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Suggested Citation:"5 Exposure to Indoor PM." National Academies of Sciences, Engineering, and Medicine. 2024. Health Risks of Indoor Exposure to Fine Particulate Matter and Practical Mitigation Solutions. Washington, DC: The National Academies Press. doi: 10.17226/27341.
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5
Exposure to Indoor PM

This chapter addresses exposure assessment methods for fine particulate matter indoors, with a focus on exposure metrics. Particle concentrations and other attributes that form the basis for these metrics—mass, surface area, number, size fraction or distribution, chemical composition and bioactivity, temporal patterns—and the state of currently available instrumentation to resolve these features with varying degrees of accuracy, sensitivity, and specificity, are then reviewed. The chapter then covers the application of these tools to measure exposure directly or indirectly, with models that resolve concentrations as a function of human location. Emerging and novel tools and approaches for characterizing and mitigating exposure uncertainty and error through better characterization of particle size, composition, spatial and temporal resolution, and human location are highlighted.

Observed trends, with an emphasis on determinants of exposure that result in exposure disparities are considered. Influencing factors, such as indoor sources, building characteristics, environmental factors, and human activities are discussed. The chapter then further explores how advances in exposure assessment can improve our understanding of health effects and practical mitigation. It closes with a summary of the findings and conclusions that flow from the literature review.

SCOPE AND INTRODUCTION

Indoor exposure to airborne particles occurs when humans inhale the air in their homes, schools, and other built environments or come into contact with the particles by other routes. Exposure may be thought of as the time-integrated airborne concentration experienced at the point of contact between humans and particles. As shown in Figure 5-1, once particles are breathed in and the human interface is crossed, particles are referred to as an intake. Inhaled particles are either breathed back out or deposited and retained in the body. The term dose applies after absorption and transport result in a final, delivered quantity, which can cause one or more health outcomes.

Suggested Citation:"5 Exposure to Indoor PM." National Academies of Sciences, Engineering, and Medicine. 2024. Health Risks of Indoor Exposure to Fine Particulate Matter and Practical Mitigation Solutions. Washington, DC: The National Academies Press. doi: 10.17226/27341.
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Image
FIGURE 5-1 The connections among sources, transformations, and mitigation, and their influence on indoor exposure to fine PM.

Exposure assessment is the process of characterizing the magnitude, frequency, and duration of individual(s) exposure to a pollutant. It is an important step in understanding the human health risks resulted from indoor PM exposure in the context of this review. Because people are indoors most of the time, being able to characterize indoor exposure is important not only to understand health impacts from indoor particle sources, but also for understanding the health impacts from indoor exposure to fine PM of outdoor origin (Morawska et al., 2013). The right choice of exposure metric depends on the application (as reviewed by Lowther et al., 2019). Appropriate exposure metrics for health studies are informed by the type of health outcome of interest, which also informs the temporal and spatial resolutions that are appropriate. Exposure assessment is also used to inform where reduction in concentrations is most needed and to validate the success of control efforts. Exposure misclassification, when individual or group exposures are not accurately characterized, can limit the ability to understand the health impacts from fine PM, for instance, around vulnerable or underserved populations (Ashayeri and Abbasabadi, 2022; Gray et al., 2013; Marshall, 2008; Tonne et al., 2018).

Personal monitors quantify individual exposure at the point of contact (Brook et al., 2011). Modeling techniques may be used to work backward along the environmental health paradigm shown in Figure 5-1 to reconstruct the contributing sources, influencing factors, or microenvironments associated with an exposure measurement. Models may also be used to predict individual or population exposures in a forward direction, by combining data on sources, environmental dynamics, human time-activity patterns, and physiological factors to extrapolate from exposure to intake, deposition, and dose.

Earlier chapters of this review have elucidated the first step in the environmental health paradigm—the emission of particles from sources and their transport and transformation in the environment, as depicted in Figure 5-1. These processes govern where, when, how much, and what types of particles are present in various environments and ultimately where they are encountered by human receptors. As a result of the complexity of sources and transformation processes described in chapters 3 and 4, human exposure to fine particulate matter has significant heterogeneity in terms of particle size and composition, which in turn creates a need for defining metrics that can properly characterize PM exposure. This chapter discusses the intermediate

Suggested Citation:"5 Exposure to Indoor PM." National Academies of Sciences, Engineering, and Medicine. 2024. Health Risks of Indoor Exposure to Fine Particulate Matter and Practical Mitigation Solutions. Washington, DC: The National Academies Press. doi: 10.17226/27341.
×

steps in Figure 5-1, whereby humans are exposed and experience a dose, which leads to the final step of health effects, covered in Chapter 6.

EXPOSURE ASSESSMENT METHODS

Exposure Metrics

Definitions Based on Mass, Surface Area, or Number

Fine PM, also referred to as PM2.5, are airborne particles with an aerodynamic diameter of 2.5 micrometers or less. PM2.5 is most commonly quantified as total mass per unit volume of air. When applied this way, the PM2.5 mass metric is nonspecific with respect to composition and integrated over all particle sizes below 2.5 micrometers. Ambient PM standards in the United States are based on this metric, with compliance traditionally monitored with offline, gravimetric filter-based methods. The resulting measurement is typically collected at a frequency of once per day and is only feasible at moderate to low spatial density due to sample handling and labor requirements.

Though the mass-based metric has been repeatedly associated with respiratory and other symptoms (as discussed in Chapter 6), its adequacy for measuring and managing the health risks from PM has been brought into question by the growing number of studies demonstrating the importance of other particle attributes such as number count, surface area, and composition (Lowther et al., 2019). As expected, the discrepancy between mass- and number-based measures is greatest for the smallest particle sizes, referred to as ultrafine particles. Therefore, the representativeness of the PM mass metric may be especially pronounced in indoor environments where people are exposed, in proximity, to fresh emissions from combustion and other indoor sources that emit in the ultrafine range, as discussed in Chapter 3.

At present, the majority of epidemiological studies are based on the PM mass metric. And while the health evidence base is being transformed by the ability to capture a wider range of particle attributes in exposure assessment studies and the understanding of the mechanisms underlying various adverse health outcomes from PM grows, the mass-based metric will continue to be a useful indicator.

Addressing Temporal Complexity

The timing and duration (short-term versus long-term) of exposure measurements are also important factors. Continuous, time-resolved data are important for resolving both short-term (acute) and long-term (chronic) exposures and the evaluation of diurnal and seasonal patterns. Insight into temporal patterns is particularly useful for source identification and mitigation planning as well as for extracting health effect time-scales, such as the lag between an exposure and its effect and the duration of exposure associated with an effect. The range of temporal metrics used in epidemiological studies for ambient PM varies from the average over a lifetime (N. Li et al., 2022; Morawska et al., 2013) to the average daily level over the preceding 5 years (S. Li et al., 2020), to an annual average with no lag (N. Li et al., 2022) and prior-day or even prior 10-min exposures (Woo et al., 2022). Some long-term studies also address the potential additive effects of multiple prior exposures. A careful treatment of timescales is particularly important for the intermittent sources described in Chapter 3, which result in highly variable indoor exposures.

