Exposure Assessment Component Research Articles

Critical reviews of exposure assessment in carcinogenic hazard identification: the IARC Monographs experience

ObjectivesTo summarise the rationale, workflow and recommendations for the conduct of exposure assessment critiques in key human studies evaluated for International Agency for Research on Cancer (IARC) Monographs on the...

Predicting environmental concentrations of nanomaterials for exposure assessment - a review

There have been major advances in the science to predict the likely environmental concentrations of nanomaterials, which is a key component of exposure and subsequent risk assessment. Considerable progress has been since the first Material Flow Analyses (MFAs) in 2008, which were based on very limited information, to more refined current tools that take into account engineered nanoparticle (ENP) size distribution, form, dynamic release, and better-informed release factors. These MFAs provide input for all environmental fate models (EFMs), that generate estimates of particle flows and concentrations in various environmental compartments. While MFA models provide valuable information on the magnitude of ENP release, they do not account for fate processes, such as homo- and heteroaggregation, transformations, dissolution, or corona formation. EFMs account for these processes in differing degrees. EFMs can be divided into multimedia compartment models (e.g., atmosphere, waterbodies and their sediments, soils in various landuses), of which there are currently a handful with varying degrees of complexity and process representation, and spatially-resolved watershed models which focus on the water and sediment compartments. Multimedia models have particular applications for considering predicted environmental concentrations (PECs) in particular regions, or for developing generic “fate factors” (i.e., overall persistence in a given compartment) for life-cycle assessment. Watershed models can track transport and eventual fate of emissions into a flowing river, from multiple sources along the waterway course, providing spatially and temporally resolved PECs. Both types of EFMs can be run with either continuous sources of emissions and environmental conditions, or with dynamic emissions (e.g., temporally varying for example as a new nanomaterial is introduced to the market, or with seasonal applications), to better understand the situations that may lead to peak PECs that are more likely to result in exceedance of a toxicological threshold. In addition, bioaccumulation models have been developed to predict the internal concentrations that may accumulate in exposed organisms, based on the PECs from EFMs. The main challenge for MFA and EFMs is a full validation against observed data. To date there have been no field studies that can provide the kind of dataset(s) needed for a true validation of the PECs. While EFMs have been evaluated against a few observations in a small number of locations, with results that indicate they are in the right order of magnitude, there is a great need for field data. Another major challenge is the input data for the MFAs, which depend on market data to estimate the production of ENPs. The current information has major gaps and large uncertainties. There is also a lack of robust analytical techniques for quantifying ENP properties in complex matrices; machine learning may be able to fill this gap. Nevertheless, there has been major progress in the tools for generating PECs. With the emergence of nano- and microplastics as a leading environmental concern, some EFMs have been adapted to these materials. However, caution is needed, since most nano- and microplastics are not engineered, therefore their characteristics are difficult to generalize, and there are new fate and transport processes to consider.

Minimally Processed Vegetables: Consumer Profile, Consumption Habits, and Perceptions of Microbiological Risk

This study was conducted to characterize the profile of Brazilian consumers of minimally processed vegetables (MPV) by analyzing consumption habits and perceptions of microbiological risk. An online survey with 27 closed-end questions was administered using Google Forms. The survey consisted of questions regarding participants’ demographic characteristics, consumption of fresh vegetables and MPV, and perception of the microbiological risk associated with the consumption of MPV. The majority of the 1,510 participants (77.5%) were women, 42.3% were 26 to 40 years of age, 42.3% were married, 19.7% were students, 54.4% had a graduate degree, and 39.4% had a monthly family income between 5 and 15 times the minimum wage. Most participants (95.3%) reported consuming fresh vegetables, but only 45.4% reported the consumption of MPV. Among MPV consumers, convenience and practicality were the main determining factors (77.8%) in the purchase of these products. The high price of MPV was the main limiting factor (66.4%). Pearson’s chi-square test indicated that only monthly family income was positively associated with the consumption of vegetables and the perception of microbiological risks related to MPV. These data contribute to a better understanding of the profile of Brazilian MPV consumers and will be important as an exposure assessment component of vegetable risk assessment models.

Advancing exposure assessment approaches to improve wildlife risk assessment.

