Characterizing Individual Sources Of 

Uncertainty in EFED Standard Aquatic Risk 

Assessments Using Best Available Data

Paul Hendley (Phasera Ltd.)

Jeffrey Giddings (Compliance Services International)

based on research conducted for the 

Pyrethroid Working Group (PWG)

The Pyrethroid Working Group (PWG) is a US task force whose members include eight primary pyrethroid registrants (AMVAC Chemical Corporation, 

BASF Corporation, Bayer CropScience LP, Cheminova A/S, DuPont Crop Protection, FMC Corporation, Syngenta Crop Protection LLC, Valent U.S.A. 

Corporation).

Assessing Risks to Endangered and Threatened Species from Pesticides

3rd Interagency Workshop on Joint Interim Approaches to NAS Recommendations 

October 6, 2014 
Examining individual sources of uncertainty

 Uncertainty is part of all risk assessments and is addressed 

through assumptions (explicit and implicit) about factors 

potentially affecting exposure and effects.

 Components of uncertainty can be considered individually

and examined using focused probabilistic analysis.

 This is feasible because high-quality monitoring data are 

now available for many real-world variables such as:

 Spatial data on hydrology and soils

 Highly localized weather

 Yearly field–by-field crop locations

 Multi-year/site/AI water and sediment concentrations

October 6, 2014 (c) 2014 Pyrethroid Working Group. All rights reserved. 2
May 1996: FIFRA SAP on risk assessment process

 “...current process cautious and protective in 

terms of adverse environmental effects but 

serves only as screen because it reveals little 

information on likelihood of damage...”

 SAP recommended that the process be expanded 

to include probabilistic assessments of risk, 

identify uncertainties.

 Led to 1997 EPA initiation of ECOFRAM* panel.

*Ecological Committee On FIFRA Risk Assessment Methods

October 6, 2014 (c) 2014 Pyrethroid Working Group. All rights reserved. 3
ECOFRAM Aquatic Exposure Concept – Tiering

Concentration

DIAGRAM E

Key

Min Max

Median is area of most

intense color

Tier 1 - Single value ideally

at 90%ile of expected Tier 2

Tier 2 - Conservative initial

probabilistic estimate

Tier 3 - Refined estimate may

include landscape/chemistry

Tier 4 - Most sophisticated -

may included monitoring etc.

Real World Actual Exp.

may never be known exactly.

0 ppb (Log Scale)

Screening 

Level 

Assessment

October 6, 2014 (c) 2014 Pyrethroid Working Group. All rights reserved. 4
PWG pyrethroid risk assessment - background

 Registration Review under way for 9 synthetic pyrethroids

 Highly active insecticide class

 ~30 year history of safe use

 Vast range of US crops across the AIs

 Economically significant for food production

 Tier II exposure modeling indicated that

 Plants and mollusks are not at risk, fish “on borderline”

 Arthropods (including insects and crustaceans) are predicted to 

have substantial RQs for many crop uses

October 6, 2014 (c) 2014 Pyrethroid Working Group. All rights reserved. 5
BUT large database of water/sediment monitoring indicates Tier II 

modeling overestimates pyrethroid concentrations

 Bulk water column EECs are much greater than concentrations 

measured in whole water samples. Example: deltamethrin use on 

cotton, soybeans, vegetables, and outdoor residential.

October 6, 2014 (c) 2014 Pyrethroid Working Group. All rights reserved. 6

Modeling (ng/L) Monitoring (ng/L)

Agricultural Residential Agricultural

(n=229)

Urban

(n=420)

90th %ile 46-569 125 90th %ile <RLa <RL

95th %ile 62-691 158 95th %ile <RL <RL

Max 73-761 162 99th %ile 2.8 16

a RL = reporting limit, 0.5-1 ng/L for most samples

Conclusion: Statistically valid monitoring data are at odds with Tier II exposure 

predictions with potential regulatory implications.
Understanding role of sources of uncertainty in EEC/monitoring 

discrepancy helps both FIFRA and ESA assessments

 PWG set itself task to 

“Evaluate sources of uncertainty in standard 

FIFRA aquatic exposure modeling which 

contribute to more or less conservatism in 

exposure estimates and examine how their 

impact may be quantified using recent bestavailable data”

