2024
LaRoe, J. (Applied Analysis Solutions, LLC); Kern, M. (Balance EcoSolutions, LLC); Holmes, C.M. (Applied Analysis Solutions, LLC)
Non-Agricultural Use Data Layers: Options and Methodologies for Supporting ESA Overlap Analysis Presentation
10.20.2024.
Abstract | Links | Tags: 2024, Poster, SETAC North America
@misc{NA24UDL,
title = {Non-Agricultural Use Data Layers: Options and Methodologies for Supporting ESA Overlap Analysis},
author = {LaRoe, J. (Applied Analysis Solutions, LLC) and Kern, M. (Balance EcoSolutions, LLC) and Holmes, C.M. (Applied Analysis Solutions, LLC)},
url = {https://appliedanalysis.solutions/wp-content/uploads/2024/10/SETAC-NA-2024-NonAgricultural-Use-Data-Layers-Options-and-Methodologies.pdf, View Poster},
year = {2024},
date = {2024-10-20},
urldate = {2024-10-20},
abstract = {Exposure assessments for Endangered Species have benefitted from quality spatial data on use sites for agricultural applications of pesticides. There is less development of available data for assessments in non-agricultural settings where pesticides are applied and need to be assessed, like forests, railroads, golf courses, parks, and other commercially managed vegetation. The emphasis on agricultural use sites has produced refined methodologies as well as defined and delineated data sets (e.g., Use Data Layers). However, there is ambiguity within non-agricultural uses in terms of delineating potential use sites and developing robust and comprehensive spatial datasets, particularly at national scales. Recent advancements in data processing/platforms along with the scale and extent of available spatial data present an opportunity to develop more comprehensive datasets for non-agricultural overlap analyses (relationship of use sites to species habitats). We have created customized approaches leveraging detailed datasets, like Open Street Map, to help fill this data gap. We incorporated additional information from other sources, such as historical high resolution aerial imagery, to support boundary editing and refinement of application areas. We present a combination of data sources and methods that can be tailored to curate data for specific objectives, tools and strategies to refine potential exposure for non-agricultural scenarios, as well as applied examples using golf courses, railroad vegetation management, and forests.},
keywords = {2024, Poster, SETAC North America},
pubstate = {published},
tppubtype = {presentation}
}
Exposure assessments for Endangered Species have benefitted from quality spatial data on use sites for agricultural applications of pesticides. There is less development of available data for assessments in non-agricultural settings where pesticides are applied and need to be assessed, like forests, railroads, golf courses, parks, and other commercially managed vegetation. The emphasis on agricultural use sites has produced refined methodologies as well as defined and delineated data sets (e.g., Use Data Layers). However, there is ambiguity within non-agricultural uses in terms of delineating potential use sites and developing robust and comprehensive spatial datasets, particularly at national scales. Recent advancements in data processing/platforms along with the scale and extent of available spatial data present an opportunity to develop more comprehensive datasets for non-agricultural overlap analyses (relationship of use sites to species habitats). We have created customized approaches leveraging detailed datasets, like Open Street Map, to help fill this data gap. We incorporated additional information from other sources, such as historical high resolution aerial imagery, to support boundary editing and refinement of application areas. We present a combination of data sources and methods that can be tailored to curate data for specific objectives, tools and strategies to refine potential exposure for non-agricultural scenarios, as well as applied examples using golf courses, railroad vegetation management, and forests.
Insinga, L. (Applied Analysis Solutions, LLC); Kay, S. (Pyxis Regulatory Consulting, Inc)
Refinements to Use Data Layers (UDLs) Used in Endangered Species Assessments of Pesticides Presentation
10.20.2024.
