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Exposure assessment

Exposure assessment is the scientific process of estimating or measuring the magnitude, frequency, and duration of human or ecological contact with environmental agents, such as chemical, physical, or biological stressors, at boundaries like the skin, lungs, or gastrointestinal tract, to characterize potential health risks.^[1] This multidisciplinary field integrates data from environmental monitoring, human activity patterns, biomonitoring, and modeling to evaluate exposure routes (e.g., inhalation, ingestion, dermal absorption) and pathways, supporting risk assessment frameworks established by organizations like the U.S. Environmental Protection Agency (EPA) and the National Research Council (NRC).^[2] Originating in the early 20th century within epidemiology and industrial hygiene, it gained formal recognition in the 1983 NRC "Red Book" as a core component of risk analysis, evolving with advances in analytical techniques and computational models—including recent developments as of 2025 such as new approach methodologies (NAMs) and AI-enhanced probabilistic modeling—to address uncertainties in exposure estimates.^[1]^[3] Key methods in exposure assessment include direct measurement using personal monitoring devices to capture real-time contact, indirect approaches relying on environmental concentrations combined with exposure factors (e.g., time spent indoors), and biological markers like biomarkers in blood or urine to reconstruct past exposures via reverse dosimetry.^[2]^[1] Probabilistic modeling, such as Monte Carlo simulations, accounts for variability across populations—considering factors like age, location, and behavior—and quantifies uncertainties from data limitations or assumptions.^[1] These techniques are applied in occupational settings by the Centers for Disease Control and Prevention (CDC) to characterize workplace hazards like silica dust or per- and polyfluoroalkyl substances (PFAS), and in broader environmental contexts to assess pollutants in air, water, and soil.^[4] The importance of exposure assessment lies in its role in informing public health protection, regulatory decisions, and intervention strategies, such as reducing emissions from industrial sources or designing safer consumer products, by identifying vulnerable populations (e.g., children, pregnant individuals, or low-income communities) and tracking exposure trends over time.^[1] Quality assurance, peer review, and transparent documentation ensure assessments are robust and reproducible, aligning with EPA guidelines updated in 2019 to incorporate high-throughput screening and integrated exposure models like SHEDS (Stochastic Human Exposure and Dose Simulation).^[1] Ultimately, it bridges environmental science and toxicology, enabling evidence-based actions to minimize adverse outcomes from stressors like pesticides, carcinogens, or climate-related hazards.^[4]

Definition and Fundamentals

Definition

Exposure assessment is the process of estimating or measuring the magnitude, frequency, and duration of contact between a receptor—such as a human, animal, or ecosystem—and a chemical, biological, or physical agent.^[5] This involves evaluating the intensity and routes of such contact, often quantified as the concentration of the agent multiplied by the time of contact at the receptor's boundary (e.g., skin, lungs, or gastrointestinal tract).^[6] The approach can be quantitative, providing numerical estimates, or qualitative, describing potential exposure scenarios without precise measurements.^[5] Within the broader framework of risk assessment, exposure assessment serves as one of four key components, alongside hazard identification, dose-response assessment, and risk characterization.^[7] It provides essential estimates of exposure or dose that are integrated with toxicity data from dose-response assessments to inform risk characterization, enabling the evaluation of potential health or ecological impacts.^[8] This integration supports regulatory decision-making, such as prioritizing site cleanups or setting exposure limits, by linking environmental contaminants to affected populations or ecosystems.^[9] Basic terminology in exposure assessment distinguishes between related concepts: exposure refers to the contact with an agent at the outer boundary of the receptor, without necessarily implying absorption.^[6] Intake describes the amount of the agent entering the body, such as through ingestion or inhalation, crossing an exposure surface that is not an absorption barrier.^[10] Dose, in contrast, represents the amount absorbed and available for interaction with target tissues, often termed absorbed dose or internal dose once uptake occurs.^[10] The term "exposure assessment" was formalized in the 1980s by the U.S. Environmental Protection Agency (EPA), particularly in response to the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) of 1980, also known as Superfund legislation, which mandated systematic evaluation of hazardous waste site risks.^[9] This development built on earlier foundations in epidemiology and industrial hygiene from the early 20th century but gained prominence with the 1983 National Research Council report, which outlined it as a core element of risk assessment.

