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Welch SA, Grung M, Madsen AL, Jannicke Moe S. Development of a probabilistic risk model for pharmaceuticals in the environment under population and wastewater treatment scenarios. INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT 2024; 20:1715-1735. [PMID: 38771172 DOI: 10.1002/ieam.4939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 04/01/2024] [Accepted: 04/12/2024] [Indexed: 05/22/2024]
Abstract
Preparing for future environmental pressures requires projections of how relevant risks will change over time. Current regulatory models of environmental risk assessment (ERA) of pollutants such as pharmaceuticals could be improved by considering the influence of global change factors (e.g., population growth) and by presenting uncertainty more transparently. In this article, we present the development of a prototype object-oriented Bayesian network (BN) for the prediction of environmental risk for six high-priority pharmaceuticals across 36 scenarios: current and three future population scenarios, combined with infrastructure scenarios, in three Norwegian counties. We compare the risk, characterized by probability distributions of risk quotients (RQs), across scenarios and pharmaceuticals. Our results suggest that RQs would be greatest in rural counties, due to the lower development of current wastewater treatment facilities, but that these areas consequently have the most potential for risk mitigation. This pattern intensifies under higher population growth scenarios. With this prototype, we developed a hierarchical probabilistic model and demonstrated its potential in forecasting the environmental risk of chemical stressors under plausible demographic and management scenarios, contributing to the further development of BNs for ERA. Integr Environ Assess Manag 2024;20:1715-1735. © 2024 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
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Affiliation(s)
- Samuel A Welch
- Norwegian Institute for Water Research (NIVA), Oslo, Norway
| | - Merete Grung
- Norwegian Institute for Water Research (NIVA), Oslo, Norway
| | | | - S Jannicke Moe
- Norwegian Institute for Water Research (NIVA), Oslo, Norway
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2
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Sample BE, Johnson MS, Hull RN, Kapustka L, Landis WG, Murphy CA, Sorensen M, Mann G, Gust KA, Mayfield DB, Ludwigs JD, Munns WR. Key challenges and developments in wildlife ecological risk assessment: Problem formulation. INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT 2024; 20:658-673. [PMID: 36325881 PMCID: PMC10656671 DOI: 10.1002/ieam.4710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 10/05/2022] [Accepted: 10/10/2022] [Indexed: 06/16/2023]
Abstract
Problem formulation (PF) is a critical initial step in planning risk assessments for chemical exposures to wildlife, used either explicitly or implicitly in various jurisdictions to include registration of new pesticides, evaluation of new and existing chemicals released to the environment, and characterization of impact when chemical releases have occurred. Despite improvements in our understanding of the environment, ecology, and biological sciences, few risk assessments have used this information to enhance their value and predictive capabilities. In addition to advances in organism-level mechanisms and methods, there have been substantive developments that focus on population- and systems-level processes. Although most of the advances have been recognized as being state-of-the-science for two decades or more, there is scant evidence that they have been incorporated into wildlife risk assessment or risk assessment in general. In this article, we identify opportunities to consider elevating the relevance of wildlife risk assessments by focusing on elements of the PF stage of risk assessment, especially in the construction of conceptual models and selection of assessment endpoints that target population- and system-level endpoints. Doing so will remain consistent with four established steps of existing guidance: (1) establish clear protection goals early in the process; (2) consider how data collection using new methods will affect decisions, given all possibilities, and develop a decision plan a priori; (3) engage all relevant stakeholders in creating a robust, holistic conceptual model that incorporates plausible stressors that could affect the targets defined in the protection goals; and (4) embrace the need for iteration throughout the PF steps (recognizing that multiple passes may be required before agreeing on a feasible plan for the rest of the risk assessment). Integr Environ Assess Manag 2024;20:658-673. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
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Affiliation(s)
| | - Mark S. Johnson
- US Army Public Health Center, Aberdeen Proving Ground, MD, USA
| | - Ruth N. Hull
- Gary D. Williams & Associates Inc., Campbellville, Ontario, Canada
| | | | | | | | | | - Gary Mann
- Azimuth Consulting Group Inc., Vancouver, British Columbia, Canada
| | - Kurt A. Gust
- Research Development and Engineering Center, Engineer Research and Development Center, US Army Corps of Engineers, MS, Vicksburg, USA
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3
