Uncertainties in human health risk assessment of environmental contaminants: A review and perspective

Addressing uncertainties in human health risk assessment is a critical issue when evaluating the effects of contaminants on public health. A range of uncertainties exist through the source-to-outcome continuum, including exposure assessment, hazard and risk characterisation. While various strategies have been applied to characterising uncertainty, classical approaches largely rely on how to maximise the available resources. Expert judgement, defaults and tools for characterising quantitative uncertainty attempt to fill the gap between data and regulation requirements. The experiences of researching 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) illustrated uncertainty sources and how to maximise available information to determine uncertainties, and thereby provide an ‘adequate’ protection to contaminant exposure. As regulatory requirements and recurring issues increase, the assessment of complex scenarios involving a large number of chemicals requires more sophisticated tools. Recent advances in exposure and toxicology science provide a large data set for environmental contaminants and public health. In particular, biomonitoring information, in vitro data streams and computational toxicology are the crucial factors in the NexGen risk assessment, as well as uncertainties minimisation. Although in this review we cannot yet predict how the exposure science and modern toxicology will develop in the long-term, current techniques from emerging science can be integrated to improve decision-making.     

Addressing uncertainties in the ERICA Integrated Approach

Like any complex environmental problem, ecological risk assessment of the impacts of ionising radiation is confounded by uncertainty. At all stages, from problem formulation through to risk characterisation, the assessment is dependent on models, scenarios, assumptions and extrapolations. These include technical uncertainties related to the data used, conceptual uncertainties associated with models and scenarios, as well as social uncertainties such as economic impacts, the interpretation of legislation, and the acceptability of the assessment results to stakeholders. The ERICA Integrated Approach has been developed to allow an assessment of the risks of ionising radiation, and includes a number of methods that are intended to make the uncertainties and assumptions inherent in the assessment more transparent to users and stakeholders. Throughout its development, ERICA has recommended that assessors deal openly with the deeper dimensions of uncertainty and acknowledge that uncertainty is intrinsic to complex systems. Since the tool is based on a tiered approach, the approaches to dealing with uncertainty vary between the tiers, ranging from a simple, but highly conservative screening to a full probabilistic risk assessment including sensitivity analysis. This paper gives on overview of types of uncertainty that are manifest in ecological risk assessment and the ERICA Integrated Approach to dealing with some of these uncertainties.     

Precaution,uncertainty and causation in environmental decisions

What measures of uncertainty and what causal analysis can improve the management of potentially severe, irreversible or dreaded environmental outcomes? Environmental choices show that policies intended to be precautionary (such as adding MTBE to petrol) can cause unanticipated harm (by mobilizing benzene, a known leukemogen, in the ground water). Many environmental law principles set the boundaries of what should be done but do not provide an operational construct to answer this question. Those principles, ranging from the precautionary principle to protecting human health from a significant risk of material health impairment, do not explain how to make environmental management choices when incomplete, inconsistent and complex scientific evidence characterizes potentially adverse environmental outcomes. Rather, they pass the task to lower jurisdictions such as agencies or authorities. To achieve the goals of the principle, those who draft it must deal with scientific casual conjectures, partial knowledge and variable data. In this paper we specifically deal with the qualitative and quantitative aspects of the European Union's (EU) explanation of consistency and on the examination of scientific developments relevant to variability and uncertain data and causation. Managing hazards under the precautionary principle requires inductive, empirical methods of assessment. However, acting on a scientific conjecture can also be socially unfair, costly, and detrimental when applied to complex environmental choices. We describe a constructive framework rationally to meet the command of the precautionary principle using alternative measures of uncertainty and recent statistical methods of causal analysis. These measures and methods can bridge the gap between conjectured future irreversible or severe harm and scant scientific evidence, thus leading to more confident and resilient social choices. We review two sets of measures and computational systems to deal with uncertainty and link them to causation through inductive empirical methods such as Bayesian Networks. We conclude that primary legislation concerned with large uncertainties and potential severe or dreaded environmental outcomes can produce accurate and efficient choices. To do so, primary legislation should specifically indicate what measures can represent uncertainty and how to deal with uncertain causation thus providing guidance to an agency's rulemaking or to an authority's writing secondary legislation. A corollary conclusion with legal, scientific and probabilistic implications concerns how to update past information when the state of information increases because a failure to update can result in regretting past choices. Elected legislators have the democratic mandate to formulate precautionary principles and are accountable. To preserve that mandate, imbedding formal methods to represent uncertainty in the statutory language of the precautionary principle enhances subsequent judicial review of legislative actions. The framework that we propose also reduces the Balkanized views and interpretations of probabilities, possibilities, likelihood and uncertainty that exists in environmental decision-making.     

