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Environmental Science & Technology 08/2011; 45(15):6235-6. · 4.80 Impact Factor
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ABSTRACT: Concentrations of 38 gas-phase organic air toxics were measured over a 2-yr period at four different sites in and around Pittsburgh, PA, to investigate spatial variations in health risks from chronic exposure. The sites were chosen to represent different exposure regimes: a downtown site with substantial mobile source emissions; two residential sites adjacent to one of the most heavily industrialized zones in Pittsburgh; and a regional background site. Lifetime cancer risks and non-cancer hazard quotients were estimated using a traditional and interactive risk models. Although study average concentrations of specific air toxics varied by as a much as a factor of 26 between the sites, the additive cancer risks of the gas-phase organic air toxics varied by less than a factor of 2, ranging from 6.1 x 10(-5) to 9.5 x 10(-5). The modest variation in risks reflects the fact that two regionally distributed toxics, formaldehyde and carbon tetrachloride (CCl4), contributed more than half of the cancer risk at all four sites. Benzene contributed substantial cancer risks at all sites, whereas trichloroethene and 1,4-dichlorobenzene only contributed substantial cancer risks at the downtown site. Only acrolein posed a non-cancer risk. Diesel particulate matter is estimated to pose a much greater cancer risk in Pittsburgh than other classes of air toxics including gas-phase organic, metals, polycyclic aromatic hydrocarbons, and coke oven emissions. Health risks of air toxics in Pittsburgh are comparable with those in other urban areas in the United States.
Journal of the Air & Waste Management Association (1995) 03/2010; 60(3):271-86. · 1.52 Impact Factor
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ABSTRACT: Statistical models are developed for bromine incorporation in the trihalomethane (THM), trihaloacetic acids (THAA), dihaloacetic acid (DHAA), and dihaloacetonitrile (DHAN) subclasses of disinfection byproducts (DBPs) using distribution system samples from plants applying only free chlorine as a primary or residual disinfectant in the Information Collection Rule (ICR) database. The objective of this study is to characterize the effect of water quality conditions before, during, and post-treatment on distribution system bromine incorporation into DBP mixtures. Bayesian Markov Chain Monte Carlo (MCMC) methods are used to model individual DBP concentrations and estimate the coefficients of the linear models used to predict the bromine incorporation fraction for distribution system DBP mixtures in each of the four priority DBP classes. The bromine incorporation models achieve good agreement with the data. The most important predictors of bromine incorporation fraction across DBP classes are alkalinity, specific UV absorption (SUVA), and the bromide to total organic carbon ratio (Br:TOC) at the first point of chlorine addition. Free chlorine residual in the distribution system, distribution system residence time, distribution system pH, turbidity, and temperature only slightly influence bromine incorporation. The bromide to applied chlorine (Br:Cl) ratio is not a significant predictor of the bromine incorporation fraction (BIF) in any of the four classes studied. These results indicate that removal of natural organic matter and the location of chlorine addition are important treatment decisions that have substantial implications for bromine incorporation into disinfection byproduct in drinking waters.
Environmental Science and Technology 02/2010; 44(4):1232-9. · 5.23 Impact Factor
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ABSTRACT: We present a model that integrates the economic input-output approach of life cycle assessment with environmental fate, exposure, and risk assessment to estimate the spatial distribution of air toxic health risks due to sector-specific economic activity in the U.S. The model is used to relate the economic activity and exposure potential (population density and meteorology) associated with point source emissions of the heavy metal and carcinogen, hexavalent chromium, or Cr(VI), on a county basis. Total direct annual airborne emissions of Cr(VI) in the U.S. were 44 tonnes in 2002, with 97% from facilities in four major sectors: power generation, wood, plastics, and chemicals, metals, and scientific services. These include 6 tonnes of Cr(VI) emitted in the supply chains of these sectors. A highly variable national distribution of lifetime cancer risk is predicted, with a population-weighted mean of 2.7 x 10(-7), but with hot-spot counties with lifetime risks as high as 6 x 10(-6). Furthermore, high exposures and risks tend to occur in more highly populated counties. In particular, the population of Los Angeles County is exposed to the highest level of risk in the country and almost three-quarters of the total predicted cancer incidence due to inhalation of airborne Cr(VI) emissions. This finding can be attributed largely to the use of Cr(VI) as a corrosion inhibitor by the scientific services sector facilities in the county, the use of shorter facility stacks, and their sitting within a highly populated area. These results indicate that linking economic activity, emission estimates, and fate and transport models for air toxics can inform both life cycle impact and comparative health risk assessments, allowing us to better target emission reductions to minimize hot-spots of risk.