Tapered element oscillating microbalances and beta attenuation instruments (Lowther et al., 2019) offer a means to measure time-resolved particle mass concentrations, but the cost and

Suggested Citation:"5 Exposure to Indoor PM." National Academies of Sciences, Engineering, and Medicine. 2024. Health Risks of Indoor Exposure to Fine Particulate Matter and Practical Mitigation Solutions. Washington, DC: The National Academies Press. doi: 10.17226/27341.
×

complexity pose a barrier for use in indoor exposure studies. Optical particle counters (OPCs), based on principles of light scattering, measure particle number concentrations within a certain size range at a reduced cost relative to real-time mass concentration measurement techniques, enabling dense monitoring networks. The number concentration data can be converted to a lower-fidelity mass measurement by making assumptions about particle shape, density, and composition. Low-cost sensors, discussed below, offer a practical means of acquiring time-resolved data, but with a further decrease in reliability, accuracy, and precision, especially when the light scattering data are converted to a mass estimate.

Addressing Size Complexity

Fine particle sizes range over a 16 million-fold span in mass between the smallest and largest particles (NAE, 2022). OPCs are used to measure size-resolved number counts of particles 0.3–10 micrometers in diameter. Condensation particle counters and instruments based on electrical mobility—scanning mobility particle sizers, diffusion chargers, fast mobility particle sizers—are used to evaluate count- or size-distribution-based exposure metrics for the smaller, submicron and ultrafine fraction (Lowther et al., 2019). Multistage impactors and aerodynamic particle sizers provide size- and time-resolved measurements for a wide range of particle sizes, based on their time of flight.

As the conventional PM2.5 mass metric has come under scrutiny, attempts have been made to determine whether the breakpoint or diameter thresholds used to delineate coarse and fine—and fine and ultrafine—particles should be amended (Morawska et al., 2008). The reasoning is that health effects are likely to be source-specific, and so by selecting size fraction breakpoints based on the particle size distribution (PSD) profiles of major source-types, a size-based metric will better encompass other health-relevant particle attributes. Regardless of the broad consensus on metrics, source-specific PSDs can be used, when available, to customize exposure assessment tools to the sources under investigation.

Addressing Composition Complexity

Chemically, the composition of fine particles includes elemental and organic carbon, with vastly diverse chemical compositions, as well as crustal materials, inorganic salts, metals, microbes, allergens, and other constituents (NAE, 2022). The microbial components can have variable immunologic and inflammatory effects. Recently, it has also been reported that radionuclides attached to PM were associated with respiratory effects (Vieira et al., 2019, Wang et al., 2023). Chapter 3 presents composition profiles associated with common sources of indoor PM. Particle measurement instruments capture composition variability with different levels of sensitivity, specificity, and resolution. An indirect way to account for composition differences is to measure source-specific exposures.

Many species are present in a wide range of particle sizes, extending beyond the 2.5-micrometer diameter cut-off for fine particles, making it hard to determine to what extent a composition-specific measure such as the amount of black carbon, polycyclic aromatic hydrocarbons, metals, allergens, or flame retardants, or the infectious agent load, are fine particle measures in any given context. The link to fine particle exposure is even harder to predict for attributes such as oxidative potential or reactive oxygen species that are related to particles and gases in an air sample. Health effects studies need to be attentive to the independent but overlapping effect from exposures to multiple parameters from the same underlying source. For instance, as described by Biel et al. (2020), urban populations are often simultaneously exposed to multiple air pollution measures and noise, which are independently associated with cardiovascular disease.

Suggested Citation:"5 Exposure to Indoor PM." National Academies of Sciences, Engineering, and Medicine. 2024. Health Risks of Indoor Exposure to Fine Particulate Matter and Practical Mitigation Solutions. Washington, DC: The National Academies Press. doi: 10.17226/27341.
×

Exposed populations or individuals may vary in their susceptibility to various composition measures, which suggests that the most impactful metric depends not just on the source profile, but also on the human receptors.

Addressing Practical Barriers to Exposure Assessment

In addition to capabilities such as size resolution, time resolution, and composition resolution, particle measurement devices are selected for exposure assessments based on their ease-of-use features, including cost, form-factor and weight, operating noise, labor to install and maintain, power requirements, connectivity, and data-processing capabilities. Emerging tools and methods for acquiring or predicting spatially dense data at scale—low-cost sensors, HVAC filter media as opportunistic samplers, participatory research, mobile monitoring, satellite data, and models— are discussed below.

The use of low-cost particulate matter sensors has grown rapidly, with the majority of papers reporting their use published in the last 5 years (NAE, 2022). Internet-of-things based particle monitors typically cost $100–500. Most sensors function as bulk nephelometers, but lower-cost miniaturized versions of single-particle counting laser technology that offers greater size resolution and accuracy are also now available (Particles Plus, 2023). Black carbon sensors are also available. Implementation on urban, building, and personal scales has generated unprecedented amounts of data that can be integrated to develop more accurate personal exposure models at scale (Pantelic et al., 2022).

Numerous studies have compared performance among various sensor and device types. Researchers have found a generally high correlation between readings from low-cost sensors and reference instrument readings, although accuracy decreases in uncontrolled “real world” environments where particle attributes are unpredictable, heterogeneous, and dynamic (Demanega et al., 2021; Sá et al., 2022). Low-cost sensors can also have issues related to their limits of detection and temperature and humidity compensation. Techniques and recommendations have been developed to guide sensor selection and placement and to boost performance, including methods based on machine learning, neural networks, and fusion with higher fidelity data (Chojer et al., 2022; Fritz et al., 2022; Y. Li et al., 2017; Omidvarborna et al., 2021; Park et al., 2017). However, there are as of yet no standards or widely accepted protocols for quality control and appropriate use.

Community-based participatory research and citizen science are increasingly used as mechanisms to increase the availability of PM data, especially around communities of color and those with lower socioeconomic status, as reviewed by Commodore et al. (2017). These communities tend to disproportionately reside close to ambient sources such as industrial facilities and freeways (Johnston et al., 2020) and to face social inequalities in the distribution of sensors (Mullen et al., 2022). The accessibility of low-cost sensors has expanded monitoring via citizen- and community-based science, though the focus has been mainly outdoors (Colorado Dept. of Public Health and Environment, 2023; EPA, 2021; State of California, n.d.).

Filters installed in HVAC systems can serve as opportunistic PM sampling devices (Haaland and Siegel, 2017; Mahdavi et al., 2021). House dust also serves as a convenient particle reservoir that can be analyzed for insight into the chemical or biological composition of PM, but it is harder to parse for the previously (or potentially) airborne fraction. Other measurement techniques to fill spatial data gaps efficiently, without recourse to large numbers of devices, are mobile monitoring (indoor through robots, or outdoors through vehicles) and satellites (Knibbs et al., 2018; Y. Li et al., 2017).

Suggested Citation:"5 Exposure to Indoor PM." National Academies of Sciences, Engineering, and Medicine. 2024. Health Risks of Indoor Exposure to Fine Particulate Matter and Practical Mitigation Solutions. Washington, DC: The National Academies Press. doi: 10.17226/27341.
×

A variety of deterministic and empirical models have also been developed to fill “gaps” in ambient and indoor air quality data, including models on outdoor to indoor infiltration (e.g., Chapizanis et al., 2021; Gariazzo et al., 2015; Özkaynak et al., 2013; Vette et al., 2013). This topic is covered in Chapter 4 on transport and transformation processes. A distinct class of exposure metrics that bypasses concentration measurements or estimates is based on models of those transport and transformation processes. They include indirect indicators such as infiltration rate, intake fraction, air change rate, and indoor/outdoor ratio (Baxter et al., 2013; Breen et al., 2018, 2019; Shi et al., 2017).