The exposure assessment component of a Wildlife Ecological Risk Assessment aimstoestimate the magnitude, frequency, and duration of exposure to a chemical or environmental contaminant, along with characteristics of the exposed population. This can be challenging in wildlife as there is often high uncertainty and error caused by broad-based, interspecific extrapolation and assumptions often because of a lack of data. Both the US Environmental Protection Agency (USEPA) and European Food Safety Authority (EFSA) have broadly directed exposure assessments to include estimates of the quantity (dose or concentration), frequency, and duration of exposure to a contaminant of interest while considering "all relevant factors." This ambiguity in the inclusion or exclusion of specific factors (e.g., individual and species-specific biology, diet,orproportion time in treated or contaminated area) can significantly influence the overall risk characterization. In this review, we identify four discretecategories of complexity that should beconsideredin an exposure assessment-chemical, environmental, organismal, and ecological. These may require more data, but a degree of inclusion at all stages of the risk assessment is critical to moving beyond screening-level methods that have a high degree of uncertainty and suffer from conservatism and a lack of realism. We demonstrate that there are many existing and emerging scientific tools and cross-cutting solutions for tackling exposure complexity. To foster greater application of these methods inwildlife exposure assessments, we present a new framework for risk assessors to construct an "exposure matrix." Using three case studies, we illustrate how the matrix can better inform, integrate, and more transparently communicate the important elements of complexity and realism in exposure assessments for wildlife. Modernizing wildlife exposure assessments is long overdue and will require improved collaboration, data sharing, application of standardized exposure scenarios, better communication of assumptions and uncertainty, and postregulatory tracking. Integr Environ Assess Manag 2024;20:674-698. © 2023 SETAC.

Toxicological approaches for the quantitative inhalation risk assessment of toxic metals from tobacco smoke: application on the deterministic and probabilistic inhalation risk assessment of cadmium for Austrian smokers

Objective The objective of this study was the assessment of risks from inhalation exposure of Austrian smokers to cadmium through established toxicological approaches with emphasis on the exposure assessment component, which is challenging regarding the actual amount of metal that is inhaled and the simulation of the smoking pattern. Materials and methods Exposure assessment comprised an estimation of the proportion of cadmium inhaled through smoking and actual occurrence data in tobacco products and survey smoking habits, which were integrated in alternative scenarios through a deterministic and a probabilistic Monte Carlo simulation method. Risks were characterized through the comparison of the exposure with health-based guidance values, as well as through the assessment of the excess lifetime cancer risk (ELCR), the non-cancer hazard quotient (NCHQ), and the margin of exposure (MOE). The strengths, the uncertainties, and the limitations of the different methodologies were discussed. Results and discussion Upper exposures are close or exceed the Permitted Daily Exposure. Respiratory ELCRs are unacceptable compared to the benchmark range of 1.0E–06 to 1.0E–04. Renal and respiratory NCHQs exceed the target value of 1.0 by 3- to 17-fold. MOEs are not protective enough for cancer and non-cancer effects. The amount of cadmium that reaches the lung is a key source of uncertainty. Conclusion Probabilistic estimates provide a refined capture of the actual inhalation exposure. Risk estimates and gender and age profiles are alarming, especially for young smokers. Application of toxicological approaches, combined with realistic assessment of the inhalation exposure levels, can support risk communication and management.

Generation and Classification of Activity Sequences for Spatiotemporal Modeling of Human Populations.

Human activity encompasses a series of complex spatiotemporal processes that are difficult to model but represent an essential component of human exposure assessment. A significant empirical data source, like the American Time Use Survey (ATUS), can be leveraged to model human activity. However, tractable models require a better stratification of activity data to inform about different, but classifiable groups of individuals, that exhibit similar activity sequences and mobility patterns. Using machine learning algorithms, we developed an unsupervised classification and sequence generation method that is capable of generating coherent and stochastic sequences of activity from the ATUS data. This classification, when combined with any spatiotemporal exposure profile, allows the development of stochastic models of exposure patterns and records for groups of individuals exhibiting similar activity behaviors.

