Public Reactions to Risk Messages Communicating Different Sources of Uncertainty: An Experimental Test

There is an abundant literature on the challenge of integrating uncertainties in experts’ risk assessments, but the evidence on the way they are understood by the public is scarce and mixed. This study aims to better understand the effect of communicating different sources of uncertainty in risk communication. A causal design was employed to test the effect of communicating risk messages varying in type of advisory warning (no risk and suggests no protective measure, or risk and recommends a protective measure) and sources of uncertainty (no uncertainty, divergence between experts, contradictory data, or lack of data) on public reactions. Participants from the general public (N = 434) were randomly assigned to read and react to variants of a fictitious government message discussing the presence of a new micro-organism found in tap water. Multiple analysis of variance showed that to report uncertainty from divergence between experts or from contradictory data reduced the adherence to the message, but not to mention the lack of data. Moreover, the communication of diverse sources of uncertainty did not affect trust in the government when the advisory warning stated there was a risk and recommended a protective measure. These findings have important implications for risk communication.     

Uncertainty Is Part of Making Decisions

Advances in computer technology and applied statistics have provided the opportunity for the non-statistician to investigate uncertainty in a quantitative manner. The following discussion argues, notwithstanding the possible misuse of uncertainty analysis, that uncertainty is always present and that decisions based on human or ecological risk assessment would benefit from disclosure of uncertainty in the estimated risks.     

Extrapolation in Risk Assessment: Improving the Quantification of Uncertainty,and Improving Information to Reduce the Uncertainty

Risk assessments inevitably extrapolate from the known to the unknown. The resulting calculation of risk involves two fundamental kinds of uncertainty: uncertainty owing to intrinsically unpredictable (random) components of the future events, and uncertainty owing to imperfect prediction formulas (parameter uncertainty and error in model structure) that are used to predict the component that we think is predictable. Both types of uncertainty weigh heavily both in health and ecological risk assessments. Our first responsibility in conducting risk assessments is to ensure that the reported risks correctly reflect our actual level of uncertainty (of both types). The statistical methods that lend themselves to correct quantification of the uncertainty are also effective for combining different sources of information. One way to reduce uncertainty is to use all the available data. To further sharpen future risk assessments, it is useful to partition the uncertainty between the random component and the component due to parameter uncertainty, so that we can quantify the expected reduction in uncertainty that can be achieved by investing in a given amount of future data. An example is developed to illustrate the potential for use of comparative data, from toxicity testing on other species or other chemicals, to improve the estimates of low-effect concentration in a particular case with sparse case-specific data.     

Concentration of Radiocesium in the Wild Japanese Monkey (Macaca fuscata) over the First 15 Months after the Fukushima Daiichi Nuclear Disaster

Following the massive earthquake that struck eastern Japan on March 11, 2011, a nuclear reactor core meltdown occurred at the Fukushima Daiichi Nuclear Power Plant, operated by Tokyo Electric Power Company, and was followed by the release of large amounts of radioactive materials. The objective of this study was to measure the concentration of radiocesium ¹³⁴Cs and ¹³⁷Cs in the muscle of Japanese monkeys (Macaca fuscata) inhabiting the forest area of Fukushima City and to determine the change in concentration over time as well as the relationship with the level of soil contamination. Cesium concentrations in the muscle of monkeys captured at locations with 100,000–300,000 Bq/m² were 6,000–25,000 Bq/kg in April 2011 and decreased over 3 months to around 1,000 Bq/kg. However, the concentration increased again to 2,000–3,000 Bq/kg in some animals during and after December 2011 before returning to 1,000 Bq/kg in April 2012, after which it remained relatively constant. This pattern of change in muscle radiocesium concentration was similar to that of the change in radiocesium concentration in atmospheric fallout. Moreover, the monkeys feed on winter buds and the cambium layer of tree bark potentially containing higher concentrations of radiocesium than that in the diet during the rest of the year. The muscle radiocesium concentration in the monkeys related significantly with the level of soil contamination at the capture locations.     

