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Stone is excited to again be participating in the American Chemical Society's (ACS) Fall 2025 Meeting, from Sunday, August 17, to Thursday, August 21, in Washington, D.C.

We have been a longtime supporter of the conference and this year we'll be a Gold-levl sponsor of the ACS Division of Agrochemicals (AGRO) symposia. Our employee-owners are eager to share another year's worth of experience in multiple presentations within AGRO. Our scientists and modelers will cover diverse topics such as spray drift from unmanned aerial systems, pesticide loss mitigation, ecological risk assessments, and GLP field studies.

For more information on our participation and to connect with our team at the conference, please see the details below.

SYMPOSIA AND PRESENTATIONS

(All presentations are in Mountain Daylight Time.)

Monday, August 18, 2025

Session: Refined Risk Assessment and Implementation of Pesticide Mitigation Practices for Species Protection (8 to 11:35 a.m., Hall E – Room 14, Walter E. Washington Convention Center)

8:05 - 8:30 a.m.

Presentation: Evaluating EPA’s Tier-3 scenarios for refining pesticide exposure and mitigation needs under ESA (4312134)

Presenter: Lula Ghebremichael. Co-Authors: Michael Winchell (Stone Environmental), Nathan Snyder, Frank Donaldson (BASF Corporation) Gerco Hoogeweg

This study evaluates EPA’s Pesticide in Water Calculator (PWC) Tier-3 scenarios as a potential science-based approach for refining pesticide exposure estimates and mitigation needs for ESA compliance. By examining test-case pesticides with varying properties and crop uses, a team from the Crop Life America (CLA’s) Exposure Working Group assess how Tier-3 refinements account for key factors such as soil, slope, and weather conditions that influence predicted environmental exposure concentrations (EECs) in terrestrial and aquatic habitats. The analysis also explores the broader applicability of Tier-3 refinements across multiple compounds and their alignment with the current regulatory framework. Findings from this study will be shared at the conference.

8:30 to 8:55 a.m.

Presentation:  Geospatial refinement of pesticide aquatic exposure modeling to improve the risk characterization and mitigation analysis for endangered species (4326841)

Presenter: Hendrik Rathjens (Stone Environmental). Co-Authors: Michael Winchell, Scott Teed (Stone Environmental) Christopher Holmes, Lula Ghebremichael, Ralph Warren (BASF Corporation) Huajin Chen (Bayer Crop Science) Patrick Havens (Corteva Agriscience Indianapolis Global Business Center) Tilghman Hall (Bayer) Paul Whatling, Rebecca Haynie

New strategies for efficiently conducting endangered species pesticide risk assessments and determining necessary mitigations for reducing pesticide exposure have been developed by the US EPA, including distinct strategies for herbicides, insecticides, and rodenticides. These strategies have purposefully relied upon conservative screening-level exposure modeling methods and assumptions, both for efficiency and to ensure species protection. In the case of aquatic exposure, this high level of conservatism consequentially leads to very high levels of exposure reductions needed to meet species protection goals, often 99% (100x) to 99.9% (1000x). These screening-level exposure estimates lead to both an inaccurate interpretation of pesticide use risk and mis-inform where and to what extent mitigations are needed to protect species. An alternative approach is to incorporate aquatic exposure modeling methods that account for local environmental conditions and agronomic practices into the risk assessment process. This leads to more realistic species-specific exposure estimates and resulting risk characterization and mitigation strategies. We have developed a multi-step aquatic exposure modeling methodology designed for national endangered species assessments that incorporates four key geospatial refinement elements: geographically-explicit exposure scenarios, Percent Cropped Area, pesticide use site proximity to aquatic habitat, and Percent Crop Treated. The methodology, designed to be both flexible and efficient, has been tested for the insecticide dimethoate and compared with the exposure, risk assessment, and mitigation requirement results from a dimethoate case study conducted following the US EPA’s Draft Insecticide Strategy. The new methods resulted in species-specific exposure estimates substantially lower than the generalized screening-level exposure estimates from the Draft Insecticide Strategy, leading to more realistic risk characterization and a more effective and practical mitigation approach. The exposure modeling approach developed in this assessment can be readily applied to endangered species risk assessments for other pesticides and can be extended when updated datasets (e.g., Cropland Data Layer) become available.