Inhalation Intake, Deposition, and Dose

As illustrated in Figure 5-1, intake, deposition, and dose occur downstream from the point of contact between a pollutant and the human interface. Intake is evaluated as the product of the concentration in the breathing zone, and the breathing rate. The intake fraction (iF) is a dimensionless parameter representing the intake of PM per unit of emissions. The iF can be used to evaluate the effect of building, human, and pollutant-specific factors on exposure without measuring or modeling environmental concentrations (Hodas et al., 2016). Inhalation rate can be used to personalize dose based on activity type. Yoon et al. (2012) demonstrated the use of heart rate monitors to evaluate breathing rates, which they used in turn to estimate total inhalation mass.

Models of the amount of inhaled particles deposited in the human respiratory system depend additionally on human factors such as lung morphology and breathing patterns, level of activity and its effect on respiratory minute volume, fluid dynamic properties of the environment, and particle properties including size and composition, as reviewed by Hofmann (2011). Dosimetry models may be used to estimate whole-lung or regional (extrathoracic, tracheobronchial, and alveolar-interstitial) deposition.

Deposition metrics are based on mass (Patel et al., 2020; Sánchez-Soberón et al., 2018), number, and surface area (Pañella et al., 2017) and may be presented as rates (Liao et al., 2006) or fractions (Martins et al., 2015) or as a function of composition (Wang et al., 2022). Prominent respiratory tract dosimetry models are maintained by task groups of the National Council on Radiation Protection and Measurements and of the International Commission on Radiological Protection (Yeh et al., 1996). Newton et al. (2021) presented an innovative empirical alternative to dosimetry models: a polyurethane foam sampler whose particle capture mechanisms are posited to simulate the behavior of the human lung.

Finally, the absorbed dose may be estimated by combining intake measures with pharmacokinetic models (EPA, 2015) or directly from biomarker data.

Exposure Assessment Approaches

Direct Measurement

Personal monitoring via wearable or “point-of-contact” (EPA, 2015) sensors is considered the gold standard for exposure assessment. It is commonly used to evaluate the accuracy of exposures modeled and estimated through indirect methods (e.g., Ha et al., 2020; Nethery et al., 2008). Personal measurements capture total exposure, integrated over all sources and microenvironments, at the individual level, over the time-frame studied.

The contribution of various sources, influencing factors, and microenvironments to the total measured exposure can be modeled or reconstructed in conjunction with time-activity and other contextual data, to inform mitigation. For example, Buonanno et al. (2012) used data collected with a global positioning system (GPS) logger and activity diaries to interpret exposure

Suggested Citation:"5 Exposure to Indoor PM." National Academies of Sciences, Engineering, and Medicine. 2024. Health Risks of Indoor Exposure to Fine Particulate Matter and Practical Mitigation Solutions. Washington, DC: The National Academies Press. doi: 10.17226/27341.
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results. Using a similar study design, which is echoed across many studies (e.g.,Braniš and Kolomazníková, 2010), Uzun et al. (2022) measured personal exposures to black carbon and applied time-activity diaries to attribute the fractional contribution from transportation versus home-based activities on weekends versus weekdays, while Milà et al. (2018) used questionnaires, GPS, and wearable camera data to model the sources and factors influencing exposure and to identify dominant microenvironments. Extrapolation from individual to population exposures and from exposure measured in a discrete time window to long-term exposure also relies on modeling techniques.

Historically, personal PM monitoring has been cumbersome, requiring study subjects to carry relatively large, heavy, and delicate measurement devices in backpacks and requiring researchers to download data manually after collection (Buonanno et al., 2014). Large-scale personal monitoring studies have been made more feasible by the development and proliferation of low-cost, portable, connected sensors (e.g., L. Li et al., 2021).

Exposure Reconstruction

Direct exposure monitoring of particulate matter (PM) in indoor spaces gives an estimate of the potential exposures of individuals in that environment. When combined with environmental and human data, such monitoring can be used to estimate inhalation intake and lung deposition. However, the techniques discussed above fall short of elucidating the dose of PM that an individual receives. Exposure reconstruction uses internal body measurements, or biomarkers, to directly measure the absorbed dose and to infer exposure from multiple pathways and sources (EPA, 2015).

Exposures to environmental pollutants are highly heterogeneous across populations, and individuals who are chronically exposed to these substances are viewed as being at higher risk for developing biological signals of exposure or cellular alterations indicative of exposure effects. The signals or alterations, called biomarkers of exposure or effect, are essential tools in understanding the potential effects of exposures on human health. The health effects associated with exposure to particulate matter is likely linked to biotransformation processes that result in the formation of reactive metabolites, or reactive species of oxygen and nitrogen, that can damage cells, cause chronic inflammation, and lead to disease processes in the human body.

Biological monitoring provides the ability to assess the uptake or dose by an organism that often is the result of personal factors and individual susceptibility. In environmental science, biomarkers are divided into three types: markers of internal exposure, markers of effect or response, and markers of susceptibility. The biomonitoring approach implies that internal exposure to a toxicant can be determined by measuring the toxic substance or chemical or its metabolites, i.e., reaction products that can be found in the blood, urine, saliva, or exhaled breath.

The literature on the utility of using biomarkers of exposure to indoor particulate matter is currently limited, but the research on particulate matter in air pollution and biomonitoring is directly applicable. Many epidemiological studies have shown a relationship between outdoor PM2.5 and DNA damage, though the mechanism is unclear. Most frequently the hypothesis is that substances attached to the particulate matter play an important role in the DNA damage. The extent to which the chemicals associated with outdoor pollution also apply to indoor pollution is not clear, further complicating the ability to compare the utility of biomonitoring in indoor environments. The discussion on biomarkers of susceptibility and effect is further expanded on in Chapter 6, while this discussion focuses on the feasibility of biological monitoring of exposure to particulate matter, primarily in the indoor environment.

Suggested Citation:"5 Exposure to Indoor PM." National Academies of Sciences, Engineering, and Medicine. 2024. Health Risks of Indoor Exposure to Fine Particulate Matter and Practical Mitigation Solutions. Washington, DC: The National Academies Press. doi: 10.17226/27341.
×

The ideal biomarker for exposure to particulate matter should be sensitive, specific, biologically relevant, practical, inexpensive, and available. To date there is not a specific biomarker for PM in the indoor or outdoor environment that meets these criteria. In studying indoor exposures, the population characteristics, the practicality of collecting biological samples, seasonal variations in exposure, the nature (e.g., composition) of PM, and background comparison ranges all need consideration. A study by Hachesu et al. (2019) found that phagocytized carbon load in airway macrophages could serve as a biomarker of internal particulate matter in the human body; however, macrophage sampling has limited utility in epidemiological studies, given that bronchoalveolar lavage is needed to obtain the sample of lung macrophages (low practicality) and that no background comparison ranges are available. Small studies have been done that are compartment specific for elements associated with particulate matter exposure. For example, Zetlan et al. (2023) measured metals and inflammation in the nasal epithelial lining fluid of patients with chronic obstructive pulmonary disease (COPD) exposed to air pollution. While this finding could be associated with particulate matter exposure, it does not precisely measure exposure to particulate matter.

