# Estimating **Aquatic Plant Community**  **Hazard Concentrations**  **for Pesticide Effects**  FIFRA Scientific Advisory Panel Review January 31, 2012

**Glen Thursby**  ORD Atlantic Ecology Division Narragansett , RI 

# Sujatha **Sankula** O PP Environmental Fate & Ef fects Division Wa shington, DC 

# Michael Lew **is** ORD Gulf Ecology Division Gulf Breeze, FL 1

# Definitions

• **FIFRA -**  **5 data set**

• **Full data set**

• **HC5**

2 

# • **Reference HC5**

# Problem Definition

3 

# Lack of consensus **of minimum data requirements**

**for deriving criteria for plants** 

# Primary **Conclusion** 

# Can derive **a reasonable**  **estimate of**  **plant effects**  **using FIFRA**  **- 5**  **data**

4

# • “Results of at least one acceptable test with a saltwater [freshwater] alga or vascular plant.” 

# OW **Plant Data for Aquatic Life Criteria** 

• “A plant value is the result of a 96 - hr test conducted with an alga or a chronic test conducted with an aquatic vascular plant.”

• “If plants are among the aquatic organisms most sensitive to the material, results of a test with a plant in another phylum (division) should also be available.”

5



# • Acceptable test • Important aquatic plant species • Biologically important endpoint**Navicula pelliculosa**  **Anabaena flos**  **- aquae**  

# OPP **Plant MDR for Pesticide Registration** 

# FIFRA **- 5** 

**Navicula pelliculosa** 

**Sk eletonema**  **costatum Lemna**  **gibba Pseudokirchneriella**  **subcapitata** 6

# FIFRA **- 5**  **minimum aquatic plant**  **requirement exceeds**  **“ requirement”**  **in the**  **OW 1985**  **Guidelines for**  **ALC**

7

# Problem Definition, **cont’d PEP**  **- carbo**  **xylase RuBisCO** 

8

# Problem Definition, **cont’d**


# Given that there are minimal requirements **. . .** 

# Are **useful estimates of an aquatic plant Hazard Concentration**  **possible with FIFRA -**  **5 data as the minimum?** 

9

# Methods



# • **Developed database**  **for 10 pesticides**

• Included pesticides with FIFRA - 5 & at least 5 other aquatic plant species

• Data from ECOT OX & OPP toxicity databases

10 

## • QA/QC of data

• Combined FW & SW

• Combined vascular & non - vascular

• Combined measured & unmeasured

# Methods, **cont’d**



# • **Endpoints**

• EC50 values
• Standard durations
• 3,4,5 d for microalgae • 7 - 14 d for duckweeds • 14 - 35 d for other vascular plants

11 

## • Multiple endpoints include

• Biomass, cell counts, chl a, photosynthetic rate, growth rate, etc.


## • Atypical endpoints excluded

• Va riable fluorescence, enzyme or AT P concentration, etc


## • Most sensitive EC50 value used

### Compound **Class Mode**  **- of**  **- Action**  **# Species**  **% V**

**ascular % SW**  At ra zine Herbicide Photosystem II 54 26 24 Diur on Herbicide Photosystem II 32 25 31 Ir garol Herbicide Photosystem II 38 24 55 Linur on Herbicide Photosystem II 10 40 0 

# Pesticides used in Plant **Analy ses** 

Metribuzin Herbicide Photosystem II 18 50 6 Pr ometryn Herbicide Photosystem II 13 31 15 Diquat Herbicide Membr ane Disruption 13 23 8 Metolachlor Herbicide Amino Acid S ynthesis 17 35 6 Metsulfur on Methyl Herbicide Cell Division 19 63 5 Pe ntachlor ophenol Fungicide Mitochondrial AT Pa se 27 37 26

12

# Methods, **cont’d**

• **Species Sensitivity Distributions**  

# Used two approaches to compare HC5 estimates from FIFRA - 5 data sets with reference HC5 values

• **Species Sensitivity Distributions** 

• Extrapolation Factors

130. 5 0. 6 0. 7 0. 8 0. 9 1. 0**C u** **m u** **l a** **t i** **v e** **P r** **o p** **o r** **t i** **o n** **- -** **F (** **c )**  

