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Decisions about the development of new marketed technologies or products invariably come with consequences for economy, society and the environment. Environmental and health risk assessment on the one hand and sustainability assessment on the other hand are tools that offer different but complementary information about such consequences. Conflicts or synergies between the two tools may arise when there are trade-offs between considerations of specific risks and safety versus long-term sustainability. There is a compelling case for a combined assessment of both sustainability and risks, also in support of a successful safe and sustainable-by-design (SSbD) approach, but this is not straightforward. We offer a roadmap showing when the two assessment tools should be applied together and how to combine them in a consistent way, to support more robust decision-making. Four alternative approaches are evaluated against six performance criteria to recommend an approach that makes use of the broader and more generic sustainability assessment as a baseline and includes iterative applications of risk and sustainability assessment elements to increase specificity, reliability and relevance of the assessment results. The recommended approach provides a basis for better-informed decisions about technology choices for policy and societal stakeholders. [ABSTRACT FROM AUTHOR]

Over the last decade the health and environmental research communities have made significant progress in collecting and improving access to genomic, toxicology, exposure, health, and disease data useful to health risk assessment. One of the barriers to applying these growing volumes of information in fields such as risk assessment is the lack of informatics tools to organize, curate, and evaluate thousands of journal publications and hundreds of databases to provide new insights on relationships among exposure, hazard, and disease burden. Many fields are developing ontologies as a way of organizing and analyzing large amounts of complex information from multiple scientific disciplines. Ontologies include a vocabulary of terms and concepts with defined logical relationships to each other. Building from the recently published exposure ontology and other relevant health and environmental ontologies, this article proposes an ontology for health risk assessment (RsO) that provides a structural framework for organizing risk assessment information and methods. The RsO is anchored by eight major concepts that were either identified by exploratory curations of the risk literature or the exposure-ontology working group as key for describing the risk assessment domain. These concepts are: (1) stressor, (2) receptor, (3) outcome, (4) exposure event, (5) dose-response approach, (6) dose-response metric, (7) uncertainty, and (8) measure of risk. We illustrate the utility of these concepts for the RsO with example curations of published risk assessments for ionizing radiation, arsenic in drinking water, and persistent pollutants in salmon. [ABSTRACT FROM AUTHOR]

Human exposure to preformed dialkylphosphates (DAPs) in food or the environment may affect the reliability of DAP urinary metabolites as biomarkers of organophosphate (OP) pesticide exposure. We conducted a study to investigate the presence of DAPs In indoor residential environments and their association with children's urinary DAP levels. We collected dust samples from homes in farmworker and urban communities (40 homes total, n = 79 samples) and up to two urine samples from resident children ages 3-6 years. We measured six DAPs in all samples and eight DAP-devolving OP pesticides in a subset of dust samples (n = 54). DAPs were detected in dust with diethylphosphate (DEP) being the most frequently detected (>60%); detection frequencies for other DAPs were <50%. DEP dust concentrations did not significantly differ between communities, nor were concentrations significantly correlated with concentrations of chlorpyrifos and diazinon, the most frequently detected dlethyl-OP pesticides (Spearman p = -0.41 to 0.38, P>0.05). Detection of DEP, chlorpyrifos, or diazinon, was not associated with DEP and/or DEP + diethylthiophosphate detection in urine (Kappa coefficients =--0.33 to 0.16). Finally, estimated non-dietary ingestion intake from DEP in dust was found to be <5% of the dose calculated from DEP levels in urine, suggesting that ingestion of dust is not a significant source of DAPs in urine if they are excreted unchanged. [ABSTRACT FROM AUTHOR]

Reliable exposure-based chemical characterization tools are needed to evaluate and prioritize in a rapid and efficient manner the more than tens of thousands of chemicals in current use. This study applies intake fraction (iF), the integrated incremental intake of a chemical per unit of emission, for a suite of indoor released compounds. A fugacity-based indoor mass-balance model was used to simulate the fate and transport of chemicals for three release scenarios: direct emissions to room air and surface applications to carpet and vinyl. Exposure through inhalation, dermal uptake, and nondietary ingestion was estimated. To compute iF, cumulative intake was summed from all exposure pathways for 20 years based on a scenario with two adults and a 1-year-old child who ages through the simulation. Overall iFs vary by application modes: air release (3.1 × 10-3 to 6.3 × 10-3), carpet application (3.8 × 10-5 to 6.2 × 10-3), and vinyl application (9.0 × 10-5 to 1.8 × 10-2). These iF values serve as initial estimates that offer important insights on variations among chemicals and the potential relative contribution of each pathway over a suite of compounds. The approach from this study is intended for exposure-based prioritization of chemicals released inside homes. [ABSTRACT FROM AUTHOR]

In this article the authors discuss the development of an Exposure Ontology (ExO) to formalize the conceptualization of exposure science and extend the ability to integrate and analyze exposure information with a broader context of environmental health. They argue that environmental health information resources lack the kind of exposure data necessary to translate molecular insights, understand environmental factors to diseases, and assess human health and ecological risks. Topics include an overview of exposure science, the existence of a framework for integrating exposure data with environmental health information resources, and the implementation of a phased strategy for developing the ExO.

