccua Insect Caddisfly

Rhyacophila rotunda Insect Caddisfly

Petrophila Genus Insect Aquatic Catapillar

Amphizoa Genus Insect Trout Stream Beetle

Helichus striatus Insect None

Agabus Genus Insect Predaceous Diving Beetles

Oreodytes Genus Insect Predaceous Diving Beetles

Uvarus Genus Insect Predaceous Diving Beetles

Cleptelmis Genus Insect Riffle Beetle

Dubiraphia Genus Insect Riffle Beetle

Heterlimnius Genus Insect Riffle Beetle

Lara Genus Insect Riffle Beetle

Narpus Genus Insect Riffle Beetle

Optioservus Genus Insect Riffle Beetle

Ordobrevia nubifera Insect Riffle Beetle

Zaitzevia parvula Insect Riffle Beetle

Hydrochus Genus Insect Water Scavenger Beetles

Paracymus Genus Insect Water Scavenger Beetles

Psephenus Genus Insect Water Penny Beetles

Atherix Genus Insect Watersnipe Flies

Bezzia Genus Insect Biting Midges

Culicoides Genus Insect Biting Midges

Stilobezzia Genus Insect Biting Midges

Chironomus Genus Insect Midges

Cladotanytarsus Genus Insect Midges

Cryptochironomus Genus Insect Midges

Dicrotendipes Genus Insect Midges

Endochironomus Genus Insect Midges

Lauterborniella agrayloides Insect Midges

Microspectra Genus Insect Midges

Microtendipes Genus Insect Midges

Paratanytarsus Genus Insect Midges

Paratendipes Genus Insect Midges

Polypedilum Genus Insect Midges

Rheotanytarsus Genus Insect Midges

Stempellinella Genus Insect Midges

Stempellina Genus Insect Midges

Tanytarsus Genus Insect Green Midge

Diamesa Genus Insect Midges

Pagastia Genus Insect Midges

Potthastia longimana Insect Midges

Brillia Genus Insect Midges

Cardiocladius Genus Insect Midges

Chaetocladius Genus Insect Midges

Corynoneura Genus Insect Midges

Cricotopus Genus Insect Midges

Eukiefferiella Genus Insect Midges

Heleniella Genus Insect Midges

Lopescladius Genus Insect Midges

Mesocricotopus Genus Insect Midges

Nanocladius Genus Insect Midges

Orthocladius Genus Insect Midges

Paraphaenocladius Genus Insect Midges

Paratrichocladius Genus Insect Midges

Rheocricotopus Genus Insect Midges

Synorthocladius Genus Insect Midges

Thienemanniella Genus Insect Midges

Tvetenia Genus Insect Midges

Apsectrotanypus Genus Insect Midges

Brundiniella Genus Insect Midges

Helopelopia Genus Insect Midges

Krenopelopia Genus Insect Midges

Pentaneura Genus Insect Midges

Procladius Genus Insect Midges

Thienemannimyia Genus Insect Midges

Chelifera Genus Insect True Fly

Hemerodromia Genus Insect Dance Fly

Limnophora Genus Insect True Fly

Pericoma Genus Insect Midges

Glutops Genus Insect True Fly

Ptychopteridae Family Insect Phantom Crane Flies

Sciomyzidae Family Insect Marsh Fly

Prosimulium Genus Insect Black Fly

Simulium Genus Insect Black Fly

Nemotelus Genus Insect Soldier Fly

Tabanus Genus Insect Horse Fly

Antocha Genus Insect Limoniid Crane Flies

Dicranota Genus Insect Limoniid Crane Flies

Hexatoma Genus Insect Limoniid Crane Flies

Tiputa Genus Insect True Fly

Appendix B.

Chlorpyrifos residue data taken from WDOE sampling campaigns between 2003-2008 and the

FEQL 2006 study. Data labeled FEQL represent the samples collected during the FEQL 2006

study.