Key Concepts

Exposure assessment relies on key quantitative metrics to characterize the magnitude, frequency, and duration of contact with stressors, enabling the estimation of potential health impacts. A fundamental metric is the average daily dose (ADD), which represents the mass of a contaminant per unit body weight per day, calculated as ADD = (C × IR × EF × ED) / (BW × AT), where C is the concentration of the stressor in the medium, IR is the intake rate (amount contacted or ingested per unit time), EF is the exposure frequency, ED is the exposure duration, BW is body weight, and AT is the averaging time (e.g., lifetime in days for chronic exposures).^[6] Closely related is the chronic daily intake (CDI), which averages intake over a lifetime (typically 70 years) to assess long-term exposures, often expressed in similar units and derived from similar parameters but normalized over an averaging time equal to the lifetime.^[11] These metrics provide a standardized basis for comparing exposures across populations and integrating with risk assessment processes.^[8] Assessors distinguish between central tendency and high-end exposure estimates to capture variability in population exposures. Central tendency estimates represent average or typical exposures, often using arithmetic means of input parameters to reflect the experience of most individuals in the exposed group.^[8] In contrast, high-end estimates focus on the upper tail of the distribution, such as the 90th or 95th percentile, to evaluate risks for highly exposed subgroups by selecting upper-bound values for key variables.^[12] Point estimates, which use fixed values for deterministic analyses, can yield these categories, while probabilistic approaches employ distributions (e.g., Monte Carlo simulations) to generate full ranges of possible exposures, offering greater insight into uncertainty and variability.^[6] Time-weighted average (TWA) exposure accounts for varying concentrations over time, providing a representative measure for periods with fluctuating exposures. The TWA is computed as E = \sum (C_i \times t_i) / T_{total}, where C_i is the concentration during time interval t_i, and T_{total} is the overall exposure period (e.g., an 8-hour workday or lifetime).^[6] This approach is essential for chronic assessments, as it normalizes intermittent or variable contacts into an equivalent constant exposure level, facilitating comparisons against health-based thresholds.^[13] Bioavailability plays a critical role by quantifying the fraction of an exposure that actually reaches a target site, such as systemic circulation, influencing the effective dose. It is typically expressed as a factor between 0 and 1, incorporating elements like absorption efficiency, gastrointestinal uptake, or dermal penetration, and is adjusted in dose equations (e.g., multiplying ADD by a bioavailability factor) to refine estimates.^[6] Factors affecting bioavailability include chemical speciation, particle size, and physiological conditions, with site-specific measurements often used to avoid overestimation of risks from total concentrations.^[14]

Applications and Contexts

Environmental Applications

Exposure assessment in environmental science evaluates the risks posed by pollutants in air, water, soil, and other media to ecosystems and human populations through ambient exposures. This process integrates data on contaminant sources, transport mechanisms, and receptor characteristics to quantify potential exposures, informing regulatory decisions and remediation strategies. By focusing on ecological and environmental pathways, it distinguishes from health-specific applications by emphasizing broader ecosystem dynamics and long-term ambient risks.^[15] Multimedia exposure assessment addresses combined routes of contaminant movement across environmental compartments, such as air, water, soil, and biota, to predict overall ecological and human risks. For instance, multimedia models simulate the fate, transport, and bioaccumulation of chemicals like volatile methylsiloxanes, revealing how emissions partition into multiple media and accumulate in organisms. A key example is bioaccumulation in aquatic food chains, where persistent organic pollutants like polychlorinated biphenyls (PCBs) concentrate in fish tissues, leading to human exposure through consumption of contaminated seafood. This approach highlights the need for integrated modeling to capture indirect exposures via ecological magnification, as outlined in frameworks for organic contaminant risk assessment.^[16]^[17]^[18] In Superfund site remediation under the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) of 1980, exposure assessment quantifies risks from contaminated groundwater to guide cleanup actions. Site assessments evaluate contaminant migration from sources like leaking landfills into aquifers, estimating exposure concentrations for nearby populations via drinking water or irrigation pathways. For example, at sites with volatile organic compounds in groundwater, baseline risk assessments integrate hydrogeological data to set remediation goals, ensuring protectiveness of human health and the environment as required by CERCLA processes. This application has been central to the cleanup of 459 Superfund sites since 1980 (as of March 2025), prioritizing sites based on exposure potential.^[9]^[19]^[20]^[21] The U.S. Environmental Protection Agency's Exposure Factors Handbook (2011 edition) serves as a primary framework for population-based environmental data in exposure assessments. It compiles physiological and behavioral parameters, such as inhalation rates, soil ingestion, and fish consumption patterns, tailored for U.S. populations to estimate exposures to environmental contaminants. Updated from prior versions, the handbook supports probabilistic modeling by providing distributions of factors like body weight and activity levels, enabling site-specific applications in ecological risk evaluations.^[22]^[23] Emerging concerns in environmental exposure assessment include climate change effects, which alter pollutant distribution and vector habitats, potentially increasing exposure risks. Warmer temperatures and shifting precipitation patterns expand the range of vectors like mosquitoes, accelerating pathogen development and transmission of diseases such as West Nile virus. For instance, climate-driven habitat changes have led to higher incidence in temperate regions, necessitating adaptive exposure models that incorporate meteorological projections. Assessments now integrate these factors to predict amplified ecological exposures in vulnerable areas.^[24]^[25]^[26]

