Integrating climate model projections into environmental risk assessment: A probabilistic modeling approach.

The Society of Environmental Toxicology and Chemistry (SETAC) convened a Pellston workshop in 2022 to examine how information on climate change could be better incorporated into the ecological risk assessment (ERA) process for chemicals as well as other environmental stressors. A major impetus for this workshop is that climate change can affect components of ecological risks in multiple direct and indirect ways, including the use patterns and environmental exposure pathways of chemical stressors such as pesticides, the toxicity of chemicals in receiving environments, and the vulnerability of species of concern related to habitat quality and use. This article explores a modeling approach for integrating climate model projections into the assessment of near- and long-term ecological risks, developed in collaboration with climate scientists. State-of-the-art global climate modeling and downscaling techniques may enable climate projections at scales appropriate for the study area. It is, however, also important to realize the limitations of individual global climate models and make use of climate model ensembles represented by statistical properties. Here, we present a probabilistic modeling approach aiming to combine projected climatic variables as well as the associated uncertainties from climate model ensembles in conjunction with ERA pathways. We draw upon three examples of ERA that utilized Bayesian networks for this purpose and that also represent methodological advancements for better prediction of future risks to ecosystems. We envision that the modeling approach developed from this international collaboration will contribute to better assessment and management of risks from chemical stressors in a changing climate. Integr Environ Assess Manag 2024;20:367-383. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Avoided wildfire impact modeling with counterfactual probabilistic analysis.

Assessing the effectiveness and measuring the performance of fuel treatments and other wildfire risk mitigation efforts are challenging endeavors. Perhaps the most complicated is quantifying avoided impacts. In this study, we show how probabilistic counterfactual analysis can help with performance evaluation. We borrow insights from the disaster risk mitigation and climate event attribution literature to illustrate a counterfactual framework and provide examples using ensemble wildfire simulations. Specifically, we reanalyze previously published fire simulation data from fire-prone landscapes in New Mexico, USA, and show applications for post-event analysis as well as pre-event evaluation of fuel treatment scenarios. This approach found that treated landscapes likely would have reduced fire risk compared to the untreated scenarios. To conclude, we offer ideas for future expansions in theory and methods.

Causal Bayesian networks in assessments of wildfire risks: Opportunities for ecological risk assessment and management.

Wildfire risks and losses have increased over the last 100 years, associated with population expansion, land use and management practices, and global climate change. While there have been extensive efforts at modeling the probability and severity of wildfires, there have been fewer efforts to examine causal linkages from wildfires to impacts on ecological receptors and critical habitats. Bayesian networks are probabilistic tools for graphing and evaluating causal knowledge and uncertainties in complex systems that have seen only limited application to the quantitative assessment of ecological risks and impacts of wildfires. Here, we explore opportunities for using Bayesian networks for assessing wildfire impacts to ecological systems through levels of causal representation and scenario examination. Ultimately, Bayesian networks may facilitate understanding the factors contributing to ecological impacts, and the prediction and assessment of wildfire risks to ecosystems. Integr Environ Assess Manag 2021;17:1168-1178. Published 2021. This article is a U.S. Government work and is in the public domain in the USA.

Increased Use of Bayesian Network Models Has Improved Environmental Risk Assessments.