# Methods, **Species Sensitivity Distribution** 

**Log normal distribution**

0. 0 0. 1 0. 2 0. 3 0. 4

- 2 0 2 4 6 8 10**C u** **m u** **l a** **t i** **v e** **P r** **o p** **o r** **t i** **o n** 

**Ln EC5**  **0** 

###      

     c c F ) (



## c = ln EC50 μ = mean of ln EC50s σ = std dev of ln EC50s 143 4 5 6 7 8 9 10 

# Methods, **SSD cont’d** 

# 

      

# )

( 1 

# c F c 

# μLnEC50

- 3 - 2 - 1 0 1 2

- 3 - 2 - 1 0 1 2 3

15 

## Slope = estimate of std dev of ln EC50s Intercept = estimate of the mean of ln EC50s Φ - 1 

### F(c)3. 0 4. 0 5. 0 6. 0 

# Methods, **HC5 calculation** LnEC50 

## FIFRA **- 5**  **Data**

0. 0 1. 0 2. 0 3. 0

- 2. 5 - 2. 0 - 1. 5 - 1. 0 - 0. 5 0. 0 0. 5 1. 0 1. 5 

## Ln **(HC5)**

Φ - 1 

### F(c)Ln


## F(c) = 0.05

166. 0 8. 0 10. 0 12. 0 

# Methods, **SSD cont’d**

**Full Data Set** EC50

0. 0 2. 0 4. 0 6. 0

- 2. 5 - 2. 0 - 1. 5 - 1. 0 - 0. 5 0. 0 0. 5 1. 0 1. 5 2. 0 2. 5 

### Φ - 1 

### F(c)LnEC50

17

### 6. 0

8. 0 10. 0 12. 0**L n** **( E** **C 5** **0 )**  

## At **razi ne**  

# A 

# HC5 Estimation for **Atrazine**

Full dat a set HC5 = 12.7μg /L

0. 0 2. 0 4. 0

- 2. 5 - 2. 0 - 1. 5 - 1. 0 - 0. 5 0. 0 0. 5 1. 0 1. 5 2. 0 2. 5**L n** **( E** **C 5** **0 )** 



## NO **RM SI**  **NV( p)**  

## Φ **- 1**  

## F(c)
18 

## FIFRA - 5 HC5 = 15.3μg /L6. 0 8. 0 10. 0 12. 0**L n** **( E** **C 5** **0 )**  

### Di **ur on**  

## B**( E** **C 5** **0 )**  

# HC5 Estimation for Diuron

FIFRA - 5 HC5 = 5.8 μg /L

- 2. 0 0. 0 2. 0 4. 0

- 2. 0 - 1. 5 - 1. 0 - 0. 5 0. 0 0. 5 1. 0 1. 5 2. 0**L n** **( E** **C 5** **0 )** 



### NO **RM SI**  **NV( p)**  **Φ -**  **1** 

### F(c)**L n** **( E** **C 5** **0 )** 

19



## Full dat a set HC5 = 1.57 HC5 = 5.8 μg /L

### 5. 0

6. 0 7. 0 8. 0 9. 0**L n** **( E** **C 5** **0 )**  

## Me **tri buz**  **in** 

## E 

# HC5 Estimation for Metribuzin

Full dat a set HC5 = 7.65 μg /L

0. 0 1. 0 2. 0 3. 0 4. 0

- 2. 0 - 1. 5 - 1. 0 - 0. 5 0. 0 0. 5 1. 0 1. 5 2. 0**L n** **( E** **C 5** **0 )** 



## Φ **- 1**  

## F( **c)**
20 

## FIFRA - 5 HC5 = 1.8 μg /LAT Z

DIU LIN MT C MBZ PC P

PM T 1 10 100**R e** **f e** **r e** **n c** **e H** **C 5** **( μ** **g /** **L )**  

# Summary **o f SSD Results** 

**FIFRA -**  **5 more conservative** 

DQ T

IRG MSF

0. 01 0. 1 1

0. 01 0. 1 1 10 100**R e** **f e** **r e** **n c** **e H** **C 5** **( μ** **g /** **L )** 



### FI **FRA -**  **5 HC5**  **( μg**  **/L )**  21 **Reference more**  **conservative**

# • Also wanted to accommodate in the HC5

estimation a provision by which the relationship to the reference HC5 would improved as the number of EC50 values in the FIFRA data set increases. **Summary o**  **f SSD Results, cont’d**

