UNITED STATES ENVIRONMENTAL PROTECTION AGENC Y WAS HIN G T O N

. D .C. 20 4 60

OFFICE OF CHEMICA L SAFETY AND POLLUT I ON PREVENTION

PC Code: 027602 DP Barcodes: 412791 and 425073 Date: February 20,2015

MEMORANDUM

SUBJECT:

FROM:

THROUGH: Review of Three Report s Related to a 3-year Flubendiamide Water Monitoring Project 

### in Support of the Conditiona l Registration of Flubendiamide

Stephen Wente, Ph : D., Biologist

# ~ Z. 

# jz**"'~** .5- Environmental Risk Branch 1 Environmental Fate and Effects Division (7507 P) \**~**

## ~~**~** 

# :1-\)..0\\? Sujatha Sankula, Ph

. D., Branch Chief

. 

## $- 

# 7

Environmental Risk Branch 1 Environmental Fate and Effects Divi sion (750 7P)

Edward

# Odenkirch~n, Ph.D

., Senior Advisor 

# a_ Q,.J

# ~ Immediate Office Environmental Fate and Effects Division (7507P)

TO: Carme n Rodia

, Risk Manager Review er Richard Gebken, Risk Manager

Introduction Debbie McCall, Br?lnch Chief Invertebrate & Vertebrate Branch 2 Registration Di v ision (7504P)

Flubendiamide, an insecticid e, was conditionally registered in 2008 for aerial and/or ground application to com, cotton, tobacco, pom e 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 chr o nic toxicity to aquatic invertebrates 1 • Submitted fate data indicate flubendiamide slow l y converts to it s des-iodo degradate, which does not breakdown. EFED modeling (03296 1 3 + ) predicts t hat

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 la bels require a 15 ft VFS buffer around aquatic areas); and 2) the Agency overestimates aq uatic exposure because EFED modeling cannot account for the effect of VFSs. According to th e flubendiamide preliminary acceptance letter, the Registration Division stated, the “Agency believes that the effi cacy 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 concer ns, 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. Th e 3-year monitoring study of water column, sediments, and pore water in 3 ponds (2 in Geor gia and 1 in North Carolina) was submitted in December of 2014.

EFED has reviewed the monitoring data and a ssociated studies and has identified several issues with this monitoring data. Despite th ese issues, EFED believes the monitoring data shows clear evidence that both flubendiamid e and des-iodo accumulate in the ponds monitored. The accumulation measured in th e first three years of the pond data least impacted by the identified issues largely matches the initial 3 ye ars of concentration predictions of EFED’s aquatic exposure mo deling. Because EFED’s modeling does not account for the effect of VFSs, but still larg ely 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 Ag ency adequately reflect the risks posed by flubendiamide applications and rejects the regis trant’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 Flube ndiamide in Sediment and Surface Water” (MRID 49415303), “Flubendiamide Aquatic Risk – Summary of Surface Water Monitoring and Toxicity Testing” (MRID 49415302), and “Aqua tic Exposure Assessment for Flubendiamide and its Metabolite Des-iodo Flubendiamide ba sed 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 indi vidual 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 adj acent 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). Th e 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 watershe d area to the standard pond that EFED uses in exposure modeling and the requirement that th e entire watershed be planted to one crop. Additionally, an attempt was made to select ponds with watersheds that had similar characteristics to EFED standard scenario s 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 fl owed 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 s ites (2 in GA and 2 in NC).) Monthly water and sediment samples, with a few exceptions, were ta ken from each monitoring site for three years. Water quality parameters including pH, te mperature, conductivity, dissolved oxygen, and oxidation/reduction potential (O RP) 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 flubendiamid e product BeltTM were made to the watershed of the pond(s) at each location ever y year during the study period.

Pore water was separated from sediment samp les by vacuum filtration at about 10 PSI to quantify the benthic water resi due. Flubendiamide and des-iodo in the water column, pore water and sediment extracts were analyzed by LC/M S/MS, using isotopically -labelled internal standards for quantitation. The me thod detection limits were 0.004 μ g/L for flubendiamide and des-iodo in water and pore water samples, and 0.02 μ g/kg for flubendiamide and des-iodo in sediment samples.

