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Ferroalloy production can release a number of metals into the environment, of which manganese (Mn) is of major concern. Other elements include lead, iron, zinc, copper, chromium, and cadmium. Mn exposure derived from settled dust and suspended aerosols can cause a variety of adverse neurological effects to chronically exposed individuals. To better estimate the current levels of exposure, this study quantified the metal levels in dust collected inside homes (n=85), outside homes (n=81), in attics (n=6), and in surface soil (n=252) in an area with historic ferroalloy production. Metals contained in indoor and outdoor dust samples were quantified using inductively coupled plasma optical emission spectroscopy, whereas attic and soil measurements were made with a X-ray fluorescence instrument. Mean Mn concentrations in soil (4600 μg/g) and indoor dust (870 μg/g) collected within 0.5 km of a plant exceeded levels previously found in suburban and urban areas, but did decrease outside 1.0 km to the upper end of background concentrations. Mn concentrations in attic dust were ~120 times larger than other indoor dust levels, consistent with historical emissions that yielded high airborne concentrations in the region. Considering the potential health effects that are associated with chronic Mn inhalation and ingestion exposure, remediation of soil near the plants and frequent, on-going hygiene indoors may decrease residential exposure and the likelihood of adverse health effects. [ABSTRACT FROM AUTHOR]

Characterization of environmental exposures to population subgroups within the National Children's Study (NCS), or other large-scale human environmental health studies is essential for developing a high-quality data platform for subsequent investigations. A computational formulation utilizing the tiered exposure ranking framework is presented for calculating inhalation exposure indices (EIs) for population subgroups. This formulation employs a probabilistic approach and combines information from diverse, publicly available exposure-relevant databases and information on biological mechanisms, for ranking study locations or population subgroups with respect to potential for specific end point-related environmental exposures. These EIs capture and summarize, within a set of numerical values/ranges, complex distributions of potential exposures to multiple airborne contaminants. These estimates capture spatial and demographic variability within each study segment, and allow for the relative comparison of study locations based on different statistical metrics of exposures. The EI formulation was applied to characterize and rank segments within Queens County, NY, which is one of the Vanguard centers for the NCS. Inhalation EI estimates relevant to respiratory outcomes, and potentially to pregnancy outcomes (low birth weight and preterm birth rates) were calculated at the study segment level. Results indicate that there is substantial variability across the study segments in Queens County, NY, and within segments, and showed an exposure gradient across the study segments that can help guide and target environmental and personal exposure sampling efforts in this county. The results also serve as an example application of the EI for use in other exposure and outcome studies. [ABSTRACT FROM AUTHOR]

A Bayesian hierarchical model was developed to estimate the parameters in a physiologically based pharmacokinetic (PBPK) model for chloroform using prior information and biomarker data from different exposure pathways. In particular, the model provides a quantitative description of the changes in physiological parameters associated with hot-water bath and showering scenarios. Through Bayesian inference, uncertainties in the PBPK parameters were reduced from the prior distributions. Prediction of biomarker data with the calibrated PBPK model was improved by the calibration. The posterior results indicate that blood flow rates varied under two different exposure scenarios, with a two-fold increase of the skin's blood flow rate predicted in the hot-bath scenario. This result highlights the importance of considering scenario-specific parameters in PBPK modeling. To demonstrate the application of a probability approach in toxicological assessment, results from the posterior distributions from this calibrated model were used to predict target tissue dose based on the rate of chloroform metabolized in liver. This study demonstrates the use of the Bayesian approach to optimize PBPK model parameters for typical household exposure scenarios. [ABSTRACT FROM AUTHOR]

A conceptual/computational framework for exposure reconstruction from biomarker data combined with auxiliary exposure-related data is presented, evaluated with example applications, and examined in the context of future needs and opportunities. This framework employs physiologically based toxicokinetic (PBTK) modeling in conjunction with numerical “inversion” techniques. To quantify the value of different types of exposure data “accompanying” biomarker data, a study was conducted focusing on reconstructing exposures to chlorpyrifos, from measurements of its metabolite levels in urine. The study employed biomarker data as well as supporting exposure-related information from the National Human Exposure Assessment Survey (NHEXAS), Maryland, while the MENTOR-3P system (Modeling ENvironment for TOtal Risk with Physiologically based Pharmacokinetic modeling for Populations) was used for PBTK modeling. Recently proposed, simple numerical reconstruction methods were applied in this study, in conjunction with PBTK models. Two types of reconstructions were studied using (a) just the available biomarker and supporting exposure data and (b) synthetic data developed via augmenting available observations. Reconstruction using only available data resulted in a wide range of variation in estimated exposures. Reconstruction using synthetic data facilitated evaluation of numerical inversion methods and characterization of the value of additional information, such as study-specific data that can be collected in conjunction with the biomarker data. Although the NHEXAS data set provides a significant amount of supporting exposure-related information, especially when compared to national studies such as the National Health and Nutrition Examination Survey (NHANES), this information is still not adequate for detailed reconstruction of exposures under several conditions, as demonstrated here. The analysis presented here provides a starting point for introducing improved designs for future biomonitoring studies, from the perspective of exposure reconstruction; identifies specific limitations in existing exposure reconstruction methods that can be applied to population biomarker data; and suggests potential approaches for addressing exposure reconstruction from such data.Journal of Exposure Science and Environmental Epidemiology (2009) 19, 149–171; doi:10.1038/jes.2008.9; published online 26 March 2008 [ABSTRACT FROM AUTHOR]