Date Concentration (µg/L) Data Qualifier

4/8/2003 8.50E-02 J

4/16/2003 1.60E-03 J

4/22/2003 1.60E-03 J

4/30/2003 1.60E-03 J

3/31/2004 2.60E-02 U

4/5/2004 0.002 J

4/14/2004 2.60E-02 U

4/21/2004 3.00E-03 NJ

4/28/2004 6.00E-03 J

3/2/2005 0.027 U

3/9/2005 2.50E-02 U

3/16/2005 2.60E-02 U

3/23/2005 5.40E-02

3/29/2005 0.012 J

4/6/2005 2.60E-02 U

4/13/2005 0.027 U

4/20/2005 8.90E-03 J

4/20/2005 9.20E-03 J

4/27/2005 2.50E-02 U

4/5/2006 0.024 J

4/11/2006 0.031 U

4/18/2006 4.40E-03 NJ

4/25/2006 0.01 J

3/6/2007 3.20E-02 U

3/14/2007 0.25 UJ

3/21/2007 2.20E-02 J

3/27/2007 4.40E-02

4/4/2007 3.80E-02 J

4/11/2007 1.10E-02 NJ

4/17/2007 3.20E-02 U

4/24/2007 3.20E-02 U

4/25/2007 3.30E-02 UJ

4/26/2007 0.019 NJ

4/27/2007 3.20E-02 U

4/28/2007 2.20E-02 J

4/29/2007 0.02 NJ

4/30/2007 0.02 NJ

3/10/2008 0.034 U

3/18/2008 0.033 U

3/26/2008 0.017 J

4/1/2008 0.024 J

4/7/2008 0.0092 J

4/16/2008 0.032 U

4/21/2008 0.033 U

4/28/2008 0.033 U

5/6/2003 1.60E-03 J

5/15/2003 0.027 U

5/20/2003 2.50E-02 U

5/27/2003 3.20E-03 NJ

5/27/2003 0.002 J

6/4/2003 2.80E-02 U

6/11/2003 2.60E-02 U

6/17/2003 0.024 U

6/25/2003 2.50E-02 U

7/9/2003 2.50E-02 U

7/23/2003 2.60E-02 U

8/6/2003 2.60E-02 U

8/20/2003 0.027 U

5/5/2004 0.012 J

5/5/2004 1.30E-02 J

5/12/2004 7.00E-03 J

5/19/2004 2.50E-02 U

5/26/2004 0.024 U

6/2/2004 6.20E-03 J

6/9/2004 8.50E-03 NJ

6/16/2004 8.80E-03 NJ

6/23/2004 4.40E-03 NJ

6/30/2004 0.0028 NJ

7/14/2004 2.50E-02 U

7/21/2004 2.60E-02 U

7/28/2004 0.024 U

8/4/2004 2.50E-02 U

8/11/2004 2.50E-02 U

8/18/2004 2.60E-02 U

8/25/2004 2.60E-02 U

8/25/2004 2.50E-02 U

5/3/2005 0.027 U

5/11/2005 0.023 J

5/18/2005 2.60E-02 U

5/25/2005 0.027 U

6/1/2005 0.027 U

6/8/2005 0.027 U

6/15/2005 2.60E-02 U

6/22/2005 2.80E-02 U

6/29/2005 2.60E-02 U

7/6/2005 2.60E-02 U

7/13/2005 0.027 U

7/19/2005 0.027 U

7/25/2005 2.60E-02 U

8/2/2005 0.027 U

8/11/2005 2.60E-02 U

8/17/2005 0.027 U

8/24/2005 2.60E-02 U

8/30/2005 2.60E-02 U

5/2/2006 3.20E-02 U

5/9/2006 1.10E-02 J

5/17/2006 1.10E-02 J

5/23/2006 1.30E-02 J

5/30/2006 3.20E-02 U

6/5/2006 0.031 U

6/13/2006 9.70E-03 J

6/19/2006 1.10E-02 J

6/27/2006 0.012 J

7/3/2006 0.031 U

7/12/2006 8.80E-03 J

7/17/2006 3.30E-02 U

7/24/2006 3.20E-02 U

8/1/2006 0.012 NJ

8/8/2006 3.30E-02 U

8/14/2006 1.70E-02 J

8/22/2006 0.0086 J

8/29/2006 0.016 J

5/1/2007 0.02 J

5/1/2007 0.02 NJ

5/2/2007 9.80E-03 J

5/3/2007 9.10E-03 NJ

5/4/2007 8.20E-03 NJ

5/5/2007 0.02 J

5/6/2007 1.10E-02 NJ

5/7/2007 1.30E-02 J

5/8/2007 3.30E-02 UJ

5/9/2007 2.20E-02 J

5/10/2007 1.80E-02 J

5/11/2007 0.014 J

5/12/2007 1.30E-02 J

5/13/2007 1.10E-02 NJ

5/14/2007 3.20E-02 U

5/15/2007 6.40E-03 J

5/15/2007 6.10E-03 J

5/22/2007 2.80E-02 J

5/29/2007 3.00E-02 J

6/5/2007 2.80E-02 J

6/11/2007 2.60E-02 J

6/19/2007 3.20E-02 U

6/25/2007 3.30E-02 U

7/5/2007 3.20E-02 U

7/11/2007 3.20E-02 U

7/18/2007 0.039

7/25/2007 0.031 J

7/30/2007 3.20E-02

8/8/2007 1.50E-02 J

8/15/2007 3.30E-02 U

8/21/2007 0.012 J

8/27/2007 3.70E-02

5/7/2008 0.022 J

5/12/2008 0.033 U

5/20/2008 0.033 U

5/26/2008 0.033 U

6/2/2008 0.033 U

6/10/2008 0.0047 J

6/18/2008 0.033 U

6/23/2008 0.033 U

7/1/2008 0.032 U

7/9/2008 0.033 U

7/15/2008 0.033 U

7/22/2008 0.033 U

7/28/2008 0.033 U

8/4/2008 0.033 U

8/12/2008 0.033 U

8/20/2008 0.033 U