Experimental Design and Data Quality Issues EFED identified six major issues with the monito ring study that affect the interpretability of the study. The first four issues concern the experi mental treatment of the watershed: 1) the variability in crops grown on the pond watersheds; 2) the variability in the da te 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). Becaus e the participation of the growers was voluntary, the regi strant 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 watershe d and from cotton (2011 and 2012) to peanut (2013) in the watershed of the GA ponds. The application dates we re quite variable in the NC pond watershed with 15 months between the 1 st 

### and 2 nd 

### application, 12.5 months between the 2 nd 

### and 3 rd 

### , and 6.5 months between the 3 rd 

### and 4 th 

### application with a s econd application in 2014 occurring a month later. The a pplication rates also varied in the NC pond watershed from 0.06 to 0.09 lb/A. Both the application dates (all in Augu st) 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 th e monitoring data; making it harder to detect trends in the data. The fourth issue (low applicati on 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 main tained 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 th e same sentence “Primary entry points of runoff into the ponds originated from the southe ast 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 Nationa l 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 so il and chemical loadings to waterbodies. They occupy the main drainage pathways though which the majority of the pe sticide 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 th e treated field and waterbodies, but do not require grassed waterways. The presence of the grassed wa terways 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 th e 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 dimi nished relative to what would be expected in the absence of the grassed waterways].

The final issue with the submitted monitoring da ta 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, EF ED expects the pore water and water column concentrations to equilibrate ov er time for both flubendiamide and des-iodo with only short-term excursions from nearly equal concentrations af ter drift and storm events. However, the observed pond pore water concentrations were typically much lower than the observed water column concentrations from samples collected fr om the same pond and at the same time.

To show the magnitude and pervas iveness 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 detecti on limit for both pore water and water column samples. If the pore water to wate r column concentration were equa l, this ratio should equal 1. In the NC and GA pond samples (Figure 2a and c, resp ectively), almost all of the observed ratios plot below 1 (equilibrium) with many equal t o, or less than, 0.1 indicating the pore water concentration is 10 times lower than the corres ponding water column con centration for many of these samples. For comparison, similar ratio s 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 1400Flubendiamide Pore Water to WaterColumn 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 1400Flubendiamide Pore Water to WaterColumn 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 1400Flubendiamide Pore Water to WaterColumn 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 1400Flubendiamide Pore Water to WaterColumn Concentration Ratio (unitless)

Days After Initial Application Equilibrium

Flubendiamide

Desiodo

d
Figure 2. Comparison of observed pore water to water column concentr ation 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 th e pond pore water concentrations ar e 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 const itute 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 str eam (the Tar River) is a large rive r at the sites sampled. Sediment depths are likely deeper and bett er 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 stre am water column and pore water concentrations were relatively low, so that early in the mon itoring time frame, ratios could not be calculated because one or both c oncentrations fell below the detection limit. However, the later ratios from the GA perennial st ream 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 samp le sites than the NC perennial stream is at

### 7 the NC sample sites). (Observed pore wate r 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 wate r 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 woul d be much lower than model predicted ‘non-diluted’ por e water concentrations.

Accumulation EFED used the “LifeReg” regressi on procedure in SAS statistical so ftware 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 fitt ed 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 por e 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 tr ends 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

TimeConcentration (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

TimeConcentration (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)



### P ore Wate r

0.001

0.01

0.1

1

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

TimeConcentration (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 (chan g e from be g innin g 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 TimeConcentration (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 TimeConcentration (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)

P ore Wate r

0.001

0.01

0.1

1

Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14 TimeConcentration (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 TimeConcentration (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 TimeConcentration (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)



### P ore Wate r

0.001

0.01

0.1

1

Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14
TimeConcentration (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 Expo sure 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 wa tershed characteristics that were similar to EFED standard scenarios. However, EFED expos ure 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 comp arison 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 app lication timing and rates of pesticides applied each year, and assumes wind direction always de posits 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 scenario 3 