Identifying key issues in environmental wetland research using scaling and uncertainty analysis

Quantification of the response of wetlands to environmental change and of climate change to wetland processes, is a pressing, but complex, issue. Findings of widely diverse investigations have to be generalised to identify the key issues and the gaps in knowledge. Two tools for generalisation, scaling and uncertainty analysis, were applied here to two case studies and act as stepping stones for analysis of key issues. The first case study is on methane emissions from wetland rice fields and the second is on the restoration of wet dune slacks. The type of problems encountered depends on the scale at which a process is studied. At the biogeochemical processes scale, knowledge on some key parameters, but especially on interactions between key parameters is limiting. At the vegetation scale, feedbacks between processes become especially important, whereas at even larger scales increasingly stringent approximations of interactions have to be made. Accumulating uncertainties at the landscape scale need careful evaluation. Apart from uncertainties in the approximations and their interactions, those introduced by spatial and temporal variability as well as by different data sources highly influence the accuracy of response estimates, while these last sources of uncertainty are neglected in many studies. Only by explicitly accounting for scaling effects and their resulting uncertainties, the interactions between wetlands and environment can be understood.     

Development of quantitative estimates of uncertainty in environmental risk assessments when the scientific data base is inadequate

The reasons for developing quantitative estimates of uncertainty in environmental risk assessments are discussed along with a method for developing them which involves scientific judgement. In the situation considered here the regulatory needs are ahead of the science, which makes the development of the estimates on uncertainty more difficult, but not impossible. Quantitative estimates for all uncertainties involved in the estimation of risk resulting from exposure to volatile organic compounds (VOCs) in drinking water are developed and tabulated. By far the largest contribution to the uncertainty in the risk estimates for VOCs in drinking water are due to uncertainty in the extrapolation of the dose-response curve to low levels. The uncertainty due to extrapolation is on the order of 10⁴ and 10⁶. Other components of the analysis may contribute uncertainties of a few orders of magnitude. In general the largest uncertainties are in the toxicological data base and the manipulation of it needed to estimate risk. The data base and manipulations needed to estimate exposure due to VOCs in drinking water were at the more an order to magnitudes in uncertainty.     

Extension of sensitivity and uncertainty analysis for long term dose assessment of high level nuclear waste disposal sites to uncertainties in the human behaviour

Biosphere dose conversion factors are computed for the French high-level geological waste disposal concept and to illustrate the combined probabilistic and deterministic approach. Both ¹³⁵Cs and ⁷⁹Se are used as examples. Probabilistic analyses of the system considering all parameters, as well as physical and societal parameters independently, allow quantification of their mutual impact on overall uncertainty. As physical parameter uncertainties decreased, for example with the availability of further experimental and field data, the societal uncertainties, which are less easily constrained, particularly for the long term, become more and more significant. One also has to distinguish uncertainties impacting the low dose portion of a distribution from those impacting the high dose range, the latter having logically a greater impact in an assessment situation. The use of cumulative probability curves allows us to quantify probability variations as a function of the dose estimate, with the ratio of the probability variation (slope of the curve) indicative of uncertainties of different radionuclides. In the case of ¹³⁵Cs with better constrained physical parameters, the uncertainty in human behaviour is more significant, even in the high dose range, where they increase the probability of higher doses. For both radionuclides, uncertainties impact more strongly in the intermediate than in the high dose range. In an assessment context, the focus will be on probabilities of higher dose values. The probabilistic approach can furthermore be used to construct critical groups based on a predefined probability level and to ensure that critical groups cover the expected range of uncertainty.     

