ALBERT

Notes: Recent events have increased awareness of the risk posed by terrorist attacks. Bacillus anthracis has resurfaced in the 21st century as a deadly agent of bioterrorism because of its potential for causing massive civilian casualties. This analysis presents the results of a computer simulation of the dispersion of anthrax spores in a typical 50-story, high-rise building after an intentional release during a bioterrorist incident. The model simulates aerosol dispersion in the case of intensive, small-scale convection, which equalizes the concentration of anthrax spores over the building volume. The model can be used to predict the time interval required for spore dispersion throughout a building after a terrorist attack in a high-rise building. The analysis reveals that an aerosol release of even a relatively small volume of anthrax spores during a terrorist incident has the potential to quickly distribute concentrations that are infectious throughout the building.

Notes: Human health risk assessments use point values to develop risk estimates and thus impart a deterministic character to risk, which, by definition, is a probability phenomenon. The risk estimates are calculated based on individuals and then, using uncertainty factors (UFs), are extrapolated to the population that is characterized by variability. Regulatory agencies have recommended the quantification of the impact of variability in risk assessments through the application of probabilistic methods.In the present study, a framework that deals with the quantitative analysis of uncertainty (U) and variability (V) in target tissue dose in the population was developed by applying probabilistic analysis to physiologically-based toxicokinetic models. The mechanistic parameters that determine kinetics were described with probability density functions (PDFs). Since each PDF depicts the frequency of occurrence of all expected values of each parameter in the population, the combined effects of multiple sources of U/V were accounted for in the estimated distribution of tissue dose in the population, and a unified (adult and child) intraspecies toxicokinetic uncertainty factor UFH-TK was determined.The results show that the proposed framework accounts effectively for U/V in population toxicokinetics. The ratio of the 95th percentile to the 50th percentile of the annual average concentration of the chemical at the target tissue organ (i.e., the UFH-TK) varies with age. The ratio is equivalent to a unified intraspecies toxicokinetic UF, and it is one of the UFs by which the NOAEL can be divided to obtain the RfC/RfD. The 10-fold intraspecies UF is intended to account for uncertainty and variability in toxicokinetics (3.2×) and toxicodynamics (3.2×). This article deals exclusively with toxicokinetic component of UF.The framework provides an alternative to the default methodology and is advantageous in that the evaluation of toxicokinetic variability is based on the distribution of the effective target tissue dose, rather than applied dose. It allows for the replacement of the default adult and children intraspecies UF with toxicokinetic data-derived values and provides accurate chemical-specific estimates for their magnitude. It shows that proper application of probability and toxicokinetic theories can reduce uncertainties when establishing exposure limits for specific compounds and provide better assurance that established limits are adequately protective. It contributes to the development of a probabilistic noncancer risk assessment framework and will ultimately lead to the unification of cancer and noncancer risk assessment methodologies.

Notes: The treatment of uncertainties associated with modeling and risk assessment has recently attracted significant attention. The methodology and guidance for dealing with parameter uncertainty have been fairly well developed and quantitative tools such as Monte Carlo modeling are often recommended. However, the issue of model uncertainty is still rarely addressed in practical applications of risk assessment. The use of several alternative models to derive a range of model outputs or risks is one of a few available techniques. This article addresses the often-overlooked issue of what we call “modeler uncertainty,” i.e., difference in problem formulation, model implementation, and parameter selection originating from subjective interpretation of the problem at hand. This study uses results from the Fruit Working Group, which was created under the International Atomic Energy Agency (IAEA) BIOMASS program (BIOsphere Modeling and ASSessment). Model-model and model-data intercomparisons reviewed in this study were conducted by the working group for a total of three different scenarios. The greatest uncertainty was found to result from modelers' interpretation of scenarios and approximations made by modelers. In scenarios that were unclear for modelers, the initial differences in model predictions were as high as seven orders of magnitude. Only after several meetings and discussions about specific assumptions did the differences in predictions by various models merge. Our study shows that parameter uncertainty (as evaluated by a probabilistic Monte Carlo assessment) may have contributed over one order of magnitude to the overall modeling uncertainty. The final model predictions ranged between one and three orders of magnitude, depending on the specific scenario. This study illustrates the importance of problem formulation and implementation of an analytic-deliberative process in risk characterization.

Notes: This article presents the results of an analysis of the accident history data reported under section 112(r) of the Clean Air Act Amendments. These data provide a fairly complete record of the consequences of reportable accidental releases occurring during the time frame 1995–1999 in the U.S. chemical industry and covering 77 toxic and 63 flammable substances subject to the provisions of section 112(r). As such, these results are of fundamental interest to the affected communities, regulators, and insurers, as well as to owners and managers in the chemical industry. The results show the statistical associations between accident frequency and severity and a number of characteristics of reporting facilities, including their size, the hazardousness of the processes and chemicals inventoried, and the regulatory programs (in addition to section 112(r)) to which these facilities are subject. The results are interpreted in light of economic drivers of protective activity and regulatory priorities for monitoring and enforcement.

