Systematic Evidence Mapping of Potential Exposure Pathways for Per- and Polyfluoroalkyl Substances Based on Measured Occurrence in Multiple Media.

Given that human biomonitoring surveys show per- and polyfluoroalkyl substances (PFAS) to be ubiquitous, humans can be exposed to PFAS through various sources, including drinking water, food, and indoor environmental media. Data on the nature and level of PFAS in residential environments are required to identify important pathways for human exposure. This work investigated important pathways of exposure to PFAS by reviewing, curating, and mapping evidence for the measured occurrence of PFAS in exposure media. Real-world occurrence for 20 PFAS was targeted primarily in media commonly related to human exposure (outdoor and indoor air, indoor dust, drinking water, food, food packaging, articles, and products, and soil). A systematic-mapping process was implemented to conduct title-abstract and full-text screening and to extract PECO-relevant primary data into comprehensive evidence databases. Parameters of interest included the following: sampling dates and locations, numbers of collection sites and participants, detection frequencies, and occurrence statistics. Detailed data were extracted on PFAS occurrence in indoor and environmental media from 229 references and on PFAS occurrence in human matrices where available from those references. Studies of PFAS occurrence became numerous after 2005. Studies were most abundant for PFOA (80% of the references) and PFOS (77%). Many studies analyzed additional PFAS, particularly, PFNA and PFHxS (60% of references each). Food (38%) and drinking water (23%) were the commonly studied media. Most studies found detectable levels of PFAS, and detectable levels were reported in a majority of states in the United States. Half or more of the limited studies for indoor air and products detected PFAS in 50% or more of the collected samples. The resulting databases can inform problem formulation for systematic reviews to address specific PFAS exposure queries and questions, support prioritization of PFAS sampling, and inform PFAS exposure measurement studies. The search strategy should be extended and implemented to support living evidence review in this rapidly advancing area.

Assessment of Non-Occupational 1,4-Dioxane Exposure Pathways from Drinking Water and Product Use.

1,4-Dioxane is a persistent and mobile organic chemical that has been found by the United States Environmental Protection Agency (USEPA) to be an unreasonable risk to human health in some occupational contexts. 1,4-Dioxane is released into the environment as industrial waste and occurs in some personal-care products as an unintended byproduct. However, limited exposure assessments have been conducted outside of an occupational context. In this study, the USEPA simulation modeling tool, Stochastic Human Exposure and Dose Simulator-High Throughput (SHEDS-HT), was adapted to estimate the exposure and chemical mass released down the drain (DTD) from drinking water consumption and product use. 1,4-Dioxane concentrations measured in drinking water and consumer products were used by SHEDS-HT to evaluate and compare the contributions of these sources to exposure and mass released DTD. Modeling results showed that compared to people whose daily per capita exposure came from only products (2.29 × 10-7 to 2.92 × 10-7 mg/kg/day), people exposed to both contaminated water and product use had higher per capita median exposures (1.90 × 10-6 to 4.27 × 10-6 mg/kg/day), with exposure mass primarily attributable to water consumption (75-91%). Last, we demonstrate through simulation that while a potential regulatory action could broadly reduce DTD release, the proportional reduction in exposure would be most significant for people with no or low water contamination.

Advances on a Decision Analytic Approach to Exposure-Based Chemical Prioritization.

The volume and variety of manufactured chemicals is increasing, although little is known about the risks associated with the frequency and extent of human exposure to most chemicals. The EPA and the recent signing of the Lautenberg Act have both signaled the need for high-throughput methods to characterize and screen chemicals based on exposure potential, such that more comprehensive toxicity research can be informed. Prior work of Mitchell et al. using multicriteria decision analysis tools to prioritize chemicals for further research is enhanced here, resulting in a high-level chemical prioritization tool for risk-based screening. Reliable exposure information is a key gap in currently available engineering analytics to support predictive environmental and health risk assessments. An elicitation with 32 experts informed relative prioritization of risks from chemical properties and human use factors, and the values for each chemical associated with each metric were approximated with data from EPA's CP_CAT database. Three different versions of the model were evaluated using distinct weight profiles, resulting in three different ranked chemical prioritizations with only a small degree of variation across weight profiles. Future work will aim to include greater input from human factors experts and better define qualitative metrics.

