Chapter 14
Pesticides in Surface Water: Monitoring, Modeling, Risk Assessment, and Management Downloaded from pubs.acs.org by BETHEL UNIV on 04/06/19. For personal use only.
Modeling the Mitigating Effects of Conservation Practices for Pyrethroid Uses in Agricultural Areas of California Yuzhou Luo* Department of Pesticide Regulation, California Environmental Protection Agency, Sacramento, California 95814, United States *E-mail:
[email protected].
An integrated modeling system is proposed to simulate the effects of conservation and mitigation practices on pesticide off-site movement and fate in aquatic system. The system is based on the incorporation of AgDrift and the Vegetative Filter Strip Model (VFSMOD) to the existing Pesticide Root-Zone Model and Variable Volume Water Model (PRZM-VVWM), and new development of pesticide simulations for a vegetative filter strips (VFS). The integrated model is demonstrated with agricultural uses of pyrethroids in California to evaluate the mitigation practices mandated in product labels. Results suggest that a well-maintained VFS is efficient in removing suspended solids and associated pyrethroids. The efficiency is dependent on the soil texture, pesticide properties, and pesticide application methods. Therefore, these factors should be properly characterized by both soil surveys and model simulations before the development and implementation of a VFS.
© 2019 American Chemical Society
Introduction Environmental modeling approaches have been widely accepted to simulate pesticide fate and transport. The models Pesticide Root-Zone Model (PREM) and Variable Volume Water Model (VVWM) are used by the U.S. Environmental Protection Agency (USEPA) (1) and the California Department of Pesticide Regulation (CDPR) (2) to assess potential pesticide risks to surface water. By altering relevant input values, those models are also capable of simulating some conservative practices of pesticide applications, such as in-field (e.g., reduced application rate, cover crop) and in-pond (e.g., sedimentation pond) practices. However, they are not sufficient for evaluating structural best management practices (BMPs), such as a vegetative filter strip (VFS), which require additional modeling capabilities with considerations of pesticide behaviors between the treated fields and a receiving water body. VFS has been implemented in agricultural areas worldwide for decades, demonstrating their effectiveness in water quality improvement (3, 4). VFS is required on the labels for agricultural uses of pyrethroid products (5). Pyrethroid insecticides are applied to a variety of crops throughout California. Due to their aquatic toxicity and persistence, off-site movement of these chemicals into surface water is of concern. Monitoring studies and toxicity tests in the early 2000s showed pyrethroid-related sediment toxicity in agriculturally-influenced areas of California (6–8). Water bodies impaired by pyrethroids have been included in the 303(d) list since 2006 (9). In 2008, USEPA stipulated label changes with updated spray drift language for all pyrethroid products used on agricultural crops (5). The label changes include requirements on spray buffer zone, VFS, and other conditions for agricultural applications of pyrethroid products. Modeling efforts for VFS effectiveness on pyrethroids are not sufficiently presented in the literature. For example, the USEPA ecological risk assessment (ERA) for agricultural uses of pyrethroids did not consider VFS (10). A similar ERA by the Pyrethroid Working Group (PWG) predicted environmental concentration of pyrethroids with label-required spray buffer zone and VFS (11). However, model simulations before VFS installation were not conducted, so the mitigating effects of VFS were not characterized. CDPR also conducted an ERA that incorporated the mitigating effects of buffers in reducing spray drift to aquatic system (12), but the mitigation from label requirements for VFS was not sufficiently addressed. This study aims to extend the modeling capability of PRZM-VVWM for evaluating the mitigating effects of spray buffer and VFS for pesticides. Similar to the previous study, spray buffer is simulated by AgDrift (13), and the results are presented as application efficiency and drift fraction to be used by PRZM and VVWM. For VFS, two additional modeling components are incorporated: hydrological simulations based on the vegetative filter strip model (VFSMOD) (14–16), and pesticide simulations developed in this study (“Improved Modeling Approaches for Pesticide Registration Evaluation for Surface Water Protection in California”). PREM software, with more details introduced in another chapter of this book, is used as a platform for model development and integration. The integrated 276
model is tested with the label-required spray buffer and VFS for pyrethroid uses in agricultural areas of California.