Given the lack of a specific marker of biological absorption of particulate matter, it is more often the case that various compounds that attach to particulate matter, such as polycyclic aromatic hydrocarbons (PAHs), volatile organic compounds, endotoxins, allergens, or metals, are used to assess exposure. For example, the main metabolite of pyrene, urinary 1-hydroxypyrene (1-OHP), is frequently used to estimate overall biological exposure to PAHs present in air pollution and has been shown to be a suitable biomarker of exposure. PAHs are compounds released during the incomplete combustion of fossil fuels, wood, incense, coal, and oil products present in indoor air, and they are widely known for their toxicity, mutagenicity, and carcinogenicity. Airborne PAHs can be found in both a gaseous phase and also bound to particulate matter, depending on moisture, temperature, volatility and other factors.

There is evidence that children attending schools in urban areas are exposed to higher concentrations of airborne PM and PAHs, and higher levels of PAH metabolites have been found in the urine of children in urban schools compared to children in non-urban schools. Oliveira et al. (2019) completed a review of 17 studies, including a small number carried out in U.S. schools, on the exposure of children in school environments to particulate matter and PAH through biomonitoring in school environments. These studies found that median PM10 and PM2.5 exceeded World Health Organization guidelines in European and Asian schools and that Asian schools had higher levels of both PM and PAHs than other countries. Levels of PAH metabolites were increased in children from schools in polluted areas. The results of this review point out a major limitation of biomarkers of exposure—the inability to attribute the biological load to the source of exposure, in this case the indoor school environment or the outdoor air pollution sources. Still, the authors stressed that PAH exposure is directly associated with indoor and outdoor levels of PM, principally the smallest fractions, and that there would be utility in studying the synergistic effects of both PM and PAHs in future studies.

The National Health and Nutrition Examination Survey (NHANES) is the most comprehensive source for human biomonitoring data in the United States (EPA, 2015). PAH metabolites are included in the battery of chemicals that are assessed in human urine, and PAH levels have been shown to be higher in populations who smoke. More intricate relationships between the presence of PAH biomarkers and the indoor air environment have not been published from the NHANES data.

Suggested Citation:"5 Exposure to Indoor PM." National Academies of Sciences, Engineering, and Medicine. 2024. Health Risks of Indoor Exposure to Fine Particulate Matter and Practical Mitigation Solutions. Washington, DC: The National Academies Press. doi: 10.17226/27341.
×

Indirect Estimation

Exposure models, also known as scenario evaluations (EPA, 2015), rely on data on time-activity, which is the amount of time people spend doing various activities, in various locations. This information may be combined with information about particle concentrations in those locations to predict exposure when personal monitoring is not feasible or desirable (e.g., Lane et al., 2015). Exposure models may be based on real contextual data or on hypothetical scenarios of interest, making models the tool of choice to predict the impact of changes anticipated due to policy measures, climate change, and other exposure determinants. The accuracy of an exposure model depends on the spatial and temporal granularity of the underlying time-activity and location-specific concentration data. More complex integrative models can be used to quantify exposure for a population of interest (EPA, 2015).

Population-level surveys and residential addresses obtained from census tracts and administrative registers are sources of position and time-activity data that are freely available and are, as such, a practical if low-fidelity resource for large-scale exposure characterizations. Examples of population-surveys from different geographies are the National Human Activity Pattern Survey (Klepeis et al., 2001; Zhang and Batterman, 2009), the Canadian Human Activity Pattern Survey (Leech and Smith-Doiron, 2006), the Consolidated Human Activity Database (Che et al., 2015), the London Travel Demand Survey (Smith et al., 2016), and the Exposure Factors Handbook of Chinese Population (Shen et al., 2021). The limitations of these data are that they are static and coarse-grained and do not capture stochastic and adaptive behavior variability, resulting in exposure prediction errors and misclassification for pollutants that are spatially heterogeneous (Özkaynak et al., 2013).

Higher-fidelity human data may be obtained by acquiring individual-level time-activity budgets with questionnaires and diaries (e.g., Takaro et al., 2015; J. Kang et al., 2021, reviewed for assessing children’s exposure by Branco et al., 2014; Kaufman et al., 2012). Questions about the timing and frequency of potential particle emitting activities such as cooking, cleaning, or the use of candles are included when source attribution is a goal. Limitations of this data class is that it is resource-intensive to collect. Automated methods reduce the processing overhead associated with surveys and diaries, but self-reported time-activity data are intrinsically limited in terms of the temporal and spatial granularity that is feasible and the subjective reporting bias involved.

Particle concentration data may also be obtained at varying levels of resolution, ranging from regional (from central stations and satellites) to zip code or address level (from atmospheric dispersion models, spatial interpolation techniques, empirical models, neighborhood sensor networks or mobile monitoring), all the way to concentrations adjusted by indoor infiltration rate or directly measured from indoor microenvironments, as reviewed by Özkaynak et al. (2013).

New technology and tools enable dynamic, objective mobility and activity tracking. Measurement and modeling tools to assess particle levels at increasingly granular scales and to match the scales at which human data are collected have also become increasingly sophisticated. Most of the newly available location-activity and concentration tracking methods take advantage of advances in sensor technology and “big data” capabilities, including connectivity and cloud storage and processing.

One class of location-activity tracking tools captures macro-scale individual mobility. These rely on GPS and geographic information system (GIS) mapping technologies, often in conjunction with smartphones and applications like the Google Maps program, for real-time and historical data (Gulliver and Briggs, 2005; Pañella et al., 2017; Yarza et al., 2020; Yu et al., 2019). Milà et al. (2018) integrated GPS with wearable cameras to attribute exposures to time of day, location, and activities in South India. Micro-scale—i.e. within a building—mobility

Suggested Citation:"5 Exposure to Indoor PM." National Academies of Sciences, Engineering, and Medicine. 2024. Health Risks of Indoor Exposure to Fine Particulate Matter and Practical Mitigation Solutions. Washington, DC: The National Academies Press. doi: 10.17226/27341.
×

tracking was demonstrated by Quinn et al. (2020) through the combined use of GPS and motion, temperature, and light sensors. Researchers have demonstrated other approaches to monitor occupancy and activities associated with indoor PM, such as cooking and range hood use. Examples include Dedesko et al. (2015), who tested low-cost, non-invasive methods to estimate occupancy and occupant activities in hospital patient rooms, based on data from CO2 sensors and non-directional doorway beam-break sensors; Johnson et al. (2020), who adapted a temperature sensor to track stove use; and Zhao et al. (2020), who applied a similar approach to Johnson et al. in the United States, using anemometers to measure range hood use. Pollard et al. (2023) evaluated indoor positioning systems (IPS) for the study of the movement and interaction of people in offices, finding that just over a week of data collection was sufficient for characterizing typical movement behaviors in these settings. While the committee is not aware of similar IPS efforts in residences or schools, a feasibility study demonstrated that networks of motion sensors could be useful for characterizing in-home human behavior and associated impacts on indoor air quality (Lin et al., 2017).