Abstract | Links | Tags: 2024, Presentation, SETAC North America
@misc{2024NA-CSB,
title = {Refinements to Use Data Layers (UDLs) Used in Endangered Species Assessments of Pesticides},
author = {Insinga, L. (Applied Analysis Solutions, LLC) and Kay, S. (Pyxis Regulatory Consulting, Inc)},
url = {https://appliedanalysis.solutions/wp-content/uploads/2024/10/SETAC-NA-2024-CSB-Based-UDLs.pdf, View Presentation},
year = {2024},
date = {2024-10-20},
urldate = {2024-10-20},
abstract = {In their Endangered Species (ES) assessments for pesticides, the US Environmental Protection Agency (EPA), US Fish and Wildlife Service (FWS) and National Marine Fisheries Service (NMFS) use datasets called Use Data Layers (UDLs) to spatially delineate potential pesticide use sites. For agricultural uses, the UDLs are currently generated from the US Department of Agriculture’s (USDA) Cropland Data Layer (CDL) raster dataset, which is created using a machine learning classification algorithm applied to satellite imagery, agricultural ground truth data, and other ancillary datasets. The current approach for generating agricultural UDLs sources 5 years of CDL datasets which are processed by grouping the more than 100 individual CDL cultivated classes into 13 general UDL classes. While this approach is robust, there are some challenges. One challenge with generating agricultural UDLs is the presence of “spurious pixels” or highly fragmented areas of the UDL that are known to be false-positive classifications (i.e., non-agricultural raster cells classified as agriculture) carried through from the source CDL dataset. The presence of spurious pixels can result in inaccurate estimations of co-occurrence between UDLs and listed species locations, and it is desirable to remove spurious pixels to the greatest extent possible. However, it is difficult to identify and filter spurious pixels in a programmatic way given the nature of the remotely sensed source dataset. Furthermore, it is undesirable to remove actual agriculture from the UDLs, which can occur if agricultural pixels are misidentified as spurious. To address this issue, we propose to mitigate the presence of spurious pixels by incorporating additional processing steps to generate more accurate UDLs. This method builds on the USDA’s refinement of the base raster CDL data into a vector Crop Sequence Boundary (CSB) dataset that uses publicly available data and employs a transparent yet efficient processing workflow. This new methodology virtually eliminates spurious pixels from the UDLs and provides the added benefit of significantly improved processing efficiency leading to a reduction in processing time from weeks for the original CDL based approach to overnight using the new CSB based approach.},
keywords = {2024, Presentation, SETAC North America},
pubstate = {published},
tppubtype = {presentation}
}
In their Endangered Species (ES) assessments for pesticides, the US Environmental Protection Agency (EPA), US Fish and Wildlife Service (FWS) and National Marine Fisheries Service (NMFS) use datasets called Use Data Layers (UDLs) to spatially delineate potential pesticide use sites. For agricultural uses, the UDLs are currently generated from the US Department of Agriculture’s (USDA) Cropland Data Layer (CDL) raster dataset, which is created using a machine learning classification algorithm applied to satellite imagery, agricultural ground truth data, and other ancillary datasets. The current approach for generating agricultural UDLs sources 5 years of CDL datasets which are processed by grouping the more than 100 individual CDL cultivated classes into 13 general UDL classes. While this approach is robust, there are some challenges. One challenge with generating agricultural UDLs is the presence of “spurious pixels” or highly fragmented areas of the UDL that are known to be false-positive classifications (i.e., non-agricultural raster cells classified as agriculture) carried through from the source CDL dataset. The presence of spurious pixels can result in inaccurate estimations of co-occurrence between UDLs and listed species locations, and it is desirable to remove spurious pixels to the greatest extent possible. However, it is difficult to identify and filter spurious pixels in a programmatic way given the nature of the remotely sensed source dataset. Furthermore, it is undesirable to remove actual agriculture from the UDLs, which can occur if agricultural pixels are misidentified as spurious. To address this issue, we propose to mitigate the presence of spurious pixels by incorporating additional processing steps to generate more accurate UDLs. This method builds on the USDA’s refinement of the base raster CDL data into a vector Crop Sequence Boundary (CSB) dataset that uses publicly available data and employs a transparent yet efficient processing workflow. This new methodology virtually eliminates spurious pixels from the UDLs and provides the added benefit of significantly improved processing efficiency leading to a reduction in processing time from weeks for the original CDL based approach to overnight using the new CSB based approach.
Christian, D. (Applied Analysis Solutions, LLC); Kern, M. (Balance EcoSolutions, LLC); Holmes, C.M. (Applied Analysis Solutions, LLC); Kay, S. (Pyxis Regulatory Consulting, Inc); Cowles, J. (Tessenderlo Kerley, Inc.); Henry, K. (Tessenderlo Kerley, Inc.)
10.20.2024.
Abstract | Links | Tags: 2024, Presentation, SETAC North America
@misc{2024NA-PULAs,
title = {Creating More Refined Pesticide Mitigation Areas Informed by Our Familiarity With Species Spatial Data in Support of Carbamate Biological Opinion Development},
author = {Christian, D. (Applied Analysis Solutions, LLC) and Kern, M. (Balance EcoSolutions, LLC) and Holmes, C.M. (Applied Analysis Solutions, LLC) and Kay, S. (Pyxis Regulatory Consulting, Inc) and Cowles, J. (Tessenderlo Kerley, Inc.) and Henry, K. (Tessenderlo Kerley, Inc.)},
url = {https://appliedanalysis.solutions/wp-content/uploads/2024/10/SETAC-NA-2024-PULAs-Carbamate-BO.pdf, View Presentation},
year = {2024},
date = {2024-10-20},
abstract = {Pesticide mitigation areas are designed to ensure a pesticide’s use will be protective of listed endangered or threatened species and their critical habitat. A species range can be geographically broad and can include areas unsuitable for habitat due to various environmental factors. There is a need to create more spatially refined pesticide mitigation areas (e.g., Pesticide Use Limitation Areas, PULAs) to better identify habitats where mitigation may be required. We have implemented a process during recent work with carbamates which has allowed us to explore more detailed habitat definitions to refine areas within a species range that, according to the most recent documentation from FWS, are required for a species’ continued recovery. We have worked to develop a “core map” representing locations of the species where mitigations are needed based on species range, critical habitat, known locations, and suitable habitat. We have focused on using the best publicly available spatial datasets in conjunction with the most recent FWS species documentation to build detailed core maps for a wide variety of endangered species. Experience has shown us that, while challenges with resources and uncertainty exist, this range refinement process is possible and valuable for assessment of risk to many endangered species. This presentation lays out the lessons learned and choices made while developing more refined pesticide mitigation areas, based on our extensive experience with species range, critical habitat, potentially suitable habitat and known locations in support of the carbamate BOs.},
keywords = {2024, Presentation, SETAC North America},
pubstate = {published},
tppubtype = {presentation}
}
Pesticide mitigation areas are designed to ensure a pesticide’s use will be protective of listed endangered or threatened species and their critical habitat. A species range can be geographically broad and can include areas unsuitable for habitat due to various environmental factors. There is a need to create more spatially refined pesticide mitigation areas (e.g., Pesticide Use Limitation Areas, PULAs) to better identify habitats where mitigation may be required. We have implemented a process during recent work with carbamates which has allowed us to explore more detailed habitat definitions to refine areas within a species range that, according to the most recent documentation from FWS, are required for a species’ continued recovery. We have worked to develop a “core map” representing locations of the species where mitigations are needed based on species range, critical habitat, known locations, and suitable habitat. We have focused on using the best publicly available spatial datasets in conjunction with the most recent FWS species documentation to build detailed core maps for a wide variety of endangered species. Experience has shown us that, while challenges with resources and uncertainty exist, this range refinement process is possible and valuable for assessment of risk to many endangered species. This presentation lays out the lessons learned and choices made while developing more refined pesticide mitigation areas, based on our extensive experience with species range, critical habitat, potentially suitable habitat and known locations in support of the carbamate BOs.
LaRoe, J. (Applied Analysis Solutions, LLC); Holmes, C.M. (Applied Analysis Solutions, LLC); Schad, T. (Bayer CropScience)
05.05.2024.
Abstract | Links | Tags: 2024, Poster, SETAC Europe
@misc{EU24birds,
title = {Spatial Approaches to Field Study Characterisation and Representativeness to Address EFSA Guidance on the Risk Assessment for Birds and Mammals},
author = {LaRoe, J. (Applied Analysis Solutions, LLC) and Holmes, C.M. (Applied Analysis Solutions, LLC) and Schad, T. (Bayer CropScience)},
url = {https://appliedanalysis.solutions/wp-content/uploads/2024/05/SETAC-EU-2024-Birds-and-mammals.pdf, View Poster},
year = {2024},
date = {2024-05-05},
urldate = {2024-05-05},
abstract = {New EFSA guidance on the Risk assessment for Birds and Mammals includes updates relating to the improvement of terrestrial vertebrate (TV) field studies for use in risk assessment of Plant Protection Products containing pesticide active substances. Within the guidance, two concepts are discussed: Field Study Characterisation and Field Study Representativeness. To address these concepts, a past terrestrial vertebrate field study was assessed and updated to address the new guidance needs for characterisation and representativeness.
Field study characterisation involved collection and analysis of spatial data available for the study area and relevant to the species, including agricultural fields, grasslands, woody vegetation, forest, water/riparian areas. Percent area metrics were generated for several distances around the study fields, as well as examination of high-resolution imagery suitable for expert evaluation. Weather conditions during the field study were compared against 30-year monthly norms to verify the study year was not outside of expected variation from long-term averages.
Field study representativeness was assessed at multiple scales, including EU, EFSA zone and Member State. The potential for species interaction between “habitat” (a proxy defined by spatial land cover data) with “use sites” (assumed treated fields by crop type derived from agricultural maps) was processed using gridded spatial data at a resolution of 10m. Shared border between habitat and use sites served as an interaction indicator summarized at the 1-km scale (i.e., how many of the 10mx10m use site grid cells were adjacent to habitat cells). The 1-km scale is suitable for further aggregation at various sizes based on species attributes. In this case, local landscape interactions were assessed by developing “scenarios” a 5kmx5km summation of the interaction indicator which was quantified across all EU member states.
The scenario(s) encompassing the study fields were placed into the full distribution of all possible scenarios (EU, EFSA zone, Member State) to assess the study location relative to all possible use site / habitat interactions (i.e., possible field study locations). The quantitative approaches developed here can also be used to prepare upcoming generic terrestrial vertebrate field study designs and support optimized study site selection to meet the new EFSA guidance requirements.},
keywords = {2024, Poster, SETAC Europe},
pubstate = {published},
tppubtype = {presentation}
}
New EFSA guidance on the Risk assessment for Birds and Mammals includes updates relating to the improvement of terrestrial vertebrate (TV) field studies for use in risk assessment of Plant Protection Products containing pesticide active substances. Within the guidance, two concepts are discussed: Field Study Characterisation and Field Study Representativeness. To address these concepts, a past terrestrial vertebrate field study was assessed and updated to address the new guidance needs for characterisation and representativeness.
