# Pesticide Aquatic Exposure Scenarios and Modeling for Juvenile Pacific Salmonid Floodplain Habitat Nick Poletika a 

# Zhenxu Tang b 

# Scott Jackson c

a Dow AgroSciences LLC b Bayer CropScience c BASF Corporation 1

# Background  

# In recent biological opinions from the National Marine Fisheries Service addressing concerns with the impact of pesticides on Pacific salmonid populations listed as endangered/threatened, there has been a great emphasis on juveniles occupying shallow habitat in floodplains.  

# Current exposure models and scenarios do not represent this habitat well.  

# We present an approach to exposure modeling and risk assessment for commercial agriculture in floodplains where pesticide use is frequent and flood control systems are present. 2

# Off - Channel Features of Importance in Agricultural Areas  

# Three basic types with connection to the main channel a

 

# Side channels • Flowing water bodies with clearly identifiable upstream and downstream connections to the main channel  

# Alcoves • Water bodies that maintain a downstream connection to the main channel at summer low flow, but have no upstream connection during low flow  

# Backwater pools • A pool formed in free - flowing streams by an eddy along channel margins downstream from obstructions such as bars, rootwads , or boulders

a http://oregonstate.edu/dept/ODFW/freshwater/inventory/index.htmc 3

# Examples of Side Channels and Alcoves

4 Source: http://www.fsl.orst.edu/pnwerc/wrb/Atlas_web_compressed/3.Water_Resources/3d.off - channel%20habs_web.pdf 

### Side Channel Alcove Alcove Side Channel5 

# Example of Backwater Pool

Pool Hwy 99

# Scenario Selection  

# Different transport/mixing mechanisms  

# Side channels – stream flow  

# Alcoves – hyporheic flow  

# Backwater pools – eddy current exchange  

# Imply the need for different process models  

# Stream flow – advection/dispersion  

# Hyporheic flow – empirical decay model based on dye studies  

# Backwater pools – computational fluid dynamics 6

# Scenario Selection, Cont.  

# Side channel advection/dispersion can be simulated by existing research and regulatory models.  

# Alcoves and backwater pools require more novel approaches.  

# Selected side channel to take advantage of existing models (and measured data)

7

# Data for Side Channel Scenario  

# Lower Mokelumne River, CA, below Camanche Dam

 

# Data collected by biologists from the East Bay Municipal Utility District a  

# Habitat was measured in conjunction with seining to sample fish in four side channels.  

# Depth and velocity taken at the seining location; width also measured  

# 133 measurements from 1996 - 2004

a Joe Merz, personal communication 8

## Mokelumne River Side Channel Locations in Relation to Ag. Land

9

# Downstream Side Channel Landscape Features

10

# Are Variables Correlated?

DEPTH (m) 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6VELOCITY (m sec-1)

0.0 0.2 0.4 0.6 0.8 1.0 1.2 r2= 0.179 p<0.001

WIDTH (m) 8 9 10 11 12 13 14 15 16VELOCITY (m sec-1)

0.0 0.2 0.4 0.6 0.8 1.0 1.2 r2= 0.107 p<0.001

11 

# Depth, Width vs Velocity

# Are Variables Correlated? Month vs Depth, Velocity, Pesticide

TIME (Month) 1 2 3 4 5 6 7 8 9 10 11 12DEPTH (m)

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

TIME (Month) 1 2 3 4 5 6 7 8 9 10 11 12VELOCITY (m sec-1)

0.0 0.2 0.4 0.6 0.8 1.0 1.2

12 TIME (Month) 1 2 3 4 5 6 7 8 9 10 11 12PESTICIDE FLUX IN RUNOFF (g cm-2day-1)

0.00 0.05 0.10 0.15 0.20 1961 All 30 Years 1990

# Compound Probability  

# Overall scenario target probability for exposure potential (p)

 

# Example: p = 0.05 to 0.1, a typical worst case  

# If controlling variables are independent, then  

# p = p 1 

# x p 2 

# x p 3 

# . . . x p n 

# , where • n = number of variables • p 1,2,3 ... n 

## = probability of a value for each variable 

#  

# Assume independence of key variables driving exposure in the side channel

 

# Depth (D), width (W), velocity (V), pesticide mass in runoff (M) 13

# Compound Probability, Cont.  

# Example scenarios for p = 0.05 to 0.1  

# p M 

# = 0.1, p D 

# = 0.5, W, V fixed for p D 

# = 0.5  

# p M 

# = 0.1, p V 

# = 0.5, D, W fixed for p V 

# = 0.5  

# p = 0.1 x 0.5 = 0.05  

# Definitions • p M 

# = USEPA OPP 90 th 

# percentile annual maximum pesticide mass in runoff in 30 years of simulation • Fixed is the mean measured value of the variable associated with all instances of the variable set to p = 0.5 14

# Field - Scale Exposure Models  

# Individual spray drift and daily runoff events simulated using the Pesticide Root Zone Model (PRZM) as implemented in a customized version of the PRZM - EXAMS (PE) Version 5 shell a 