The predictions of two source-to-dose models are evaluated with observed data collected in a village polluted by an operating secondary lead smelter. Both models were built up from several sub-models linked together and run using Monte-Carlo simulation. The first model system provides the distribution of the media-specific lead concentrations (air, soil, fruit, vegetables, and blood) in the whole area investigated. The second model provides an estimate of the concentration of exposure of specific individuals living in the study area. The predictions of the first model system were improved by performing a sensitivity analysis and using field data to correct the default value provided for the leaf wet density. However, in this case study, the model system tends to overestimate the exposure due to exposed vegetables. The second model was tested for nine children with contrasting exposure conditions. It managed to capture the blood levels for eight of them. In the last case, the exposure of the child by pathways not considered in the model may explain the failure of the model. The interest of this integrated model is to provide outputs with lower variance than the first model system, but further tests are necessary to conclude about its accuracy. [ABSTRACT FROM AUTHOR]

The objective of this study is to increase the understanding and transparency of chemical biotransfer modeling into meat and milk and explicitly confront the uncertainties in exposure assessments of chemicals that require such estimates. In cumulative exposure assessments that include food pathways, much of the overall uncertainty is attributable to the estimation of transfer into biota and through food webs. Currently, the most commonly used meat and milk-biotransfer models date back two decades and, in spite of their widespread use in multimedia exposure models, few attempts have been made to advance or improve the outdated and highly uncertain Kow regressions used in these models. Furthermore, in the range of Kow where meat and milk become the dominant human exposure pathways, these models often provide unrealistic rates and do not properly reflect the transfer dynamics. To address these issues, we developed a dynamic three-compartment cow model (called CKow), distinguishing lactating and nonlactating cows. For chemicals without available overall removal rates in the cow, a correlation is derived from measured values reported in the literature to predict this parameter from Kow. Results on carry over rates (COR) and biotransfer factors (BTF) demonstrate that a steady-state ratio between animal intake and meat concentrations is almost never reached. For meat, empirical data collected on short-term experiments need to be adjusted to provide estimates of average longer term behaviors. The performance of the new model in matching measurements is improved relative to existing models—thus reducing uncertainty. The CKow model is straightforward to apply at steady state for milk and dynamically for realistic exposure durations for meat COR. [ABSTRACT FROM AUTHOR]

The article discusses human exposure assessment as a step in estimating environmental and public health problems due to chemical emissions by an industrial product or service. The exposure assessment process involves measuring or modeling the magnitude, frequency and duration of contact between a harmful agent and a target population. The magnitude and variation of exposures to contaminants depend on the concentrations of contaminants in the exposure media and the exposure factors of the population. Human exposure factors include the behavioral, sociological and physiological characteristics of a population determining contact rates with food, air, water and soils.

The effectiveness of a probabilistic risk assessment (PRA) depends on the quality and relevance of the output from exposure and risk models, which, in turn, depends on the critical inputs to the assessment. These critical inputs are often in the form of probabilistic exposure factor distributions that are derived for the given risk scenario. Deriving probabilistic distributions for model inputs can be time consuming and subjective. The absence of a standard approach for developing these distributions can result in PRAs that are inconsistent and difficult to review by regulatory agencies. We present an approach that reduces subjectivity in the distribution development process without limiting the flexibility needed to prepare relevant PRAs. The approach requires two steps. First, we analyze data pooled at a population scale to (i) identify the most robust demographic descriptors within the population for a given exposure factor, (ii) partition the data into subsets based on these variables, and (iii) construct archetypal distributions for each subpopulation. Second, we sample from these archetypal distributions according to site- or scenario-specific conditions to simulate exposure factor values and use these values to construct the scenario-specific input distribution. The archetypal distributions developed through Step 1 provide a consistent basis for developing scenario-specific distributions so risk assessors will not have to repeatedly collect and analyze raw data for each new assessment. We demonstrate the approach for two commonly used exposure factors—body weight (BW) and exposure duration (ED)—using data that are representative of the U.S. population. For these factors we provide a first set of subpopulation-based archetypal distributions and demonstrate methods for using these distributions to construct relevant scenario-specific probabilistic exposure factor distributions. [ABSTRACT FROM AUTHOR]