Environmental and ecological risk assessments are defined as the process for evaluating the likelihood that the environment may be impacted as a result of exposure to stressors. Although this definition implies the calculation of probabilities, risk assessments traditionally rely on nonprobabilistic methods such as calculation of a risk quotient. Bayesian network (BN) models are a tool for probabilistic and causal modeling, increasingly used in many fields of environmental science. Bayesian networks are defined as directed acyclic graphs where the causal relationships and the associated uncertainty are quantified in conditional probability tables. Bayesian networks inherently incorporate uncertainty and can integrate a variety of information types, including expert elicitation. During the last 2 decades, there has been a steady increase in reports on BN applications in environmental risk assessment and management. At recent annual meetings of the Society of Environmental Toxicology and Chemistry (SETAC) North America and SETAC Europe, a number of applications of BN models were presented along with new theoretical developments. Likewise, recent meetings of the European Geosciences Union (EGU) have dedicated sessions to Bayesian modeling in relation to water quality. This special series contains 10 articles based on presentations in these sessions, reflecting a range of BN applications to systems, ranging from cells and populations to watersheds and national scale. The articles report on recent progress in many topics, including climate and management scenarios, ecological and socioeconomic endpoints, machine learning, diagnostic inference, and model evaluation. They demonstrate that BNs can be adapted to established conceptual frameworks used to support environmental risk assessments, such as adverse outcome pathways and the relative risk model. The contributions from EGU demonstrate recent advancements in areas such as spatial (geographic information system [GIS]-based) and temporal (dynamic) BN modeling. In conclusion, this special series supports the prediction that increased use of Bayesian network models will improve environmental risk assessments. Integr Environ Assess Manag 2021;17:53-61. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Assessing Coral Reef Condition Indicators for Local and Global Stressors Using Bayesian Networks.

Coral reefs are highly valued ecosystems currently threatened by both local and global stressors. Given the importance of coral reef ecosystems, a Bayesian network approach can benefit an evaluation of threats to reef condition. To this end, we used data to evaluate the overlap between local stressors (overfishing and destructive fishing, watershed-based pollution, marine-based pollution, and coastal development threats), global stressors (acidification and thermal stress), and management effectiveness with indicators of coral reef health (live coral index, live coral cover, population bleaching, colony bleaching, and recently killed corals). Each of the coral health indicators had Bayesian networks constructed globally and for Pacific, Atlantic, Australia, Middle East, Indian Ocean, and Southeast Asia coral reef locations. Sensitivity analysis helped evaluate the strength of the relationships between different stressors and reef condition indicators. The relationships between indicators and stressors were also evaluated with conditional analyses of linear and nonlinear interactions. In this process, a standardized direct effects analysis was emphasized with a target mean analysis to predict changes in the mean value of the reef indicator from individual changes to the distribution of the predictor variables. The standardized direct effects analysis identified higher risks in the Middle East for watershed-based pollution with population bleaching and in Australia for overfishing and destructive fishing with living coral. For thermal stress, colony bleaching and recently killed coral in the Indian Ocean were found to have the strongest direct associations along with living coral in the Middle East. For acidification threat, Australia had a relatively strong association with colony bleaching, and the Middle East had the strongest overall association with recently killed coral, although extrapolated spatial data were used for the acidification estimates. The Bayesian network approach helped to explore the relationships among existing databases used for policy development in coral reef management by examining the sensitivity of multiple indicators of reef condition to spatially distributed stress. Integr Environ Assess Manag 2021;17:165-187. Published 2020. This article is a US Government work and is in the public domain in the USA.

Conceptual Bayesian networks for contaminated site ecological risk assessment and remediation support.

The causal pathways of stressors that lead to impacts on individuals, populations, and communities of organisms are useful to know for designing alternatives that manage or remediate ecological risks. The ecological risk assessment (ERA) framework (USEPA, 1998b) can help to identify and prioritize management of risks. One key product of the problem formulation step in an ERA, that captures and represents causal knowledge, is the conceptual site model (CSM). The CSM is a graphical depiction of the risk environment that traces the fate and transport pathways of contaminants from sources of contamination (e.g., a leaking storage tank) to receptors (i.e., the ecological endpoints of concern in the risk assessment). The CSM guides the development of methods for assessing ecological risk scenarios and for remediation design alternatives. The qualitative and quantitative aspects of Bayesian networks may support CSM development and risk characterization. Bayesian networks provide a graphical platform geared toward probabilistic modeling making them important candidates for calculating risks in environmental assessments. The diagrammatic representation of causal Bayesian networks (i.e., the directed acyclic graphs) also adds explanatory depth for developing the evidence-base for risk characterization and remediation interventions. We call these qualitative graphs conceptual Bayesian networks (CBNs). The components of CBNs can be used to represent the variables and relationships between sources of contamination, media transfer, bioaccumulation, and risk. The connections help to compose, piece together, and explore hypothesized relationships that bring about high-risk scenarios. Causal pathway analysis of the CBNs provides visualizations of exposure pathways from initial and intermediate sources to receptors. Remediation options that would interrupt or stop the transport of contaminants to ecological receptors can then be identified. Even if the CBN is not quantified, the structures can support mechanistic and statistical designs for exposure and effects analysis and risk characterization and evaluate information needs for resolving uncertainties. This paper will examine these and other unexplored benefits of CBNs to assessment and management of contaminated sites.

