Dataset Information

Bayesian Networks in Environmental Risk Assessment: A Review.

ABSTRACT: Human activities both depend upon and have consequences on the environment. Environmental risk assessment (ERA) is a process of estimating the probability and consequences of the adverse effects of human activities and other stressors on the environment. Bayesian networks (BNs) can synthesize different types of knowledge and explicitly account for the probabilities of different scenarios, therefore offering a useful tool for ERA. Their use in formal ERA practice has not been evaluated, however, despite their increasing popularity in environmental modeling. This paper reviews the use of BNs in ERA based on peer-reviewed publications. Following a systematic mapping protocol, we identified studies in which BNs have been used in an environmental risk context and evaluated the scope, technical aspects, and use of the models and their results. The review shows that BNs have been applied in ERA, particularly in recent years, and that there is room to develop both the model implementation and participatory modeling practices. Based on this review and the authors' experience, we outline general guidelines and development ideas for using BNs in ERA. Integr Environ Assess Manag 2021;17:62-78. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

SUBMITTER: Kaikkonen L

PROVIDER: S-EPMC7821106 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Bayesian Networks in Environmental Risk Assessment: A Review.

Kaikkonen Laura L Parviainen Tuuli T Rahikainen Mika M Uusitalo Laura L Lehikoinen Annukka A

Integrated environmental assessment and management 20201006 1

Human activities both depend upon and have consequences on the environment. Environmental risk assessment (ERA) is a process of estimating the probability and consequences of the adverse effects of human activities and other stressors on the environment. Bayesian networks (BNs) can synthesize different types of knowledge and explicitly account for the probabilities of different scenarios, therefore offering a useful tool for ERA. Their use in formal ERA practice has not been evaluated, however, ...[more]

PMID: 32841493

Similar Datasets

Project description: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.

Dataset Information

Bayesian Networks in Environmental Risk Assessment: A Review.

Publications

Bayesian Networks in Environmental Risk Assessment: A Review.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets