A Caution for Monte Carlo Risk Assessment of Long Term Exposures based on Short-Term Exposure Study Data

Monte Carlo risk assessments commonly take as input empirical or parametric exposure distributions from specially designed exposure studies. The exposure studies typically have limited duration, since their design is based on statistical and practical factors (such as cost and respondent burden). For these reasons, the exposure period studied rarely corresponds to the biologic exposure period, which we define as the time at risk that is relevant for quantifying exposure that may result in health effects. Both the exposure period studied and the biologic exposure period will often differ from the exposure interval used in a Monte Carlo analysis. Such time period differences, which are often not accounted for, can have dramatic effects on the ultimate risk assessment. When exposure distributions are right skewed and/or follow a lognormal distribution, exposure will usually be overestimated for percentiles above the median by direct use of exposure study empirical data, since biologic exposure periods are generally longer than the exposure periods in exposure assessment studies. We illustrate the effect that biologic exposure time period and response error can have on exposure distributions, using soil ingestion as an example. Beginning with variance components from lognormally distributed soil ingestion estimates, we illustrate the effect of different modeling assumptions, and the sensitivity of the resulting analyses to these assumptions. We develop a strategy for determining appropriate exposure input distributions for soil ingestion, and illustrate this using data on soil ingestion in children.     

Population-Based Fish Consumption Survey and Probabilistic Methylmercury Risk Assessment

A fish consumption survey was developed and administered by telephone to 820 Wyoming fishing license holders. Survey respondents provided the frequency, species, and quantity of Wyoming-caught and store-bought fish consumed for license holder and household members. Deterministic and probabilistic methylmercury exposure distributions were estimated by multiplying fish consumption by species-specific mercury concentrations for each household member. Risk assessments were conducted for children, women of childbearing age, and the rest of the population by comparing methylmercury exposure distributions to levels of concern. The results indicate that probabilistic risk assessment likely provides a more realistic view of the risk to the study population. The results of this study clearly indicate that: (1) there is no level of fish consumption that is without risk of methylmercury exposure, (2) fish advisories may be warranted for children and women of childbearing age, and (3) that store-bought fish generally contribute more to methylmercury exposure than do Wyoming-caught fish.     

Do Current Standards of Practice in Canada Measure What is Relevant to Human Exposure at Contaminated Sites? I: A Discussion of Soil Particle Size and Contaminant Partitioning in Soil

Human health risk estimates for sites with contaminated soils are often based on the assumption that the bulk concentration of substances in outdoor soil samples is a reasonable predictor of exposures via incidental soil ingestion, soil particle inhalation, and dermal absorption. Most underlying conceptual models are grossly simplistic, however, when considered in light of (i) biases in the distribution of contaminants across soil particle sizes, (ii) the size range of particles in soils and dusts that is environmentally available, and (iii) factors that influence desorption from particles and uptake into humans. The available studies indicate that contaminant distribution across soil particle size fractions varies widely between different soil types and contaminant delivery mechanisms, and it cannot be assumed that higher masses of contaminants per unit mass of soil are correlated with smaller particles sizes. Soil data gathered in support of detailed human health risk assessments, therefore, should allow for the examination of distribution across particle sizes of contaminants of concern, and consider those size fractions most critical to human exposure. Soil evaluations for health risk assessments of metals/metalloids should also consider mineralogical characterization.     