Occupational and Public Health Applications

In occupational health, exposure assessment plays a critical role in evaluating worker risks to chemical, physical, and biological hazards in the workplace. Organizations like the Occupational Safety and Health Administration (OSHA) have established permissible exposure limits (PELs) since the passage of the Occupational Safety and Health Act in 1970, which set the first federal standards in 1971 for over 400 toxic substances to protect workers from excessive exposure.^[27] For instance, monitoring for solvents such as benzene or toluene involves personal sampling devices to measure airborne concentrations against PELs, ensuring levels remain below thresholds like 1 ppm for benzene over an 8-hour time-weighted average. Similarly, noise exposure assessment uses dosimeters to quantify sound levels, with OSHA's standard (29 CFR 1910.95) mandating monitoring when exposures approach 85 decibels (dBA) and requiring hearing conservation programs if the PEL of 90 dBA is exceeded.^[28] In public health applications, exposure assessment informs epidemiological studies by quantifying environmental contaminants' links to adverse outcomes, enabling targeted interventions. A prominent example is the 2014 Flint water crisis in Michigan, where switching to a corrosive water source led to lead leaching from pipes, resulting in elevated blood lead levels (BLLs) in children; post-switch data from 2015 showed the incidence of children with BLLs ≥5 μg/dL increasing from 2.4% to 4.9%, particularly in socioeconomically disadvantaged neighborhoods.^[29] This assessment involved biomonitoring through blood tests and water sampling, highlighting how exposure data can trace community-wide health impacts and support public policy responses, such as the establishment of a lead exposure registry.^[30] Consumer exposure assessment evaluates risks from everyday products, focusing on pathways like dermal contact with phthalates in plastics, which are used as plasticizers in items such as flooring, toys, and personal care products. Dermal uptake occurs through direct skin contact, with studies estimating daily exposures of 0.1–10 μg/kg body weight for di(2-ethylhexyl) phthalate (DEHP) via handling or mouthing of contaminated items, particularly in children.^[31] Regulatory bodies like the U.S. Environmental Protection Agency (EPA) use scenario-based modeling to assess these risks, incorporating factors like product usage frequency and skin absorption rates to determine if exposures exceed tolerable daily intake levels.^[32] Exposure assessment integrates with epidemiology in cohort studies to calculate attributable risk fractions, which quantify the proportion of disease incidence directly linked to a specific exposure. In prospective cohort designs, researchers estimate the population attributable fraction (PAF) as the excess cases preventable by eliminating the exposure, using formulas like PAF = (Pe × (RR - 1)) / (1 + Pe × (RR - 1)), where Pe is the exposure prevalence and RR is the relative risk.^[33] This approach has been applied in occupational cohorts to attribute fractions of respiratory diseases to solvent exposures, informing preventive strategies and resource allocation in public health.^[34]

Exposure Pathways

Routes of Exposure

Routes of exposure refer to the primary biological and physical pathways by which hazardous agents, such as chemicals, particulates, or biological contaminants, come into contact with and are absorbed by receptors, typically humans or other organisms. The three main routes—inhalation, ingestion, and dermal absorption—account for the majority of exposures in environmental and occupational settings, with absorption efficiency varying significantly by route and agent properties. These pathways determine the dose received and influence the subsequent health effects, as the site of entry affects distribution and metabolism within the body.^[35] Inhalation occurs when gases, vapors, or airborne particles are taken up through the respiratory tract, primarily via the lungs, where they can be absorbed into the bloodstream. For gases and vapors, absorption is often nearly complete, approaching 100% due to the large surface area of the alveoli and efficient gas exchange mechanisms. Particulate matter uptake depends on aerodynamic particle size; for instance, particles with diameters of 2.5 micrometers or smaller (PM2.5) represent the respirable fraction capable of penetrating deep into the alveoli, while larger thoracic particles (up to 10 micrometers) may deposit in upper airways. Factors such as breathing rate, lung ventilation, and particle solubility further modulate deposition and absorption efficiency.^[36]^[37] Ingestion involves the oral intake of contaminants through food, water, or inadvertent swallowing of soil, dust, or other media, leading to absorption primarily in the gastrointestinal tract. In adults, this route is common via contaminated drinking water or diet, but in children, hand-to-mouth behavior significantly increases nondietary ingestion risks, as young children frequently touch surfaces and then place hands or objects in their mouths, potentially transferring soil or dust. Absorption rates for ingested agents vary by solubility and chemical form, often assumed at 50-100% for many soluble compounds, though less soluble materials like lead may have lower uptake (up to 50% in children). This route is particularly relevant for persistent environmental pollutants bioaccumulating in food chains.^[38]^[39]^[36] Dermal absorption happens when agents penetrate the skin barrier, entering the systemic circulation through the stratum corneum and underlying layers, influenced by the chemical's lipid solubility, molecular weight, and exposure duration. Highly lipophilic compounds, as indicated by a high octanol-water partition coefficient, absorb more readily due to the skin's lipid-rich structure, while hydrophilic substances penetrate poorly. Prolonged contact, such as from immersion or repeated handling, enhances uptake, with absorption rates typically ranging from 10-50% for many organic chemicals over typical exposure periods, though this can vary widely (e.g., 1-3% for some pesticides in children). Skin integrity, hydration, and vehicle (e.g., solvent) also play key roles in this route.^[40]^[41]^[42] Other routes, such as ocular exposure or injection, are less common but can result in rapid and high-efficiency absorption in specific scenarios. Ocular contact involves agents reaching the eyes or mucous membranes, where they may be absorbed through the thin, vascularized tissues, often leading to local irritation or systemic effects if the agent is highly permeable. Injection, typically accidental in occupational settings like needlestick injuries, bypasses external barriers for direct entry into the bloodstream, achieving nearly 100% absorption akin to intravenous administration. These routes are rare in general exposure assessments but critical in healthcare or laboratory environments.^[43]^[44]^[36]