Uncertainty analysis in integrated assessment: the users’ perspective

Integrated Assessment (IA) models aim at providing information- and decision-support to complex problems. This paper argues that uncertainty analysis in IA models should be user-driven in order to strengthen science–policy interaction. We suggest an approach to uncertainty analysis that starts with investigating model users’ demands for uncertainty information. These demands are called “uncertainty information needs”. Identifying model users’ uncertainty information needs allows focusing the analysis on those uncertainties which users consider relevant and meaningful. As an illustrative example, we discuss the case of examining users’ uncertainty information needs in the SEAMLESS Integrated Framework (SEAMLESS-IF), an IA model chain for assessing and comparing alternative agricultural and environmental policy options. The most important user group of SEAMLESS-IF are policy experts at the European and national level. Uncertainty information needs of this user group were examined in an interactive process during the development of SEAMLESS-IF and by using a questionnaire. Results indicate that users’ information requirements differed from the uncertainty categories considered most relevant by model developers. In particular, policy experts called for addressing a broader set of uncertainty sources (e.g. model structure and technical model setup). The findings highlight that investigating users’ uncertainty information needs is an essential step towards creating confidence in an IA model and its outcomes. This alone, however, may not be sufficient for effectively implementing a user-oriented uncertainty analysis in such models. As the case study illustrates, it requires to include uncertainty analysis into user participation from the outset of the IA modelling process.     

Regional industrial growth and environmental impacts in the Bohai Sea rim region of China: uncertainty in location choice

Regional industrial growth is facing the problems of no control and disorder in rapidly transitioning China, especially in mega-regional areas. These problems have significantly intensified the use of regional resources and the level of environmental stress. The integration of industrial development and the environmental pollution pressure simulation at the mega-regional level must be supported at the planning stage. In this study, a Computational System for Regional Industrial Distribution Simulation and Environmental Impact Assessment (RESEA) that combines a multi-nominal logit model and uncertainty analysis was developed. This system aimed to explore efficient industrial spatial distribution simulations and potential environmental pressures at the mega-regional level. This study also developed an uncertainty analysis framework to identify and apply a bottom-up system with aggregate and sparse data following the basic processes of an HSY algorithm and Global-Formed Regional Sensitivity Analysis, which is capable of considering both input uncertainty and parameter uncertainty. By applying the RESEA system, a process of model estimation and sensitivity analysis was implemented based on historic data from 2002 to 2008 for the Bohai Sea rim region in China. The future industry distribution for the year 2015 was later aggregated based on the chosen sizes and locations of newly added industrial plants. Finally, the pollution loads of surface water into every sub-region were calculated, and the potential environmental impacts of different strategies were discussed.     

区域水环境系统脆弱性指标体系及综合决策模型研究

目前水环境系统脆弱性研究主要局限于水环境系统的单方面脆弱性研究,过于片面化,需要综合考虑水资源、水环境质量、旱涝灾害、社会经济以及生态环境等方面的脆弱性。根据水环境系统脆弱性是系统状态受到系统压力引起的敏感性以及系统状态对系统压力的适应性响应的观点,采用“压力-状态-响应”模式构建区域水环境系统脆弱性指标体系。区域水环境系统脆弱性研究中客观存在诸多不确定性,利用联系数描述上述不确定性过程中差异度系数在［-1,1］上取值仍存在不确定性,为此,构造三角模糊数刻画差异度系数取值的不确定性,给出三角模糊联系数的基本形式,取置信水平得出置信区间联系数,采用期望-方差排序法对置信区间联系数进行决策分析,建立基于三角模糊联系数的系统综合决策模型。此模型用于安徽省水环境系统脆弱性评价的结果表明,安徽省区域水环境系统脆弱等级介于［2.731 3.620］之间,脆弱性状况不容乐观,应采取相应措施改善区域水环境质量,降低区域水环境系统脆弱性,保障区域社会经济及生态环境的可持续发展     