 Understanding the combined impact of 

individual sources of uncertainty allows for

 More realistic aquatic EECs 

 Confidence in protective nature of assessments

 For ESA, most effective if analysis is part of 

Step 1: avoid unwarranted may-affect 

determinations, narrow the scope of Step 2

October 6, 2014 (c) 2014 Pyrethroid Working Group. All rights reserved.

Number of 

species of 

concern

Source: Intrinsik

7
PWG’s approach to characterize potential uncertainties

October 6, 2014 (c) 2014 Pyrethroid Working Group. All rights reserved. 8
Scope of PWG evaluation and data sources

 Initially identified ~70 sources of uncertainty in the standard assessment of 

pyrethroids

 Several non-conservative items, most are conservative or variable

 Many are inherent in model algorithms

 Focused on those related to standard modeling scenarios

 e.g. wind speed/direction, extent of crop in watersheds, severity of selected soil/weather 

scenarios relative to national distribution, etc.

 Sought best-available data sources to provide rigorous quantification of effect of 

each source of uncertainty (ideally via accepted models). Unfiltered government 

databases of environmental data (although not error free) have high credibility.

 NASS CDL and Pesticide Use

 NHD+

 NEXRAD

 USGS and CDPR monitoring data

October 6, 2014 (c) 2014 Pyrethroid Working Group. All rights reserved. 9
Four examples of PWG analysis (out of ~70)

October 6, 2014 (c) 2014 Pyrethroid Working Group. All rights reserved. 10

Assumption How this issue is addressed in the PWG 

exposure analysis

Potential significance 

(SMA= standard model 

assumption)

Minimum specified 

application interval is always 

used for aerial applications.

Included. In real world, aerial foliar pyrethroid 

applications only made when needed due to insect 

pressure. This rarely necessitates more than two 

applications in close succession.

SMA. Conservative.

Growers ignore instructions 

to avoid application when 

imminent rainstorms are 

anticipated.

Included. In real world, insecticide applications are 

rarely made during or before rain due to tractor 

access issues and/or concerns about loss of efficacy 

due to washoff.

SMA. Conservative.

Regional use patterns and 

application technologies are 

not significantly different 

from the national average.

Included except in special cases (e.g., California 

vegetables). In some areas there may be more or 

less PCA, PTA, ground/air applications, etc. Insect 

pressure will vary spatially and by year, leading to 

region-specific application patterns.

PWG Hypothesis. Variable.

Wind speed is always 10 

mph for aerial applications

Included, Quantified. PWG modeling uses default 

fixed values. 

SMA. Variable but largely very 

conservative.
Example 1 – Wind speed co-occurrence for multiple applications

 Quantifies impact of model defaults for aerial applications (wind speed 

always 10 mph and temperature/RH are constant).

 Retains protective assumption that wind direction is always toward water 

body.

 Extracts wind speed, temperature and RH data from SAMSON stations for 

6 times on all days.

 Assumes aerial application sequence occurs at same time “N” days apart 

(repeated for different start dates) and employs labeled 150-ft no-spray 

buffer & droplet size (medium coarse).

 AgDrift computes drift load using specific wind speed, temperature and RH 

values for each aerial spray event over 30 years.

 Resulting drift loads used as input to receiving water model (AGRO-2014) 

to predict concentrations in water and sediment.