Model Development Conceptual Model VFS modeling in this study separates the “hydrological simulation” and “pesticide simulation” (Figure 1). For hydrological simulation, VFSMOD v4.3.1 (17) is integrated with PRZM-VVWM. During a runoff event aggregated at daily time step, VFSMOD predicts infiltration of the incoming water runoff (ΔQ, infiltration as fraction of input water) and deposition of suspended solids (ΔE, deposition as fraction of input solids). The edge-of-field fluxes for water and solids will be adjusted by the VFSMOD-predicted trapping efficiencies before the VVWM simulation. The pesticide simulation for VFSs is developed in this study based on environmental characteristics, chemical properties, and modeling results from PRZM (edge-of-field fluxes) and VFSMOD (ΔQ and ΔE). During a dry period between two runoff events, pesticide degradation and availability for the next runoff event are simulated. Therefore, pesticide residues trapped from the previous runoff event remaining in a VFS will be continuously modeled and subject to reentering to the runoff in the next event. The proposed framework also incorporates AgDrift v2.1.1 (13) for the effects of the spray buffer zone required in the label changes of pyrethroid products used on agricultural crops. In the modeling system, AgDrift simulates pesticide deposition to the treated field, the receiving water body, and the VFS.
Figure 1. Model integration for pesticide risk assessments with conservation practices. 277
Hydrological Simulations by VFSMOD VFSMOD is a runoff event-based model, while PRZM and VVWM run at a daily time step. Therefore, the daily runoff entering a VFS is assumed to be one aggregated event. The same assumption is applied to the runoff generation in many surface water models at daily time step, such as SWAT (Soil & Water Assessment Tool) (18). The aggregated daily runoff is characterized by total runoff volume (predicted by PRZM), duration, and runoff distribution over the duration. For rainfall-induced runoff, its duration is calculated as (rainfall volume)/(rainfall intensity). The rainfall volume for each day is retrieved from weather data associated with crop scenarios (1), and the rainfall intensity is set as 2 mm/hour. The default intensity is determined based on data analysis for the Sacramento area by the National Oceanic and Atmospheric Administration (NOAA) (19) (for a 24-hour period, 1-year recurrence interval). This default value can be adjusted from the daily rainfall intensity data if available. For irrigation tail water, the duration is calculated by (irrigation volume)/(irrigation rate), where the volume is predicted by PRZM and the rate is predefined in the crop scenarios (1). Triangular hydrography (with peak flow rate = 2× average rate, and peak time = (runoff duration)/2.67) is used for distributing the total runoff over its duration (20). VFSMOD is parameterized with PRZM modeling results (soil water content and edge-of-field fluxes of water and solids) and USEPA crop scenarios (specifying weather data, soil properties, and crop management parameters) (Figure 1). It’s assumed that the treated field and adjacent VFS have the same soil type, so the soil properties in the crop scenarios for the top soil layer (Table 1) are used in the VFSMOD hydrological simulation, including bulk density (BD), field capacity (THEFC), wilting point (THEWP), and organic carbon content (OC, reported as percentage, converted to dimensionless value for VFSMOD). In addition, VFSMOD also needs saturated hydraulic conductivity (VKS), average suction at wetting front (SAV), and contents of clay and sand. Those parameters are determined based on USDA soil texture classification: a soil class is first assigned to each crop scenario by matching its THEFC and THEWP, and then the representative values under this soil class will be retrieved for modeling (21, 22). For example, the “CA alfalfa” scenario, with THEFC = 0.42 and THEWP = 0.36, is classified as “Clay” soil and the required parameters are set as the representative values of “Clay”: VFS = 1.67×10-7 m/s, and SAV = 0.32 m.
Table 1. Variables used for hydrological and pesticide simulations in a VFS Description
Symbol
Notes
Soil properties in the top layer (for both the treated field and VFS) Slope of the treated field (%) Bulk density
(g/cm3)
Field capacity (-)
SLP
Crop scenario
BD
Crop scenario
THEFC
Crop scenario Continued on next page.
