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Health Risks of Indoor Exposure to Fine Particulate Matter and Practical Mitigation Solutions (2024)

Chapter: 4 Particle Dynamics and Building Characteristics that Influence Indoor PM

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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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4
Particle Dynamics and Building Characteristics that Influence Indoor PM

This chapter describes the mechanisms that affect indoor particle dynamics, how those mechanisms are measured or modeled, and how building characteristics affect these mechanisms. It concludes with recommendations for research to enhance knowledge of indoor particle dynamics in order to improve understanding of the health effects of indoor particulate matter (PM) and the effectiveness of practical mitigation measures. The fundamental dynamics described in this chapter apply to a broad range of particle sizes beyond the PM2.5 (2.5 μm and smaller) size range; however, the quantitative information presented in later sections of this chapter are focused on all particle sizes that contribute to PM2.5, given the scope of this report. The National Academies Why Indoor Chemistry Matters report (NASEM, 2022) addresses fate, transport, and transformation issues related to chemical species like polycyclic aromatic hydrocarbons that exist in the PM2.5 range.

PARTICLES IN INDOOR ENVIRONMENTS

The fate of particles in the indoor environment governs the magnitude and route of occupant exposure to indoor PM and depends on a variety of mechanistic processes and the factors that affect those processes. Broadly, the major categories of mechanistic processes that affect indoor PM concentrations include PM (1) sources, (2) losses, and (3) transformations (Nazaroff, 2004).

The relative importance of specific PM sources, losses, and transformation processes depends on the nature of the PM sources as well as the type and location of the building in which an occupant resides and how the building is designed, built, and operated. Differences in building types and their operational characteristics are relevant for the fate of indoor PM because they influence how PM enters from outdoors (e.g., via infiltration through leaks or via mechanical outdoor air intakes), how and when PM is generated indoors (e.g., what types of activities, appliances, and fuels are present), and what types of practical mitigation measures can be implemented effectively (e.g., can central air cleaning be used?).

There are nearly 130 million occupied housing units in the United States, of which approximately 81 million are single-family detached homes, 32 million are multi-family homes, 8 million are single-family attached homes, and 7 million are manufactured/mobile homes (U.S. Census Bureau, 2022). The vast majority of existing homes rely on infiltration (i.e., air leaks), natural ventilation (i.e., window openings), and intermittent exhaust (e.g., bathroom and kitchen exhaust fans) for outdoor air ventilation rather than on dedicated mechanical ventilation systems (ASHRAE, 2017). There are also approximately 6 million commercial buildings (EIA, 2022) in the United States, the majority of which are designed to use mechanical heating, ventilation, and

Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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.
×

air conditioning (HVAC) systems that intentionally deliver outdoor air for ventilation; infiltration is also often not negligible in these building types, albeit not by design (Emmerich and Persily, 2014). There are also approximately 130,000 elementary and secondary schools in the United States (National Center for Education Statistics, 2022), with a much larger total number of classroom units within these schools, which vary widely in the types of HVAC systems that are installed and how they are operated (Batterman et al., 2017; Jaramillo and Ermann, 2012; McNeill et al., 2022). Importantly, a large fraction of schools have HVAC systems that are in need of significant retrofits or replacement (Chan et al., 2020; GAO, 2020), which affects the types of indoor PM mitigation measures that can be deployed and the effects they can have.

Some differences in building characteristics that can affect the fate, transport, and transformation of indoor PM are also associated with differences in geographic and socioeconomic factors that may contribute to disparities in exposure to indoor PM and associated health effects. For example, lower-priced homes tend to be leakier, with greater amounts of outdoor air infiltration (Chan et al., 2005, 2013) and thus greater amounts of outdoor pollutant entry (Stephens, 2015). Larger homes have been associated with lower indoor particle concentrations (Klepeis et al., 2017), and home size scales with income (U.S. Census Bureau, 2022). And more frequent kitchen range hood use has been associated with higher income and education levels (Zhao et al., 2020). However, the extent to which such factors contribute to disparities in indoor PM exposure has not been explored in depth or at scale to date.

