Panos G. Georgopoulos

Growth and Deposition of Hygroscopic Particulate Matter in the Human Lungs

Transport and fate of inhaled particulate matter in the human lungs is calculated for realistic physicochemical conditions by a new dosimetry model. The model solves a variant of the general dynamic equation for the size evolution of respirable particles within the human tracheobronchial airways, starting at the tracheal entrance. We focus on ambient anthropogenic aerosols, which are of concern in inhalation toxicology because of their potential irritant and toxic effects on humans. The aerosols considered are polydisperse with respect to size and heterodisperse with respect to thermodynamic state and chemical composition, having initially bimodal lognormal size distribution that evolves with time as a result of condensation-evaporation and deposition processes. The architecture of the human lung is described by Weibels symmetric bronchial tree. Simulations reveal that, due to the rapid growth of submicron-sized particles, increased number and mass fractions of the particle population can be found in the intermediate size range 0.1 < φ < 1

Efficient Sensitivity/Uncertainty Analysis Using the Combined Stochastic Response Surface Method and Automated Differentiation: Application to Environmental and Biological Systems

Estimation of uncertainties associated with model predictions is an important component of the application of environmental and biological models. Traditional methods for propagating uncertainty, such as standard Monte Carlo and Latin Hypercube Sampling, however, often require performing a prohibitive number of model simulations, especially for complex, computationally intensive models. Here, a computationally efficient method for uncertainty propagation, the Stochastic Response Surface Method (SRSM) is coupled with another method, the Automatic Differentiation of FORTRAN (ADIFOR). The SRSM is based on series expansions of model inputs and outputs in terms of a set of well-behaved standard random variables. The ADIFOR method is used to transform the model code into one that calculates the derivatives of the model outputs with respect to inputs or transformed inputs. The calculated model outputs and the derivatives at a set of sample points are used to approximate the unknown coefficients in the series expansions of outputs. A framework for the coupling of the SRSM and ADIFOR is developed and presented here. Two case studies are presented, involving (1) a physiologically based pharmacokinetic model for perchloroethylene for humans, and (2) an atmospheric photochemical model, the Reactive Plume Model. The results obtained agree closely with those of traditional Monte Carlo and Latin hypercube sampling methods, while reducing the required number of model simulations by about two orders of magnitude.

Impact of Biogenic Emission Uncertainties on the Simulated Response of Ozone and Fine Particulate Matter to Anthropogenic Emission Reductions

ABSTRACT The role of emissions of volatile organic compounds and nitric oxide from biogenic sources is becoming increasingly important in regulatory air quality modeling as levels of anthropogenic emissions continue to decrease and stricter health-based air quality standards are being adopted. However, considerable uncertainties still exist in the current estimation methodologies for biogenic emissions. The impact of these uncertainties on ozone and fine particulate matter (PM2.5) levels for the eastern United States was studied, focusing on biogenic emissions estimates from two commonly used biogenic emission models, the Model of Emissions of Gases and Aerosols from Nature (MEGAN) and the Biogenic Emissions Inventory System (BEIS). Photochemical grid modeling simulations were performed for two scenarios: one reflecting present day conditions and the other reflecting a hypothetical future year with reductions in emissions of anthropogenic oxides of nitrogen (NOx). For ozone, the use of MEGAN emissions resulted in a higher ozone response to hypothetical anthropogenic NOx emission reductions compared with BEIS. Applying the current U.S. Environmental Protection Agency guidance on regulatory air quality modeling in conjunction with typical maximum ozone concentrations, the differences in estimated future year ozone design values (DVF) stemming from differences in biogenic emissions estimates were on the order of 4 parts per billion (ppb), corresponding to approximately 5% of the daily maximum 8-hr ozone National Ambient Air Quality Standard (NAAQS) of 75 ppb. For PM2.5, the differences were 0.1–0.25 μg/m3 in the summer total organic mass component of DVFs, corresponding to approximately 1–2% of the value of the annual PM2.5 NAAQS of 15 μg/m3. Spatial variations in the ozone and PM2.5 differences also reveal that the impacts of different biogenic emission estimates on ozone and PM2.5 levels are dependent on ambient levels of anthropogenic emissions. IMPLICATIONS The findings presented in this study demonstrate that uncertainties in biogenic emission estimates due to different emission models can have a significant effect on the model estimates of ozone and PM2.5 concentrations; specifically, the changes in these concentrations due to reductions in anthropogenic emissions considered in regulatory modeling scenarios. These results point to the need for further research aimed at improving biogenic emission estimates as well as better characterizing their dependency on environmental factors and the fate of these emissions once released into the atmosphere.

