Exposure Misclassification and Threshold Concentrations in Time Series Analyses of Air Pollution Health Effects

Linear, no-threshold relationships are typically reported for time series studies of air pollution and mortality. Since regulatory standards and economic valuations typically assume some threshold level, we evaluated the fundamental question of the impact of exposure misclassification on the persistence of underlying personal-level thresholds when personal data are aggregated to the population level in the assessment of exposure-response relationships. As an example, we measured personal exposures to two particle metrics, PM2.5 and sulfate (SO4(2-)), for a sample of lung disease patients and compared these with exposures estimated from ambient measurements Previous work has shown that ambient:personal correlations for PM2.5 are much lower than for SO4(2-), suggesting that ambient PM2.5 measurements misclassify exposures to PM2.5. We then developed a method by which the measured:estimated exposure relationships for these patients were used to simulate personal exposures for a larger population and then to estimate individual-level mortality risks under different threshold assumptions. These individual risks were combined to obtain the population risk of death, thereby exhibiting the prominence (and the value) of the threshold in the relationship between risk and estimated exposure. Our results indicated that for poorly classified exposures (PM2.5 in this example) population-level thresholds were apparent at lower ambient concentrations than specified common personal thresholds, while for well-classified exposures (e.g., SO4(2-)), the apparent thresholds were similar to these underlying personal thresholds. These results demonstrate that surrogate metrics that are not highly correlated with personal exposures obscure the presence of thresholds in epidemiological studies of larger populations, while exposure indicators that are highly correlated with personal exposures can accurately reflect underlying personal thresholds.     

Assessment of Interindividual and Geographic Variability in Human Exposure to Fine Particulate Matter in Environmental Tobacco Smoke

Environmental tobacco smoke (ETS) is a major contributor to indoor human exposures to fine particulate matter of 2.5 μm or smaller (PM_2.5). The Stochastic Human Exposure and Dose Simulation for Particulate Matter (SHEDS‐PM) Model developed by the U.S. Environmental Protection Agency estimates distributions of outdoor and indoor PM_2.5 exposure for a specified population based on ambient concentrations and indoor emissions sources. A critical assessment was conducted of the methodology and data used in SHEDS‐PM for estimation of indoor exposure to ETS. For the residential microenvironment, SHEDS uses a mass‐balance approach, which is comparable to best practices. The default inputs in SHEDS‐PM were reviewed and more recent and extensive data sources were identified. Sensitivity analysis was used to determine which inputs should be prioritized for updating. Data regarding the proportion of smokers and “other smokers” and cigarette emission rate were found to be important. SHEDS‐PM does not currently account for in‐vehicle ETS exposure; however, in‐vehicle ETS‐related PM_2.5 levels can exceed those in residential microenvironments by a factor of 10 or more. Therefore, a mass‐balance‐based methodology for estimating in‐vehicle ETS PM_2.5 concentration is evaluated. Recommendations are made regarding updating of input data and algorithms related to ETS exposure in the SHEDS‐PM model. Interindividual variability for ETS exposure was quantified. Geographic variability in ETS exposure was quantified based on the varying prevalence of smokers in five selected locations in the United States.     

An Empirical Bayes Approach to Estimating the Relation of Mortality to Exposure to Particulate Matter

As part of its assessment of the health risks associated with exposure to particulate matter (PM), the U.S. Environmental Protection Agency analyzed the risks associated with current levels, and the risk reductions that might be achieved by attainment of alternative PM standards, in two locations in the United States, Philadelphia, and Los Angeles. The concentration-response function describing the relation between a health endpoint and ambient PM concentrations is an important component, and a source of substantial uncertainty, in such risk analyses. In the absence of location-specific estimates, the concentration-response functions necessary for risk assessments in Philadelphia and Los Angeles must be inferred from the available information in other locations. Although the functional form of the concentration-response relations is assumed to be the same everywhere, the value of the PM coefficient in that function may vary from one location to another. Under this model, a distribution describes the probability that the PM coefficient in a randomly selected location will lie in any range of interest. An empirical Bayes estimation technique was used to improve the estimation of location-specific concentration-response functions relating mortality to short-term exposure to particles of aerodynamic diameter less than or equal to 2.5 microm (PM-2.5), for which functions have previously been estimated in several locations. The empirical Bayes-adjusted parameter values and their SEs were used to derive an estimate of the distribution of PM-2.5 coefficients for mortality associated with short-term exposures. From this distribution, distributions of relative risks corresponding to different specified changes in PM-2.5 concentrations could be derived.     