8/25/2008 0.033 U

9/3/2003 0.027 J

9/17/2003 2.50E-02 J

10/1/2003 2.10E-02 J

10/1/2003 0.023 J

10/8/2003 0.027 J

10/22/2003 2.60E-02 U

9/1/2004 2.50E-02 U

9/8/2004 5.20E-02

9/15/2004 0.1

9/22/2004 7.40E-02

9/29/2004 0.02 J

10/6/2004 3.80E-02

10/13/2004 2.50E-02 J

10/20/2004 2.50E-02 U

10/27/2004 2.50E-02 U

9/8/2005 1.50E-02 J

9/13/2005 2.50E-02 U

9/21/2005 0.4

9/28/2005 0.047

10/5/2005 1.70E-02 J

10/12/2005 1.70E-02 J

9/6/2006 3.50E-02

9/7/2006 6.00E-02 FEQL

9/7/2006 2.00E-02 FEQL

9/8/2006 6.70E-02 FEQL

9/8/2006 6.40E-02 FEQL

9/11/2006 9.10E-02 FEQL

9/11/2006 5.10E-02 FEQL

9/13/2006 0.12

9/14/2006 0.058 FEQL

9/14/2006 0.082 FEQL

9/19/2006 3.70E-02

9/26/2006 8.60E-02

10/2/2006 2.80E-02 J

10/10/2006 0.027 J

10/17/2006 1.30E-02 J

10/23/2006 1.10E-02 J

10/30/2006 0.012 J

9/4/2007 0.047

9/12/2007 5.90E-02

9/17/2007 5.00E-02

9/26/2007 7.50E-02

9/26/2007 7.40E-02

10/1/2007 0.039

10/9/2007 0.12

10/17/2007 3.30E-02 U

10/22/2007 3.20E-02 U

10/30/2007 3.20E-02 U

9/2/2008 0.0041 J

9/10/2008 0.018 J

9/15/2008 0.023 J

9/23/2008 0.0088 J

10/1/2008 0.0058 J

10/6/2008 0.01 J

10/15/2008 0.0079 NJ

10/22/2008 0.031 U

10/29/2008 0.033 U

(Note: Data qualifier definitions are provided on the next page.)

Qualifier Definition

U The analyte not detected at or above the reported sample quantification limit.

The analyte was positively identified, and the associated numerical value is the

approximate concentration of the analyte in the sample (either certain quality

control criteria were not met or the concentration of the analyte was below the

sample quantification limit).

UJ The analyte was not detected at or above the reported sample quantification limit.

However, the reported quantification limit is approximate and may be imprecise.

The analysis indicates the presence of an analyte that has been "tentatively

identified," and the associated numerical value represents its approximate

concentration

Blank The analyte was detected at or above the reported sample quantification limit.

Burke et al. (2006).

Appendix C.

Lognormal concentration models for March thru April, May thru August and September thru

October.

Figure C1. Distribution of chlorpyrifos residues in water samples collected during March thru

April and their fit to a lognormal concentration model.

Figure C2. Distribution of chlorpyrifos residues in water samples collected during May thru

August and their fit to a lognormal concentration model.

Figure C3. Distribution of chlorpyrifos residues in water samples collected during September

thru October and their fit to a lognormal concentration model.

Appendix D.

Aquatic animal and lotic invertebrate 96-h LC50 lognormal models.

Figure D1. Lognormal fit of 96-h LC50 aquatic animal data.

Figure D2. Lognormal fit of 96-h LC50 lotic invertebrate data.

Appendix E.

1D Monte Carlo simulation of the RQ probability distribution results.

Figure E1. Aquatic animal RQ probability distribution results for March thru April from the 1D

Monte Carlo simulation.

Figure E2. Aquatic animal RQ probability distribution results for May thru August from the 1D

Monte Carlo simulation.

Figure E3. Aquatic animal RQ probability distribution results for September thru October from

the 1D Monte Carlo simulation.