Integrating exposure to chemicals in building materials during use stage.

PURPOSE: There do not currently exist scientifically defensible ways to consistently characterize the human exposures (via various pathways) to near-field chemical emissions and associated health impacts during the use stage of building materials. The present paper thus intends to provide a roadmap which summarizes the current status and guides future development for integrating into LCA the chemical exposures and health impacts on various users of building materials, with a focus on building occupants. METHODS: We first review potential human health impacts associated with the substances in building materials and the methods used to mitigate these impacts, also identifying several of the most important online data resources. A brief overview of the necessary steps for characterizing use stage chemical exposures and health impacts for building materials is then provided. Finally, we propose a systematic approach to integrate the use stage exposures and health impacts into building material LCA and describe its components, and then present a case study illustrating the application of the proposed approach to two representative chemicals: formaldehyde and methylene diphenyl diisocyanate (MDI) in particleboard products. RESULTS AND DISCUSSION: Our proposed approach builds on the coupled near-field and far-field framework proposed by Fantke et al. (Environ Int 94:508-518, 2016), which is based on the product intake fraction (PiF) metric proposed by Jolliet et al. (Environ Sci Technol 49:8924-8931, 2015), The proposed approach consists of three major components: characterization of product usage and chemical content, human exposures, and toxicity, for which available methods and data sources are reviewed and research gaps are identified. The case study illustrates the difference in dominant exposure pathways between formaldehyde and MDI and also highlights the impact of timing and use duration (e.g., the initial 50 days of the use stage vs. the remaining 15 years) on the exposures and health impacts for the building occupants. CONCLUSIONS: The proposed approach thus provides the methodological basis for integrating into LCA the human health impacts associated with chemical exposures during the use stage of building materials. Data and modeling gaps which currently prohibit the application of the proposed systematic approach are discussed, including the need for chemical composition data, exposure models, and toxicity data. Research areas that are not currently focused on are also discussed, such as worker exposures and complex materials. Finally, future directions for integrating the use stage impacts of building materials into decision making in a tiered approach are discussed.

Episodic Impacts from California Wildfires Identified in Las Vegas Near-Road Air Quality Monitoring.

Air pollutant concentrations near major highways are usually attributed to a combination of nearby traffic emissions and regional background, and generally presumed to be additive in nature. During a near-road measurement study conducted in Las Vegas, NV, the effects of distant wildfires on regional air quality were indicated over a several day period in the summer of 2009. Area-wide elevated particulate levoglucosan (maximum of 0.83 µg/m(3)) and roadside measurements of ultraviolet light-absorbing particulate matter (UVPM) in comparison to black carbon (Delta-C) were apparent over the three-day period. Back-trajectory modeling and satellite images supported the measurement results and indicated the transport of air pollutants from wildfires burning in southern California. Separating roadside measurements under apparent biomass burning event (Delta-C > 1000 ng m(-3)) and nonevent (Delta-C < 1000 ng m(-3)) periods, and constraining to specific days of week, wind speed range, wind direction from the road and traffic volume range, roadside carbon monoxide, black carbon, total particle number count (20-200 nm), and accumulation mode particle number count (100-200 nm) increased by 65%, 146%, 58%, and 366%, respectively, when biomass smoke was indicated. Meanwhile, ultrafine particles (20-100 nm) decreased by 35%. This episode indicates that the presence of aged wildfire smoke may interact with freshly emitted ultrafine particles, resulting in a decrease of particles in the ultrafine mode.

Conceptual Framework To Extend Life Cycle Assessment Using Near-Field Human Exposure Modeling and High-Throughput Tools for Chemicals.