INDOOR PARTICLE DYNAMICS: DEFINING MECHANISMS

The dynamic characteristics of indoor PM can be broadly classified into three fundamental processes: sources, losses, and transformations. Important sources of indoor PM are described in more detail in the previous chapter and are not the focus of this chapter. In brief summary, the sources of indoor PM include:

  • Entry/delivery from outdoors through ventilation and/or infiltration,
  • Primary indoor emissions,
  • Resuspension from settled dust, and
  • Formation as a byproduct from homogeneous or heterogeneous reactions (e.g., oxidation reactions between oxidants and volatile organic compounds [VOCs]).

Indoor PM simultaneously undergoes any number of other losses, also referred to as “sinks”, including:

  • Deposition to surfaces,
  • Removal by ventilation/exfiltration, and
  • Removal by central or in-room air cleaning/filtration.

Indoor PM is also subject to a number of transformation processes, which can act as either sources, sinks, or merely a change in aerosol properties, including but not limited to:

  • Transport, including intra-zonal transport (e.g., mixing within a room) and inter-zonal transport (e.g., from room to room)
  • Coagulation (i.e., smaller particles combine to form larger particles or aggregates), and
  • Change in properties such as composition, size, phase state, surface charge, viability (for biological particles), affected by processes such as aerosol aging, oxidation, evaporation, condensation, and partitioning.
Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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.
×

Many of these source, loss, and transformation processes interact to influence the size, composition, and concentrations of particles in indoor air and are influenced by factors such as indoor and ambient environmental conditions (e.g., temperature and relative humidity) and building operational characteristics. For gaining a deeper understanding of some of these fundamental aerosol transformations, the ambient atmospheric chemistry and physics community has a number of resources available (Seinfeld and Pandis, 2016; Zhang et al., 2022). And as ambient atmospheric scientists have recently turned their attention to indoor air, several resources describe how some of these aerosol processes interact in indoor environments (Abbatt and Wang, 2020), including the 2022 NASEM report Why Indoor Chemistry Matters.

Figure 4-1 illustrates how sources, sinks, and transformation mechanisms interact to affect indoor PM. Table 4-1 further describes these mechanisms and key parameters that influence the strength or importance of each measure for indoor PM. The parameters in Table 4-1 include those that arise from an inherent property of the building and its systems, those that are a function of the operation of the building (e.g., how the building is operated at any given moment), those that are a function of weather or outdoor pollution conditions, and those that are a function of particle size, composition, or the presence of gas-phase pollutants. These layers of interacting factors and often high temporal dynamism makes general statements about practical mitigation challenging. For example, a building with open windows will generally have diminished marginal benefit on reducing indoor particle concentrations from the use of air cleaning because of competition by the additional ventilation and high rates of delivery of outdoor fine PM. The extent of the impact of these factors depends on parameters such as the inside–outside temperature difference, the wind speed and direction, the number, extent, and location of open windows, and the concentration of ambient fine PM. Thus, the sign and the magnitude of the impact of a given air cleaner on fine PM is specific to the details of the specific application. The same logic is necessary to consider for other mitigation measures as well.

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FIGURE 4-1 Sources, sinks, and transformations of indoor particulate matter (PM).
Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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.
×

TABLE 4-1 Sources, Sinks, and Transformations of Indoor PM and Key Affecting Parameters