Figure E4. Lotic invertebrate RQ probability distribution results for March thru April from the

1D Monte Carlo simulation.

Figure E5. Lotic invertebrate RQ probability distribution results for May thru August from the

1D Monte Carlo simulation.

Figure E6. Lotic invertebrate RQ probability distribution results for September thru October

from the 1D Monte Carlo simulation.

Appendix F.

Joint probability table of results for the aquatic animal assemblage and lotic invertebrates.

Table F-1. Joint probability results table of results for the aquatic animal assemblage.

Percent of

Species

Affected

Concentration

(ppb) Percent

Exceedance

Percent

Exceedance

Percent

Exceedance

March thru

April May thru Aug. Sept. thru Oct.

0.1 0.000395 99.960 100.000 100.000

0.2 0.000800 99.632 100.000 99.988

0.3 0.001235 98.832 100.000 99.937

0.4 0.001698 97.528 99.994 99.808

0.5 0.002188 95.761 99.961 99.566

0.6 0.002704 93.602 99.849 99.186

0.7 0.003246 91.127 99.568 98.648

0.8 0.003813 88.410 99.004 97.944

0.9 0.004404 85.515 98.034 97.071

1 0.005018 82.500 96.551 96.031

2 0.012430 52.888 55.437 78.910

3 0.022100 31.756 17.609 57.923

4 0.034072 18.780 4.154 39.975

5 0.048453 11.110 0.852 26.657

6 0.065386 6.608 0.163 17.426

7 0.085039 3.959 0.030 11.256

8 0.107598 2.390 0.006 7.218

9 0.133273 1.454 0.001 4.608

10 0.162290 0.890 0.000 2.933

11 0.194896 0.548 0.000 1.864

12 0.231356 0.340 0.000 1.183

13 0.271958 0.212 0.000 0.751

14 0.317012 0.132 0.000 0.476

15 0.366852 0.083 0.000 0.302

16 0.421838 0.052 0.000 0.191

17 0.482358 0.033 0.000 0.121

18 0.548827 0.021 0.000 0.077

19 0.621697 0.013 0.000 0.049

20 0.701449 0.008 0.000 0.031

30 2.015498 0.000 0.000 0.000

40 4.966567 0.000 0.000 0.000

50 11.538383 0.000 0.000 0.000

60 26.806097 0.000 0.000 0.000

70 66.055270 0.000 0.000 0.000

80 189.798839 0.000 0.000 0.000

90 820.345755 0.000 0.000 0.000

99.9 336963.354926 0.000 0.000 0.000

Table F-2. Joint probability analysis results for lotic invertebrates.

Percent of

Species

Affected

Concentration

(ppb) Percent

Exceedance

Percent

Exceedance

Percent

Exceedance

March thru

May thru

August Sept. thru Oct.

0.1 0.002421 94.819 99.924 99.411

0.2 0.003592 89.481 99.263 98.236

0.3 0.004578 84.660 97.665 96.790

0.4 0.005470 80.309 95.142 95.195

0.5 0.006303 76.359 91.861 93.515

0.6 0.007096 72.750 88.022 91.790

0.7 0.007859 69.435 83.812 90.043

0.8 0.008599 66.375 79.389 88.291

0.9 0.009320 63.539 74.877 86.547

1 0.010026 60.900 70.372 84.817

2 0.016649 41.865 34.264 69.035

3 0.022969 30.460 15.819 56.335

4 0.029259 22.926 7.342 46.242

5 0.035626 17.659 3.470 38.179

6 0.042126 13.836 1.673 31.686

7 0.048794 10.984 0.822 26.416

8 0.055655 8.811 0.411 22.109

9 0.062730 7.130 0.209 18.566

10 0.070034 5.810 0.107 15.637

11 0.077583 4.764 0.056 13.204

12 0.085390 3.926 0.029 11.173

13 0.093470 3.250 0.016 9.473

14 0.101835 2.701 0.008 8.045

15 0.110498 2.252 0.005 6.842

16 0.119473 1.883 0.002 5.826

17 0.128773 1.578 0.001 4.966

18 0.138412 1.326 0.001 4.237

19 0.148405 1.117 0.000 3.618

20 0.158766 0.942 0.000 3.090

30 0.286459 0.181 0.000 0.645

40 0.474312 0.035 0.000 0.128

50 0.759908 0.006 0.000 0.022

60 1.217468 0.001 0.000 0.003

70 2.015859 0.000 0.000 0.000

80 3.637175 0.000 0.000 0.000

90 8.245452 0.000 0.000 0.000

99.9 238.554333 0.000 0.000 0.000

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