WATERBORNE EVENTS

ACS Fall 2024

Waterborne Environmental attending this year's ACS Fall 2024: Elevating Chemistry conference from August 18th-22nd in Denver, Colorado. We  sent experts who presented short courses and papers (abstracts below), and meet with colleagues within the industry. 

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Waterborne Talks

Title: Evaluation of Exposure Model Predictability Utilizing Field-Scale Estimates of Pesticide Application Timing from Remote Sensing

Sunday, 12:10-12:35PM, August 18th | Location: Room 603 (Colorado Convention Center)

Nick Guth*, Presenter; Zechariah Stone*; Nick Guth*; Maura Roberts*; Sarah Terrell*; Richard Brain

A key source of uncertainty modeling pesticide exposures in surface waters is knowledge of the application timing of pesticides, considering this information is often unavailable. However, due to recent improvements in satellite imagery, both in resolution and re-visit period, a machine learning model was recently developed using Sentinel-2 satellite imagery and environmental conditions, such as soil type, slope, climate, etc., to estimate field-scale, crop planting dates as a proxy to pesticide application timing for pre-emergent and early post-emergent corn herbicides. To evaluate the performance of the machine learning model at estimating field-scale crop planting, model estimates were compared to corn planting dates collected for several Midwestern U.S. corn fields during the crop growth seasons from 2018 to 2022.

Furthermore, a case study was developed to characterize the impact of utilizing these field-scale estimates of pesticide application timing on the predictability of exposure modeling. Using this case study, model performance was assessed against observed pesticide concentrations using different pesticide application timing estimates for the watershed(s). Ultimately, the impact of refined, pesticide application timing estimates towards improving model exposure predictability in surface water was characterized and compared.


Title: Aged sorption accepted in Europe, when North America?

Wednesday, 10:55-11:20AM EDT (Colorado Convention Center)

Shiran Qiu, Presenter. With Nathan Snyder

Aged sorption is an important process that often has been shown to modulate the bioavailability and mobility of many organic chemicals in soil. In the regulatory evaluation of leaching potential, well-understood models are available to represent this phenomenon. A guidance on how to evaluate aged sorption was published in 2021 by UK Chemical Regulation Division and European Commission. In North America, however, aged sorption is not widely recognized and accepted in regulatory evaluations. This may lead to overly conservative assessments of potential leaching risk. In this case study, aged sorption behavior of a new active substance and its soil metabolite were demonstrated in both laboratory and field soils. Inverse modeling was used to derive aged sorption parameters and soil degradation rates in equilibrium following the European aged sorption guidance, resulting in significant refinement of potential risk to groundwater resources. By applying this methodology with US-based modeling tools and soil-climate scenarios, a similar level of refinement was observed. This demonstrates that a more realistic representation of chemical behavior in the environment can aid in a more pragmatic evaluation of leaching potential.


Title: Identification of Agricultural Best Management Practices Using Remote Sensing

Wednesday, 4:05pm - 4:30PM, August 21st | Location: Room 501 (Colorado Convention Center)

Andy Jacobson*, Presenter; Maura Roberts*; Sarah Terrell*; Zechariah Stone*; Richard Brain

The United States (US) Environmental Protection Agency (EPA) has proposed a menu of runoff mitigation measures, as part of both their Endangered Species Act (ESA) workplan and their draft herbicide strategy, to protect listed species identified as potentially at risk from predicted pesticide exposures, based on the Agency’s screening-level, risk assessment approach. To evaluate the potential ramifications of the EPA’s proposed mitigation measures, as well as to refine pesticide risk assessments, an inventory of existing, implemented agricultural best management practices (BMPs) for pesticide runoff in the contiguous US is needed. However, due to the number of funding sources available for conservation practice implementation, including federal, state, local, non-governmental organization (NGO), and private sector sources, as well as the fact that agricultural BMPs are often voluntarily adopted by growers without the support of conservation programs or funding, it is impossible to get an absolute accounting of implemented BMPs. Furthermore, federal and state reporting of BMP implementation is often limited to state-level aggregation, or county-level at best.

Therefore, due to recent improvements in satellite imagery, in both resolution and re-visit period, remote sensing and artificial intelligence (AI) were utilized to develop a comprehensive system for the unsupervised identification of existing, implemented agricultural BMPs. While a remote sensing approach may not be applicable to all agricultural BMPs, it can be used globally to improve future inventories by identifying both permanent (e.g., grassed waterways, terraces) and non-permanent (e.g., cover crops, conservation tillage) practices. Using existing datasets of identified BMPs and their corresponding locations, the developed system was trained, tested, and evaluated based on its accuracy for BMP identification. Ultimately, the BMP identifications resulting from this developed system may be utilized to evaluate the potential ramifications of the EPA’s proposed menu of mitigation measures, as well as to refine future pesticide risk assessments.