Assessment of Inhalation Exposures and Potential Health Risks to the General Population that Resulted from the Collapse of the World Trade Center Towers

In the days following the collapse of the World Trade Center (WTC) towers on September 11, 2001 (9/11), the U.S. Environmental Protection Agency (EPA) initiated numerous air monitoring activities to better understand the ongoing impact of emissions from that disaster. Using these data, EPA conducted an inhalation exposure and human health risk assessment to the general population. This assessment does not address exposures and potential impacts that could have occurred to rescue workers, firefighters, and other site workers, nor does it address exposures that could have occurred in the indoor environment. Contaminants evaluated include particulate matter (PM), metals, polychlorinated biphenyls, dioxins, asbestos, volatile organic compounds, particle-bound polycyclic aromatic hydrocarbons, silica, and synthetic vitreous fibers (SVFs). This evaluation yielded three principal findings. (1) Persons exposed to extremely high levels of ambient PM and its components, SVFs, and other contaminants during the collapse of the WTC towers, and for several hours afterward, were likely to be at risk for acute and potentially chronic respiratory effects. (2) Available data suggest that contaminant concentrations within and near ground zero (GZ) remained significantly elevated above background levels for a few days after 9/11. Because only limited data on these critical few days were available, exposures and potential health impacts could not be evaluated with certainty for this time period. (3) Except for inhalation exposures that may have occurred on 9/11 and a few days afterward, the ambient air concentration data suggest that persons in the general population were unlikely to suffer short-term or long-term adverse health effects caused by inhalation exposures. While this analysis by EPA evaluated the potential for health impacts based on measured air concentrations, epidemiological studies conducted by organizations other than EPA have attempted to identify actual impacts. Such studies have identified respiratory effects in worker and general populations, and developmental effects in newborns whose mothers were near GZ on 9/11 or shortly thereafter. While researchers are not able to identify specific times and even exactly which contaminants are the cause of these effects, they have nonetheless concluded that exposure to WTC contaminants (and/or maternal stress, in the case of developmental effects) resulted in these effects, and have identified the time period including 9/11 itself and the days and few weeks afterward as a period of most concern based on high concentrations of key pollutants in the air and dust.     

Diesel Emissions and Lung Cancer1

This paper uses two different methods to assess the potential risk of human lung cancer from exposure to diesel engine emissions. One method analyzes the best available epidemiological evidence on the lung cancer risks of persons exposed in their occupations to diesel engine emissions. The second conducts a comparative analysis of laboratory and epidemiological data on diesel engine emissions and two chemically related environmental exposures–coke oven emissions and roofing tar emissions. The estimates of potential risk derived from these two distinct methods are compared. The sources of uncertainty in each method are explicitly characterized. The value of these estimates for comparing the potential lung cancer risks from exposure to diesel engine emissions with other personal and societal risks are discussed. Also considered are the limitations of these results in predicting the possible excess incidence of lung cancer from ambient exposure to diesel emissions.     

Health Effects Caused by Primary Fine Particulate Matter (PM2.5) Emitted from Buses in the Helsinki Metropolitan Area, Finland