Mechanism Key parameters that affect that mechanism
Sources
Indoor emissions Occupant activity type and schedule, source type and frequency (see Chapter 3)
Resuspension Surface area, surface roughness, prior deposition and surface loading, air flow near surface, occupant activity
Formation (e.g., secondary organic aerosols) Concentrations of precursors in air (homogeneous reactions) and on surfaces (heterogeneous reaction), temperature and humidity
Entry from outdoors Mechanical ventilation: HVAC type (e.g., central or local), outdoor air flow rate, HVAC control strategy (e.g., damper position and schedule), outdoor air cleaner/filter efficiency
Natural ventilation: Window/opening size and operational behavior, driving forces (wind speed and direction, inside–outside temperature differences, HVAC induced pressures), penetration factors
Infiltration: Building leakage area and geometry, driving forces (wind speed and direction, inside–outside temperature differences, HVAC-induced pressures), penetration factors
Sinks (losses)
Removal to outdoors Mechanical ventilation (central exhaust): HVAC type, flow rate, HVAC control strategy (e.g., damper position and schedule) Mechanical ventilation (local exhaust): Flow rate, capture efficiency, occupant behavior and operation schedule, local exhaust location relative to source, mixing
Natural ventilation: Window/other opening size and operational behavior, driving forces (wind speed and direction, inside–outside temperature differences, HVAC-induced pressures)
Exfiltration: Building leakage area and geometry, driving forces (wind speed and direction, inside–outside temperature differences, HVAC-induced pressures)
Deposition to surfaces Surface area, material properties (e.g., material, roughness), and orientation, particle deposition velocity, space and surface air flow characteristics
Air cleaning In-duct: Flow rate through air cleaner/filter relative to space volume, installed removal/filtration efficiency, system runtime, mixing
In-room: Flow rate through air cleaner relative to space volume, installed removal/filtration efficiency, air cleaner runtime, location relative to source
Transformations
Transport Intra-zonal transport (mixing): Source characteristics (e.g., point or area), zone/room volume, room air flow characteristics (HVAC, fans, buoyancy, activities), operation of local sinks (e.g., air cleaners, local exhaust)
Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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.
×
Inter-zonal transport (between zones): Space layout, HVAC layout, leakage area of walls/partitions, driving forces (e.g., pressure differences caused by wind, temperature, HVAC operation)
Coagulation Particle concentration (high concentrations needed), particle size distributions (smaller sizes needed)
Change in aerosol properties (e.g., size, phase state, charge, composition, viability) Initial composition, size, and surface area; residence time; environmental conditions (e.g., temperature, humidity, pH); presence of sunlight; presence and concentration of interacting compounds

MEASURING INDOOR PARTICLE DYNAMICS

A major challenge in understanding individual sources, sinks, and transformations is that measurements of indoor PM concentrations, size distributions, or composition in buildings alone generally do not yield insights into the presence or magnitude of any particular mechanisms. Rather, indoor PM concentration measurements yield a measure of the net result of any number of competing or interacting processes (i.e., the concentration that remains after competing mechanisms interact). Moreover, it may not always be critical to assess specific mechanisms in the context of a health or mitigation study. For example, measurements of indoor and outdoor concentrations in buildings under relatively tightly controlled conditions, such as air cleaner on versus air cleaner off conditions, can yield insights into the effectiveness of an intervention while not necessarily yielding direct measurements of the magnitude of individual sinks or transformation processes. Also worth noting is that recent advancements in low-cost PM sensors that provide real-time displays of indoor PM concentrations to building occupants may also be useful in promoting behavioral interventions that affect indoor PM concentrations (Klepeis et al., 2013). However, it is possible to use a combination of (1) mathematical models and (2) targeted/scripted measurements to quantify the magnitude of particle sources, sinks, and transformations in real buildings. Laboratory measurements with certain parameters constrained also yield fundamental insight into these processes. In either field or lab tests, mathematical models are used to establish a theoretical framework for quantifying mechanisms that one observes or expects to observe and then are applied to measurements of indoor PM concentrations that result from targeted or scripted experiments, such as intentional perturbation experiments, to parameterize those models and quantify specific mechanisms.