Environmental Science and Technology 02/2010; 44(6):2131-7. · 5.23 Impact Factor
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ABSTRACT: Designing a robust sensor network to detect accidental contaminants in water distribution systems is a challenge given the uncertain nature of the contamination events (what, how much, when, where and for how long) and the dynamic nature of water distribution systems (driven by the random consumption of consumers). We formulate a set of scenario-based minimax and minimax regret models in order to provide robust sensor-placement schemes that perform well under all realizable contamination scenarios, and thus protect water consumers. Single-and multi-objective versions of these models are then applied to a real water distribution system. A heuristic solution method is applied to solve the robust models. The concept of "sensitivity region" is used to visualize trade-offs between multiple objectives.
European Journal of Operational Research 01/2010; 202(3):707-716. · 1.82 Impact Factor
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ABSTRACT: Polychlorinated biphenyl molecules can be biologically dechlorinated through sequential losses of a chlorine atom, following 840 pathways from higher chlorinated to lesser-chlorinated congeners and biphenyl. Previously, eight recurring sets of pathways, herein referred to as explicitly reported pathways in dechlorination processes, have been identified through qualitative analysis of shifts in congener masses in field and laboratory studies. Dechlorination process generalizations were qualitatively extrapolated based on limited attributes of the congeners dechlorinated in the explicitly reported pathways. They are valuable because they allow comparisons of dechlorination patterns across laboratory experiments and contaminated sites. However, due to analytical limitations and a paucity of studies, the explicitly reported pathways in dechlorination processes likely do not represent all of the pathways that could occur at contaminated sites. This work presents an alternative, quantitative, and replicable approach to the identification of candidate pathways for inclusion in dechlorination process generalizations through use of classification trees. This method considers 46 structural and property attributes of dechlorination pathways. Trees fit for pathway inclusion in each of the eight dechlorination processes with alternative assumptions are compared in terms of critical congener attributes. The classification trees correctly classify explicitly reported pathways into dechlorination processes at rates of 0.90 to 0.99. While many of the attributes used in the original generalizations were also selected as predictors by the classification trees, the extra attributes allow identification of additional dechlorination pathways that can be considered as candidates for monitoring in future studies.
Environmental Science and Technology 12/2009; 44(8):2842-8. · 5.23 Impact Factor
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ABSTRACT: Hazardous air pollutants or air toxics are pollutants that are known or suspected to cause serious health effects. This paper presents a methodology to quantify source contributions to air toxics health risks. First, a linear, no-threshold risk model was used to identify gas-phase organic air toxics that contribute significantly to cancer risks. Next, Positive Matrix Factorization (PMF) was performed on high time-resolved measurements of these air toxics, and the additive cancer risks associated with each factor was determined. Finally, the PMF factors were linked to sources and source classes (mobile, nonmobile, secondary/background) using a combination of meteorological data and comparisons with published source profiles. The analysis was performed using data from three sites in Pittsburgh, Pennsylvania: a downtown site near a heavily traveled bus route, a residential site adjacent to a heavily industrialized area, and an urban background site. At all three sites emissions from nonmobile sources were the dominant contributors to the cancer risks from air toxics included in the PMF model, including benzene and other air toxics often associated with mobile source emissions. Emissions from both large industrial sources, such as coke works and chemical facilities, and smaller point sources, such as dry cleaners, contributed significantly to the cancer risks at all sites. This method can provide insight for decision makers to prioritize sources for risk reduction.
Environmental Science and Technology 12/2009; 43(24):9439-44. · 5.23 Impact Factor
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ABSTRACT: A Bayesian statistical water quality model is demonstrated to predict fecal-indicator bacterial concentrations for waterbodies without sufficient monitoring data for data-intensive modeling techniques. Using a truncated bivariate normal likelihood function and the readily available observations of flow and bacterial concentration, the Bayesian approach propagates the uncertainty in the model parameterization to the final predictions of in-stream bacterial concentration. The proposed model captures the variation in the magnitude of bacterial loading between dry and wet conditions by estimating separate sets of model parameters for different flow conditions, but also has the capability to pool the data among flow conditions. The model can be used in two ways: first, the model specifies the percent of time that the recreational season in-stream concentration is not in compliance with the relevant water quality standard, and second, the model estimates the necessary bacterial load reduction for multiple flow conditions to meet the relevant water quality standard. Using an eleven year monitoring record for a site sampled at a monthly frequency on the Youghiogheny River in southwestern Pennsylvania, USA, the model parameters are updated and posterior predictions are generated for each 2-year increment. After six years of sampling, the predicted percent of time that the recreational season in-stream bacterial concentration is not in compliance with the relevant water quality is 82% with 95% CI(80,85), and the bacterial load reductions required to meet the standard are 14.7 CI(14.6,14.8), 14.5 CI(14.3, 14.6), and 14.0 CI(13.9, 14.2) log(10)(cfu/day) for the high, normal, and dry flow conditions, respectively. The change in estimated load reduction and percent exceedance resulting from additional monitoring for this site becomes small after six years of sampling, indicating that a decision does not need to be postponed for additional monitoring.