Extrapolation for Exposure Duration in Oral Toxicity: A Quantitative Analysis of Historical Toxicity Data

For human risk assessment, experimental data often have to be extrapolated for exposure duration, which is generally done by means of default values. The purpose of the present study was twofold. First, to derive a statistical distribution for differences in exposure duration that can be used in a probabilistic concept for combining assessment factors in risk characterization. Second, to obtain insight in the magnitude of the change in No-Observed-Adverse-Effect-Level (NOAEL) with exposure duration, which will lead to more science-based assessment factors for exposure duration. A large historical database, including 198 substances, was consulted. Ratios were calculated for pairs of NOAELs for systemic toxicity from oral toxicity studies with the same species (rats or mice, various strains) and different exposure duration categories. The Geometric Mean (GM), Geometric Standard Deviation (GSD), and the 90th and 95th percentile values were determined. The traditionally applied default factors for subacute to semichronic (10), for semichronic to chronic (10), and for subacute to chronic exposure (100) corresponded with the 93, 87, and 99-percentiles of the respective distributions. Options are presented for a set of default values and probabilistic distributions for assessment factors for exposure duration based on data from the consulted historical database.     

Review of Information Resources to Support Human Exposure Assessment Models

Efforts to model human exposures to chemicals are growing more sophisticated and encompass increasingly complex exposure scenarios. The scope of such analyses has increased, growing from assessments of single exposure pathways to complex evaluations of aggregate or cumulative chemical exposures occurring within a variety of settings and scenarios. In addition, quantitative modeling techniques have evolved from simple deterministic analyses using single point estimates for each necessary input parameter to more detailed probabilistic analyses that can accommodate distributions of input parameters and assessment results. As part of an overall effort to guide development of a comprehensive framework for modeling human exposures to chemicals, available information resources needed to derive input parameters for human exposure assessment models were compiled and critically reviewed. Ongoing research in the area of exposure assessment parameters was also identified. The results of these efforts are summarized and other relevant information that will be needed to apply the available data in a comprehensive exposure model is discussed. Critical data gaps in the available information are also identified. Exposure assessment modeling and associated research would benefit from the collection of additional data as well as by enhancing the accessibility of existing and evolving information resources.     

Distributions for Key Exposure Factors Controlling the Uptake of Xenobiotic Chemicals by Great Blue Herons (Ardea herodius) through Ingestion of Fish

A variety of exposure factors ultimately control the uptake of xenobiotic chemicals by colonial piscivorous birds, such as herons and egrets. These factors, which include feeding rate for various food sources, feeding territory size, and body weight, are critical to the implementation of models commonly used to predict ecological risks. A critical evaluation of literature on avian behavior, physiology, and ecology was conducted in order to identify those variables that most strongly influence uptake of chemicals by herons and egrets. After developing a database of such exposure factors, we identified each factor's range, central tendency, and distribution. Sufficient information was available to allow the development of statistical distributions for body weight, fraction of diet from fish, prey length, distance to foraging site, and feeding territory size for great blue herons. The five distributions presented in this paper are intended to support probabilistic assessments of potential risks to great blue herons. Distributions of exposure factors for great blue herons have not been previously published. Without such data, application of all but the most simple mechanistic models will require point estimates, most of which will be overly conservative and will not support accurate characterization of actual exposures of herons at hazardous waste sites.     

Soil Cleanup Goal for Dioxin Using Probabilistic Risk Assessment Techniques

State regulators in Florida recently approved a first-of-its-kind probabilistic risk assessment (PRA) for determining an alternative residential Soil Cleanup Target Level (SCTL) for dioxin (32 ng/kg TEQ). The default residential SCTL (7 ng/kg TEQ) is based on a single, deterministic calculation with numerous conservative assumptions, resulting in an overly conservative value far beyond the regulatory mandate (i.e., 10^?6 increase in cancer risk). Conversely, this PRA used a Monte Carlo simulation to estimate risk for all members of a large population using a combination of scientific data and professional judgment, with final details developed during negotiations with regulators. The simulation parameters were defined probabilistically and reflect the ranges of values for the following exposure variables: body weight, exposure duration, exposure frequency, fraction from contaminated source, soil ingestion rate, and relative bioavailability. Other variable and uncertain parameters were treated deterministically per direction from the regulators. The state also required that a pre-supposed high-risk subpopulation be analyzed separate from the full receptor population. Despite the conservativeness of the alternative SCTL, this PRA represents a significant step toward more realistic estimates of human health risks caused by environmental contaminant exposure.     