Alternative Metrics for Assessing the Relative Effectiveness of NOx and VOC Emission Reductions in Controlling Ground-Level Ozone

This study presents a new set of metrics quantifying the response of photochemical air pollution systems to changes in 03 precursor levels. Extending the traditional approach of using domain-wide maximum ozone values as the metric for guiding the development of emission control strategies, the new metrics incorporate attributes of the spatial and temporal pervasiveness and the severity of the ozone episodes considered for strategy development, as well as the impact on potential exposures to ozone. The usefulness of using various alternative criteria to better understand the directionality of the impact of emission controls is demonstrated via a set of 26 simulations of a three-day period of the severe July 1988 episode over the New Jersey-Philadelphia-Delaware Valley area. These simulations model the effect of across-the-board reductions of VOC and NO by 25, 50, 75, and 100% of the base case. The impact of these reductions is found to be dependent on the control objective (e.g., reduction of exposure vs. reduction of the maximum) as well as on the targeted level of the control objective (e.g., reduction of exposures below 120 ppb vs. reduction of exposures below 80 ppb).

Mechanistic modeling of emergency events: Assessing the impact of hypothetical releases of anthrax

A modular system for source-to-dose-to-effect modeling analysis has been developed based on the modeling environment for total risk studies (MENTOR),((1)) and applied to study the impacts of hypothetical atmospheric releases of anthrax spores. The system, MENTOR-2E (MENTOR for Emergency Events), provides mechanistically consistent analysis of inhalation exposures for various release scenarios, while allowing consideration of specific susceptible subpopulations (such as the elderly) at the resolution of individual census tracts. The MENTOR-2E application presented here includes atmospheric dispersion modeling, statistically representative samples of individuals along with corresponding activity patterns, and population-based dosimetry modeling that accounts for activity and physiological variability. Two hypothetical release scenarios were simulated: a 100 g release of weaponized B. anthracis over a period of (a) one hour and (b) 10 hours, and the impact of these releases on population in the State of New Jersey was studied. Results were compared with those from simplified modeling of population dynamics (location, activities, etc.), and atmospheric dispersion of anthrax spores. The comparisons showed that in the two release scenarios simulated, each major approximation resulted in an overestimation of the number of probable infections by a factor of 5 to 10; these overestimations can have significant public health implications when preparing for and responding effectively to an actual release. This is in addition to uncertainties in dose-response modeling, which result in an additional factor of 5 to 10 variation in estimated casualties. The MENTOR-2E system has been developed in a modular fashion so that improvements in individual modules can be readily made without impacting the other modules, and provides a first step toward the development of models that can be used in supporting real-time decision making.

Modeling of Personal Exposures to Ambient Air Toxics in Camden, New Jersey: An Evaluation Study

Abstract This study presents the Individual Based Exposure Modeling (IBEM) application of MENTOR (Modeling ENvironment for TOtal Risk studies) in a hot spot area, where there are concentrated local sources on the scale of tens to hundreds of meters, and an urban reference area in Camden, NJ, to characterize the ambient concentrations and personal exposures to benzene and toluene from local ambient sources. The emission-based ambient concentrations in the two neighborhoods were first estimated through atmospheric dispersion modeling. Subsequently, the calculated and measured ambient concentrations of benzene and toluene were separately combined with the time-activity diaries completed by the subjects as inputs to MENTOR/IBEM for estimating personal exposures resulting from ambient sources. The modeling results were then compared with the actual personal measurements collected from over 100 individuals in the field study to identify the gaps in modeling personal exposures in a hot spot. The modeled ambient concentrations of benzene and toluene were generally in agreement with the neighborhood measurements within a factor of 2, but were underestimated at the high-end percentiles. The major local contributors to the benzene ambient levels are from mobile sources, whereas mobile and stationary (point and area) sources contribute to the toluene ambient levels in the study area. This finding can be used as guidance for developing better air toxic emission inventories for characterizing, through modeling, the ambient concentrations of air toxics in the study area. The estimated percentage contributions of personal exposures from ambient sources were generally higher in the hot spot area than the urban reference area in Camden, NJ, for benzene and toluene. This finding demonstrates the hot spot characteristics of stronger local ambient source impacts on personal exposures. Non-ambient sources were also found as significant contributors to personal exposures to benzene and toluene for the population studied.