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Oldenkamp R, Benestad RE, Hader JD, Mentzel S, Nathan R, Madsen AL, Jannicke Moe S. Incorporating climate projections in the environmental risk assessment of pesticides in aquatic ecosystems. INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT 2024; 20:384-400. [PMID: 37795750 DOI: 10.1002/ieam.4849] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 09/21/2023] [Accepted: 10/03/2023] [Indexed: 10/06/2023]
Abstract
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 synergize 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 applications. Here, we describe a Bayesian network model that does. It incorporates a two-step univariate regression model based on a 30-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 is reduced 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 increased to 30%-35% when a more conservative precautionary factor of 100 instead of 30 was used. Integr Environ Assess Manag 2024;20:384-400. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
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Affiliation(s)
- Rik Oldenkamp
- Amsterdam Institute for Life and Environment (A-LIFE)-Section Chemistry for Environment and Health, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | | | - John D Hader
- Department of Environmental Science, Stockholm University, Stockholm, Sweden
| | - Sophie Mentzel
- Norwegian Institute for Water Research (NIVA), Oslo, Norway
| | - Rory Nathan
- Department of Infrastructure Engineering, University of Melbourne, Melbourne, Victoria, Australia
| | - Anders L Madsen
- Hugin Expert A/S, Alborg, Denmark
- Department of Computer Science, Aalborg University, Aalborg, Denmark
| | - S Jannicke Moe
- Norwegian Institute for Water Research (NIVA), Oslo, Norway
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4
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Rowland FE, Kotalik CJ, Marcot BG, Hinck JE, Walters DM. A novel approach to assessing natural resource injury with Bayesian networks. INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT 2024; 20:562-573. [PMID: 37664978 DOI: 10.1002/ieam.4836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 08/16/2023] [Accepted: 08/25/2023] [Indexed: 09/05/2023]
Abstract
Quantifying the effects of environmental stressors on natural resources is problematic because of complex interactions among environmental factors that influence endpoints of interest. This complexity, coupled with data limitations, propagates uncertainty that can make it difficult to causally associate specific environmental stressors with injury endpoints. The Natural Resource Damage Assessment and Restoration (NRDAR) regulations under the Comprehensive Environmental Response, Compensation, and Liability Act and Oil Pollution Act aim to restore natural resources injured by oil spills and hazardous substances released into the environment; exploration of alternative statistical methods to evaluate effects could help address NRDAR legal claims. Bayesian networks (BNs) are statistical tools that can be used to estimate the influence and interrelatedness of abiotic and biotic environmental variables on environmental endpoints of interest. We investigated the application of a BN for injury assessment using a hypothetical case study by simulating data of acid mine drainage (AMD) affecting a fictional stream-dwelling bird species. We compared the BN-generated probability estimates for injury with a more traditional approach using toxicity thresholds for water and sediment chemistry. Bayesian networks offered several distinct advantages over traditional approaches, including formalizing the use of expert knowledge, probabilistic estimates of injury using intermediate direct and indirect effects, and the incorporation of a more nuanced and ecologically relevant representation of effects. Given the potential that BNs have for natural resource injury assessment, more research and field-based application are needed to determine their efficacy in NRDAR. We expect the resulting methods will be of interest to many US federal, state, and tribal programs devoted to the evaluation, mitigation, remediation, and/or restoration of natural resources injured by releases or spills of contaminants. Integr Environ Assess Manag 2024;20:562-573. Published 2023. This article is a U.S. Government work and is in the public domain in the USA. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
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Affiliation(s)
- Freya E Rowland
- US Geological Survey, Columbia Environmental Research Center, Columbia, Missouri, USA
| | - Christopher J Kotalik
- US Geological Survey, Columbia Environmental Research Center, Columbia, Missouri, USA
| | - Bruce G Marcot
- US Forest Service, Pacific Northwest Research Station, Portland, Oregon, USA
| | - Jo Ellen Hinck
- US Geological Survey Natural Resource Damage Assessment and Restoration and Disaster Supplemental Science Coordinator, Natural Hazards Mission Area, Reston, Virginia, USA
| | - David M Walters
- US Geological Survey, Columbia Environmental Research Center, Columbia, Missouri, USA
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Moe SJ, Brix KV, Landis WG, Stauber JL, Carriger JF, Hader JD, Kunimitsu T, Mentzel S, Nathan R, Noyes PD, Oldenkamp R, Rohr JR, van den Brink PJ, Verheyen J, Benestad RE. Integrating climate model projections into environmental risk assessment: A probabilistic modeling approach. INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT 2024; 20:367-383. [PMID: 38084033 PMCID: PMC11247537 DOI: 10.1002/ieam.4879] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 11/28/2023] [Accepted: 12/04/2023] [Indexed: 01/11/2024]
Abstract
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).