Mathematical Modeling of Indoor PM Sources, Sinks, and Transformations

The earliest mathematical model for predicting indoor aerosol size distributions and concentrations dates to 1973 with an application in a computer facility at Bell Laboratories (Lum and Graedel, 1973). Nazaroff and Cass (1989) presented what is believed to be the first comprehensive mathematical model for predicting the concentration and fate of PM in indoor air that included both size resolution and chemical composition of indoor PM and accounted for indoor emissions, ventilation, filtration, deposition on surfaces, and coagulation. The model was validated using measured aerosol size distributions resulting from combustion of a cigarette in a single room, setting a precedent for how measurements and models can be combined to yield mechanistic insights into indoor PM. Such modeling efforts have since been extended to estimate

Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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.
×

or predict the effects of numerous other processes in a variety of indoor environments, including but not limited to predicting shifts in gas-particle partitioning with outdoor-to-indoor transport in homes (Hodas and Turpin, 2014); simulating residential PM2.5 infiltration across the U.S. housing stock factoring in size- and chemically-resolved penetration factors, evaporative losses, deposition losses, and filtration (Logue et al., 2015); predicting the effects of oxidative aging (i.e., continuously changing aerosol chemistry evolving by oxidative chemistry) on organic aerosol concentrations in residences under varying conditions (Cummings and Waring, 2019); and predicting the impacts of the phase state (e.g., semisolid or liquid) of indoor organic aerosols of outdoor origin on gas-particle partitioning (Cummings et al., 2022). Such models are also applied to measured indoor PM concentration data to estimate parameters such as emission rates (Chan et al., 2018), envelope penetration factors (Rim et al., 2010; Zhao and Stephens, 2017), indoor deposition loss rates (Lee et al., 2014), and filtration losses and filtration efficiency (Stephens and Siegel, 2012, 2013). Experimental investigations of mechanistic source, sink, or transformation processes often begin with controlled laboratory chambers, where parameters can be tightly controlled to yield observations that can be used to parameterize models, and then commonly extend to field measurements in individual homes or groups of homes to yield further insights in real buildings. Common approaches to measuring sources, sinks, and transformations of indoor PM are described in the next sections, with the goal of illustrating how such measurements are made in the event that they may be useful for incorporation into indoor PM health or intervention studies.

Measuring Indoor PM Sources

Because indoor PM in buildings results from a mixture of ambient sources that enter through ventilation/infiltration plus indoor sources, targeted in-situ measurements must be used to characterize the relative contributions of ambient and non-ambient sources from field measurements of indoor and outdoor PM concentrations. Such efforts generally begin with characterizing the time-averaged infiltration factor, or the fraction of ambient PM that infiltrates (i.e., enters) and persists indoors (a value bounded by 0 and 1) over a certain time period, which may vary by the nature of ventilation air, the magnitude of air change rate, ambient particle size distributions, or other building characteristics. Once the infiltration factor is known and applied to calculate the fraction of indoor PM originating from outdoors, the remaining fraction of indoor PM can be estimated to be generated from indoor sources (Özkaynak et al., 1996; Wilson et al., 2000). This approach can be applied to concurrent indoor and outdoor PM concentrations using either (1) time-integrated gravimetric measurements combined with a chemical tracer of ambient-infiltrated PM (e.g., sulfur, which historically has been assumed to have predominantly outdoor sources and minimal indoor sources) (Wallace and Williams, 2005) or (2) time-resolved measurements of PM concentrations (or surrogates of PM concentrations, see Chapter 5) with algorithms applied to mathematically remove the influence of indoor sources (Kearney et al., 2014; MacNeill et al., 2012, 2014).

Numerous studies have used such approaches and estimated that the time-averaged infiltration factor for ambient PM2.5 in residences commonly ranges from as low as ~0.1 to as high as nearly 1, with an average of ~0.5 (the average infiltration factor for ultrafine particles is lower, around ~0.3) (Chen and Zhao, 2011). Other recent studies using low-cost optical particle counters to approximate PM2.5 concentrations have found lower mean values of PM2.5 infiltration factors in U.S. homes of ~0.25 to ~0.4 (J. Bi et al., 2021; Liang et al., 2021). Use of air cleaning systems can reduce infiltration factors to even lower than 0.1 (Singer et al. 2017). With

Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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.
×

sufficiently broad characterizations of infiltration factors across a specific building stock, it is possible to model infiltration factors with reasonable accuracy using factors such as frequency of window opening, use of forced air heating or cooling, and use of air cleaning/filtration (Allen et al., 2012; Tang et al., 2018), which in turn could be used in epidemiology studies to characterize population exposures. An analysis of indoor and outdoor PM2.5 samples from the RIOPA study further showed that measured air change rates were a strong predictor of infiltration factors, but that air change rates were difficult to accurately predict using simple indicator variables (Meng et al., 2009). Infiltration factors have been less well characterized in schools, especially in the United States; a few studies in European schools have estimated PM2.5 infiltration factors ranging from ~0.3 to ~0.8, likely varying by factors such as the source and rate of ventilation air delivery and the type and use of HVAC systems and filtration (Korhonen et al., 2021; Rivas et al., 2015).

Other studies have also explored the infiltration of outdoor PM in greater depth by attempting to estimate the penetration factor of the building envelope (and any connected systems that may draw in outside air). The penetration factor, which is also bounded by 0 and 1, describes the fraction of ambient PM that passes through the building’s boundary between inside and outside (i.e., its enclosure, or envelope) (Liu and Nazaroff, 2001). The parameter is fundamentally important because it characterizes the fraction of the PM in outdoor air that enters a building, allowing one to understand the relative impacts of the building envelope versus indoor sinks such as deposition or air cleaning on indoor PM of outdoor origin. However, it is notoriously difficult to measure, as approaches to measuring it are time-consuming, cumbersome, and invasive to occupants, while also requiring solving for two unknowns (penetration factors and indoor loss rate constants) using only one mass balance equation applied to measured concentrations from the space (Diapouli et al., 2013). Approaches to estimating penetration factors for PM2.5 using statistical methods combined with integrated PM2.5 mass measurements have suggested that penetration factors in U.S. homes may be close to 1 (Meng et al., 2005; Wilson et al., 2000), whereas specific measurements of size-resolved penetration factors suggest that values range from ~0.2 to ~1 depending on particle size and various building factors (Chen and Zhao, 2011; Rim et al., 2010), which in turn suggests that ambient PM2.5 infiltration factors may also range in magnitude depending on the same factors. A 2017 study used targeted measurements in an unoccupied apartment unit to estimate size-resolved penetration factors for particles approximately 0.01–2.5 μm in size with doors and windows closed, which were then used to estimate penetration factors for an integral measure of PM2.5 by scaling to concurrent outdoor size distributions, resulting in a mean estimated PM2.5 penetration factor of ~0.73 (Zhao and Stephens, 2017). A 2010 study of size-resolved ultrafine particle penetration into an unoccupied test house revealed that both infiltration factors and underlying penetration factors were approximately two times higher with a single window open approximately 3 inches (7.5 cm), depending on particle size (Rim et al., 2010).

As mentioned, the same approaches that are used to estimate infiltration factors can also be used to estimate the contribution of indoor sources to total indoor PM2.5. Applications of such approaches have shown that the contribution of indoor sources to indoor PM2.5 may range from negligible to nearly dominant, depending on how much ambient PM2.5 infiltrates and persists indoors and on the magnitude of indoor source strengths (Kearney et al., 2014; MacNeill et al., 2014). Therefore, numerous studies have quantified the strength of indoor sources using in-situ (i.e., in-home) measurements with either scripted (or documented) field experiments with specific sources (Hussein et al., 2006; Sain et al., 2018; Stephens et al., 2013; Wallace, 2006;

Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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.
×

Zhao et al., 2021) or unscripted experiments to capture whole-house emission rates (Chan et al., 2018), or, more commonly, using controlled chamber experiments with specific sources (Afshari et al., 2005; Azimi et al., 2016; Géhin et al., 2008; Licina et al., 2017; Vance et al., 2017). In any of these approaches, emission rates from indoor PM sources can be estimated using mass balances applied to control volumes with a number of assumptions such as well-mixed conditions and measurements or estimates of parameters such as PM loss rates and test space volume. Such field-based approaches provide insight into sources as they behave in the field, albeit with less well controlled conditions, while lab-based approaches offer greater control and provide the ability to easily isolate specific sources. However, it is worth noting that emission rates measured in a laboratory or chamber might not accurately predict the emission rates measured in the field. Similar approaches have also been used to estimate PM resuspension rates from settled dust (Ferro et al., 2004; Qian and Ferro, 2008) and the formation rates of PM (and other mechanistic factors such as yields) resulting from indoor reactions (Petrick et al., 2011; Wang and Waring, 2014; Youssefi and Waring, 2014). Results from this literature were summarized in Chapter 3.

