UNITED STATES ENVIRONMENTAL PROTECTION AGENCY 

WASHINGTON. D.C. 20460 

OFFICE OF CHEMICAL SAFETY AND POLLUTION PREVENTION 

PC Code: 027602 

DP Barcodes: 412791 and 425073 

Date: February 20,2015 

MEMORANDUM 

SUBJECT: 

FROM: 

THROUGH: 

Review of Three Reports Related to a 3-year Flubendiamide Water Monitoring 

Project in Support of the Conditional Registration of Flubendiamide 

Stephen Wente, Ph:D., Biologist ~ Z. jz "'~ .5-

Environmental Risk Branch 1 

Environmental Fate and Effects Division (7507P) \ ~ 

~~ ~ :1-\)..0\\? Sujatha Sankula, Ph.D., Branch Chief . $- 7 

Environmental Risk Branch 1 

Environmental Fate and Effects Division (7507P) 

Immediate 

Edward 

Environmental 

Odenkirch~n, Office 

Fate and 

Ph.D

Effects 

., Senior 

Division 

Advisor 

(7507P) 

a_ Q,.J ~ 

TO: Carmen Rodia, Risk Manager Reviewer 

Richard Gebken, Risk Manager 

Introduction 

Debbie McCall, Br?lnch Chief 

Invertebrate & Vertebrate Branch 2 

Registration Division (7504P) 

Flubendiamide, an insecticide, was conditionally registered in 2008 for aerial and/or ground 

application to com, cotton, tobacco, pome fruit, stone fruit, tree nuts, grapes, cucurbit vegetables, 

fruiting vegetables, leafy vegetables, and brassica leafy vegetables. Registrant-submitted effects 

studies indicate that both the parent (flubendiamide) and degradate (des-iodo) exhibit chronic 

toxicity to aquatic invertebrates 1

• Submitted fate data indicate flubendiamide slowly converts to 

its des-iodo degradate, which does not breakdown. EFED modeling (0329613+) predicts that 

1 Flubendiamide's mode of action is taxa-specific to an unknown degree (targets lepidopteran ryanodine receptors). 

EFED does not have endpoints specific to Jepidopterans. There are numerous species of aquatic lepidopterans of 

which four are listed species. 

1 
2 

flubendiamide and its degradate (des-iodo) will accumulate in aquatic systems eventually 

exceeding Agency levels of concern (LOCs). 

The registrant has argued that: 1) vegetative filter strips (VFSs) would prevent accumulation 

from exceeding Agency LOCs (flubendiamide labels require a 15 ft VFS buffer around aquatic 

areas); and 2) the Agency overestimates aquatic exposure because EFED modeling cannot 

account for the effect of VFSs. According to the flubendiamide preliminary acceptance letter, the 

Registration Division stated, the “Agency believes that the efficacy of vegetative buffers for 

flubendiamide use is uncertain.” The conditions of registration required a VFS study and, if the 

VFS study did not allay the Agency’s concerns, a pond monitoring study. EFED identified a 

major modeling error in the VFS study (MRIDs 48175602, 48175604, and 48175606) and asked 

the registrant to correct it and re-submit (D382010). The VFS study was never re-submitted, 

therefore, the monitoring study was required. The 3-year monitoring study of water column, 

sediments, and pore water in 3 ponds (2 in Georgia and 1 in North Carolina) was submitted in 

December of 2014. 

EFED has reviewed the monitoring data and associated studies and has identified several 

issues with this monitoring data. Despite these issues, EFED believes the monitoring data 

shows clear evidence that both flubendiamide and des-iodo accumulate in the ponds 

monitored. The accumulation measured in the first three years of the pond data least 

impacted by the identified issues largely matches the initial 3 years of concentration 

predictions of EFED’s aquatic exposure modeling. Because EFED’s modeling does not 

account for the effect of VFSs, but still largely matches the monitoring data, EFED believes 

the effect of VFSs is not large enough to mitigate the ecological risks posed by 

flubendiamide applications. Therefore, EFED concludes the original and subsequent 

ecological risk assessments performed by the Agency adequately reflect the risks posed by 

flubendiamide applications and rejects the registrant’s argument that the label-required 15 

ft VFSs would prevent accumulation from exceeding Agency LOCs. 

The registrant submitted three reports related to this monitoring study: 1) “Monitoring for 

Flubendiamide and its Metabolite Des-iodo Flubendiamide in Sediment and Surface Water” 

(MRID 49415303), “Flubendiamide Aquatic Risk – Summary of Surface Water Monitoring and 

Toxicity Testing” (MRID 49415302), and “Aquatic Exposure Assessment for Flubendiamide 

and its Metabolite Des-iodo Flubendiamide based on a 3-Year Monitoring Study” (MRID 

49415301). This memo provides EFED’s analysis of the monitoring data provided in the 3-year 

monitoring study, summarizes the individual registrant reports, and responds to the major issues 

raised in these reports. 

EFED’s Analysis of the Monitoring Data 

The residues of flubendiamide and its metabolite Des-iodo were monitored in three ponds in two 

locations: one pond in Louisburg, NC, and two adjacent ponds (attached by a culvert) in Omega, 

GA (MRID 49415303). The monitoring study ponds in North Carolina (NC) (Negley et al. 2011; 

MRID 48535201) and Georgia (GA) (Hanzas et al. 2011; MRID 48644901) were approved by 

the Agency (D394006 and D398132, respectively). The ponds were selected from areas with 

high flubendiamide use based on confidential 2009 U.S. sales data. Ponds were selected based on 
3 

the similarity of their surface area and watershed area to the standard pond that EFED uses in 

exposure modeling and the requirement that the entire watershed be planted to one crop. 

Additionally, an attempt was made to select ponds with watersheds that had similar 

characteristics to EFED standard scenarios for the crop planted in that watershed. 

Although not requested by the Agency, the registrant also sampled intermittent and perennial 

streams near the monitored ponds. The intermittent stream sites were up and downstream of 

where the discharge of the pond(s) flowed into the intermittent stream, while the perennial

stream sites were up and downstream of where the discharge of the intermittent stream flowed 

into the perennial stream. (Both Georgia ponds flowed into the same intermittent and perennial 

streams, so the total number of monitoring sites included 3 ponds, 4 intermittent stream sites (2 

in GA and 2 in NC), and 4 perennial streams sites (2 in GA and 2 in NC).) Monthly water and 

sediment samples, with a few exceptions, were taken from each monitoring site for three years. 

Water quality parameters including pH, temperature, conductivity, dissolved oxygen, and 

oxidation/reduction potential (ORP) were measured on-site during each sampling event. 