Life Cycle Assessment (LCA) is a decision-making tool that accounts for multiple impacts across the life cycle of a product or service. This paper presents a conceptual framework to integrate human health impact assessment with risk screening approaches to extend LCA to include near-field chemical sources (e.g., those originating from consumer products and building materials) that have traditionally been excluded from LCA. A new generation of rapid human exposure modeling and high-throughput toxicity testing is transforming chemical risk prioritization and provides an opportunity for integration of screening-level risk assessment (RA) with LCA. The combined LCA and RA approach considers environmental impacts of products alongside risks to human health, which is consistent with regulatory frameworks addressing RA within a sustainability mindset. A case study is presented to juxtapose LCA and risk screening approaches for a chemical used in a consumer product. The case study demonstrates how these new risk screening tools can be used to inform toxicity impact estimates in LCA and highlights needs for future research. The framework provides a basis for developing tools and methods to support decision making on the use of chemicals in products.

SHEDS-HT: an integrated probabilistic exposure model for prioritizing exposures to chemicals with near-field and dietary sources.

United States Environmental Protection Agency (USEPA) researchers are developing a strategy for high-throughput (HT) exposure-based prioritization of chemicals under the ExpoCast program. These novel modeling approaches for evaluating chemicals based on their potential for biologically relevant human exposures will inform toxicity testing and prioritization for chemical risk assessment. Based on probabilistic methods and algorithms developed for The Stochastic Human Exposure and Dose Simulation Model for Multimedia, Multipathway Chemicals (SHEDS-MM), a new mechanistic modeling approach has been developed to accommodate high-throughput (HT) assessment of exposure potential. In this SHEDS-HT model, the residential and dietary modules of SHEDS-MM have been operationally modified to reduce the user burden, input data demands, and run times of the higher-tier model, while maintaining critical features and inputs that influence exposure. The model has been implemented in R; the modeling framework links chemicals to consumer product categories or food groups (and thus exposure scenarios) to predict HT exposures and intake doses. Initially, SHEDS-HT has been applied to 2507 organic chemicals associated with consumer products and agricultural pesticides. These evaluations employ data from recent USEPA efforts to characterize usage (prevalence, frequency, and magnitude), chemical composition, and exposure scenarios for a wide range of consumer products. In modeling indirect exposures from near-field sources, SHEDS-HT employs a fugacity-based module to estimate concentrations in indoor environmental media. The concentration estimates, along with relevant exposure factors and human activity data, are then used by the model to rapidly generate probabilistic population distributions of near-field indirect exposures via dermal, nondietary ingestion, and inhalation pathways. Pathway-specific estimates of near-field direct exposures from consumer products are also modeled. Population dietary exposures for a variety of chemicals found in foods are combined with the corresponding chemical-specific near-field exposure predictions to produce aggregate population exposure estimates. The estimated intake dose rates (mg/kg/day) for the 2507 chemical case-study spanned 13 orders of magnitude. SHEDS-HT successfully reproduced the pathway-specific exposure results of the higher-tier SHEDS-MM for a case-study pesticide and produced median intake doses significantly correlated (p<0.0001, R2=0.39) with medians inferred using biomonitoring data for 39 chemicals from the National Health and Nutrition Examination Survey (NHANES). Based on the favorable performance of SHEDS-HT with respect to these initial evaluations, we believe this new tool will be useful for HT prediction of chemical exposure potential.

High-throughput models for exposure-based chemical prioritization in the ExpoCast project.