Fine particle (PM(2.5)) emissions from traffic have been associated with premature mortality. The current work compares PM(2.5)-induced mortality in alternative public bus transportation strategies as being considered by the Helsinki Metropolitan Area Council, Finland. The current bus fleet and transportation volume is compared to four alternative hypothetical bus fleet strategies for the year 2020: (1) the current bus fleet for 2020 traffic volume, (2) modern diesel buses without particle traps, (3) diesel buses with particle traps, and (4) buses using natural gas engines. The average population PM(2.5) exposure level attributable to the bus emissions was determined for the 1996-1997 situation using PM(2.5) exposure measurements including elemental composition from the EXPOLIS-Helsinki study and similar element-based source apportionment of ambient PM(2.5) concentrations observed in the ULTRA study. Average population exposure to particles originating from the bus traffic in the year 2020 is assumed to be proportional to the bus emissions in each strategy. Associated mortality was calculated using dose-response relationships from two large cohort studies on PM(2.5) mortality from the United States. Estimated number of deaths per year (90% confidence intervals in parenthesis) associated with primary PM(2.5) emissions from buses in Helsinki Metropolitan Area in 2020 were 18 (0-55), 9 (0-27), 4 (0-14), and 3 (0-8) for the strategies 1-4, respectively. The relative differences in the associated mortalities for the alternative strategies are substantial, but the number of deaths in the lowest alternative, the gas buses, is only marginally lower than what would be achieved by diesel engines equipped with particle trap technology. The dose-response relationship and the emission factors were identified as the main sources of uncertainty in the model.     

Determination of Less-Than-Lifetime Exposures to Point Source Emissions

This paper presents a method of estimating long-term exposures to point source emissions. The method consists of a Monte Carlo exposure model (PSEM or Point Source Exposure Model) that combines data on population mobility and mortality with information on daily activity patterns. The approach behind the model can be applied to a wide variety of exposure scenarios. In this paper, PSEM is used to characterize the range and distribution of lifetime equivalent doses received by inhalation of air contaminated by the emissions of a point source. The output of the model provides quantitative information on the dose, age, and gender of highly exposed individuals. The model is then used in an example risk assessment. Finally, future uses of the model's approach are discussed.     

Combining Food Frequency and Survey Data to Quantify Long-Term Dietary Exposure: A Methyl Mercury Case Study

Twenty-four-hour recall data from the Continuing Survey of Food Intake by Individuals (CSFII) are frequently used to estimate dietary exposure for risk assessment. Food frequency questionnaires are traditional instruments of epidemiological research; however, their application in dietary exposure and risk assessment has been limited. This article presents a probabilistic method of bridging the National Health and Nutrition Examination Survey (NHANES) food frequency and the CSFII data to estimate longitudinal (usual) intake, using a case study of seafood mercury exposures for two population subgroups (females 16 to 49 years and children 1 to 5 years). Two hundred forty-nine CSFII food codes were mapped into 28 NHANES fish/shellfish categories. FDA and state/local seafood mercury data were used. A uniform distribution with minimum and maximum blood-diet ratios of 0.66 to 1.07 was assumed. A probabilistic assessment was conducted to estimate distributions of individual 30-day average daily fish/shellfish intakes, methyl mercury exposure, and blood levels. The upper percentile estimates of fish and shellfish intakes based on the 30-day daily averages were lower than those based on two- and three-day daily averages. These results support previous findings that distributions of "usual" intakes based on a small number of consumption days provide overestimates in the upper percentiles. About 10% of the females (16 to 49 years) and children (1 to 5 years) may be exposed to mercury levels above the EPA's RfD. The predicted 75th and 90th percentile blood mercury levels for the females in the 16-to-49-year group were similar to those reported by NHANES. The predicted 90th percentile blood mercury levels for children in the 1-to-5-year subgroup was similar to NHANES and the 75th percentile estimates were slightly above the NHANES.     

Caveats for Causal Interpretations of Linear Regression Coefficients for Fine Particulate (PM2.5) Air Pollution Health Effects

Recent linear regression analyses have concluded that decreasing levels of fine particulate matter (PM2.5) air pollution have increased life expectancy in the United States. These findings have left unresolved questions about the causal relation between reductions in PM2.5 levels and changes in cause‐specific (especially, cardiovascular disease, CVD) mortality risks. Their robustness (e.g., sensitivity to deletion of a single data point) has also been questioned. We investigate these issues in the National Mortality and Morbidity Air Pollution Study database. Comparing changes in PM2.5 levels and cause‐specific mortality rates for elderly people in 24 cities between two periods separated by a decade (1987–1989 and 1999–2000) shows that reductions in PM2.5 were significantly associated with increases in respiratory mortality rates and with decreases in CVD mortality rates. CVD and all‐cause mortality risks fell equally for all months of the year over this period, but average PM2.5 levels increased significantly for winter months. This casts doubts on the causal interpretation that declines in PM2.5 over the decade caused reduced short‐term mortality risks. Nonlinear regression suggests that reduced or negative marginal health benefits are associated with reductions of PM2.5 below 1999–2000 levels (about 15 μg/m³). Such nonlinear relations imply that risk communication statements that project a constant incremental reduction in mortality risks per unit reduction in PM2.5 do not adequately reflect the realistic possibility of nonlinear exposure‐response relations and diminishing returns to further exposure reductions.     