Composite water and sediment (top 2 inches) samples were collected during each monthly 

sampling event. Applications of the flubendiamide product BeltTM were made to the watershed of 

the pond(s) at each location every year during the study period. 

Pore water was separated from sediment samples by vacuum filtration at about 10 PSI to 

quantify the benthic water residue. Flubendiamide and des-iodo in the water column, pore water 

and sediment extracts were analyzed by LC/MS/MS, using isotopically-labelled internal 

standards for quantitation. The method detection limits were 0.004 μg/L for flubendiamide and 

des-iodo in water and pore water samples, and 0.02 μg/kg for flubendiamide and des-iodo in 

sediment samples. 

Experimental Design and Data Quality Issues 

EFED identified six major issues with the monitoring study that affect the interpretability of the 

study. The first four issues concern the experimental treatment of the watershed: 1) the 

variability in crops grown on the pond watersheds; 2) the variability in the date of application(s); 

3) the variability in the application rates; and 4) the magnitude of the study application rates 

compared to the maximum annual label application rates (Table 1). Because the participation of 

the growers was voluntary, the registrant did not have much control over the treatment of the 

watersheds. The crops rotated in both watersheds – from tobacco (2011) to soybean (2012 and 

2013) to tobacco (2014) in the NC pond watershed and from cotton (2011 and 2012) to peanut 

(2013) in the watershed of the GA ponds. The application dates were quite variable in the NC 

pond watershed with 15 months between the 1st and 2nd application, 12.5 months between the 2nd

and 3rd, and 6.5 months between the 3rd and 4th application with a second application in 2014 

occurring a month later. The application rates also varied in the NC pond watershed from 0.06 to 

0.09 lb/A. Both the application dates (all in August) and rates (all 0.09 lb/A) in the watershed of 

the GA ponds were much more consistent. 
4 

Table 1. Timeline of applications within the watersheds of the monitoring study ponds and comparison to the 

maximum annual application rates allowed by flubendiamide labels. 

Year Crop Application Date 

Rate Applied 

(lb/A) 

Label Maximum 

Annual Rate 

(lb/A) 

Percent of 

Maximum Annual 

Rate (%) 

North Carolina Pond Watershed 

2011 Tobacco May 26 0.06 0.375 16 

2012 Soybean Aug 27 0.075 0.188 40 

2013 Soybean Nov 12 0.09 0.188 48 

2014 Tobacco May 31 0.0675 0.375 34 June 28 0.06 

Georgia Ponds Watershed 

2011 Cotton August 18 (25% of area)

August 23, (75% of area) 0.09 0.282 32 

2012 Cotton August 13 0.09 0.282 32 

2013 Peanut August 30 0.09 0.375 24 

The first three issues (variation in crops grown, application dates, and application rates) would be 

expected to add variability to the monitoring data; making it harder to detect trends in the data. 

The fourth issue (low application rates) reduces the magnitude of the trends, which makes it 

harder to detect trends from the noise in the data. 

The fifth issue concerns the installation of maintained grass swales (grass waterways) in the 

watershed of the GA ponds. On page 15 of the GA Site selection Report (Hanzas, et al. 2011; 

MRID 48644901), it is stated “Primary entry points of runoff into the ponds originate from the 

southeast via two distinct, un-cropped (but not vegetated) drainage pathways.” However the 

Interim Report 1 (MRID 48892501; after the first year of monitoring data) p. 13, the Interim 

Report 2 (MRID 49139801; after the second year of monitoring data) p. 13, and the Final 

Monitoring Report (MRID 49415303) p. 15, all state the same sentence “Primary entry points of 

runoff into the ponds originated from the southeast via three maintained grass swales.” The 

Agency obtained aerial photography of the GA ponds and watershed from September 16, 2010 

(Figure 5a) and September 13, 2013 (Figure 5b) from the National Agriculture Imagery Program 

(NAIP)2

. 

 

2 http://fsa.usda.gov/FSA/apfoapp?area=home&subject=prog&topic=nai
5 

2010 2013 

Figure 1. Aerial photography of the Georgia pond watershed taken before (a) and after (b) installation of 

grass waterways. 

The purpose of grassed waterways is to reduce soil and chemical loadings to waterbodies. They 

occupy the main drainage pathways though which the majority of the pesticide in runoff and 

attached to eroded soil would travel. Grassed waterways are designed to trap eroded soil and 

allow runoff and the chemicals in the runoff to infiltrate into the ground. The flubendiamide 

labels require a 15 ft VFS between the treated field and waterbodies, but do not require grassed 

waterways. The presence of the grassed waterways would be expected to reduce the 

accumulation of flubendiamide and des-iodo in the GA ponds and therefore, make it more 

difficult to identify accumulation trends in the GA ponds. Additionally, the trends measured 

from water column, sediment, and pore water in the GA ponds would be diminished [i.e., the 

magnitude (steepness) of those trends would be diminished relative to what would be expected in 

the absence of the grassed waterways]. 

The final issue with the submitted monitoring data concerns the magnitude of the pore water 

concentrations compared to the water column concentrations from samples collected from the 

same pond and at the same time. In the ponds, EFED expects the pore water and water column 

concentrations to equilibrate over time for both flubendiamide and des-iodo with only short-term 

excursions from nearly equal concentrations after drift and storm events. However, the observed 

pond pore water concentrations were typically much lower than the observed water column 

concentrations from samples collected from the same pond and at the same time. 

To show the magnitude and pervasiveness of this discrepancy, the ratio of the pore water to 

water column concentration was plotted over time for all pond samples that had measured 

concentrations that were above the detection limit for both pore water and water column 

samples. If the pore water to water column concentration were equal, this ratio should equal 1. In 

the NC and GA pond samples (Figure 2a and c, respectively), almost all of the observed ratios 

plot below 1 (equilibrium) with many equal to, or less than, 0.1 indicating the pore water 

concentration is 10 times lower than the corresponding water column concentration for many of 

these samples. For comparison, similar ratios are plotted for samples from the up and 

downstream perennial stream sites (Figure 2b and d). The perennial stream site ratios tend to 
6 

straddle a ratio value of 1 indicating much more equality between pore water and water column 

concentrations. 

