6
Indoor Chemistry and Exposure
This chapter begins by introducing exposure routes and defining some of the factors that influence exposure in indoor environments, drawing in part from the 1991 National Research Council (NRC) report Human Exposure Assessment for Airborne Pollutants (NRC, 1991). Some exposure variables are linked to environmental health disparities, and these are discussed in the context of indoor chemistry. Subsequent sections cover the intersection of indoor chemistry and exposure modeling and measurement science for exposure. The chapter concludes with a list of priority research needs identified by the committee.
EXPOSURE ROUTES
Exposure to chemicals indoors can occur by three routes: inhalation, ingestion, and dermal uptake (Figure 6-1) (Feld-Cook and Weisel, 2021). Inhalation includes the uptake of gas- or particle-phase chemicals into the respiratory tract. Particle-phase chemicals deposit into the nasopharynx, bronchi, or deep lung (alveolar) region, with a general trend of smaller particles penetrating deeper into the respiratory tract (Heyder et al., 1986). The most penetrating particle size is 0.1–0.5 micrometers (µm), but even small ultrafine particles (UFPs) are lost by diffusion to the upper respiratory tract (Hofmann, 2011). Some particles are small enough to cross the lung epithelial tissue and pass into the circulatory system (Nakane, 2012), and particles have also been shown to enter the brain directly (Block and Calderón-Garcidueñas, 2009). Dermal uptake involves the deposition of gas-phase chemicals on skin or direct contact with surfaces that contain chemicals. Deposition or contact is followed by migration into the epidermis where chemicals may be metabolized, undergo active transport, and be taken up into the dermis. Chemicals may cross capillary membranes and potentially partition into the bloodstream and be absorbed. Ingestion is mediated by hand-to-mouth behavior and is most common for particles (dust) (Ott et al., 2006). Additionally, some fraction of inhaled aerosols (larger particles) that are moved up the bronchi by mucociliary clearance to be swallowed can be ingested and enter the gastrointestinal tract (Wanner et al., 1996).
Exposure levels and the relative contribution of exposure routes to total exposure burden are influenced by factors such as age, human behaviors, environment and surroundings, and the
physical and chemical properties of the agents to which people are exposed. For example, the inhalation rate (volume of air inhaled per day) varies with an individual’s age and body mass index and can increase significantly during physical activity (EPA, 2011). In addition, estimates of dust ingestion are larger for children than adults (EPA, 2017), while dermal uptake of volatile organic compounds (VOCs) increases with age (Morrison et al., 2016). Dermal uptake involves partitioning of gas-phase organic chemicals into the skin; thus, rates are influenced by physicochemical properties, including molecular weights and gas-phase partitioning coefficients (Weschler and Nazaroff, 2014). Transdermal uptake for some semivolatile organic compounds (SVOCs) can rival that of inhalation intake (Weschler and Nazaroff, 2012).
A number of conceptual frameworks exist to describe how environmental stressors ultimately affect human health. The environmental health paradigm (also known as the source to receptor model) presents a biological response pathway (Figure 6-2). The pathway from a source of exposure to its effect(s) is complex. Exposure to environmental stressors occurs at the interface between sources and receptors. Typically, receptors are people who may subsequently experience physical and/or mental health outcomes, resulting from or being influenced by the exposure. Multiple approaches exist for assessing chemical exposure in individuals. Indirect assessments can include surveys of behavior and time spent near chemical sources. Indoor concentrations may be measured by stationary devices or grab samples (e.g., dust wipes) as proxies for personal exposure.
Stationary indoor measurements made in more than one microenvironment can be combined with time-weighted activity information to yield estimated personal exposure concentrations; further translation to a dose requires additional information on intake (i.e., inhalation, ingestion, or dermal absorption) rates. The spatial and temporal variability in concentrations and human movement can best be captured by direct personal monitoring, which also minimizes uncertainty or exposure measurement error inherent in using more indirect approaches (e.g., questionnaires, microenvironmental models, and outdoor measurements). Personal exposure monitoring can reduce exposure misclassification; however, because such monitoring can be burdensome and resource-intensive, surrogate and proxy measures of exposure are commonly used but may still be referred to as measures of exposure. Finally, biomonitoring provides a more direct measurement of internal dose and includes measurements of chemicals and chemical metabolites in biospecimens (e.g., blood, urine, saliva, hair, cord blood, breast milk, and nails). Different biological matrices may reflect variable time integration of exposure (e.g., a few days for urine versus a few months for hair). However, biomonitoring data and measures of internal dose cannot provide insight on how exposure occurred and what the source, or sources, may be. The National Health and Nutrition Examination Survey (NHANES) is an example of a biomonitoring dataset that measures chemicals in blood and urine, some of which are present indoors.
While understanding exposure to chemicals in the indoor environment is in a nascent stage, several factors are known to modify human exposure. Important sources of particles and gas-phase chemicals, as discussed in detail in Chapter 2, include combustion, resuspension of particles caused by walking and cleaning, applications of cleaning agents and personal care products, and off-gassing of volatile and semivolatile chemicals from building material surfaces and myriad consumer products. These sources may contribute to indoor chemistry in both nonresidential and residential environments. In residential settings, several additional common activities contribute substantially to indoor chemistry, including cooking, space-heating, and behaviors associated with natural, mechanical, and unintentional ventilation of the occupied space. The location of a building, the integrity of the building envelope, and the presence of building air treatment systems are important factors that influence indoor chemical transformations and reactions, as well as indoor exposures to chemicals that can intrude from outdoors.
EXPOSURE DEFINITIONS, SETTINGS, AND TIMING
Exposure to a given chemical reflects the integration of concentrations of that chemical that an individual comes into contact with over the duration of time spent in contact with the agent across various settings and microenvironments.
To translate an exposure concentration (typically expressed in units of mass per volume or mixing ratio) into an intake dose, the exposure concentration is multiplied by an exposure duration (expressed in units of time) and by an intake rate that depends on individual characteristics (e.g., age, height, body mass index), activities (e.g., sedentary, active), and route (inhalation, ingestion, and dermal absorption as the major pathways of relevance in indoor environments) (EPA, 2011).
Settings, tasks, activities, behaviors, and features and conditions of indoor environments change over time, resulting in indoor environmental exposures that vary both within individuals over time and space and between individuals, even in the same indoor environment. Understanding variability in exposures is important for informing design of measurement and monitoring campaigns; assigning estimates of exposure to participants in a health study; identifying determinants of exposure; evaluating adherence with exposure guidelines; and, ultimately, evaluating the distributions of impacts of interventions and control measures intended to prevent or mitigate harmful exposures.
Given the challenging nature of measuring exposure continuously (or even the concentrations that individuals are exposed to over time), the microenvironmental model posits that dose can be
approximated by capturing time-averaged concentrations, time-activity patterns, and intake rates within key or major microenvironments where individuals spend time (Branco et al., 2014). These microenvironments often include indoor locations, like the home; occupational or school settings; transit settings; and outdoor microenvironments. Therefore, according to the microenvironmental model, an individual’s total dose of a given chemical is the sum of doses across all known microenvironments. These concepts provide a framework for characterizing and understanding variability in exposures. Specifically, this framework can provide insight on settings, activities, or characteristics that might determine or lead to elevated exposures, especially for sensitive or susceptible individuals and subpopulations across concentrations, time, and intake rates.