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Affiliation(s)
- S Jannicke Moe
- Norwegian Institute for Water Research (NIVA), Oslo, Norway
| | - Kevin V Brix
- EcoTox LLC, Miami, Florida, USA
- RSMAES, University of Miami, Miami, Florida, USA
| | - Wayne G Landis
- College of the Environment, Western Washington University, Bellingham, Washington, USA
| | - Jenny L Stauber
- CSIRO Environment, Lucas Heights, Sydney, NSW, Australia
- La Trobe University, Wodonga, Victoria, Australia
| | - John F Carriger
- Center for Environmental Solutions and Emergency Response, Office of Research and Development, USEPA, Land Remediation and Technology Division, Cincinnati, Ohio, USA
| | - John D Hader
- Department of Environmental Science, Stockholm University, Stockholm, Sweden
| | - Taro Kunimitsu
- CICERO Center for International Climate Research, Oslo, Norway
| | - Sophie Mentzel
- Norwegian Institute for Water Research (NIVA), Oslo, Norway
| | - Rory Nathan
- Department of Infrastructure Engineering, University of Melbourne, Melbourne, Victoria, Australia
| | - Pamela D Noyes
- Center for Public Health and Environmental Assessment, Office of Research and Development, USEPA, Integrated Climate Sciences Division, Washington, DC, USA
| | - Rik Oldenkamp
- Chemistry for Environment and Health, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jason R Rohr
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, USA
| | - Paul J van den Brink
- Aquatic Ecology and Water Quality Management Group, Wageningen University, Wageningen, The Netherlands
| | - Julie Verheyen
- Laboratory of Evolutionary Stress Ecology and Ecotoxicology, KU Leuven, Belgium
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Liu J, Xu X, Zou C, Lin N, Zhang K, Shan N, Zhang H, Liu R. A Bayesian network-GIS probabilistic model for addressing human disturbance risk to ecological conservation redline areas. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 344:118400. [PMID: 37331314 DOI: 10.1016/j.jenvman.2023.118400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/16/2023] [Accepted: 06/12/2023] [Indexed: 06/20/2023]
Abstract
Population growth and associated ecological space occupation are posing great risks to regional ecological security and social stability. In China, "Ecological Conservation Redline" (ECR) that prohibited urbanization and industrial construction has been proposed as a national policy to resolve spatial mismatches and management contradictions. However, unfriendly human disturbance activities (e.g., cultivation, mining, and infrastructure construction) still exist within the ECR, posing a great threat to ecological stability and safety. In this article, a Bayesian network (BN)-GIS probabilistic model is proposed to spatially and quantitatively address the human disturbance risk to the ECR at the regional scale. The Bayesian models integrate multiple human activities, ecological receptors of the ECR, and their exposure relationships for calculating the human disturbance risk. The case learning method geographic information systems (GIS) is then introduced to train BN models based on the spatial attribute of variables to evaluate the spatial distribution and correlation of risks. This approach was applied to the human disturbance risk assessment for the ECR that was delineated in 2018 in Jiangsu Province, China. The results indicated that most of the ECRs were at a low or medium human disturbance risk level, while some drinking water sources and forest parks in Lianyungang City possessed the highest risk. The sensitivity analysis result showed the ECR vulnerability, especially for cropland, that contributed most to the human disturbance risk. This spatially probabilistic method can not only enhance model's prediction precision, but also help decision-makers to determine how to establish priorities for policy design and conservation interventions. Overall, it presents a foundation for later ECR adjustments as well as for human disturbance risk supervision and management at the regional scale.