Pond Samples Perennial Stream Samples 

0.01

0.1

1

10

100

0 200 400 600 800 1000 1200 1400

Flubendiamide Pore Water to Water 

Column Concentration Ratio (unitless)

Days After Initial Application

Equilibrium

Flubendiamide

Desiodo

a

0.01

0.1

1

10

100

0 200 400 600 800 1000 1200 1400

Flubendiamide Pore Water to Water 

Column Concentration Ratio (unitless)

Days After Initial Application

Equilibrium

Flubendiamide

Desiodo

b

0.01

0.1

1

10

100

0 200 400 600 800 1000 1200 1400

Flubendiamide Pore Water to Water 

Column Concentration Ratio (unitless)

Days After Initial Application

Equilibrium

Flubendiamide

Desiodo

c

0.01

0.1

1

10

100

0 200 400 600 800 1000 1200 1400

Flubendiamide Pore Water to Water 

Column Concentration Ratio (unitless)

Days After Initial Application

Equilibrium

Flubendiamide

Desiodo

d

Figure 2. Comparison of observed pore water to water column concentration ratios for flubendiamide and 

des-iodo from the NC pond (a) the NC perennial stream (b) and GA ponds (c) the GA perennial stream (d) 

samples. 

A potential explanation for why the pond pore water concentrations are so much lower relative to 

the pond water column concentrations may be that the depth of sediment and pore water 

contaminated with flubendiamide and des-iodo may be very shallow relative to the total depth of 

sediment and pore water extracted (~2 inches) during sample collection. Consequently, the pond 

sediment and pore water samples would constitute a mixture of flubendiamide/des-iodo and 

uncontaminated sediment and pore water, thus diluting the concentration flubendiamide and desiodo in the sample. 

The NC perennial stream exhibits pore water to water column concentration ratios that are much 

closer to 1 (Figure 2b). This stream (the Tar River) is a large river at the sites sampled. Sediment 

depths are likely deeper and better mixed due to turbulent flow in the river, which may make it 

easier to sample sediment and pore water sample from a surficial layer with less dilution from 

deeper uncontaminated sediment and pore water. The GA perennial stream water column and 

pore water concentrations were relatively low, so that early in the monitoring time frame, ratios 

could not be calculated because one or both concentrations fell below the detection limit. 

However, the later ratios from the GA perennial stream sites (Figure 2d) were distributed closer 

to 1 than the pond ratios, but further from 1 than the Tar River (NC perennial stream) ratios (the 

GA perennial stream is much smaller at the GA sample sites than the NC perennial stream is at 
7 

the NC sample sites). (Observed pore water to water column concentration ratios for 

flubendiamide and des-iodo for all stream sites are depicted in Appendix B.) 

Assuming the measured sediment and pore water concentrations from the pond samples are 

biased low, it would be harder to detect trends in the sediment and pore water data because the 

observed rate of accumulation will have been diminished due to dilution with the 

uncontaminated layers. Additionally, these ‘diluted’ samples would be much lower than model 

predicted ‘non-diluted’ pore water concentrations. 

Accumulation 

EFED used the “LifeReg” regression procedure in SAS statistical software to fit trend lines to 

the pond concentration data because some of the data are only known to be less than the method 

detection limit (left-censored). This procedure better accounts for the presence of this leftcensored data without biasing the fitted trend estimates. The fitted trends increase with time 

(accumulate) in all of the 18 time-series data sets collected from these ponds [3 ponds × 3 media 

(water column, sediments, and pore water) × 2 chemicals = 18 time series data sets]. Fitting these 

trends as exponential trends (i.e., fitting a linear trend to the natural log of the concentration 

observations) indicated that 13 of these 18 trends were statistically significant at the p = 0.05 

level of confidence (Figures 3, 4, and 5) despite the issues with this data described in the 

previous section. (The exponential trends appear as linear trends in these figures because the yaxis is presented as a log scale). 
8 

Concentration Data Over Time Accumulation (change from 

beginning to end of trend line) 

Water Column

0.001

0.01

0.1

1

10

Apr-11 Oct-11 Apr-12 Oct-12 Apr-13 Oct-13 Apr-14 Oct-14

Time

Concentration (

μg/L)

Flubendiamide

Desiodo

Flubendiamide: Increased 2.60× 

(0.071 to 0.19 μg/L) 

Upward exponential trend is not 

statistically significant (P = 0.2840) 

Des-iodo: Increased 1.68× 

(0.018 to 0.031 μg/L) 

Upward exponential trend is not 

statistically significant (P = 0.4675) 

Sediment

0.01

0.1

1

10

100

Apr-11 Oct-11 Apr-12 Oct-12 Apr-13 Oct-13 Apr-14 Oct-14

Time

Concentration (

μg/kg)

Flubendiamide

Desiodo Flubendiamide: Increased 10.0× 

(0.26 to 2.57 μg/kg) 

Upward exponential trend is statistically 

significant (P = 0.0088) 

Des-iodo: Increased 10.55× 

(0.023 to 0.33 μg/kg) 

Upward exponential trend is statistically 

significant (P = 0.0002) 

Pore Water

0.001

0.01

0.1

1

Apr-11 Oct-11 Apr-12 Oct-12 Apr-13 Oct-13 Apr-14 Oct-14

Time

Concentration (

μg/L)

Flubendiamide

Desiodo Flubendiamide: Increased 7.84× 

(0.0084 to 0.66 μg/L) 

Upward exponential trend is statistically 

significant (P = 0.0005) 

Des-iodo: Increased 6.99× 

(0.0044 to 0.030 μg/L) 

Upward exponential trend is statistically 

significant (P < 0.0001) 

Figure 3. Accumulation of flubendiamide and des-iodo in the water column (a), sediment (b), and pore water 

(c) of North Carolina pond.
9 

Concentration Data Over Time Accumulation (change from beginning to 

end of trend line) 

Water Column

0.001

0.01

0.1

1

10

Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14

Time

Concentration (μg/L)

Flubendiamide

Desiodo Flubendiamide: Increased 6.6× 

(0.045 to 0.30 μg/L) 

Upward exponential trend is statistically 

significant (P = 0.0002) 

Des-iodo: Increased 10.1× 

(0.013 to 0.13 μg/L) 

Upward exponential trend is statistically 

significant (P < 0.0001) 

Sediment

0.01

0.1

1

10

100

Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14

Time

Concentration (μg/Kg)

Flubendiamide

Desiodo

Flubendiamide: Increased 20.4× 

(0.19 to 3.80 μg/kg) 

Upward exponential trend is statistically 

significant (P < 0.0001) 

Des-iodo: Increased 34.6× 

(0.037 to 1.27 μg/kg) 

Upward exponential trend is statistically 

significant (P < 0.0001) 

Pore Water

0.001

0.01

0.1

1

Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14

Time

Concentration (μg/L)

Flubendiamide

Desiodo

Flubendiamide: Increased 12.5× 

(0.0039 to 0.048 μg/L) 

Upward exponential trend is statistically 

significant (P < 0.0001) 

Des-iodo: Increased 10.7× 

(0.0042 to 0.045 μg/L) 

Upward exponential trend is statistically 

significant (P < 0.0001) 

Figure 4. Accumulation of flubendiamide and des-iodo in the water column (a), sediment (b), and pore water 