Exposure Scenarios

Exposure scenarios in exposure assessment integrate various routes of exposure into cohesive, contextual narratives that represent realistic or hypothetical situations in which individuals or populations may come into contact with stressors. These scenarios serve as frameworks for evaluating exposures by combining assumptions about environmental conditions, human behaviors, and chemical characteristics to estimate intake or uptake. Routes such as ingestion, inhalation, and dermal contact act as building blocks within these scenarios, allowing assessors to simulate combined exposures across pathways.^[6] Scenario development typically employs either deterministic or probabilistic methods to quantify exposures. Deterministic approaches use single-point estimates, such as mean or upper-bound values for parameters like concentration and intake rates, to produce a fixed exposure estimate, often applied in initial screening to simplify calculations. In contrast, probabilistic methods, such as Monte Carlo simulations, incorporate variability and uncertainty by sampling from probability distributions of input parameters, yielding a range of possible exposure outcomes with associated probabilities; this is particularly useful for capturing real-world heterogeneity in behaviors and environments. The choice between these methods depends on data availability and assessment goals, with probabilistic techniques recommended when sufficient distributional data exist to refine estimates beyond point values.^[6]^[45]^[46] A common example is the residential soil ingestion scenario for children, which accounts for age-specific hand-to-mouth behaviors leading to incidental ingestion of contaminated soil. In this scenario, young children aged 3 to less than 6 years are assumed to ingest an upper percentile rate of 200 mg of soil per day, derived from tracer element studies and modeling that reflect typical play activities in home environments. This rate integrates ingestion as the primary route while considering factors like soil concentration and contact frequency to estimate potential lead or contaminant uptake.^[47] Assessments often follow tiered approaches, progressing from screening-level scenarios with conservative assumptions—such as maximum concentrations and high-end intake rates—to refined, site-specific evaluations using measured data and probabilistic modeling for greater accuracy. Screening tiers prioritize bounding estimates to quickly identify low-risk situations, while higher tiers incorporate detailed observations to reduce uncertainty in complex cases. Key factors influencing scenario design include exposure duration and frequency, distinguishing acute (short-term, high-intensity) from chronic (long-term, repeated) exposures, as well as population subgroups like infants, who exhibit higher relative intake due to behaviors, or the elderly, who may face reduced mobility but prolonged residence in contaminated areas. These elements ensure scenarios align with the temporal and demographic aspects of potential risks.^[6]^[6]

Assessment Methods

Direct Measurement

Direct measurement in exposure assessment involves empirical techniques that quantify exposure to hazardous agents through the collection of real-time or sampled data from individuals or their environments. These methods provide direct evidence of exposure levels, capturing the magnitude, frequency, and duration of contact with contaminants such as chemicals, particulates, or biological agents. Unlike predictive modeling approaches, direct measurement relies on observational data to establish actual exposure scenarios, offering high specificity for validation in regulatory and research contexts.^[48]^[49] Personal monitoring represents a core component of direct measurement, utilizing portable devices worn by individuals to assess exposure in occupational or daily settings. Common tools include badges, air pumps, and sensors that sample airborne contaminants; for instance, diffusive samplers passively collect volatile organic compounds (VOCs) in air by allowing analytes to diffuse onto adsorbent materials over time, enabling lightweight and pump-free operation for extended periods. These devices, such as badge-style samplers, are particularly effective for monitoring worker exposure to gases and vapors without interfering with normal activities, providing time-integrated concentrations that reflect personal dose.^[48]^[50] Biological monitoring complements personal methods by measuring internal exposure through biomarkers in bodily fluids or tissues, integrating all routes of uptake such as inhalation, ingestion, and dermal absorption. This approach analyzes samples like blood or urine to detect parent compounds or metabolites, offering a direct indicator of absorbed dose and systemic effects. A representative example is the use of cotinine, a nicotine metabolite, in urine to quantify tobacco smoke exposure, where urinary levels above 50 ng/mL often indicate active smoking and levels between 1 and 50 ng/mL may reflect secondhand exposure.^[51]^[52]^[53] Environmental sampling employs fixed-site monitors to capture ambient concentrations of pollutants at specific locations, providing population-level data for exposure assessment in community or workplace settings. These stationary devices continuously or periodically measure air, water, or soil contaminants using standardized protocols; for example, EPA Method TO-15 involves collecting air samples in evacuated canisters followed by gas chromatography/mass spectrometry analysis to quantify up to 97 VOCs at parts-per-billion levels. Fixed-site networks, such as those operated under the Clean Air Act, track criteria pollutants like ozone and particulate matter, informing broader exposure patterns while highlighting spatial variability.^[54]^[55] Advantages of direct measurement include the provision of real-time, verifiable data that minimizes reliance on assumptions, with modern equipment like wearable sensors enhancing precision. Since the 2010s, GPS-linked personal sensors have enabled spatiotemporal tracking of exposures, such as integrating air quality readings with location data to map individual mobility and pollutant hotspots. These tools offer immediate feedback for intervention, though they require calibration and can be resource-intensive compared to indirect modeling techniques.^[56]