The United States Environmental Protection Agency (U.S. EPA) must characterize potential risks to human health and the environment associated with manufacture and use of thousands of chemicals. High-throughput screening (HTS) for biological activity allows the ToxCast research program to prioritize chemical inventories for potential hazard. Similar capabilities for estimating exposure potential would support rapid risk-based prioritization for chemicals with limited information; here, we propose a framework for high-throughput exposure assessment. To demonstrate application, an analysis was conducted that predicts human exposure potential for chemicals and estimates uncertainty in these predictions by comparison to biomonitoring data. We evaluated 1936 chemicals using far-field mass balance human exposure models (USEtox and RAIDAR) and an indicator for indoor and/or consumer use. These predictions were compared to exposures inferred by Bayesian analysis from urine concentrations for 82 chemicals reported in the National Health and Nutrition Examination Survey (NHANES). Joint regression on all factors provided a calibrated consensus prediction, the variance of which serves as an empirical determination of uncertainty for prioritization on absolute exposure potential. Information on use was found to be most predictive; generally, chemicals above the limit of detection in NHANES had consumer/indoor use. Coupled with hazard HTS, exposure HTS can place risk earlier in decision processes. High-priority chemicals become targets for further data collection.

Computational estimates of daily aggregate exposure to PFOA/PFOS from 2011 to 2017 using a basic intake model.

BACKGROUND: Human exposure to per- and polyfluoroalkyl substances has been modeled to estimate serum concentrations. Given that the production and use of these compounds have decreased in recent years, especially PFOA and PFOS, and that additional concentration data have become available from the US and other industrialized countries over the past decade, aggregate median intakes of these two compounds were estimated using more recent data. METHODS: Summary statistics from secondary sources were collected, averaged, and mapped for indoor and outdoor air, water, dust, and soil for PFOA and PFOS to estimate exposures for adults and children. European dietary intake estimates were used to estimate daily intake from food. RESULTS: In accordance with decreased concentrations in media, daily intake estimates among adults, i.e., 40 ng/day PFOA and 40 ng/day PFOS, are substantially lower than those reported previously, as are children's estimates of 14 ng/day PFOA and 17 ng/day PFOS. Using a first-order pharmacokinetic model, these results compare favorably to the National Health and Nutrition Examination Survey serum concentration measurements. CONCLUSION: Concomitant blood concentrations support this enhanced estimation approach that captures the decline of PFOA/PFOS serum concentration over a decade.

A Scoping Assessment of Implemented Toxicokinetic Models of Per- and Polyfluoro-Alkyl Substances, with a Focus on One-Compartment Models.

Toxicokinetic (TK) models have been used for decades to estimate concentrations of per-and polyfluoroalkyl substances (PFAS) in serum. However, model complexity has varied across studies depending on the application and the state of the science. This scoping effort seeks to systematically map the current landscape of PFAS TK models by categorizing different trends and similarities across model type, PFAS, and use scenario. A literature review using Web of Science and SWIFT-Review was used to identify TK models used for PFAS. The assessment covered publications from 2005-2020. PFOA, the PFAS for which most models were designed, was included in 69 of the 92 papers, followed by PFOS with 60, PFHxS with 22, and PFNA with 15. Only 4 of the 92 papers did not include analysis of PFOA, PFOS, PFNA, or PFHxS. Within the corpus, 50 papers contained a one-compartment model, 17 two-compartment models were found, and 33 used physiologically based pharmacokinetic (PBTK) models. The scoping assessment suggests that scientific interest has centered around two chemicals-PFOA and PFOS-and most analyses use one-compartment models in human exposure scenarios.

Toward a blended ontology: applying knowledge systems to compare therapeutic and toxicological nanoscale domains.

Bionanomedicine and environmental research share need common terms and ontologies. This study applied knowledge systems, data mining, and bibliometrics used in nano-scale ADME research from 1991 to 2011. The prominence of nano-ADME in environmental research began to exceed the publication rate in medical research in 2006. That trend appears to continue as a result of the growing products in commerce using nanotechnology, that is, 5-fold growth in number of countries with nanomaterials research centers. Funding for this research virtually did not exist prior to 2002, whereas today both medical and environmental research is funded globally. Key nanoparticle research began with pharmacology and therapeutic drug-delivery and contrasting agents, but the advances have found utility in the environmental research community. As evidence ultrafine aerosols and aquatic colloids research increased 6-fold, indicating a new emphasis on environmental nanotoxicology. User-directed expert elicitation from the engineering and chemical/ADME domains can be combined with appropriate Boolean logic and queries to define the corpus of nanoparticle interest. The study combined pharmacological expertise and informatics to identify the corpus by building logical conclusions and observations. Publication records informatics can lead to an enhanced understanding the connectivity between fields, as well as overcoming the differences in ontology between the fields.