This article presents an integrated, biologically based, source-to-dose assessment framework for modeling multimedia/multipathway/multiroute exposures to arsenic. Case studies demonstrating this framework are presented for three US counties (Hunderton County, NJ; Pima County, AZ; and Franklin County, OH), representing substantially different conditions of exposure. The approach taken utilizes the Modeling ENvironment for TOtal Risk studies (MENTOR) in an implementation that incorporates and extends the approach pioneered by Stochastic Human Exposure and Dose Simulation (SHEDS), in conjunction with a number of available databases, including NATA, NHEXAS, CSFII, and CHAD, and extends modeling techniques that have been developed in recent years. Model results indicate that, in most cases, the food intake pathway is the dominant contributor to total exposure and dose to arsenic. Model predictions are evaluated qualitatively by comparing distributions of predicted total arsenic amounts in urine with those derived using biomarker measurements from the NHEXAS — Region V study: the population distributions of urinary total arsenic levels calculated through MENTOR and from the NHEXAS measurements are in general qualitative agreement. Observed differences are due to various factors, such as interindividual variation in arsenic metabolism in humans, that are not fully accounted for in the current model implementation but can be incorporated in the future, in the open framework of MENTOR. The present study demonstrates that integrated source-to-dose modeling for arsenic can not only provide estimates of the relative contributions of multipathway exposure routes to the total exposure estimates, but can also estimate internal target tissue doses for speciated organic and inorganic arsenic, which can eventually be used to improve evaluation of health risks associated with exposures to arsenic from multiple sources, routes, and pathways.Journal of Exposure Science and Environmental Epidemiology (2008) 18, 462–476; doi:10.1038/sj.jes.7500637; published online 12 December 2007 [ABSTRACT FROM AUTHOR]

Advances in computing processing power and in availability of environmental and biological data have allowed the development and application of comprehensive modeling systems that utilize a holistic, integrated, approach for assessing the interactions of environmental and biological systems across multiple scales of spatiotemporal extent and biological organization. This approach allows mechanism-based environmental health risk assessments in a person-oriented framework, which accounts for simultaneous exposures to contaminants from multiple media, routes, and pathways. The conceptual basis and example applications of the Modeling ENvironment for TOtal Risk (MENTOR), and the DOse– Response Information ANalysis system (DORIAN) are presented. [ABSTRACT FROM AUTHOR]

The collapse of the world trade center (WTC) produced enhanced levels of airborne contaminants in New York City and nearby areas on September 11, 2001 through December, 2001. This catastrophic event revealed the vulnerability of the urban environment, and the inability of many existing air monitoring systems to operate efficiently in a crisis. The contaminants released circulated within the street canyons, but were also lifted above the urban canopy and transported over large distances, reflecting the fact that pollutant transport affects multiple scales, from single buildings through city blocks to mesoscales. In this study, ground-and space-based observations were combined with numerical weather forecast fields to initialize fine-scale numerical simulations. The effort is aimed at reconstructing pollutant dispersion from the WTC in New York City to surrounding areas, to provide means for eventually evaluating its effect on population and environment. Atmospheric dynamics were calculated with the multi-grid Regional Atmospheric Modeling System (RAMS), covering scales from 250 m to 300 km and contaminant transport was studied using the Hybrid Particle and Concentration Transport (HYPACT) model that accepts RAMS meteorological output. The RAMS/HYPACT results were tested against PM2.5 observations from the roofs of public schools in New York City (NYC), Landsat images, and Multi-angle Imaging SpectroRadiometer (MISR) retrievals. Calculations accurately reproduced locations and timing of PM2.5 peak aerosol concentrations, as well as plume directionality. By comparing calculated and observed concentrations, the effective magnitude of the aerosol source was estimated. The simulated pollutant distributions are being used to characterize levels of human exposure and associated environmental health impacts. [ABSTRACT FROM AUTHOR]