Exposure Settings
Exposure settings that fall within the scope of this report include nonindustrial indoor settings, including but not limited to the following: homes and places of permanent and temporary residence, office settings, schools, hospitals, prisons, public venues (indoors), spaces for community gatherings, retail environments, restaurants, and transport environments. Reviews and individual studies have illustrated the variability in the identities and concentrations of harmful compounds in a range of indoor settings, including residential environments (e.g., Diaz Lozano Patino and Siegel, 2018; Logue et al., 2011), light commercial buildings (e.g., Mandin et al., 2017; Ng et al., 2012; Nirlo et al., 2014; Zaatari et al., 2014), early childcare centers and schools (e.g., Bradman et al., 2014, 2017; Erlandson et al., 2019; Gaspar et al., 2014, 2018; Givehchi et al., 2019; Hoang et al., 2017), and hospitals and public utilities (e.g., Chamseddine, 2019; Śmiełowska et al., 2017), among others. The referenced sources serve as examples but are not intended to provide a comprehensive or systematic review. It is important to acknowledge that indoor gas and aerosol phase concentrations are more common in the literature, and equivalent measurements, studies, and reviews of surface and dust composition are less common. Estimating exposures to chemicals in dust via ingestion and/or dermal absorption has been a particular challenge for scientific and practitioner communities alike.
Some settings have potential for more intense indoor exposures; for instance, indoor settings or microenvironments with high concentrations of some chemicals might include residences near major roadways or other strong outdoor point sources, and service-oriented work settings where workers and patrons alike may experience high exposures (e.g., restaurants and beauty and nail salons). Homes where solid fuels are used for cooking and heating pose significant risks to health through exposures to pollutants emitted during incomplete (and sometimes unvented) combustion, including organic carbon, elemental carbon, UFPs, inorganic ions, carbohydrates, and VOCs and SVOCs, in addition to better understood pollutants, such as fine particulate matter (PM2.5), carbon monoxide (CO), and nitrogen oxides (NO, NO2). These settings are more prevalent outside the United States, yet some U.S. households continue to use solid fuels, mostly for space heating, either as a primary or supplemental energy source depending on factors such as access, convenience, cost, and household preferences. A large body of work documents emission rates and factors associated with solid fuel combustion, indoor concentrations of byproducts of incomplete combustion, and exposures and health effects associated with these exposures (Champion, 2017; Noonan et al., 2015; Rogalsky et al., 2014; Semmens et al., 2015). For example, daily average (24-h) concentrations of commonly measured indoor pollutants (e.g., PM2.5, CO, and NO2) in these settings have been measured ranging into the hundreds of micrograms per cubic meter (for PM2.5), the tens of parts per million (ppm; for CO), and the tens to hundreds of parts per billion (for NO2). In the United States, just less than half of households rely on natural gas as a primary heating fuel, while approximately one-third use natural gas as their primary cooking fuel. When in use, natural gas appliances in homes can emit oxides of nitrogen—namely, nitrogen oxide (NO) and nitrogen dioxide (NO2). Low level exposure to NO2 has been associated with increased bronchial reactivity in some people with asthma, as well as decreased lung function among
individuals with chronic obstructive pulmonary disease and increased risk of respiratory infections, especially in young children. Sustained exposure to moderate to high levels of NO2 can also contribute to the development of bronchitis. Formaldehyde is another air pollutant for which exposure tends to be intensified indoors relative to outdoors. For instance, living in newer, more modern, energy-efficient homes is associated with higher indoor concentrations of formaldehyde (Huang et al., 2017b; Langer et al., 2016), terpenes, and other VOCs (Derbez et al., 2018; Langer et al., 2015).
In total, indoor environments are chemically diverse. Some settings, like homes, have been the focus of many studies in the scientific literature, and they are indeed important because of how much time people spend in them. Yet, adverse chemical exposures can occur in a much wider range of indoor settings despite, in some cases, the more limited time people spend in those settings.
Exposure Timing, Duration, and Time-Activity Patterns
On average, people in the United States tend to spend the majority (69 percent) of their indoor time in their homes, and overall time spent indoors can exceed 90 percent when transit environments (e.g., cars, buses, trains) are taken into consideration (Klepeis et al., 2001). A range of methods can be applied to develop understanding of these time-activity budgets, which can vary with age and stage of life as well as with other social, cultural, economic, and demographic factors. For example, the Bureau of Labor Statistics and the U.S. Environmental Protection Agency (EPA) gather data through nationally representative survey instruments to characterize distributions of where and how people spend their time, sub-divided among multiple age and socioeconomic and sociodemographic groups. Time-activity budgets might also consider specific subgroups of the population that spend a lot of time in settings with the potential for indoor exposures to reach harmful levels, even some of the time. Examples include children in daycare facilities, newborns in neonatal intensive care units, expectant and new mothers, elderly people in nursing homes, individuals with lower mobility, and adults with asthma in their residence and work locations. Data on time-activity patterns are also routinely gathered in exposure and health-based epidemiological studies. Nationally sourced data provide broadly representative and potentially generalizable information; however, datasets such as these can mute variability that may exist at individual- and sub-group levels. Recently, GPS and location and motion sensors embedded in smartphones and wearables have been used to derive space and time-resolved time-activity data for exposure and health studies. Crowdsourced commercial services and apps are also providing access to large amounts of time-activity data (e.g., Google Timelines); however, data privacy considerations and generalizability/representation across and within regions, populations, and over time remain an issue. Some populations remain difficult to reach. As such, information on time-activity patterns is still limited among some subpopulations in the United States, including refugees, migrant or unhoused populations, populations for whom English is not a primary language, and those who are not connected to smartphones or the internet. The reasons why some people are difficult to access or gather information from are likely associated with other socioeconomic and demographic factors that could also be associated with greater likelihood of experiencing elevated or adverse exposures, including in indoor environments, and/or greater susceptibility to the effects of those chemical exposures.
Exposure Factors, Behaviors, and Intake Rates
Greater insight on the chemical composition of indoor air would be achieved if the scientific community had better data on some indoor activities, including window-opening, cooking, cleaning, and using personal care and leisure products. Furthermore, understanding the periodicity of activities such as these and others is important for determining the acute, chronic, and/or episodic nature of indoor chemical exposures, as well as anticipating how those exposures might change under disruptive circumstances (e.g., shifts to remote work, as have occurred throughout the
COVID-19 pandemic). Improved measurements of intake rates could improve exposure estimates and modeling. Intake rates vary with individual and demographic characteristics, life stage, and comorbidities and health conditions that jointly and independently influence exposure and susceptibility to harm resulting from exposure to indoor chemicals. For example, children’s dust ingestion rates are elevated compared to adults’ due to crawling on floors and hand-to-mouth behavior. Similarly, inhalation rates of pregnant women increase significantly during the first trimester and stay elevated throughout pregnancy (LoMauro and Aliverti, 2015).
Susceptibility factors also come into play when thinking about exposure and health implications. Given similar levels of chemical exposure, different subpopulations can be more susceptible to adverse effects. As an example, children have less developed immune systems and receive higher doses of chemicals per body weight compared to adults and thus might be more susceptible to adverse effects of chemical contaminants, even if they are exposed to comparable concentrations in the indoor environment. Developing fetuses are also susceptible to chemical exposures, and timing of exposure relative to conception, gestation, and developmental windows of various organ systems can be critical to determine risk of adverse health effects as described in the Developmental Origins of Health and Disease paradigm (Haugen et al., 2015). Chemical exposures can interfere with proper placentation, modify epigenetic programming and gene expression, and act in direct and indirect ways to adversely affect fetal health in-utero, at birth, or later in childhood and across the life course (Almieda et al., 2019; Harley et al., 2017; Haugen et al., 2015; Wigle et al., 2008). In addition, exposure to environmental contaminants may occur simultaneously and repeatedly with exposures to multiple other chronic social stressors, and the interaction of chemical and social exposures may increase biological susceptibility or vulnerability to adverse health outcomes. This has been termed a “double jeopardy,” where individuals living in disadvantaged neighborhoods, historically marginalized populations, racial and ethnic minorities, communities of color, and low-income groups can be disproportionately exposed to multiple environmental contaminants and can have higher susceptibility or vulnerability to their adverse effects (Morello-Frosch et al., 2011; Morello-Frosch and Lopez, 2006; Morello-Frosch and Shenassa, 2006). The terms “vulnerability” and “susceptibility” have overlapping meanings and could be used interchangeably. However, this report uses susceptibility to refer more to inherent physiological factors that may predispose an individual or sub-population to an elevated adverse response to an exposure. Vulnerability, on the other hand, refers to external factors (e.g., social, economic, and demographic) that may interact with chemical exposures to influence their effect (Bell et al., 2013). Several studies have documented these environmental health disparities or social inequalities in terms of exposure to chemicals in the environment. Biological mechanisms like allostatic load have been postulated to explain this increased susceptibility or vulnerability on a physiological level (Beckie, 2012; Carlson and Chamberlain, 2005; Szanton et al., 2005). An overview of some of the most commonly reported settings and determinants of environmental health disparities in relation to exposures experienced in the indoor environment is provided in the next section.
ENVIRONMENTAL HEALTH DISPARITIES AND EXPOSURE VARIABLES
Exposure to indoor air pollutants varies across individual households, yet certain exposures affect subsets of the population differently. These differential exposures derive from variables that influence indoor air chemistry. As an example, the location, build quality, age, and condition of housing are variables recognized in the literature as factors that can contribute to disparate exposures. The literature also establishes that certain indoor air exposures are observed disproportionately among communities of color and low-income households, such as particulate matter (PM) and lead (Baxter et al., 2007; Hauptman et al., 2021). The literature on indoor air quality and disparities is still a growing field, however, and not as robust as that on ambient air quality and disparities.
A scoping review of the indoor air pollution literature in high-income countries found significant gaps in the current understanding of inequalities related to indoor exposures and socioeconomic status (Ferguson et al., 2020).
In the United States, indoor chemical exposures coupled with frequent or elevated social stressors are believed to contribute to downstream disparities in health outcomes. The U.S. Office of Disease Prevention and Health Promotion (2008) defines a health disparity as “a particular type of health difference that is closely linked with social, economic, and/or environmental disadvantage.” Thus, understanding sources of exposure variability is critical to reducing disparities and achieving health equity. This section focuses on disparities in exposure and exposure determinants in the United States.
The literature on indoor air pollution and variable exposure across different socioeconomic, racial, and ethnic groups is somewhat limited. This is in spite of the environmental justice and heathy housing research which consistently finds that low-income, Black, Hispanic, and Indigenous communities are more likely to live in substandard housing (Jacobs, 2011; Seltenrich, 2012).
To understand exposure disparities thus requires consideration of differences in indoor air pollutant variables, which include nearby sources of outdoor pollutants, construction materials and practices, energy efficiency practices, energy use and home heating, occupancy rates, occupant practices and behaviors, indoor environmental maintenance patterns, and climate change. Indoor exposures to chemicals have been linked to the location, age, and condition of the structures in which people live, work, play, and congregate; and to the poor quality of our residences and work settings, as well as a lack of standardized maintenance and sanitation. These have had a well-documented impact on health that dates back to the 19th century (Adamkiewicz et al., 2011).
This report cannot thoroughly discuss emerging indoor chemistry issues without addressing climate: climate change may influence the condition and quality of indoor environments, the chemistry that arises indoors, and the resulting exposures and exposure variability in numerous ways. A 2016 National Climate Assessment Report recommended consideration of how vulnerable populations experience disproportionate risks in response to climate change (U.S. Global Change Research Program, 2016). Thus, the committee sought to provide examples from emerging science on the role climate change may play in widening or narrowing differences in indoor environmental exposures and related health outcomes. Comprehensive coverage of all documented exposure determinants and their links to environmental health disparities would be too broad to achieve within the scope of this report. However, drawing from the available literature, this section highlights categories of exposure determinants that are especially relevant for health disparities related to indoor environments.
Indoor Pollutants of Outdoor Origin
Indoor air chemistry is influenced by proximity to outdoor polluting sources and ambient air pollutant concentrations, which vary based on land-use types, zoning, topography, and geography. Redlining, a real estate practice dating to the 1930s, produced a pattern in which communities of color are more likely to live in neighborhoods with a high density of polluting sources and land uses and heavily polluted airsheds. This historical pattern creates pervasive differences in exposure to outdoor air pollution by race and ethnicity. Although exposure disparities exist for a wide range of source types, research has shown significant disparities in exposure from four sources: industry, light-duty gasoline vehicles, construction, and diesel PM (Gee and Payne-Sturges, 2004; Miranda et al., 2011; Tessum et al., 2021). In the United States, “non-Hispanic blacks are consistently overrepresented in communities with the poorest air quality” with respect to PM2.5 and ozone (Miranda et al., 2011). Communities of color are also more likely to occupy housing that is adjacent to major roadways, where mobile sources emit hydrocarbons, carbon monoxide, nitrogen oxides, and fine
or UFPs from diesel (HEI, 2010; Perez et al., 2013; Tessum et al., 2021). Mobile sources also emit chemicals classified by EPA as Hazardous Air Pollutants, such as 1,3-butadiene, acetaldehyde, benzene, and formaldehyde, which can infiltrate indoor spaces. Many American Indian and Alaska Natives live on reservations near anthropogenic sources of air pollutants, including oil and gas extraction, mining, and other major industrial emitters (Kramer et al., 2020). While infiltration rates vary spatially and temporally, in substandard housing infiltration rates of chemicals and PM of outdoor origin may be higher due to greater leakiness (e.g., poorly maintained structures, cracks and openings in floors and walls); a lack of mechanical ventilation, or, alternatively, increased reliance on mechanical ventilation; and less air filtration and cleaning compared to more modern or properly maintained residences (Underhill et al., 2018). Shrestha et al. (2019a) found that homes with higher annual average infiltration rates were associated with higher mold growth, higher levels of dust, and unacceptable odor levels. Window-opening was positively associated with lower-income housing, apartment homes, and rental properties, which may increase exposure to air pollutants of outdoor origin while decreasing exposure to indoor-sourced pollutants (Morrison et al., 2022). Few studies are available that document substandard ventilation and filtration in low-income housing, but higher indoor concentrations of ambient pollutants have been observed in low-income and public housing and could be indicative of broader conditions (Colton et al., 2014; Tessum et al., 2021; Zota et al., 2005). As an example, an assessment of residential air leakage in the United States found that older, smaller homes were leakier than newer, larger homes (Chan et al., 2005). Because age and size of housing are linked to housing prices, the authors note that older, smaller homes are more likely to be occupied by low-income families.
Construction Practices and Materials
Construction practices also affect building systems for air handling. In turn, air handling system design, installation, operation, and maintenance all influence the potential for indoor exposure to harmful chemicals, either of indoor or outdoor origin (see Chapter 5 for more details). Low-income, public, and multifamily housing units are more likely to be constructed with low-grade building materials, which may emit higher concentrations of, or result in higher dust-borne concentrations of, VOCs and SVOCs, including phthalates, flame retardants, antimicrobials, petroleum chemicals, chlorinated solvents, and formaldehyde (Bi et al., 2018; Colton et al., 2014; Dodson et al., 2017; Wan et al., 2020). Older housing is also more likely to contain legacy pollutants, such as polychlorinated biphenyls.
Where households have aging carpets, the carpets can act as a reservoir for allergens, endotoxins, lead dust (Becher et al., 2018), and other chemicals. Observing asthma triggers among 112 low-income urban housing units, Krieger et al. (2000) found that 76.8 percent of children’s bedrooms had carpeting. Sun et al. (2022) recently observed that the presence of carpeting, along with other household characteristics (e.g., age of home; wall, roof, flooring, and insulation materials; surface paints and coatings; household energy systems for cooking, heating, and lighting; air handling systems; and appliances), were correlated with the presence of multiple biocontaminants. Vinyl flooring in low-income homes was associated with increased levels of benzyl butyl phthalate and di-(2-ethylhexyl) phthalate relative to homes without vinyl flooring (Bi et al., 2018).
Energy Efficiency Factors
Energy efficiency measures can improve occupant comfort while reducing space heating demands. In both residential and nonresidential settings, building energy performance assessments can identify opportunities for energy efficiency improvements, which may be achieved through building and building envelope upgrades and retrofits. Yet, improper implementation of energy
efficiency upgrades can result in over-tightening of the building envelope (Manuel, 2011) and inadequate air exchange rates, and may contribute to indoor air quality issues, including higher concentrations of chemicals emitted indoors, higher relative humidity, and microbial growth (Collins and Dempsey, 2019; Du et al., 2019). Local- to national-scale programs focused on improving building energy efficiency have historically been associated with mixed impacts on indoor air quality. A study examining the impact of energy renovation in multifamily residences found lower air exchange rates and higher concentration of carbon dioxide (CO2), formaldehyde, and VOCs (Földváry et al., 2017). Leivo et al. (2018) also found lower air exchange rates and higher CO2 levels (in units they observed without mechanical exhaust ventilation) after energy retrofitting. Less and Walker (2014) found that homes with dedicated outdoor ventilation systems had air change rates that were not significantly different than homes relying on natural ventilation. Problems with outdoor air ventilation systems included incorrect installations, clogged vents, and ventilation systems turned off by the occupants. However, insight into the potential but avoidable indoor air quality hazards that can be introduced through poor implementation of energy efficiency upgrades in aging workplaces, homes, and other nonresidential settings has led to improvements in the practice of weatherization and building energy efficiency performance upgrades (Fisk et al., 2020; Shrestha et al., 2019a). Several voluntary standards and rating systems are available to guide weatherization practices and energy efficiency retrofits, from industry (ASHRAE, Building Performance Institute) to government (Energy Star).
Indoor Climate Control
Indoor air chemistry is modulated by the age, efficiency, and condition of heating and cooling systems. Low-income, rural, rental, and multifamily homes tend to be lower on the energy ladder, in which lower economic status drives higher use of biomass and other solid fuels for heat and cooking (van der Kroon et al., 2013) and greater energy insecurity due to the cost burden of heating, cooling, and ventilation. In 2015, the most recent year for which data were collected, 11 percent of households surveyed reported keeping their homes at an unhealthy or unsafe temperature (EIA, 2015a). Low-income U.S. households are more likely to rely on electricity, wood, fuel oil, or propane systems as opposed to natural gas. These energy systems each have unique emissions characteristics and produce different impacts on indoor air chemistry. Whether they are more or less harmful than natural gas is influenced by many variables. Yet from a disparities lens, households without natural gas are less likely to have a central furnace or heating, ventilation, and air-conditioning (HVAC) system and associated air exchange and filtration—48.7 percent of single-family attached homes in the United States have a central furnace, as compared to 4.6 percent of multifamily units (2–4 units) and 8.7 percent of multifamily units (5 or more units) (EIA, 2015a). Disparities in indoor air quality can also arise when space-heating systems are aging, leaky, and poorly maintained by the occupants or the property manager. Poorly vented or unvented combustion indoors can result in indoor exposure to byproducts of incomplete combustion including carbon monoxide (Vicente et al., 2020) and polycyclic aromatic hydrocarbons (Tiwari et al., 2013).
Low-income households have a high energy burden: 31 percent of U.S. households report having difficulty adequately heating or cooling their homes (EIA, 2015b). Households unable to afford adequate heat are more likely to experience low indoor temperatures, which may also be associated with increased indoor humidity in some settings, as well as condensation of moisture on indoor surfaces, and microbial growth (Zhang and Yoshino, 2010). Higher or uncontrolled (i.e., more variable) temperatures in homes are also a factor in levels of relative humidity and influence chemical emission rates (Haghighat and De Bellis, 1998). Low-income homes are also less able to cool their homes than non-low-income households (U.S. Department of Health and Human Services, 2017), contributing to unsafe conditions (Clinch and Healy, 2000).
When window-opening is used for home cooling, it can contribute to higher rates of outdoor air pollution infiltration. Many low-income communities are situated within urban heat islands (UHI), where urbanization has contributed to a phenomenon in which the impervious surfaces and absence of tree canopy contribute to higher temperatures (Yang et al., 2020). UHI may be exacerbated by thermal events that are modeled to increase due to climate change (Perkins et al., 2012).