A Bayesian network approach to refining ecological risk assessments: Mercury and the Florida panther (Puma concolor coryi).

Traditionally hazard quotients (HQs) have been computed for ecological risk assessment, often without quantifying the underlying uncertainties in the risk estimate. We demonstrate a Bayesian network approach to quantitatively assess uncertainties in HQs using a retrospective case study of dietary mercury (Hg) risks to Florida panthers (Puma concolor coryi). The Bayesian network was parameterized, using exposure data from a previous Monte Carlo-based assessment of Hg risks (Barron et al., 2004. ECOTOX 13:223), as a representative example of the uncertainty and complexity in HQ calculations. Mercury HQs and risks to Florida panthers determined from a Bayesian network analysis were nearly identical to those determined using the prior Monte Carlo probabilistic assessment and demonstrated the ability of the Bayesian network to replicate conventional HQ-based approaches. Sensitivity analysis of the Bayesian network showed greatest influence on risk estimates from daily ingested dose by panthers and mercury levels in prey, and less influence from toxicity reference values. Diagnostic inference was used in a high-risk scenario to demonstrate the capabilities of Bayesian networks for examining probable causes for observed effects. Application of Bayesian networks in the computation of HQs provides a transparent and quantitative analysis of uncertainty in risks.

A Decision Analysis Approach to Electronics Standard Development Informed by Life Cycle Assessment Using Influence Diagrams.

Life cycle assessment (LCA) provides holistic information on systems including the trade-offs between environmental impacts and the drivers of such impacts. Coupling life cycle assessment with a decision analysis (DA) method can help ensure that a life cycle assessment is focused on pertinent decision performance measures. In this paper, a framework integrating life cycle assessment with a decision analysis method to enhance the application of life cycle assessment is presented with a real-world case study of developing a material inclusion criterion for sustainable electronics standards. The proposed DA-LCA framework is a five-step process that tracks the flow of information between the steps of decision analysis and life cycle assessment. The case study considered the level of post-consumer-recycled or biobased content in laptop enclosures. Elicitation with a mock stakeholder panel was used to structure a means-ends network and create a utility-based influence diagram to link changes in material inclusion to environmental objectives using life cycle impact scores. Unlike typical life cycle assessment, the decision analysis approach allows for explicit incorporation of non-environmental factors and better constrains product options. Using this approach, the optimum decision for a possible range of 0-30% material content is 5% or 10%, depending on weighting. The DA-LCA framework can provide a blueprint for placing life cycle assessment results in context for decision-makers.

An introduction to Bayesian networks as assessment and decision support tools for managing coral reef ecosystem services.

Quantifying ecosystem goods and services can help evaluate policies aimed at protecting present and future generations from losing ecosystem benefits. Explicating and quantifying the relationships among risk factors, ecological structure and function, and delivery of ecosystem goods and services requires analytical methodologies that propagate uncertainties. The capabilities of Bayesian networks in generating predictions and accounting for uncertainty are explored with a focus on coral reef ecosystem service assessments. The qualitative aspects of Bayesian networks can be applied to conceptual frameworks developed for coral reef ecosystem service assessments. This is demonstrated using qualitative graphs that describe the relationships between coral reef condition endpoints and benefits from ecosystem services including property protection, recreational opportunities, fish for fisheries, and biochemical metabolites for commercial products developed from reef organisms. Bayesian networks help weigh uncertainties between management decision impacts on stressors and the corresponding delivery of ecosystem services. Quantitative capabilities for inferences are examined in hypothetical scenarios evaluating how decisions affect coral reef ecosystem services and economic benefits and resilience to episodic stress. The described methods facilitate visualizing the potential impacts on ecosystem services from alternative scenarios.