278
Table 1. (Continued). Variables used for hydrological and pesticide simulations in a VFS Description
Symbol
Notes
Wilting point (-)
THEWP
Crop scenario
Organic carbon content (-)
OC
Crop scenario
Soil texture class
-
Determined by THEFC and THEWP in each crop scenario
Saturated hydraulic conductivity (m/s)
VKS
Representative value for the soil texture class (21)
Average suction at wetting front (m)
SAV
Representative value for the soil texture class (21)
Content of clay and sand (-)
CLAY, SAND
Representative value for the soil texture class (22)
Soil water content (-)
THET
Predicted by PRZM
Input water volume (m3)
Q
PRZM-predicted edge-of-field runoff plus rainfall over the VFS
Trapping efficiency of water, infiltration as fraction of Q (-)
ΔQ
Predicted by VFSMOD
Input sediment (kg)
E
by PRZM
Trapping efficiency of sediment, deposited solids divided by E (-)
ΔE
by VFSMOD
Hydrological variables
Pesticide- related variables (mg/m2[treated field], unless stated otherwise) Soil adsorption coefficient (L/kg)
Kd
=KOC×OC, where KOC is organic carbon-normalized soil adsorption coefficient
Phase distribution factor (-)
Fph
Q/E/Kd
Edge-of-field pesticide flux in dissolved phase
RFLX
by PRZM
Edge-of-field pesticide flux in solid-bound phase
EFLX
by PRZM
Input pesticide, in dissolved and solid-bound phases
mid, mip
Based on RFLX, EFLX, and residues in the VFS
Output pesticide, in dissolved and solid-bound phases
mod, mop
Pesticide loadings to receiving water body
Trapped pesticide by a VFS, in dissolved and solid-bound phases
mfd, mfp
See the section “Pesticide Simulations in VFS”
Trapping efficiencies of pesticide (-), in dissolved, solid-bound, and total phases
ΔPd, ΔPp, ΔP
Eqs. (1) and (2)
Continued on next page.
279
Table 1. (Continued). Variables used for hydrological and pesticide simulations in a VFS Description
Symbol
Notes
Pesticide residue in the mixing layer of the VFS (2 cm), in dissolved and solid-bound phases
mrp, mrd
Figure 2
Pesticide loss to subsurface
msub
Dissolved pesticide transported with infiltration (mfd-mrd)
Pesticide at the end of a dry period (before the next runoff event), in dissolved and solid-bound phases
m′rd, m′rp
Eq. (3)
Notes: All hydrological and pesticide variables are presented as daily time series for the 30-year simulation period; “(-)” denotes that the variable is dimensionless; in this study, mip=EFLX.
Pesticide Simulations in VFS In 2009, an empirical regression equation was developed to estimate the removal efficiency of total pesticides (combined for dissolved and solid-bound phases) by VFS. In the early efforts of continuous simulations for VFS, it’s assumed that trapped pesticide masses by the VFS are immediately removed from the simulation domain (17, 23). Later, the VFSMOD developers proposed a mass balance component to estimate pesticide residue towards the next runoff event (14, 24). It’s assumed that all pesticide residues (in both dissolved and solid-bound phases) in a VFS will re-enter the runoff and available for transport in the subsequent runoff event. This assumption may underestimate pesticide removal efficiency, especially for hydrophobic components such as pyrethroids. A new approach for pesticide removal and mass balance in VFS is proposed in this study (Figure 2), by considering two facts: [1] PRZM provides pesticide edge-of-field fluxes separately in dissolved and solid-bound phases, and [2] solid detachment (i.e., resuspension) is not simulated in VFSMOD. The removal efficiencies, for pesticide in dissolve and solid-bound phases, respectively, are assumed to be proportional to the VFSMOD-predicted infiltration (ΔQ) and deposition (ΔE) fractions in the VFS,
where ΔPd and ΔPp are trapping efficiency of pesticides in dissolved and solidbound phases, respectively, ΔP is the total efficiency, and mid and mip are the incoming pesticide masses in corresponding phases (Table 1). The same approach has been used in other models such as SWAT (18). The pesticide mass trapped by a VFS (mfd in dissolved phase and mfp in solid-bound phase) are calculated as 280
It’s assumed that the trapped solid-bound mass is immediately incorporated with the mixing layer (mfp = mrp). In dissolved phase, however, some of the trapped mass will move downward by infiltration. The retained portion in the mixing layer (mrd) is calculated by assuming that the infiltration concentration equals that of the pore water (24).