Notes: Unlike other waste streams, municipal solid waste (MSW) is collected manually, and MSW collection has recently been found to be among the highest-risk occupations in the United States. However, as for other occupational groups, actual total injury rates, including the great majority of injuries not compensated and those compensated by other insurance, are not known. In this article a predictive Bayesian method of assessing total injury rates from available information without computation is presented, and used to assess the actual numbers of musculoskeletal and dermal injuries requiring clinical care of MSW workers in Florida. Closed-form predictive Bayesian distributions that narrow progressively in response to information, representing both uncertainty and variability, are presented. Available information included workers' compensation (WC) data, worker population data, and safety records for one private and one public collection agency. Subjective input comprised epidemiological and medical judgment based on a review of 165 articles. The number of injuries was assessed at 3,146 annually in Florida, or 54 ± 18 injuries per 100 workers per year with 95% confidence. Further, WC data indicate that the injury rate is 50% higher for garbage collectors specifically, indicating a rate of approximately 80 per 100 workers. Results, though subject to uncertainty in worker numbers and classification and reporting bias, agreed closely with a survey of 251 MSW collectors, of whom 75% reported being injured (and 70% reported illness) within the past 12 months. The approach is recommended for assessment of total injury rates and, where sufficient information exists, for the more difficult assessment of occupational disease rates.

Notes: Statistical fatigue life of a ductile alloy specimen is traditionally divided into three stages, namely, crack nucleation, small crack growth, and large crack growth. Crack nucleation and small crack growth show a wide variation and hence a big spread on cycles versus crack length graph. Relatively, large crack growth shows a lesser variation. Therefore, different models are fitted to the different stages of the fatigue evolution process, thus treating different stages as different phenomena. With these independent models, it is impossible to predict one phenomenon based on the information available about the other phenomenon. Experimentally, it is easier to carry out crack length measurements of large cracks compared to nucleating cracks and small cracks. Thus, it is easier to collect statistical data for large crack growth compared to the painstaking effort it would take to collect statistical data for crack nucleation and small crack growth. This article presents a fracture mechanics-based stochastic model of fatigue crack growth in ductile alloys that are commonly encountered in mechanical structures and machine components. The model has been validated by Ray (1998) for crack propagation by various statistical fatigue data. Based on the model, this article proposes a technique to predict statistical information of fatigue crack nucleation and small crack growth properties that uses the statistical properties of large crack growth under constant amplitude stress excitation. The statistical properties of large crack growth under constant amplitude stress excitation can be obtained via experiments.

Notes: An occupational risk assessment for manganese (Mn) was performed based on benchmark dose analysis of data from two epidemiological studies providing dose-response information regarding the potential neurological effects of exposure to airborne Mn below the current Occupational Safety and Health Administration (OSHA) Permissible Exposure Level (PEL) of 5 mg Mn/m3. Based on a review of the scientific evidence regarding the toxicity of Mn, it was determined that the most appropriate measure of exposure to airborne Mn for the subclinical effects measured in these studies is recent (rather than historical or cumulative) concentration of Mn in respirable (rather than total) particulate. For each of the studies analyzed, the individual exposure and response data from the original study had been made available by the investigators. From these two studies benchmark concentrations calculated for eight endpoints ranged from 0.09 to 0.27 mg Mn/m3. From our evaluation of these results, and considering the fact that the subtle, subclinical effects represented by the neurological endpoints tested in these studies do not represent material impairment, we believe an appropriate occupational exposure guideline for manganese would be in the range of 0.1 to 0.3 mg Mn/m3, based on the respirable particulate fraction only, and expressed as an 8-hour time-weighted average.

Notes: The Safe Drinking Water Act Amendments of 1996 required U.S. utilities to report on drinking water quality to their customers annually, beginning in fall 1999, on the assumption that such reports would alert them to quality problems and perhaps mobilize pressure for improvement. A random sample of New Jersey customers read alternative versions of a water quality report, in an experiment on reactions to water quality information under U.S. Environmental Protection Agency (USEPA) rules. Experiment design was 2 × 3 + 1: two versions each—one with, one without, a violation of a health standard—of a report that was (1) Qualitative (without water quality numbers, thus not meeting USEPA rules); (2) Basic, with minimal information meeting the rules; or (3) Extended, adding reading aids and utility performance information; plus a control instrument without any hypothetical report. Results of ANOVA suggest the reports will have less effect than hoped or feared. These manipulations were successful: people reading the Qualitative versions were less likely to say that the report gave the amounts of substances found in the water, and those reading Violation versions were more likely to report a violation of a health standard. The main differences in responses to the report involved the judged adequacy of the information, and to a lesser extent responses on a Concern scale (constructed from measures of concern, judged risk, clean-up intentions, distrust of utility information, and doubt that the utility was doing all it could to improve water quality). Overall judgments of water quality and utility performance did not change, either relative to the controls or in before versus after responses. Qualitative reports performed worse than others, confirming the decision to have utilities report actual contaminant levels. Extended reports did only slightly better than the Basic versions on these measures. Many respondents had trouble identifying the presence or absence of substance amounts or violations, despite their seeming obviousness (e.g., in a “bottom line” summary on the front page of each report), suggesting many were not processing this information carefully. However, the pattern of responses for those who accurately identified the presence or absence of substance amounts or violations did not differ substantially from that for the group as a whole. Generic risk beliefs (serious local environmental problems; lack of control over risks to one's health) dominated demographic variables, attitudes toward utility water quality or trustworthiness, and the content and format of water quality reports in influencing concern about drinking water quality. Previous empirical and theoretical evidence for lack of change in public risk attitudes due to one-time or infrequent communications—e.g., role of personal experience, perseverance of prior trust or distrust—seems to be confirmed for annual water quality reports.