Indirect Measurement and Modeling

Indirect measurement and modeling in exposure assessment rely on surrogate data sources, mathematical simulations, and probabilistic frameworks to estimate contaminant concentrations and human exposures without real-time direct observation of the target individual or environment. These techniques are essential for scenarios where direct sampling is impractical due to high costs, spatial-temporal limitations, or the need to evaluate hypothetical or future exposures, enabling predictions grounded in emission rates, transport mechanisms, and behavioral patterns. By integrating environmental physics with statistical variability, indirect methods facilitate population-level risk evaluations and support regulatory decision-making.^[13] A core component is exposure modeling, which uses simplified mass balance equations to predict contaminant levels in defined compartments. In indoor air applications, the one-box model assumes uniform mixing within a single room and derives the steady-state concentration C as

C = \frac{S}{Q + kV},

where S represents the source emission rate (e.g., in μg/s), Q the ventilation rate (m³/h), k the deposition or removal rate constant (h⁻¹), and V the room volume (m³). This equation balances pollutant influx from sources against losses via ventilation and surface deposition, providing a quick estimate for inhalation exposures in residential or occupational settings. The model has been foundational in life cycle assessments and indoor pollutant studies, often serving as a baseline for more complex refinements.^[57] Proxy data enhance model accuracy by incorporating real-world behavioral surrogates that influence exposure duration and intensity. For instance, activity pattern databases capture daily routines such as time spent in microenvironments (e.g., home, workplace, outdoors) or ingestion-related behaviors, allowing inference of contact frequencies without individual monitoring. The U.S. EPA's Consolidated Human Activity Database (CHAD) aggregates nearly 180,000 individual study days of survey data from national and regional studies, detailing patterns like indoor occupancy (typically 80-90% of time for adults) and location-specific activities, which inform probabilistic exposure simulations for chemicals in air, water, or soil. CHAD data have been pivotal in constructing representative cohorts for U.S. populations, improving the realism of modeled exposures compared to uniform assumptions.^[58]^[59] Advanced software tools operationalize these concepts through integrated simulations. The EPA's Stochastic Human Exposure and Dose Simulation (SHEDS) model employs Monte Carlo methods to generate probabilistic distributions of exposures by stochastically sampling from activity diaries (e.g., CHAD), microenvironmental concentrations, and intake parameters across pathways like inhalation and dermal contact. SHEDS has been validated for applications such as pesticide residues and air toxics, producing outputs like 95th percentile exposure estimates that account for inter-individual variability, and it supports high-throughput screening for thousands of chemicals. This tool exemplifies how indirect modeling bridges deterministic equations with uncertainty quantification for aggregate risk assessments.^[60] Hierarchical approaches structure modeling from rudimentary to refined levels, starting with mass balance principles like the one-box model for screening and escalating to computational fluid dynamics (CFD) for precise dispersion predictions. Basic mass balance models offer computational efficiency for broad scenarios, estimating average concentrations via input-output fluxes, while CFD solves Navier-Stokes equations to simulate airflow, turbulence, and pollutant trajectories in three dimensions, capturing gradients in non-idealized spaces like ventilated rooms. This progression enhances resolution for critical cases, such as industrial emissions or urban air quality, with simpler tiers calibrated using empirical benchmarks to ensure reliability.^[61]

Specialized Approaches

Receptor-Based Approach

The receptor-based approach in exposure assessment employs a bottom-up methodology that begins with the exposed individual or population, known as the receptor, to estimate contact with contaminants through their behaviors, locations, and susceptibilities. This process integrates environmental concentrations with receptor-specific factors, such as daily activities and physiological traits, to quantify exposure via pathways like ingestion, inhalation, or dermal contact. It is particularly suited for scenarios involving microexposure events in daily life, such as hand-to-mouth behaviors or time spent in contaminated microenvironments, and relies on tools like probabilistic modeling to account for variability across populations.^[1] The methodology follows a structured sequence of steps to build exposure estimates. First, the receptor population is identified, considering demographics like age, lifestage, socioeconomic status, and genetic factors that influence susceptibility. Next, receptor activities are characterized using data from time-activity diaries, surveys, or models to map behaviors (e.g., dietary habits, occupational tasks, or subsistence practices like fishing) and locations (e.g., residential yards, workplaces, or urban versus rural settings). Finally, contact is estimated by route of exposure, combining these factors with measured or modeled contaminant concentrations in relevant media such as soil, water, air, or food. This approach often incorporates probabilistic techniques, such as Monte Carlo simulations, to represent distributions of exposure factors rather than point estimates.^[1]^[23] A representative example is the assessment of human health risks from pesticide residues in residential settings, where children may face elevated exposure through soil ingestion and dermal contact. The chronic daily intake (CDI) is calculated using the formula:

