© 2014 by Pyrethroid Working Group. All rights reserved. 

# SWMM **- AGRO: A Modeling System**  **and Scenarios for Pesticide Aquatic Exposure Assessments**  **in Urban Residential Environments** 
**Michael Winchell 1** 

## , **Scott Jackson**  **2**

## , **Lauren Padilla 1**  **1.** 

### Stone Environmental, **Inc. 2.**  

### BASF Corporation **EMPM Meeting, October 28 th**  

### , 2014

# Contents

 

# Background  

### What is SWMM -AGRO?  

### Model requirements  

# Model Development  

### SWMM  

### AGRO - 2014  

# Model Application  

# SWMM -AGRO Interface  

# Summary and Conclusions

© 2014 by Pyrethroid Working Group. All rights reserved

# Background: What is SWMM-AGRO?
 

## SWMM -AGRO is a graphical user interface (GUI) that seamlessly integrates EPA’s Stormwater Management Model (SWMM) with the AGRO - 2014 receiving water model (Padilla and Winchell, 2013 ) 

## .  

## SWMM version 5.022, customized with CDPR’s pesticide washoff algorithm ( Luo et al., 2014) 

## , is included with the modeling package.

© 2014 by Pyrethroid Working Group. All rights reserved  

## SWMM -AGRO allows registrants, EPA scientists, and others to run the same residential pesticide exposure scenarios that were used in the PWG’s urban exposure assessments, as well as to simulate custom scenarios.

# Background: Modeling Approach Requirements
 

## The urban pesticide modeling approach must:

 Capture the heterogeneity in use sites found in an urban environment.  Account for the hydrologic processes in residential watersheds.  Allow for flexibility in use assumptions.  Permit evaluation of mitigation effectiveness.  

## Model must be validated with monitoring data 

# .

© 2014 by Pyrethroid Working Group. All rights reserved

# Model Development: SWMM-AGRO Model Selection  

## Storm Water Management Model (SWMM): US EPA  

### Watershed scale, urban/residential water quantity and quality model  

### Strength in handling of sub - hourly runoff and flow routing  

### Able to model multiple surface types (lawn, driveway, etc.)  

## AGRO: Canadian Center for Environmental Modeling (CEMC)  

### Water Quality Model: Quantitative Water, Air, Sediment Interaction (QWASI) Fugacity model (Mackay, 2001) 

### .  

### Accounts for chemical mass exchange between water column (dissolved and sorbed compartments), benthic layer (dissolved and sorbed compartments), and air.  

### Simulation of sediment dynamics, including handling of i ncoming sediment; important for high Koc pyrethroids.  

### AGRO - 2014 (Padilla and Winchell, 2013) 

### includes improvement in parameterization based on comparison with cosm study data 

## .

© 2014 by Pyrethroid Working Group. All rights reserved

# Model Development: Study Location

© 2014 by Pyrethroid Working Group. All rights reserved  

## Aliso Viejo, Orange County, CA  

## Part of CA DPR / UC Riverside monitoring program (Oki and Haver, 2011 ) 

# .  

## Drainage area: 67.2 acres  

## 307 homes  

## Dwelling unit density: 4.6 units/acre.

# Model Development: National Housing Density Assessment
 

## Objective: Determine if, from a housing density perspective, Aliso Viejo represents a “high vulnerability” site.  

## Approach: Use housing density as a surrogate for residential use site density.  

## Datasets:  US Census Block: Housing units per census block  National Land Cover Dataset 2006 (NLCD): Land cover classifications used to identify with single family housing blocks.

© 2014 by Pyrethroid Working Group. All rights reserved 

### Census Block Data

# Model Development: Density Assessment Results  

## Targeted analysis of all residential census blocks within the lower 48 states:  “Developed, Low Intensity” and/or “Developed ,Medium Intensity” acreage is > 0 (to focus on single family home areas)  “Developed, High Intensity” acreage is < 10% of the census block (to avoid apartment complexes and commercial/industrial areas)  

## In California:  Total number of census blocks assessed: 417,767  Aliso Viejo housing unit density percentile in CA: 72 nd % - ile  

## In lower 48 states:  Total number of census blocks assessed: 6,279,464  Aliso Viejo housing unit density percentile in USA: 88 th % - ile  

## Aliso Viejo represents a high vulnerability site in terms of potential use site density of homes potentially receiving PCO/LCO treatments.

© 2014 by Pyrethroid Working Group. All rights reserved

# Modeling Development: Conceptual Model  

## Aliso Viejo n eighborhood was spatially delineated.  

## Particular attention to impervious use sites.  Lower driveway  Upper driveway (within 5 ft)  Garage door  Impervious within 5 -ft foundation perimeter  Patios/walkways away from building  

## Impervious areas near lawns (1.5 ft) receive irrigation.  

## A fraction of impervious surfaces (other than driveway) flow into adjacent lawns. © 2014 by Pyrethroid Working Group. All rights reserved

# Model Development: Incorporation of CDPR Washoff Method  

## The standard SWMM model provides three washoff options (exponential equation, rating curve, event mean concentration), which all are a function of flow rate.  