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Affiliation(s)
- Jing Liu
- Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of the People's Republic of China, Nanjing, 210042, China
| | - Xiaojuan Xu
- Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of the People's Republic of China, Nanjing, 210042, China
| | - Changxin Zou
- Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of the People's Republic of China, Nanjing, 210042, China.
| | - Naifeng Lin
- Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of the People's Republic of China, Nanjing, 210042, China
| | - Kun Zhang
- Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of the People's Republic of China, Nanjing, 210042, China.
| | - Nan Shan
- Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of the People's Republic of China, Nanjing, 210042, China
| | - Hanwen Zhang
- Institute of Strategic Planning, Chinese Academy for Environmental Planning, Beijing, 100012, China
| | - Renzhi Liu
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing, 100875, China
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7
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Chu Q, Li Y, Wang X. Bayesian inference of heavy metals exposure in crayfish for assessing human non-carcinogenic health risk. Food Chem Toxicol 2023; 173:113595. [PMID: 36608734 DOI: 10.1016/j.fct.2022.113595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/21/2022] [Accepted: 12/27/2022] [Indexed: 01/05/2023]
Abstract
Contaminant concentrations often presented left censorship that below the limit of detection (LOD), which may contain true zero values because of no residue. In this study, we analyzed the concentrations of lead (Pb), cadmium (Cd), mercury (Hg), arsenic (As), and chromium (Cr) in 391 crayfish samples collected from 24 provinces of China, modelled the concentrations with two different models in a Bayesian framework, and evaluated health risk by estimating the target hazard quotient (THQ). The highest concentration was associated with Cr in Northwest China (0.1753 ± 0.1520 mg/kg w. w.), and the minimum concentration was Cd in Southwest China (0.0052 ± 0.0144 mg/kg w. w.), all heavy metal concentrations were below their safety limits. The posterior means of not detect rates P0 of Pb, Cd, Hg, As, and Cr obtained with two models were both nearly equal to the observed not detect rates (51.15%, 36.83%, 27.37%, 64.71%, and 43.73%, respectively). The posterior probabilistic density lines for Pb, Hg, and Cd concentrations obtained with two models were similar, and fitted the empirical distributions well. The posterior density lines of THQs showed that the non-carcinogenic risk of As and Hg were significant high. Moreover, Bayesian approach presented a better understanding of the percentage of population exposed to potential risk.
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Affiliation(s)
- Qi Chu
- School of Mathematics and Statistics, Beijing Technology and Business University, Beijing, 100048, China
| | - Ying Li
- School of Mathematics and Statistics, Beijing Technology and Business University, Beijing, 100048, China
| | - Xueli Wang
- School of Mathematics and Statistics, Beijing Technology and Business University, Beijing, 100048, China.
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Govender IH, Sahlin U, O'Brien GC. Bayesian Network Applications for Sustainable Holistic Water Resources Management: Modeling Opportunities for South Africa. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2022; 42:1346-1364. [PMID: 34342043 PMCID: PMC9290082 DOI: 10.1111/risa.13798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 07/10/2021] [Accepted: 07/12/2021] [Indexed: 05/11/2023]
Abstract
Anthropogenic transformation of land globally is threatening water resources in terms of quality and availability. Managing water resources to ensure sustainable utilization is important for a semiarid country such as South Africa. Bayesian networks (BNs) are probabilistic graphical models that have been applied globally to a range of water resources management studies; however, there has been very limited application of BNs to similar studies in South Africa. This article explores the benefits and challenges of BN application in the context of water resources management, specifically in relation to South Africa. A brief overview describes BNs, followed by details of some of the possible opportunities for BNs to benefit water resources management. These include the ability to use quantitative and qualitative information, data, and expert knowledge. BN models can be integrated into geographic information systems and predict impact of ecosystem services and sustainability indicators. With additional data and information, BNs can be updated, allowing for integration into an adaptive management process. Challenges in the application of BNs include oversimplification of complex systems, constraints of BNs with categorical nodes for continuous variables, unclear use of expert knowledge, and treatment of uncertainty. BNs have tremendous potential to guide decision making by providing a holistic approach to water resources management.