Field study characterisation involved collection and analysis of spatial data available for the study area and relevant to the species, including agricultural fields, grasslands, woody vegetation, forest, water/riparian areas. Percent area metrics were generated for several distances around the study fields, as well as examination of high-resolution imagery suitable for expert evaluation. Weather conditions during the field study were compared against 30-year monthly norms to verify the study year was not outside of expected variation from long-term averages.
Field study representativeness was assessed at multiple scales, including EU, EFSA zone and Member State. The potential for species interaction between “habitat” (a proxy defined by spatial land cover data) with “use sites” (assumed treated fields by crop type derived from agricultural maps) was processed using gridded spatial data at a resolution of 10m. Shared border between habitat and use sites served as an interaction indicator summarized at the 1-km scale (i.e., how many of the 10mx10m use site grid cells were adjacent to habitat cells). The 1-km scale is suitable for further aggregation at various sizes based on species attributes. In this case, local landscape interactions were assessed by developing “scenarios” a 5kmx5km summation of the interaction indicator which was quantified across all EU member states.
The scenario(s) encompassing the study fields were placed into the full distribution of all possible scenarios (EU, EFSA zone, Member State) to assess the study location relative to all possible use site / habitat interactions (i.e., possible field study locations). The quantitative approaches developed here can also be used to prepare upcoming generic terrestrial vertebrate field study designs and support optimized study site selection to meet the new EFSA guidance requirements.
Field study characterisation involved collection and analysis of spatial data available for the study area and relevant to the species, including agricultural fields, grasslands, woody vegetation, forest, water/riparian areas. Percent area metrics were generated for several distances around the study fields, as well as examination of high-resolution imagery suitable for expert evaluation. Weather conditions during the field study were compared against 30-year monthly norms to verify the study year was not outside of expected variation from long-term averages.
Field study representativeness was assessed at multiple scales, including EU, EFSA zone and Member State. The potential for species interaction between “habitat” (a proxy defined by spatial land cover data) with “use sites” (assumed treated fields by crop type derived from agricultural maps) was processed using gridded spatial data at a resolution of 10m. Shared border between habitat and use sites served as an interaction indicator summarized at the 1-km scale (i.e., how many of the 10mx10m use site grid cells were adjacent to habitat cells). The 1-km scale is suitable for further aggregation at various sizes based on species attributes. In this case, local landscape interactions were assessed by developing “scenarios” a 5kmx5km summation of the interaction indicator which was quantified across all EU member states.
The scenario(s) encompassing the study fields were placed into the full distribution of all possible scenarios (EU, EFSA zone, Member State) to assess the study location relative to all possible use site / habitat interactions (i.e., possible field study locations). The quantitative approaches developed here can also be used to prepare upcoming generic terrestrial vertebrate field study designs and support optimized study site selection to meet the new EFSA guidance requirements.
Holmes, C.M. (Applied Analysis Solutions, LLC); Insinga, L. (Applied Analysis Solutions, LLC); Hodges, J. (Unilever Safety & Environmental Assurance Centre); Bietz, J. (Clariant Produkte GmbH); Beuter, L. (Nouryon AB); Schowanek, D. (Procter & Gamble); Dawich, J. (Innospec Limited); Maloney, E.M. (Shell Global Solutions International BV); Geurts, M. (Nouryon Chemicals B.V.); Wilhelm, S. (BASF Personal Care & Nutrition GbmH); Vitale, C.M. (Procter & Gamble); Anselmo, H. (Global Safety Assurance, Reckitt)
05.05.2024.
Abstract | Links | Tags: 2024, Poster, SETAC Europe
@misc{EU23MAF,
title = {Addressing the Impact of the Mixture Allocation Factor (MAF) on Environmental Risk Assessment: Refining Regulatory Exposure Predictions Using Spatial Data and Modelling Approaches},
author = {Holmes, C.M. (Applied Analysis Solutions, LLC) and Insinga, L. (Applied Analysis Solutions, LLC) and Hodges, J. (Unilever Safety & Environmental Assurance Centre) and Bietz, J. (Clariant Produkte GmbH) and Beuter, L. (Nouryon AB) and Schowanek, D. (Procter & Gamble) and Dawich, J. (Innospec Limited) and Maloney, E.M. (Shell Global Solutions International BV) and Geurts, M. (Nouryon Chemicals B.V.) and Wilhelm, S. (BASF Personal Care & Nutrition GbmH) and Vitale, C.M. (Procter & Gamble) and Anselmo, H. (Global Safety Assurance, Reckitt)},
url = {https://appliedanalysis.solutions/wp-content/uploads/2024/05/SETAC-EU-2024-ERASM-gridded-MAF.pdf, View Poster},
year = {2024},
date = {2024-05-05},
urldate = {2024-05-05},
abstract = {Under the Chemical Strategy for Sustainability (CSS), the European Commission has proposed the use of a mixture allocation factor (MAF) to address uncertainties and risks associated with unintentional chemical mixtures in the environment. Currently it is thought that a MAF may need to be applied to all single chemical registrations for the higher tonnage bands. It is expected that additional exposure and/or effect data generation will be needed for the risk assessment, and in some cases further risk management measures may be required to ensure environmental safety.