Approximations for Estimating Change in Life Expectancy Attributable to Air Pollution in Relation to Multiple Causes of Death Using a Cause Modified Life Table

There is considerable debate as to the most appropriate metric for characterizing the mortality impacts of air pollution. Life expectancy has been advocated as an informative measure. Although the life‐table calculus is relatively straightforward, it becomes increasingly cumbersome when repeated over large numbers of geographic areas and for multiple causes of death. Two simplifying assumptions were evaluated: linearity of the relation between excess rate ratio and change in life expectancy, and additivity of cause‐specific life‐table calculations. We employed excess rate ratios linking PM_2.5 and mortality from cerebrovascular disease, chronic obstructive pulmonary disease, ischemic heart disease, and lung cancer derived from a meta‐analysis of worldwide cohort studies. As a sensitivity analysis, we employed an integrated exposure response function based on the observed risk of PM_2.5 over a wide range of concentrations from ambient exposure, indoor exposure, second‐hand smoke, and personal smoking. Impacts were estimated in relation to a change in PM_2.5 from 19.5 μg/m³ estimated for Toronto to an estimated natural background concentration of 1.8 μg/m³. Estimated changes in life expectancy varied linearly with excess rate ratios, but at higher values the relationship was more accurately represented as a nonlinear function. Changes in life expectancy attributed to specific causes of death were additive with maximum error of 10%. Results were sensitive to assumptions about the air pollution concentration below which effects on mortality were not quantified. We have demonstrated valid approximations comprising expression of change in life expectancy as a function of excess mortality and summation across multiple causes of death.     

An Exposure‐Response Threshold for Lung Diseases and Lung Cancer Caused by Crystalline Silica

Whether crystalline silica (CS) exposure increases risk of lung cancer in humans without silicosis, and, if so, whether the exposure‐response relation has a threshold, have been much debated. Epidemiological evidence is ambiguous and conflicting. Experimental data show that high levels of CS cause lung cancer in rats, although not in other species, including mice, guinea pigs, or hamsters; but the relevance of such animal data to humans has been uncertain. This article applies recent insights into the toxicology of lung diseases caused by poorly soluble particles (PSPs), and by CS in particular, to model the exposure‐response relation between CS and risk of lung pathologies such as chronic inflammation, silicosis, fibrosis, and lung cancer. An inflammatory mode of action is described, having substantial empirical support, in which exposure increases alveolar macrophages and neutrophils in the alveolar epithelium, leading to increased reactive oxygen species (ROS) and nitrogen species (RNS), pro‐inflammatory mediators such as TNF‐alpha, and eventual damage to lung tissue and epithelial hyperplasia, resulting in fibrosis and increased lung cancer risk among silicotics. This mode of action involves several positive feedback loops. Exposures that increase the gain factors around such loops can create a disease state with elevated levels of ROS, TNF‐alpha, TGF‐beta, alveolar macrophages, and neutrophils. This mechanism implies a “tipping point” threshold for the exposure‐response relation. Applying this new model to epidemiological data, we conclude that current permissible exposure levels, on the order of 0.1 mg/m³, are probably below the threshold for triggering lung diseases in humans.     