### with the same input values as appear in Table 1 of the aquatic exposure repor t (MRID 49415301) with the exceptions that the benthic metabolism half-life value of 855 days was us ed (rather than the registrant modified value of 7300 da ys), the soil half-life of 0 (stable) was used (rather than the 10,000 day value in the aquatic expo sure report), the efficiency (0.95) and drift (0.05) fractions were changed to 0.99 and 0.0082 4 

### because Table 1 indicates that these were ground applications under the application method secti on of this table, and standa rd pond dimensions were used. (EFED did not use the registrant modified weathe r files, files because they only provided to the agency in a pdf format as part of the report.)

The monitoring report does not contain sufficien t information to identify a unique set of SWCC parameters for comparison with the NC pond data . For example, the re port does not indicate whether the wind direction on th e application date would have blown drift toward the pond. Therefore, three SWCC scenarios were run with di fferent combinations of application rates and spray drift assumptions to bound reasonable SW CC parameterizations for the NC pond. The highest rates applied to the NC pond watershed (0.09 lb/A) with th e EFED’s current spray drift fraction (0.0082) is shown solid li nes in Figures 6a to f). The lo west rates applied to the NC pond watershed (0.06 lb/A) with the EF ED’s current spray drift fracti on (0.0082) is shown as dashed lines in Figures 6a to f. The third SWCC scenar io used the lowest rate s applied to the NC pond watershed (0.06 lb/A) with no drif t (to simulate the lowest reas onable 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 concentra tions 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 concentra tions plot both above and below the SWCC predictions, but the concentrati ons 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 obse rved 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 ha lf of the monitoring. The respective sediment concentrations (Figure 6c and d) and pore wate r concentrations (Figure 6e and f) all plot somewhat low compared to the SWCC prediction s, consistent with th e hypothesis that these samples are diluted with the underlying uncontaminat ed sediment and pore water lying below the higher surficial sediment and por e water concentrations (see prev ious discussion). Overall, the Agency believes the monitoring data tracks reason ably 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 m odeling 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 1400Water 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 1400Water 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 1400Sediment 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 1400Sediment 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 1400Pore 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 1400Pore 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 th e North Carolina pond for wa ter 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 app lication 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 compar es exposure model predictions and observed concentrations for the GA ponds. The SWCC modeling used the MS cotton 5 

### (solid lines in Figures 7a to f) and NC cotton 6 

### (dashed lines in Figures 6a to f) scenarios with the same input values as described for the NC scenario (only on e 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 fl ubendiamide reaching the pond in the MS and NC cotton scenarios and would have been indistingui shable from the predictions including drift. (Note: Figures 7a through f are presen ted with the y-axis as a log scale.)

Almost all of the GA ponds concentration da ta plots below the SWCC predictions. The interpretation of the GA ponds data is confounde d by the presence of grassed waterways in the watershed. The combination of grassed wate rways and VFSs (only VFSs are required by flubendiamide labels) would be expected to dimi nish 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 wate r 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 gene ral 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 1400Water 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 1400Water 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 1400Sediment 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 1400Sediment 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 1400Pore 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 1400Pore Water Des-iodo (μg/L)

Days After Initial Application Pond 1 Pond 2

MS Cotton NC Cotton

f
Figure 7. Comparison of Surface Water Concentrat ion 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 expo sure estimates to Agency levels of concern (LOCs). Aquatic exposure is predicted over 30 y ears in Figure 8 for the NC tobacco scenario. These model results are based on the same parame ters as the predictions that fit the NC pond data well, but use the maximum label rates instea d (4 applications of 0.09 lb/A for an annual maximum of 0.375 lb/A assuming it is continuous ly planted to tobacco). Chronic aquatic invertebrate endpoints are also included in Fi gure 8. Because these chronic endpoints have an LOC of 1, an exposure exceeding an e ndpoint also exceeds the Agency LOC ( i.e. , the LOC and the endpoint are the same number). Drawing a ve rtical 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 fo r 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 scen arios exceed LOCs in shorter time periods).