ebTrack: an environmental bioinformatics system built upon ArrayTrack™

AbstractebTrack is being developed as an integrated bioinformatics system for environmental research and analysis by addressing the issues of integration, curation, management, first level analysis and interpretation of environmental and toxicological data from diverse sources. It is based on enhancements to the US FDA developed ArrayTrack™ system through additional analysis modules for gene expression data as well as through incorporation and linkages to modules for analysis of proteomic and metabonomic datasets that include tandem mass spectra. ebTrack uses a client-server architecture with the free and open source PostgreSQL as its database engine, and java tools for user interface, analysis, visualization, and web-based deployment. Several predictive tools that are critical for environmental health research are currently supported in ebTrack, including Significance Analysis of Microarray (SAM). Furthermore, new tools are under continuous integration, and interfaces to environmental health risk analysis tools are being developed in order to make ebTrack widely usable. These health risk analysis tools include the Modeling ENvironment for TOtal Risk studies (MENTOR) for source-to-dose exposure modeling and the DOse Response Information ANalysis system (DORIAN) for health outcome modeling. The design of ebTrack is presented in detail and steps involved in its application are summarized through an illustrative application.

An Evaluation of the Role of Risk-Based Decision-Making in a Former Manufactured Gas Plant Site Remediation

Abstract Environmental remediation decisions are driven by the need to minimize human health and ecological risks posed by environmental releases. The Risk Assessment Guidance for Superfund Sites enunciates the principles of exposure and risk assessment that are to be used for reaching remediation decisions for sites under Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA). Experience with remediation management under CERCLA has led to recognition of some crucial infirmities in the processes for managing remediation: cleanup management policies are ad hoc in character, mandates and practices are strongly conservative, and contaminant risk management occurs in an artificially narrow context. The purpose of this case study is to show how a policy of risk-based decision-making was used to avoid customary pitfalls in site remediation. This case study describes the risk-based decision-making process in a remedial action program at a former manufactured gas plant site that successfully achieved timely and effective cleanup. The remediation process operated outside the confines of the CERCLA process under an administrative consent order between the utility and the New Jersey Department of Environmental Protection. A residential use end state was negotiated as part of this agreement. The attendant uncertainties, complications, and unexpected contingencies were overcome by using the likely exposures associated with the desired end state to structure all of the remediation management decisions and by collecting site-specific information from the very outset to obtain a detailed and realistic characterization of human health risks that needed to be mitigated. The lessons from this case study are generalizable to more complicated remediation cases, when supported by correspondingly sophisticated technical approaches.

Development and application of a methodology for determining background groundwater quality at the Savannah River Site.

Abstract A statistical methodology formulated for defining background or baseline levels of constituents of concern in groundwater is presented. The methodology was developed for the case where prior delineation of unimpacted areas is not possible because of site history and a large set of ground-water monitoring measurements exists. Consideration was given to spatial and temporal trends, outliers, and final segregation of wells into impacted or unimpacted categories to develop probability distributions and summary statistics for each constituent evaluated. The formulated approaches were applied to groundwater monitoring data for the U.S. Department of Energy Savannah River Site facility, and results for four representative constituents (aluminum, arsenic, mercury, and tritium) are discussed.

Comparative evaluation of computationally ecient uncertainty propagation methods through application to regional-scale air quality models

This work presents the comparative evaluation of two computationally ecient uncertainty propagation techniques: the Stochastic Response Surface Method (SRSM) and the High Dimensional Model Representation (HDMR) method. The evaluation is performed in relation to the applicability to these methods to complex numerical models, specifically those dealing with simulating regional-scale air quality. The air quality model used in the application case study is a Eulerian type three-dimensional grid-based model, and involves a large set of non-linear partial and ordinary dierential equations to describe atmospheric transport and chemistry, thus making it impractical to use traditional Monte Carlo based techniques for performing uncertainty analysis. The application case study focuses on studying uncertainties in ozone levels estimated by a regulatory air quality model due to uncertainties in biogenic emissions of ozone precursors. Preliminary results show that 95th confidence interval for the peak ozone levels spans a range of over ±15% from the mean value, indicating significant uncertainties with respect to the health impact and regulatory compliance. Both the SRSM and HDMR methods provide similar estimates, thus serving to cross-validate each other, while requiring a small number of model simulations.