Figure 2. Diagram of pesticide simulations in vegetative filter strip with variables defined in Table 1. Once trapped in a VFS, pesticides are subject to degradation and partitioning during the dry period between runoff events. The first-order kinetics are used for degradation, with the aerobic soil metabolism half-life of the pesticide. Partitioning is modeled based on instantaneous equilibrium between dissolved and solid-bound phases in the mixing layer of 2 cm,
where mr is the total pesticide residue in the VFS on a simulate day, with m′rd and m′rp denoting its portioning in dissolved and solid-bound phases, respectively, and THET is the soil water content in the VFS for the calculation day, which is estimated as the PRZM-predicted soil water content in the treated field (Table 1). During the subsequent runoff event, some of the pesticide residues in the VFS will be remobilized by the incoming runoff for transport in the VFS again. VFSMOD doesn’t simulate particle detachment from the top soil of a VFS. Therefore, it’s assumed that incoming runoff only extract dissolved pesticide residue in the mixing layer (m′rd). Solid-bound pesticide residue (m′rp) remains in the VFS, and acts as a reservoir that will store or release pesticide by partitioning in the next dry period. This assumption is less conservative but reflects the 281
variations on the VFS effectiveness according to the physiochemical properties of pesticides, especially the adsorption coefficient.
Modeling Results and Discussion Simulation Design In the previous modeling efforts for pyrethroids in agricultural areas of California (12), PRZM-VVWM have been configured for six pyrethroids (bifenthrin, cyfluthrin, cypermethrin, esfenvalerate, lambda-cyhalothrin, and permethrin), and the modeling results before the 2008 label changes were validated with monitoring data. The integrated system in this study is used to evaluate the mitigating effects of label-required conservation practices, especially the 10-ft VFS. Similar to registration evaluations and ecological risk assessment, the mitigating effect of a VFS will be simulated with prescribed crop scenarios and weather data for the 30-year period of 1961–1990. Daily concentrations of pyrethroids in sediment, in OC-normalized format (µg/kg[OC]), are predicted by VVWM and further reported as the 1-in-10-year estimated environmental concentration (EEC). Taking bifenthrin as an example, two sets of model simulations are conducted and results are presented in this chapter: with and without VFS. The effects of VFS are characterized by the relative changes of pesticide masses in VFS and concentrations in the receiving water. It’s anticipated that the modeling results will provide screening-level analysis on the mitigation effectiveness with minimum input data as required for pesticide registration review (25). Note that the actual effectiveness of a VFS would be dependent on more factors such as effects of spatial variability and long-term operations (26). According to the label changes with updated spray drift language for all pyrethroid products used on agricultural crops (5), pyrethroid products can be applied onto fields only where there is a maintained VFS of at least 10 feet between the field and down gradient aquatic habitat. USEPA referred to a USDA publication for information on constructing and maintaining VFS (4). However, there is no specific requirement for the area ratio between the VFS and the field to be treated. For estimating the theoretical maximum effectiveness of the 10-ft VFS as required in the product labels, this study assumes that a VFS is installed for each 10-ha agricultural field. The VFS has the same length as the 10-ha field, i.e., 316 m [square root of 10 ha] or 1,037 ft. This reflects an area ratio of about 1:100 (VFS:Field). Again, the modeling results only represent the upper bound of the mitigation effects by introducing a 10-ft VFS, while the actual effects cannot be estimated unless the area ratio is further defined by field surveys or additional regulatory actions. Data Acquisition Physicochemical properties and environmental fate data for the pyrethroids are retrieved from the USEPA ERA (10). Foliar degradation is assumed to be stable according to the USEPA guidance for model input data (27). 282
Pyrethroid products were reviewed by USEPA, and summarized as crops and application methods in a model-ready format (10), with the maximum application rate, the maximum number of application per year/season, and the minimum application interval. The data for bifenthrin uses in California are used in this study (Table 2). Other model input data for pesticide applications are set as the default values (28).