Harmonizing the Incorporation of Uncertainty and Variability Into Risk Assessment: An International Approach

International harmonization of risk assessment approaches affords a number of opportunities and advantages. Overall, harmonization will lead to more efficient use of resources, but also will lead to better understanding amongst scientists and regulators worldwide. It is with these goals in mind that in 1994 the International Programme on Chemical Safety (IPCS) initiated its Project on the Harmonization of Approaches to the Assessment of Risk from Exposure to Chemicals (Harmonization Project). An ongoing activity under this project addresses uncertainty and variability in risk assessment. The goal of the overall activity is to promote harmonization of risk assessment methodologies for noncancer endpoints. However, given the common links in uncertainty and variability that apply across a range of end-point-specific activities, these links are identified wherever possible. This paper provides an overview of the IPCS Harmonization Project and reviews the activity and future plans related to uncertainty and variability.     

Uncertainty Analysis of Pesticide Residues in Drinking Water Risk Assessment

Pesticide residues in drinking water can vary significantly from day to day. However, water quality monitoring performed under the Safe Drinking Water Act (SDWA) at most community water systems (CWSs) is typically limited to four data points per year over a few years. Due to this limited sampling, likely maximum residues may be underestimated in risk assessment. In this work, a statistical methodology is proposed to study two types of uncertainties in observed samples and their propagated effect in risk estimates. The methodology was demonstrated using data from 16 CWSs that have three independent databases of atrazine residue to estimate the uncertainty of risk in infants and children. The results showed that in 85% of the CWSs, chronic risks predicted with the proposed approach may be two- to four-folds higher than that predicted with the current approach, wheras intermediate risks may be two- to three-folds higher in 50% of the CWSs. In 12% of the CWSs, however, the proposed methodology showed a lower intermediate risk. A closed-form solution of propagated uncertainty was developed to demonstrate the number of years (seasons) of data and sampling frequency needed to reduce the uncertainty of risk estimates. In general, this methodology provided good insight into the importance of addressing uncertainty of observed water quality data and the need to predict likely maximum residues in risk assessment.     

Identifying Uncertainty in Environmental Risk Assessments: The Development of a Novel Typology and Its Implications for Risk Characterization

Environmental risk analysts need to draw from a clear typology of uncertainties when qualifying risk estimates and/or significance statements about risk. However, categorizations of uncertainty within existing typologies are largely overlapping, contradictory, and subjective, and many typologies are not designed with environmental risk assessments (ERAs) in mind. In an attempt to rectify these issues, this research provides a new categorization of uncertainties based, for the first time, on the appraisal of a large subset of ERAs, namely 171 peer-reviewed environmental weight-of-evidence assessments. Using this dataset, a defensible typology consisting of seven types of uncertainty (data, language, system, extrapolation, variability, model, and decision) and 20 related sub-types is developed. Relationships between uncertainties and the techniques used to manage them are also identified and statistically evaluated. A highly preferred uncertainty management option is to take no action when faced with uncertainty, although where techniques are applied they are commensurate with the uncertainty in question. Key observations are applied in the form of guidance for dealing with uncertainty, demonstrated through ERAs of genetically modified higher plants in the European Union. The presented typology and accompanying guidance will have positive implications for the identification, prioritization, and management of uncertainty during risk characterization.     

Uncertainty in the Extrapolation from Individual Effects to Impacts upon Landscapes

Uncertainty is inherent in extrapolating from effects upon the individual to alterations in ecological structure or function. Subtle differences in individuals within a population can give rise to significant evolutionary events. Populations are part of ecological structures, systems that are clearly complex, requiring an understanding of the interacting components, stochastic inputs and spatial scales. A series of patch-dynamic models is used to illustrate the importance of spatial arrangement and initial population size in predicting effects at a landscape level. The importance of understanding the spatial structure of a population in uncertainty reduction is addressed.     

Quantifying Uncertainty in Predictions of Invasiveness

Using the Australian weed risk assessment (WRA) model as an example, we applied a combination of bootstrapping and Bayesian techniques as a means for explicitly estimating the posterior probability of weediness as a function of an import risk assessment model screening score. Our approach provides estimates of uncertainty around model predictions, after correcting for verification bias arising from the original training dataset having a higher proportion of weed species than would be the norm, and incorporates uncertainty in current knowledge of the prior (base-rate) probability of weediness. The results confirm the high sensitivity of the posterior probability of weediness to the base-rate probability of weediness of plants proposed for importation, and demonstrate how uncertainty in this base-rate probability manifests itself in uncertainty surrounding predicted probabilities of weediness. This quantitative estimate of the weediness probability posed by taxa classified using the WRA model, including estimates of uncertainty around this probability for a given WRA score, would enable bio-economic modelling to contribute to the decision process, should this avenue be pursued. Regardless of whether or not this avenue is explored, the explicit estimates of uncertainty around weed classifications will enable managers to make better informed decisions regarding risk. When viewed in terms of likelihood of weed introduction, the current WRA model outcomes of ‘accept’, ‘further evaluate’, or ‘reject’, whilst not always accurate in terms of weed classification, appear consistent with a high expected cost of mistakenly introducing a weed. The methods presented have wider application to the quantitative prediction of invasive species for situations where the base-rate probability of invasiveness is subject to uncertainty, and the accuracy of the screening test imperfect     