Occupancy Rates
High occupancy loads are a hallmark of low-income households (WHO, 2018), but overcrowding also occurs in areas with limited housing stock, such as rural areas and cold-climate regions. Overcrowding is a primary contributor to higher indoor temperatures and higher humidity, which in turn are positively associated with increases in formaldehyde and VOC emissions from building materials (Huangfu et al., 2019). Higher occupancy also introduces a higher concentration of human bioeffluents, including dermal (skin oils, skin flakes) and exhaled bioeffluents such as carbon dioxide and certain VOCs (Liu et al., 2016; Tsushima et al., 2018). As different latitudes and geographic regions of the globe become less tolerable due to extreme weather, human migration will place pressure on, and potentially increase occupancy rates in, existing housing stock. Coates and Norton (2021) describe how climate changes drive migration, overcrowding, and poverty, and increase opportunities for infectious disease transmission. Overcrowding is also associated with heat risks, due in part to poorly ventilated dwellings (Pelling et al., 2021).
Occupant Practices, Consumer Product Use, and Behaviors
Indoor air chemistry is influenced by occupant practices, consumer product use, and behaviors, which vary by region, culture, ethnicity, race, and family structure. Consumer and personal care products, often unique to personal and societal cultures, emit VOCs and may contain endocrine-disrupting chemicals (EDCs). For example, households using incense as a regular practice could have higher concentrations of PM (Yang et al., 2012) and inorganic gases. Certain beauty products contain parabens, preservatives, and EDCs. The majority of samples in a study of commonly used hair care products targeted for Black women showed androgen antagonist properties (James-Todd et al., 2021). Cleaning products commonly used in the United States contain phthalates and phenols, with empirical evidence indicating that the cost of “safer” or “green” alternatives may limit access to higher-income households (Finisterra do Paço and Raposo, 2010). In addition, cleaning products that might be particularly popular or common among specific groups of people or communities can be a dominant source of exposure to one or several chemicals emitted from such products. For example, Hispanic households in Boston showed higher exposures to a restricted pesticide, cyfluthrin, because products were available at local bodegas (Adamkiewicz et al., 2011). Sales of phased-out spray pesticides have been shown to persist in low-income, minority neighborhoods (Carlton et al., 2004).
Operation and Maintenance Patterns
Public policies exist to minimize disinvestment and disrepair in low-income housing (Travis, 2019), yet many states have had to enact legislation to protect tenants (Sabbeth, 2019). Deferred maintenance and neglect of both residential and nonresidential buildings influence indoor air chemistry through increases in indoor dampness, microbial contamination, contaminants from older and poorly maintained or unvented combustion appliances, higher temperatures and humidity, and lower ventilation rates. Deferred maintenance, particularly in public housing, is also linked to pest infestations (Julien et al., 2008), increasing the potential for higher indoor concentrations of insecticides and pesticides, as well as pest allergens that can be triggers for asthma exacerbation. Finally, concentrations of lead
in household dust are higher in low-income and multifamily housing (Benfer, 2017), where racial bias is linked to low compliance with lead-safe work practices by property managers. Rather than perform lead inspections before a child is lead poisoned, for example as a routine part of housing maintenance, U.S. lead poisoning policies and practices tend to follow a “wait and see” approach (Benfer, 2017).
Climate Change, Disparities, and Emerging Indoor Chemistry Issues
Climate change and its attendant extreme weather events are potentially widening disparities in indoor exposures among low-income households and communities of color compared to moderate-to high-income and White communities. In the 2011 Institute of Medicine report Climate Change, the Indoor Environment, and Health, the effect of poverty on indoor air quality is discussed in significantly greater detail than will be addressed in this report, and readers are referred to that report for in-depth consideration of these interrelated issues (IOM, 2011). Beyond poverty, EPA’s 2021 report on climate change and social vulnerability models climate events and notes that race and ethnicity are associated with disproportionate impacts.
Box 6-1 summarizes several emergent issues related to indoor chemistry and climate change. All of these factors are anticipated to evolve as climate change progresses and will influence indoor pollutant concentrations (Nazaroff, 2013). In addition, factors that already influence personal exposure to chemicals indoors, such as seasonality and underlying drivers of seasonality (e.g., temperature, humidity), may drive exposure variability even further as greater extremes are reached and previously extreme values are sustained over longer time periods. For example, personal exposure measurements of flame retardants and plasticizers in adults and children in the United States revealed significant seasonal variability in exposure that was difficult to explain but also replicated in another study (Hoffman et al., 2017; Phillips et al., 2018).
THE INTERSECTION OF INDOOR CHEMISTRY AND EXPOSURE MODELING
Exposure models sit at the intersection of chemistry, human activities in microenvironments, and health impacts. Exposure models serve a wide range of uses, including filling in data gaps, quantifying exposures and the relative importance of various exposure pathways, setting indoor air quality standards
for occupational and public health protection, prioritizing chemicals for risk evaluation, and evaluating approaches to exposure mitigation. Combined, these outputs of exposure models improve our understanding of how indoor air chemistry impacts human health outcomes. Earlier parts of this report describe key data and information gaps that may necessitate the use of models (e.g., to improve understanding of SVOC partitioning). The breadth of models dictates that a given model be used with care and be applied appropriately (i.e., “fit for purpose”)—fully cognizant of its limitations. In the indoor air chemistry arena, near-field exposure models are most relevant. Far-field exposure models, which describe environmental behaviors of pollutants in the outdoor environment, will not be discussed here, although they are often used in the exposure and health literature to approximate personal exposures.
The Modeling Landscape
Diverse exposure models have been developed to address research and practical needs, including the ones listed above. To fully describe all exposure models in terms of their applicability, strengths, and limitations is outside the scope of this report. A set of exposure models commonly used for predicting near-field exposures is provided for reference in Appendix D.
A complete model incorporates all components necessary to accurately quantify exposure. To generalize to “any contaminant and situation,” this means including all possible factors and processes, including source composition and emission rates, chemical partitioning, chemical transformations, building factors (like air exchange rates), and human factors and behaviors. In reality, a specific exposure model is designed for a particular purpose by emphasizing certain components or aspects of these components. For example, some exposure models (e.g., USEtox) incorporate detailed transport processes with just a few chemical transformations, while others (e.g., CONTAM) are capable of processing complex gas-phase chemistry but can only characterize inhalation exposure. Note that CONTAM is an exception in that, in general, only a limited number of exposure models integrate detailed chemistry and sophisticated air flows.
Exposure models can be mapped across several dimensions—most obviously, spatial and temporal. Other dimensions include exposure pathway, number of chemistries considered, and the degree to which human behavior is incorporated into the model (Isaacs and Wambaugh, 2021). Regardless of dimension, a model can be characterized by its level of complexity as it relates to model application/utility. Figure 6-3 provides a graphical representation of the model complexity continuum.
Typically, more complex models more accurately replicate physical or biological processes. But increased complexity typically comes with increased challenges in developing, using, and evaluating the model. For example, much higher spatial and temporal resolution in exposure models can be achieved but often at the expense of computational demands. While more complex models may describe processes in greater detail, users have to decide whether the cost of using a particular model is commensurate with the level of effort associated with its complexity. Model developers, practitioners, and those who use the results have to assess whether a model is the preferred option for a given purpose and weigh the tradeoffs between model granularity or resolution versus complexity, ease of use, and “fit for purpose.”