Table 2. Application methods of bifenthrin products in California Crop(s)
Application interval (days)
Spray method, rate (kg[AI]/ha), and frequency
Almond
15
Airblast, (0.2242×2) + (0.1121×1)
Cole crops, grapes, lettuce
7
Aerial, 0.1121×5
Corn
14
(Aerial, 0.1121×2) + (Ground, 0.2242×1)
Cotton
3
Aerial, 0.1121×3
Table 3. Drift fraction for agricultural applications of pyrethroid products with required spray buffers (calculated for the USEPA pond) AgDrift model settings
Drift fraction
Application efficiency
Aerial
Drop size distribution (DSD): ASABE medium; wind speed: 15 mph
0.037 (150 ft)
0.95
Airblast
Orchards: spare (Young, Dormant)
0.015 (25 ft)
0.99
Ground
Boom height: high; DSD: ASABE fine to medium/coarse; data percentile: 90th
0.007 (25 ft)
0.99
The requirement of a spray buffer zone is part of the 2008 USEPA label changes with updated spray drift language for all pyrethroid products used on agricultural crops (5). The label changes required 150-ft buffer zone for aerial applications and 25-ft buffer zone for airblast and ground applications. In addition, requirements on wind speed and direction, temperature inversion, and droplet size are also specified (Table 3). Drift fractions of pyrethroid uses are calculated by AgDrift 2.1.1 for the USEPA pond. Other model input parameters are selected according to the USEPA guidance on modeling off-site deposition of pesticides via spray drift for ecological assessments (29). For application efficiency, to be consistent with USEPA guidance (27) and the ERAs by PWG and USEPA, this study uses the values of 0.95 for aerial and 0.99 for airblast and ground applications. 283
VFS Effectiveness The integrated model reports event-based removal efficiencies of runoff, solids, and pesticides at daily time step. To better present the mitigating effects, the overall efficiency by a VFS is also calculated. The overall efficiency is defined as the relative change between the total influent and total effluent masses during the 30-year simulation period of 1961–1990. The overall efficiency for bifenthrin removal (ΔP) in the demonstrated case studies range from 60% (cotton) to 97% (almond and grapes) (Table 4). For hydrophobic pesticides like bifenthrin, sedimentation of suspended solids is the primary mechanism for pesticide trapping through a VFS. The effects of infiltration on the dissolved pesticide are secondary, but still contribute to the total removal especially during high flow events. So the optimal conditions for VFS implementation would be: (1) high efficiency of sediment trapping (ΔE), (2) high fraction of incoming pesticide in sediment-bound phase (indicated by E/Q, or by the VFSMOD output variable 1/Fph=E*Kd/Q, where Q is incoming flow and Kd is soil adsorption coefficient), and (3) high infiltration rate (ΔQ). Previous field and modeling studies suggest that a VFS is generally effective in removing sediment from runoff (i.e., high ΔE). In this study, the predicted ΔE ranges from 74% to 99% (Table 4), with an average of 80% weighted by incoming sediment loadings. For reference, a median efficiency of 91.3% for sediment trapping was summarized from 181 experimental events reported in 16 studies (3) with a median VFS width of 10 m (compared to 10-ft or 3.05-m VFS required for pyrethroid applications and simulated in this study).
Table 4. Effects of the label-required vegetative filter strips on bifenthrin applications on selected crops in California ΔP
Q
ΔQ
Fph
ΔE
ΔEEC
Almond
97%
2.2
70%
99%
4.4
56%
Cole crops
94%
4.8
6.7%
93%
1
65%
Grapes
97%
0.2
53%
99%
2.9
20%
Lettuce
86%
4.5
29%
80%
0.55
58%
Corn
82%
4.3
22%
74%
2
64%
Cotton
60%
1.6
13%
99%
17.3
24%
Notes: ΔP = overall efficiency of pesticide removal, Q = incoming flow (presented as the mean value over runoff events, mm), ΔQ = infiltration (normalized by incoming flow), ΔE = sediment trapping, Fph = phase distribution factor, and ΔEEC = EEC reduction relative to the modeling results without VFS.
284
With consistently high ΔE for most of the runoff events, the variation of pesticide removal efficiency is mainly related to 1/Fph and ΔQ. This is consistent with the previous study of VFSMOD sensitivity analysis on pesticide reduction, which concluded that pesticide reduction through surface runoff mechanisms was identified as an important process for pesticides with a wide range of properties (30). Both of 1/Fph and ΔQ are dependent on soil properties. This study assumes that the VFS has the same soil properties as in the corresponding crop scenario. For example, the smaller overall mass reduction (ΔP) predicted for the “cotton” scenario is associated with its lower 1/Fph and lower ΔQ compared to other scenarios (Table 4). The “cotton” scenario is prescribed with clay soil, compared to other scenarios with sandy loam (“lettuce”, “almond”) or loam (“grapes”, “corn”) soils. Clay soils are associated with lower saturated hydraulic conductivity, higher runoff potential, and lower soil erodibility. In addition to the removal efficiency (ΔP), the mitigating effect of a VFS is also presented as the relative change of EEC in the receiving water before and after the installation of the label-required VFS (ΔEEC, Table 4). The ΔEEC are predicted as ~20% for “cotton” and “grapes” scenarios, and 50–60% for others. ΔEEC is smaller than ΔP because ΔEEC includes the contribution of spray drift to the receiving water, which is not mitigated by a VFS. In terms of ΔEEC, therefore, the overall effectiveness of a VFS could be reduced for the scenarios with more relative contribution by drift. A linear relationship is observed as ΔEEC = (1 – %Drift)*ΔP, where ΔEEC and ΔP are predicted EEC reduction and mass reduction with VFS implementation, and %Drift is the relative contribution of total pesticide input by spray drift. In addition, since the EEC is calculated as the 1-in-10-year daily concentrations, the reduction of EEC by a VFS could be moderated by larger runoff events with higher pyrethroid concentrations. In this case, as concluded in a previous study by Muñoz-Carpena et al. (24), EEC is greatly influenced by individual runoff event and thus not an appropriate indicator to evaluate the overall effects of a VFS.