The European Union System for the Evaluation of Substances (EUSES), also accessed via the CHESAR v1 tool is the standard model to determine exposure in support of chemical safety assessments (CSAs) under REACH. In the spirit of continuously refining safety assessments and models, we envisage an opportunity to update exposure model background data in EUSES/CHESAR in line with the evolution of wastewater treatment infrastructure in EU, and availability of better spatial datasets. This would ensure that regulatory modelling remains aligned with most updated data available and the situation in the field.
In this work we explore field data and spatially explicit modelling approaches, some of which are probabilistic, in order to compare them with the current EUSES default parameters. We developed a spatially explicit model which applied the EUSES PEC calculation to estimate environmental concentrations at freshwater discharge locations of almost 22,000 European Urban Wastewater Treatment Plants (UWWTPs). Specifically, using publicly available data, we examined per capita water use, UWWTP connectivity and local UWWTP dilution factors to generate EU-wide distributions and to place these in context with default EUSES values.
We demonstrate that spatially explicit exposure models are feasible for use in regulatory modelling and can give more realistic predicted environmental concentrations (PECs). Case studies have been performed comparing PEClocal values for freshwater using CHESAR v1 including both the current default data and the spatial approach. We expect that higher tier exposure data will be increasingly needed to ascertain that risk quotients remain acceptable even with a MAF applied.},
keywords = {2024, Poster, SETAC Europe},
pubstate = {published},
tppubtype = {presentation}
}
Under the Chemical Strategy for Sustainability (CSS), the European Commission has proposed the use of a mixture allocation factor (MAF) to address uncertainties and risks associated with unintentional chemical mixtures in the environment. Currently it is thought that a MAF may need to be applied to all single chemical registrations for the higher tonnage bands. It is expected that additional exposure and/or effect data generation will be needed for the risk assessment, and in some cases further risk management measures may be required to ensure environmental safety.
The European Union System for the Evaluation of Substances (EUSES), also accessed via the CHESAR v1 tool is the standard model to determine exposure in support of chemical safety assessments (CSAs) under REACH. In the spirit of continuously refining safety assessments and models, we envisage an opportunity to update exposure model background data in EUSES/CHESAR in line with the evolution of wastewater treatment infrastructure in EU, and availability of better spatial datasets. This would ensure that regulatory modelling remains aligned with most updated data available and the situation in the field.
In this work we explore field data and spatially explicit modelling approaches, some of which are probabilistic, in order to compare them with the current EUSES default parameters. We developed a spatially explicit model which applied the EUSES PEC calculation to estimate environmental concentrations at freshwater discharge locations of almost 22,000 European Urban Wastewater Treatment Plants (UWWTPs). Specifically, using publicly available data, we examined per capita water use, UWWTP connectivity and local UWWTP dilution factors to generate EU-wide distributions and to place these in context with default EUSES values.
We demonstrate that spatially explicit exposure models are feasible for use in regulatory modelling and can give more realistic predicted environmental concentrations (PECs). Case studies have been performed comparing PEClocal values for freshwater using CHESAR v1 including both the current default data and the spatial approach. We expect that higher tier exposure data will be increasingly needed to ascertain that risk quotients remain acceptable even with a MAF applied.
The European Union System for the Evaluation of Substances (EUSES), also accessed via the CHESAR v1 tool is the standard model to determine exposure in support of chemical safety assessments (CSAs) under REACH. In the spirit of continuously refining safety assessments and models, we envisage an opportunity to update exposure model background data in EUSES/CHESAR in line with the evolution of wastewater treatment infrastructure in EU, and availability of better spatial datasets. This would ensure that regulatory modelling remains aligned with most updated data available and the situation in the field.
In this work we explore field data and spatially explicit modelling approaches, some of which are probabilistic, in order to compare them with the current EUSES default parameters. We developed a spatially explicit model which applied the EUSES PEC calculation to estimate environmental concentrations at freshwater discharge locations of almost 22,000 European Urban Wastewater Treatment Plants (UWWTPs). Specifically, using publicly available data, we examined per capita water use, UWWTP connectivity and local UWWTP dilution factors to generate EU-wide distributions and to place these in context with default EUSES values.
We demonstrate that spatially explicit exposure models are feasible for use in regulatory modelling and can give more realistic predicted environmental concentrations (PECs). Case studies have been performed comparing PEClocal values for freshwater using CHESAR v1 including both the current default data and the spatial approach. We expect that higher tier exposure data will be increasingly needed to ascertain that risk quotients remain acceptable even with a MAF applied.
Holmes, C.M. (Applied Analysis Solutions, LLC); Insinga, L. (Applied Analysis Solutions, LLC); Kay, S. (Pyxis Regulatory Consulting, Inc); Kern, M. (Balance EcoSolutions, LLC); Henry, K. (Tessenderlo Kerley, Inc.)
05.05.2024.
Abstract | Links | Tags: 2024, Poster, SETAC Europe
@misc{EU24PWC+,
title = {Promoting Defensible Science in Aquatic Exposure Estimation by Integrating Landscape-Level Data into US Endangered Species Assessments},
author = {Holmes, C.M. (Applied Analysis Solutions, LLC) and Insinga, L. (Applied Analysis Solutions, LLC) and Kay, S. (Pyxis Regulatory Consulting, Inc) and Kern, M. (Balance EcoSolutions, LLC) and Henry, K. (Tessenderlo Kerley, Inc.)},
url = {https://appliedanalysis.solutions/wp-content/uploads/2024/05/SETAC-EU-2024-PWCPlus.pdf, View Poster},
year = {2024},
date = {2024-05-05},
urldate = {2024-05-05},
abstract = {Prospective aquatic exposure modeling is a key component of the assessment of potential jeopardy of endangered species during the preparation of a risk assessment related to federal US pesticide (re)registration. The “Biological Evaluation” prepared by the US Environmental Protection Agency (USEPA) provides the foundation of aquatic exposure estimates using well-established pesticide fate and transport models applied to a standardized set of crop/soil/weather scenarios. Exposure estimates are based on 30 years of model simulation representing labeled uses of the pesticide assuming maximum application rates, maximum number of applications, and minimum re-application intervals, as well as assuming treated crop is adjacent to the aquatic environment. This modeling forms the basis from which subsequent refinements incorporating spatial, temporal, and pesticide usage variability can be applied. Further refinement of the screening-level modeling is essential to more accurately inform the weight of evidence process used by the US Fish and Wildlife Service and National Marine Fisheries Service to determine species jeopardy in the next stage of the assessment, the “Biological Opinion”.
To support this refinement, we present a highly efficient, quantitative approach that builds on the USEPA’s aquatic modeling by increasing the spatial/temporal context and resolution of exposure estimates to species of interest and producing well-defined and reproducible species-specific estimated aquatic concentrations. Utilizing data for each species, a set of high-resolution catchments, landscape-based crop proximity and density, and pesticide usage, inputs were processed to produce landscape-level exposure concentrations suitable for aggregation at multiple spatial scales appropriate for the species being examined. These refinements show that aquatic concentrations based on screening level assumptions of pesticide use and hypothetical water body scenarios may occur at some locations at limited times, but they are far less likely to occur within species range/habitat than is assumed in the screening-level risk assessments.
The methodologies presented are a quantitative approach to refining aquatic exposure estimates that incorporate variations in landscape and agronomic practices near endangered species locations and provide essential information to help inform localized mitigations for individual species across specific use patterns.},
keywords = {2024, Poster, SETAC Europe},
pubstate = {published},
tppubtype = {presentation}
}
Prospective aquatic exposure modeling is a key component of the assessment of potential jeopardy of endangered species during the preparation of a risk assessment related to federal US pesticide (re)registration. The “Biological Evaluation” prepared by the US Environmental Protection Agency (USEPA) provides the foundation of aquatic exposure estimates using well-established pesticide fate and transport models applied to a standardized set of crop/soil/weather scenarios. Exposure estimates are based on 30 years of model simulation representing labeled uses of the pesticide assuming maximum application rates, maximum number of applications, and minimum re-application intervals, as well as assuming treated crop is adjacent to the aquatic environment. This modeling forms the basis from which subsequent refinements incorporating spatial, temporal, and pesticide usage variability can be applied. Further refinement of the screening-level modeling is essential to more accurately inform the weight of evidence process used by the US Fish and Wildlife Service and National Marine Fisheries Service to determine species jeopardy in the next stage of the assessment, the “Biological Opinion”.
To support this refinement, we present a highly efficient, quantitative approach that builds on the USEPA’s aquatic modeling by increasing the spatial/temporal context and resolution of exposure estimates to species of interest and producing well-defined and reproducible species-specific estimated aquatic concentrations. Utilizing data for each species, a set of high-resolution catchments, landscape-based crop proximity and density, and pesticide usage, inputs were processed to produce landscape-level exposure concentrations suitable for aggregation at multiple spatial scales appropriate for the species being examined. These refinements show that aquatic concentrations based on screening level assumptions of pesticide use and hypothetical water body scenarios may occur at some locations at limited times, but they are far less likely to occur within species range/habitat than is assumed in the screening-level risk assessments.
The methodologies presented are a quantitative approach to refining aquatic exposure estimates that incorporate variations in landscape and agronomic practices near endangered species locations and provide essential information to help inform localized mitigations for individual species across specific use patterns.
To support this refinement, we present a highly efficient, quantitative approach that builds on the USEPA’s aquatic modeling by increasing the spatial/temporal context and resolution of exposure estimates to species of interest and producing well-defined and reproducible species-specific estimated aquatic concentrations. Utilizing data for each species, a set of high-resolution catchments, landscape-based crop proximity and density, and pesticide usage, inputs were processed to produce landscape-level exposure concentrations suitable for aggregation at multiple spatial scales appropriate for the species being examined. These refinements show that aquatic concentrations based on screening level assumptions of pesticide use and hypothetical water body scenarios may occur at some locations at limited times, but they are far less likely to occur within species range/habitat than is assumed in the screening-level risk assessments.