Evaluation of Software for Propagating Uncertainty Through Risk Assessment Models

Quantitative uncertainty analysis has become a common component of risk assessments. In risk assessment models, the most robust method for propagating uncertainty is Monte Carlo simulation. Many software packages available today offer Monte Carlo capabilities while requiring minimal learning time, computational time, and/or computer memory. This paper presents an evalu ation of six software packages in the context of risk assessment: Crystal Ball, @Risk, Analytica, Stella II, PRISM, and Susa-PC. Crystal Ball and @Risk are spreadsheet based programs; Analytica and Stella II are multi-level, influence diagram based programs designed for the construction of complex models; PRISM and Susa-PC are both public-domain programs designed for incorpo rating uncertainty and sensitivity into any model written in Fortran. Each software package was evaluated on the basis of five criteria, with each criterion having several sub-criteria. A ‘User Preferences Table’ was also developed for an additional comparison of the software packages. The evaluations were based on nine weeks of experimentation with the software packages including use of the associated user manuals and test of the software through the use of example problems. The results of these evaluations indicate that Stella II has the most extensive modeling capabilities and can handle linear differential equations. Crystal Ball has the best input scheme for entering uncertain parameters and the best reference materials. @Risk offers a slightly better standard output scheme and requires a little less learning time. Susa-PC has the most options for detailed statistical analysis of the results, such as multiple options for a sensitivity analysis and sophisticated options for inputting correlations. Analytica is a versatile, menu- and graphics-driven package, while PRISM is a more specialized and less user friendly program. When choosing between software packages for uncertainty and sensitivity analysis, the choice largely depends on the specifics of the problem being modeled. However, for risk assessment problems that can be implemented on a spreadsheet, Crystal Ball is recommended because it offers the best input options, a good output scheme, adequate uncertainty and sensitivity analysis, superior reference materials, and an intuitive spreadsheet basis while requiring very little memory.     

Skunkworks in the Embers of the Cedar Fire: Enhancing Resilience in the Aftermath of Disaster

A coalition of environmental activists and professionals created the San Diego Fire Recovery Network (SDFRN) while the largest wildfire in California history was still burning at the city’s edge in October 2003. Acting quickly while the citizenry questioned governmental ability to protect their rapidly growing region, SDFRN proposed to reduce fire risk in a way that altered residential knowledge practices and identity while reshaping governance relationships. While this effort stalled after governmental agencies restored public confidence through massive fire prevention initiatives, SDFRN’s efforts may not have been in vain. Retained within collective memory, SDFRN contributed to community resilience by diversifying possible responses to environmental change and uncertainty. In this way, flexible, informal learning organizations such as SDFRN may serve as “skunkworks,” seizing on disaster in order to incubate social–ecological relationships that might avert greater tragedies to come.

Risk Analysis,Uncertainty Factors,and the Susceptibilities of Children

This article examines the use of uncertainty factors (UFs) by the Agency for Toxic Substances and Disease Registry (ATSDR) when developing health guidance values known as minimal risk levels (MRLs) in environmental risk analysis as it applies to children. Improvements in the chemical-specific databases often reveal new information and thereby reduce uncertainty or alternatively raise new concerns. As a result, MRLs can and will change. Children, in particular, are not “small adults” and in some instances demonstrate greater risks of exposure to environmental toxicants and greater susceptibility for adverse health effects once exposed. Recent experience with risk analysis for three toxicants (organic mercury, dioxin, and manganese) is recounted to demonstrate how ATSDR has revised MRLs as the emerging science generates greater knowledge and awareness of children's special vulnerabilities to toxic substances in the environment.     

