Environmental Risk Management Decision-Making in a Societal Context

Recent studies point to the need for improved understanding of environmental management frameworks designed to combine qualitative public and quantitative technical inputs in decision-making processes. Flux in public perception and concern about risks imply frameworks must be iterative in nature and incorporate a variety of assessment triggers in the form of decision points. A conceptual model is proposed here to explain the de facto operation of standard risk analytic frameworks within the broader sociopolitical milieu of public policy. The model is presented as a decision flow diagram that emphasizes setting environmental management goals based on societal input and the formulation of decision criteria for selecting management actions to achieve those goals. Prospective and retrospective decision control points operate to select management options that, respectively, avoid or reduce actual or predicted effects. Feedback loops that modify risk management outcomes are identified. Technical and scientific inputs (i.e., risk analysis) are assigned an essential information role within the framework and are responsible for informing the management process with the results of appropriately conducted and reviewed investigations. The proposed model is intended primarily to indicate how environmental risk management decision-making and associated technical assessments may be influenced by social pressures. It is hoped this understanding will lead to analytical transparency and better public communication of the environmental implications of policy options.     

Arsenic in Michigan Soil: Spatial Stratification for Environmental Regulatory Purposes

State environmental regulatory agencies in the U.S. often establish a default background standard for naturally occurring elements in the soil, water, and air. The background standard is determined and then used as a benchmark across the entire jurisdiction. A variety of statistical techniques are used to determine this standard, but often ignore any inherent spatial dependencies within the jurisdiction. If the analysis indicates a specific site exceeds the default standard, additional background sampling and analysis must usually be performed. Frequently, this additional sampling is found to be unnecessary simply because the natural background levels were elevated for this particular site. Conversely, potential contamination may be overlooked in areas where the natural background levels are much lower. Thus, a single default background standard seems inadequate within this context.

This paper proposes the use of dissimilarity coefficients based on kriging estimates as a means to regionalize background standards. Along with cluster analysis techniques, these dissimilarity coefficients provide a means to stratify the population into geographic sub-areas. A regulatory agency may now define multiple default background standards based on geographic location. To illustrate, this paper examines a case study concerning residential soil arsenic for 83 Michigan counties.     

Probabilistic Analysis of Human Health Risks Associated with Background Concentrations of Inorganic Arsenic: Use of a Margin of Exposure Approach

Substantial evidence exists from epidemiological and mechanistic studies supporting a sublinear or threshold dose–response relationship for the carcinogenicity of ingested arsenic; nonetheless, current regulatory agency evaluations have quantified arsenic risks using default, generic risk assessment procedures that assume a linear, no-threshold dose–response relationship. The resulting slope factors predict risks from U.S. background arsenic exposures that exceed certain regulatory levels of concern, an outcome that presents challenges for risk communication and risk management decisions. To better reflect the available scientific evidence, this article presents the results of a Margin of Exposure (MOE) analysis to characterize risks associated with typical and high-end background exposures of the U.S. population to arsenic from food, water, and soil. MOE values were calculated by comparing a no-observable-adverse-effect-level (NOAEL) derived from the epidemiological literature with exposure estimates generated using a probabilistic (Monte Carlo) model. The plausibility and conservative nature of the exposure and risk estimates evaluated in this analysis are supported by sensitivity and uncertainty analyses and by comparing predicted urinary arsenic concentrations with empirical data. Using the more scientifically supported MOE approach, the analysis presented in this article indicates that typical and high-end background exposures to inorganic arsenic in U.S. populations do not present elevated risks of carcinogenicity.     

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.     