A prospective cohort study on ambient air pollution and respiratory morbidities including childhood asthma in adolescents from the western Cape Province: study protocol

BackgroundThere is evidence from existing literature that ambient air pollutant exposure in early childhood likely plays an important role in asthma exacerbation and other respiratory symptoms, with greater effect among asthmatic children. However, there is inconclusive evidence on the role of ambient air pollutant exposures in relation to increasing asthma prevalence as well as asthma induction in children. At the population level, little is known about the potential synergistic effects between pollen allergens and air pollutants since this type of association poses challenges in uncontrolled real life settings. In particular, data from sub-Sahara Africa is scarce and virtually absent among populations residing in informal residential settlements.Methods/designA prospective cohort study of 600 school children residing in four informal settlement areas with varying potential ambient air pollutant exposure levels in the Western Cape in South Africa is carried-out. The study has two follow-up periods of at least six-months apart including an embedded panel study in summer and winter. The exposure assessment component models temporal and spatial variability of air quality in the four study areas over the study duration using land-use regression modelling (LUR). Additionally, daily pollen levels (mould spores, tree, grass and weed pollen) in the study areas are recorded. In the panel study asthma symptoms and serial peak flow measurements is recorded three times daily to determine short-term serial airway changes in relation to varying ambient air quality and pollen over 10-days during winter and summer. The health outcome component of the cohort study include; the presence of asthma using a standardised ISAAC questionnaire, spirometry, fractional exhaled nitric-oxide (FeNO) and the presence of atopy (Phadiatop).DiscussionThis research applies state of the art exposure assessment approaches to characterize the effects of ambient air pollutants on childhood respiratory health, with a specific focus on asthma and markers of airway inflammation (FeNO) in South African informal settlement areas by considering also pollen counts and meteorological factors. The study will generate crucial data on air pollution and asthma in low income settings in sub-Sahara Africa that is lacking in the international literature.

A Dynamic Model to Assess Microbial Health Risks Associated with Beneficial Uses of Biosolids - Phase 1

Maximum allowable levels for chemical contaminants in biosolids were developed for the Part 503 rule using risk-based methodologies. However, maximum allowable levels of microbiological contaminants in the Part 503 rule were based on specific treatment methodologies rather than risk levels, because it was determined at that time that risk assessment methodologies were not sufficiently developed. Given the current interest in the beneficial uses of biosolids and the projected rapid growth of biosolids reuse, there is increasing interest in the development of a microbial risk assessment methodology for regulatory and operational decision making.This document presents a methodology for assessing risks to human health from pathogens via exposure to biosolids. The methodology integrates two fundamental components: an exposure assessment component and a health risk component. The exposure assessment component is used to quantify pathogen levels in the environment and serves as input to the health effects component. The health effects component is used to quantify health risks using a model that explicitly accounts for properties unique to an infectious disease process, specifically secondary transmission and immunity.To demonstrate the applicability of these risk-based methods developed for biosolids exposure, numerical simulations were carried out for a case study example in which the route of exposure was direct consumption of biosolids-amended soil. The output from the case study yielded a decision tree that differentiates between conditions in which the risk from biosolids exposure is high and those conditions in which the relative risk from biosolids is low. This decision tree illustrates the interaction among the important factors in quantifying risk. For the case study example, those factors include biosolids treatment processes, the pathogen shedding rate of infectious individuals, secondary transmission and immunity. Further work in determining biosolids exposures is required before this methodology can be used in a comprehensive risk assessment.This title belongs to WERF Research Report SeriesISBN: 9781843396345 (Print)ISBN: 9781780403212 (eBook)

Derivation of human health-based ambient water quality criteria: a consideration of conservatism and protectiveness goals.

Under the terms of the Clean Water Act, criteria for the protection of human health (Human Health Ambient Water Quality Criteria [HHWQC]) are traditionally derived using equations recommended by the US Environmental Protection Agency (USEPA) that include parameters for exposure assessment. To derive "adequately protective" HHWQC, USEPA proposes the use of default values for these parameters that are a combination of medians, means, and percentile estimates targeting the high end (90th percentile) of the general population. However, in practice, in nearly all cases, USEPA's recommended default assumptions represent upper percentiles. This article considers the adequacy of the exposure assessment component of USEPA-recommended equations to yield criteria that are consistent with corresponding health protection targets established in USEPA recommendations or state policies, and concludes that conservative selections for exposure parameters can result in criteria that are substantially more protective than the health protection goals for HHWQC recommended by USEPA, due in large part to the compounding effect that occurs when multiple conservative factors are combined. This situation may be mitigated by thoughtful selection of exposure parameter values when using a deterministic approach, or by using a probabilistic approach based on data distributions for many of these parameters.