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Affiliation(s)
| | - Ullrika Sahlin
- Centre for Environmental and Climate Science (CEC)Lund UniversityLundSweden
| | - Gordon C. O'Brien
- School of Biology and Environmental Sciences, Faculty of Agriculture and Natural SciencesUniversity of MpumalangaNelspruitSouth Africa
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Mentzel S, Grung M, Tollefsen KE, Stenrød M, Petersen K, Moe SJ. Development of a Bayesian network for probabilistic risk assessment of pesticides. INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT 2022; 18:1072-1087. [PMID: 34618406 DOI: 10.1002/ieam.4533] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 09/28/2021] [Accepted: 10/01/2021] [Indexed: 06/13/2023]
Abstract
Conventional environmental risk assessment of chemicals is based on a calculated risk quotient, representing the ratio of exposure to effects of the chemical, in combination with assessment factors to account for uncertainty. Probabilistic risk assessment approaches can offer more transparency by using probability distributions for exposure and/or effects to account for variability and uncertainty. In this study, a probabilistic approach using Bayesian network modeling is explored as an alternative to traditional risk calculation. Bayesian networks can serve as meta-models that link information from several sources and offer a transparent way of incorporating the required characterization of uncertainty for environmental risk assessment. To this end, a Bayesian network has been developed and parameterized for the pesticides azoxystrobin, metribuzin, and imidacloprid. We illustrate the development from deterministic (traditional) risk calculation, via intermediate versions, to fully probabilistic risk characterization using azoxystrobin as an example. We also demonstrate the seasonal risk calculation for the three pesticides. Integr Environ Assess Manag 2022;18:1072-1087. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
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Affiliation(s)
| | - Merete Grung
- Norwegian Institute for Water Research, Oslo, Norway
| | - Knut Erik Tollefsen
- Norwegian Institute for Water Research, Oslo, Norway
- Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Marianne Stenrød
- Division for Biotechnology and Plant Health, Norwegian Institute of Bioeconomy Research, Ås, Norway
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10
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Wolf R, Tollefsen KE. A Bayesian Approach to Incorporating Spatiotemporal Variation and Uncertainty Limits into Modeling of Predicted Environmental Concentrations from Chemical Monitoring Campaigns. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:1699-1709. [PMID: 33525880 DOI: 10.1021/acs.est.0c06268] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Environmental monitoring studies provide key information to assess ecosystem health. Results of chemical monitoring campaigns can be used to identify the exposure scenarios of regulatory concern. In environmental risk assessment (ERA), measured concentrations of chemicals can be used to model predicted environmental concentrations (PECs). As the PEC is, by definition, a predicted variable, it is highly dependent on the underlying modeling approach from which it is derived. We demonstrate the use of Bayesian distributional regression models to derive PECs by incorporating spatiotemporal conditional variances, and limits of quantification (LOQ) and detection (LOD) as de facto data censoring. Model accuracies increase when incorporating spatiotemporal conditional variances, and the inclusion of LOQ and LOD results in potentially more robust PEC distributions. The methodology is flexible, credibly quantifies uncertainty, and can be adjusted to different scientific and regulatory needs. Posterior sampling allows to express PECs as distributions, which makes this modeling procedure directly compatible with other Bayesian ERA approaches. We recommend the use of Bayesian modeling approaches with chemical monitoring data to make realistic and robust PEC estimations and encourage the scientific debate about the benefits and challenges of Bayesian methodologies in the context of ERA.
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Affiliation(s)
- Raoul Wolf
- Norwegian Institute for Water Research (NIVA), Gaustadalléen 21, 0349 Oslo, Norway
| | - Knut Erik Tollefsen
- Norwegian Institute for Water Research (NIVA), Gaustadalléen 21, 0349 Oslo, Norway
- Norwegian University of Life Science (NMBU), Universitetstunet 3, 1430 Ås, Norway
- Centre for Environmental Radioactivity (CERAD CoE), Fougnerbakken 1, 1432 Ås, Norway
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11
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Liu J, Liu R, Yang Z, Kuikka S. Quantifying and predicting ecological and human health risks for binary heavy metal pollution accidents at the watershed scale using Bayesian Networks. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 269:116125. [PMID: 33250289 DOI: 10.1016/j.envpol.2020.116125] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 11/02/2020] [Accepted: 11/17/2020] [Indexed: 05/25/2023]
Abstract
The accidental leakage of industrial wastewater containing heavy metals from enterprises poses great risks to resident health, social instability, and ecological safety. During 2005-2018, heavy metal mixed pollution accidents comprised approximately 33% of the major environmental ones in China. A Bayesian Networks-based probabilistic approach is developed to quantitatively predict ecological and human health risks for heavy metal mixed pollution accidents at the watershed scale. To estimate the probability distributions of joint ecological exposure once a heavy metal mixed pollution accident occurs, a Copula-based joint exposure calculation method, comprised of a hydro-dynamic model, emergent heavy metal pollution transport model, and the Copula functions, is embedded. This approach was applied to the risk assessment of acute Cr6+-Hg2+ mixed pollution accidents at 76 electroplating enterprises in 24 risk sub-watersheds of the Dongjiang River downstream watershed. The results indicated that nine sub-watersheds created high ecological risks, while only five created high human health risks. In addition, the ecological and human health risk levels were highest in the tributary (the Xizhijiang River), while the ecological risk was more critical in the river network, and the human health risk was more serious in the mainstream of the Dongjiang River. The quantitative risk assessment provides a substantial support to incident prevention and control, risk management, as well as regulatory decision making for electroplating enterprises.