The methodologies presented are a quantitative approach to refining aquatic exposure estimates that incorporate variations in landscape and agronomic practices near endangered species locations and provide essential information to help inform localized mitigations for individual species across specific use patterns.
Insinga, L. (Applied Analysis Solutions, LLC); Holmes, C.M. (Applied Analysis Solutions, LLC); Hodges, J. (Unilever Safety & Environmental Assurance Centre)
Estimating Marine Chemical Emissions from Discharges of Sewage Treatment Plants Directly and from Freshwater Rivers Presentation
05.05.2024.
Abstract | Links | Tags: 2024, Poster, SETAC Europe
@misc{EU24STP,
title = {Estimating Marine Chemical Emissions from Discharges of Sewage Treatment Plants Directly and from Freshwater Rivers},
author = {Insinga, L. (Applied Analysis Solutions, LLC) and Holmes, C.M. (Applied Analysis Solutions, LLC) and Hodges, J. (Unilever Safety & Environmental Assurance Centre)},
url = {https://appliedanalysis.solutions/wp-content/uploads/2024/05/SETAC-EU-2024-Marine-Exposure-Model.pdf, View Poster},
year = {2024},
date = {2024-05-05},
urldate = {2024-05-05},
abstract = {Changes in demographics are leading to greater urbanization and higher coastal populations which in turn is increasing the need to better understand chemical loading into coastal and marine environments. Many Home and Personal Care Products (HPCPs) are disposed of down the drain by the consumer, whereupon the ingredients typically are released into freshwater rivers having passed through upstream sewage treatment plants (STPs). Further transport to the marine environment can take place. However, there is a currently paucity of approaches to adequately estimate the extent of this emission route into the environment.
Following on from previous work from our group estimating chemical mass discharged directly into marine environments, here we present an approach which utilizes data on hydrologic travel distance/time for inland STPs combined with estimated aquatic half-lives to estimate emissions from upstream STPs into rivers flowing to coastal areas.
We have developed a spatially explicit fate and transport model to estimate emissions into the marine environment across 88 countries. This global model utilizes the HydroAtlas river dataset and covers 44,000 STPs in 34 countries which discharge into both freshwater and coastal waters directly. Ingredient mass discharged from freshwater STPs was estimated using hydrologic routing from the STP and estimated aquatic half-lives to the coastal discharge location. Discharged mass and percent of mass reaching the coast can then be calculated.
Resulting information can be used to refine emission estimates of ingredients into marine environments based on population demographics, disposal scenarios, and STP hydrologic distance to the coast. Results can also be used to inform more refined marine exposure modelling for risk assessment purposes.},
keywords = {2024, Poster, SETAC Europe},
pubstate = {published},
tppubtype = {presentation}
}
Changes in demographics are leading to greater urbanization and higher coastal populations which in turn is increasing the need to better understand chemical loading into coastal and marine environments. Many Home and Personal Care Products (HPCPs) are disposed of down the drain by the consumer, whereupon the ingredients typically are released into freshwater rivers having passed through upstream sewage treatment plants (STPs). Further transport to the marine environment can take place. However, there is a currently paucity of approaches to adequately estimate the extent of this emission route into the environment.
Following on from previous work from our group estimating chemical mass discharged directly into marine environments, here we present an approach which utilizes data on hydrologic travel distance/time for inland STPs combined with estimated aquatic half-lives to estimate emissions from upstream STPs into rivers flowing to coastal areas.
We have developed a spatially explicit fate and transport model to estimate emissions into the marine environment across 88 countries. This global model utilizes the HydroAtlas river dataset and covers 44,000 STPs in 34 countries which discharge into both freshwater and coastal waters directly. Ingredient mass discharged from freshwater STPs was estimated using hydrologic routing from the STP and estimated aquatic half-lives to the coastal discharge location. Discharged mass and percent of mass reaching the coast can then be calculated.
Resulting information can be used to refine emission estimates of ingredients into marine environments based on population demographics, disposal scenarios, and STP hydrologic distance to the coast. Results can also be used to inform more refined marine exposure modelling for risk assessment purposes.
Following on from previous work from our group estimating chemical mass discharged directly into marine environments, here we present an approach which utilizes data on hydrologic travel distance/time for inland STPs combined with estimated aquatic half-lives to estimate emissions from upstream STPs into rivers flowing to coastal areas.
We have developed a spatially explicit fate and transport model to estimate emissions into the marine environment across 88 countries. This global model utilizes the HydroAtlas river dataset and covers 44,000 STPs in 34 countries which discharge into both freshwater and coastal waters directly. Ingredient mass discharged from freshwater STPs was estimated using hydrologic routing from the STP and estimated aquatic half-lives to the coastal discharge location. Discharged mass and percent of mass reaching the coast can then be calculated.