#  

# Fate and transport simulated by a customized spreadsheet implementation of the AGRO Quantitative Water Air Sediment Interaction (QWASI) fugacity model b 

#  

# Both models were run on a 1 - day time step 15 a http://www.epa.gov/oppefed1/models/water/index.htm b http://www.trentu.ca/academic/aminss/envmodel/models/AGROv120.html

# Scenario Definition - PRZM  

# EPA farm pond standard scenario (10 - ha field)  

# Crop and location • CAcotton_WirrigSTD.txt  

### Fresno County, CA - MLRA 17  

# Weather • w93193.dvf  

### Fresno  

# Chemical • Chlorpyrifos  

### 1 application on 1 Jul at 1.12 kg a.i ./A  

### Aerial application with 76.2 - m spray drift buffer resulting in 1.7% drift  

### Runoff the same day: p = 0.5 in irrigation scenario 16

# Scenario Definition – AGRO

17 Name Farm Pond Dimensions Water Surface Area (m2) 10000 Water Volume (m3) 20000 Sediment Active Layer Depth (m) 0.05 Concentration of Solids Aerosol Particles (ug/m3) 30 Inflow Particles (mg/L) 2 Suspended Sediment (mg/L) 30 Sediment Particles (m3 /m3) 0.5 Density of Solids (kg/m3) in Water 2400 in Sediment 2400 in Aerosols 1500 Organic Carbon Fraction of Solids in Water 0.067 in Sediment 0.04 in Inflow 0.067 in Resuspended Sediment 0.04 Flows River water inflow (m3 /h) 0.0001 Water outflow (m3 /h) 0.0001 Deposition rate of solids to sediment (g/m2/day) 80 Burial Rate of Solids (g/m2/day) 40 Resuspension Rate of Solids (g/m2/day) 40 Mass Transfer Coefficients Aerosol Dry Deposition (m/h) 10 Scavenging Ratio (vol air/vol rain) 200000 Rain Rate (m/y) 1 Volatilization (air side) (m/h) 1 Volatilization (water side) (m/h) 0.01 Sediment-Water diffusion (m/h) 0.0004 Variable Sedimentation Settling Time 90% (days) 7 Resuspension Percentage (% of deposition) 50 Side Channel p D a = 0.5 = 0.66 m p V b = 0.5 = 0.19 m sec - 1 10405 m 2 10244 m 2

6818 m 3 3747 m 3

11994 m 3 h - 1 3116 m 3 h - 1 11994 m 3 h - 1 3116 m 3 h - 1

a V = 0.393 m sec - 1 , W = 12.93 m b D = 0.366 m, W = 12.73 m 

### Mokelumne River typical velocities below Camanche Dam: 0.8 m sec - 1 

### (low discharge) 1.4 m sec - 1 

### (high discharge) Pasternack et al., 2003. River Res Appl 19:1 - 21.

# Relative Sensitivity of Sorption and Flow for p V 

# and p D 

# = 0.5 (Flows of 3116 and 11994 m 3 

# h - 1 

# , respectively)

18 Chlorpyrifos Koc = 6070, log Kow = 4.7

[Channel] / [Pond] p V = 0.5 0.272 0.275 0.277 p D = 0.5 0.0712 0.0726 0.0738 Koc = 60.7, log Kow = 2.7 Koc = 607,000, log Kow = 6.7 EXAMS Peak = 3159 ng L - 1

# Effects Data  

# Sensitive aquatic invertebrate, Daphnia magnaa

 

# Short term exposure to chlorpyrifos evaluated for survival at 21 days • 120 ng L - 1 

# , 24 h resulted in 95 ± 5% survival • 250 ng L - 1 

# , 24 h resulted in 85 ± 10% survival  

# Conclude that EC 50 

# > 250 ng L - 1 

# for 24 - h exposure with no latent responses  

# (Guideline study 48 - h EC 50 

# = 100 ng L - 1 

# ) 19 a Naddy et al. 2000. Environ Toxicol Chem 19:423 - 431.

# Risk Assessment  

# Risk Quotient (RQ)  

# Estimated Environmental Concentration / EC 50  

# Standard EFED RQ: EXAMS EEC, Peak / Guideline Study EC 50 

# = 3159 / 100 = 31.6  

# AGRO Farm Pond, p = 0.1, 48 h • RQ = 1054 / 100 = 10.5  

# AGRO Side Channel, p = 0.05, 24 h • p D 

# = 0.5: RQ = 77 / > 250 = < 0.31 • p V 

# = 0.5: RQ = 293 / > 250 = < 1.2 20

# Conclusions  

## Pesticide exposure can be estimated in low velocity shallow water representing vulnerable juvenile salmonid habitat at the field scale using available models.  

## With sufficient uncorrelated measurement data, a specific scenario vulnerability level can be set assuming simple compound probability.  

## At a scenario probability level of p = 0.05, our example demonstrated that flow velocity limited predicted dissolved concentrations.  

## Short -term effects data can be used with time -weighted average exposures to assess risk at times as short as 1 day.  

## When this is done, the RQs decrease significantly, suggesting that the risk to the juvenile salmonid prey base is less than that predicted by standard scenarios.

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