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Affiliation(s)
- Jing Liu
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing, 100875, China.
| | - Renzhi Liu
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing, 100875, China.
| | - Zhifeng Yang
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing, 100875, China.
| | - Sakari Kuikka
- University of Helsinki, Finland, Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, P.O Box 65, Viikinkaari 1, FI-00014, Helsinki, Finland.
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Kaikkonen L, Parviainen T, Rahikainen M, Uusitalo L, Lehikoinen A. Bayesian Networks in Environmental Risk Assessment: A Review. INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT 2021; 17:62-78. [PMID: 32841493 PMCID: PMC7821106 DOI: 10.1002/ieam.4332] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/23/2020] [Accepted: 08/21/2020] [Indexed: 05/06/2023]
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).
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Affiliation(s)
- Laura Kaikkonen
- Ecosystems and Environment Research ProgrammeUniversity of HelsinkiHelsinkiFinland
- Helsinki Institute of Sustainability ScienceUniversity of HelsinkiHelsinkiFinland
| | - Tuuli Parviainen
- Ecosystems and Environment Research ProgrammeUniversity of HelsinkiHelsinkiFinland
- Helsinki Institute of Sustainability ScienceUniversity of HelsinkiHelsinkiFinland
| | - Mika Rahikainen
- Bioeconomy StatisticsNatural Resource Institute FinlandHelsinkiFinland
| | - Laura Uusitalo
- Programme for Environmental InformationFinnish Environment InstituteHelsinkiFinland
| | - Annukka Lehikoinen
- Ecosystems and Environment Research ProgrammeUniversity of HelsinkiHelsinkiFinland
- Helsinki Institute of Sustainability ScienceUniversity of HelsinkiHelsinkiFinland
- Kotka Maritime Research CentreKotkaFinland
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Sahlin U, Helle I, Perepolkin D. "This Is What We Don't Know": Treating Epistemic Uncertainty in Bayesian Networks for Risk Assessment. INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT 2021; 17:221-232. [PMID: 33151017 PMCID: PMC7839433 DOI: 10.1002/ieam.4367] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 07/22/2020] [Accepted: 11/02/2020] [Indexed: 05/20/2023]
Abstract
Failing to communicate current knowledge limitations, that is, epistemic uncertainty, in environmental risk assessment (ERA) may have severe consequences for decision making. Bayesian networks (BNs) have gained popularity in ERA, primarily because they can combine variables from different models and integrate data and expert judgment. This paper highlights potential gaps in the treatment of uncertainty when using BNs for ERA and proposes a consistent framework (and a set of methods) for treating epistemic uncertainty to help close these gaps. The proposed framework describes the treatment of epistemic uncertainty about the model structure, parameters, expert judgment, data, management scenarios, and the assessment's output. We identify issues related to the differentiation between aleatory and epistemic uncertainty and the importance of communicating both uncertainties associated with the assessment predictions (direct uncertainty) and the strength of knowledge supporting the assessment (indirect uncertainty). Probabilities, intervals, or scenarios are expressions of direct epistemic uncertainty. The type of BN determines the treatment of parameter uncertainty: epistemic, aleatory, or predictive. Epistemic BNs are useful for probabilistic reasoning about states of the world in light of evidence. Aleatory BNs are the most relevant for ERA, but they are not sufficient to treat epistemic uncertainty alone because they do not explicitly express parameter uncertainty. For uncertainty analysis, we recommend embedding an aleatory BN into a model for parameter uncertainty. Bayesian networks do not contain information about uncertainty in the model structure, which requires several models. Statistical models (e.g., hierarchical modeling outside the BNs) are required to consider uncertainties and variability associated with data. We highlight the importance of being open about things one does not know and carefully choosing a method to precisely communicate both direct and indirect uncertainty in ERA. Integr Environ Assess Manag 2021;17:221-232. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
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Affiliation(s)
- Ullrika Sahlin
- Centre for Environmental and Climate ResearchLund UniversitySweden
| | - Inari Helle
- Faculty of Biological and Environmental Sciences and Helsinki Institute of Sustainability Science (HELSUS)University of Helsinki, Finland, and Natural Resources Institute Finland (Luke)Helsinki
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Jannicke Moe S, Carriger JF, Glendell M. Increased Use of Bayesian Network Models Has Improved Environmental Risk Assessments. INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT 2021; 17:53-61. [PMID: 33205856 PMCID: PMC8573810 DOI: 10.1002/ieam.4369] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 11/09/2020] [Accepted: 11/09/2020] [Indexed: 05/04/2023]
Abstract
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).