One example is that air is commonly assumed to be well mixed in simpler models (Huang et al., 2017a). This allows such models to be applied to high throughput screening and other less computationally intensive applications. However, more complex models include computational fluid dynamics, which provides the detail needed to understand chemical concentrations near a stationary individual (Rim et al., 2018). To date, most indoor computational fluid dynamic models do not account for the movement of building occupants within spaces.
For indoor chemistry, several dimensions of complexity are particularly important. One dimension is the level of human activity and behavior detail included in the model. Personal activities are associated with both physicochemical processes underlying complex indoor chemistry and
personal exposures. Models may integrate various levels of detail with regard to human time-activity and mobility patterns that determine exposure duration and intensity. To assess the complexity, the user might ask whether the model averages activity information over a 24-hour period or if it allows consideration of single event exposures to provide insights at shorter timescales. Does the model allow the user to specify key demographic factors or sub-population characteristics, perhaps taking into account personal characteristics (e.g., age, body mass index, and socioeconomic factors)? Increasingly, more details are being incorporated into models based on consumer product use patterns, or more sophisticated habits and practices data.
A second complexity axis is the extent to which physicochemical processes are incorporated into a given model. Although many exposure models incorporate chemistry, often this is done in a simplistic way. A notable differentiator among models is the ability of a model to handle different types of chemicals (e.g., VOCs versus SVOCs). Chapter 3 discusses recent improvements in models to help explain SVOC partitioning, and Chapter 4 discusses how parameterization of partitioning processes is embedded in some models but not in others. Chapter 4 also highlights the benefits of integrating models to capture the complexity of transformations in the indoor environment. Current exposure models have yet to incorporate some of the recent advances in chemistry models.
A third axis of complexity is that of the biological/physiological detail of the exposure mechanism in dosimetry and toxicokinetic/toxicodynamic models that accounts for metabolism, metabolites, disposition among tissues, and excretion. For instance, Lakey et al. (2017) highlight the benefit of more complicated dynamic models over steady-state models in their ability to capture transdermal uptake of SVOCs. Exposure models have also been coupled with dosimetry models and toxicokinetic/toxicodynamic models developed to predict internal dose.
Although users may decide that a high degree of complexity is not needed for a particular application, generally much could be gained from incorporating increased levels of complexity across multiple dimensions into exposure models for indoor chemistry in order to more fully simulate real-world conditions.
Several reviews of contemporary exposure models are available in the literature. Huang et al. (2017a) considered near-field exposure models suitable for life cycle assessment, chemical alternatives assessment, and high throughput screening risk assessments. This review outlines individual exposure scenarios as examples (e.g., transfer of chemicals in sprays to near-person air) and assesses strengths and weaknesses of algorithms and models for these scenarios. Of note, Huang et al. (2017a) observed that even in highly mature indoor fate transport models, the role of human occupants on chemical fate and transport is not adequately considered.
Cowan-Ellsberry et al. (2020) compare five international consumer product exposure models used for regulatory decision making. The authors outline several factors and key differences among models of which users need to be aware so that selected models are applied in a manner that is consistent with the intended use of the results (or “fit for purpose”). Efforts to simulate similar exposures using these different models were hampered by difficulties ensuring that the models used the same conditions. These challenges stemmed from inconsistencies and a lack of transparency in factors including product type definition, specifications of the exposure route for a given product, details of the exposure scenario, and output exposure metrics. Ultimately, the authors determined that additional consistency across models would be beneficial, and they highlight possible adoption of the standardized product taxonomy by the Organisation for Economic Co-operation and Development (OECD, 2017). They also stated that built-in input databases create challenges, particularly in terms of identifying the source of uncertainties as due to model algorithms versus input datasets.
The models presented above—designed for different purposes—have various levels of intrinsic complexity. They can be grouped according to the NRC’s Exposure Science in the 21st Century exposure model groupings (NRC, 2012). The first set of models are process-based models, while others are classified as activity-based models. In the indoor chemistry context, process models include both fugacity- or equilibrium-based chemistry or mass balance-based models to characterize chemical transport and fate, exposure routes and pathways, and primary and secondary sources (e.g., emissions). Activity-based models predict exposures based on the behavior of individuals and populations. In the indoor realm, activity-based models may evaluate exposures based on consumer product use patterns, building occupancy rates, habits and practices, or occupational behaviors. Many near-field exposure models include both process with activity elements.
Exposure Model Uncertainties
When a model cannot fully characterize an exposure scenario, uncertainty arises. In the context of indoor chemistry, the primary uncertainty originates from insufficient characterization of homogeneous and heterogeneous reactions, leading to inadequate prediction of exposures to transformation products in aerosols and on surfaces (Zhou et al., 2019). Another primary uncertainty arises from inadequately describing chemical partitioning processes across complex surfaces (including clothing) in the indoor environment for SVOCs, leading to mischaracterized exposure routes. In addition, some parameters are presumed to be well known but might not be characterized adequately for a specific exposure scenario. For instance, ventilation or air exchange have been widely used and often assumed constant in exposure assessment, when, in fact, more specificity may be warranted to support modeling. For example, quantifying elevated exposures due to proximity to a source or where chemical reactions happen requires more detailed consideration of ventilation near a person, and the bulk air exchange rate may be less important. Further amplifying uncertainty are microenvironmental extremes in temperature and moisture that have an outsized impact on local chemistry (e.g., very hot or very cold surfaces in HVAC systems).
Even if a model includes all key physicochemical processes and can adequately characterize a certain exposure scenario, uncertainties remain owing to unknowns in parameterization of all parameters incorporated in the model. Major uncertainties in model parameterization include
emission factors, partitioning coefficients, personal activities and product use and co-use patterns, chemical emission profiles from products, physicochemical properties for chemicals of emerging concern (e.g., per- and polyfluoroalkyl substances), and modification of physicochemical properties by environmental conditions.
Uncertainties could be addressed by using several models simultaneously to make predictions. For example, the Systematic Empirical Evaluation of Models framework was applied to develop a consensus model of chemical exposures (Ring et al., 2019). This effort employed 13 models to create a consensus-based metamodel. The article contains concise descriptions of the individual models used that provide a useful overview of several relevant fate and transport and near-field exposure models, including SHEDS-HT, FINE, RAIDAR-ICE, and the product intake fraction framework. The application of multiple appropriate models to predict exposures allowed assessment of 479,926 chemicals with increased confidence in predicted outcomes.
A quantitative or qualitative uncertainty analysis can provide transparency about the uncertainties associated with a given exposure model, thus providing the user of the model or the interpreter of modeling results with the requisite understanding of the limits of the model. At the very least, a model’s applicability domain needs to be documented, including the type of chemicals, the exposure scenarios, and the spatial and temporal scales over which the model can be applied.