Environmental Risk Assessment of Dams by Using Multi-Criteria Decision-Making Methods: A Case Study of the Polrood Dam,Guilan Province,Iran

A dam's construction always imposes some risks to the environment. In this article, the environmental risks of the Polrood dam, located in a northern province of Iran, during its construction phase, were identified, ranked, and evaluated. The risk factors were initially identified by Delphi questionnaire and then rated using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Subsequently, the Analytical Hierarchy Process (AHP) was applied to classify the risk factors into four major categories, and Expert Choice software was used to weight them. Based on the obtained results, Physicochemical; Biological; Economic, Social, and Cultural; and Health and Safety risks were rated by the weights of 0.124, 0.080, 0.048, and 0.021, respectively. Among the physicochemical risks, Erosion and Sedimentation weighted 0.061 and 0.047, respectively, were identified as the most serious risks. Among the biological risks, the highest negative impact was assigned to Land Cover with the weight of 0.189. In the third category, the most important risk was Population Displacement, weighted 0.114. Workers’ Falls with the weight of 0.109 was also determined as the highest risk in the last category. A comprehensive risk response plan will be required for dealing with the identified risks and their mitigation.     

The Use of Probabilistic Risk Assessment in Establishing Drinking Water Quality Objectives

There has been a trend in recent years toward the use of probabilistic methods for the analysis of uncertainty and variability in risk assessment. By developing a plausible distribution of risk, it is possible to obtain a more complete characterization of risk than is provided by either best estimates or upper limits. We describe in this paper a general framework for evaluating uncertainty and variability in risk estimation and outline how this framework can be used in the establishment of drinking water quality objectives. In addition to characterizing uncertainty and variability in risk, this framework also facilitates the identification of specific factors that contribute most to uncertainty and variability. The application of these probabilistic risk assessment methods is illustrated using tetrachloroethylene and trihalomethanes as examples.     

Perspectives on the Conduct and Use of Economic Analysis in Federal Environmental Decisionmaking

This article lays out the broad rationale for conducting economic analyses of major environmental regulations—principally, benefit-cost analysis—as well as some of the pertinent critiques. What can public agencies expect to gain from such activities? What are the reasons to be wary of the results? The paper reviews the recent experience of the federal government in conducting such analyses, with particular reference to the relevant Presidential Executive Orders issued over the past three decades. Finally, the paper examines some of the key methodological issues, often involving interdiscripliary topics, critical to the conduct of such analyses. Overall, it is concluded that the economic analysis of proposed environmental regulations can help improve the allocation of society's resources while at the same time engendering an understanding of who benefits and who pays for any given regulatory action. Additionally, properly conducted economic analyses encourage transparency and accountability in the decisionmaking process, provide a framework for consistent data collection and identification of gaps in knowledge, and allow for the aggregation of many dissimilar effects (e.g., those on health, visibility, and crops) into one measure of net benefits expressed in a single currency.     

基于蛋白质组学对螺旋藻砷胁迫响应机制的研究

通过研究螺旋藻(Spirulina sp.)在砷离子胁迫下的蛋白质组变化,从蛋白质表达水平解释螺旋藻对砷离子胁迫的响应机理。螺旋藻经过不同浓度砷离子胁迫7 d后,提取蛋白质进行凝胶电泳,并对差异蛋白进行质谱分析。结果表明螺旋藻在2.0 ppm砷酸盐中暴露10 min光合放氧速率降低27.3%,培养24 h后细胞内的金属硫蛋白、叶绿素、类胡萝卜素及藻胆蛋白相对含量均明显降低。蛋白组学共鉴定出75个差异蛋白,其中26个显著上调,49个呈现下调。这些差异蛋白表明砷离子主要通过破坏螺旋藻光合色素蛋白,干扰电子传递过程,导致能量合成受损,使得依赖光合作用产生能量进行的跨膜运动、蛋白质合成等相关过程受到影响;同时,活性氧清除与防御相关蛋白呈现上调,螺旋藻细胞内抗氧化系统被激活。     

The Everyday Life Context of Increasing Energy Demands: Time Use Survey Data in a Decomposition Analysis

Industrial ecologists have modeled with precision the material foundations of industrial systems, but given less attention to the demand for products and the drivers of structural changes in these systems. This article suggests that time use data complement data on monetary expenditure and can be used to elucidate the everyday life context in which the changes in the economy take place. It builds upon the claim that goods are not direct sources of utility, but enter specific household activities as inputs. A second argument for the proposed approach is that it can be used to introduce and foster human agency in analyses of production systems. The article uses Finnish time use survey data, consumption expenditure data, and data on the sectoral energy intensities of financial output in the Finnish economy. First, a measure of the energy intensity of activities is derived by relating consumer time use and the required direct and indirect energy requirements. Second, the results include a decomposition of changes in the energy requirements of private consumption in Finland during the 1990s. It is shown that although the same activities on average require increasing energy inputs per unit of time, Finns have simultaneously changed the structure of their everyday life toward less energy-intensive activities.     