Assessment of the Annual Additional Effective Doses amongst Minamisoma Children during the Second Year after the Fukushima Daiichi Nuclear Power Plant Disaster

An assessment of the external and internal radiation exposure levels, which includes calculation of effective doses from chronic radiation exposure and assessment of long-term radiation-related health risks, has become mandatory for residents living near the nuclear power plant in Fukushima, Japan. Data for all primary and secondary children in Minamisoma who participated in both external and internal screening programs were employed to assess the annual additional effective dose acquired due to the Fukushima Daiichi nuclear power plant disaster. In total, 881 children took part in both internal and external radiation exposure screening programs between 1^st April 2012 to 31^st March 2013. The level of additional effective doses ranged from 0.025 to 3.49 mSv/year with the median of 0.70 mSv/year. While 99.7% of the children (n = 878) were not detected with internal contamination, 90.3% of the additional effective doses was the result of external radiation exposure. This finding is relatively consistent with the doses estimated by the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR). The present study showed that the level of annual additional effective doses among children in Minamisoma has been low, even after the inter-individual differences were taken into account. The dose from internal radiation exposure was negligible presumably due to the success of contaminated food control.     

High Prevalence of Post-Traumatic Stress Symptoms in Relation to Social Factors in Affected Population One Year after the Fukushima Nuclear Disaster

Objective

This study investigated post-traumatic stress symptoms in relation to the population affected by the Fukushima Nuclear Disaster, one year after the disaster. Additionally, we investigated social factors, such as forced displacement, which we hypothesize contributed to the high prevalence of post-traumatic stress. Finally, we report of written narratives that were collected from the impacted population.

Design and Settings

Using the Impact of Event Scale-Revised (IES-R), questionnaires were sent to 2,011 households of those displaced from Fukushima prefecture living temporarily in Saitama prefecture. Of the 490 replies; 350 met the criteria for inclusion in the study. Multiple logistic regression analysis was performed to examine several characteristics and variables of social factors as predictors of probable post-traumatic stress disorder, PTSD.

Results

The mean score of IES-R was 36.15±21.55, with 59.4% having scores of 30 or higher, thus indicating a probable PTSD. No significant differences in percentages of high-risk subjects were found among sex, age, evacuation area, housing damages, tsunami affected, family split-up, and acquaintance support. By the result of multiple logistic regression analysis, the significant predictors of probable PTSD were chronic physical diseases (OR = 1.97), chronic mental diseases (OR = 6.25), worries about livelihood (OR = 2.27), lost jobs (OR = 1.71), lost social ties (OR = 2.27), and concerns about compensation (OR = 3.74).

Conclusion

Although there are limitations in assuming a diagnosis of PTSD based on self-report IES-R, our findings indicate that there was a high-risk of PTSD strongly related to the nuclear disaster and its consequent evacuation and displacement. Therefore, recovery efforts must focus not only on medical and psychological treatment alone, but also on social and economic issues related to the displacement, as well.     

Probabilistic Forecast and Uncertainty Assessment of Hydrologic Design Values Using Bayesian Theories

Researches on hydrologic extreme events have great significance in reducing and avoiding the severe losses and impacts caused by natural disasters. When forecasting hydrologic design values of the hydrologic extreme events of interest by the conventional hydrologic frequency analysis (HFA) model, the results cannot take uncertainties and risks into account. In this article, in order to overcome conventional HFA model's disadvantages and to improve hydrologic design values’ forecast results, an improved HFA model named AM-MCMC-HFA is proposed by employing the AM-MCMC algorithm (adaptive Metropolis-Markov chain Monte Carlo) to HFA process. Differing with conventional HFA model, which is seeking single optimal forecast result, the AM-MCMC-HFA model can not only get the optimal but also the probabilistic forecast results of hydrologic design values. By applying to two obviously different hydrologic series, the performances of the model proposed have been verified. Analysis results show that four factors have great influence on hydrologic design values’ reliability, and also indicate that AM-MCMC-HFA has the ability of assessing the uncertainties of parameters and hydrologic design values. Therefore, by using the AM-MCMC-HFA model, hydrologic designs tasks can be operated more reasonably, and more rational decisions can be made by governmental decision-makers and public in practice.     