\text{CDI} = \frac{\text{IR} \times \text{C} \times \text{EF} \times \text{ED}}{\text{AT}}

Here, IR is the ingestion rate (e.g., soil intake in g/day, such as 50-200 mg/day for toddlers based on hand-to-mouth activity); C is the contaminant concentration in the medium (e.g., pesticide level in soil, mg/g); EF is the exposure frequency (e.g., days/year spent in the yard); ED is the exposure duration (e.g., years of residence); and AT is the averaging time (typically ED for chronic non-cancer effects or lifetime for cancer risks). To derive this, start with the amount ingested per event (IR × C), multiply by the number of events over the exposure period (EF × ED) to get the total intake, then divide by AT to obtain the average daily amount over the relevant timeframe; for body-weight-normalized dose, divide further by average body weight (BW). This equation, adapted from standard exposure factors, allows estimation of aggregate risks from multiple microexposures, such as a child ingesting 100 mg/day of pesticide-laden soil at 0.01 mg/g concentration over 350 days/year for 6 years, averaged over 70 years.^[1]^[23]^[62] This approach offers advantages in capturing variability among vulnerable groups, such as children who exhibit higher soil ingestion rates (e.g., up to 10 times adults due to mouthing behaviors) or tribal populations with elevated fish consumption leading to greater bioaccumulative pesticide intake. By prioritizing receptor-specific data, it provides more tailored and realistic risk estimates compared to source-oriented methods, enhancing the precision of public health protections.^[1]

Source-Based Approach

The source-based approach to exposure assessment employs a top-down methodology that initiates with the characterization of contaminant releases from identified sources, followed by modeling of their dispersion, transformation, and environmental fate to estimate concentrations at potential human or ecological receptors. This method is particularly suited for scenarios where source emissions can be reliably quantified, allowing predictions of exposure potential across broader areas without direct measurement at every receptor location. Unlike forward-tracing from receptors, it traces contaminants outward from emission points, facilitating proactive risk management in environmental and occupational settings.^[1] The process typically involves several key steps. First, source strength is quantified through emission inventories or direct measurements, capturing factors such as release rates, temperatures, and flow volumes for point sources like industrial stacks. Second, environmental fate and transport are simulated using physicochemical parameters, including partitioning coefficients that describe how contaminants distribute between media such as air, water, and soil—for instance, the octanol-water partition coefficient (Kow) influences bioaccumulation potential in aquatic systems. Finally, receptor concentrations are predicted via dispersion models that account for meteorological conditions, topography, and chemical degradation, yielding time-averaged exposure estimates.^[63] A prominent example in atmospheric applications is the use of Gaussian plume models, which assume pollutants disperse in a bell-shaped plume under steady-state conditions, incorporating wind speed, atmospheric stability, and plume rise to calculate downwind concentrations. These models form the basis for advanced tools like AERMOD, a steady-state dispersion model developed collaboratively by the U.S. Environmental Protection Agency (EPA) and the American Meteorological Society, promulgated in 2006 as the preferred regulatory tool for evaluating pollutant impacts from industrial sources such as stack emissions. AERMOD integrates planetary boundary layer processes for more accurate near- and far-field predictions, often applied to assess compliance with air quality standards.^[64]^[65] This approach is widely applied in regulatory permitting processes, such as Prevention of Significant Deterioration (PSD) reviews under the Clean Air Act, where it informs emission limits to protect ambient air quality, and in pollution control strategies to prioritize source reductions for persistent pollutants like volatile organic compounds. By focusing on source-receptor pathways, it supports cost-effective interventions, though it may require validation with receptor-based measurements for site-specific refinements.^[66]

