Development of a Bayesian network for probabilistic risk assessment of pesticides

Mentzel, Sophie; Grung, Merete; Tollefsen, Knut Erik; Stenrød, Marianne; Petersen, Karen Koefoed; Moe, S. Jannicke

doi:10.1002/ieam.4533

Cited by 14 publications

(27 citation statements)

References 50 publications

(89 reference statements)

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“…As monitoring of environmental pesticide concentration is costly and time-consuming, future climate conditions need to be incorporated better in risk assessment. The complexity and lack of well characterized processes in pesticide risk assessment can be overcome by taking advantage of the BNs’ ability to use data from various different sources, which is one of their benefits (Hamilton and Pollino, 2012, Troldborg et al, 2021, Mentzel et al, 2021, Gibert et al, 2018). Moreover, they can be constructed as causal models that help comprehend hazard pathways and vulnerability relations better and with that assist in risk prioritization (Sperotto et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…The NOEC distribution is based on NOEC and NOEL values, apart from Clopyralid for which only NOEC toxicity data was available. If multiple values for the same species occurred in the data set, the mean was used as a data point to derive the distribution (Mentzel et al, 2021). The number of observations for this study varied depending on the chemical, and whether it was an EC50 or NOEC toxicity test.…”

Section: Pesticide Effectsmentioning

confidence: 99%

“…Nevertheless, some of the results are difficult to communicate and thereby challenging for decision-makers to interpret and understand (Verdonck, 2003, FOCUS, 2007), possibly because they are often based on cumulative distribution curves (EUFRAM, 2006). We propose the use of Bayesian networks (BN) as a more user-friendly and intuitive method for probabilistic risk assessment of pesticides (Mentzel et al 2021). To demonstrate this, we developed a BN model to assess environmental risk of pesticides under future scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…Probabilistic risk assessment make use of probability distributions to characterize uncertainty in all parts of the risk characterization (Mentzel et al, 2021, EUFRAM, 2006.…”

Section: Introductionmentioning

confidence: 99%

“…Probabilistic risk assessment make use of probability distributions to characterize uncertainty in all parts of the risk characterization (Mentzel et al, 2021, EUFRAM, 2006). Ergo, fully probabilistic risk characterization can better account for spatial and temporal variability of both chemical concentrations and species sensitivity (EUFRAM, 2006, Verdonck, 2003, Fairbrother et al, 2015, Solomon et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic risk assessment of pesticides under future agricultural and climate scenarios using a Bayesian network

Mentzel

Grung

Holten

et al. 2022

Preprint

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The use of Bayesian networks (BN) for environmental risk assessment has increased in recent years. One reason is that they offer a more transparent way to characterize risk and evaluate uncertainty than the traditional risk assessment paradigms. In this study, we explore a new approach to probabilistic risk assessment by developing and applying a BN as a meta-model for a Norwegian agricultural site. The model uses predictions from a process-based pesticide exposure model (World Integrated System for Pesticide Exposure - WISPE) in the exposure characterization and species sensitivity data from toxicity tests in the effect characterization. The probability distributions for exposure and effect are then combined into a risk characterization (i.e. the probability distribution of a risk quotient), which is a common measure of the exceedance of an environmentally safe exposure threshold. In this way, we aim to use the BN model to better account for variabilities of both pesticide exposure and effects to the aquatic environment than traditional risk assessment. Furthermore, the BN is able to link different types of future scenarios to the exposure assessment, taking into account both effects of climate change on pesticides fate and transport, and changes in pesticide application. We used climate projections from IPCC scenario A1B and two global circulation models (ECHAM5-r3 and HADCM3-Q0), which projected daily values of temperature and precipitation for Northern Europe until 2100. In Northern Europe, increased temperature and precipitation is expected to cause an increase in weed infestations, plant disease and insect pests, which in turn can result in altered agricultural practices, such as the use of new crop types and changes in pesticide application patterns. We used the WISPE model to link climate and pesticide application scenarios, environmental factors such as soil properties and field slope together with chemical properties (e.g. half-life in soil, water solubility, soil adsorption), to predict the pesticide exposure in streams adjacent to the agricultural fields. The model was parameterized and evaluated for five selected pesticides: the herbicides clopyralid, fluroxypyr-meptyl, and 2-(4-chloro-2-methylphenoxy) acetic acid (MCPA), and the fungicides prothiocanzole and trifloxystrobin. This approach enabled the estimation and visualization of probability distribution of the risk quotients representing the alternative climate models and application scenarios for the future time horizons 2050 and 2075. The currently used climate projections resulted in only minor changes in future risk directly through the meteorological variables. A stronger increase in risk was predicted for the scenarios with increased pesticide application, which in turn can represent an adaptation to a future climate with higher pest pressures. Further advancement of BN modelling as demonstrated herein is anticipated to aid targeted management of ecological risks in support of future research, industry and regulatory needs.