Physiologically based pharmacokinetic (PBPK) modeling is a well-established toxicological tool designed to relate exposure to a target tissue dose. The emergence of federal and state programs for environmental health tracking and the availability of exposure monitoring through biomarkers creates the opportunity to apply PBPK models to estimate exposures to environmental contaminants from urine, blood, and tissue samples. However, reconstructing exposures for large populations is complicated by often having too few biomarker samples, large uncertainties about exposures, and large interindividual variability. In this paper, we use an illustrative case study to identify some of these difficulties, and for a process for confronting them by reconstructing population-scale exposures using Bayesian inference. The application consists of interpreting biomarker data from eight adult males with controlled exposures to trichloroethylene (TCE) as if the biomarkers were random samples from a large population with unknown exposure conditions. The TCE concentrations in blood from the individuals fell into two distinctly different groups even though the individuals were simultaneously in a single exposure chamber. We successfully reconstructed the exposure scenarios for both subgroups - although the reconstruction of one subgroup is different than what is believed to be the true experimental conditions. We were however unable to predict with high certainty the concentration of TCE in air.Journal of Exposure Analysis and Environmental Epidemiology (2004) 14, 204-213. doi:10.1038/sj.jea.7500314 [ABSTRACT FROM AUTHOR]

The human toxicity potential, a weighting scheme used to evaluate toxic emissions for life cycle assessment and toxics release inventories, is based on potential dose calculations and toxicity factors. This paper evaluates the variance in potential dose calculations that can be attributed to the uncetainty in chemical-specific input parameters as well as the variability in exposure factors and landscape parameters. A knowledge of the uncertainty allows us to assess the robustness of a decision based on the toxicity potential; a knowledge of the sources of uncertainty allows us to focus our resources if we want to reduce the uncertainty. The potential dose of 236 chemicals was assessed. The chemicals were grouped by dominant exposure route, and a Monte Carlo analysis was conducted for one representative chemical in each group. The variance is typically one to two orders of magnitude. For comparison, the point estimates in potential dose for 236 chemicals span ten orders of magnitude. Most of the variance in the potential dose is due to chemical-specific input parameters, especially half-lives, although exposure factors such as fish intake and the source of drinking water can be important for chemicals whose dominant exposure is through indirect routes. Landscape characteristics are generally of minor importance. [ABSTRACT FROM AUTHOR]

Soil is the thin outer zone of the earth's crust that supports rooted plants and is the product of climate and living organisms acting on rock. Throughout the world, soils are contaminated to some extent by local, regional, and global pollution sources of both natural and human origin. Sources of soil contamination are identified and discussed. With the possible exception of agricultural applications of pesticides and fertilizers, most contaminant releases to soil are not easily quantified and, as a result, remain highly uncertain. In establishing a comprehensive framework for human exposure to soil contaminants, it is revealed that such exposure occurs through multiple transfer processes. The process for linking human exposure to soil contact is considered and it is found that the magnitude and persistence of exposure depend not only on the level of soil contamination but also on physical and chemical properties of soil, chemical properties of the contaminant, and the frequency and duration of human factors such as occupational and recreational activities or the consumption of home-grown food, which result in direct and indirect soil contact. All of these factors possess some degree of variance that leads to probability distributions for representing total exposure and risk. [ABSTRACT FROM AUTHOR]

The article discusses the use of multimedia mass-balance modeling in environmental science and decision-making. According to the authors, a fundamental challenge of environmental chemistry is understanding and characterizing the amount of chemical pollutants in various media, such as air, water, and soil, and the ability to estimate chemical mass flows through these various media and geographical regions. They suggest that a quantitative modeling approach is needed that describes the environmental fate of chemicals in order to assess the hazards and risks posed to humans by chemical products. Topics include an overview of the development and evolution of the multimedia mass-balance model, its conceptualization and design, and the role of mass-balance models in science.

Approximately 13 million U.S. children lessthan 6 years old spend some time in early childhood education (ECE) facilities where they may be exposed to potentially harmful chemicals during critical periods of development. We measured five phthalate esters in indoor dust (n = 39) and indoor and outdoor air (n = 40 and 14, respectively) at ECE facilities in Northern California. Dust and airborne concentrations were used to perform a probabilistic health risk assessment to compare estimated exposures with risk levels established for chemicals causing reproductive toxicity and cancer under California's Proposition 65. Di(2-ethylhexyl) phthalate (DEHP) and butyl benzyl phthalate (BBzP) were the dominant phthalates present in floor dust (medians = 172.2 and 46.8 μg/g, respectively), and dibutyl phthalate (DBP), diethyl phthalate (DEP), and diisobutyl phthalate (DIBP) were the dominant phthalates in indoor air (medians = 0.52, 0.21, and 0.10 μg/m3, respectively). The risk assessment results indicate that 82-89% of children in California ECE had DBP exposure estimates exceeding reproductive health benchmarks. Further, 8-11% of children less than 2 years old had DEHP exposure estimates exceeding cancer benchmarks. This is the largest study to measure phthalate exposure in U.S. ECE facilities and findings indicate wide phthalate contamination and potential risk to developing children. [ABSTRACT FROM AUTHOR]