Note that in addition to serving as an input parameter for exposure models, time-activity pattern data may also serve as a qualitative proxy for a quantitative exposure measurement. For instance, Leech and Smith-Doiron (2006) reported that COPD patients spent more time indoors at home and were more likely to have air conditioning than controls from the general population. Whether or not the association implies a causal link between time indoors and the development of COPD, the finding supported the recommendation that mitigation strategies emphasize source control in the patient’s home. Data on the presence or absence of known sources such as candles and incense (Chapter 3) can also serve as a crude, qualitative proxy for exposure.

EXPOSURE TRENDS AND DISPARITIES

Exposure Trends in Homes

A literature review by Ilacqua et al. (2021) found that indoor PM concentrations in homes over the past three decades (1990–2019) displayed generally decreasing trends in concentrations of all size fractions in North American and European studies. In the United States, indoor PM2.5 concentrations have been decreasing at a rate of about 0. 4 ± 0. 1 μg/m3 per year (87 studies, from 1987 to 2019), and concentrations of PM10 have been decreasing at a rate of 1.0 ± 0.4 μg/m3 per year (31 studies, from 1987 to 2014). Based on these downward trends, the estimated mean indoor PM2.5 concentrations for 2016 was 5.2 μg/m3, and the estimated mean indoor PM10 concentrations for 2014 was 9.7 μg/m3. Downward trends were also observed when the regression analyses were performed at the city-level using published data from Baltimore, Boston, Detroit, Los Angeles, and New York City. In contrast, there are fewer studies on ultrafine particle (UFP) number concentrations in U.S. homes (four studies, from 2006 to 2015), and regression analysis found no significant changes over the years. The review found that outdoor air pollution remains a major influence on indoor concentrations of PM of all sizes. But large variabilities in indoor PM concentrations in homes suggest that indoor sources and interventions are important factors that can affect human exposure.

The general downward trend of indoor PM exposure is expected to continue, according to a modeling study by Fazli et al. (2021) showing a decrease in population-average indoor concentrations of pollutants of ambient origin in U.S. residences from the baseline year 2010s to 2050s assuming business-as-usual conditions. Model predictions suggested that population

Suggested Citation:"5 Exposure to Indoor PM." National Academies of Sciences, Engineering, and Medicine. 2024. Health Risks of Indoor Exposure to Fine Particulate Matter and Practical Mitigation Solutions. Washington, DC: The National Academies Press. doi: 10.17226/27341.
×

weighted mean indoor concentrations of PM2.5 are expected to decrease slightly, largely due to a reduction in indoor PM2.5 of outdoor origin. The model study made assumptions about changes that retrofits and new constructions would bring about, such as reductions in outdoor air ventilation rates because of structures becoming more airtight, decreases in window use, and increases in HVAC system runtimes, and acknowledged that it did not consider more transformative changes to the housing stock, such as deep energy retrofits, or adding high efficiency mechanical ventilation and filtration more widely. There are considerable uncertainties on the impact of home retrofits on indoor PM concentrations, according to a review by Fisk et al. (2020). In one study, I. Kang et al. (2022) show that adding mechanical ventilation in 40 Chicago area homes, including filter upgrades among those with central forced air systems, resulted in a reduction in the indoor-to-outdoor PM ratio. The study suggests that the magnitude of reduction is largest in homes that received continuous ventilation systems, compared with homes that received intermittent systems. More data are needed to assess how other approaches to home retrofits can affect indoor PM exposure.

The COVID-19 pandemic prompted concerns about an increase in indoor exposure owing to the increase in the time being spent at home, but very few studies have measured the effect of this change (Adam et al., 2021). Increases in exposure to PM emitted from indoor sources such as cooking, cleaning, candles, and environmental tobacco smoke (ETS) are described as some of the primary concerns, especially among susceptible populations such as children, elderly, and those living in crowded housing in poor conditions. For example, a modeling paper by Dobson et al. (2022) reported an increase in exposure to ETS from COVID lockdown measures for the U.K. population, but changes in PM2.5 exposure were minimal for most individuals despite the simulated increase in cooking activity. To the best of our knowledge, there is no available assessment of how indoor PM exposure may have changed since the pandemic for the U.S. population.

Exposure Trends in Schools

Several reviews on exposure to air pollutants in schools (Mejía et al., 2011; Morawska et al., 2017; Oliveira et al., 2019; Salthammer et al., 2016) summarized measurements of fine PM concentrations, including in some U.S. schools. PM exposures in schools are shaped by indoor emissions and resuspension caused by occupant activities and the effect of ventilation and surface sinks. School proximity to traffic has been identified as a crucial factor affecting indoor exposure. The review also points out significant temporal and spatial variations in exposure in different microenvironments within the school (e.g., classrooms, gymnasium). Similar to the case with residential studies, very few studies have measured UFP number concentrations in schools. Overall, the reviews point to a need to monitor personal exposure of children to PM in schools, with a focus on particle size and composition, in order to better understand the associated risks for the health of children.

In 2014, the U.S. Environmental Protection Agency (EPA) awarded a number of grants to study school factors and environmental conditions related to children and teacher/staff health and performance. Several of these studies included PM monitoring. Ren et al. (2020) measured PM in seven high schools in Texas over 2 years (2015–2017), and found that the average PM2.5 concentrations in classrooms were low compared with health guidelines due to air filtration as part of HVAC use. The study observed that flooring type had an effect on the resuspension of PM10, where carpet flooring was associated with significantly higher indoor concentrations compared with classrooms with vinyl composition tile flooring. Kabirikopaei et al. (2021)

Suggested Citation:"5 Exposure to Indoor PM." National Academies of Sciences, Engineering, and Medicine. 2024. Health Risks of Indoor Exposure to Fine Particulate Matter and Practical Mitigation Solutions. Washington, DC: The National Academies Press. doi: 10.17226/27341.
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measured fine and coarse PM counts in 220 classrooms from 39 schools in the Midwestern United States over 2 years (2015–2017). The study found associations of student achievement scores with a number of building and environmental factors, including fine PM counts. Majd et al. (2019) measured PM and other indoor air pollutants in 16 schools in an urban area in the mid-Atlantic region in three different seasons (2015–2017). Monitored schools did not have central HVAC systems and relied on opening windows for ventilation. The study points out the significance of outdoor sources in proximity to schools, including the length of the nearby roads (as proxy of total nearby traffic volume) and the number of nearby industrial facilities, on indoor exposure. A related study (Zaeh et al., 2021) measured PM2.5 and other indoor air pollutants in seven schools from the same region before and after building renovations. Renovations included HVAC retrofits, window replacement, and other major improvements; one of the seven schools was completely replaced with new construction. Study data showed substantial reductions in indoor PM2.5 concentrations post-renovation.

Factors Associated with Fine PM Exposure

A review by Morawska et al. (2013) outlined a number of factors that affect indoor exposure to fine PM. In summary, both proximity to outdoor sources (e.g., traffic, industrial emissions) and the presence of indoor sources (see Chapter 3) are important determinants of indoor exposure. The transport and fate of indoor PM depends on a range of building and environmental factors (see Chapter 4), and together they influence the particle size and composition of indoor PM. Time activities of humans and their behaviors can affect the intake and deposition of fine particles in the lung, and human susceptibility will determine the health risks resulting from indoor exposure (see Chapter 6). A review by Hodas et al. (2016) identifies major factors influencing the inhalation of PM2.5 using intake fraction (iF) as the metric. Variability in iF is driven by a combination of building parameters such as building size, air exchange rate, and interzonal mixing; human factors such as inhalation rates, occupancy, and time-activity patterns; and pollutant characteristics such as particle size distribution, physical and chemical processes like deposition, resuspension, and transformation.