Distribution of Daily Breathing Rates For Use in California's Air Toxics Hot Spots Program Risk Assessments

Data were combined from a study measuring breathing rates at various activities and two activity pattern studies to generate breathing rate distributions for children and adults. The children and adult breathing rate distributions were combined using a Monte Carlo technique to generate a breathing rate distribution for a lifetime spanning ages 0 to 70. The children's breathing rate distribution has a mean, standard deviation, median and 95^th percentile of 452, 67.7, 441, and 581 L/kg-day, respectively. The adult breathing rate distribution has a mean, standard deviation, median and 95^th percentile of 232, 64.6, 209, and 381 L/kg-day, respectively. The simulated 70-year distribution has a mean, standard deviation, median and 95^th percentile of 271, 57.9, 253, and 393 L/kg-day, respectively. The adult breathing rate distribution is based on 24-hour recall activity data that would not necessarily capture average activity patterns and therefore breathing rates. We utilized the human energy expenditure literature to validate the breathing rate distribution. We conclude that the breathing rate distribution is reasonable for chronic long-term risk assessment in California's Air Toxics Hot Spots program.     

The Use of Epidemiology in Environmental Risk Assessment

Epidemiology provides estimates of the concentration–response relation for environmental and occupational toxicants in the species of interest, in or close to the dose range of interest. As such, when available, they provide the primary source for risk assessments. Further information can be acquired by using modern biostatistical techniques to assess the shape of the dose response relation, examine effect modification, and assure control for confounding. These approaches are particularly effective if they are done in the context of a meta-analysis or hierarchical model. This is illustrated with examples from the air pollution literature.     

Use of a Portable Biofeedback Device to Improve Insomnia in a Combat Zone,a Case Report

Insomnia is a common problem in situations of stress. Some forms of stress, however, may contraindicate the use of traditional, pharmacological interventions. Working in a combat zone is such a situation. Alternative means of improving sleep are clearly needed for Service Members. We report a case involving a medical provider who was serving in a military, emergency-services facility in Iraq, and who presented with anxiety, depressed mood, and insomnia. Symptoms were sub-threshold for major depressive disorder or acute stress disorder. Mood and anxiety symptoms responded to traditional therapy techniques, but problems with insomnia remained. The patient was given a portable biofeedback device that employs an infrared sensor photoplethysmograph to measure heart rate variability (HRV) from peripheral finger pulse. One week later, sleep was significantly improved. Symptom improvement lasted to at least 6 weeks while in theater. One year later, a check-in with the patient revealed that after returning home, he had been diagnosed with post traumatic stress disorder (PTSD). PTSD symptoms had resolved after 6 months of psychopharmacology and cognitive behavioral therapy. These results indicate that biofeedback may be a useful means of improving sleep in a combat zone, but that such improvements may not necessarily prevent the development of more serious symptoms later. No clear causality can be inferred from a single case, and further study is needed to determine if this finding have wider applicability.     

Evaluation of Sampling-Based Methods for Sensitivity Analysis: Case Study for the E. coli Food Safety Process Risk Model

This article evaluates selected sensitivity analysis methods applicable to risk assessment models with two-dimensional probabilistic frameworks, using a microbial food safety process risk model as a test-bed. Six sampling-based sensitivity analysis methods were evaluated including Pearson and Spearman correlation, sample and rank linear regression, and sample and rank stepwise regression. In a two-dimensional risk model, the identification of key controllable inputs that can be priorities for risk management can be confounded by uncertainty. However, despite uncertainty, results show that key inputs can be distinguished from those that are unimportant, and inputs can be grouped into categories of similar levels of importance. All selected methods are capable of identifying unimportant inputs, which is helpful in that efforts to collect data to improve the assessment or to focus risk management strategies can be prioritized elsewhere. Rank-based methods provided more robust insights with respect to the key sources of variability in that they produced narrower ranges of uncertainty for sensitivity results and more clear distinctions when comparing the importance of inputs or groups of inputs. Regression-based methods have advantages over correlation approaches because they can be configured to provide insight regarding interactions and nonlinearities in the model.     