22 

# • Appendix F: “. . .the 25 th 

# percentile of the HC5 value”10 100**R e** **f e** **r e** **n c** **e H** **C 5** **( μ** **g /** **L )**  

## Summary

**of SSD Results, cont’d** 
**FIFRA -**  **5 more conservative** 

0. 01 0. 1 1

0. 01 0. 1 1 10 100**R e** **f e** **r e** **n c** **e H** **C 5** **( μ** **g /** **L )** 



### FI **FR A**  **- 5**  **HC5 (**  **μg /L**  **)** 2310 100**R e** **f e** **r e** **n c** **e H** **C 5** **( μ** **g /** **L )**  

## Summary

**of SSD Results, cont’d** 

0. 01 0. 1 1

0. 01 0. 1 1 10 100**R e** **f e** **r e** **n c** **e H** **C 5** **( μ** **g /** **L )** 



### FI **FR A**  **- 5**  **HC5 (**  **μg /L**  **)** 243. 0 4. 0 5. 0 6. 0EC50 

## FIFRA **- 5**  **“plus”** 

## Summary

**of SSD Results, cont’d** 

• Estimate of μ and σ

0. 0 1. 0 2. 0

- 2. 5 - 2. 0 - 1. 5 - 1. 0 - 0. 5 0. 0 0. 5 1. 0 1. 5 

### Φ - 1 

### F(c)Ln

25



## • Estimate of μ and σ improve as N increases

• Estimate of HC5 should improve

# Summary **o f SSD Results, cont’d** 

• Advantage of using the SSD approach over some of the other approaches (e.g., some function of just the lowest value) — **it uses all of the available data.** 

26 

# • Some preliminary work suggests that just increasing the number of EC50 values may reduce uncertainty with less bias than using the percentile approach suggested in Appendix F.

# • Species Sensitivity Distributions

• **Extrapolation Factors**  

# Used two 

# approaches to generate HC5 values

27

# Use of Extrapolation Factors

• Potential use of extrapolation factors still uses SSDs for the estimation of the reference HC5 from the full data set.

• A comparison is made between the lowest

28 

# • A comparison is made between the lowest FIFRA - 5 EC50 and this reference HC5.

# Use of Extrapolation Factors

Lemna **31.5** Skeletonema 35.7 Navicula 60.0 **Example: Atrazine**  FIFRA - 5 EC50s (μg/L )

29 

# Navicula 60.0 Pseudokirchneriella 73.2 Anabaena 224.9 Reference HC5 (μg/L) **15.3**

**Ratio = 2.1**

# Extrapolation **Factors, cont’d**

8. 0 10. 0 12. 0**L n** **( E** **C 5** **0 )**  

### Atr **azi ne** 

30 0. 0 2. 0 4. 0 6. 0

- 2. 5 - 2. 0 - 1. 5 - 1. 0 - 0. 5 0. 0 0. 5 1. 0 1. 5 2. 0 2. 5**L n** **( E** **C 5** **0 )** 

**Φ -**  **1 F(**  **c)** 

## proportion less than this value = 0.19

# Extrapolation **Factors, cont’d** Compound Ratio (lowest/HC5) % of full data set lower than FIFRA

value Atrazine 2.1 19 Diuron 4.5 31 Irgarol 1.5 8 Linuron 4.0 31

31



## Geomean **of Ratios = 2.6**  Linuron 4.0 31 Metribuzin 1.1 6 Prometryn 0.8 4 Diquat **6.6** 17 Metolachlor 2.7 13 Metsulfuron methyl 6.4 25 Pentachlorophenol 2.9 13AT Z

DIU LIN MBZ

PMT MT C PC P 10 100**R e** **f e** **r e** **n c** **e H** **C 5** **( μ** **g /** **L )**  

## Extrapolation **Factor , cont’d**  **Low est**  **FIFRA**

**V alue** 

**FIFRA -**  **5 more conservative** 

IRG DQ T

MSF

0. 01 0. 1 1

0. 01 0. 1 1 10 100**R e** **f e** **r e** **n c** **e H** **C 5** **( μ** **g /** **L )** 