There is extensive literature on factors associated with higher exposure to ambient fine PM. For example, Marshall et al. (2006) calculated the inhalation of diesel fine PM and other ambient air pollutants by people living in California’s South Coast Air Basin. The analysis revealed that exposure concentrations in different microenvironments, population mobility, and temporal correlations between ambient concentrations and breathing rates affected the calculated inhalation intake by 40 percent, on average. As a result, subpopulations who are non-whites and economically disadvantaged households had higher inhalation intake than the population as a whole.

Many studies have identified risk factors among economically disadvantaged communities that are associated with higher indoor PM exposure in homes. Adamkiewicz et al. (2011) measured indoor concentrations of multiple pollutants in economically disadvantaged households and found that exposures are driven by the combined influences of indoor sources, outdoor sources, physical structures, and residential activity patterns. However, the study also found that exposure is not the sole determinant of health risk. Other individual and neighborhood characteristics with strong ties to economic disadvantages also influence how environmental exposures can affect health and may heighten the influence of indoor environmental exposures. This point is echoed by Escobedo et al. (2014), who carried out a study of in-home PM2.5 exposure in an economically disadvantaged Latino community in Boulder, Colorado. The

Suggested Citation:"5 Exposure to Indoor PM." National Academies of Sciences, Engineering, and Medicine. 2024. Health Risks of Indoor Exposure to Fine Particulate Matter and Practical Mitigation Solutions. Washington, DC: The National Academies Press. doi: 10.17226/27341.
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researchers emphasized that even though the levels of in-home PM2.5 were low (all non-smoking homes, where cooking was likely the primary source), prior exposures from abroad for immigrant communities and contributions from work environments could add to the overall health burden.

Studies that focus on strong participant engagement in targeted subpopulations have identified factors associated with fine PM exposure. For example, Do et al. (2021) used high-resolution wearable sensors to study behavior-dependent patterns of PM2.5 exposure in high-traffic, industrialized regions of Southern California. Results from the study indicate that participants from the most economically disadvantaged community, despite their high level of mobility and low variability in ambient PM2.5 concentrations, experienced overall higher personal exposure, mostly because of high PM in homes where participants spent the majority of their time. The study of 18 participants (half of them college students) showed that acute exposures (less than one hour) at high concentrations (> 35 μg/m3) in these microenvironments: homes, work/university, restaurants, suspected smoking/vaping. Another example is by Webb et al. (2021), who used a community-based participatory research approach to measure PM2.5 concentrations in two tribal communities. Two of the 15 homes monitored for PM2.5 showed daily concentrations exceeding 35 μg/m3 (EPA 24-h standard for outdoor PM2.5). These data point to indoor sources, such as smoking and candles and potentially woodstoves, as potential contributors.

Studies on Exposure Disparities

A study on the exposure disparities to outdoor PM2.5 found that U.S. public housing developments are significantly overrepresented in areas with higher outdoor air pollutants (Chakraborty et al., 2022). Housing and occupant factors can further exacerbate such disparities in terms of indoor exposure. Multifamily homes that are smaller in size, higher in occupant densities, and located in areas close to outdoor sources are associated with higher indoor PM exposure and health outcomes. Baxter et al. (2007), as part of a prospective birth cohort study assessing asthma etiology in urban Boston, Massachusetts, collected indoor and outdoor PM2.5 samples in 43 homes across multiple seasons from 2003 to 2005. The studied homes represented economically disadvantaged households and consisted almost entirely of multifamily residences. The study found cooking time, humidifier use, cleaning activities, and occupant density were associated with PM2.5 and trace elemental concentrations. The study points out particular concerns and a need for more research in urban areas where more people reside in multifamily homes with higher occupant densities.

Stevenson et al. (2001) found racial disparities in housing (e.g., year built, crowding) and community factors that are associated with asthma morbidity. More recently, Grant et al. (2023) provided an overview of disparities for children with asthma from their exposure to indoor allergens. The review points to increased exposure to PM from various sources, including proximity to traffic-related air pollutants and indoor PM sources, that are adversely affecting both homes and schools, particularly in urban inner-city neighborhoods. The review also points out that environmental exposures and influences affecting pediatric urban asthma are complex and intertwined. Thus, multimodal interventions targeting allergen, mold, and air pollution exposures in conjunction with changes on income, housing, and other social inequalities will be needed to meaningfully change pediatric asthma.

A review by Diaz Lozano Patinõ and Siegel (2018) on indoor environmental quality in subsidized and public housing (also referred to as social housing) found evidence that residents

Suggested Citation:"5 Exposure to Indoor PM." National Academies of Sciences, Engineering, and Medicine. 2024. Health Risks of Indoor Exposure to Fine Particulate Matter and Practical Mitigation Solutions. Washington, DC: The National Academies Press. doi: 10.17226/27341.
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may be disproportionately exposed to higher levels of PM2.5, largely because of the higher prevalence of smoking in the building, compared with non-social-housing multifamily homes. The review also found that there are strong indicators that residing in social housing is associated with negative health effects, with a high prevalence of respiratory problems. In contrast, Zhao et al. (2021) found that in recently (2013–2017) constructed or renovated non-smoking, mechanically ventilated multifamily homes in California, the measured PM2.5 concentrations were similar to those observed in larger, less densely occupied single-family detached homes of similar vintage and cooking frequency. This underscores the potential of using building controls, such as mechanical ventilation, to mitigate indoor exposure.

Chu et al. (2021) found disparities by homeownership, where renters in multifamily housing experienced a higher proportion of PM2.5 concentrations from non-ambient sources due to a combination of behavioral and building factors amenable to interventions. The research team worked with a community-based organization to recruit renters and homeowners and conducted week-long PM2.5 measurements in 71 homes in Greater Boston, Massachusetts. By concurrently monitoring both outdoor and indoor PM2.5 using real-time and time-integrated gravimetric methods and using information gathered from home visual assessment, participant interview and daily activity logs, the study estimated PM2.5 from non-ambient origin and found associations with indoor source activities. The researchers found that the majority of indoor PM2.5 was of non-ambient origin, with increasing contributions in homes with higher indoor PM2.5. Major source predictors of non-ambient PM2.5 were cooking, smoking (reported among renters in multifamily homes only), increased range hood use, and being in heating season.