Water Research 11/2009; 44(3):1006-16. · 4.86 Impact Factor
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ABSTRACT: Diarrheal illness is a leading cause of child mortality in developing nations. Previous longitudinal studies have attempted to identify the factors that contribute to child mortality, but few have examined the determinants of diarrheal illness at a country level. Here we demonstrate the use of Classification and Regression Trees (CART) to predict diarrheal illness from a 192-country data set of country-level attributes and compare the performance of CART with a linear regression model. The CART model identifies improvements in rural sanitation as the most important spending priority for reducing diarrheal illness. We estimate that reducing unmet rural sanitation need worldwide by 65% would save the equivalent of 1.2 million lives annually.
Environmental Science and Technology 03/2009; 43(4):993-9. · 5.23 Impact Factor
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ABSTRACT: Some 1,976 sites at closed military bases in the United States are contaminated with unexploded ordnance (UXO) left over from live-fire weapons training. These sites present risks to civilians who might come into contact with the UXO and cause it to explode. This paper presents the first systems analysis model for assessing the explosion risks of UXO at former military training ranges. We develop a stochastic model for estimating the probability of exposure to and explosion of UXO, before and after site cleanup. An application of the model to a 310-acre parcel at Fort Ord, California, shows that substantial risk can remain even after a site is declared clean. We estimate that risk to individual construction workers of encountering UXO that explodes would range from 4 x 10(-4) to 5 x 10(-2), depending on model assumptions, well above typical Occupational Safety and Health Administration (OSHA) and U.S. Environmental Protection Agency (EPA) target risk levels of 10(-4) to 10(-6). In contrast, a qualitative UXO risk assessment method, the Munitions and Explosives of Concern Hazard Assessment (MEC HA), developed by an interagency work group led by the EPA, indicates that the explosion risk at the case study site is low and "compatible with current and determined or reasonably anticipated future risk." We argue that a quantitative approach, like that illustrated in this paper, is necessary to provide a more complete picture of risks and the opportunities for risk reduction.
Environmental Science and Technology 02/2009; 43(2):259-65. · 5.23 Impact Factor
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ABSTRACT: This paper summarizes the findings of a statistical analysis of the locations of metallic anomalies detected at the Pueblo
Precision Bombing Range Number 2 in Otero County, Colorado, and at the Victorville Precision Bombing Range in San Bernardino
County, California. The purpose of the study is to explore whether statistical properties of the pattern of anomaly locations
can be used to discriminate areas likely to contain unexploded ordnance (UXO) left over from previous bombing practice from
those unlikely to contain UXO. Techniques for discriminating areas with and without UXO are needed because historic records
have left an incomplete account of previous military training activities, so that locations historically used for target practice
are often unknown. This study differs from previous research on metallic anomaly data at former military training ranges in
that it analyzes the spatial pattern of the discrete locations of the anomalies, rather than the average number of anomalies
per unit area. The results indicate that differences in spatial pattern may be a distinguishing feature between areas that
were used for target practice and those that are unlikely to contain UXO, even when a large number of ferrous rocks and other
inert metallic anomalies are present. We found that at both of the former bombing ranges, the anomaly patterns in sample areas
that are distant from all known bombing targets are consistent with a complete spatial randomness pattern, while those near
the target areas fit a radially symmetric, bivariate Gaussian pattern. Furthermore, anomaly location patterns generated by
surveys with airborne metal detectors have the same statistical properties as the patterns generated by surveys with on-ground
detectors, even though the airborne systems detect only a subset of the anomalies found by the ground-based detectors. Thus,
pattern information revealed by airborne surveys with metal detectors may be useful in identifying areas where careful searches
for UXO are needed.