Representing causal knowledge in environmental policy interventions: Advantages and opportunities for qualitative influence diagram applications.

This article develops and explores a methodology for using qualitative influence diagrams in environmental policy and management to support decision-making efforts that minimize risk and increase resiliency. Influence diagrams are representations of the conditional aspects of a problem domain. Their graphical properties are useful for structuring causal knowledge relevant to policy interventions and can be used to enhance inference and inclusivity of multiple viewpoints. Qualitative components of influence diagrams are beneficial tools for identifying and examining the interactions among the critical variables in complex policy development and implementation. Policy interventions on social-environmental systems can be intuitively diagrammed for representing knowledge of critical relationships among economic, environmental, and social attributes. Examples relevant to coastal resiliency issues in the US Gulf Coast region are developed to illustrate model structures for developing qualitative influence diagrams useful for clarifying important policy intervention issues and enhancing transparency in decision making. Integr Environ Assess Manag 2018;14:381-394. Published 2018. This article is a US Government work and is in the public domain in the USA.

Representing causal knowledge in environmental policy interventions: Advantages and opportunities for qualitative influence diagram applications.

This article develops and explores a methodology for using qualitative influence diagrams in environmental policy and management to support decision making efforts that minimize risk and increase resiliency. Influence diagrams are representations of the conditional aspects of a problem domain. Their graphical properties are useful for structuring causal knowledge relevant to policy interventions and can be used to enhance inference and inclusivity of multiple viewpoints. Qualitative components of influence diagrams are beneficial tools for identifying and examining the interactions among the critical variables in complex policy development and implementation. Policy interventions on social-environmental systems can be intuitively diagrammed for representing knowledge of critical relationships among economic, environmental, and social attributes. Examples relevant to coastal resiliency issues in the U.S. Gulf Coast region are developed to illustrate model structures for developing qualitative influence diagrams useful for clarifying important policy intervention issues and enhancing transparency in decision making. This article is protected by copyright. All rights reserved.

IDENTIFYING AND STRUCTURING OBJECTIVES FOR A CORAL REEF PROTECTION PLAN AT THE U.S. ENVIRONMENTAL PROTECTION AGENCY.

Region 2 of the U.S. Environmental Protection Agency initiated a Coral Reef Protection Plan (CRPP) in 2014 to reduce anthropogenic stress on Caribbean coral reefs. The CRPP is intended to foster institutional practices that improve reef condition and focus regulatory and nonregulatory decision making on minimizing pollutant release to coastal systems. A framework incorporating two sets of objectives was constructed to examine the short- and long-term costs and benefits of tasks. The first set of objectives was derived from existing tasks in the CRPP and was intended to support an update of the CRPP for 2015. Fundamental objectives were constructed to communicate the end objectives across tasks and means objectives were constructed to communicate the means for achieving them. The second set of objectives was created to reflect costs and benefits of the CRPP beyond 2015. These objectives contained fundamental objectives comprising broad social, economic, learning and governance topics. The means objectives included tasks such as building capacity, providing regulatory oversight, and learning and reducing uncertainties. The second set of objectives also included strategic objectives that identify long-range benefits such as coral reef integrity and reef ecosystem services. The process of defining objectives helped to ascertain and better elucidate the important consequences for the CRPP. Understanding objectives not only provides a roadmap for coral reef protection but can help Region 2 communicate internally and externally with other agencies, industry, and the public.

Bayesian Networks Improve Causal Environmental Assessments for Evidence-Based Policy.

Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the problem, using this knowledge with probabilistic calculus to combine multiple lines of evidence, and minimizing biases in predicting or diagnosing causal relationships. Too often, sources of uncertainty in conventional weight of evidence approaches are ignored that can be accounted for with Bayesian networks. Specifying and propagating uncertainties improve the ability of models to incorporate strength of the evidence in the risk management phase of an assessment. Probabilistic inference from a Bayesian network allows evaluation of changes in uncertainty for variables from the evidence. The network structure and probabilistic framework of a Bayesian approach provide advantages over qualitative approaches in weight of evidence for capturing the impacts of multiple sources of quantifiable uncertainty on predictions of ecological risk. Bayesian networks can facilitate the development of evidence-based policy under conditions of uncertainty by incorporating analytical inaccuracies or the implications of imperfect information, structuring and communicating causal issues through qualitative directed graph formulations, and quantitatively comparing the causal power of multiple stressors on valued ecological resources. These aspects are demonstrated through hypothetical problem scenarios that explore some major benefits of using Bayesian networks for reasoning and making inferences in evidence-based policy.

A Bayesian network model for predicting aquatic toxicity mode of action using two dimensional theoretical molecular descriptors.

The mode of toxic action (MoA) has been recognized as a key determinant of chemical toxicity, but development of predictive MoA classification models in aquatic toxicology has been limited. We developed a Bayesian network model to classify aquatic toxicity MoA using a recently published dataset containing over one thousand chemicals with MoA assignments for aquatic animal toxicity. Two dimensional theoretical chemical descriptors were generated for each chemical using the Toxicity Estimation Software Tool. The model was developed through augmented Markov blanket discovery from the dataset of 1098 chemicals with the MoA broad classifications as a target node. From cross validation, the overall precision for the model was 80.2%. The best precision was for the AChEI MoA (93.5%) where 257 chemicals out of 275 were correctly classified. Model precision was poorest for the reactivity MoA (48.5%) where 48 out of 99 reactive chemicals were correctly classified. Narcosis represented the largest class within the MoA dataset and had a precision and reliability of 80.0%, reflecting the global precision across all of the MoAs. False negatives for narcosis most often fell into electron transport inhibition, neurotoxicity or reactivity MoAs. False negatives for all other MoAs were most often narcosis. A probabilistic sensitivity analysis was undertaken for each MoA to examine the sensitivity to individual and multiple descriptor findings. The results show that the Markov blanket of a structurally complex dataset can simplify analysis and interpretation by identifying a subset of the key chemical descriptors associated with broad aquatic toxicity MoAs, and by providing a computational chemistry-based network classification model with reasonable prediction accuracy.

Identifying evaluation considerations for the recovery and restoration from the 2010 Gulf of Mexico oil spill: An initial appraisal of stakeholder concerns and values.

Understanding what can be achieved and what should be avoided by environmental management decisions requires an understanding of values, or what is cared about in a decision. Decision analysis provides tools and processes for constructing objectives that transparently reflect the values being considered in environmental management decisions. The present study demonstrates parts of the initial decision analysis steps for identifying a decision context and constructing objectives for the recovery and long-term restoration of the Gulf of Mexico following the 2010 Deepwater Horizon oil spill. From a review of multiple reports, including those developed by policy makers and nongovernmental organizations, a preliminary structuring of concerns and considerations into objectives was derived to highlight features of importance in the recovery from the spill and long-term restoration. The fundamental objectives constructed for the long-term restoration context reflect broader concerns regarding well-being and quality of life. When developed through stakeholder engagement processes, clarifying objectives can potentially 1) lend insight into the values that can be affected, 2) meaningfully include stakeholders in the decision-making process, 3) enhance transparency and communication, and 4) develop high-impact management strategies reflecting broad public interests. This article is a US government work and is in the public domain in the United States of America.

Broader perspective on ecosystem sustainability: consequences for decision making.