Bayesian Updating of Model-Based Risk Estimates Using Imperfect Public Health Surveillance Data

Model-based estimation of the human health risks resulting from exposure to environmental contaminants can be an important tool for structuring public health policy. Due to uncertainties in the modeling process, the outcomes of these assessments are usually probabilistic representations of a range of possible risks. In some cases, health surveillance data are available for the assessment population over all or a subset of the risk projection period and this additional information can be used to augment the model-based estimates. We use a Bayesian approach to update model-based estimates of health risks based on available health outcome data. Updated uncertainty distributions for risk estimates are derived using Monte Carlo sampling, which allows flexibility to model realistic situations including measurement error in the observable outcomes. We illustrate the approach by using imperfect public health surveillance data on lung cancer deaths to update model-based lung cancer mortality risk estimates in a population exposed to ionizing radiation from a uranium processing facility.     

Importance of dose‐response model form in probabilistic risk assessment: A case study of health effects from methylmercury in fish

We compared the effect of uncertainty in dose‐response model form on health risk estimates to the effect of uncertainty and variability in exposure. We used three different dose‐response models to characterize neurological effects in children exposed in utero to methylmercury, and applied these models to calculate risks to a native population exposed to potentially contaminated fish from a reservoir in British Columbia. Uncertainty in model form was explicitly incorporated into the risk estimates. The selection of dose‐response model strongly influenced both mean risk estimates and distributions of risk, and had a much greater impact than altering exposure distributions. We conclude that incorporating uncertainty in dose‐response model form is at least as important as accounting for variability and uncertainty in exposure parameters in probabilistic risk assessment.     

Human Exposure Pathways of Heavy Metals in a Lead-Zinc Mining Area,Jiangsu Province,China

Heavy metal pollution is becoming a serious issue in developing countries such as China, and the public is increasingly aware of its adverse health impacts in recent years. We assessed the potential health risks in a lead-zinc mining area and attempted to identify the key exposure pathways. We evaluated the spatial distributions of personal exposure using indigenous exposure factors and field monitoring results of water, soil, food, and indoor and outdoor air samples. The risks posed by 10 metals and the contribution of inhalation, ingestion and dermal contact pathways to these risks were estimated. Human hair samples were also analyzed to indicate the exposure level in the human body. Our results show that heavy metal pollution may pose high potential health risks to local residents, especially in the village closest to the mine (V1), mainly due to Pb, Cd and Hg. Correspondingly, the residents in V1 had higher Pb (8.14 mg/kg) levels in hair than those in the other two villages. Most of the estimated risks came from soil, the intake of self-produced vegetables and indoor air inhalation. This study highlights the importance of site-specific multipathway health risk assessments in studying heavy-metal exposures in China.     

The Use of Epidemiology in Risk Assessment: Challenges and Opportunities

The assessment of risk from environmental and occupational exposures incorporates and synthesizes data from a variety of scientific disciplines including toxicology and epidemiology. Epidemiological data have offered valuable contributions to the identification of human health hazards, estimation of human exposures, quantification of the exposure–response relation, and characterization of risks to specific target populations including sensitive populations. As with any scientific discipline, there are some uncertainties inherent in these data; however, the best human health risk assessments utilize all available information, characterizing strengths and limitations as appropriate. Human health risk assessors evaluating environmental and occupational exposures have raised concerns about the validity of using epidemiological data for risk assessment due to actual or perceived study limitations. This article highlights three concerns commonly raised during the development of human health risk assessments of environmental and occupational exposures: (a) error in the measurement of exposure, (b) potential confounding, and (c) the interpretation of non-linear or non-monotonic exposure–response data. These issues are often the content of scientific disagreement and debate among the human health risk assessment community, and we explore how these concerns may be contextualized, addressed, and often ameliorated.     