Resulting information can be used to refine emission estimates of ingredients into marine environments based on population demographics, disposal scenarios, and STP hydrologic distance to the coast. Results can also be used to inform more refined marine exposure modelling for risk assessment purposes.
Holmes, C.M. (Applied Analysis Solutions, LLC); Schad, T. (Bayer CropScience); Bub, S. (Rheinland‐Pfälzische Technische Universität); Wang, M. (WSC Scientific GmbH); Hammel, K. (Bayer AG)
A Spatiotemporally Explicit Modeling Approach for Exposire and Risk Assessment of Off-field Soil Organisms Presentation
05.05.2024.
Abstract | Links | Tags: 2024, Poster, SETAC Europe
@misc{EU24xOffFieldSoil,
title = {A Spatiotemporally Explicit Modeling Approach for Exposire and Risk Assessment of Off-field Soil Organisms},
author = {Holmes, C.M. (Applied Analysis Solutions, LLC) and Schad, T. (Bayer CropScience) and Bub, S. (Rheinland‐Pfälzische Technische Universität) and Wang, M. (WSC Scientific GmbH) and Hammel, K. (Bayer AG)},
url = {https://appliedanalysis.solutions/wp-content/uploads/2024/05/SETAC-EU-2024-xOffFieldSoil.pdf, View Poster
https://github.com/xlandscape/xOffFieldSoilRisk, GitHub Repository},
year = {2024},
date = {2024-05-05},
urldate = {2024-05-05},
abstract = {Natural and semi-natural habitats of soil living organisms in cultivated landscapes can be subject to unintended exposure by active substances of pesticides used in adjacent fields. Spray-drift deposition and runoff are considered major exposure routes into such off-field areas. In this work we develop a model (xOffFieldSoil) and associated scenarios to estimate exposure of off-field soil habitats at flexible levels of realism (https://github.com/xlandscape/xOffFieldSoilRisk). The modular approach consists of components each addressing a specific aspect of exposure processes, e.g., pesticide use, drift deposition, runoff generation and filtering, and PECsoil estimation. The approach is spatiotemporally explicit and operates at scales ranging from local edge-of-field to large landscapes. The outcome can be aggregated and presented as comprehensible endpoints to the risk assessor, considering ecologically relevant dimensions and scales for species protection. The approach also assesses the effect of mitigation options, e.g., field margins, in-field buffers, or drift-reducing technology. Presented scenarios start with a schematic edge-of-field situation and extend to real-world landscapes of up to 5 km x 5 km. A case study was conducted for two active substances of different environmental fate characteristics. Results are presented as a collection of percentiles over time and space, as contour plots and as maps. The results show that exposure patterns of off-field soil organisms are of a complex nature due to spatial and temporal variabilities combined with landscape structure and event-based processes. Our concepts and preliminary analysis demonstrate that more realistic exposure data can be meaningfully consolidated to serve in standard-tier risk assessments. The real-world landscape-scale scenarios indicate risk hot-spots which support the identification of efficient risk mitigation. As a next step, the spatiotemporally explicit exposure data can be directly coupled to ecological effect models (e.g., for earthworms or collembola) to conduct risk assessments at biological entity levels.},
keywords = {2024, Poster, SETAC Europe},
pubstate = {published},
tppubtype = {presentation}
}
Natural and semi-natural habitats of soil living organisms in cultivated landscapes can be subject to unintended exposure by active substances of pesticides used in adjacent fields. Spray-drift deposition and runoff are considered major exposure routes into such off-field areas. In this work we develop a model (xOffFieldSoil) and associated scenarios to estimate exposure of off-field soil habitats at flexible levels of realism (https://github.com/xlandscape/xOffFieldSoilRisk). The modular approach consists of components each addressing a specific aspect of exposure processes, e.g., pesticide use, drift deposition, runoff generation and filtering, and PECsoil estimation. The approach is spatiotemporally explicit and operates at scales ranging from local edge-of-field to large landscapes. The outcome can be aggregated and presented as comprehensible endpoints to the risk assessor, considering ecologically relevant dimensions and scales for species protection. The approach also assesses the effect of mitigation options, e.g., field margins, in-field buffers, or drift-reducing technology. Presented scenarios start with a schematic edge-of-field situation and extend to real-world landscapes of up to 5 km x 5 km. A case study was conducted for two active substances of different environmental fate characteristics. Results are presented as a collection of percentiles over time and space, as contour plots and as maps. The results show that exposure patterns of off-field soil organisms are of a complex nature due to spatial and temporal variabilities combined with landscape structure and event-based processes. Our concepts and preliminary analysis demonstrate that more realistic exposure data can be meaningfully consolidated to serve in standard-tier risk assessments. The real-world landscape-scale scenarios indicate risk hot-spots which support the identification of efficient risk mitigation. As a next step, the spatiotemporally explicit exposure data can be directly coupled to ecological effect models (e.g., for earthworms or collembola) to conduct risk assessments at biological entity levels.