Water Column Pore Water N C Tobacco

0 5 10 15 20 25 30 35 40

0 5 10 15 20 25 30 35Water 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 35Pore Water Concentration (μg/L)

Years Flubendiamide Des-iodo Flubendiamide NOEC Des-iodo NOEC

b
Figure 8. Accumulations of flubendiam ide and its des-iodo degradate exceed ing chronic risk endpoints in the standard pond water column (a) and pore water (b) ba sed on ground applications to North Carolina tobacco at the maximum allowed application rate.



### Additional monitoring at stream sites near these ponds found bot h 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 contam ination in streams is currently pervasive in regions where flubendiamide is used.

The next section addresses each of the three s ubmitted studies individually. For each study, a brief summary is provided with a list of issu es raised in the study along with EFED comments on those issues.

Monitoring for Flubendiamide and its Metabolit e Des-iodo Flubendiami de in Sediment and Surface Water (MRID 49415303)

Summary: The objective of this study wa s to assess the potential for flubendiamide and its des-iodo metabolite to accumulate in aquatic environmen ts (water and sediment) following drift and runoff of flubendiamide into surface wate r with multiple years of applications.

EFED Issue: Much of the report only discu sses measurements above the lim it of quantitation (LOQ) rather than the method detection limits (MDL). Fo r 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 acco rding to the Agency’s modeling from values below the LOQ. The Agency has discussed this issue with the re gistrant 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 br ought the grassed waterway s to the attention of the Agency so that a different site could be monitored), usi ng only the values above the LOQ may have been an option.

Registrant Comment: “Overall, the results show negligible concentr ations of des-iodo flubendiamide in water, porewater or sediment, and no indication of forma tion of des-iodo flubendiamide in the water or sediment ( i.e. , a decline in flubendiamide in sediment orwater 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 applicati on, and then declining. There is no indication of accumulation of flube ndiamide or des-iodo flubendiamide in porewater, water or sediment in the pond, interm ittent streams or permanent streams.” (p. 27)

“These results indicate that lo w levels of flubendiamide residue s can occur due to runoff from fields with recent applications of flubendiam ide 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 y ear-to-year variations, and no indication of signifi cant 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 da ta 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 statisti cally significant. The Agency strongly disagrees with the registrant’s assessmen t 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 endpoi nts to use for a flubendiamide and des-iodo flubendiamide sediment risk assessment are:

 Flubendiamide overlying water – NOEC 33 μ g/L

 Flubendiamide pore water – NOEC 2.56 μ g/L

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

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

EFED Response: EFED has evaluated all of thes e studies and provided a Data Evaluation Record (DER) for each with the exception of the aforementioned midge st udy that has yet to be submitted to the Agency. Some of these registrant-calcu lated endpoints differ slightly from the Agency determined endpoints. If the registrant beli eves the Agency-calculated endpoint s are in error, the appropriate course of action would be to rebut the i ndividual DERs. This re port (MRID 49415302) does not contain sufficient explanation and analysis for the Agency to r econsider the endpoints. However for purposes of evaluating the studies submitted with the monitoring study (MRIDs 49415301 to 49415303), the Agency will use the registrant-calcula ted endpoints to avoid diverting focus from the issues the Agency has with the submitte d monitoring and aquatic exposure reports.

Aquatic Exposure Assessment for Flubendi amide and its Metabolite Des-iodo Flubendiamide based on a 3-Year Monitoring Study (MRID 49415301)

Summary: The overall objective of this report was to compar e the results from a 3-year monitoring study at two locations with the potential aquatic esti mated 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 factor s governing the fate of flubendiamide and des-iodo at the field sites.