October 6, 2014 (c) 2014 Pyrethroid Working Group. All rights reserved. 11
Drift loads for sequence of 4 or 2 aerial applications are over-estimated 

most of the time; time-of-day differences obvious

October 6, 2014 (c) 2014 Pyrethroid Working Group. All rights reserved. 12

0

20

40

60

80

100

0 20 40 60 80 100 120 140

Pro

b

a

bilit

y (%)

Percent of Default Annual Aerial Drift Load

MS Cotton - Application Set 1

Default

0400

0800

1200

1600

2000

0000

0

20

40

60

80

100

0 20 40 60 80 100 120 140 160 180 200

Pro

b

a

bilit

y (%)

Percent of Default Drift Load

CA Onion - Application Set 1

Default

0400

0800

1200

1600

2000

0000

Conclusions: Except at 12:00 and 16:00, drift loads substantially reduced compared to default.

Applies to all AIs, not just pyrethroids.

NOTE: this does not take DIRECTION of wind into account!

Default: no variation, 10 mph on every application over 30-year period.
Reduced drift loads can lead to substantial reductions in water 

concentrations

October 6, 2014 (c) 2014 Pyrethroid Working Group. All rights reserved. 13

4 aerial 

apps

2 aerial 

apps

Magnitude of impact: Across range of crops, incorporating wind speed/drift parameter distributions 

will reduce 90th percentile EEC estimates for pyrethroids in sediment and water by 1.1 – 7X.

0.000

0.005

0.010

0.015

0.020

0 0.2 0.4 0.6 0.8 1

C

o

n

c

e

n

t

r

a

tio

n

s, μ

g

/

L

Exceedance Probability

MS Cotton 24-hr TWA Water Column

Default

0400

0.000

0.002

0.004

0.006

0.008

0 0.2 0.4 0.6 0.8 1

C

o

n

c

e

n

t

r

a

tio

n

s, μ

g

/

L

Exceedence Probability

CA Onion 24-hr TWA Water Column

Default

0800

(Sediment concentrations are also reduced in scenarios where erosion is not a major factor.) 
Example 2 – Landscape-related uncertainties 

 PWG analysis identified two key assumptions inherent in 

standard EFED scenarios:

1. Soil/landform/weather characteristics of Tier II scenarios reflect 

~90th percentile runoff/erosion vulnerability for national 

distribution of chosen crop.

2. Percent of cropped area (PCA) in realistic scale watersheds is 

100% of the watershed area.

 Key government datasets were combined to create “added 

value” GIS layers to examine the impact of these assumptions 

on exposure predictions.

October 6, 2014 (c) 2014 Pyrethroid Working Group. All rights reserved. 14
National Hydrography Data plus (NHD+) catchments are an 

excellent scale for regulatory assessment and uncertainty evaluation

 There are a large number of 

them (~2.5 million)

 Each includes a single 

stream reach

 Entire US land area 

accounted for 

 NHD+ catchments comprise 

a range of areas highly 

relevant to farming 

practices at the local scale 

 90th percentile ~2.4 mi2

 50th percentile ~0.56 mi2 

(360A)

October 6, 2014 (c) 2014 Pyrethroid Working Group. All rights reserved. 15

Cotton Soybean

Catchments with 

crop, 2008-2012 246,956 893,703

Catchments with 

crop, 2012 138,707 658,633
Data processing example for NHD+ catchments 

 Three catchments (90-

650 acres) with different 

landscape patterns

 Different cropping density 

(pink areas = cotton)

 Proximity to flowing 

water (200m)

 Crop in proximity to 

flowing water

 Soils and cropping

A

C

B

0 0.25 0.5 1

Miles

October 6, 2014 (c) 2014 Pyrethroid Working Group. All rights reserved. 16
PWG has classified potential vulnerability to runoff/erosion of US 

EPA model scenarios using complete national assessment

October 6, 2014 (c) 2014 Pyrethroid Working Group. All rights reserved. 17

Conclusion: Relative runoff/ erosion potential for standard FIFRA Tier II scenarios varies and in 

some cases can be an overestimate and in others an underestimate of 90th centile goal

MS Cotton and TX 

cotton Tier II 

scenarios at 97.5th

and 97.3rd

percentiles

57,720 soil/weather station 

combinations on which cotton was 

cropped between 2008-2012

MS Soybean Tier II 

scenario at 89.7th

percentile

229,110 soil/weather station 

combinations on which soybean 

was cropped between 2008-2012
PCA in most catchments is much less than 100%

October 6, 2014 (c) 2014 Pyrethroid Working Group. All rights reserved. 18

PWG has classified distribution of watershed landscape compositions 

for every NHD+ catchment which NASS CDL reported as growing the 

crop-of-interest between 2008 and 2012.