Measuring Indoor PM Sinks

The impact of indoor PM sinks, especially those associated with mitigation measures such as air cleaning, can be measured in two main ways: (1) measurements of the resulting effectiveness of an intervention or (2) direct measurements of the magnitude or rate of an indoor sink process. To measure the effectiveness of a mitigation intervention, indoor PM concentrations can be measured with and without an intervention, and comparisons between test conditions can yield insight into the magnitude of impact, holding other important parameters such as ventilation rates constant (or accurately measuring them concurrently). Such approaches have been widely used to quantify the impacts of air cleaning interventions such as portable HEPA air cleaners (Batterman et al., 2012) and in-duct particle filters (Bennett et al., 2018; 2022) on indoor PM concentrations and the impacts of air filtration or UV air cleaners on indoor concentrations of airborne microbes (Kunkel et al., 2017; Lai et al., 2003). Similar test approaches in controlled chambers and smaller-scale field studies have also been useful in quantifying the effectiveness and demonstrating some of the potential consequences of air cleaning technologies that rely on the addition of reactive constituents to air, such as the formation of secondary organic aerosols and gas-phase oxidation byproducts from the operation of ozone-generating ionizing and other oxidizing air cleaners in the presence of unsaturated organic compounds (Joo et al., 2021; Waring et al., 2008; Ye et al., 2021; Zeng et al., 2022).

Both indirect and direct methods have been used to quantify the magnitude or rate of specific indoor PM sink processes in buildings. Historically, statistical approaches have been used to indirectly estimate the magnitude of PM sinks such as total indoor loss rate constants from time-integrated concurrent indoor and outdoor concentration measurements (e.g. Meng et al., 2005; Williams et al., 2003). With the development of real-time and time-resolved instrumentation to monitor particle concentrations, methods to directly quantify indoor PM sinks have emerged. Direct measurements of indoor PM sinks generally involve analyzing time-series concentrations that characterize an elevation period followed by a decay towards background levels. Such approaches can be used with intentional perturbation experiments that involve purposeful elevation of indoor PM (e.g., He et al., 2005; Lee et al., 2014; Wallace et al., 2004) or with natural experiments that involve exploring resulting concentration data to find periods of concentration elevation and decay that naturally occurred with regular occupancy and activity

Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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.
×

(Chan et al., 2018; Hussein et al., 2005). Either way, estimates of first-order indoor particle loss rate constants can be made using mass or number balance approaches applied to the resultant data with a number of appropriate assumptions, which allows for direct quantification of such sinks (Thatcher et al., 2002).

In the absence of other information, such estimates of total PM loss rates will account for losses due to the combined effects of surface deposition, loss by ventilation/exfiltration, and losses by any air cleaning strategy or other sink or transformation process that might be present (Boedicker et al., 2021). Simultaneous measurements of other parameters such as air change rates can account for some of these interacting mechanisms and allow for isolating the impacts of, for example, surface deposition or air cleaning alone. Comparisons of loss rates measured between different conditions in a building can also allow for directly quantifying the impact of a change in condition (e.g., an intervention), assuming other mechanisms remain constant or are measured and accounted for. For example, comparing loss rates between different in-duct filter or portable air cleaner configurations can make it possible to quantify the impact that higher efficiency filtration or stand-alone air cleaning has on loss rates in a space, which also allows for estimating the in-situ clean air delivery rate (CADR) of the filter or air cleaning system (Alavy and Siegel, 2020; MacIntosh et al., 2008; Stephens and Siegel, 2012, 2013). Additionally, the impact of improved particle filtration on indoor PM concentrations can also be assessed by measuring PM concentrations upstream and downstream of a filter and quantifying airflow rates in buildings; recent studies have applied such approaches to characterize the impacts of interventions on PM loss rates in residences with central HVAC systems with various efficiency filters (Li and Siegel, 2020) and also in a renovated school that received a combination of MERV 8 and MERV 16 filters (Laguerre et al., 2020).