Registrant Comment: “For GA, the SWCC overestimated peak flubendia mide 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, respec tively.” (p. 7)

EFED Response: The Agency agrees the SWCC concentration pr edictions based on the MS cotton and NC cotton scenarios are higher than the concentrations observed in th e GA pond. However, the Agency ascribes these discrepancies to problems with th e registrant’s data. The Agency believes the presence of the grassed waterways in the wate rshed 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 i ssue. 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 receiv ed an off-season, bare ground application in November of 2013 which led to greater runoff th an would be expected in a typical growing

season 7 

### . However, for des-iodo, the SWCC over-predi cted water and pore water concentrations by a factor of 2 and 7, respectively.” (p. 7)

EFED Response: The Agency believes the SWCC predictions fit th e water column data quite well (Figure 6a and b) and believes the differences in pore water concen trations (Figures 6e and f) are better ascribed to the previously discussed sample dilution issue.

Registrant Comment: “The model also predicted exponential accumula tion 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 accumula tion was observed in the field study.

EFED Issue: The registrant developed a series of increasingly complex model adjustment s in order to get the SWCC predictions to align with the water colu mn and pore water observations. The justification for making these adjustments was based almost en tirely 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, a nd pore water with 13 of the 18 pond accumulation trends identified as statistically si gnificant. 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 ma tch to the SWCC modeling (Figures 6a and b). This same model parameterization (after ad justing 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 ecologi cal risk assessments performed by the Agency adequately reflect th e 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 gr ower 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. Baye r CropScience. Report Number: US0453. 16 pp. (MRID 49415302)

Hanzas, J.P., B. Toth, and J. White. 2011. Geor gia 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 Meta bolite des-iodo Flubendiamide in Sediment and Surface Water” Bayer CropScien ce. Report Number: US0220. 50 pp. (MRID 48535201)

Pérez Ovilla, O. 2014. Aquatic Exposure Assessme nt for Flubendiamide and its Metabolite Desiodo Flubendiamide based on a 3-Year M onitoring 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 Re port 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 Re port 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 Re port. Bayer CropScience. Study Number: MEAMP011. 518 pp. (MRID 49415303)20Appendix A. Additional MonitoringData from Flowing-Water SitesEFED does not anticipate continuous accumulation at these flowing-water sites becauseany accumulation is continuously (water)orperiodically (sediment) flushed downstream. Data from the Georgia and North Carolina flowing-water sites (located at differentpointsin the larger watersheds thatcontain the GA and NC ponds) with trend lines (solid for flubendiamide and dashed for des-iodo) arepresented in Figure A1 and A2, respectively. Because some of the data time-series from streamsites have few concentrationsmeasured above the detection limit, the trend lines appear counter-intuitive.Water ColumnSedimentPore WaterUpstream Intermittent Stream Site0.0010.010.1110Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14Time

Concentration (μg/L)FlubendiamideDesiodo0.010.1110100Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14Time

Concentration (μg/Kg)FlubendiamideDesiodo0.0010.010.11Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14Time

Concentration (μg/L)FlubendiamideDesiodoDownstream Intermittent Stream Site0.0010.010.11Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14Time

Concentration (μg/L)FlubendiamideDesiodo0.010.1110100Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14Time

Concentration (μg/Kg)FlubendiamideDesiodo0.0010.010.11Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14Time

Concentration (μg/L)FlubendiamideDesiodo21Water ColumnSedimentPore WaterUpstream Perennial Stream Site0.00010.0010.010.11Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14Time

Concentration (μg/L)FlubendiamideDesiodo0.0010.010.1110Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14Time

Concentration (μg/Kg)FlubendiamideDesiodo0.00010.0010.010.11Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14Time

Concentration (μg/L)FlubendiamideDesiodoDownstream Perennial Stream Site0.0010.010.11Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14Time

Concentration (μg/L)FlubendiamideDesiodo0.0010.010.1110Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14Time

Concentration (μg/Kg)FlubendiamideDesiodo0.00010.0010.010.11Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14Time

Concentration (μg/L)FlubendiamideDesiodoFigure A1. Georgia monitoring data from stream sites.22North Carolina Flowing-water Sites (locatedat different points in a larger watershed that contains theNorth Carolina Pond)Water ColumnSedimentPore WaterUpstream Intermittent Stream Site0.0010.010.11Apr-11 Oct-11 Apr-12 Oct-12 Apr-13 Oct-13 Apr-14 Oct-14Time