Conclusions: 10- to 200-m PCA distributions at catchment scale vary by crop but 

assumption of 100% PCA is not supported!

86,853 catchments

90th percentile = 3.2%

Cotton Sunflower

138,707 catchments

90th percentile = 13.7%

Soybean

658.633 catchments

90th percentile = 37.5%

(Metric is percent cropped area (PCA) in 10- to 200-m zone around NHD+ stream segment)
Incorporating PCA into FIFRA modeling scenarios

October 6, 2014 (c) 2014 Pyrethroid Working Group. All rights reserved. 19

CA Onion

MS 

Soybean

MS/TX 

Cotton

Tier II 30-year EEC distributions for 

standard MS & TX cotton scenarios 

90th percentiles 0.012 μg/L

EEC distributions for identical input 

but incorporating PCA distributions

90th percentiles 0.0016 ug/L

Conclusions: Impact of watershed scale landscape PCA on EECs is highly 

significant. Effect varies by crop but remains dependent on Tier II 

soil/weather scenario selection.

Magnitude of impact: Across range of crops, PCA distributions reduce 

90th percentile EECs for sediment and water by 3-10X.
What was learned

 PWG used this uncertainty approach 

to refine the pyrethroid risk 

assessment.

 Including landscape thinking does not 

mean that aquatic EECs predicted by 

standard EFED scenarios will never 

occur, but that their probability of 

occurrence in the real world is much 

lower than Tier II estimates.

 Another important learning was that 

there was a need for a further 

categorization of uncertainty. Two 

distinct regulatory questions apply 

for aquatic assessments.

October 6, 2014 (c) 2014 Pyrethroid Working Group. All rights reserved.

PWG NEXT STEP – Select several potential 

sources of uncertainty and categorize 

against these criteria.

20
Potential source of uncertainty Potential 

impact Quantification Estimate (EEC Multiplier) Impacts single 

pond scenario

Impacts 

probability of 

exposed ponds

Systematic uncertainties in SPME KOC model inputs. Conservative. 1.25 – 1.4X due to correction factor used for model inputs. Y

Adsorption to aquatic plants/associated biofilms in water bodies 

(quantified via EXAMS - 300 mg dry wt/L biomass). Conservative. 13X for water column, 2X for sediment and pore water. Y

Degradation by plant surfaces and associated biofilms. Conservative. Estimated 1.5 – 3X. Y

Variation in the number of applications made per season. Conservative. Estimated 1 – 1.5X. Y

Variation from modeled 30 year continuous cropping regimes. Conservative. 1 – 1.3X. Y

Variation in frequency of applications. Conservative. Estimated 1 – 1.2X. Y

Incorporation of soil photolysis. Variable –non- conservative. Estimated 0.7 – 1X. Y

Variable volume water body – evaporation. Non conservative. 0.7 – 1X. Y

Variation in wind direction relative to water body. Conservative. Estimated 1 – 1.3X. Y

Variable deposition/mixing of drift Variable 0.5 - 2X - creates more- and less- potentially exposure areas Y 

Variable deposition/mixing of erosion Variable 0.5-2X - creates more- and less- potentially exposure areas Y 

Variation in wind speed at time of application. Conservative quantified. 1.1 – 5X. Y

Interception of drift by intervening vegetation. Conservative. 1 – 2X. Y

Effect of riparian buffers at edge-of-field to modify runoff/erosion. Conservative. 1 – 2X (effect mainly on sediment). Y