(c) of Georgia pond #1. 
10 

Concentration Data Over Time Accumulation (change from 

beginning to end of trend line) 

Water Column

0.001

0.01

0.1

1

10

Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14

Time

Concentration (μg/L)

Flubendiamide

Desiodo Flubendiamide: Increased 2.02× 

(0.096 to 0.19 μg/L) 

Upward exponential trend is not 

statistically significant (P = 0.1588) 

Des-iodo: Increased 3.30× 

(0.020 to 0.065 μg/L) 

Upward exponential trend is statistically 

significant (P = 0.0008)

Sediment

0.01

0.1

1

10

100

Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14

Time

Concentration (μg/Kg)

Flubendiamide

Desiodo Flubendiamide: Increased 2.30× 

(0.30 to 0.69 μg/kg) 

Upward exponential trend is not 

statistically significant (P = 0.1803 

Des-iodo: Increased 3.38× 

(0.033 to 0.11 μg/kg) 

Upward exponential trend is statistically 

significant (P = 0.0377) 

Pore Water

0.001

0.01

0.1

1

Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14

Time

Concentration (μg/L)

Flubendiamide

Desiodo Flubendiamide: Increased 3.71× 

(0.0071 to 0.026 μg/L) 

Upward exponential trend is statistically 

significant (P = 0.0044) 

Des-iodo: Increased 2.31× 

(0.0041 to 0.0094 μg/L) 

Upward exponential trend is not 

statistically significant (P = 0.0547) 

Figure 5. Accumulation of flubendiamide and des-iodo in the water column (a), sediment (b), and pore water 

(c) of Georgia pond #2. 
11 

Comparison of Observed Concentrations in the Monitoring Study to Exposure Model Predictions 

During the site selection phase of the monitoring study, the registrant made an attempt to select 

combinations of crops to be planted and pond watershed characteristics that were similar to 

EFED standard scenarios. However, EFED exposure scenarios are designed to represent highend exposures and have many parameters embedded within the standard scenarios that would 

likely need adjustment to make a valid comparison between exposure model predictions to 

observed concentrations in a strict sense (field slopes, etc). Additionally, the SWCC cannot be 

parameterized for crop rotations, cannot account for VFSs (or the grassed waterways in the 

watershed of the GA ponds), assumes similar application timing and rates of pesticides applied 

each year, and assumes wind direction always deposits drift into the pond(s). Therefore the 

comparisons presented should be considered very “rough”. 

Figure 6 provides the comparison between exposure model predictions and observed 

concentrations for the NC pond. The SWCC modeling used the NC tobacco scenario3

 with the 

same input values as appear in Table 1 of the aquatic exposure report (MRID 49415301) with the 

exceptions that the benthic metabolism half-life value of 855 days was used (rather than the 

registrant modified value of 7300 days), the soil half-life of 0 (stable) was used (rather than the 

10,000 day value in the aquatic exposure report), the efficiency (0.95) and drift (0.05) fractions 

were changed to 0.99 and 0.00824

 because Table 1 indicates that these were ground applications 

under the application method section of this table, and standard pond dimensions were used. 

(EFED did not use the registrant modified weather files, files because they only provided to the 

agency in a pdf format as part of the report.) 

The monitoring report does not contain sufficient information to identify a unique set of SWCC 

parameters for comparison with the NC pond data. For example, the report does not indicate 

whether the wind direction on the application date would have blown drift toward the pond. 

Therefore, three SWCC scenarios were run with different combinations of application rates and 

spray drift assumptions to bound reasonable SWCC parameterizations for the NC pond. The 

highest rates applied to the NC pond watershed (0.09 lb/A) with the EFED’s current spray drift 

fraction (0.0082) is shown solid lines in Figures 6a to f). The lowest rates applied to the NC pond 

watershed (0.06 lb/A) with the EFED’s current spray drift fraction (0.0082) is shown as dashed 

lines in Figures 6a to f. The third SWCC scenario used the lowest rates applied to the NC pond 

watershed (0.06 lb/A) with no drift (to simulate the lowest reasonable exposure scenario) and is 

show as dotted lines in Figures 6a to f. (Note: Figures 6a through f are presented with the y-axis 

as a log scale.) 

The observed water column flubendiamide concentrations display a lot of scatter in Figure 6a, 

but contain concentrations that plot both above and below the SWCC predictions. Similarly, the 

observed water column des-iodo concentrations plot both above and below the SWCC 

predictions, but the concentrations that plot above the SWCC predictions occur toward the 

 

3

 The crop in the NC pond watershed rotated from tobacco to soybean for two years and back to tobacco. EFED 

does not have mixed crop scenarios, but does have soybean scenarios from states other than NC. However, EFED 

simply used the scenario modeled by the registrant (MRID 49415301) without further exploration of alternative 

scenarios. 

4

 Calculated with AgDrift based on a high boom ground application with a droplet size of ASAE fine to medium 

coarse (DV50 of 341um). 
12 

beginning of the monitoring. Additionally, the observed water column des-iodo concentrations 

display a lot less scatter (Figure 6b) than the flubendiamide concentrations (Figure 6a) and 

follow the trend much better in the latter half of the monitoring. The respective sediment 

concentrations (Figure 6c and d) and pore water concentrations (Figure 6e and f) all plot 

somewhat low compared to the SWCC predictions, consistent with the hypothesis that these 

samples are diluted with the underlying uncontaminated sediment and pore water lying below the 

higher surficial sediment and pore water concentrations (see previous discussion). Overall, the 

Agency believes the monitoring data tracks reasonably well with the modeled data and therefore, 

supports the previous predictions of aquatic exposure modeling and the prior flubendiamide risk 

assessments despite the fact that EFED’s modeling cannot account any effect of the VFSs. 