Although the concept of ecosystem sustainability has a long-term focus, it is often viewed from a static system perspective. Because most ecosystems are dynamic, we explore sustainability assessments from three additional perspectives: resilient systems; systems where tipping points occur; and systems subject to episodic resetting. Whereas foundations of ecosystem resilience originated in ecology, recent discussions have focused on geophysical attributes, and it is recognized that dynamic system components may not return to their former state following perturbations. Tipping points emerge when chronic changes (typically anthropogenic, but sometimes natural) push ecosystems to thresholds that cause collapse of process and function and may become permanent. Ecosystem resetting occurs when episodic natural disasters breach thresholds with little or no warning, resulting in long-term changes to environmental attributes or ecosystem function. An example of sustainability assessment of ecosystem goods and services along the Gulf Coast (USA) demonstrates the need to include both the resilient and dynamic nature of biogeomorphic components. Mountain road development in northwest Yunnan, China, makes rivers and related habitat vulnerable to tipping points. Ecosystems reset by natural disasters are also presented, emphasizing the need to understand the magnitude frequency and interrelationships among major disturbances, as shown by (i) the 2011 Great East Japan Earthquake and resulting tsunami, including how unsustainable urban development exacerbates geodisaster propagation, and (ii) repeated major earthquakes and associated geomorphic and vegetation disturbances in Papua New Guinea. Although all of these ecosystem perturbations and shifts are individually recognized, they are not embraced in contemporary sustainable decision making.

Influence diagrams as decision-making tools for pesticide risk management.

The pesticide policy arena is filled with discussion of probabilistic approaches to assess ecological risk, however, similar discussions about implementing formal probabilistic methods in pesticide risk decision making are less common. An influence diagram approach is proposed for ecological risk-based decisions about pesticide usage. Aside from technical data, pesticide risk management relies on diverse sources, such as stakeholder opinions, to make decisions about what, how, where, and when to spray. Bayesian influence diagrams allow multiple lines of evidence, including process related information from existing data and expert judgment, in 1 inclusive decision model. In ecological risk assessments, data informally incorporated for pesticide usage decisions, such as field and laboratory effect studies along with chemical monitoring and modeling data, can be formally incorporated and expressed in linked causal diagrams. A case study is presented from the perspective of an environmental manager wishing to efficiently control pests while minimizing risk to local aquatic receptors. Exposure modeling results and toxicity studies were incorporated, and an ecological risk assessment was carried out but combined with hypothetical information on spraying efficacy and valuation of outcomes that would be necessary for making risk management decisions. The variables and their links in the influence diagram are ones that are important to a manager and can be manipulated to optimally control pests while protecting nontarget resources.

Minimizing risks from spilled oil to ecosystem services using influence diagrams: the Deepwater Horizon spill response.

Decision science tools can be used in evaluating response options and making inferences on risks to ecosystem services (ES) from ecological disasters. Influence diagrams (IDs) are probabilistic networks that explicitly represent the decisions related to a problem and their influence on desired or undesired outcomes. To examine how IDs might be useful in probabilistic risk management for spill response efforts, an ID was constructed to display the potential interactions between exposure events and the trade-offs between costs and ES impacts from spilled oil and response decisions in the DWH spill event. Quantitative knowledge was not formally incorporated but an ID platform for doing this was examined. Probabilities were assigned for conditional relationships in the ID and scenarios examining the impact of different response actions on components of spilled oil were investigated in hypothetical scenarios. Given the structure of the ID, potential knowledge gaps included understanding of the movement of oil, the ecological risk of different spill-related stressors to key receptors (e.g., endangered species, fisheries), and the need for stakeholder valuation of the ES benefits that could be impacted by a spill. Framing the Deepwater Horizon problem domain in an ID conceptualized important variables and relationships that could be optimally accounted for in preparing and managing responses in future spills. These features of the developed IDs may assist in better investigating the uncertainty, costs, and the trade-offs if large-scale, deep ocean spills were to occur again.

Acute toxicity and effects analysis of endosulfan sulfate to freshwater fish species.