Model integration provides the opportunity to connect models that may be more advanced on a given complexity axis to obtain more detailed understanding along the biological response pathway from sources, to emissions, to concentrations and exposure, as illustrated by Figure 6-2. Yet, existing exposure modeling approaches are fragmented. Modular structures analogous to the Modelling Consortium for Chemistry of Indoor Environments are beneficial for exposure applications but can only be undertaken if integration is considered in model design. As an example, a modular mechanistic approach has been proposed to improve predictions of SVOC exposures indoors (Eichler et al., 2021).
Furthermore, opportunities have been identified for model development in the future. Inhalation exposure models are fairly mature, but models that quantify chemical partitioning from articles to skin need improvement (Huang et al., 2017a). Cowan-Ellsberry et al. (2020) identify several opportunities to improve models. The need for standardization is a prevalent theme. Standardization of product taxonomies and chemical formulations, for example, would facilitate model integration while making it easier to gauge uncertainties and compare results. Both Ring et al. (2019) and Cowan-Ellsberry et al. (2020) call for more extensive biomonitoring (more populations, more chemicals) to provide input data, validate model results, and improve statistics for probabilistic models.
As exposure modeling matures, models may support work to better understand aggregate exposures from multiple sources of a given chemical as well as cumulative exposures from multiple chemicals. As researchers work to measure exposure to chemical mixtures and understand their impacts, modeling advances could facilitate analysis of the complex interactions of multiple chemicals on human receptors to ultimately understand health risks at the individual and population level.
A notable practical limitation of the exposure models described here is lack of integration with building thermal analysis models that are used to predict the expected energy use of buildings. Some couplings exist between tools like CONTAM and energy simulation programs, which are rarely used except by experts. Programs such as Energy Plus simulate the variation of ventilation rates, HVAC air flows, and indoor temperature and humidity, which vary as occupancy and thermal loads vary. Consequently, it is not a straightforward task to estimate annual exposures and the impact of control measures on annual energy use and cost that are highly relevant to the assessment of existing buildings and design of new buildings. Until modeling of exposures is integrated with such models of building performance in a relatively easy-to-use way, the routine quantitative analysis of indoor air quality will be impeded.
MEASUREMENT SCIENCE FOR EXPOSURE
The primary goal of exposure assessment is to obtain accurate, precise, and biologically relevant personal or population exposure estimates in the most efficient and cost-effective way. Exposure data collection frequently involves tradeoffs among accuracy and precision, time resolution, number of contaminants monitored, technological limitations, and burden and resource constraints. As noted above, exposure can be classified, measured, or modeled with direct or indirect methods, and tradeoffs exist for all methods of exposure assessment. This section addresses measurement science specific to exposure applications. Chapter 2 provides more information on measurement approaches for quantifying the chemical composition of different indoor phases.
Exposure can be classified using dichotomous values such as “exposed” or “not exposed” to a particular substance. Classifications can also incorporate multiple categories of exposure, such as “no,” “low,” “medium,” and “high,” or occupational exposure categories based on job exposure matrix questionnaires (Choi, 2020). Such classifications can also be obtained by expert assessment. As a direct method, measurements are often considered a more objective means of assessing exposure than questionnaires. The most common measurement utilized is the concentration of a given agent in a representative area. Area monitors are used to estimate exposure to individuals living within a certain proximity (e.g., ambient reference monitors) or indoor locations where individuals spend a lot of time (e.g., living rooms), and personal samplers can be used to measure individual exposure.
Indoor environmental samplers can operate passively or actively with the aid of a pumping mechanism that purposefully draws air over sampling media. Passive gas sampling relies on diffusion of gas molecules onto a sorbent medium and can measure many classes of compounds quantitatively and correlate well with measurements of internal dose or metabolites of exposure. Diffusive air sampling uses minimal testing equipment and requires minimal expertise to implement, making it an often more affordable choice for conducting ongoing air monitoring. However, diffusive sampling is not amenable to sampling aerosols and PM, since particles do not follow the same principles of diffusion as gases and vapors. With diffusive sampling, the uptake rate is fixed; in contrast, with active sampling, it is possible to vary the sampling flow rate and thus collect the required sample over a range of preferred time periods (although optimal performance of size-selective aerosol samplers requires a fixed flow rate). Diffusive samplers tend to be lighter and less obtrusive than active samplers and, therefore, potentially preferable for personal monitoring. In addition to diffusive sampling, passive samplers may instead rely on particle settling. In this case, the passive sample being collected is a particle-based sample rather than a gas sample.
Over the past several years, there has been a proliferation of consumer-grade monitors that quantify various chemicals, many of which have even been expressly developed for indoor applications (Ometov et al., 2021; Zhang and Srinivasan, 2020). Sensors that are widely used typically report concentrations of CO2, PM2.5, PM10, and/or total VOC with time resolutions of seconds to minutes (Chojer et al., 2020). The monitors have been purchased predominantly by the average consumer interested in indoor and outdoor air quality or by researchers aiming to achieve somewhat larger-scale measurements of some common pollutants or indicators of indoor and outdoor air quality for research purposes. The lower cost of these consumer-grade sensors allows greater monitoring of personal and room scale indoor concentrations in exposure and health studies. In addition, some of these monitors incorporate web- and app-based data logging and public display that allow for real time monitoring of exposures, especially in quickly changing situations like impacts of wildfire events on indoor spaces.
However, consumer-grade monitors often use different, miniaturized, or less costly technologies than federal reference monitors to quantify the constituent of concern, resulting in potential accuracy and precision performance issues. For example, consumer-grade PM2.5 monitors typically rely upon light scattering nephelometer sensors to count particles. These are limited by the wavelength they use and are unable to detect particles smaller than ~0.3 μm. To calibrate these consumer-grade PM2.5 monitors, the amount of scattered light is correlated to the PM2.5 mass concentration measured in a laboratory setting via gravimetric analysis (on a filter using a microbalance), beta
attenuation, and light scattering at other wavelengths. However, the calibration equation is dependent upon the size distribution, density, chemical composition, and optical properties of the collected sample, which is not always representative of the indoor PM2.5 mixture being measured.
While much work has been done to deliberate performance targets (Duvall et al., 2021; Williams et al., 2019) and evaluate the outdoor performance of consumer-grade sensors (Bi et al., 2020; Holder et al., 2020; Wallace et al., 2021), efforts to evaluate performance of these sensors in indoor environments are more limited. Indoor consumer-grade PM2.5 sensors have been shown to vary from laboratory instruments by up to a factor of 2, while PM10 value variation can be even higher (Demanega et al., 2021; Wang et al., 2020). As a result of the variation in indoor measurements, Wang et al. (2020) conclude that indoor PM2.5 measurements are semi-quantitative; they can be used to identify episodic events and relative changes in the same indoor setting or room but may not report accurate absolute values without additional calibration measurements co-located in the experimental setting.