Exposure Prioritization (Ex Priori): A Screening-Level High-Throughput Chemical Prioritization Tool.

To estimate potential chemical risk, tools are needed to prioritize potential exposures for chemicals with minimal data. Consumer product exposures are a key pathway, and variability in consumer use patterns is an important factor. We designed Ex Priori, a flexible dashboard-type screening-level exposure model, to rapidly visualize exposure rankings from consumer product use. Ex Priori is Excel-based. Currently, it is parameterized for seven routes of exposure for 1108 chemicals present in 228 consumer product types. It includes toxicokinetics considerations to estimate body burden. It includes a simple framework for rapid modeling of broad changes in consumer use patterns by product category. Ex Priori rapidly models changes in consumer user patterns during the COVID-19 pandemic and instantly shows resulting changes in chemical exposure rankings by body burden. Sensitivity analysis indicates that the model is sensitive to the air emissions rate of chemicals from products. Ex Priori's simple dashboard facilitates dynamic exploration of the effects of varying consumer product use patterns on prioritization of chemicals based on potential exposures. Ex Priori can be a useful modeling and visualization tool to both novice and experienced exposure modelers and complement more computationally intensive population-based exposure models.

The Residential Population Generator (RPGen): Parameterization of Residential, Demographic, and Physiological Data to Model Intraindividual Exposure, Dose, and Risk.

Exposure to chemicals is influenced by associations between the individual's location and activities as well as demographic and physiological characteristics. Currently, many exposure models simulate individuals by drawing distributions from population-level data or use exposure factors for single individuals. The Residential Population Generator (RPGen) binds US surveys of individuals and households and combines the population with physiological characteristics to create a synthetic population. In general, the model must be supported by internal consistency; i.e., values that could have come from a single individual. In addition, intraindividual variation must be representative of the variation present in the modeled population. This is performed by linking individuals and similar households across income, location, family type, and house type. Physiological data are generated by linking census data to National Health and Nutrition Examination Survey data with a model of interindividual variation of parameters used in toxicokinetic modeling. The final modeled population data parameters include characteristics of the individual's community (region, state, urban or rural), residence (size of property, size of home, number of rooms), demographics (age, ethnicity, income, gender), and physiology (body weight, skin surface area, breathing rate, cardiac output, blood volume, and volumes for body compartments and organs). RPGen output is used to support user-developed chemical exposure models that estimate intraindividual exposure in a desired population. By creating profiles and characteristics that determine exposure, synthetic populations produced by RPGen increases the ability of modelers to identify subgroups potentially vulnerable to chemical exposures. To demonstrate application, RPGen is used to estimate exposure to Toluene in an exposure modeling case example.

Enhancing life cycle chemical exposure assessment through ontology modeling.

In its 2014 report, A Framework Guide for the Selection of Chemical Alternatives, the National Academy of Sciences placed increased emphasis on comparative exposure assessment throughout the life cycle (i.e., from manufacturing to end-of-life) of a chemical. The inclusion of the full life cycle greatly increases the data demands for exposure assessments, including both the quantity and type of data. High throughput tools for exposure estimation add to this challenge by requiring rapid accessibility to data. In this work, ontology modeling was used to bridge the domains of exposure modeling and life cycle inventory modeling to facilitate data sharing and integration. The exposure ontology, ExO, is extended to describe human exposure to consumer products, while an inventory modeling ontology, LciO, is formulated to support automated data mining. The core ontology pieces are connected using a bridging ontology and discussed through a theoretical example to demonstrate how data from LCA can be leveraged to support rapid exposure modeling within a life cycle context.

