Jayme Coyle, Bradley Barnhart, Raymond Harbison, Kan Shao, A Wallace Hayes, Giffe Johnson
{"title":"概率风险评估方法更好地保护易感人群。","authors":"Jayme Coyle, Bradley Barnhart, Raymond Harbison, Kan Shao, A Wallace Hayes, Giffe Johnson","doi":"10.1093/inteam/vjaf101","DOIUrl":null,"url":null,"abstract":"<p><p>Across all of its program areas, the United States Environmental Protection Agency conducts risk assessments to evaluate the potential adverse effects of environmental hazards on ecological and human health. Traditionally, these assessments rely on deterministic methods that use point estimates for key parameters and incorporate uncertainty factors and precautionary assumptions to account for uncertainties in data and variability in environmental conditions, exposure pathways, and population characteristics. However, these approaches are unnecessarily conservative for the general population yet fail to transparently account for the vulnerabilities of susceptible populations. Probabilistic risk assessment (PRA) offers a more refined approach that utilizes distributional data to better characterize uncertainty and response. By leveraging empirical data and probabilistic modeling, PRA allows for a more transparent, precise quantification of risk that ensures targeted protection for susceptible populations. This article examines how PRA enhances each phase of risk assessment-hazard identification, dose-response assessment, exposure assessment, and risk characterization-to produce a more scientifically robust assessment of risk for susceptible populations, including both human and ecological receptors. We review the literature and discuss practical applications of PRA in regulatory contexts to illustrate its advantages over deterministic approaches. Finally, we discuss key implementation challenges and propose future research directions to advance risk assessment methodologies and improve policy decision-making.</p>","PeriodicalId":13557,"journal":{"name":"Integrated Environmental Assessment and Management","volume":" ","pages":""},"PeriodicalIF":8.4000,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Probabilistic Risk Assessment Approaches Better Protect Susceptible Populations.\",\"authors\":\"Jayme Coyle, Bradley Barnhart, Raymond Harbison, Kan Shao, A Wallace Hayes, Giffe Johnson\",\"doi\":\"10.1093/inteam/vjaf101\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Across all of its program areas, the United States Environmental Protection Agency conducts risk assessments to evaluate the potential adverse effects of environmental hazards on ecological and human health. Traditionally, these assessments rely on deterministic methods that use point estimates for key parameters and incorporate uncertainty factors and precautionary assumptions to account for uncertainties in data and variability in environmental conditions, exposure pathways, and population characteristics. However, these approaches are unnecessarily conservative for the general population yet fail to transparently account for the vulnerabilities of susceptible populations. Probabilistic risk assessment (PRA) offers a more refined approach that utilizes distributional data to better characterize uncertainty and response. By leveraging empirical data and probabilistic modeling, PRA allows for a more transparent, precise quantification of risk that ensures targeted protection for susceptible populations. This article examines how PRA enhances each phase of risk assessment-hazard identification, dose-response assessment, exposure assessment, and risk characterization-to produce a more scientifically robust assessment of risk for susceptible populations, including both human and ecological receptors. We review the literature and discuss practical applications of PRA in regulatory contexts to illustrate its advantages over deterministic approaches. Finally, we discuss key implementation challenges and propose future research directions to advance risk assessment methodologies and improve policy decision-making.</p>\",\"PeriodicalId\":13557,\"journal\":{\"name\":\"Integrated Environmental Assessment and Management\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":8.4000,\"publicationDate\":\"2025-07-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Integrated Environmental Assessment and Management\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1093/inteam/vjaf101\",\"RegionNum\":4,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Integrated Environmental Assessment and Management","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1093/inteam/vjaf101","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Across all of its program areas, the United States Environmental Protection Agency conducts risk assessments to evaluate the potential adverse effects of environmental hazards on ecological and human health. Traditionally, these assessments rely on deterministic methods that use point estimates for key parameters and incorporate uncertainty factors and precautionary assumptions to account for uncertainties in data and variability in environmental conditions, exposure pathways, and population characteristics. However, these approaches are unnecessarily conservative for the general population yet fail to transparently account for the vulnerabilities of susceptible populations. Probabilistic risk assessment (PRA) offers a more refined approach that utilizes distributional data to better characterize uncertainty and response. By leveraging empirical data and probabilistic modeling, PRA allows for a more transparent, precise quantification of risk that ensures targeted protection for susceptible populations. This article examines how PRA enhances each phase of risk assessment-hazard identification, dose-response assessment, exposure assessment, and risk characterization-to produce a more scientifically robust assessment of risk for susceptible populations, including both human and ecological receptors. We review the literature and discuss practical applications of PRA in regulatory contexts to illustrate its advantages over deterministic approaches. Finally, we discuss key implementation challenges and propose future research directions to advance risk assessment methodologies and improve policy decision-making.
期刊介绍:
Integrated Environmental Assessment and Management (IEAM) publishes the science underpinning environmental decision making and problem solving. Papers submitted to IEAM must link science and technical innovations to vexing regional or global environmental issues in one or more of the following core areas:
Science-informed regulation, policy, and decision making
Health and ecological risk and impact assessment
Restoration and management of damaged ecosystems
Sustaining ecosystems
Managing large-scale environmental change
Papers published in these broad fields of study are connected by an array of interdisciplinary engineering, management, and scientific themes, which collectively reflect the interconnectedness of the scientific, social, and environmental challenges facing our modern global society:
Methods for environmental quality assessment; forecasting across a number of ecosystem uses and challenges (systems-based, cost-benefit, ecosystem services, etc.); measuring or predicting ecosystem change and adaptation
Approaches that connect policy and management tools; harmonize national and international environmental regulation; merge human well-being with ecological management; develop and sustain the function of ecosystems; conceptualize, model and apply concepts of spatial and regional sustainability
Assessment and management frameworks that incorporate conservation, life cycle, restoration, and sustainability; considerations for climate-induced adaptation, change and consequences, and vulnerability
Environmental management applications using risk-based approaches; considerations for protecting and fostering biodiversity, as well as enhancement or protection of ecosystem services and resiliency.