Uncertainty analysis of soil organic carbon stock change in Canadian cropland from 1991 to 2001

National estimates of changes in the amount of soil organic carbon (SOC) in cropland requires an assessment of uncertainty for accounting and reporting under the United Nations Framework Convention on Climate Change (UNFCCC) and the Kyoto Protocol. Canada has data sets on SOC stocks in croplands, historical changes in SOC levels due to management practices, and historical changes in the area of land devoted to certain soil management practices. We conducted an analysis of uncertainty of the change in SOC levels due to management practices in Canada from 1991 to 2001 using Monte Carlo analysis and a simple model. Probability distribution functions were determined for each of the inputs of the model, enabling us to assess the uncertainty for the output. The storage rate of SOC in cropland soils of Canada for the 10‐year period ranged from 3.2 to 8.3 Mt C yr^?1 at 95% confidence, with a mean of 5.7 Mt C yr^?1. Approximately 67% (about 3.8 Mt C yr^?1) of the increase in SOC storage in Canada occurred in Saskatchewan where the cropland area under no‐till increased from 10% to 35%, and the area of summer‐fallow declined from 43% to 20% during the study period. The large uncertainty in the effect of no‐till on SOC stock changes in the Gray‐Brown Luvisols of Ontario contributed most to the variance in the model output. If trends in agricultural management continue for the next 10‐year census period, the estimated SOC storage would comprise between 7% and 19% of the gap required to achieve the 6% reduction in 1990 greenhouse gas emission levels for Canada under the Kyoto Protocol.     

Stochastic and Subjective Uncertainty in the Assessment of Radiation Exposure at the Waste Isolation Pilot Plant

The Waste Isolation Pilot Plant (WIPP) is under development by the U.S. Department of Energy as a geologic (i.e., deep underground) disposal facility for transuranic waste. An analysis is presented of possible radiation exposures associated with inadvertent drilling intrusions through the WIPP using future drilling rates obtained in accordance with requirements specified by the U.S. Environmental Protection Agency (EPA) in 40 CFR 194, Subpart B. The analysis attempts to maintain a separation between stochastic (i.e., aleatory) and subjective (i.e., epistemic) uncertainty as implied in the EPA regulations 40 CFR 191, Subpart B, and 40 CFR 194. The results of the analysis are presented as distributions of complementary cumulative distribution functions (CCDFs) for radiation exposure to oil field workers, where the individual CCDFs arise from stochastic uncertainty (i.e., many possible patterns of drill ing intrusions are possible over the 10,000?yr period specified in 40 CFR 191, Subpart B) and the distributions of CCDFs arise from subjective uncertainty (i.e., many inputs to the analysis have fixed, but poorly known, values). The projected radiation exposures during the 10,000?yr period following decommissioning of the WIPP in the year 2033 were found to be small (typically less than 0.001 person-Sv), with the dominant exposure pathway being the incidental ingestion of contaminated dirt by drilling crew members.     

Analysis and Portrayal of Uncertainty in a Food-Web Exposure Model

The results of quantitative risk assessments are key factors in a risk manager's decision of the necessity to implement actions to reduce risk. The extent of the uncertainty in the assessment will play a large part in the degree of confidence a risk manager has in the reported significance and probability of a given risk. The two main sources of uncertainty in such risk assessments are variability and incertitude. In this paper we use two methods, a second-order two-dimensional Monte Carlo analysis and probability bounds analysis, to investigate the impact of both types of uncertainty on the results of a food-web exposure model. We demonstrate how the full extent of uncertainty in a risk estimate can be fully portrayed in a way that is useful to risk managers. We show that probability bounds analysis is a useful tool for identifying the parameters that contribute the most to uncertainty in a risk estimate and how it can be used to complement established practices in risk assessment. We conclude by promoting the use of probability analysis in conjunction with Monte Carlo analyses as a method for checking how plausible Monte Carlo results are in the full context of uncertainty.     

Uncertainty in Health Risks from Artificial Lighting due to Disruption of Circadian Rhythm and Melatonin Secretion: A Review

Incandescent lighting in many domestic and commercial applications is in the process of replacement by more efficient light sources, such as the compact fluorescent light (CFL) and the light emitting diode (LED). For household use, both CFL and LED sources have a significant blue component in the emitted spectrum in comparison to the warmer incandescent globes and this has been the cause of emerging health concerns. Recent research suggests that the blue light bandwidth in the visible spectrum has a significant impact on physical health, including disruption of the internal body clock and suppression of melatonin secretion at night. This disruptive effect has been linked to a range of illnesses, including breast cancer, prostate cancer, heart disease, obesity, and diabetes. There have also been positive effects observed, including re-setting the body clock to the required sleep pattern, boosting mood, alertness, cognitive performance, and alleviating seasonal affective depression (SAD). In this article, an introduction and review of recent research is provided, relevant health issues are highlighted and discussed, and uncertainty analysis completed for the dose–response curve for melatonin suppression as a function of incident photon density.