10:40 – 11:05 a.m.

Presentation: Pesticide Mitigation Assessment Tool: Quantitative evaluation of field-level mitigation practice effectiveness on pesticide loss (4319411)

Presenter: Jody Stryker (Stone Environmental) Co-Authors: Michael Winchell (Stone Environmental), Lula Ghebremichael, Tony Burd (Syngenta), Zhenxu Tang (Bayer CropScience), Richard Brain, Tilghman Hall (Bayer), Robin Sur (Bayer CropScience LP)

Agronomic mitigations are increasingly required by regulatory authorities in the US to offset potential risks that agricultural pesticides pose to threatened and endangered species. However, research has demonstrated that effectiveness of these practices varies significantly based on site-specific field characteristics, cropping system, compound characteristics, and specific mitigation practice or practices implemented. This research employs the USDA-supported Agricultural Policy/Environmental eXtender model to quantitatively evaluate the field-scale effectiveness of common EPA-recognized agronomic mitigation practices under diverse agricultural conditions across the continental US. We demonstrate how geographic region, field characteristics (slope, soil, and weather) and cropping systems influence the effectiveness of these mitigation practices. The same modeling approach is integrated into the Pesticide Mitigation Assessment Tool (PMAT), a site-specific agronomic modeling tool that predicts pesticide runoff and erosion from agricultural fields based on site-specific conditions. PMAT compares each user-defined field to a high vulnerability benchmark scenario, determining field-specific vulnerability based on modeling of all soil - weather combinations within the crop footprints of 10 EPA crop groups. This enhances the approach used in the EPA’s Herbicide Strategy by maintaining a site-specific assessment while accounting for variability in soil, weather, and crop conditions, enabling more precise mitigation recommendations. The tool then automatically evaluates feasible mitigation practices and combinations, providing field- and compound-specific estimates of effectiveness. Users receive a tailored menu of mitigation solutions that meet the pesticide use requirements while protecting endangered species. Recent PMAT updates streamline model inputs and expand mitigation options to include varying widths of vegetated filter strips and riparian buffers, and mulching. These enhancements improve field-specific evaluations of vulnerability and potential mitigation effectiveness, supporting regulatory efforts to refine site specific mitigation requirements. A case study will illustrate its application.

 

Tuesday, August 19, 2025

Sesssion: Water Monitoring Study Design and Interpretation for Agrochemical Exposure Assessment (8:50 a.m. – Noon. Hall E – Room 13, Walter E. Washington Convention Center)

 

10:15 – 10:40 a.m.

Presentation: Model-based vulnerability mapping to contextualize surface water pesticide monitoring on large scales (4325319)

Presenter: Jens Kiesel (Stone Environmental). Co-Authors: Hendrik Rathjens (Stone Environmental), Robin Sur (Bayer CropScience LP)

A major challenge in pesticide monitoring is extrapolating results from intensively studied catchments to unmonitored areas. Given limited resources, region-wide monitoring is unfeasible, emphasizing the need for scientifically robust methods to transfer insights between sites. A critical goal is enabling knowledge transfer from monitored to unmonitored regions. When monitoring data demonstrates safe pesticide use in one area, this information should be systematically applicable to unmonitored areas with similar or lower vulnerability profiles. This requires contextualizing catchments and assessing their relative vulnerability to pesticide contamination in surface waters. A regional vulnerability index offers a means to compare catchments, interpret existing monitoring data in a broader landscape context, and support targeted selection of future monitoring sites and mitigation efforts. While a validated framework exists for groundwater vulnerability assessment and data transfer, no equivalent method has been established for surface waters.
To address this gap, we developed a catchment-scale vulnerability assessment based on a process-driven SWAT+ model that simulates pesticide transport in streams. The model was exemplarily built for the region of Flanders, Belgium, a hydrologically complex, 15,000 sq km area divided into 10 sq km-sized subbasins and more than 400,000 hydrological response units with 5-year individual crop rotations. Calibration and plausibility assessments of the model were conducted on water balance components, pesticide monitoring time series, and sparse monitoring data from selected catchments.
The spatio-temporal model outputs were translated to catchment vulnerability rankings based on pesticide concentrations in surface waters. Maps and exceedance probability curves were generated on the subbasin scale for three active substances, allowing spatial comparison of catchment vulnerability. We demonstrate the application of this framework by contextualizing existing monitoring data, resulting in a high-resolution vulnerability classification for the entire Flanders region.
This vulnerability-based approach represents a significant advancement in environmental risk assessment by maximizing the utility of limited monitoring resources while providing comprehensive spatial coverage for regulatory decision-making, monitoring site selection, and targeted mitigation planning.