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Affiliation(s)
- S Jannicke Moe
- Norwegian Institute for Water Research (NIVA), Oslo, Norway
- Address correspondence to
| | - John F Carriger
- United States Environmental Protection Agency, Office of Research and Development, Center for Environmental Solutions and Emergency Response, Land Remediation and Technology Division, Environmental Decision Analytics Branch, Cincinnati, Ohio
| | - Miriam Glendell
- James Hutton Institute, Craigiebuckler, Aberdeen, United Kingdom
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15
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Moe SJ, Wolf R, Xie L, Landis WG, Kotamäki N, Tollefsen KE. Quantification of an Adverse Outcome Pathway Network by Bayesian Regression and Bayesian Network Modeling. INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT 2021; 17:147-164. [PMID: 32965776 PMCID: PMC7820971 DOI: 10.1002/ieam.4348] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Revised: 06/30/2020] [Accepted: 09/22/2020] [Indexed: 05/04/2023]
Abstract
The adverse outcome pathway (AOP) framework has gained international recognition as a systematic approach linking mechanistic processes to toxicity endpoints. Nevertheless, successful implementation into risk assessments is still limited by the lack of quantitative AOP models (qAOPs) and assessment of uncertainties. The few published qAOP models so far are typically based on data-demanding systems biology models. Here, we propose a less data-demanding approach for quantification of AOPs and AOP networks, based on regression modeling and Bayesian networks (BNs). We demonstrate this approach with the proposed AOP #245, "Uncoupling of photophosphorylation leading to reduced ATP production associated growth inhibition," using a small experimental data set from exposure of Lemna minor to the pesticide 3,5-dichlorophenol. The AOP-BN reflects the network structure of AOP #245 containing 2 molecular initiating events (MIEs), 3 key events (KEs), and 1 adverse outcome (AO). First, for each dose-response and response-response (KE) relationship, we quantify the causal relationship by Bayesian regression modeling. The regression models correspond to dose-response functions commonly applied in ecotoxicology. Secondly, we apply the fitted regression models with associated uncertainty to simulate 10 000 response values along the predictor gradient. Thirdly, we use the simulated values to parameterize the conditional probability tables of the BN model. The quantified AOP-BN model can be run in several directions: 1) prognostic inference, run forward from the stressor node to predict the AO level; 2) diagnostic inference, run backward from the AO node; and 3) omnidirectionally, run from the intermediate MIEs and/or KEs. Internal validation shows that the AOP-BN can obtain a high accuracy rate, when run is from intermediate nodes and when a low resolution is acceptable for the AO. Although the performance of this AOP-BN is limited by the small data set, our study demonstrates a proof-of-concept: the combined use of Bayesian regression modeling and Bayesian network modeling for quantifying AOPs. Integr Environ Assess Manag 2021;17:147-164. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
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Affiliation(s)
| | - Raoul Wolf
- Norwegian Institute for Water Research (NIVA)OsloNorway
| | - Li Xie
- Norwegian Institute for Water Research (NIVA)OsloNorway
- Norwegian University of Life Sciences (NMBU), Faculty of Environmental Sciences and Natural Resource Management (MINA), ÅsNorway
- Centre for Environmental Radioactivity, Norwegian University of Life Sciences (NMBU), ÅsNorway
| | - Wayne G Landis
- Institute of Environmental Toxicology, Huxley College of the EnvironmentWestern Washington UniversityBellinghamWashingtonUSA
| | | | - Knut Erik Tollefsen
- Norwegian Institute for Water Research (NIVA)OsloNorway
- Norwegian University of Life Sciences (NMBU), Faculty of Environmental Sciences and Natural Resource Management (MINA), ÅsNorway
- Centre for Environmental Radioactivity, Norwegian University of Life Sciences (NMBU), ÅsNorway