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Section: Discussionmentioning

confidence: 99%

Section: Pesticide Effectsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Probabilistic risk assessment make use of probability distributions to characterize uncertainty in all parts of the risk characterization (Mentzel et al, 2021, EUFRAM, 2006.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Probabilistic risk assessment of pesticides under future agricultural and climate scenarios using a Bayesian network

Mentzel

Grung

Holten

et al. 2022

Preprint

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Using a Bayesian Network Model to Predict Risk of Pesticides on Aquatic Community Endpoints in a Rice Field—A Southern European Case Study

Mentzel,

Martínez‐Megías,

Grung

et al. 2023

Enviro Toxic and Chemistry

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Bayesian network (BN) models are increasingly used as tools to support probabilistic environmental risk assessments (ERA), as they can better account for uncertainty compared to the simpler approaches commonly used in traditional ERA. We used BNs as meta‐models to link various sources of information in a probabilistic framework, to predict the risk of pesticides to aquatic communities under given scenarios. The research focused on rice fields surrounding a Spanish Natural Park Albufera, considering three selected pesticides: acetamiprid (insecticide), MCPA (herbicide), and azoxystrobin (fungicide). The developed BN linked the inputs and outputs of two pesticide models: a process‐based exposure model (RICEWQ), and probabilistic effects model (PERPEST) using case‐based reasoning with data from microcosm and mesocosm experiments. The model characterised risk at three levels in a hierarchy: biological endpoints (e.g., molluscs, zooplankton, insects, etc.), endpoint groups (plants, invertebrates, vertebrates, and community processes), and community. The pesticide risk to a biological endpoint was characterised as the probability of an effect for a given pesticide concentration interval. The risk to an endpoint group was calculated as the joint probability of effect on any of the endpoints in the group. Likewise, community‐level risk was calculated as the joint probability of any of the endpoint groups being affected. This approach enabled comparison of risk to endpoint groups across different pesticide types. For example, in a scenario for year 2050, the predicted risk of the insecticide to the community (40% probability of effect) was dominated by the risk to invertebrates (36% risk). In contrast, herbicide‐related risk to the community (63%) was resulting from risk to both plants (35%) and invertebrates (38%); the latter might here represent indirect effects of toxicity through the food chain. This novel approach combines the quantification of spatial variability of exposure with probabilistic risk prediction for different components of aquatic ecosystems.

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Incorporating climate projections in the environmental risk assessment of pesticides in aquatic ecosystems

Oldenkamp,

Benestad,

Hader

et al. 2023

Integr Envir Assess & Manag

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Global climate change will significantly impact the biodiversity of freshwater ecosystems, both directly and indirectly via the exacerbation of impacts from other stressors. Pesticides form a prime example of chemical stressors that are expected to synergise with climate change. Aquatic exposures to pesticides might change in magnitude due to increased runoff from agricultural fields, and in composition as application patterns will change due to changes in pest pressures and crop types. Any prospective chemical risk assessment that aims to capture the influence of climate change should properly and comprehensively account for the variabilities and uncertainties that are inherent to projections of future climate. This is only feasible if they probabilistically propagate extensive ensembles of climate model projections. However, current prospective risk assessments typically make use of process‐based models of chemical fate that do not typically allow for such high‐throughput application. Here we describe a Bayesian network model that does. It incorporates a two‐step univariate regression model based on a thirty‐day antecedent precipitation index, circumventing the need for computationally laborious mechanistic models. We show its feasibility and application potential in a case study with two pesticides in a Norwegian stream: the fungicide trifloxystrobin and herbicide clopyralid. Our analysis showed that variations in pesticide application rates as well as precipitation intensity lead to variations in in‐stream exposures. When relating to aquatic risks, the influence of these processes reduces and distributions of risk are dominated by effect‐related parameters. Predicted risks for clopyralid were negligible, but the probability of unacceptable future environmental risks due to exposure to trifloxystrobin (i.e., a risk quotient > 1) was 8‐12%. This percentage further increases to 30‐35% when a more conservative precautionary factor of 100 instead of 30 was used.

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Development of a Bayesian network for probabilistic risk assessment of pesticides

Cited by 14 publications

References 50 publications

Probabilistic risk assessment of pesticides under future agricultural and climate scenarios using a Bayesian network

Probabilistic risk assessment of pesticides under future agricultural and climate scenarios using a Bayesian network

Using a Bayesian Network Model to Predict Risk of Pesticides on Aquatic Community Endpoints in a Rice Field—A Southern European Case Study

Incorporating climate projections in the environmental risk assessment of pesticides in aquatic ecosystems

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