It is worth noting that the in-situ methods described above originate from controlled chamber studies that are routinely used to characterize the performance of air cleaners (Offermann et al., 1985; Shaughnessy and Sextro, 2006) and have also been used to yield mechanistic insights into factors that affect various sink processes, such as air speeds and surface characteristics, on deposition loss rates (Lai et al., 2002; Thatcher et al., 2002). Such controlled chamber test approaches are useful because they allow for the direct quantification of parameters such as CADRs or equivalent air change rates of air cleaners, both for PM broadly (Sultan et al., 2011; Waring et al., 2008) and for specific constituents of PM such as varying chemical compositions or source types (Peck et al., 2016) and microbial viability (Eadie et al., 2022; Kujundzic et al., 2006; Miller-Leiden et al., 1996). These measures conceivably translate to real indoor environments as well; however, such studies are limited in that laboratory performance may not accurately reflect performance in the field, for a variety of reasons. Many fewer studies in the literature have measured in-situ CADRs or other sink mechanisms in real field settings compared with controlled chamber studies, likely because of the increased complexity involved in doing so.

Recent advances have also been made in the experimental characterization of local mitigation strategies such as residential kitchen range hoods (Kim et al., 2018) and the placement of air cleaners near the breathing zone of occupants (DuBois et al., 2022). The capture efficiency of range hoods characterizes the fraction of particles generated through cooking that are removed by operating an exhaust fan over the cooking area. Capture efficiencies for fine PM have been shown to range from less than 10 percent to greater than 80 percent, depending on factors such as the exhaust hood flow rate, the burner location (i.e., front versus back), and particle size (Lunden

Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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.
×

et al., 2015; Rim et al., 2012b; Sun et al., 2018). However, such measurements have not been made at scale to characterize variability among different building stocks.

Measuring Indoor PM Transformations

Indoor PM of both indoor and outdoor origin is subject to a number of transformation processes as particles interact with each other and the environment. Transformation processes can act as a source or a sink, or lead to changes in aerosol properties such as size distribution or toxicological profile, depending on a number of factors and conditions. For example, when particles are first emitted from an indoor source such as cooking, if particles are small enough (e.g., <50 nm) and at high enough concentrations (e.g., >20,000 particles/cm3), coagulation can occur whereby smaller particles collide with like-size or larger particles to form yet larger particles (Rim et al., 2012a). Thus, coagulation simultaneously acts as a loss for the colliding particles and as a source for the larger aggregate particles that are created, affecting the overall size distribution but not total particle mass. For small nanoparticles at high concentrations, coagulation can be a dominant loss mechanism and be much greater than room ventilation (Jeong et al., 2021). However, coagulation is not considered a dominant mechanism for larger particles which generally contribute more to indoor PM2.5 mass concentrations, and understanding coagulation processes is probably not critical for understanding the impacts of most practical mitigation measures under most circumstances.