Efficiency of sampling and analysis of asbestos fibers on filter media: implications for exposure assessment.

To measure airborne asbestos and other fibers, an air sample must represent the actual number and size of fibers. Typically, mixed cellulose ester (MCE, 0.45 or 0.8 microm pore size) and, to a much lesser extent, capillary-pore polycarbonate (PC, 0.4 microm pore size) membrane filters are used to collect airborne asbestos for count measurement and fiber size analysis. In this research study, chrysotile asbestos (fibers both shorter and longer than 5 microm) were generated in an aerosol chamber and sampled by 25 mm diameter MCE filter media to compare the fiber retention efficiency of 0.45 microm pore size filters vs. 0.8 microm pore size filter media. In addition, the effect of plasma etching times on fiber densities was evaluated. This study demonstrated a significant difference in fiber retention efficiency between 0.45 microm and 0.8 microm pore size MCE filters for asbestos aerosols (structures longer than or equal to 0.5 microm length). The fiber retention efficiency of a 0.45 microm pore size MCE filter is statistically significantly higher than that of the 0.8 microm pore size MCE filter. However, for asbestos structures longer than 5 microm, there is no statistically significant difference between the fiber retention efficiencies of the 0.45 microm and 0.8 microm pore size MCE filters. The mean density of asbestos fibers (longer than or equal to 0.5 microm) increased with etching time. Doubling the etching time increased the asbestos filter loading in this study by an average of 13%. The amount of plasma etching time had no effect on the filter loading for fibers longer than 5 microm. Many asbestos exposure risk models attribute health effects to fibers longer than 5 microm. In these models, both the 0.45 microm and 0.8 microm pore size MCE filter can produce suitable estimates of the airborne asbestos concentrations. However, some models suggest a more significant role for asbestos fibers shorter than 5 microm. Exposure monitoring for these models should consider only the 0.45 microm pore size MCE filters as recommended by the U.S. Environmental Protection Agency Asbestos Hazard Emergency Response Act (AHERA) protocol and other methods.

Advancements in Life Cycle Human Exposure and Toxicity Characterization.

BACKGROUND: The Life Cycle Initiative, hosted at the United Nations Environment Programme, selected human toxicity impacts from exposure to chemical substances as an impact category that requires global guidance to overcome current assessment challenges. The initiative leadership established the Human Toxicity Task Force to develop guidance on assessing human exposure and toxicity impacts. Based on input gathered at three workshops addressing the main current scientific challenges and questions, the task force built a roadmap for advancing human toxicity characterization, primarily for use in life cycle impact assessment (LCIA). OBJECTIVES: The present paper aims at reporting on the outcomes of the task force workshops along with interpretation of how these outcomes will impact the practice and reliability of toxicity characterization. The task force thereby focuses on two major issues that emerged from the workshops, namely considering near-field exposures and improving dose­response modeling. DISCUSSION: The task force recommended approaches to improve the assessment of human exposure, including capturing missing exposure settings and human receptor pathways by coupling additional fate and exposure processes in consumer and occupational environments (near field) with existing processes in outdoor environments (far field). To quantify overall aggregate exposure, the task force suggested that environments be coupled using a consistent set of quantified chemical mass fractions transferred among environmental compartments. With respect to dose­response, the task force was concerned about the way LCIA currently characterizes human toxicity effects, and discussed several potential solutions. A specific concern is the use of a (linear) dose­response extrapolation to zero. Another concern addresses the challenge of identifying a metric for human toxicity impacts that is aligned with the spatiotemporal resolution of present LCIA methodology, yet is adequate to indicate health impact potential. CONCLUSIONS: Further research efforts are required based on our proposed set of recommendations for improving the characterization of human exposure and toxicity impacts in LCIA and other comparative assessment frameworks. https://doi.org/10.1289/EHP3871.

Comparative, collaborative, and integrative risk governance for emerging technologies.