Variation in natural and man-made filter strip widths. Conservative. 1 – 2X (effect mainly on sediment). Y

Potential Drift deposition from >200 m. Non conservative. 0.7 – 1.0X. Y

Distribution of PCA across potentially treated watersheds Conservative Quantified. Typically 3-10X for sediment & water for major crops Y

PCA distribution PLUS full distribution of environmental 

vulnerabilities Variable Quantified. 0.9-1.1 for water column. 0.5-2X for sediment and pore water IN ADDITION TO PCA distribution Y

Fraction of percent of crop area treated (PTA). Conservative. Water column 5.5X, 23X & 130X. Sediment = 4X, 150X and 970X. 

(Onion, Soybean & Cotton) at 90th centile Y

Fraction of catchment area draining to watershed exit (i.e., 

uncertainty about enclosed depressions). Conservative. Estimated 1.1X – 1.3X. Y

Individual market share of pyrethroids nationally and by region. Conservative. 3 – 100X (deltamethrin > 100X both nationally and in CA). Y

Variation in fraction of CoI in catchment treated on the same day. Conservative. Estimated 1 – 1.8X. Y

Variation in actual rate of pyrethroid applied (GfK Kynetec data). Conservative. 1 – 1.15X. Y

Variation in selection of application methods (ground/air) and 

subsequent handling (banding/incorporation).

Variable. Most likely to be 

conservative. 0.9 – 1.3X. Y

Variation in intervals between applications. Conservative. Estimated 1 – 1.2X. Y

Fraction of watersheds with sediment /erosion control structures. Conservative. 1 – 1.4X (regional). (effect mainly on sediment). Y

Fraction of CoI area using conservation tillage, associated realism 

of LS CA and P parameters in models. Conservative. 0.8 – 1.1X (effect mainly on sediment). Y

Impact of sediment delivery ratios at field/catchment scales. Conservative. 1 – 4X (effect mainly on sediment). Y

October 6, 2014 (c) 2014 Pyrethroid Working Group. All rights reserved.
Potential source of uncertainty Potential 

impact Quantification Estimate (EEC Multiplier) Impacts single 

pond scenario

Impacts 

probability of 

exposed ponds

Systematic uncertainties in SPME KOC model inputs. Conservative. 1.25 – 1.4X due to correction factor used for model inputs. Y

Adsorption to aquatic plants/associated biofilms in water bodies 

(quantified via EXAMS - 300 mg dry wt/L biomass). Conservative. 13X for water column, 2X for sediment and pore water. Y

Degradation by plant surfaces and associated biofilms. Conservative. Estimated 1.5 – 3X. Y

Variation in the number of applications made per season. Conservative. Estimated 1 – 1.5X. Y

Variation from modeled 30 year continuous cropping regimes. Conservative. 1 – 1.3X. Y

Variation in frequency of applications. Conservative. Estimated 1 – 1.2X. Y

Incorporation of soil photolysis. Variable –non- conservative. Estimated 0.7 – 1X. Y

Variable volume water body – evaporation. Non conservative. 0.7 – 1X. Y

Variation in wind direction relative to water body. Conservative. Estimated 1 – 1.3X. Y

Variable deposition/mixing of drift Variable 0.5 - 2X - creates more- and less- potentially exposure areas Y 

Variable deposition/mixing of erosion Variable 0.5-2X - creates more- and less- potentially exposure areas Y 

Variation in wind speed at time of application. Conservative - quantified. 1.1 – 7X. Y

Interception of drift by intervening vegetation. Conservative. 1 – 2X. Y

Effect of riparian buffers at edge-of-field to modify runoff/erosion. Conservative. 1 – 2X (effect mainly on sediment). Y

Variation in natural and man-made filter strip widths. Conservative. 1 – 2X (effect mainly on sediment). Y

Potential Drift deposition from >200 m. Non conservative. 0.7 – 1.0X. Y

Distribution of PCA across potentially treated watersheds Conservative Quantified. Typically 3-10X for sediment & water for major crops Y

PCA distribution PLUS full distribution of environmental 

vulnerabilities Variable Quantified. 0.9-1.1 for water column. 0.5-2X for sediment and pore water IN ADDITION TO PCA distribution Y

Fraction of percent of crop area treated (PTA). Conservative. Water column 5.5X, 23X & 130X. Sediment = 4X, 150X and 970X. 