Stochastic Environmental Research and Risk Assessment 01/2009; 23(2):203-214. · 1.52 Impact Factor
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Mitchell J Small
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ABSTRACT: The distributional approach for uncertainty analysis in cancer risk assessment is reviewed and extended. The method considers a combination of bioassay study results, targeted experiments, and expert judgment regarding biological mechanisms to predict a probability distribution for uncertain cancer risks. Probabilities are assigned to alternative model components, including the determination of human carcinogenicity, mode of action, the dosimetry measure for exposure, the mathematical form of the dose-response relationship, the experimental data set(s) used to fit the relationship, and the formula used for interspecies extrapolation. Alternative software platforms for implementing the method are considered, including Bayesian belief networks (BBNs) that facilitate assignment of prior probabilities, specification of relationships among model components, and identification of all output nodes on the probability tree. The method is demonstrated using the application of Evans, Sielken, and co-workers for predicting cancer risk from formaldehyde inhalation exposure. Uncertainty distributions are derived for maximum likelihood estimate (MLE) and 95th percentile upper confidence limit (UCL) unit cancer risk estimates, and the effects of resolving selected model uncertainties on these distributions are demonstrated, considering both perfect and partial information for these model components. A method for synthesizing the results of multiple mechanistic studies is introduced, considering the assessed sensitivities and selectivities of the studies for their targeted effects. A highly simplified example is presented illustrating assessment of genotoxicity based on studies of DNA damage response caused by naphthalene and its metabolites. The approach can provide a formal mechanism for synthesizing multiple sources of information using a transparent and replicable weight-of-evidence procedure.
Risk Analysis 11/2008; 28(5):1289-308. · 2.37 Impact Factor
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ABSTRACT: This article reports on a study to quantify expert beliefs about the explosion probability of unexploded ordnance (UXO). Some 1,976 sites at closed military bases in the United States are contaminated with UXO and are slated for cleanup, at an estimated cost of $15-140 billion. Because no available technology can guarantee 100% removal of UXO, information about explosion probability is needed to assess the residual risks of civilian reuse of closed military bases and to make decisions about how much to invest in cleanup. This study elicited probability distributions for the chance of UXO explosion from 25 experts in explosive ordnance disposal, all of whom have had field experience in UXO identification and deactivation. The study considered six different scenarios: three different types of UXO handled in two different ways (one involving children and the other involving construction workers). We also asked the experts to rank by sensitivity to explosion 20 different kinds of UXO found at a case study site at Fort Ord, California. We found that the experts do not agree about the probability of UXO explosion, with significant differences among experts in their mean estimates of explosion probabilities and in the amount of uncertainty that they express in their estimates. In three of the six scenarios, the divergence was so great that the average of all the expert probability distributions was statistically indistinguishable from a uniform (0, 1) distribution-suggesting that the sum of expert opinion provides no information at all about the explosion risk. The experts' opinions on the relative sensitivity to explosion of the 20 UXO items also diverged. The average correlation between rankings of any pair of experts was 0.41, which, statistically, is barely significant (p= 0.049) at the 95% confidence level. Thus, one expert's rankings provide little predictive information about another's rankings. The lack of consensus among experts suggests that empirical studies are needed to better understand the explosion risks of UXO.
Risk Analysis 08/2008; 28(4):825-41. · 2.37 Impact Factor
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ABSTRACT: Characterizing all possible chemical mixtures in drinking water is a potentially overwhelming project, and the task of assessing each mixture's net toxicity even more daunting. We propose that analyzing occurrence information on mixtures in drinking water may help to narrow the priorities and inform the approaches taken by researchers in mixture toxicology. To illustrate the utility of environmental data for refining the mixtures problem, we use a recent compilation of national ground-water-quality data to examine proposed U.S. Environmental Protection Agency (EPA) and Agency for Toxic Substances and Disease Registry (ATSDR) models of noncancer mixture toxicity. We use data on the occurrence of binary and ternary mixtures of arsenic, cadmium, and manganese to parameterize an additive model and compute hazard index scores for each drinking-water source in the data set. We also use partially parameterized interaction models to perform a bounding analysis estimating the interaction potential of several binary and ternary mixtures for which the toxicological literature is limited. From these results, we estimate a relative value of additional toxicological information for each mixture. For example, we find that according to the U.S. EPA's interaction model, the levels of arsenic and cadmium found in U.S. drinking water are unlikely to have synergistic cardiovascular effects, but the same mixture's potential for synergistic neurological effects merits further study. Similar analysis could in future be used to prioritize toxicological studies based on their potential to reduce scientific and regulatory uncertainty. Environmental data may also provide a means to explore the implications of alternative risk models for the toxicity and interaction of complex mixtures.