## Recently research from CDPR by Luo et al. (2014) described a new, theoretically- based washoff algorithm specifically designed for predicting pesticide washoff from hard surfaces.  

## The Luo washoff method was incorporated into the SWMM model code base, then tested and calibrated using Pathway ID study data.

© 2014 by Pyrethroid Working Group. All rights reserved

# Model Development: SWMM Model Calibration Approach  

## The monitoring study led by Oki and Haver (2011) provided the following datasets for calibration of the Aliso Viejo scenario:  Daily and hourly flow from the stormwater outfall  30 bifenthrin concentration analyses from both wet and dry periods  Estimated weekly and annual pyrethroid mass load  

## Flow calibrated through adjustment of:  Irrigation practices  Hydrologic connectivity of impervious surfaces  Subsurface flow contributions  Routing parameterization  

## Bifenthrin mass and concentrations calibrated by adjusting:  Irrigation practices  Hydrologic connectivity of impervious surfaces  Washoff parameters

© 2014 by Pyrethroid Working Group. All rights reserved

# Model Development: Neighborhood Pyrethroid Use Assumptions  

## A best estimate of the extent and frequency of actual pyrethroid use in California was required for scenario calibration.  

## Bifenthrin was chosen based on its relatively extensive use and frequency of detections in the monitoring data.  

## Key assumptions include:  75.9% of households use outdoor insecticides (Winchell, 2013)  Some households are treated every 6 weeks, and some every 12 weeks  Fraction of use sites treated with bifenthrin (of households using insecticides) at these intervals was estimated from survey data and were set as follows:

© 2014 by Pyrethroid Working Group. All rights reserved Use Site Estimated

Total Percent Treated (%) Estimated

Pe rcent Treated Every 6 Weeks (%) Estimated Percent

Treated Every 12 Weeks (%) Foundation Perimeter 25.7 13.1 12.6 Patios/Walkways 24.9 12.7 12.2 Driveways 24.1 10.6 13.5 Lawns 24.4 5.4 18.9

# Model Development: SWMM Hydrology Calibration  

## Mean daily flows and hourly flows are well simulated:

© 2014 by Pyrethroid Working Group. All rights reserved  

## Flow Bias:

- 2%  

## Daily NSE (Nash Sutcliffe Efficiency): 0.83 (Nash and Sutcliffe, 1970)  

## Hourly NSE: 0.57  

## Surface runoff rarely occurs from lawns.

# Model Development: SWMM Bifenthrin Load Calibration  

## The simulated cumulative bifenthrin mass load over 1 year was compared to the observed mass load.  

## Over the 1 year period, the model predicted 10% more mass load than observed.  

## Given high bias in simulated mass, calibration is conservative.

© 2014 by Pyrethroid Working Group. All rights reserved

# Model Development: AGRO2014,Cosm Data Calibration (Drift)

© 2014 by Pyrethroid Working Group. All rights reserved  AGRO - 2014 best simulates the observed data (given the observed range in TSS)  AGRO - 2014 is more conservative than EXAMS and VVWM  Observed data from Leistra et al., 2003

# Model Development: AGRO2014,Cosm Data Validation (Drift)

© 2014 by Pyrethroid Working Group. All rights reserved  AGRO - 2014 is generally in good agreement with observed, slightly more conservative than EXAMS and VVWM.  Uncertainty in pond volume and TSS, and therefore concentrations.  Default 30 mg/L TSS assumed in all models.  Observed data from Springer et al. , 1996

# Model Development: AGRO2014,Cosm Data Validation (Erosion)

© 2014 by Pyrethroid Working Group. All rights reserved  AGRO - 2014 is generally in good agreement with observed, and more conservative than EXAMS and VVWM.  Observed erosion event has faster response rate than drift event.  Observed data from Springer et al. , 1996

# SWMM-AGRO-2014 Application: Simulation of Regional Aquatic EECs  

## Regional parameterizations were developed for the Southeast, South Central, Northwest, North Central, Northeast, and Mid -Atlantic.  