Various emerging technologies challenge existing governance processes to identify, assess, and manage risk. Though the existing risk-based paradigm has been essential for assessment of many chemical, biological, radiological, and nuclear technologies, a complementary approach may be warranted for the early-stage assessment and management challenges of high uncertainty technologies ranging from nanotechnology to synthetic biology to artificial intelligence, among many others. This paper argues for a risk governance approach that integrates quantitative experimental information alongside qualitative expert insight to characterize and balance the risks, benefits, costs, and societal implications of emerging technologies. Various articles in scholarly literature have highlighted differing points of how to address technological uncertainty, and this article builds upon such knowledge to explain how an emerging technology risk governance process should be driven by a multi-stakeholder effort, incorporate various disparate sources of information, review various endpoints and outcomes, and comparatively assess emerging technology performance against existing conventional products in a given application area. At least in the early stages of development when quantitative data for risk assessment remain incomplete or limited, such an approach can be valuable for policymakers and decision makers to evaluate the impact that such technologies may have upon human and environmental health.

Beyond responsible conduct in research: new pedagogies to address macroethics of nanobiotechnologies.

A team of engineers, scientists, ethicists, and educational specialists are enhancing Duke University's Responsible Conduct of Research (RCR) program to ensure that graduate-level researchers in emerging fields are adequately prepared when confronted with macroethical issues associated with applications of new and emerging medical technologies. The focus is on nanoscale laboratory research conducted in the Center for Biologically Inspired Materials and Material Systems and the Center for Biological Tissue Engineering. Most present RCR programs address methodological ethics of the individual researcher or practitioner (i.e., microethical) issues, analogous to Kohlberg's theory of moral development. The resultant model from this project is the basis for departmental, center, and other more targeted ethical challenges stemming from research in emerging technologies, designed to provide comprehensive RCR training. The research successfully identified new ways of teaching students about macroethical issues (i.e., those that affect society). Three main dimensions of ethics in nanotechnology-related research are being stressed, namely, awareness, ethical decision making, and behavior. Workshops appear to enhance awareness of the ethical issues associated with emerging technologies. To date, attempts to affect decision making have been difficult, although in this study workshops were an effective means of identifying strategies to address ethical issues. A principal lesson learned has been the importance of providing a context for macroethical issues. For example, the workshop where an expert presented the technical aspects of environmental consequences of carbon nanotubes led to statistically significant differences between pre- and postworkshop understanding of societal risks. Conversely, in a workshop without the technical introduction, little difference was observed. This indicates that the stage of students' ethical understanding is an important determinant of effectiveness of the education method needed.

Comparison of soil sampling and analytical methods for asbestos at the Sumas Mountain Asbestos Site-Working towards a toolbox for better assessment.

Established soil sampling methods for asbestos are inadequate to support risk assessment and risk-based decision making at Superfund sites due to difficulties in detecting asbestos at low concentrations and difficulty in extrapolating soil concentrations to air concentrations. Environmental Protection Agency (EPA)'s Office of Land and Emergency Management (OLEM) currently recommends the rigorous process of Activity Based Sampling (ABS) to characterize site exposures. The purpose of this study was to compare three soil analytical methods and two soil sampling methods to determine whether one method, or combination of methods, would yield more reliable soil asbestos data than other methods. Samples were collected using both traditional discrete ("grab") samples and incremental sampling methodology (ISM). Analyses were conducted using polarized light microscopy (PLM), transmission electron microscopy (TEM) methods or a combination of these two methods. Data show that the fluidized bed asbestos segregator (FBAS) followed by TEM analysis could detect asbestos at locations that were not detected using other analytical methods; however, this method exhibited high relative standard deviations, indicating the results may be more variable than other soil asbestos methods. The comparison of samples collected using ISM versus discrete techniques for asbestos resulted in no clear conclusions regarding preferred sampling method. However, analytical results for metals clearly showed that measured concentrations in ISM samples were less variable than discrete samples.