Projection of Health Benefits from Ambient Ozone Reduction Related to the Use of Methyl tertiary Butyl Ether (MTBE) in the Reformulated Gasoline Program

To estimate potential public health benefits from ozone (O₃) pollution reduction attributable to the use of methyl tertiary-butyl ether (MTBE) in gasoline, O₃ dose-response estimates from the biomedical literature were combined with model estimates of O₃ reduction. Modeling employed EPA MOBILE5a and Complex models to predict emission changes, industry AQIRP techniques to predict ambient O₃ changes, and the National Exposure Model to predict human exposures. Human health effects considered were lung function decrements and respiratory irritant symptoms (using dose-response functions measured in laboratory and field studies), and increased death rates (using concentration-response functions inferred statistically from public-health data). Other reported health effects, such as lung inflammation, increases in asthma attacks, and hospitalizations, were not addressed because of inadequate dose-response information. Even for the health responses considered, quantitation of improvements due to MTBE use is problematical, because MTBE affects only a small percentage of existing O₃ pollution, and because exposure-response relationships are not well understood for population subgroups most likely to be affected. Nevertheless, it is reasonable to conclude that even small MTBE-associated reductions in peak ambient O₃ levels (1–5 ppb, according to model estimates) should yield considerable public health benefits. Tens of millions of Americans are potentially exposed to O₃ in the concentration range associated with health effects. Even if only a small percentage of them are susceptible, any incremental reduction in O₃ (as with MTBE use) must mitigate or prevent effects for a meaningful number of people. Better quantitative estimates of benefit must await a more detailed understanding of each link in the chain of causation.     

Retrospective Temporal and Spatial Mobility of Adult Iowa Women

Human exposure assessments require a linkage between toxicant concentrations in occupied spaces and the receptor's mobility pattern. Databases reporting distinct populations' mobility in various parts of the home, time outside the home, and time in another building are scarce. Temporal longitudinal trends in these mobility patterns for specific age and gender groups are nonexistent. This paper describes subgroup trends in the spatial and temporal mobility patterns within the home, outside the home, and in another building for 619 Iowa females that occupied the same home for at least 20 years. The study found that the mean time spent at home for the participants ranged from a low of 69.4% for the 50-59 year age group to a high of 81.6% for the over 80-year-old age group. Participants who lived in either one- or two- story homes with basements spent the majority of their residential occupancy on the first story. Trends across age varied for other subgroups by number of children, education, and urbadrural status. Since all of these trends were nonlinear, they indicate that error exists when assuming a constant, such as a 75% home occupancy factor, which has been advocated by some researchers and agencies. In addition, while aggregate data, such as presented in this report, are more helpll in deriving risk estimates for population subgroups, they cannot supplant good individual-level data for determining risks.     

Human Interindividual Variability in Susceptibility to Airborne Particles

Part of the explanation for the persistent epidemiological findings of associations between mortality and morbidity with relatively modest ambient exposures to airborne particles may be that some people are much more susceptible to particle-induced responses than others. This study assembled a database of quantitative observations of interindividual variability in pharmacokinetic and pharmacodynamic parameters likely to affect particle response. The pharmacodynamic responses studied included data drawn from epidemiologic studies of doses of methacholine, flour dust, and other agents that induce acute changes in lung function. In general, the amount of interindividual variability in several of these pharmacodynamic response parameters was greater than the variability in pharmacokinetic (breathing rate, deposition, and clearance) parameters. Quantitatively the results indicated that human interindividual variability of breathing rates and major pharmacokinetic parameters-total deposition and tracheobronchial clearance-were in the region of Log(GSD) = 0.1 to 0.2 (corresponding to geometric standard deviations of 10(.1)-10(.2) or 1.26-1.58). Deposition to the deep lung (alveolar region) appeared to be somewhat more variable: Log(GSD) of about 0.3 (GSD of about 2). Among pharmacodynamic parameters, changes in FEV1 in response to ozone and metabisulfite (an agent that is said to act primarily on neural receptors in the lung) were in the region of Log(GSD) of 0.2 to 0.4. However, similar responses to methacholine, an agent that acts on smooth muscle, seemed to have still more variability (0.4 to somewhat over 1.0, depending on the type of population studied). Similarly high values were suggested for particulate allergens. Central estimates of this kind of variability, and the close correspondence of the data to lognormal distributions, indicate that 99.9th percentile individuals are likely to respond at doses that are 150 to 450-fold less than would be needed in median individuals. It seems plausible that acute responses with this amount of variability could form part of the mechanistic basis for epidemiological observations of enhanced mortality in relation to ambient exposures to fine particles.     