Studies on vulnerable populations residing in other types of housing institutions are sparse in the United States. For example, Reddy et al. (2021) found only one study in the country (Tebbe, 2017), which evaluated indoor air quality in four nursing homes in Ohio. In 2022, there was a relevant study published using wildfire smoke exposure measurements at four skilled nursing facilities in the western United States (Montrose et al., 2022). Residents in nursing homes spend large amounts of time indoors, and their advanced age and susceptibility to prolonged exposure are reasons for concerns. Studies from Europe had reported inadequate ventilation and high concentrations of other indoor air pollutants (e.g., NO2, formaldehyde) in nursing homes (Bentayeb et al., 2015). Early childhood education is another area where there are few studies of fine PM exposure and the potential impacts on the health and development on young children. Early childhood education facilities differ from K–12 schools in terms of building characteristics (e.g., home-based settings are common) and occupant activities. Several exploratory studies (Gaspar et al., 2018; Gilden et al., 2022; Quirós-Alcalá et al., 2016) measured indoor particle concentrations in early childhood education facilities over short periods of time (e.g., over the course of a day). These studies found that indoor levels of PM were either the same or higher than outdoor levels. Resuspension and PM that originated from outdoors from proximity to traffic and the use of windows for natural ventilation are among the contributing factors. In addition, common indoor sources such as scented candles, air fresheners, and cleaning products may also have contributed to indoor PM levels.

Suggested Citation:"5 Exposure to Indoor PM." National Academies of Sciences, Engineering, and Medicine. 2024. Health Risks of Indoor Exposure to Fine Particulate Matter and Practical Mitigation Solutions. Washington, DC: The National Academies Press. doi: 10.17226/27341.
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INDOOR EXPOSURE ASSESSMENT TO INFORM HEALTH AND MITIGATION

Advances in exposure assessment of indoor PM are important not only for improving the understanding of health impacts—they are also important to motivate practical actions to mitigate. This section will discuss how exposure assessment can improve the understanding of the health disparities from indoor exposure to fine PM of outdoor origin, and the health implications from indoor PM more broadly. The role of exposure assessment to raise awareness on the importance of indoor PM to health is also discussed.

Disparities in Indoor Exposure to Fine PM of Outdoor Origin

While there are examples of epidemiological studies of outdoor fine PM considering building factors as modifiers (Allen et al., 2012; Breen et al., 2014; Hodas et al., 2012; Hystad et al., 2009), the majority of the literature examining exposure inequality to air pollutants of outdoor origin does not consistently incorporate factors that modify indoor exposures (Bell and Ebisu, 2012; Jones et al., 2014). Continuing to improve the characterization of housing and behavioral factors can help improve understanding of inequalities in exposure to outdoor PM. For example, ambient air pollution epidemiological studies that apply building outdoor air change rates (ACH) as a covariate or modifying factor have produced less exposure measurement error and, thus, more precise effect estimates of associations between residential exposure to ambient air pollution and health outcomes, compared with traditional analyses (Sarnat et al., 2013). Rosofsky et al. (2019) used spatially and temporally resolved estimates of PM2.5 concentrations and calculated ACH to analyze exposure inequality and found that neighborhoods containing parcels with both high ambient PM2.5 and high ACH disproportionately included non-White, economically disadvantaged, and low-educational-attainment populations. Stratified analyses also confirmed an a priori hypothesis that historically marginalized populations experience a cumulative burden of both high ACH and high ambient air pollution concentrations and that the exposure inequalities are magnified when ACH and ambient PM2.5 are overlaid.

Studies that directly measure or approximate indoor exposure to fine PM of outdoor origin, rather than relying on outdoor measurements alone, illustrate the importance of considering indoor environments. For example, in the NEXUS study, Vette et al. (2013) included indoor sampling at participants’ homes and at two schools to better characterized how building factors such as infiltration could impact near-road exposures and the resulting health effects among children with asthma in Detroit, Michigan. Lane et al. (2015, 2016) demonstrated the value of using time-activity adjustments on exposure assessments to better understand the impact of UFPs on systemic inflammation biomarkers and cardiovascular disease risk. These study findings reinforce the importance of the indoor environment for examining differences in exposure patterns and associations among racial/ethnic sub-populations for causal interpretation.

Indoor Exposure to Fine PM for Understanding of Health Implications

The ability to characterize exposure with sufficient specificity (e.g., speciation, size, spatial, and temporal variations) is critical to understanding health impacts from indoor fine PM. One example is a study by Isiugo et al. (2019) which found that reduced lung function is more strongly associated with indoor particles, in particular indoor PM associated with smoldering

Suggested Citation:"5 Exposure to Indoor PM." National Academies of Sciences, Engineering, and Medicine. 2024. Health Risks of Indoor Exposure to Fine Particulate Matter and Practical Mitigation Solutions. Washington, DC: The National Academies Press. doi: 10.17226/27341.
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organics (e.g. wood burning fireplace), than with outdoor particles or black carbon. Studies that are designed to understand the health impacts from indoor fine PM have consistently found that indoor exposures are often higher and more variable than outdoor exposures. Zusman et al. (2021), in an effort to understand indoor exposures in a large cohort of adults with COPD, recruited across 12 clinic centers in the United States. The study, known as SPIROMICS Air, monitored 2-week integrated PM2.5 concentrations indoors and outdoors and found indoor concentrations to be higher and more variable than outdoor concentrations. Advancement in modeling of indoor PM exposures from information such as home and behavioral survey data and socioeconomic and meteorological parameters can improve understanding of health effects from fine PM exposures.

There are other examples of health cohort studies that included indoor exposure as part of the environmental assessment. The National Human Exposure Assessment Survey (NHEXAS) was designed to establish environmental exposure estimates and trends in a number of study cohorts in the United States. As part of NHEXAS, Williams et al. (2013) conducted personal air monitoring for selected PAHs in children and adults residing in urban, suburban, and rural areas near Baltimore, Maryland. The study found notably higher PAH exposures among participants living in urban and suburban areas compared with rural areas. More recently, the Environmental Influences on Child Health Outcomes (ECHO) Program has been evaluating environmental factors affecting children’s health (Buckley et al., 2020). Apart from biomonitoring, several ECHO cohorts will include air sampling and other environmental monitoring in the homes of study participants. Such data will be invaluable in shedding light on the role of in-home exposure, including chemical components of fine PM, on children’s health.

Increasing Public Awareness of Exposure to Indoor PM

There is a wide difference in public knowledge concerning outdoor air quality versus the hazards associated with indoor air quality. To design high-quality epidemiological studies of indoor PM exposure and its health effects, advances in exposure monitoring are needed. Advances in exposure monitoring in indoor environments are beginning to help overcome some of the challenges in measuring exposure and documenting mitigation effectiveness. Low-cost PM monitors are enabling larger sample sizes in cross-sectional studies. Such methods are also allowing longer-term monitoring so that changes pre and post intervention can be properly captured.

Making exposure visible is critical to motivating actions to mitigate, and participatory PM2.5 monitoring is important to increasing environmental health literacy and raising awareness. One example is the A Day in the Life project by youth living in disadvantaged communities in Southern California (Johnston et al., 2020). The project used air monitoring coupled with photography and mapping to increase youth-centered understanding of personal exposures, fine PM sources, and vulnerability to air quality. Rickenbacker et al. (2020) measured indoor air quality, including fine PM, and collected quality-of-life surveys from 41 homes in Pittsburgh, Pennsylvania. This study is another example of community–academic partnership that is driven by the desire from participants to learn based on their unique set of personal concerns.