Has the mist been peered through? Revisiting the building blocks of human health risk assessment for electronic cigarette use

Electronic cigarettes, battery-powered nicotine delivery devices, have been increasingly used in the past decade. This critical review provides a qualitative research synthesis of the human health risks associated with E-vapor inhalation in the peer-reviewed literature and our own preliminary experimental results. E-cigarettes may be as efficient as traditional cigarettes in nicotine delivery, especially for experienced users, and studies suggest lower emissions of air toxics from E-cigarette vapor and lower second- and third-hand vapor exposures. Some toxic emissions may however surpass those of traditional cigarettes, especially under high voltage vaping conditions. Experimentally, E-vapor/E-liquid exposures reduce cell viability and promote pro-inflammatory cytokine release. User vulnerability to concomitant environmental agent exposures, such as viruses and bacteria, may potentially be increased. While evidence to date suggests that E-cigarettes release fewer toxins and carcinogens compared to cigarettes, E-vapor is not safe and might adversely affect human immune functions. Major knowledge gaps hinder risk quantification and effective regulation of E-cigarette products including: lack of long-term exposure studies, lack of understanding of biological mechanisms associated with exposure, and lack of integration of exposure and toxicity assessments. Better data are needed to inform human health risk assessments and understand the public health impact of E-vapor exposures.     

Equine Risk Assessment for Insecticides Used in Adult Mosquito Management

Since West Nile virus (WNV) was introduced to New York City in 1999, it has subsequently spread through the Americas, creating human and animal health risks. Our equine risk assessment focused on three pyrethroid insecticides (phenothrin, resmethrin, and permethrin), pyrethrins, and two organophosphate insecticides (malathion and naled). Piperonyl butoxide, a synergist commonly used in pyrethroids, was also assessed. The objective was to use deterministic and probabilistic risk assessment methodologies to evaluate health risks to horses from vector management tactics used for control of adult mosquitoes. Our exposure estimates were derived from the Kenaga nomogram for food deposition, AgDRIFT® for deposition onto soil and hair, AERMOD for ambient air concentrations, and PRZM-EXAMS for water concentrations. We used the risk quotient (RQ) method for our assessment with the RQ level of concern (LOC) set at 1.0. RQs were determined by comparing the exposure to no-observable-effect-levels. Acute deterministic RQs ranged from 0.0004 for phenothrin to 0.2 for naled. Subchronic deterministic RQs ranged from 0.001 for phenothrin to 0.6 for naled. The probabilistic assessment revealed estimates of deterministic acute and subchronic RQs were highly conservative. Our assessment revealed that risks to horses from adult mosquito insecticides are low and not likely to exceed the LOC.     

Bioaccessibility and other key parameters in assessing oral exposure to PAH-contaminated soils and dust: A critical review

Soil ingestion is an important pathway for human exposure to polycyclic aromatic hydrocarbon (PAH)-contaminated soils and dust for children (via ingesting hand residue) as well as for adults (via occupational exposure). An appropriate selection of exposure parameter values is essential for having an accurate risk assessment. This review addresses key parameters for estimating oral exposure to PAH-contaminated soils/dust, discusses their variability and uncertainty, and provides recommendations for value selection. Bioaccessibility (contaminant fraction solubilized in gastro-intestinal tract, available for entering bloodstream and reaching target organs) and soil ingestion rate are two key parameters for exposure assessment (usually characterized by large variability and/or uncertainty), followed by exposure frequency/duration and body weight.     