Comparison of methods for analyzing left-censored occupational exposure data.

The National Institute for Environmental Health Sciences (NIEHS) is conducting an epidemiologic study (GuLF STUDY) to investigate the health of the workers and volunteers who participated from April to December of 2010 in the response and cleanup of the oil release after the Deepwater Horizon explosion in the Gulf of Mexico. The exposure assessment component of the study involves analyzing thousands of personal monitoring measurements that were collected during this effort. A substantial portion of these data has values reported by the analytic laboratories to be below the limits of detection (LOD). A simulation study was conducted to evaluate three established methods for analyzing data with censored observations to estimate the arithmetic mean (AM), geometric mean (GM), geometric standard deviation (GSD), and the 95th percentile (X0.95) of the exposure distribution: the maximum likelihood (ML) estimation, the β-substitution, and the Kaplan-Meier (K-M) methods. Each method was challenged with computer-generated exposure datasets drawn from lognormal and mixed lognormal distributions with sample sizes (N) varying from 5 to 100, GSDs ranging from 2 to 5, and censoring levels ranging from 10 to 90%, with single and multiple LODs. Using relative bias and relative root mean squared error (rMSE) as the evaluation metrics, the β-substitution method generally performed as well or better than the ML and K-M methods in most simulated lognormal and mixed lognormal distribution conditions. The ML method was suitable for large sample sizes (N ≥ 30) up to 80% censoring for lognormal distributions with small variability (GSD = 2-3). The K-M method generally provided accurate estimates of the AM when the censoring was <50% for lognormal and mixed distributions. The accuracy and precision of all methods decreased under high variability (GSD = 4 and 5) and small to moderate sample sizes (N < 20) but the β-substitution was still the best of the three methods. When using the ML method, practitioners are cautioned to be aware of different ways of estimating the AM as they could lead to biased interpretation. A limitation of the β-substitution method is the absence of a confidence interval for the estimate. More research is needed to develop methods that could improve the estimation accuracy for small sample sizes and high percent censored data and also provide uncertainty intervals.

Calculation of chemical elimination half-life from blood with an ongoing exposure source: The example of perfluorooctanoic acid (PFOA)

Determination of the chemical clearance rate from human blood is a critical component of toxicokinetic exposure assessment. Analysis of temporal biomonitoring data without consideration of ongoing exposure results in calculation of apparent elimination half-life values that are longer than the intrinsic value. The intrinsic elimination half-life is solely a function of the rate of elimination while the apparent elimination half-life reflects the processes of both elimination and ongoing exposure. Confusion between intrinsic and apparent half-life values can lead to misinterpretation of biomonitoring data and can result in exaggerated predictions in subsequent modeling efforts. This work provides a review of the first-order equations that have been developed to calculate intrinsic and apparent half-life values and the potential bias that can result from confusing these two values. Published human biomonitoring data for perfluorooctanoic acid (PFOA) are analyzed using these equations to provide examples of low, medium and high bias in determination of the intrinsic elimination half-life from plasma or serum, the components of blood typically analyzed for PFOA. An approach is also provided to estimate the extent of exposure reduction that is indicated by declining longitudinal or cross-sectional biomonitoring data. Based on the evaluation methodology presented in this work, the intrinsic elimination half-life of PFOA in humans is 2.4years, representing the average of independent estimates of 2.5years (95% CI, 2.4–2.7) and 2.3years (95% CI, 2.1–2.4). The declining concentration of PFOA in blood of the general USA adult population represents an estimated exposure reduction of 20–30% over the period 1999–2008.

0269 An International Historical Cohort Study of Workers in the Hard-Metal Industry: Mid-Study Epidemiology Update

ObjectivesA multinational occupational epidemiological study of workers exposed to tungsten carbide with a cobalt binder (WCCo) is underway in the US and Europe. The epidemiological component will investigate total and...

0050 An International Historical Cohort Study of Workers in the Hard-Metal Industry: Exposure Assessment

ObjectivesA multinational occupational epidemiological study is underway to investigate the total and cause-specific mortality of workers exposed to tungsten carbide with a cobalt binder (WCCo). The study includes 12 US...

57 An international historical cohort study of workers in the hard-metal industry

ObjectivesIn 2006, IARC found limited evidence in humans and sufficient evidence in animals that tungsten carbide (WC) with cobalt binder (WCCo) acted as a lung carcinogen (Group 2A). Our historical...