Regulatory Frameworks

Occupational Exposure Limits

Occupational exposure limits (OELs) establish the maximum allowable concentrations of hazardous substances in workplace air to protect workers from adverse health effects over a working lifetime. In the United States, the Occupational Safety and Health Administration (OSHA) enforces Permissible Exposure Limits (PELs), which are legal standards primarily adopted in 1971 from earlier federal guidelines and American Conference of Governmental Industrial Hygienists (ACGIH) recommendations.^[67] These PELs specify the highest concentration levels to which workers may be exposed without expected harm, serving as enforceable benchmarks for compliance. Complementing PELs, ACGIH publishes Threshold Limit Values (TLVs), voluntary guidelines first issued in 1946 as Maximum Allowable Concentrations for 148 substances, with the TLV term introduced in 1956 to denote levels below which adverse effects are unlikely.^[68] OELs are derived from toxicological data, typically starting with the no-observed-adverse-effect level (NOAEL) identified from human or animal studies, which represents the highest exposure without detectable harm. This NOAEL is then adjusted by applying safety or uncertainty factors to account for variability in sensitivity, data quality, and extrapolation needs; for instance, a factor of 10 is commonly used for interspecies differences when animal data is extrapolated to humans.^[69] These factors ensure the limit provides a margin of safety, with the process emphasizing inhalation studies where possible and case-by-case evaluation to balance protection and feasibility.^[69] Monitoring for compliance involves measuring airborne concentrations against specific metrics, including the 8-hour time-weighted average (TWA), which calculates the average exposure over an 8-hour workday in a 40-hour workweek and must not be exceeded.^[70] Short-term exposure limits (STELs) supplement TWAs by restricting peak exposures, typically to a 15-minute average not to be surpassed during the shift, preventing acute effects from brief high-level contacts.^[70] OSHA requires employers to assess exposures when potential hazards exist and maintain levels below these limits through engineering controls, administrative measures, or personal protective equipment. Globally, OELs vary by jurisdiction, with the European Union establishing them under the REACH regulation framework effective from 2007, which mandates risk assessments and derivation of derived no-effect levels (DNELs) for occupational scenarios as part of chemical registration.^[71] The European Commission sets indicative OELs through directives like 98/24/EC, promoting harmonization while allowing member states to adopt binding limits tailored to local needs. These EU approaches integrate with broader chemical safety strategies but focus on workplace protections distinct from ambient environmental guidelines.

Environmental Exposure Standards

Environmental exposure standards establish thresholds for contaminants in air, water, and other media to protect public health and ecosystems in non-occupational settings, differing from occupational limits that focus on worker protections. These standards are developed based on scientific assessments of toxicity, exposure patterns, and sensitive populations, aiming to prevent adverse effects at population levels. In the United States, the Environmental Protection Agency (EPA) plays a central role in setting such standards under laws like the Clean Air Act and Clean Water Act.^[72] The National Ambient Air Quality Standards (NAAQS), established by the EPA under the Clean Air Act of 1970 and first promulgated in 1971, set permissible levels for six criteria air pollutants: carbon monoxide, lead, nitrogen dioxide, ozone, particulate matter, and sulfur dioxide. These primary standards are designed to protect public health with an adequate margin of safety, particularly for vulnerable groups like children and those with respiratory conditions, while secondary standards safeguard public welfare including visibility and ecosystems. For ground-level ozone, a key pollutant contributing to smog, the EPA revised the primary 8-hour standard in 2015 to 70 parts per billion, reflecting updated scientific evidence on respiratory and cardiovascular risks. NAAQS are periodically reviewed every five years, with levels adjusted based on epidemiological and toxicological data to ensure ongoing protection.^[72]^[73]^[74] Reference doses (RfD) provide chronic oral exposure thresholds for non-carcinogenic chemicals, representing an estimate of daily intake likely without appreciable health risks over a lifetime for the general population, including sensitive subgroups. The RfD is derived from the no-observed-adverse-effect level (NOAEL) or similar points of departure by dividing by uncertainty factors (UF) typically ranging from 10 to 1,000 to account for interspecies extrapolation, intraspecies variability, and data limitations. For example, the EPA's Integrated Risk Information System (IRIS) database lists an RfD for inorganic arsenic at 0.00006 mg/kg-day (0.06 µg/kg-day) as of the 2025 assessment, based on human epidemiological studies of ischemic heart disease and diabetes with UFs applied for chronic exposure duration.^[75]^[8]^[76] These values guide risk assessments for environmental contaminants in food, water, and soil, ensuring exposures remain below levels associated with developmental or systemic toxicity. Under Section 304(a) of the Clean Water Act, the EPA develops national recommended water quality criteria for toxins to protect aquatic life and human health in surface waters. These criteria specify maximum concentrations of pollutants, such as heavy metals or pesticides, that should not cause acute or chronic harm to fish, invertebrates, or other organisms, while also considering bioaccumulation risks for human consumption via fish. For instance, the acute criterion for cadmium in freshwater is 1.8 µg/L (at a water hardness of 100 mg/L as CaCO3) to prevent lethality in sensitive species like salmonids, derived from toxicity tests and water chemistry adjustments. States adopt these into their standards to maintain designated uses like recreation or drinking water sources, with updates reflecting new ecotoxicological data.^[77]^[78]^[79] Internationally, the World Health Organization (WHO) provides global air quality guidelines to inform national policies, with a 2021 update significantly tightening limits for fine particulate matter (PM2.5) to an annual mean of 5 µg/m³ and a 24-hour mean of 15 µg/m³, based on evidence linking even low levels to cardiovascular disease and mortality. These guidelines also cover ozone (100 µg/m³ as a peak season 8-hour mean) and other pollutants, offering interim targets for countries with higher pollution burdens to progressively align with the strictest levels. Adopted by over 100 nations in policy frameworks, they emphasize reducing ambient exposures to minimize global health impacts from air pollution.^[80]^[81]