Copper is an essential trace element and adverse health effects can potentially be associated with both very low and very high intakes. Accurate estimates of inhalation and ingestion (food and drinking water) exposures are therefore needed in order to realistically assess any effects of the distribution of copper intakes within the general population. The work presented here demonstrates an application of a customized subset of the MENTOR/SHEDS-4M computational system (Modeling ENvironment for TOtal Risk studies, employing the Stochastic Human Exposure and Dose Simulation approach, for Multimedia, Multipathway, Multiroute exposures to Multiple co-occurring contaminants. The application utilized data from the National Human Exposure Assessment Survey (NHEXAS) for USEPA Region V as well as from a variety of other available databases. The case study, using a statistical population-based modeling framework, was performed for Eaton County, MI. The results of the simulations, aggregated for six age subgroups of the general population, suggest that food intake is the major pathway for total copper exposure, while drinking water can have significant contributions at the tail of the distribution of intakes. Specifically, it was estimated that over 80% of the county population received potential doses of copper from food that were lower than the Institute of Medicine (IOM) Recommended Dietary Allowance (RDA) value of 900 μg/day. Furthermore, the total combined potential dose from food and water was only about two times greater than the recommended value only for individuals with intakes in the range above the 99th percentile of both food and water intakes. The values were well below the upper tolerable intake value of 10,000 μg/day. The inhalation route consistently acted as only a minor contributor to the total exposure. [ABSTRACT FROM AUTHOR]

A novel source-to-dose modeling study of population exposures to fine particulate matter (PM2.5) and ozone (O3) was conducted for urban Philadelphia. The study focused on a 2-week episode, 11–24 July 1999, and employed the new integrated and mechanistically consistent source-to-dose modeling framework of MENTOR/SHEDS (Modeling Environment for Total Risk studies/Stochastic Human Exposure and Dose Simulation). The MENTOR/SHEDS application presented here consists of four components involved in estimating population exposure/dose: (1) calculation of ambient outdoor concentrations using emission-based photochemical modeling, (2) spatiotemporal interpolation for developing census-tract level outdoor concentration fields, (3) calculation of microenvironmental concentrations that match activity patterns of the individuals in the population of each census tract in the study area, and (4) population-based dosimetry modeling. It was found that the 50th percentiles of calculated microenvironmental concentrations of PM2.5 and O3 were significantly correlated with census-tract level outdoor concentrations, respectively. However, while the 95th percentiles of O3 microenvironmental concentrations were strongly correlated with outdoor concentrations, this was not the case for PM2.5. By further examining the modeled estimates of the 24-h aggregated PM2.5 and O3 doses, it was found that indoor PM2.5 sources dominated the contributions to the total PM2.5 doses for the upper 5 percentiles, Environmental Tobacco Smoking (ETS) being the most significant source while O3 doses due to time spent outdoors dominated the contributions to the total O3 doses for the upper 5 percentiles. The MENTOR/SHEDS system presented in this study is capable of estimating intake dose based on activity level and inhalation rate, thus completing the source-to-dose modeling sequence. The MENTOR/SHEDS system also utilizes a consistent basis of source characterization, exposure factors, and human activity patterns in conducting population exposure assessment of multiple co-occurring air pollutants, and this constitutes a primary distinction from previous studies of population exposure assessment, where different exposure factors and activity patterns would be used for different pollutants. Future work will focus on incorporating the effects of commuting patterns on population exposure/dose assessments as well as on extending the MENTOR/SHEDS applications to seasonal/annual studies and to other areas in the U.S.Journal of Exposure Analysis and Environmental Epidemiology (2005) 15, 439–457. doi:10.1038/sj.jea.7500422; published online 16 February 2005 [ABSTRACT FROM AUTHOR]

Objectives: The traditional manner to evaluate whether regulatory controls meet their public health goals of reducing adverse health effects associated with exposure to environmental pollutants is to compare measured concentrations of the target pollutant in the environment with a standard. A complementary approach is also to measure health-based indicators, e.g., changes in the prevalence of adverse health outcomes attributed to the pollutant. This manuscript presents the concepts of using asthma emergency room (ER) visits and hospital admission as potential health-based indicators for ozone. Methods: The frequency of ER visits and hospital admissions for asthma in New Jersey in 1995 was compared with daily ozone concentrations, to establish the consistency of the relationship and the presence of potential confounders, and to establish whether routinely documented adverse outcomes in asthmatics could serve as health-based indicators. Results: A mathematical model relating ER visits and hospital admissions of asthmatics to ozone concentration was developed for 1995, which was to be used as a baseline year within a health-based indicator program. A coherent relationship was found between same-day ambient air ozone concentrations and ER visits and 2-day time-lagged ambient ozone and hospital admissions during 1995; pollen was identified as a confounder and the association between ER visits and ozone concentration was similar to that determined for 1986 to 1990. Conclusions: Sufficient databases exist for ER visits by asthmatics in Northern and Central New Jersey, and throughout the state for hospital admissions, for these health outcomes to be used as health-based indicators, complementing air-monitoring data in assessing whether improvements in public health are occurring because of reduction in emissions of precursors of ozone. [ABSTRACT FROM AUTHOR]

A response to a commentary about their article "Reconstructing Population Exposures to Environmental Chemicals From Biomarkers: Challenges and Opportunities" is presented. The authors claim that their paper mainly focus on the formulation and evaluation of approaches for mathematical reconstruction of human exposures. They emphasize that the exposure and dose reconstruction involevs estimation of different entities.