Quality of Evidence Must Guide Risk Assessment of Asbestos

In 2011, we reported on the sensitivity of lung cancer potency estimates for asbestos to the quality of the exposure assessment component of underlying evidence. Both this meta-analysis and a separate reassessment of standards published by the Health Council of the Netherlands (Gezondheidsraad) have been commented on by Berman and Case. A criticism is that we used a truncated data set. We incrementally excluded poorer-quality studies to evaluate trends in meta-analyzed lung cancer potency estimates (meta-K (L) values). This was one of three analysis approaches we presented. The other two used the full set of studies: a meta-analysis stratified by covariates and dichotomized by poorer and better exposure assessment aspects; and a meta-regression modeling both asbestos fiber type and these covariates. They also state that our results are not robust to removal of one study. We disagree with this claim and present additional sensitivity analyses underpinning our earlier conclusion that inclusion of studies with higher-quality asbestos-exposure assessment yield higher meta-estimates of the lung cancer risk per unit of exposure. We reiterate that potency differences for predominantly chrysotile- versus amphibole-asbestos-exposed cohorts are difficult to ascertain when meta-analyses are restricted to studies with fewer exposure assessment limitations. We strongly argue that the existence of any uncertainty related to potency issues should not hamper the development of appropriate evidence-based guidelines and stringent policies in order to protect the public from hazardous environmental and occupational exposures.

Geocoding rural addresses in a community contaminated by PFOA: a comparison of methods

BackgroundLocation is often an important component of exposure assessment, and positional errors in geocoding may result in exposure misclassification. In rural areas, successful geocoding to a street address is limited by rural route boxes. Communities have assigned physical street addresses to rural route boxes as part of E911 readdressing projects for improved emergency response. Our study compared automated and E911 methods for recovering and geocoding valid street addresses and assessed the impact of positional errors on exposure classification.MethodsThe current study is a secondary analysis of existing data that included 135 addresses self-reported by participants of a rural community study who were exposed via public drinking water to perfluorooctanoate (PFOA) released from a DuPont facility in Parkersburg, West Virginia. We converted pre-E911 to post-E911 addresses using two methods: automated ZP4 address-correction software with the U.S. Postal Service LACS database and E911 data provided by Wood County, West Virginia. Addresses were geocoded using TeleAtlas, an online commercial service, and ArcView with StreetMap Premium North America NAVTEQ 2008 enhanced street dataset. We calculated positional errors using GPS measurements collected at each address and assessed exposure based on geocoded location in relation to public water pipes.ResultsThe county E911 data converted 89% of the eligible addresses compared to 35% by ZP4 LACS. ArcView/NAVTEQ geocoded more addresses (n = 130) and with smaller median distance between geocodes and GPS coordinates (39 meters) than TeleAtlas (n = 85, 188 meters). Without E911 address conversion, 25% of the geocodes would have been more than 1000 meters from the true location. Positional errors in TeleAtlas geocoding resulted in exposure misclassification of seven addresses whereas ArcView/NAVTEQ methods did not misclassify any addresses.ConclusionsAlthough the study was limited by small numbers, our results suggest that the use of county E911 data in rural areas increases the rate of successful geocoding. Furthermore, positional accuracy of rural addresses in the study area appears to vary by geocoding method. In a large epidemiological study investigating the health effects of PFOA-contaminated public drinking water, this could potentially result in exposure misclassification if addresses are incorrectly geocoded to a street segment not serviced by public water.

Exposure to chemicals and metals and risk of amyotrophic lateral sclerosis: A systematic review

Environmental exposure to chemicals and metals may contribute to the risk of sporadic amyotrophic lateral sclerosis (ALS). Two systematic reviews of the literature on these topics performed according to the well-established MOOSE guidelines are presented. Literature cited in MEDLINE, EMBASE, CINAHL, and Cochrane databases (up to March 2007) as well as references of relevant articles were screened for case-control or cohort studies investigating the associations between sporadic ALS and exposure to chemical agents or metals. Methodology of selected studies was appraised according to Armon's classification system for ALS risk factor studies as well as a newly developed classification system for quality of exposure assessment. Seven of the 38 studies concerning exposure to chemicals and three of the 50 studies concerning exposure to metals fulfilled the validity criteria. In two independent studies meeting the validity criteria, a significant association with increased ALS risk was reported for exposure to pesticides. This systematic review demonstrated the difficulty in attaining a high level of evidence due to lack of high quality of methodological and exposure assessment components. Although pesticide exposure was identified as candidate risk factor, more well-designed studies are needed to provide a definitive answer about exogenous factors of ALS.