Estimates of the Health Risk Reductions Associated with Attainment of Alternative Particulate Matter Standards in Two U.S. Cities

As part of its periodic re-evaluation of particulate matter (PM) standards, the U.S. Environmental Protection Agency estimated the health risk reductions associated with attainment of alternative PM standards in two locations in the United States with relatively complete air quality data: Philadelphia and Los Angeles. PM standards at the time of the analysis were defined for particles of aerodynamic diameter less than or equal to 10 microm, denoted as PM-10. The risk analyses estimated the risk reductions that would be associated with changing from attainment of the PM-10 standards then in place to attainment of alternative standards using an indicator measuring fine particles, defined as those particles of aerodynamic diameter less than or equal to 2.5 microm and denoted as PM-2.5. Annual average PM-2.5 standards of 12.5, 15, and 20 microg/m3 were considered in various combinations with daily PM-2.5 standards of 50 and 65 microg/m3. Attainment of a standard or set of standards was simulated by a proportional rollback of "as is" daily PM concentrations to daily PM concentrations that would just meet the standard(s). The predicted reductions in the incidence of health effects varied from zero, for those alternative standards already being met, to substantial reductions of over 88% of all PM-associated incidence (e.g., in mortality associated with long-term exposures in Los Angeles, under attainment of an annual standard of 12.5 microg/m3). Sensitivity analyses and integrated uncertainty analyses assessed the multiple-source uncertainty surrounding estimates of risk reduction.     

Acute Exposure to Extremely Hazardous Substances: An Analysis of Environmental Equity

Although environmental equity research has focused primarily on chronic pollution sources, recent advances in environmental modeling and geographic information systems (GIS) provide a foundation for developing measures that can be used to evaluate differential exposure to acute pollution events. This article describes a methodology that uses facility-specific information to develop a risk surface representing the spatial distribution of accidental exposure to hazardous substances in a study area. Environmental pollution models recommended by the U.S. Environmental Protection Agency were used in conjunction with GIS software to achieve this objective. The methodology was implemented in a large metropolitan region (Hillsborough County, Florida) to examine disproportionate exposure to worst-case releases of extremely hazardous substances. The environmental inequity hypothesis was investigated by directly comparing the distribution of potential exposures within each racial (non-White versus White) and income (below poverty versus above poverty) subgroup. The results indicate that a significantly large proportion of both non-White and impoverished individuals resided in areas potentially exposed to multiple accidental releases.     

Propagation of Uncertainty in Risk Assessments: The Need to Distinguish Between Uncertainty Due to Lack of Knowledge and Uncertainty Due to Variability

In quantitative uncertainty analysis, it is essential to define rigorously the endpoint or target of the assessment. Two distinctly different approaches using Monte Carlo methods are discussed: (1) the end point is a fixed but unknown value (e.g., the maximally exposed individual, the average individual, or a specific individual) or (2) the end point is an unknown distribution of values (e.g., the variability of exposures among unspecified individuals in the population). In the first case, values are sampled at random from distributions representing various "degrees of belief" about the unknown "fixed" values of the parameters to produce a distribution of model results. The distribution of model results represents a subjective confidence statement about the true but unknown assessment end point. The important input parameters are those that contribute most to the spread in the distribution of the model results. In the second case, Monte Carlo calculations are performed in two dimensions producing numerous alternative representations of the true but unknown distribution. These alternative distributions permit subject confidence statements to be made from two perspectives: (1) for the individual exposure occurring at a specified fractile of the distribution or (2) for the fractile of the distribution associated with a specified level of individual exposure. The relative importance of input parameters will depend on the fractile or exposure level of interest. The quantification of uncertainty for the simulation of a true but unknown distribution of values represents the state-of-the-art in assessment modeling.     