Suggested Citation:"5 Exposure to Indoor PM." National Academies of Sciences, Engineering, and Medicine. 2024. Health Risks of Indoor Exposure to Fine Particulate Matter and Practical Mitigation Solutions. Washington, DC: The National Academies Press. doi: 10.17226/27341.
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FINDINGS AND CONCLUSIONS

Findings

This chapter describes different methods (e.g., direct methods such as biomarkers, wearables for personal monitoring, low-cost sensors, time-activity measurements and exposure modeling, etc.) to characterize exposure to indoor fine PM. It is challenging to characterize exposure comprehensively, given the spatial and temporal variability in particulate matter indoors, its heterogeneity in size and composition, the dynamics of human movement and behavior, and the conditions of the built environment. Our ability to fully measure and quantify exposure to indoor fine PM is intrinsically limited. Given these intrinsic limitations, characterizing exposure is still a valuable tool that helps to connect fine particulate sources to health effects and aid our understanding of mitigation effectiveness.

Indoor exposure to PM is generally decreasing in the United States, with decreasing outdoor air pollution and lower prevalence of smoking among likely contributors (Ilacqua et al. 2021). This implies a shift towards indoor exposure being even more dominated by indoor PM generated from other sources. It is expected that this trend will continue in response to decreasing reliance on fossil fuels, energy efficiency improvements, and the like. However, the reduction in indoor exposure to outdoor PM is not occurring uniformly. For example, areas affected by wildfire smoke are exposed to high levels of indoor PM of outdoor origin, and communities affected by localized outdoor PM sources are still burdened by their indoor exposure to those sources. These changes, among others, are contributing to disparities in indoor PM exposures.

Disparities exist in population exposure to indoor fine particulate matter of both outdoor and indoor origin. Disparities occur not only because of higher indoor exposure concentrations due to more activities happening in smaller, densely occupied, and interconnected (multi-family) homes or because of outdated appliances that have higher emissions or ventilation equipment that is less effective at removing PM, but also because of the susceptibility of the exposed populations leading to excess health burden. Settings where indoor PM exposures, the associated health impacts, and mitigation opportunities are particularly limited include schools and early childhood education facilities as well as institutional housing such as homeless shelters, transitional homes, skilled nursing facilities, and correctional facilities.

Low-cost sensors and personal monitoring are providing greater abilities to measure exposure, although important limitations remain. The accessibility of these lower-cost sensors has greatly expanded monitoring capabilities, but further advancements to measure particles in the ultrafine range and provide information on particle size would greatly enhance their usefulness in characterizing indoor PM exposure. Beyond improving instrument accuracy, cost, form factor (ease of use, connectivity), and other performance aspects, it is critically important to advance our understanding of how measured values are useful for determining the health impacts from exposure to fine particles or mitigation effectiveness. While indoor PM is generally expected to contribute to excess morbidity and mortality, the lack of a standardized approach to readily obtain indoor fine PM exposure levels, especially in historically marginalized communities, limits advances in our understanding of the connection between exposure and disease.

Our understanding of the sources of high intermittent exposure to indoor PM is particularly limited. There are emerging concerns about new sources, such as vaping, more

Suggested Citation:"5 Exposure to Indoor PM." National Academies of Sciences, Engineering, and Medicine. 2024. Health Risks of Indoor Exposure to Fine Particulate Matter and Practical Mitigation Solutions. Washington, DC: The National Academies Press. doi: 10.17226/27341.
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frequent cleaning and disinfection, and electronic air cleaners. Very high exposure to indoor fine PM is occurring in some microenvironments. Our understanding of the potential health impacts of these indoor sources in different built environments is partly restricted by the availability of instrumentation to characterize exposure. In particular, understanding of indoor exposure to some specific types of PM, such as UFPs and specific PM compositional constituents, remains poor. Studies point to a need for innovation to improve measurement techniques and study methods to enable better characterization of the total exposure and health impacts to fine particles in indoor environments.

Conclusions

A national effort to measure and report indoor exposure to PM using validated methods and sufficient characterization of the built environment, occupancy, and activity patterns is needed to identify critical determinants of indoor exposure to fine particles (and other indoor air pollutants) so that source-specific exposure can be assessed and to guide mitigation efforts that can target subpopulations overburdened with exposure to fine particles in homes, schools, and other building types. The data would greatly improve the existing understanding of the exposure and potential health impacts of indoor PM on the U.S. population in indoor environments including homes, schools, and other vulnerable settings.

There is a need for clear guidance on indoor PM exposure metrics, in particular to support programs implementing practical mitigations—e.g., woodstove replacement, healthy home retrofits, school HVAC upgrades, portable air cleaner deployments, etc.—and to inform building standards and practices that can bring about significant changes at scale. These programs require evaluation of their benefits to motivate funding and continuing support. Guidance on how to measure the potential reduction in indoor fine PM exposure and what metrics to use is needed so that such programs can adjust and improve over time to bring more benefits to the communities.

Collaborations to study indoor PM exposure in susceptible, underserved, and disproportionately exposed communities should be encouraged. Indoor environments and the people who live in them are diverse. They have unique characteristics that may lead to high indoor fine PM exposures that require focused attention. More targeted data on such exposures are necessary to improve the current understanding of them and ultimately to protect susceptible populations. Indoor environment researchers need to collaborate with community-based organizations and community members if they are to conduct the kinds of culturally sensitive studies that will produce information relevant to these populations and develop effective messaging on PM exposure issues to help motivate practical mitigation.

There is a need to make indoor exposure to fine PM more visible to the public, such as by using low-cost sensors which can be a powerful way to educate building occupants and to motivate them to take actions that can reduce indoor sources and increase use of mitigation measures. At the same time, there is a need to advance the capabilities of low-cost sensors in order to better characterize indoor fine PM exposures and provide sufficient specificities useful for understanding health impacts. Beyond low-cost sensors, improvements in other measurement techniques and methodologies are also important to reaching this goal.

Suggested Citation:"5 Exposure to Indoor PM." National Academies of Sciences, Engineering, and Medicine. 2024. Health Risks of Indoor Exposure to Fine Particulate Matter and Practical Mitigation Solutions. Washington, DC: The National Academies Press. doi: 10.17226/27341.
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Schools, workplaces, businesses, and even homes are places where someone could be subjected to particulate matter (PM) – a mixture of solid particles and liquid droplets found in the air. PM is a ubiquitous pollutant comprising a complex and ever-changing combination of chemicals, dust, and biologic materials such as allergens. Of special concern is fine particulate matter (PM2.5), PM with a diameter of 2.5 microns (<0.0001 inch) or smaller. Fine PM is small enough to penetrate deep into the respiratory system, and the smallest fraction of it, ultrafine particles (UFPs), or particles with diameters less than 0.1 micron, can exert neurotoxic effects on the brain. Overwhelming evidence exists that exposure to PM2.5 of outdoor origin is associated with a range of adverse health effects, including cardiovascular, pulmonary, neurological and psychiatric, and endocrine disorders as well as poor birth outcomes, with the burden of these effects falling more heavily on underserved and marginalized communities.

Health Risks of Indoor Exposure to Fine Particulate Matter and Practical Mitigation Solutions explores the state-of the-science on the health risks of exposure to fine particulate matter indoors along with engineering solutions and interventions to reduce risks of exposure to it, including practical mitigation strategies. This report offers recommendations to reduce population exposure to PM2.5, to reduce health impacts on susceptible populations including the elderly, young children, and those with pre-existing conditions, and to address important knowledge gaps.

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