Determination of Quantitative Food Consumption Levels for Use in Microbial Risk Assessments: Cheddar Cheese as an Example

Microbial risk assessment (MRA) is becoming increasingly used in the management of food safety because it can be used to quantify risks and help rank intervention strategies. The exposure assessment components of the assessments have become complex with many aspects of the contamination, survival, and growth of a pathogen in a food being taken into consideration. Insufficient consumption data constitutes an important data gap and consequently one of many sources of uncertainty in MRA even though the effects of uncertainty are smaller than those affecting bacterial concentration in foods. Therefore, food consumption data also play an important role in exposure assessment of MRA. In the United States, there are large-scale, nationwide sets of consumption data available for use in MRA, i.e., the National Health and Nutrition Examination Survey (NHANES). Newly released dietary interview data in the NHANES 2001 to 2002 survey show that it has been redesigned and that the data were sufficiently updated from previous versions to have more value for MRAs. We propose a model that can effectively use the new data sets and be incorporated into MRAs, using as an example consumption of Cheddar cheese/American-type cheese. This model included the prevalence of food eaten as well as the amount and frequency. We determined the amount of Cheddar/American cheese consumed per day with probability distribution (e.g., lognormal distribution). These could be further determined by gender, age, pregnancy, and combination food type, which we plan to do in the future. The frequency of the range of serving numbers for Cheddar/American cheese consumed per person per day and prevalence as the proportion of a population (e.g., survey respondents) eating a certain food in a day are also presented. Unlike traditional published mean values, the results of this model provide probability distribution intakes that can be compared with mean and median intakes. This allows values in the upper percentiles to be considered for inclusion in MRAs. We believe this simulation model can be adapted with different variables applicable to different foods, pathogens, and specific health risk population groups.

Assessment of food intake input distributions for use in probabilistic exposure assessments of food additives

A key component of a food chemical exposure assessment using probabilistic analysis is the selection of the most appropriate input distribution to represent exposure variables. The study explored the type of parametric distribution that could be used to model variability in food consumption data likely to be included in a probabilistic exposure assessment of food additives. The goodness-of-fit of a range of continuous distributions to observed data of 22 food categories expressed as average daily intakes among consumers from the North–South Ireland Food Consumption Survey was assessed using the BestFit® distribution fitting program. The lognormal distribution was most commonly accepted as a plausible parametric distribution to represent food consumption data when food intakes were expressed as absolute intakes (16/22 foods) and as intakes per kg body weight (18/22 foods). Results from goodness-of-fit tests were accompanied by lognormal probability plots for a number of food categories. The influence on food additive intake of using a lognormal distribution to model food consumption input data was assessed by comparing modelled intake estimates with observed intakes. Results from the present study advise some level of caution about the use of a lognormal distribution as a mode of input for food consumption data in probabilistic food additive exposure assessments and the results highlight the need for further research in this area.

Community exposure assessment and intervention effectiveness at Trinity American Corporation, Glenola, North Carolina.

This case study was a critical investigation of the analytical methodology and exposure assessment components of an intervention that led to the closure of a polyurethane foaming plant in Glenola, N.C., where plant neighbors reported a wide range of adverse health effects. Resident complaints and reports of nuisance odors and health effects persisted for many years, coming to a head in late 1995 and early 1996. Central to state and federal agency activities was the determination of the concentrations of air contaminants including toluene diisocyanate (TDI) at the plant fence line to establish an empirical foundation for resident complaints. Well over 2000 air concentration measurements were collected in the 18-month period prior to intervention and plant closure in September 1997. Results showed that flawed methodology, including poor quality assurance and improper interpretation of the data, may have led to improper conclusions and the inappropriate closing of this facility. Agency data did not show that ambient air concentrations of TDI at the plant fence line exceeded any required or recommended concentration limit. Furthermore, the identity and concentration of other air contaminants were not thoroughly investigated. Key lessons learned are that such interventions must be based on well-designed and executed exposure assessments. Resultant risk determinations must be based on sound science and methods.