Probability Density Functions Describing 24-Hour Inhalation Rates For Use in Human Health Risk Assessments

A Monte Carlo simulation was undertaken to estimate the amount of air inhaled over a typical 24-hour period by six age groups of Canadians. The objective of the simulation was to derive probability density functions that could be used to describe inhalation rates in probabilistic health risk assessments involving airborne contaminants. The six age groups considered were those typically employed in human health risk assessments in Canada: infants (aged 0 to 6 months), toddlers (aged 7 months to 4 years), children (aged 5 to 11 years), teenagers (aged 12 to 19 years), adults (aged 20 to 59 years), and seniors (aged 60 years and older). The resulting distributions are considered equally applicable to Americans as Canadians, however, since the study relied heavily on time-activity information gathered in the USA. Existing time-activity and breathing rate studies were reviewed in order to define random variables describing probable durations that North Americans spend at various levels of activity and their probable inhalation rates while at each level of activity. These random variables were combined in a Monte Carlo simulation to empirically generate probability density functions describing 24-hour inhalation rates for each age group. The simulation suggested that most age groups' 24 hour inhalation rates can be represented with log-normal probability density functions. Arithmetic mean values and standard deviations for these distributions are as follows: approximately 9.3 ± 2.4?m³/day for toddlers; 14.6 ± 3.0?m³/day for children; 15.8 ± 3.7?m³/day for teenagers; 16.2 ± 3.8?m³/day for adults; and 14.2 ± 3.3?m³/day for seniors. The distribution of infants' 24-hour inhalation rates was found to be better represented by a normal distribution than a log-normal distribution. The mean and standard deviation for the infants' normal distribution are 2.1 ± 0.58?m³/day. Inhalation rates were also estimated separately for male and female toddlers, children, teenagers, adults and seniors. These estimates suggested that males inhale on average 8% to 27% more air than females of the same age. Because infants' activity patterns and minute volumes did not appear to be strongly correlated with gender, a single probability density function was deemed satisfactory to describe male and female infants' 24-hour inhalation rates.     

Estimation of Dietary Intake of Inorganic Arsenic in U.S. Children

Arsenic is a natural component of the environment and is ubiquitous in soils, water, and the diet. Because dietary intake can be a significant source of background exposure to inorganic arsenic (the most toxicologically significant form), accurate intake estimates are needed to provide a context for risk management of arsenic exposure. Intake of inorganic arsenic by adults is fairly well characterized, but previous estimates of childhood intake were based on inorganic arsenic analyses in a limited number of foods (13 food types). This article estimates dietary intake for U.S. children (1 to 6 years of age) based on reported inorganic arsenic concentrations in 38 foods and in water used in cooking those foods (inorganic arsenic concentration of 0.8 μg/L), and U.S. Department of Agriculture food consumption data. This information is combined using a probabilistic software model to extract food consumption patterns and compute exposure distributions. The mean childhood dietary intake estimate for inorganic arsenic was 3.2 μg/day with a range of 1.6 to 6.2 μg/day for the 10th and 95th percentiles, respectively. For both the mean and 95th percentile inorganic arsenic intake rates, intake was predominantly contributed by grain and grain products, fruits and fruit juices, rice and rice products, and milk.     

The need for new methods to backcalculate soil cleanup targets in interval and probabilistic cancer risk assessments

Ordinary algebra may be used to backcalculate health‐based cleanup targets in deterministic risk assessments, but it does not work in interval or probabilistic risk assessments. Equations with interval or random variables do not follow the rules of ordinary algebra. This paper explains the need for more sophisticated methods to backcalculate soil cleanup targets when using interval or random variables.     

Prevalence of Soil Mouthing/Ingestion among Healthy Children Aged 1 to 6

Ingestion of non-food items/mouthing behavior results in exposure of children to contaminants in soil/dust. We characterize the prevalence of such behaviors in healthy children. The relative frequency of such behaviors was assessed by parent interviews for 533 children age 1 to 6. Thirty-eight percent of children put soil in their mouths at least monthly, 24% at least weekly, and 11% daily. High-risk behavior decreased quickly for children aged 2 or more, but was still reported at least monthly by 3 to 9% of parents of children up through age 6. Highest outdoor object mouthing rates occur among 1-year-old children, who are reported to play daily in sand/dirt and have generally high levels of mouthing. Such children may have higher soil/dust ingestion and higher exposure to contaminants when soil/dust contains lead or other agents. These high-risk groups may help focus educational interventions and/or risk assessments.