**Lowest FI**  **FR A**  **EC 50**  **(μ g/**  **L)** 32

**Reference more**  **conservative**10 100**R e** **f e** **r e** **n c** **e H** **C 5** **( μ** **g /** **L )**  

## Extrapolation **Factor , cont’d**  **EF = largest ratio** 

**FIFRA -**  **5 more conservative** 

0. 01 0. 1 1

0. 01 0. 1 1 10 100**R e** **f e** **r e** **n c** **e H** **C 5** **( μ** **g /** **L )** 

**Lowes t**  **FI FR**  **A EC**  **50/ Large**  **st EF**  **( μg**  **/L )**  33

**Reference more**  **conservative**

## Extrapolation **Factor , cont’d**  **EF = geomean** 

1 10 100**R e** **f e** **r e** **n c** **e H** **C 5** **( μ** **g /** **L )** 

34 0. 01 0. 1 1

0. 01 0. 1 1 10 100**R e** **f e** **r e** **n c** **e H** **C 5** **( μ** **g /** **L )** 

**Lowe st**  **FI FR**  **A EC**  **50/ Geo**  **me an**  **EF ( μg**  **/L )** 

# • With only ten compounds, and most of them

PSII inhibitors — do we have confidence in the range of ratios? **Issues w ith**  **Extrapolation Factor for Plants Use of Extrapolation Factor**  **, cont’d**

range of ratios?


# • How could we account for N > 5?

35

# Growth **Rate**

• One final issue that the plant analyses addressed is whether there is a need to standardize ef fects analyses on a single endpoint — specifically on growth rate.

• Often growth rate is desired because this endpoint may be less influenced by the duration of exposure than ef fects based on yield.

• Only three compounds (atrazine, diuron & irgarol) had enough species with growth EC50s to make comparison

• All three PSII inhibitors

36 

## • This issue is independent of SSD vs. EF

## Gro **wt h**  **Rate vs.**  **Other Endpoints:**  **Atrazine**

6. 0 8. 0 10. 0 12. 0**L n** **( E** **C 5** **0 )**  

### At **razi ne** 

37 0. 0 2. 0 4. 0 6. 0

- 2. 5 - 2. 0 - 1. 5 - 1. 0 - 0. 5 0. 0 0. 5 1. 0 1. 5 2. 0 2. 5**L n** **( E** **C 5** **0 )** 



### Φ **- 1**  

### F( **c)** EC OTOX A ll En dp oi nts

## Gro **wt h**  **Rate vs.**  **Other Endpoints:**  **Atrazine**

6. 0 8. 0 10. 0 12. 0**L n** **( E** **C 5** **0 )**  

### At **razi ne** 

38 0. 0 2. 0 4. 0 6. 0

- 2. 5 - 2. 0 - 1. 5 - 1. 0 - 0. 5 0. 0 0. 5 1. 0 1. 5 2. 0 2. 5**L n** **( E** **C 5** **0 )** 



### Φ **- 1**  

### F( **c)** EC OTOX A ll En dp oi nts EC OTOX

G ro wt h Ra te

## Gro **wt h**  **Rate vs.**  **Other Endpoints:**  **Atrazine**

6. 0 8. 0 10. 0 12. 0**L n** **( E** **C 5** **0 )**  

### At **razi ne** 

39 0. 0 2. 0 4. 0 6. 0

- 2. 5 - 2. 0 - 1. 5 - 1. 0 - 0. 5 0. 0 0. 5 1. 0 1. 5 2. 0 2. 5**L n** **( E** **C 5** **0 )** 



### Φ **- 1**  

### F( **c)** Er ickso n Data EC OTOX

A ll En dp oi nts EC OTOX

G ro wt h Ra te

## Gro **wt h**  **Rate vs.**  **Other Endpoints** 

Compound Reference

HC5 (μg/L) Grow th Rate

HC5 (μg/L) Ratio (Grow th/Ref) Atrazine 15.30 19.36 1.3 Diuron 1.57 3.38 2.2

40 

## • While there may be occasions wh ere standardization on grow th rate is desirable, it may not be necessary
.

• Using a variety of endpoints generally wi ll mean that more species w ill be represented in the SSD Irgarol 0.066 0.024 0.4

# • **FIFRA -**  **5 show**  **s promise**  **for being able**

**to estimate HC5** 

# Main Conclusions

• **SSD approach has advantage over EF since estimate for HC5**  **should**

• Standardization based on growth rate may not be necessary

41 

# since estimate for HC5 **should automatically**  **improve w ith**  **additional data**  **— uses all of the available data**