Flubendiamide Des-iodo 

0.001

0.01

0.1

1

10

0 200 400 600 800 1000 1200 1400

Water Column Flubendiamide (μg/L)

Days After Initial Application

Observed

0.09 lb/A

0.06 lb/A

No Drift

a

0.001

0.01

0.1

1

0 200 400 600 800 1000 1200 1400

Water Column Des-iodo (μg/L)

Days After Initial Application

Observed

0.09 lb/A

0.06 lb/A

No Drift

b

0.01

0.1

1

10

100

0 200 400 600 800 1000 1200 1400

Sediment Flubendiamide (μg/Kg)

Days After Initial Application

Observed

0.09 lb/A

0.06 lb/A

No Drift

c

0.001

0.01

0.1

1

10

0 200 400 600 800 1000 1200 1400

Sediment Des-iodo (μg/Kg)

Days After Initial Application

Observed

0.09 lb/A

0.06 lb/A

No Drift

d

0.001

0.01

0.1

1

10

0 200 400 600 800 1000 1200 1400

Pore Water Flubendiamide (μg/L)

Days After Initial Application

Observed

0.09 lb/A

0.06 lb/A

No Drift

e

0.001

0.01

0.1

1

0 200 400 600 800 1000 1200 1400

Pore Water Des-iodo (μg/L)

Days After Initial Application

Observed

0.09 lb/A

0.06 lb/A

No Drift

f

Figure 6. Comparison of Surface Water Concentration Calculator (SWCC) daily predictions from the North 

Carolina tobacco scenario to monitoring data from the North Carolina pond for water column flubendiamide 

(a) and des-iodo (b), sediment flubendiamide (c) and des-iodo (d), and pore water flubendiamide (e) and desiodo (f) based on the range of application rates (0.06 to 0.09 lbs/A) used in the pond watershed during 

monitoring. 
13 

EFED assumes that any reduction in pond chemical concentrations in water column, sediment, 

and pore water concentrations from VFSs would be greatest when the chemical is first used and 

would diminish with time as the VFS became saturated with flubendiamide and des-iodo. Once 

saturated, the VFS might become a net source of the contaminants to the pond rather than a net 

sink. (EFED believes that VFSs would be more efficacious for pesticides that would rapidly 

breakdown into non-toxic degradates within the VFS.) From this rough comparison, the impact 

of the VFS does not appear to be large in the NC pond data. 

Similar to the NC analysis, Figure 7 compares exposure model predictions and observed 

concentrations for the GA ponds. The SWCC modeling used the MS cotton5

 (solid lines in 

Figures 7a to f) and NC cotton6 (dashed lines in Figures 6a to f) scenarios with the same input 

values as described for the NC scenario (only one application rate 0.09 lb/A was used since this 

did not vary in the GA pond watershed). A no drift scenario does not appear in the in Figure 7 

because drift only accounts for ~2% of the flubendiamide reaching the pond in the MS and NC 

cotton scenarios and would have been indistinguishable from the predictions including drift. 

(Note: Figures 7a through f are presented with the y-axis as a log scale.) 

Almost all of the GA ponds concentration data plots below the SWCC predictions. The 

interpretation of the GA ponds data is confounded by the presence of grassed waterways in the 

watershed. The combination of grassed waterways and VFSs (only VFSs are required by 

flubendiamide labels) would be expected to diminish transport of both flubendiamide and desiodo to the ponds. The GA ponds data does appear to show the same pattern of sediment and 

pore water dilution in that the water column observations are much closer to the SWCC 

predictions (Figures 7a and b) than the sediment and pore water observations are (Figures 7c 

through d). 

 

5

 The MS cotton scenario was modeled by the registrant in MRID 49415301. 

6

 The NC cotton scenario was added by EFED because it is located in the same general region and to provide 

comparison with the MS cotton scenario. 
14 

Flubendiamide Des-iodo 

0.001

0.01

0.1

1

10

0 200 400 600 800 1000 1200 1400

Water Column Flubendiamide (μg/L)

Days After Initial Application

Pond 1 Pond 2

MS Cotton NC Cotton

a

0.001

0.01

0.1

1

10

0 200 400 600 800 1000 1200 1400

Water Column Des-iodo (μg/L)

Days After Initial Application

Pond 1 Pond 2

MS Cotton NC Cotton

b

0.01

0.1

1

10

100

1000

0 200 400 600 800 1000 1200 1400

Sediment Flubendiamide (μg/Kg)

Days After Initial Application

Pond 1 Pond 2

MS Cotton NC Cotton

c

0.01

0.1

1

10

100

0 200 400 600 800 1000 1200 1400

Sediment Des-iodo (μg/Kg)

Days After Initial Application

Pond 1 Pond 2

MS Cotton NC Cotton

d

0.001

0.01

0.1

1

10

0 200 400 600 800 1000 1200 1400

Pore Water Flubendiamide (μg/L)

Days After Initial Application

Pond 1 Pond 2

MS Cotton NC Cotton

e

0.001

0.01

0.1

1

10

0 200 400 600 800 1000 1200 1400

Pore Water Des-iodo (μg/L)

Days After Initial Application

Pond 1 Pond 2

MS Cotton NC Cotton

f

Figure 7. Comparison of Surface Water Concentration Calculator (SWCC) daily predictions from the 

Mississippi cotton and North Carolina cotton scenarios to monitoring data from the Georgia pond for water 

column flubendiamide (a) and des-iodo (b), sediment flubendiamide (c) and des-iodo (d), and pore water 

flubendiamide (e) and des-iodo (f). 

Ecological Risk 

Ecological risk is determined by comparing exposure estimates to Agency levels of concern 

(LOCs). Aquatic exposure is predicted over 30 years in Figure 8 for the NC tobacco scenario. 

These model results are based on the same parameters as the predictions that fit the NC pond 

data well, but use the maximum label rates instead (4 applications of 0.09 lb/A for an annual 

maximum of 0.375 lb/A assuming it is continuously planted to tobacco). Chronic aquatic 

invertebrate endpoints are also included in Figure 8. Because these chronic endpoints have an 

LOC of 1, an exposure exceeding an endpoint also exceeds the Agency LOC (i.e., the LOC and 

the endpoint are the same number). Drawing a vertical line down from where the exposure 

crosses the appropriate endpoint indicates the time required for flubendiamide or des-iodo 

accumulation to exceed Agency LOCs. The water column des-iodo NOEC is exceeded after 8 
15 

years in Figure 8a and the pore water des-iodo NOEC is exceeded after 23 years in Figure 8b, 

while the pore water flubendiamide NOEC is exceeded after 7 years (also in Figure 8b). [Note: 

flubendiamide has already been on the market for 5 years (2009 to 2014). Also, at the lower 

application rates used in the monitoring study, it would take ~4 times as long to exceed all of 

these LOCs.] The NC tobacco scenario is not the worst case use (other scenarios exceed LOCs in 

shorter time periods). 

Water Column Pore Water 

NC Tobacco 

0

5

10

15

20

25

30

35

40

0 5 10 15 20 25 30 35

Water Column Concentration (μg/L)

Years

Flubendiamide

Des-iodo

Flubendiamide NOEC

Des-iodo NOEC

a

0

5

10

15

20

25

30

35

40

0 5 10 15 20 25 30 35

Pore Water Concentration (μg/L)

Years

Flubendiamide

Des-iodo

Flubendiamide NOEC

Des-iodo NOEC

b

Figure 8. Accumulations of flubendiamide and its des-iodo degradate exceeding chronic risk endpoints in the 

standard pond water column (a) and pore water (b) based on ground applications to North Carolina tobacco 

at the maximum allowed application rate. 