Session: Environmental Fate, Transport, and Modeling of Agriculturally Related Chemicals (2 to 6 p.m. Hall E – Room 14, Walter E. Washington Convention Center)  

2:05 - 2:30 p.m.

Presentation: Cross-validation of flux estimates: Evaluating AERMOD, IHF, and AD models in pesticide vapor exposure assessments (4326316)

Presenter: Marco Propato (Stone Environmental). Co-Authors: Jonnie Dunne (Stone Environmental), Naresh Pai (Bayer CropScience AG)

The American Meteorological Society/EPA Regulatory Model (AERMOD) is the preferred mechanistic air dispersion model used by the U.S. EPA. It simulates the movement and dispersion of air pollutants from various source types and predicts pollutant concentrations at specific locations. AERMOD plays a crucial role in air quality assessments, informing decisions on pollutant control and public health standards. AERMOD is used to evaluate the dispersion of pollutants, including pesticide vapors, in agricultural settings and estimates the concentration of pollutants that may affect receptors downwind of agricultural fields where pesticides are applied. AERMOD provides valuable information for assessing exposure risks, not only for humans but also for ecological species and assists regulators in determining whether pollutant concentrations exceed safe levels for bystanders, workers, and wildlife. The model accounts for environmental factors such as wind speed, temperature, and topography, making it effective in predicting how volatile pesticides move. In addition to AERMOD, more phenomenological models like the Integrated Horizontal Flux (IHF) and Aerodynamic (AD) models are often employed by EPA. Unlike AERMOD, which simulates detailed dispersion and concentration, IHF and AD models focus on empirical relationships and macroscopic phenomena, typically estimating the movement of pollutants based on environmental factors such as wind speed and temperature. Recent studies of volatile compounds offer an opportunity to cross-validate flux estimates from IHF and AD models with AERMOD results. Comparing fluxes predicted by these models with actual measurements from off-field receptors helps assess the accuracy of AERMOD’s concentration estimates and its real-world predictive effectiveness. Additionally, the field study data, which include receptor measurements taken at different distances from the field edge and at different sampling intervals, provide insights into how these parameters influence AERMOD’s flux estimates and better understand the factors that impact the reliability of air quality predictions. These analyses contribute valuable information on the robustness and reliability of flux estimates. Moreover, they offer practical insights into the design of field sampling studies. Ultimately, such comparative studies and analyses improve the confidence in air quality models, ensuring more accurate risk assessments and better-informed regulatory decisions.

 

2:55 – 3:20 p.m.

Presentation: Refinement of AgDRIFT ground spray models using contemporary field deposition studies (4327033)

Presenter: Michael Winchell. Co-Authors: Sebastian Castro-Tanzi and Aaron Rice (Stone Environmental) Christopher Hassinger (BASF Corporation), Naresh Pai (Bayer CropScience AG), Patrick Havens (Corteva Agriscience Indianapolis Global Business Center), Mark Ledson, Rebecca Haynie.