公平性感知、信任与农业环境保护 ——不确定情形下农户行为选择

为深入理解农业环境政策从制定到落实的内在逻辑，从农户的公平性感知和信任出发，在农业环境保护的不确定情形下，探索性地构建农户农业环境保护行为响应理论分析框架，并基于山东、山西和陕西3省微观农户调查数据，采用路径分析、贝叶斯非线性结构方程模型和斜率分析等方法进行验证。结果表明：（1）农户农业环境保护的公平性感知对农业环境保护行为有直接和间接影响。直接影响为公平性感知越高，越能显著促进农户农业环境保护行为；间接影响为公平性感知通过提高信任进而作用于行为。（2）农业环境保护的不确定性显著阻碍了农户农业环境保护行为，且显著干扰了公平性感知向行为的转化。据此提出，农业环境保护政策的制定应考虑农户公平性感知和信任，传统命令型农业环境政策应逐步向农户参与型政策让渡；对农业环境定期检测并给予农户反馈，在降低政府长期监管成本的同时，也减少了农户农业环境保护的不确定性，从而推进农业环境保护政策的有效落实。     

Assessment of uncertainties in greenhouse gas emission profiles of livestock sectors in Africa,Latin America and Europe

The global animal food chain has a large contribution to the global anthropogenic greenhouse gas (GHG) emissions, but its share and sources vary highly across the world. However, the assessment of GHG emissions from livestock production is subject to various uncertainties, which have not yet been well quantified at large spatial scale. We assessed the uncertainties in the relations between animal production (milk, meat, egg) and the CO₂, CH₄, and N₂O emissions in Africa, Latin America and the European Union, using the MITERRA-Global model. The uncertainties in model inputs were derived from time series of statistical data, literature review or expert knowledge. These model inputs and parameters were further divided into nine groups based on type of data and affected greenhouse gas. The final model output uncertainty and the uncertainty contribution of each group of model inputs to the uncertainty were quantified using a Monte Carlo approach, taking into account their spatial and cross-correlation. GHG emissions and their uncertainties were determined per livestock sector, per product and per emission source category. Results show large variation in the GHG emissions and their uncertainties for different continents, livestock sectors products or source categories. The uncertainty of total GHG emissions from livestock sectors is higher in Africa and Latin America than in the European Union. The uncertainty of CH₄ emission is lower than that for N₂O and CO₂. Livestock parameters, CH₄ emission factors and N emission factors contribute most to the uncertainty in the total model output. The reliability of GHG emissions from livestock sectors is relatively high (low uncertainty) at continental level, but could be lower at country level.     

Areas of uncertainty in comparing risks of energy systems

In the past, the safety of technology has mainly been ensured by deterministic criteria that were based on engineering principles and on experience. Recently, however, it has become necessary to include risk assessment into safety policy. This need arises due to the order of magnitude of possible health and environmental impact from normal operation or accidents, as well as the complexity of modern systems that no longer allow for intuitive understanding of possible accident sequences. Assessment of risks of technical installations is subject to large uncertainties that are caused by lack of data and inadequate quantification and evaluation methods. This paper reviews the main areas of uncertainties with regard to their importance for decisionmaking. Uncertainties are an inherent part of any risk assessment result and can never be avoided. It is thus important to pay particular attention to them including limitations of methods, sensitivity to assumptions about parameters, and site-specific considerations. All this information must be described in great detail so that it can be integrated into the decisionmaking process. If applied in this context, risk assessment is a useful tool for identifying major contributors to risk and effective actions of risk management.     