Challenges and Limitations

Systematic Errors

Systematic errors in exposure assessment refer to consistent biases that distort exposure estimates in a predictable direction, often leading to over- or underestimation of risks across studies or populations. These errors arise from flaws in measurement techniques, modeling frameworks, or input data, and they differ from random variability by producing non-zero mean deviations from true values. Unlike stochastic uncertainties, systematic errors can propagate through regulatory decisions and health risk evaluations if not identified and corrected.^[82] One primary type of systematic error is measurement bias, which occurs when sampling devices fail to accurately capture environmental contaminants due to physical or operational limitations. For instance, aerosol samplers can underestimate particle concentrations in windy conditions because wind alters aspiration efficiency, causing non-isokinetic sampling that favors larger particles and misses finer ones relevant to inhalation exposure. This bias is particularly pronounced in outdoor air monitoring, where unaccounted meteorological effects lead to consistently lower reported levels than actual exposures.^[83] Another type involves errors from modeling assumptions that do not reflect real-world dynamics, such as assuming steady-state conditions in environments with transient pollutant releases or varying emission rates. In biomarker-based assessments, steady-state models unrealistically presume constant toxicant levels in the body, resulting in biased exposure rate estimates when actual pharmacokinetics involve time-dependent accumulation or elimination. These assumptions can overestimate or underestimate exposures in dynamic settings like industrial accidents or episodic pollution events.^[84] Sources of systematic errors often stem from inadequate or outdated exposure factors, such as activity pattern data that do not account for behavioral changes over time. For example, using pre-1990s activity data for children can lead to overestimation of soil ingestion or hand-to-mouth contact in modern urban settings, where reduced outdoor play and improved hygiene alter actual behaviors. Such outdated inputs systematically inflate risk assessments for vulnerable populations, perpetuating conservative but inaccurate regulatory thresholds.^[85] A notable historical case is the underestimation of asbestos exposures in occupational assessments before the 1980s, where early monitoring relied on coarse gravimetric methods and ignored fiber size distributions, leading to reported levels far below those causing asbestosis and mesothelioma. This systematic underestimation delayed stringent regulations, such as the U.S. OSHA permissible exposure limit reductions from 12 fibers per cubic centimeter in 1971 to 0.1 in 1994, contributing to thousands of preventable cases.^[86] To mitigate systematic errors, sensitivity analysis is employed to evaluate how variations in key parameters—such as sampler efficiency or model assumptions—affect overall exposure estimates, allowing identification of influential biases for targeted corrections. By systematically perturbing inputs and observing output changes, this approach quantifies error propagation and supports more robust assessments, though it requires validation data to confirm directional impacts.^[87]

Uncertainty and Variability

In exposure assessment, uncertainty refers to a lack of knowledge about factors influencing exposure estimates, such as incomplete data or modeling assumptions, while variability describes the inherent heterogeneity in exposure across individuals, populations, or time periods due to differences in behaviors, environments, or physiological traits.^[88] These elements are distinct yet interconnected, with variability often contributing to the range of possible exposures and uncertainty affecting the confidence in those estimates.^[89] Parameter variability arises from fluctuations in key inputs, such as body weight distributions, which vary by age, sex, and ethnicity and are derived from large-scale surveys like the Continuing Survey of Food Intakes by Individuals (CSFII).^[88] For instance, adult body weights in the U.S. follow a lognormal distribution with means around 70-80 kg, but the 95th percentile can exceed 110 kg, directly impacting dose calculations in inhalation or dermal exposure models.^[89] Scenario uncertainty, on the other hand, stems from unpredictable future conditions, such as changes in emission rates due to regulatory shifts or technological advancements, which complicate long-term projections in environmental exposure scenarios.^[89] To propagate these sources through exposure models, Monte Carlo simulation is a widely adopted technique that generates probability distributions of exposure by repeatedly sampling from input parameter distributions, allowing assessors to quantify the joint effects of variability and uncertainty.^[45] In a two-stage approach, the method first accounts for population variability by sampling across individuals, then incorporates uncertainty by varying model parameters across iterations, producing outputs like cumulative distribution functions that represent the range of plausible exposures.^[90] Variability is commonly characterized using percentiles from these distributions, with the 95th percentile often employed to estimate high-end exposures for risk assessment, capturing elevated risks without assuming worst-case scenarios for all factors.^[90] This approach enables decision-makers to identify protective thresholds, such as in Superfund site evaluations where the 95th percentile exposure informs cleanup standards.^[89] Advances in handling uncertainty include Bayesian methods, which have gained prominence since the early 2000s for updating exposure estimates as new data becomes available, integrating prior knowledge with observed evidence through probabilistic frameworks like Markov Chain Monte Carlo algorithms.^[88] These methods allow for dynamic refinement of parameter distributions, enhancing the adaptability of assessments to emerging information on variability sources.^[91]

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