Two-Dimensional Monte Carlo Simulation and Beyond: A Comparison of Several Probabilistic Risk Assessment Methods Applied to a Superfund Site

Four different probabilistic risk assessment methods were compared using the data from the Sangamo Weston/Lake Hartwell Superfund site. These were one-dimensional Monte Carlo, two-dimensional Monte Carlo considering uncertainty in the concentration term, two-dimensional Monte Carlo considering uncertainty in ingestion rate, and microexposure event analysis. Estimated high-end risks ranged from 2.0×10^?4 to 3.3×10^?3. Microexposure event analysis produced a lower risk estimate than any of the other methods due to incorporation of time-dependent changes in the concentration term.     

Social Determinants of Health in Environmental Justice Communities: Examining Cumulative Risk in Terms of Environmental Exposures and Social Determinants of Health

Residents of environmental justice (EJ) communities may bear a disproportionate burden of environmental health risk, and often face additional burdens from social determinants of health. Accounting for cumulative risk should include measures of risk from both environmental sources and social determinants. This study sought to better understand cumulative health risk from both social and environmental sources in a disadvantaged community in Texas. Key outcomes were determining what data are currently available for this assessment, clarifying data needs, identifying data gaps, and considering how those gaps could be filled. Analyses suggested that the traditionally defined EJ community in Port Arthur may have a lower environmental risk from air toxics than the rest of the City of Port Arthur (although the entire city has a higher risk than the average for the state), but may have a larger burden from social determinants of health. However, the results should be interpreted in light of the availability of data, the definitions of community boundaries, and the areal unit utilized. Continued focus on environmental justice communities and the cumulative risks faced by their residents is critical to protecting these residents and, ultimately, moving toward a more equitable distribution and acceptable level of risk throughout society.     

采用关键点控制和反馈调节,提高微生物学实验课质量

将全面质量管理理论引入微生物学实验课教学,形成以关键点控制和反馈调节为核心的教学质量保障系统,实践证明,可有效地促进学生积极思考,提高实验技能。对教学过程中“关键点”．“关键点控制”的含义和特性以及“反馈信号”、“反馈调节”的含义和类型做了系统的阐述。以具体实验为例,详细说明了实验课教学中关键点控制和反馈调节的实施过程,对于保证教学质量和客观、公正地分析评价教学效果具有重要意义。     

Pathway-Related Factors: The Potential for Human Data to Improve the Scientific Basis of Risk Assessment

The default uncertainty factors used for risk assessment are applied either to allow for different aspects of extrapolation of the dose-response curve or to allow for database deficiencies. Replacement of toxicokinetic or toxicodynamics defaults by chemical-specific data allows the calculation of a chemical-specific “data-derived factor”, which is the product of chemical-specific values and default uncertainty factors. Such chemical-specific composite values will improve the scientific basis of the risk assessment of that chemical, but the necessary chemical-specific data are rarely available. Categorical defaults related to pathways of elimination and mechanisms of toxicity could be used when the overall fate or mechanism is known, but there are no chemical-specific data sufficient to allow replacement of the default, and the development of an overall data-derived factor. The development of pathway-related categorical defaults is being undertaken using data on selected probe substrates for which adequate data are available. The concept and difficulties of this approach are illustrated using data for CYP1A2.     

Probabilistic methods for addressing uncertainty and variability in biological models: application to a toxicokinetic model

Population variability and uncertainty are important features of biological systems that must be considered when developing mathematical models for these systems. In this paper we present probability-based parameter estimation methods that account for such variability and uncertainty. Theoretical results that establish well-posedness and stability for these methods are discussed. A probabilistic parameter estimation technique is then applied to a toxicokinetic model for trichloroethylene using several types of simulated data. Comparison with results obtained using a standard, deterministic parameter estimation method suggests that the probabilistic methods are better able to capture population variability and uncertainty in model parameters.