Practical consequences of the assessment of different energy health risks

Public authorities must make decisions about energy, and the risks of alternative strategies need to be calculated including health and environmental costs. Information from various sources must be organized into a logical framework for comparing impacts. This must include the widest practicable range of health and environmental damage — public-health impact of pollution, role of accidents, disease and hazardous materials in the workplace, and odds for catastrophes. It must put each part of the energy cycle into perspective-giving particular attention to uncertainties in knowledge - to convey what is known, what is uncertain, and the importance of each factor in the overall picture. This paper gives examples of the use of health-impact assessment by decisionmakers: (1) comparative risk assessment of the health effects of coal and nuclear fuel cycles used in nuclear power plant siting and licensing hearings, and (2) health risks of acid deposition and other air-transported pollutants, carried out as part of an assessment for the U.S. Congress Office of Technology Assessment.     

基于应对不确定性的战略环境评价管理模式设计与应用

战略环境评价是目前环境评价领域中令人关注的前沿课题,但是不确定性却客观地、广泛地存在于战略环境评价中,这给战略环评的有效性实施带来很多的障碍。在介绍不确定性概念及其在战略环评中内涵的基础上,针对战略环境评价的特点,基于应对不确定性,建立了战略环境评价的管理模式,包括“早期介入、工作程序互动、战略制定与战略环评的融合、跟踪评价”的4个方面。通过这种管理模式的实施运用,来顺应战略环评的不确定性,更好地应对不确定性,能更好地满足战略环评的需要,并提高其有效性。以江苏省金坛市城南新区规划环境影响评价为例,进行了该管理模式的初步运用。研究表明：通过早期介入、战略环境评价与规划编制过程的互动及融合,能确保在规划编制阶段较早地、及时地、系统地考虑不确定的规划要素及环境因素,有利于通过战略环境评价为规划的编制与未来的实施提供科学的、客观的并易于规划编制机构接受的环境保护对策和政策建议。     

Spreading Success Beyond the Laboratory: Applying the RE-AIM Framework for Effective Environmental Communication Interventions at Scale*

The RE-AIM framework, created by Russell Glasgow and colleagues, addresses five major factors involved with sustained population-level effectiveness of public health interventions—Reach, Efficacy and/or Effectiveness, Adoption, Implementation and Maintenance. In this article, I illustrate how the framework might be applied to environmental communication interventions, and discuss how the framework needs to be adapted to important aspects of this context. Following that, I address several potential criticisms of RE-AIM or its usefulness for environmental communication scholars. Finally, I discuss how research practices of environmental communication scholars could be changed in light of the insights given by the RE-AIM framework. Much work remains to be done in order to see exactly how RE-AIM, or other frameworks focused on large-scale intervention effectiveness, can be made most useful to environmental communication scholars. However, RE-AIM already provides a valuable way to start thinking about how to best improve large-scale effectiveness of communication interventions.     

Uncertainty in the difference between maps of future land change scenarios

It is essential to measure whether maps of various scenarios of future land change are meaningfully different, because differences among such maps serve to inform land management. This paper compares the output maps of different scenarios of future land change in a manner that contrasts two different approaches to account for the uncertainty of the simulated projections. The simpler approach interprets the scenario storyline concerning the quantity of each land change transition as assumption, and then considers the range of possibilities concerning the value added by a simulation model that specifies the spatial allocation of land change. The more complex approach estimates the uncertainty of future land maps based on a validation measurement with historic data. The technique is illustrated by a case study that compares two scenarios of future land change in the Plum Island Ecosystems of northeastern Massachusetts, in the United States. Results show that if the model simulates only the spatial allocation of the land changes given the assumed quantity of each transition, then there is a clearly bounded range for the difference between the raw scenario maps; but if the uncertainties are estimated by validation, then the uncertainties can be so great that the output maps do not show meaningful differences. We discuss the implications of these results for a future research agenda of land change modeling. We conclude that a productive approach is to use the simpler method to distinguish clearly between variations in the scenario maps that are due to scenario assumptions versus variations due to the simulation model.     