(Onion, Soybean & Cotton) at 90th centile Y

Fraction of catchment area draining to watershed exit (i.e., 

uncertainty about enclosed depressions). Conservative. Estimated 1.1X – 1.3X. Y

Individual market share of pyrethroids nationally and by region. Conservative. 3 – 100X (deltamethrin > 100X both nationally and in CA). Y

Variation in fraction of CoI in catchment treated on the same day. Conservative. Estimated 1 – 1.8X. Y

Variation in actual rate of pyrethroid applied (GfK Kynetec data). Conservative. 1 – 1.15X. Y

Variation in selection of application methods (ground/air) and 

subsequent handling (banding/incorporation).

Variable. Most likely to be 

conservative. 0.9 – 1.3X. Y

Variation in intervals between applications. Conservative. Estimated 1 – 1.2X. Y

Fraction of watersheds with sediment /erosion control structures. Conservative. 1 – 1.4X (regional). (effect mainly on sediment). Y

Fraction of CoI area using conservation tillage, associated realism 

of LS CA and P parameters in models. Conservative. 0.8 – 1.1X (effect mainly on sediment). Y

Impact of sediment delivery ratios at field/catchment scales. Conservative. 1 – 4X (effect mainly on sediment). Y

October 6, 2014 (c) 2014 Pyrethroid Working Group. All rights reserved.
List includes quantitative and qualitative uncertainties

 Worked examples show uncertainty impacts can be very 

significant.

 Some items are pyrethroid specific but most apply to all AIs.

 List is dominated by protective real world factors not considered in 

standard FIFRA EECs.

 Magnitude of many effects quantified via models, others via 

Best Professional Judgment (BPJ).

 Some factors appear to be significant but are difficult to 

quantify. These qualitative factors are mostly conservative 

and serve to build confidence in the protective nature of 

EECs.

October 6, 2014 (c) 2014 Pyrethroid Working Group. All rights reserved. 23
To recap:

 Estimates of potential impacts of individual sources of uncertainty associated with 

standard FIFRA assessments have been developed.

 Some uncertainties affect EECs in exposed water bodies, others affect the likelihood of 

a particular water body being exposed.

 Several specific uncertainties were built into a full assessment which ultimately 

reduced pyrethroid RQs to acceptable levels.

 Remaining sources of uncertainty were not built into assessment.

 These remaining uncertainties will not all apply together but could provide at least 1 to 

2 orders of magnitude of conservatism.

 The non-included conservatisms and qualitative factors provide assurance that 

resulting risk assessments remain protective, and most would apply to all AIs.

 These approaches have not involved modifying many basic standard protective 

FIFRA precepts, such as:

 EECs based on maximum number of applications, maximum use rates

 Using approved modeling approaches

October 6, 2014 (c) 2014 Pyrethroid Working Group. All rights reserved. 24
Take home messages

 In many cases (including pyrethroids) large, statistically significant 

water monitoring databases indicate that EPA’s standard screeninglevel (Tier II) EECs are highly protective. 

 Many individual sources of uncertainty which contribute to these 

conservative EECs have been identified, characterized, and in some 

cases quantified using probabilistic approaches.

 Understanding the wide range of quantitative and qualitative 

uncertainty factors and their potential for modifying standard 

aquatic exposure estimates offers opportunities for reducing the 

numbers of may-affect determinations under ESA, as well as 

building confidence in the protective nature of refined exposure 

modeling.

October 6, 2014 (c) 2014 Pyrethroid Working Group. All rights reserved. 25