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The attack on the World Trade Center (WTC) created an acute environmental disaster of enormous magnitude. This study characterizes the environmental exposures resulting from destruction of the WTC and assesses their effects on health. Methods include ambient air sampling; analyses of outdoor and indoor settled dust; high-altitude imaging and modeling of the(More)
High dimensional model representation is under active development as a set of quantitative model assessment and analysis tools for capturing high-dimensional input-output system behavior based on a hierarchy of functions of increasing dimensions. The HDMR component functions are optimally constructed from zeroth order to higher orders step-by-step. This(More)
BACKGROUND Dietary exposure from food to toxic inorganic arsenic (iAs) in the general U.S. population has not been well studied. OBJECTIVES The goal of this research was to quantify dietary As exposure and analyze the major contributors to total As (tAs) and iAs. Another objective was to compare model predictions with observed data. METHODS(More)
High-dimensional model representation (HDMR) is a general set of quantitative model assessment and analysis tools for improving the efficiency of deducing high dimensional input-output system behavior. RS-HDMR is a particular form of HDMR based on random sampling (RS) of the input variables. The component functions in an HDMR expansion are optimal choices(More)
Air pollutant concentrations are inherently random variables because of their dependence on the fluctuations of a variety of meteorological and emission variables. When sets of air quality data are available, various statistical characteristics can be determined and assigned to the pollutant concentrations. If certain assumptions are made, this statistical(More)
A novel source-to-dose modeling study of population exposures to fine particulate matter (PM(2.5)) and ozone (O(3)) 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(More)
Georgopoulos and Lioy (1994) presented a theoretical framework for exposure analysis, incorporating multiple levels of empirical and mechanistic information while characterizing/reducing uncertainties. The present review summarizes efforts towards implementing that framework, through the development of a mechanistic source-to-dose Modeling ENvironment for(More)
— A computationally efficient means for propagation of uncertainty in computational models is provided by the Stochastic Response Surface Method (SRSM), which facilitates uncertainty analysis through the determination of statistically equivalent reduced models. SRSM expresses random outputs in terms of a " polynomial chaos expansion " of Hermite(More)
A Bayesian framework is presented for modeling Effects of climate change on pollen indices such as annual birch pollen count, maximum daily birch pollen count, start date of birch pollen season and the date of maximum daily birch pollen count. Annual mean CO2 concentration, mean spring temperature and the corresponding pollen index of prior year were found(More)