Limitations and Future Direction VFS modeling demonstrated in this study may have overestimated effectiveness of VFS in field conditions. Known issues and potential solutions are summarized in Table 5 for future development.
285
Table 5. Assumptions/limitations in the case studies that may overestimate VFS effectiveness Assumptions/limitations
Potential solutions
Pesticide deposition to the VFS not considered. Since a VFS is installed adjacent to a field, it may receive significant pesticide mass by deposition during application. To simplify the case studies and focus on the VFS effectiveness, this is not considered in the above simulations.
Estimation of pesticide deposition to a VFS based on the AgDrift-predicted drift fraction, e.g., 0.07 for a 10-ft VFS (Tier I Aerial as an example).
Assumption of spatially-uniform distribution of edge-of-field fluxes. The case studies assumed a well-maintained VFS with shallow overland flow of the same intensity across the entire VFS width (i.e., only one section in the vegetation treatment system). However, field observations suggest different regions in a VFS with lower runoff intensity, higher intensity, and even concentrated flow. Regions with high intensity flow or concentrated flow would reduce the overall effectiveness of a VFS.
Separate VFSMOD runs and pesticide simulations for the observed hydrograph regions during each runoff event. For example, field experiments showed that 10% of the VFS area received between 25% and 75% of the total field runoff, and the average value of 50% was suggested for modeling (31, 32).
Resuspension of sediment and associated pesticides in a VFS not considered. Soil erosion could occur with sediment discharge from VFS, especially during significant subsequent runoff event.
This is related to the core algorithm of VFSMOD and cannot be addressed without significant changes to the model itself.
Conclusion The modeling capability for continuously simulating a VFS is incorporated into the existing PRZM-VVWM framework (2). Hydrological simulations are implemented by integrating with VFSMOD, whereas pesticide simulations are based on a newly proposed algorithm. As a model demonstration, this study evaluates the uses of pyrethroid products (bifenthrin as an example) in agricultural areas of California, with label-required spray buffer and VFS. Modeling results before and after the VFS installation are compared for the removal efficiencies of water, solids, and pesticides. In summary, modeling results suggest that a well-maintained VFS is efficient on removing sediment and associated pyrethroids. The required 10-ft VFS reduce bifenthrin loadings by 60–97% from the treated fields by runoff and soil erosion (Table 4). This is related to the effective removal of suspended solids by a VFS and strong binding of pyrethroids with solids. Soil texture and properties have significant effects on the pesticide removal by a VFS, as indicated by the infiltration rate (ΔQ) and phase distribution factor (Fph). Relatively low efficiency is observed for clay soils with high runoff potential (e.g., the “cotton” scenario). In addition, high removal efficiency by a VFS does not guarantee significant reduction on EECs in receiving water for risk characterization and 286
exposure analysis. For scenarios where the mass inputs to the receiving water are dominated by spray drift (e.g., the “cotton” and “grapes” scenarios demonstrated in this study), the installation of a 10-ft VFS only reduces the EEC by ~20% even if the VFS substantially removes pesticide masses carried by runoff and soil erosion. Therefore, soil surveys and model simulations are suggested for characterizing field conditions before the development and implementation of a VFS. In addition, the modeling results are based on the assumption of continuous operation of a well-maintained VFS in idealized conditions, reflecting the maximum efficiency. More informative guidelines for field installation and maintenance of a VFS (especially on the soil properties and the VFS:Field area ratio) are recommended to secure and enhance the mitigation effects.