Risk Analysis 06/2008; 28(3):653-66. · 2.37 Impact Factor
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Journal of Industrial Ecology 02/2008; 4(3):7 - 10. · 2.09 Impact Factor
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Journal of Industrial Ecology 02/2008; 3(2‐3):4 - 7. · 2.09 Impact Factor
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ABSTRACT: A water distribution system simulation model, EPANET, is used to predict chlorine residual profiles for a representative water distribution system. The analysis focuses on the identification of vulnerable areas where chlorine residual concentration is low. Alternative vulnerability measures are considered, including the probability that the disinfectant residual will be below the required threshold at any location or time during an EPANET simulation. Sensitivity and uncertainty analyses are conducted to examine the influence of key inputs to the EPANET model, including the bulk flow decay constant, pipe wall decay constant, water demand, and roughness coefficient. The roughness coefficient is indicated to have negligible impact on the predicted results, compared to the other uncertain variables. Methods for computing and mapping the system vulnerability, given the uncertainty in the EPANET model inputs, are demonstrated. The relationship between water age and chlorine residual is investigated as a first step in developing a response surface model for the system that can both simplify future uncertainty analysis and provide insights on vulnerability that are readily applicable to other systems.
Proceedings of the Water Environment Federation. 12/2006;
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ABSTRACT: More than 40,000 km2 of former military land in the United States are contaminated with unexploded ordnance (UXO). Cleanup costs are estimated to total as much as 140 billion dollars. The amount of contaminated acreage and total costs are likely to increase as the U.S. Department of Defense (DOD) follows through on recently announced plans to close an additional 22 domestic military bases. The U.S. Environmental Protection Agency(EPA) and DOD disagree on how these sites should be characterized to assess their risks and plan for cleanup. As a result, much potentially valuable land remains idle while remediation decisions are pending. One of the sources of disagreement is how the locations of UXO should be characterized, given that the exact spatial distribution of UXO is unknown in advance of cleanup. In this paper, we propose and test a new model to represent the spatial distribution of UXO. Unlike existing DOD models, the new model accounts for the tendency of UXO to cluster, presumably around targets at which soldiers aimed during training. We fit the cluster model to geographic data on UXO locations at two former military installations and show that it describes key characteristics of the data more accuratelythan the existing DOD model. We discuss how the choice of a UXO spatial distribution model could affect important decisions about cleaning up and reusing UXO-affected property.
Environmental Science and Technology 03/2006; 40(3):931-8. · 5.23 Impact Factor
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ABSTRACT: A plant uptake model is applied to describe free cyanide and ferrocyanide transport and fate in willow (Salix eriocephala var. Michaux) grown in hydroponics. The model is applied to experimental data to determine best-fit parameter values, their associated uncertainty, and their relative importance to field-scale phytoremediation applications. The fitted model results, using least-squares optimization of the observed log concentrations, indicate that free cyanide volatilization from leaf tissue and free cyanide cell wall adsorption were negligible. The free cyanide maximum uptake rate and assimilate (noncyanide 15N) first-order leaf loss rate were the only coefficients that significantly affected the model goodness of fit and were concurrently sensitive to data uncertainty in the parameter optimization. Saturation kinetics may be applicable for free cyanide uptake into plants, but not for ferrocyanide uptake, which may occur via preferential protein-mediated or inefficient transpiration stream uptake. Within the free cyanide system, the relative magnitudes of the saturation uptake parameters and the demonstration of an active role for plants in uptake relative to transpiration suggest the potential importance of preferential diffusion through the cell membranes as reported in the literature, rather than protein-mediated uptake. The fitted 13-parameter model matched the observed data well except for the predicted stem and leaf tissue assimilate concentrations, which were significantly underestimated, particularly in the free cyanide system. These low predicted values, combined with the slightly underestimated solution free cyanide removal, suggest that noncyanide 15N redistribution in phloem should be considered.
International Journal of Phytoremediation 02/2006; 8(1):45-62. · 1.30 Impact Factor
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ABSTRACT: Environmental models are often too large and cumbersome for effective use in regulatory decision making or in the characterization of uncertainty. This paper describes and compares four response surfaces that could complement a large-scale water quality model, the U.S. National Water Pollution Control Assessment Model (NWPCAM), in simulation and regulatory decision support applications. Results show that a physically based reduced-form model that exploits the mathematical structure of the underlying water quality model is a better predictor of policy-relevant outputs than the polynomial expansions that are frequently used in response surface studies.
Environmental Modeling and Assessment 01/2006; 11(4):345-359. · 0.97 Impact Factor