Additional monitoring at stream sites near these ponds found both flubendiamide and des-iodo in 

water column, sediment and pore water samples at all eight stream sites monitored (Appendix 

A). This stream data indicates low-level contamination in streams is currently pervasive in 

regions where flubendiamide is used. 

The next section addresses each of the three submitted studies individually. For each study, a 

brief summary is provided with a list of issues raised in the study along with EFED comments on 

those issues. 

Monitoring for Flubendiamide and its Metabolite Des-iodo Flubendiamide in Sediment and 

Surface Water (MRID 49415303) 

Summary: 

The objective of this study was to assess the potential for flubendiamide and its des-iodo 

metabolite to accumulate in aquatic environments (water and sediment) following drift and 

runoff of flubendiamide into surface water with multiple years of applications. 

EFED Issue: 

Much of the report only discusses measurements above the limit of quantitation (LOQ) rather 

than the method detection limits (MDL). For example, “(d)es-iodo flubendiamide was not 

detected above the LOQ in pore water in the farm pond in North Carolina” (p. 24). Yet, the 

flubendiamide data from the NC farm pond show a statistically significant (P < 0.0001) 

exponentially increasing trend according to the Agency’s modeling from values below the LOQ. 

The Agency has discussed this issue with the registrant and has indicated that the registrant 
16 

should use all values down to the MDL. If the values between the MDL and LOQ are as 

randomly distributed as the registrant claims, including these values should make it more 

difficult to detect trends in accumulation over time. Use of these data should not spontaneously 

create trends where none actually occur. 

Limits of quantitation are typically set between 3 and 10 times the MDL. The registrant has 

chosen 10 times the MDL for an accumulation study that modeling suggests will not accumulate 

to much more than the LOQ by the end of the monitoring study. Had the registrant applied the 

pesticide at the maximum application rate (and brought the grassed waterways to the attention of 

the Agency so that a different site could be monitored), using only the values above the LOQ 

may have been an option. 

Registrant Comment: 

“Overall, the results show negligible concentrations of des-iodo flubendiamide in water, porewater or sediment, and no indication of formation of des-iodo flubendiamide in the water or 

sediment (i.e., a decline in flubendiamide in sediment or water did not result in increases in desiodo flubendiamide in sediment or water). Year-to-year variations in concentrations were 

observed, with highest residues occurring a few months after application, and then declining. 

There is no indication of accumulation of flubendiamide or des-iodo flubendiamide in porewater, water or sediment in the pond, intermittent streams or permanent streams.” (p. 27) 

“These results indicate that low levels of flubendiamide residues can occur due to runoff from 

fields with recent applications of flubendiamide products. These residues are not significantly 

accumulating after three years of applications. This is expected due to the turnover of water and 

sediment in the moving water bodies, and water from the ponds. The sediment in the ponds, 

which might be expected to have accumulating residues, only showed year-to-year variations, 

and no indication of significant accumulation.” (p. 30) 

EFED Comments: 

The report purports to look for accumulation over time, but there is no trend analysis presented. 

The Agency found that fitting trend lines to the data indicated that all 18 of the time series data 

sets from the ponds [3 ponds × 3 media [water column, sediments, and pore water] × 2 chemicals 

= 18 time series data sets] increased over time with 13 of the 18 identified as statistically 

significant. Considering just the sediment data discussed in the second quote above, five of the 

six sediment concentration trends were statistically significant. The Agency strongly disagrees 

with the registrant’s assessment of no significant accumulation. 

Flubendiamide Aquatic Risk – Summary of Surface Water Monitoring and Toxicity 

Testing (MRID 49415302) 

Summary: 

The registrant summarized the toxicity studies submitted to date for flubendiamide and des-iodo 

as well as a midge (Chironomus riparius) 28-d spiked sediment flubendiamide study that is yet 

to be submitted to the Agency. 
17 

Registrant Comment: 

The appropriate chronic risk assessment endpoints to use for a flubendiamide and des-iodo 

flubendiamide sediment risk assessment are: 

 Flubendiamide overlying water – NOEC 33 μg/L 

 Flubendiamide pore water – NOEC 2.56 μg/L 

 Des-iodo flubendiamide overlying water – NOEC 4 μg/L 

 Des-iodo flubendiamide pore water – NOEC 19.5 μg/L 

EFED Response: 

EFED has evaluated all of these studies and provided a Data Evaluation Record (DER) for each 

with the exception of the aforementioned midge study that has yet to be submitted to the Agency. 

Some of these registrant-calculated endpoints differ slightly from the Agency determined 

endpoints. If the registrant believes the Agency-calculated endpoints are in error, the appropriate 

course of action would be to rebut the individual DERs. This report (MRID 49415302) does not 

contain sufficient explanation and analysis for the Agency to reconsider the endpoints. However 

for purposes of evaluating the studies submitted with the monitoring study (MRIDs 49415301 to 

49415303), the Agency will use the registrant-calculated endpoints to avoid diverting focus from 

the issues the Agency has with the submitted monitoring and aquatic exposure reports. 

Aquatic Exposure Assessment for Flubendiamide and its Metabolite Des-iodo 

Flubendiamide based on a 3-Year Monitoring Study (MRID 49415301) 

Summary: 

The overall objective of this report was to compare the results from a 3-year monitoring study at 

two locations with the potential aquatic estimated environmental concentrations (EECs) 

produced by the SWCC model. Both standard and modified scenarios were used as a means to 

better simulate field observations and to achieve insights into the factors governing the fate of 

flubendiamide and des-iodo at the field sites. 

Registrant Comment: 

“For GA, the SWCC overestimated peak flubendiamide concentrations in water and pore water 

by a factor of 3 and 17, respectively. Peak des-iodo concentrations were over-predicted by a 

factor of 11 and 26 in water and pore water, respectively.” (p. 7) 

EFED Response: 

The Agency agrees the SWCC concentration predictions based on the MS cotton and NC cotton 

scenarios are higher than the concentrations observed in the GA pond. However, the Agency 

ascribes these discrepancies to problems with the registrant’s data. The Agency believes the 

presence of the grassed waterways in the watershed of the GA ponds render these data unusable 

for comparison with the SWCC predictions. The pore water data discrepancy, which is larger 

than the water column data, is impacted by both the presence of the grassed waterways and 

potentially, the sample dilution issue. Additionally, there are other parameters such as field slope 

that would need adjustment before a direct comparisons could be made. 
18 

Registrant Comment: 

“For NC, the SWCC under-predicted flubendiamide in water and pore water by a factor of 5 and 

3 respectively. However, the NC site received an off-season, bare ground application in 

November of 2013 which led to greater runoff than would be expected in a typical growing 

season7

. However, for des-iodo, the SWCC over-predicted water and pore water concentrations 

by a factor of 2 and 7, respectively.” (p. 7) 

EFED Response: 

The Agency believes the SWCC predictions fit the water column data quite well (Figure 6a and 

b) and believes the differences in pore water concentrations (Figures 6e and f) are better ascribed 

to the previously discussed sample dilution issue. 