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Li Y, Blazer VS, Iwanowicz LR, Schall MK, Smalling K, Tillitt DE, Wagner T. Ecological risk assessment of environmental stress and bioactive chemicals to riverine fish populations: An individual-based model of smallmouth bass Micropterus dolomieu✰. Ecol Modell 2020. [DOI: 10.1016/j.ecolmodel.2020.109322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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17
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Jiménez-Oyola S, García-Martínez MJ, Ortega MF, Bolonio D, Rodríguez C, Esbrí JM, Llamas JF, Higueras P. Multi-pathway human exposure risk assessment using Bayesian modeling at the historically largest mercury mining district. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2020; 201:110833. [PMID: 32535368 DOI: 10.1016/j.ecoenv.2020.110833] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 05/28/2020] [Accepted: 05/30/2020] [Indexed: 06/11/2023]
Abstract
The largest mercury (Hg) mining district in the world is located in Almadén (Spain), with well-known environmental impacts in the surrounding ecosystem. However, the impact of mercury on the health of the inhabitants of this area has not been documented accordingly. This study aims to carry out a probabilistic human health risk assessment using Bayesian modeling to estimate the non-carcinogenic risk related to Hg through multiple exposure pathways. Samples of vegetables, wild mushrooms, fish, soil, water, and air were analyzed, and adult residents were randomly surveyed to adjust the risk models to the specific population data. On the one hand, the results for the non-carcinogenic risk based on Hazard Quotient (HQ) showed unacceptable risk levels through ingestion of Hg-contaminated vegetables and fish, with HQ values 20 and 3 times higher, respectively, than the safe exposure threshold of 1 for the 97.5th percentile. On the other hand, ingestion of mushrooms, dermal contact with soil, ingestion of water, dermal contact with water and inhalation of air, were below the safety limit for the 97.5th percentile, and did not represent a risk to the health of residents. In addition, the probabilistic approach was compared with the conservative deterministic approach, and similar results were obtained. This is the first study conducted in Almadén, which clearly reveals the high levels of human health risk to which the population is exposed due to the legacy of two millennia of Hg mining.
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Affiliation(s)
- Samantha Jiménez-Oyola
- Department of Energy and Fuels, E.T.S. Ingenieros de Minas y Energía, Universidad Politécnica de Madrid, Ríos Rosas 21, 28003, Madrid, Spain; Escuela Superior Politécnica Del Litoral, ESPOL, Facultad de Ingeniería en Ciencias de la Tierra, Campus Gustavo Galindo, Km 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador
| | - María-Jesús García-Martínez
- Department of Energy and Fuels, E.T.S. Ingenieros de Minas y Energía, Universidad Politécnica de Madrid, Ríos Rosas 21, 28003, Madrid, Spain.
| | - Marcelo F Ortega
- Department of Energy and Fuels, E.T.S. Ingenieros de Minas y Energía, Universidad Politécnica de Madrid, Ríos Rosas 21, 28003, Madrid, Spain
| | - David Bolonio
- Department of Energy and Fuels, E.T.S. Ingenieros de Minas y Energía, Universidad Politécnica de Madrid, Ríos Rosas 21, 28003, Madrid, Spain
| | - Clara Rodríguez
- Department of Energy and Fuels, E.T.S. Ingenieros de Minas y Energía, Universidad Politécnica de Madrid, Ríos Rosas 21, 28003, Madrid, Spain
| | - José-María Esbrí
- Department of Geology and Mining Engineering, Escuela Universitaria Politécnica de Almadén, Universidad de Castilla La Mancha, Plaza Manuel Meca, 13400, Almadén, Ciudad Real, Spain
| | - Juan F Llamas
- Department of Energy and Fuels, E.T.S. Ingenieros de Minas y Energía, Universidad Politécnica de Madrid, Ríos Rosas 21, 28003, Madrid, Spain
| | - Pablo Higueras
- Department of Geology and Mining Engineering, Escuela Universitaria Politécnica de Almadén, Universidad de Castilla La Mancha, Plaza Manuel Meca, 13400, Almadén, Ciudad Real, Spain
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