Concentration ( μ g/L)FlubendiamideDesiodo0.0010.010.1110100Apr-11 Oct-11 Apr-12 Oct-12 Apr-13 Oct-13 Apr-14 Oct-14Time

Concentration ( μ g/kg)FlubendiamideDesiodo0.0010.010.11Apr-11 Oct-11 Apr-12 Oct-12 Apr-13 Oct-13 Apr-14 Oct-14Time

Concentration ( μ g/L)FlubendiamideDesiodoDownstream Intermittent Stream Site0.0010.010.11Apr-11 Oct-11 Apr-12 Oct-12 Apr-13 Oct-13 Apr-14 Oct-14Time

Concentration ( μ g/L)FlubendiamideDesiodo0.0010.010.1110Apr-11 Oct-11 Apr-12 Oct-12 Apr-13 Oct-13 Apr-14 Oct-14Time

Concentration ( μ g/kg)FlubendiamideDesiodo0.0010.010.11Apr-11 Oct-11 Apr-12 Oct-12 Apr-13 Oct-13 Apr-14 Oct-14Time

Concentration ( μ g/L)FlubendiamideDesiodo23Water ColumnSedimentPore WaterUpstream Perennial Stream Site0.0010.010.11Apr-11 Oct-11 Apr-12 Oct-12 Apr-13 Oct-13 Apr-14 Oct-14Time

Concentration ( μ g/L)FlubendiamideDesiodo0.010.1110Apr-11 Oct-11 Apr-12 Oct-12 Apr-13 Oct-13 Apr-14 Oct-14Time

Concentration ( μ g/kg)FlubendiamideDesiodo0.0010.010.11Apr-11 Oct-11 Apr-12 Oct-12 Apr-13 Oct-13 Apr-14 Oct-14Time

Concentration ( μ g/L)FlubendiamideDesiodoDownstream Perennial Stream Site0.0010.010.11Apr-11 Oct-11 Apr-12 Oct-12 Apr-13 Oct-13 Apr-14 Oct-14Time

Concentration ( μ g/L)FlubendiamideDesiodo0.0010.010.1110Apr-11 Oct-11 Apr-12 Oct-12 Apr-13 Oct-13 Apr-14 Oct-14Time

Concentration ( μ g/kg)FlubendiamideDesiodo0.0010.010.11Apr-11 Oct-11 Apr-12 Oct-12 Apr-13 Oct-13 Apr-14 Oct-14Time

Concentration ( μ g/L)FlubendiamideDesiodoFigure A1. North Carolina monitoring data from stream sites.

### 24 Appendix B. Pore Water to Water Column Conc entrations 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 intermitte nt sites (Figure B1a and b) or the pond samples (see Figure 2a in the text). The NC perennial stream (the Tar Rive r) is a large river at the sites sampled. Sediment depths are likely deeper and be tter 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 sedime nt 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 1400Flubendiamide Pore Water to WaterColumn 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 1400Flubendiamide Pore Water to WaterColumn 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 1400Flubendiamide Pore Water to WaterColumn 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 1400Flubendiamide Pore Water to WaterColumn Concentration Ratio (unitless)

Days After Initial Application Equilibrium
Flubendiamide
Desiodo

d
Figure B1. Comparison of pore water to water column concen tration 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, ra tios 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 distri buted more like the GA (Figure B2 a 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). Simila r to the NC streams, the GA intermittent and perennial streams were much cl oser 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 1400Flubendiamide Pore Water to WaterColumn 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 1400Flubendiamide Pore Water to WaterColumn 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 1400Flubendiamide Pore Water to WaterColumn 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 1400Flubendiamide Pore Water to WaterColumn Concentration Ratio (unitless)

Days After Initial Application Equilibrium
Flubendiamide
Desiodo

d
Figure B2. Comparison of pore water to water column concen tration ratios for flubendiamide and des-iodo from intermittent (a and b) and perennial (c and d) near the Georgia ponds.