Registrant Comment: 

“The model also predicted exponential accumulation of both flubendiamide and des-iodo in the 

water and pore water, which was not observed in the field study.” (p. 7) 

EFED Response: 

The Agency believes exponential accumulation was observed in the field study. 

EFED Issue: 

The registrant developed a series of increasingly complex model adjustments in order to get the 

SWCC predictions to align with the water column and pore water observations. The justification 

for making these adjustments was based almost entirely on the GA pond data and pore water data 

from both the GA and NC ponds, which the Agency believes to be inaccurate due to the presence 

of the grassed waterways (GA data) and the sample dilution issue (pore water data). 

Conclusions 

The monitoring study shows accumulation in all of the ponds monitored for both flubendiamide 

and des-iodo in water column, sediments, and pore water with 13 of the 18 pond accumulation 

trends identified as statistically significant. The VFS study (MRIDs 48175602, 48175604, and 

48175606) and monitoring studies (MRIDs 49415301 to 49415303) did not provide evidence 

that VFSs provided significant reductions in flubendiamide and des-iodo transport to aquatic 

environments. The NC pond data provide a good match to the SWCC modeling (Figures 6a and 

b). This same model parameterization (after adjusting to maximum label application rates) 

produces exposure estimates that exceed Agency chronic LOCs (Figures 8a and b) for aquatic 

invertebrates in as little as 7 years. The NC tobacco scenario is not the worst case use (other 

scenarios exceed LOCs in shorter time periods). Flubendiamide and des-iodo are expected to 

accumulate in the environment and pose chronic risk concerns for aquatic invertebrates. 

Therefore, EFED concludes the original (D329613+) and subsequent ecological risk assessments 

performed by the Agency adequately reflect the risks posed by flubendiamide applications and 

 

7

 According to the monitoring report, “The concentrations of flubendiamide and des-iodo flubendiamide were higher 

in 2013 and first part of 2014 which was mainly caused by the off-season application of BeltTM on bareground after 

soybean harvesting in 2013. Although application of BeltTM on bareground in November was not a good agricultural 

practice, the application was made to compensate for the grower not making a summertime application as expected.” 
19 

rejects the registrant’s argument that the label-required 15 ft VFSs would prevent accumulation 

from exceeding Agency LOCs. 

Literature Cited 

Dyer, D.G., and A.T. Hall. 2014. Flubendiamide Aquatic Risk – Summary of Surface Water 

Monitoring and Toxicity Testing. Bayer CropScience. Report Number: US0453. 16 pp. 

(MRID 49415302) 

Hanzas, J.P., B. Toth, and J. White. 2011. Georgia Site Selection Report for “Monitoring for 

Flubendiamide and its Metabolite des-iodo Flubendiamide in Sediment and Surface 

Water”. Bayer CropScience. Study Number: MEAMP011. 37 pp. (MRID 48644901) 

Negley, T.L., D.K. Moore, and A.C. Newcombe. 2011. North Carolina Site Selection Report for 

“Monitoring for Flubendiamide and its Metabolite des-iodo Flubendiamide in Sediment 

and Surface Water” Bayer CropScience. Report Number: US0220. 50 pp. (MRID 

48535201) 

Pérez Ovilla, O. 2014. Aquatic Exposure Assessment for Flubendiamide and its Metabolite Desiodo Flubendiamide based on a 3-Year Monitoring Study. Bayer CropScience. Report 

Number: US0453. 98 pp. (MRID 49415301) 

Xu, T. 2012. Monitoring for Flubendiamide and its Metabolite Des-iodo Flubendiamide in 

Sediment and Surface Water: Interim Report 1. Bayer CropScience. Study Number: 

MEAMP011. 349 pp. (MRID 48892501) 

Xu, T. 2013. Monitoring for Flubendiamide and its Metabolite Des-iodo Flubendiamide in 

Sediment and Surface Water: Interim Report 2. Bayer CropScience. Study Number: 

MEAMP011. 74 pp. (MRID 49139801) 

Xu, T. 2014. Monitoring for Flubendiamide and its Metabolite Des-iodo Flubendiamide in 

Sediment and Surface Water: Final Report. Bayer CropScience. Study Number: 

MEAMP011. 518 pp. (MRID 49415303) 
20 

Appendix A. Additional Monitoring Data from Flowing-Water Sites EFED does not anticipate continuous accumulation at these flowing-water sites because any accumulation is con

periodically (sediment) flushed downstream. Data from the Georgia and North Carolina flowing-water sites (locain the larger watersheds that contain the GA and NC ponds) with trend lines (solid for flubendiamide and dashed 

presented in Figure A1 and A2, respectively. Because some of the data time-series from stream sites have few comeasured above the detection limit, the trend lines appear counter-intuitive. 

Water Column Sediment Pore W

Upstream Intermittent Stream Site 

0.001

0.01

0.1

1

10

Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14

Time

Concentration (μg/L)

FlubendiamideDesiodo

0.01

0.1

1

10

100

Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14

Time

Concentration (μg/Kg)

FlubendiamideDesiodo

0.001

0.01

0.1

1

Aug-11 Feb-12 Aug-12 Fe

T

Concentration (μg/L)

Downstream Intermittent Stream Site 

0.001

0.01

0.1

1

Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14

Time

Concentration (μg/L)

FlubendiamideDesiodo

0.01

0.1

1

10

100

Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14

Time

Concentration (μg/Kg)

FlubendiamideDesiodo

0.001

0.01

0.1

1

Aug-11 Feb-12 Aug-12 Fe

T

Concentration (μg/L)
21 

Water Column Sediment Pore W

Upstream Perennial Stream Site 

0.0001

0.001

0.01

0.1

1

Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14

Time

Concentration (μg/L)

FlubendiamideDesiodo

0.001

0.01

0.1

1

10

Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14

Time

Concentration (μg/Kg)

FlubendiamideDesiodo

0.0001

0.001

0.01

0.1

1

Aug-11 Feb-12 Aug-12 Fe

Concentration (μg/L)

FlubendiamideDesiodo

Downstream Perennial Stream Site 

0.001

0.01

0.1

1

Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14

Time

Concentration (μg/L)

FlubendiamideDesiodo

0.001

0.01

0.1

1

10

Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14

Time

Concentration (μg/Kg)