Issues Related to Time Averaging of Exposure in Modeling Risks Associated with Intermittent Exposures to Lead

Typical exposures to lead often involve a mix of long-term exposures to relatively constant exposure levels (e.g., residential yard soil and indoor dust) and highly intermittent exposures at other locations (e.g., seasonal recreational visits to a park). These types of exposures can be expected to result in blood lead concentrations that vary on a temporal scale with the intermittent exposure pattern. Prediction of short-term (or seasonal) blood lead concentrations arising from highly variable intermittent exposures requires a model that can reliably simulate lead exposures and biokinetics on a temporal scale that matches that of the exposure events of interest. If exposure model averaging times (EMATs) of the model exceed the shortest exposure duration that characterizes the intermittent exposure, uncertainties will be introduced into risk estimates because the exposure concentration used as input to the model must be time averaged to account for the intermittent nature of the exposure. We have used simulation as a means of determining the potential magnitude of these uncertainties. Simulations using models having various EMATs have allowed exploration of the strengths and weaknesses of various approaches to time averaging of exposures and impact on risk estimates associated with intermittent exposures to lead in soil. The International Commission of Radiological Protection (ICRP) model of lead pharmacokinetics in humans simulates lead intakes that can vary in intensity over time spans as small as one day, allowing for the simulation of intermittent exposures to lead as a series of discrete daily exposure events. The ICRP model was used to compare the outcomes (blood lead concentration) of various time-averaging adjustments for approximating the time-averaged intake of lead associated with various intermittent exposure patterns. Results of these analyses suggest that standard approaches to time averaging (e.g., U.S. EPA) that estimate the long-term daily exposure concentration can, in some cases, result in substantial underprediction of short-term variations in blood lead concentrations when used in models that operate with EMATs exceeding the shortest exposure duration that characterizes the intermittent exposure. Alternative time-averaging approaches recommended for use in lead risk assessment more reliably predict short-term periodic (e.g., seasonal) elevations in blood lead concentration that might result from intermittent exposures. In general, risk estimates will be improved by simulation on shorter time scales that more closely approximate the actual temporal dynamics of the exposure.     

A Time‐Dependent Risk Assessment for Broken Compact Fluorescent Lamps

Environmental Protection Agency (EPA) ambient air quality guidelines are meant to limit long‐term exposures of toxins to safe levels. Unfortunately, there is little guidance for what constitutes a safe level from a one‐time (or very infrequent) short exposure(s). In the case of mercury, a review of the derivation of the EPA ambient air quality standard shows that it implicitly assumes a tissue burden model. The time dependence of the tissue burden is commonly described in terms of a half‐life, a modeling assumption that presumes that the decline in the tissue burden after a single exposure can be approximately described as an exponential decay. In this article, we use a simple exponential tissue burden model to derive a time‐dependent no observable adverse effect level (NOAEL) for mercury concentrations in air. The model predicts that tissue body burden will asymptotically approach the EPA air quality level for long exposure times, and reach workplace standard levels for exposures of a few hours. The model was used along with data on mercury levels from experimental work done by the Maine Department of Environmental Protection to evaluate the risks from a broken compact fluorescent lamp in a residential setting. Mercury levels approached the NOAEL only when the debris was left in an almost sealed room. Normal common‐sense cleaning measures: removal of debris to an outside area, and ventilation of the room for several minutes, reduced exposures to less than 1% of the NOAEL.     

Some Limitations of Aggregate Exposure Metrics

Aggregate exposure metrics based on sums or weighted averages of component exposures are widely used in risk assessments of complex mixtures, such as asbestos-associated dusts and fibers. Allowed exposure levels based on total particle or fiber counts and estimated ambient concentrations of such mixtures may be used to make costly risk-management decisions intended to protect human health and to remediate hazardous environments. We show that, in general, aggregate exposure information alone may be inherently unable to guide rational risk-management decisions when the components of the mixture differ significantly in potency and when the percentage compositions of the mixture exposures differ significantly across locations. Under these conditions, which are not uncommon in practice, aggregate exposure metrics may be "worse than useless," in that risk-management decisions based on them are less effective than decisions that ignore the aggregate exposure information and select risk-management actions at random. The potential practical significance of these results is illustrated by a case study of 27 exposure scenarios in El Dorado Hills, California, where applying an aggregate unit risk factor (from EPA's IRIS database) to aggregate exposure metrics produces average risk estimates about 25 times greater - and of uncertain predictive validity - compared to risk estimates based on specific components of the mixture that have been hypothesized to pose risks of human lung cancer and mesothelioma.