Acknowledgments The author acknowledges Xuyang Zhang, Yina Xie, Nan Singhasemanon, and Kean S. Goh with the California Department of Pesticide Regulation for valuable discussions. The author is grateful to Dr. Oscar Pérez Ovilla with Bayer CropScience for instructions on VFSMOD, and the book editors and anonymous reviewers for critical reviews.
References 1. 2.
3.
4.
5.
6.
7.
PWC (Pesticide in Water Calculator), Version 1.52; U.S. Environmental Protection Agency: Washington, DC, 2016. Luo, Y.; Deng, X.; Xie, Y.; Singhasemanon, N. Goh, K. S. Improved Modeling Approaches for Pesticide Registration Evaluation for Surface Water Protection in California. In Pesticides in Surface Water: Monitoring, Modeling, Risk Assessment, and Management; Goh, K. S.; Gan, J.; Luo, Y.; Dirk, F. Y., Eds.; ACS Symposium Series 1308; American Chemical Society: Washington, DC, 2018; Chapter 13. Chen, H.; Grieneisen, M. L.; Zhang, M. Predicting pesticide removal efficacy of vegetated filter strips: A meta-regression analysis. Sci. Total Environ. 2016, 548, 122–130. United States Department of Agriculture (USDA), Natural Resources Conservation Service (NRCS), 2000. Conservation Buffers to Reduce Pesticide Losses. https://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/ nrcs143_023819.pdf (accessed 8/20/2018). U.S. Environmental Protection Agency, 2008. Updated Spray Drift Language for Pyrethroid Agricultural Use Products. https://www.epa.gov/ sites/production/files/2015-08/documents/epapyrethroidletter.pdf (accessed 8/20/2018). Amweg, E. L.; Weston, D. P.; Ureda, N. M. Use and Toxicity of Pyrethroid Pesticides in the Central Valley, California, USA. Environ. Toxicol. Chem. 2005, 24 (4), 966–972. Central Coast Water Quality Preservation, Inc. Northern Area Ag-Waiver Monitoring (R3_CMPnorth) data, listed as references #2592 and #2593 in 287
8.
9.
10.
11.
12.
13.
14.
15.
16. 17.
18.
19.
20.
the 2010 and 2012 303(d) report by State Water Resources Control Board, 2006. Starner, K.; White, J.; Spurlock, F. Kelley, K. Pyrethroid Insecticides in California Surface Waters and Bed Sediments: Concentrations and Estimated Toxicities; California Department of Pesticide Regulation: Sacramento, CA, 2006. http://www.cdpr.ca.gov/docs/emon/surfwtr/ swposters/starner_pyreth06.pdf (accessed 8/20/2018). California’s 2006 Clean Water Act Section 303(d) List of Water Quality Limited Segments; State Water Resources Control Board: Sacramento, CA, 2006. Preliminary Comparative Environmental Fate and Ecological Risk Assessment for the Registration Review of Eight Synthetic Pyrethroids and the Pyrethrins; EPA-HQ-OPP-2010-0384-0045; U.S. Environmental Protection Agency: Washington, DC, 2016. Giddings, J.; Hendley, P.; Holmes, C.; Ritter, A.; Desmarteau, D.; Winchell, M. Wirtz, J. Ecological Risk Assessment of Agricultural Uses of Nine Synthetic Pyrethroids; PWG-ERA-18; Compliance Services International: Lakewood, WA, and Pyrethroid Working Group, Valdosta, GA, 2015. Luo, Y. Modeling Pyrethroid Uses and Conservation Practices in Agricultural Areas of California; California Department of Pesticide Regulation: Sacramento, CA 2017. http://www.cdpr.ca.gov/docs/ emon/pubs/ehapreps/analysis_memos/luo_pyrethroids_ag_modeling.pdf (accessed 8/20/2018). U.S. Environmental Protection Agency, 2018. Models for Pesticide Risk Assessment. https://www.epa.gov/pesticide-science-and-assessingpesticide-risks/models-pesticide-risk-assessment (accessed 8/20/2018). Muñoz-Carpena, R.; Ritter, A.; Fox, G. A.; Perez-Ovilla, O. Does Mechanistic Modeling of Filter Strip Pesticide Mass Balance and Degradation Processes Affect Environmental Exposure Assessments? Chemosphere 2015, 139, 410–421. Muñoz-Carpena, R.; Parsons, J. E.; Gilliam, J. W. Modeling Hydrology and Sediment Transport in Vegetative Filter Strips. J. Hydrol. 