FlubendiamideDesiodo

0.0001

0.001

0.01

0.1

1

Aug-11 Feb-12 Aug-12 Fe

Concentration (μg/L)

Figure A1. Georgia monitoring data from stream sites. 
22 

North Carolina Flowing-water Sites (located at different points in a larger watershed that contains the North Caro

Water Column Sediment Pore W

Upstream Intermittent Stream Site 

0.001

0.01

0.1

1

Apr-11 Oct-11 Apr-12 Oct-12 Apr-13 Oct-13 Apr-14 Oct-14

Time

Concentration (μg/L)

FlubendiamideDesiodo

0.001

0.01

0.1

1

10

100

Apr-11 Oct-11 Apr-12 Oct-12 Apr-13 Oct-13 Apr-14 Oct-14

Time

Concentration (μg/kg)

FlubendiamideDesiodo

0.001

0.01

0.1

1

Apr-11 Oct-11 Apr-12 Oct-

Concentration (μg/L)

Downstream Intermittent Stream Site 

0.001

0.01

0.1

1

Apr-11 Oct-11 Apr-12 Oct-12 Apr-13 Oct-13 Apr-14 Oct-14

Time

Concentration (μg/L)

FlubendiamideDesiodo

0.001

0.01

0.1

1

10

Apr-11 Oct-11 Apr-12 Oct-12 Apr-13 Oct-13 Apr-14 Oct-14

Time

Concentration (μg/kg)

FlubendiamideDesiodo

0.001

0.01

0.1

1

Apr-11 Oct-11 Apr-12 Oct-

Concentration (μg/L)
23 

Water Column Sediment Pore W

Upstream Perennial Stream Site 

0.001

0.01

0.1

1

Apr-11 Oct-11 Apr-12 Oct-12 Apr-13 Oct-13 Apr-14 Oct-14

Time

Concentration (μg/L)

FlubendiamideDesiodo

0.01

0.1

1

10

Apr-11 Oct-11 Apr-12 Oct-12 Apr-13 Oct-13 Apr-14 Oct-14

Time

Concentration (μg/kg)

FlubendiamideDesiodo

0.001

0.01

0.1

1

Apr-11 Oct-11 Apr-12 Oct-

Concentration (μg/L)

Downstream Perennial Stream Site 

0.001

0.01

0.1

1

Apr-11 Oct-11 Apr-12 Oct-12 Apr-13 Oct-13 Apr-14 Oct-14

Time

Concentration (μg/L)

FlubendiamideDesiodo

0.001

0.01

0.1

1

10

Apr-11 Oct-11 Apr-12 Oct-12 Apr-13 Oct-13 Apr-14 Oct-14

Time

Concentration (μg/kg)

FlubendiamideDesiodo

0.001

0.01

0.1

1

Apr-11 Oct-11 Apr-12 Oct-

Concentration (μg/L)

Figure A1. North Carolina monitoring data from stream sites. 
24 

Appendix B. Pore Water to Water Column Concentrations Ratios for Flowing Water Sites 

The NC perennial stream exhibits pore water to water column concentration ratios that are much 

closer to 1 (Figure B1c and d) than the intermittent sites (Figure B1a and b) or the pond samples 

(see Figure 2a in the text). The NC perennial stream (the Tar River) is a large river at the sites 

sampled. Sediment depths are likely deeper and better mixed due to turbulent flow in the river, 

which may make it easier to sample sediment and pore water sample from a surficial layer with 

less dilution from deeper uncontaminated sediment and pore water. The intermittent stream 

samples had ratios that were intermediate in that they fell closer to 1 than the pond ratios, but 

further from 1 than the perennial stream samples. 

Upstream Downstream 

Intermittent Stream Sites 

0.01

0.1

1

10

100

0 200 400 600 800 1000 1200 1400

Flubendiamide Pore Water to Water 

Column Concentration Ratio (unitless)

Days After Initial Application

Equilibrium

Flubendiamide

Desiodo

a

0.01

0.1

1

10

100

0 200 400 600 800 1000 1200 1400

Flubendiamide Pore Water to Water 

Column Concentration Ratio (unitless)

Days After Initial Application

Equilibrium

Flubendiamide

Desiodo

b

Perennial Stream Sites 

0.01

0.1

1

10

100

0 200 400 600 800 1000 1200 1400

Flubendiamide Pore Water to Water 

Column Concentration Ratio (unitless)

Days After Initial Application

Equilibrium

Flubendiamide

Desiodo

c

0.01

0.1

1

10

100

0 200 400 600 800 1000 1200 1400

Flubendiamide Pore Water to Water 

Column Concentration Ratio (unitless)

Days After Initial Application

Equilibrium

Flubendiamide

Desiodo

d

Figure B1. Comparison of pore water to water column concentration ratios for flubendiamide and des-iodo 

from intermittent (a and b) and perennial (c and d) near the North Carolina pond. 

The GA perennial stream water column and pore water concentrations were relatively low. 

Therefore, early in the monitoring time frame, ratios could not be calculated because one or both 

concentrations fell below the detection limit. The later ratios from the GA perennial stream sites 

(Figure B2c and d) were distributed more like the GA (Figure B2a and b) and NC (Figure B1a 

and b) intermittent streams (the GA perennial stream is much smaller at the GA sample sites than 

the NC perennial stream is at the NC sample sites). Similar to the NC streams, the GA 

intermittent and perennial streams were much closer to a ratio of 1 than the GA pond ratios 

(Figure 2c in the text). 
25 

Upstream Downstream 

Intermittent Stream Sites 

0.01

0.1

1

10

100

0 200 400 600 800 1000 1200 1400

Flubendiamide Pore Water to Water 

Column Concentration Ratio (unitless)

Days After Initial Application

Equilibrium

Flubendiamide

Desiodo

a

0.01

0.1

1

10

100

0 200 400 600 800 1000 1200 1400

Flubendiamide Pore Water to Water 

Column Concentration Ratio (unitless)

Days After Initial Application

Equilibrium

Flubendiamide

Desiodo

b

Perennial Stream Sites 

0.01

0.1

1

10

100

0 200 400 600 800 1000 1200 1400

Flubendiamide Pore Water to Water 

Column Concentration Ratio (unitless)

Days After Initial Application

Equilibrium

Flubendiamide

Desiodo

c

0.01

0.1

1

10

100

0 200 400 600 800 1000 1200 1400

Flubendiamide Pore Water to Water 

Column Concentration Ratio (unitless)

Days After Initial Application

Equilibrium

Flubendiamide

Desiodo

d

Figure B2. Comparison of pore water to water column concentration ratios for flubendiamide and des-iodo 

from intermittent (a and b) and perennial (c and d) near the Georgia ponds.