Certification processes for consumer-grade sensors depend on adequate test methods that address the challenges of the indoor environment. Currently, consensus test methods for indoor PM2.5 and CO2 sensors are being developed (ASTM WK62732, ASTM WK74360). Application of these methods to the marketplace will hopefully produce more accurate consumer-grade instruments that can be applied to exposure measurement campaigns. Box 6-2 describes novel measurement approaches that have recently been adopted to measure children’s exposure. See Chapter 2 for more discussion of approaches for gas-phase, particle-phase, dust-phase, and surface sampling.
Human behavior influences not only the chemistry present in the indoor environment but also the differences in our relative exposure to the chemicals discussed in this chapter. Development of personal wearable sensors and samplers (e.g., VOC monitors, optical particle sensors, silicone wristbands, FreshAir Band) may help us understand individual exposure profiles in a better light as opposed to relying upon measurement of chemicals in bulk samples of indoor air and dust. For example, recent studies found that levels of parabens and organophosphate ester flame retardants measured on silicone wristbands were more strongly correlated with urinary biomarkers than levels measured in house dust (Levasseur et al., 2021; Phillips et al., 2018).
CONCLUSIONS
To date, the foremost goal of exposure science has been to identify and characterize the inhalation, ingestion, and dermal uptake by people of harmful chemicals that can cause acute or chronic health effects. The application of exposure science to the study of indoor environments and exposures that occur therein is relatively nascent but rapidly evolving. Cost-effective policies and guidance suitable for diverse indoor environments and indoor-dwelling populations demand a thorough understanding of indoor exposure profiles. Understanding large differences in indoor exposures will also require deeper insight on the societal and systemic context in which exposures occur in residential and nonresidential environments. Environmental health disparities that are persistently observed in the United States and around the world too often remain unstudied.
The evidence base and toolkits for developing a robust and comprehensive understanding of indoor exposure profiles is growing rapidly. This evidence base has grown through multiple research channels, including field-based, laboratory-based, and modeling studies. Among field-based studies, emergent tools are addressing long-standing challenges of assessing spatial and temporal resolution on concentrations of airborne hazards, as well as diversity of chemical species in indoor air. Consumer-grade measurement tools and research-grade, high-resolution instrumentation alike are achieving wider use in indoor environments.
Researchers are also working to understand exposure to chemical mixtures. These efforts complement the strategic priorities of federal agencies, like the National Institutes of Health. For example, the National Institute of Environmental Health Sciences has identified strengthening understanding of combined exposures as a strategic priority: “Study of combined exposures, or mixtures, most closely replicate the human experience, and thus may provide unique insights to environmental health sciences” (NIEHS, n.d.). Measurement science advances applied to indoor environments and personal sampling are helping to better understand discrepancies—for example, between personal exposures and stationary monitors or indoor and outdoor area concentrations. Yet, inconsistency in chemical identifiers remains a challenge. Exposure data are collected across diverse sampling platforms, ranging from very short duration and transient (e.g., 1-hour) to chronic and longitudinal (multiple years), among populations that vary greatly in size and subject composition (Tan et al., 2018). This leads to diverse data that are not standardized and therefore not readily available to support modeling efforts.
One of the most important and fundamental needs for improving the utility of models in the exposure context is to bridge the gap between physical process models and exposure models. There is a need to connect physical process models to exposure and uptake models. Integrating frameworks can be the basis for better understanding the relationship of indoor air chemistry to exposure and even to internal dosing.
RESEARCH NEEDS
On the basis of the information discussed in this chapter, the committee arrived at the following list of specific actions recommended to advance indoor exposure science and research:
- Review current science of indoor chemistry to define gaps in current exposure assessment methods or data collection. Examples include identification of novel chemicals or chemical reaction products to include in field exposure studies (e.g., ozonolysis intermediates), evaluation of influential behaviors (e.g., window-opening), and collection of market data for products of interest (e.g., oxidizing air cleaners or fragranced products).
- Develop more harmonized measures to characterize indoor exposure disparities. A sparse number of studies reproducibly demonstrate that demographic and socioeconomic factors can enhance susceptibility to chemical exposures, but the evidence base for this conclusion is incomplete and data-poor. As patterns and predictors of indoor chemical exposures and exposure variability become better understood, it will be important to
- standardize and make widely available datasets that fully capture these differences. Future work that comprehensively characterizes indoor exposures across a more diverse array of settings would have significant value for a range of real-world applications, including individual-, community-, and policy-level decision making. Expanded exposure datasets could be used in concert with the NHANES biomonitoring dataset.
- Develop methodological and technological tools to make direct measurement of exposures easier, more convenient, and lower cost, especially to chemical mixtures, at scales that meaningfully improve the performance of exposure modeling and close gaps in understanding relationships between indoor environmental co-exposures to many chemicals and health outcomes, including persistent environmental health disparities. Tools to enhance exposure monitoring based on microenvironmental measurements should extend beyond measuring species concentrations to also track occupancy patterns in indoor environments, as these patterns can influence emissions, ventilation, and pollutant removal.
- Grow the network of data sources on human behaviors in indoor environments to become more representative of the U.S. population and establish criteria for standardization and harmonization across diverse sources, ranging from nationally distributed surveys by federal agencies, to market-based data, to individual- and community-based reporting. Frameworks have the potential to provide the structure and tools needed to harmonize data on exposure determinants (e.g., human behaviors, consumption patterns, time-activity, intake rates) so that they can be better integrated for modeling efforts, as well as to increase data accessibility.
- Deepen understanding of human behavior and time-activity patterns as they relate to indoor chemistry. Addressing this critical knowledge gap would likely contribute to greater understanding of exposure variability. For example, factors such as clothes-laundering, hand-washing, window- and door-opening, spending time indoors or outdoors, cooking, cleaning, and engaging in leisure activities can drive significant differences in chemical exposures. Detailed, representative behavioral data will be increasingly valuable for models of physical processes and exposure. In recent years, the scientific community has learned how the presence of a human body mediates indoor chemistry, including gas-phase composition, generation of VOCs, and surface reactivity. It is likely that the collection of behavioral data will accelerate in the coming years. Efforts have to be undertaken to ensure the representativeness of such data if they are to be used for model training, while protecting data privacy and sustaining the highest-caliber research ethics.
- Improve understanding of first principles that mediate and govern exposure while continuing to build datasets that can provide empirical exposure model inputs. At this time, it is not possible for exposure models to be fully developed based on process knowledge. Yet numerous data gaps limit the ability to substitute empirical data for process first principles. In order for exposure models to advance, the understanding of exposure factors will need to improve while continuing to build datasets that can provide empirical information to support exposure modeling efforts.
- Improve models through better integration of an understanding of human behavior. Human time-activity patterns (i.e., where people spend their time), habits and practices, and behavioral data associated with indoor chemistry warrant significantly more study that keeps demographic differences in mind. Opportunities also exist to support and nurture modeling consortia or modeling hubs that are cross-disciplinary and include close collaboration with experimentalists.
- Connect physical process models to exposure and uptake models. The utility of models in the exposure context would be greatly improved by research that facilitates the integration of physical process models and exposure models. Integrating frameworks are being used to provide insight into the relationship of indoor air chemistry to exposure and even to internal dosing, but more work is needed.
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