## Weather (30 -yr):  Hourly Precip .  Daily Temp.  Monthly Evap.  

## Irrigation: Based on regional climate.  

## Pesticide Use: From regional Kline survey data (Winchell, 2013 ) 

## .

© 2014 by Pyrethroid Working Group. All rights reserved

# SWMM-AGRO Interface: Capabilities

 

## Run SWMM -AGRO simulations for ALL of the scenarios reported in PWG’s urban exposure assessment report (Winchell et al., 2014) 

## .  Seven different geographic regions: California, Northwest, North Central, Northeast, Mid -Atlantic, Southeast, South Central  Both historical and current pyrethroid labels for California  

## Allow creation of custom use scenarios based on the regional scenarios provided.  

## Simulation of potential application mitigations.  

## Evaluation of a range of pesticides (not only pyrethroids).

© 2014 by Pyrethroid Working Group. All rights reserved

# SWMM-AGRO Interface: Customizing Scenarios  

## Custom scenarios created from standard scenarios.  

## Application extents (fraction treated at neighborhood scale) are defined independently for each use site.  

## Specific controls for defining the foundation perimeter treatment extent.  

## Irrigation options (none, overspray, no overspray).

© 2014 by Pyrethroid Working Group. All rights reserved

# SWMM-AGRO Interface: Pesticide Application Time Series  

## Custom application inputs created from standard scenarios.  

## Application time series are defined independently for each use site.  

## Application rate and number of apps per year, and specific dates for each application are customizable for each use site.

© 2014 by Pyrethroid Working Group. All rights reserved

# SWMM-AGRO Interface: Pesticide E- Fate Properties
 

## Pyrethroid e - fate properties are stored in the SWMM -AGRO database.  

## New pesticides may be added to the SWMM AGRO database.  

## Pesticide properties required by SWMM -AGRO are defined through the interface form.

© 2014 by Pyrethroid Working Group. All rights reserved

# SWMM-AGRO Interface: Results  

## Results are provided in the AGRO - 2014 spreadsheet output tabs, including annual maximum and 90 th 

## percentile concentrations of:  Total water column  Dissolved water column  Benthic pore water  Benthic sediment

© 2014 by Pyrethroid Working Group. All rights reserved

# Summary and Conclusions

 

## A SWMM -AGRO model and associated pesticide aquatic exposure scenarios have been developed for use in assessments of residential pesticide uses.  

## The SWMM portion of the model was designed to represent a high vulnerability residential watershed and was calibrated with hydrologic and water quality monitoring data.  

## AGRO - 2014 simulations were compared with three COSM experiments where it was shown to accurately predict the observations, and was more conservative than EXAMS or VVWM.  

## The SWMM and AGRO - 2014 models were linked in a single “SWMM -AGRO” interface that is capable of running the standard residential scenarios as well as custom scenarios.  

## The SWMM -AGRO model is available for public download at: http://www.stone - env.com/agchem/agres.php © 2014 by Pyrethroid Working Group. All rights reserved

# References

 Leistra, M., Zweers, A., Warinton, J., Crum, S., Hand, L., Beltman, W., et al. 2003. Fate of the insecticide lambda - cyhalothrin in ditch enclosures differing in vegetation density. Pest Management Science, 60, 75 - 84.  Luo, Y., B. C. Jorgenson, D.Q. Thuyet, T.M. Young, F. Spurlock, and K. S. Goh. 2014. Insecticide washoff from concrete surfaces: characterization and prediction. Environmental Science & Technology 48: 234 - 243.  Mackay, D. 2001. Multimedia Environmental Models: The Fugacity Approach - Second Edition. Lewis Publishers, Boca Raton, pp.1 - 261.  Nash, J. E. and Sutcliffe, J. V.. 1970. River flow forecasting through conceptual models, Part I - A discussion of principles. J. Hydrol. 10: 282 – 290.  Oki, L, and D. Haver. 2011. Evaluating Best Management Practices (BMPs) Effectiveness to Reduce Volumes of Runoff and Improve the Quality of Runoff from Urban Environments.  Padilla, L. E., & Winchell, M. F. 2013. Development And Testing Of An Improved Agro Model (AGRO - 2014) For Use In Predicting Aquatic And Benthic Pesticide Concentrations In Ponds. PWG Report - PWG - ERA- 03b. Stone Environmental Inc.  Springer et al. 1996. An evaluation of the impact of fenpropathrin insecticide exposure on aquatic mesocosm ecosystems. Unpublished study prepared by Wildlife International Ltd. 2169 p. EPA MRID No. 43958801.  Winchell, M.F. 2013. Pyrethroid Use Characteristics in Geographically Diverse Regions of the United States: Parameterization of Estimated Pyrethroid Treatment Extent and Frequency for Urban Exposure Modeling. PWG - ERA- 02b 

### . Stone Environmental Inc.  Winchell, M.F., L. Padilla, and S. Jackson. 2014. Development and Application of a Higher Tier Urban Modeling Approach for Estimating Pyrethroid Concentrations in Static Water Bodies Using SWMM and AGRO - 2014. PWG - ERA- 11. Stone Environmental Inc. © 2014 by Pyrethroid Working Group. All rights reserved