Endosulfan sulfate is a persistent environmental metabolite of endosulfan, an organochlorine insecticide-acaricide presently registered by the United States Environmental Protection Agency. There is, however, limited acute fish toxicity data for endosulfan sulfate. This study determines the acute toxicity (LC50s and LC10s) of endosulfan sulfate to three inland Florida native fish species (mosquitofish [Gambusia affinis]; least killifish [Heterandria formosa]; and sailfin mollies [Poecilia latipinna]) as well as fathead minnows (Pimephales promelas). Ninety-six-h acute toxicity tests were conducted with each fish species under flow-through conditions. For all of the above-mentioned fish species, 96-h LC50 estimates ranged from 2.1 to 3.5 µg/L endosulfan sulfate. The 96-h LC10 estimates ranged from 0.8 to 2.1 µg/L endosulfan sulfate. Of all of the fish tested, the least killifish appeared to be the most sensitive to endosulfan sulfate exposure. The above-mentioned data were combined with previous acute toxicity data for endosulfan sulfate and freshwater fish for an effects analysis. The effects analysis estimated hazardous concentrations expected to exceed 5, 10, and 50% of the fish species' acute LC50 or LC10 values (HC5, HC10, and HC50). The endosulfan sulfate freshwater-fish acute tests were also compared with the available freshwater-fish acute toxicity data for technical endosulfan. Technical endosulfan is a mixture of α- and ß-endosulfan. The LC50s had a wider range for technical endosulfan, and their distribution produced a lower HC10 than for endosulfan sulfate. The number of freshwater-fish LC50s for endosulfan sulfate is much smaller than the number available for technical endosulfan, reflecting priorities in examining the toxicity of the parent compounds of pesticides. The toxicity test results and effects analyses provided acute effect values for endosulfan sulfate and freshwater fish that might be applied in future screening level ecologic risk assessments. The effects analyses also discussed several deficiencies in conventional methods for setting water-quality criteria and determining ecologic effects from acute toxicity tests.

Endosulfan and its metabolite, endosulfan sulfate, in freshwater ecosystems of South Florida: a probabilistic aquatic ecological risk assessment.

Endosulfan is an insecticide-acaricide used in South Florida and is one of the remaining organochlorine insecticides registered under the Federal Insecticide Fungicide and Rodenticide Act by the U.S.EPA. The technical grade material consists of two isomers (alpha-, beta-) and the main environmental metabolite in water, sediment and tissue is endosulfan sulfate through oxidation. A comprehensive probabilistic aquatic ecological risk assessment was conducted to determine the potential risks of existing exposures to endosulfan and endosulfan sulfate in freshwaters of South Florida based on historical data (1992-2007). The assessment included hazard assessment (Tier 1) followed by probabilistic risk assessment (Tier 2). Tier 1 compared actual measured concentrations in surface freshwaters of 47 sites in South Florida from historical data to U.S.EPA numerical water quality criteria. Based on results of Tier 1, Tier 2 focused on the acute and chronic risks of endosulfan at nine sites by comparing distributions of surface water exposure concentrations of endosulfan [i.e., for total endosulfan (summation of concentrations of alpha- and beta-isomers plus the sulfate), alpha- plus beta-endosulfan, and endosulfan sulfate (alone)] with distributions of species effects from laboratory toxicity data. In Tier 2 the distribution of total endosulfan in fish tissue (whole body) from South Florida freshwaters was also used to determine the probability of exceeding a distribution of whole body residues of endosulfan producing mortality (critical lethal residues). Tier 1 showed the majority of endosulfan water quality violations in South Florida were at locations S-178 followed by S-177 in the C-111 system (southeastern boundary of Everglades National Park (ENP)). Nine surface water sampling sites were chosen for Tier 2. Tier 2 showed the highest potentially affected fraction of toxicity values (>10%) by the estimated 90th centile exposure concentration (total endosulfan) was at S-178. At all other freshwater sites there were <5% of the toxicity values exceeded. Potential chronic risk (9.2% for total endosulfan) was only found at S-178 and all other sites were <5%. Joint probability curves showed the higher probability of risk at S-178 than at S-177. The freshwater fish species which contain tissue concentrations of endosulfan (total) with the highest potential risk for lethal whole body tissue residues were marsh killifish, flagfish and mosquitofish. Based on existing surface water exposures and available aquatic toxicity data, there are potential risks of total endosulfan to freshwater organisms in South Florida. Although there are uncertainties, the presence of tissue concentrations of endosulfan in small demersal fish, is of ecological significance since these fish support higher trophic level species, such as wading birds.