Examination of model uncertainty and parameter interaction in a global carbon cycling model (GLOCO)

Simulation models play an important role in understanding the causes and consequences of climate change. In order to make full use of these models, it is necessary to establish the magnitude and sources of uncertainty associated with their predictions. This information can be used to achieve a better understanding of the simulated systems, to increase the reliability of model predictions, to guide field surveys and laboratory experiments, and to define realistic values that should be used in scientific, economic, and political discussions of future conditions. In this paper, a new tree-structured density estimation technique that extends the ability of Monte Carlo-based analyses to explore parameter interactions and uncertainty in complex environmental models was applied. The application of the technique is demonstrated using the GLOCO global carbon cycle model. The paper demonstrates that there are numerous distinct parameter combinations that can meet fairly stringent calibration criteria, and they are concentrated in relatively small subsets of the parameter space. These different subsets can be viewed as representing different ecological systems that achieve the same calibration or performance goals in fundamentally different ways. It is also shown that the simulated responses of these systems to future environmental change can lead to different conclusions regarding the interaction between factors affecting environmental processes, such as the growth of vegetation. Together, these results show how the tree-structured density estimation technique can be applied to gain a broader understanding of model performance and of ecosystem responses to change.     

LICARA nanoSCAN - A tool for the self-assessment of benefits and risks of nanoproducts

The fast penetration of nanoproducts on the market under conditions of significant uncertainty of their environmental properties and risks to humans creates a need for companies to assess sustainability of their products. Evaluation of the potential benefits and risks to build a coherent story for communication with clients, authorities, consumers, and other stakeholders is getting to be increasingly important, but SMEs often lack the knowledge and expertise to assess risks and communicate them appropriately. This paper introduces LICARA nanoSCAN, a modular web based tool that supports SMEs in assessing benefits and risks associated with new or existing nanoproducts. This tool is unique because it is scanning both the benefits and risks over the nanoproducts life cycle in comparison to a reference product with a similar functionality in order to enable the development of sustainable and competitive nanoproducts. SMEs can use data and expert judgment to answer mainly qualitative and semi-quantitative questions as a part of tool application. Risks to public, workers and consumers are assessed, while the benefits are evaluated for economic, environmental and societal opportunities associated with the product use. The tool provides an easy way to visualize results as well as to identify gaps, missing data and associated uncertainties. The LICARA nanoSCAN has been positively evaluated by several companies and was tested in a number of case studies. The tool helps to develop a consistent and comprehensive argument on the weaknesses and strengths of a nanoproduct that may be valuable for the communication with authorities, clients and among stakeholders in the value chain. LICARA nanoSCAN identifies areas for more detailed assessments, product design improvement or application of risk mitigation measures.     

Will global warming affect soil-to-plant transfer of radionuclides?

Recent assessments of global climate/environmental change are reaching a consensus that global climate change is occurring but there is significant uncertainty over the likely magnitude of this change and its impacts. There is little doubt that all aspects of the natural environment will be impacted to some degree. Soil-to-plant transfer of radionuclides has long been a significant topic in radioecology, both for the protection of humans and the environment from the effects of ionising radiation. Even after five decades of research considerable uncertainty exists as to the interplay of key environmental processes in controlling soil-plant transfer. As many of these processes are, to a lesser or greater extent, climate-dependent, it can be argued that climate/environmental change will impact soil-to-plant transfer of radionuclides and subsequent transfers in specific environments. This discussion attempts to highlight the possible role of climatic and climate-dependent variables in soil-to-plant transfer processes within the overall predictions of climate/environmental change. The work is speculative, and intended to stimulate debate on a theme that radioecology has either ignored or avoided in recent years.