In the United States, the regulatory framework for prediction of off-target spray drift deposition from ground applications is based on data generated in the 1990s by the Spray Drift Task Force. However, significant advancements in nozzle technology, pesticide application equipment, and overall stewardship practices have occurred since. Therefore, it is important to evaluate off-target spray drift deposition datasets representing current ground application practices and technology when updating current exposure modeling approaches. In addition, the current US regulatory drift models represent aggregations of application equipment and environmental conditions, making refinement of spray drift modeling to match pesticide labels difficult. The goals of this study were: 1) Compile and evaluate contemporary spray drift field study datasets from ground boom applications, 2) Analyze the data using empirical models, 3) Compare empirical drift deposition models derived from analyzed field studies with current regulatory models, 4) Determine how many spray swaths contribute to off-field drift, and 5) Consider approaches for refining the current regulatory spray drift modeling framework with the data and models compiled. Data from 29 field studies were included in the analysis, representing 175 individual application events and six different spray qualities. The multi-swath power non-linear model was used to analyze all application events, which accounts for the number of swaths treated during application and supports simulation of any number of swaths. Values of R² computed using modeled and observed data ranged from 0.53 to 0.99 (mean of 0.96). Comparison of these drift deposition curves with current US regulatory curves shows that, in general, off-target deposition is lower based on the newly derived drift curves. The broad range of contemporary application technology and environmental conditions represented in this dataset provide the basis for a regulatory drift deposition modeling framework that allows for refinement of assumptions to more closely agree with pesticide label specifications.

 

Session: Agricultural Transformations with AI and ML: Collaboration, Innovation, and Data-Driven Insights (2 to 5:35 p.m., Room: East Salon C, Walter E. Washington Convention Center)

3:50 TO 4:15 p.m.

Presentation: Mapping soil carbon in the US with machine learning (4309448)

Presenter: Hannah Rubin (Stone Environmental)

Soil carbon is a vital component of healthy, functioning terrestrial ecosystems because it governs soil structure, enhances microbial activity, and regulates nutrient availability. There are ongoing efforts to increase soil carbon and protect existing carbon stocks in agricultural land through management practices with the goal of mitigating climate change and improving soil health. Recent estimates of organic carbon within the top 30 cm of soil across the contiguous US range from 29.3 Pg C to 106.2 Pg C for the same time period (~1980-2020) and the same land area, indicating the current uncertainty in this important benchmark. Without an established baseline map of soil organic carbon, it is extremely challenging to quantify the impact of various management practices or land use changes on carbon storage in soils and prioritize environmental policies in a changing climate. Our work focuses on harmonizing past research efforts and identifying important long-term trends. We compare 8 commonly applied machine learning methods and develop a new estimate of 60.4 Pg C using the method with the best performance – a random forest model. We also map the locations with the greatest disagreement between previous estimates and discuss regional historical land use, climate change, and the challenges of spatial aggregation and interpolation. Our baseline maps provide a foundation for future efforts to decarbonize and sequester carbon on a regional scale through adoption of regenerative agricultural practices.

Wednesday, August 20, 2025

Session: Unmanned Aerial Systems (aka Drones): Pesticide Spraying and Other Agricultural Applications (2 to 6 p.m., Room: Hall E – Room 14, Walter E. Washington Convention Center

4:15 to 4:40 p.m.

Presentation: UAV pattern testing design, methods, data analysis and results from UAV spray drift studies conducted in 2023 and 2024 for the Unmanned Aerial Pesticide Application Systems Task Force (UAPASTF)(4326352)

Presenter: Timothy Dupuis. Co-Authors: Ben Brayden, Aaron Rice, and Brent Toth (Stone Environmental)

Eleven UAV spray drift trials were conducted in support of the Unmanned Aerial Pesticide Application Systems Task Force (UAPASTF) focusing on off-site movement spanning 2023 and 2024. These trials were conducted in different geographic and regulatory regions including Canada, Brazil, Hungary, Spain, USA, Australia and South Africa. As a part of these trials, UAV pattern testing was conducted to determine the effective swath width and displacement for each UAV-nozzle combination that was included in the trials. The results were used to determine flight lines and the downwind edge of field during spray drift trial applications. Spray drift trials targeted swath passes perpendicular to the wind direction, so pattern testing was conducted in a cross-wind, in conditions as close as possible to the target conditions for spray drift trial applications. This presentation will focus on pattern testing design and refinement, comparing conventional pattern testing methods to the ones used in these trials, and a summary of swath width and displacement results for various hydraulic nozzles.