1999, 214 (1), 111–129. Muñoz-Carpena, R.; Parsons, J. E. A Design Procedure for Vegetative Filter Strips Using VFSMOD-W. Trans. ASABE 2004, 47 (6), 1933–1941. Sabbagh, G. J.; Fox, G. A.; Muñoz-Carpena, R.; Lenz, M. F. Revised Framework for Pesticide Aquatic Environmental Exposure Assessment That Accounts for Vegetative Filter Strips. Environ. Sci. Technol. 2010, 44 (10), 3839–3845. Soil & Water Assessment Tool (SWAT), Version 2012, Release 664; USDA Agricultural Research Service (USDA-ARS) and Texas A&M AgriLife Research, College Station, TX, 2016. National Weather Service, National Oceanic and Atmospheric Administration, 2018. Precipitation Frequency Data Server (PFDS). https://hdsc.nws.noaa.gov/hdsc/pfds/ (accessed 8/20/2018). Muñoz-Carpena, R. Technical Note: Incoming Triangular Hydrograph from Field Long-Term PRZM/VFSMOD Assessments; Agricultural 288
21. 22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
& Biological Engineering, University of Florida: Gainesville, FL, 2013. https://abe.ufl.edu/faculty/carpena/vfsmod/FOCUS/ Tech_Note_Field_Hydrograph_VFSMOD.pdf (accessed 8/20/2018). Maidment, D. Handbook of Hydrology; McGraw Hill, 1993. Miller, D. A.; White, R. A. A Conterminous United States Multilayer Soil Characteristics Dataset for Regional Climate and Hydrology Modeling. Earth Interactions 1998, 2 (2), 1–26. Sabbagh, G. J.; Muñoz-Carpena, R.; Fox, G. A. Distinct Influence of Filter Strips on Acute and Chronic Pesticide Aquatic Environmental Exposure Assessments Across U.S. EPA Scenarios. Chemosphere 2013, 90 (2), 195–202. Muñoz-Carpena, R.; Fox, G. A.; Ritter, A.; Perez-Ovilla, O.; RodeaPalomares, I. Effect of Vegetative Filter Strip Pesticide Residue Degradation Assumptions for Environmental Exposure Assessments. Sci. Total Environ. 2018, 619–620, 977–987. U.S. Environmental Protection Agency, 2007. Data Requirements for Pesticide Registration. https://www.epa.gov/pesticide-registration/datarequirements-pesticide-registration (accessed 8/20/2018). Liu, Y.; Engel, B. A.; Flanagan, D. C.; Gitau, M. W.; McMillan, S. K.; Chaubey, I. A Review on Effectiveness of Best Management Practices in Improving Hydrology and Water Quality: Needs and Opportunities. Sci. Total Environ. 2017, 601, 580–593. U.S. Environmental Protection Agency, 2009. Guidance for Selecting Input Parameters in Modeling the Environmental Fate and Transport of Pesticides, Version 2.1. https://www.epa.gov/pesticide-science-and-assessing-pesticiderisks/guidance-selecting-input-parameters-modeling (accessed 8/20/2018). Luo, Y. Methodology for Evaluating Pesticides for Surface Water Protection: PREM Version 5 Updates; California Department of Pesticide Regulation: Sacramento, CA, 2017. Guidance on Modeling Offsite Deposition of Pesticides via Spray Drift for Ecological and Drinking Water Assessments; EPA-HQ-OPP-2013-06760002; U.S. Environmental Protection Agency: Washington, DC, 2013. Muñoz-Carpena, R.; Fox, G. A.; Sabbagh, G. J. Parameter Importance and Uncertainty in Predicting Runoff Pesticide Reduction with Filter Strips. J. Environ Qual. 2010, 39 (2), 630–641. White, M. J.; Arnold, J. G. Development of a Simplistic Vegetative Filter Strip Model for Sediment and Nutrient Retention at the Field Scale. Hydrol. Process. 2009, 23 (11), 1602–1616. Neitsch, S. L.; Arnold, J. G.; Kiniry, J. R. Williams, J. R. Soil and Water Assessment Tool, Theoretical Documentation, Version 2009. ; Texas Water Resources Institute Technical Report No. 406; Texas A&M University System: College Station, TX, 2011.
289