Particulate matter (PM) is a significant contributor to death and disease globally. This paper summarizes the work of an international expert group on the integration of human exposure to PM into life cycle impact assessment (LCIA), within the UNEP/SETAC Life Cycle Initiative. We review literature-derived intake fraction values (the fraction of emissions that are inhaled), based on emission release height and "archetypal" environment (indoor versus outdoor; urban, rural, or remote locations). Recommended intake fraction values are provided for primary PM10-2.5 (coarse particles), primary PM2.5 (fine particles), and secondary PM2.5 from SO2, NOx, and NH3. Intake fraction values vary by orders of magnitude among conditions considered. For outdoor primary PM2.5, representative intake fraction values (units: milligrams inhaled per kilogram emitted) for urban, rural, and remote areas, respectively, are 44, 3.8, and 0.1 for ground-level emissions, versus 26, 2.6, and 0.1 for an emission-weighted stack height. For outdoor secondary PM, source location and source characteristics typically have only a minor influence on the magnitude of the intake fraction (exception: intake fraction values can be an order of magnitude lower for remote-location emission than for other locations). Outdoor secondary PM2.5 intake fractions averaged over respective locations and stack heights are 0.89 (from SO2), 0.18 (NOx), and 1.7 (NH3). Estimated average intake fractions are greater for primary PM10-2.5 than for primary PM2.5 (21 versus 15), owing in part to differences in average emission height (lower, and therefore closer to people, for PM10-2.5 than PM2.5). For indoor emissions, typical intake fraction values are ∼1000-7000. This paper aims to provide as complete and consistent an archetype framework as possible, given current understanding of each pollutant. Values presented here facilitate incorporating regional impacts into LCIA for human health damage from PM. [ABSTRACT FROM AUTHOR]

We evaluated three exposure models with data obtained from measurements among workers who use "aerosol" solvent products in the vehicle repair industry and with field experiments using these products to simulate the same exposure conditions. The three exposure models were the (1) homogeneously mixed-one-box model, (2) multizone model, and (3) eddy-diffusion model. Temporally differentiated real-time breathing zone volatile organic compound (VOC) concentration measurements, integrated far-field area samples, and simulated experiments were used in estimating parameters, such as emission rates, diffusivity, and near-field dimensions. We assessed differences in model input requirements and their efficacy for predictive modeling. The One-box model was not able to resemble the temporal profile of exposure concentrations, but it performed well concerning time-weighted exposure over extended time periods. However, this model required an adjustment for spatial concentration gradients. Multizone models and diffusion-models may solve this problem. However, we found that the reliable use of both these models requires extensive field data to appropriately define pivotal parameters such as diffusivity or near-field dimensions. We conclude that it is difficult to apply these models for predicting VOC exposures in the workplace. However, for comparative exposure scenarios in life-cycle assessment they may be useful. [ABSTRACT FROM AUTHOR]

Abstract: Reliable estimates of inhalation intake of air pollution and its distribution among a specified population are important for environmental epidemiology, health risk assessment, urban planning, and environmental policy. We computed distributional characteristics of the inhalation intake of five pollutants for a group of ∼25,000 people (∼29,000 person-days) living in California''s South Coast Air Basin. Our approach incorporates four main inputs: temporally resolved information about people''s location (latitude and longitude), microenvironment, and activity level; temporally and spatially explicit model determinations of ambient concentrations; stochastically determined microenvironmental adjustment factors relating the exposure concentration to the ambient concentration; and, age-, gender-, and activity-specific breathing rates. Our study is restricted to pollutants of outdoor origin, i.e. it does not incorporate intake in a microenvironment from direct emissions into that microenvironment. Median estimated inhalation intake rates (μgd−1) are 53 for benzene, 5.1 for 1,3-butadiene, 8.7×10−4 for hexavalent chromium in fine particulate matter (Cr-PM2.5), 30 for diesel fine particulate matter (DPM2.5), and 68 for ozone. For the four primary pollutants studied, estimated median intake rates are higher for non-whites and for individuals in low-income households than for the population as a whole. For ozone, a secondary pollutant, the reverse is true. Accounting for microenvironmental adjustment factors, population mobility and temporal correlations between pollutant concentrations and breathing rates affects the estimated inhalation intake by 40% on average. The approach presented here could be extended to quantify the impact on intakes and intake distributions of proposed changes in emissions, air quality, and urban infrastructure. [Copyright &y& Elsevier]