As mechanisms such as coagulation, deposition, and ventilation are simultaneously competing following emissions of indoor particles from a source, other mechanisms are also interacting, including intra-zonal transport (e.g., dispersion or mixing within a room), inter-zonal transport (e.g., movement from room to room or unit to unit), and also processes that affect composition and size, such as evaporation, condensation, and partitioning. Intra- and inter-zonal particle dispersion has been investigated using multiple calibrated PM monitors stationed at various distances and directions from point sources. A 1999 study investigated intra-zonal dispersion of incense particles in a home along horizontal distances of up to ~5 m, finding pronounced source proximity effects during the active combustion period in which fine PM concentrations within ~1 m of a source were approximately three times greater than those ~5 m from a source (i.e., in a central location in a house on the same floor) (McBride et al., 1999). Human activity (e.g., walking and moving) also affected the direction of particle movement and dispersion, suggesting that measurements in occupied versus unoccupied spaces would result in different outcomes for PM transport. In an investigation of both intra- and inter-zonal particle dispersion resulting from incense burning on the first floor of a three-story house in France, while particle concentrations were obviously higher in close proximity to the source, incense burning also increased particle concentrations throughout the second and third stories of the home, albeit with dilution, ventilation, and deposition offering some protective effects throughout the home (Ji et al., 2010). Recent advances in low-cost PM sensors have made it possible to deploy more monitors to investigate PM transport at higher spatial and temporal resolution following indoor generation from point sources than what was previously feasible (Lau et al., 2021; Li et al., 2018). Deployment of such monitors in homes has been used to illustrate that cooking emissions from kitchens can be detected in bedrooms sometimes within minutes and usually less than an hour following emission, depending on location; that PM concentrations were generally ~30 percent lower in bedrooms than in kitchens; and that the presence of interior partitions (e.g., walls, closed doors) delays transport from kitchen to bedrooms, with the fastest transport occurring in homes with no internal walls (Sankhyan et al.,

Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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.
×

2022). The extent to which transport from a point source to other indoor locations affects spatial indoor PM concentrations varies by house size, room size, direction of airflow, and other factors (e.g., Singer et al. 2017).

Inter-zonal transport can also act as a source of indoor PM from adjacent/neighboring units in multi-family buildings. A classic, often directly noticeable, example is secondhand smoke (SHS) transfer between adjacent units. King et al. (2010) found evidence of PM2.5 from SHS transporting from smoking-permitted units to smoke-free units in 2 of 14 (14 percent) smoke-free units in 11 multifamily buildings that were investigated. Bohac et al. (2011) investigated airflows between units in six multifamily buildings in Minnesota using passive perfluorocarbon tracer (PFT) gas tests and found that the median fraction of air entering a unit that came from other units ranged from ~2 percent in a new building to ~35 percent in a 1930s duplex. Air sealing retrofits helped reduce this fraction, on average. Although PM transport was not investigated, nicotine—a semi-volatile compound that strongly adsorbs to surfaces—transferred at a much lower rate than air alone. Thus, it is plausible that PM may also transfer at lower rates than air alone due to unit-to-unit penetration factors of less than 1 (e.g., Dacunto et al. 2014), but the committee is not aware of such investigations.

Finally, numerous physical, chemical, and biological processes such as aerosol aging, oxidation, evaporation, condensation, and partitioning interact to influence important indoor PM properties such as composition, size distribution, phase state, surface charge, and, for biological particles, viability. A detailed review of such processes is beyond the scope of this report, and such characterizations are often challenging to conduct in field measurements, but it is useful to have a high-level understanding of these mechanisms. For example, semi-volatile chemical species can undergo phase changes during outdoor-to-indoor transport and affect the resulting indoor PM2.5 concentrations and composition, subject to influences by indoor and outdoor temperature differences and the availability of indoor PM for sorption (Hodas and Turpin, 2014). Such phase changes can lead to losses of PM mass as it transports from cooler outdoor air to warmer indoor air and, conversely, gains of PM mass as warmer outdoor air transports into cooler indoor environments (humidity, and thus total enthalpy, as well as PM composition, also interact to influence the magnitude and direction of partitioning, but the above simplification is useful for illustration). Avery et al. (2019) provides further insight into how aerosol composition and indoor/outdoor temperature and humidity influence the concentration and composition of indoor PM of outdoor origin in a classroom. Transformations can also interact with sources and loss rates to affect both PM and gas-phase pollutant exposure. For example, reducing PM concentrations also removes sorption sites onto which semi-volatile organic compounds (SVOCs) can no longer partition, which may shift the fraction of SVOCs that are found in the particle phase into the gas phase (Liu et al., 2013; Lunderberg et al., 2019). Such phase and compositional changes may also influence the toxicity of indoor PM of ambient origin. For example, one 2021 study characterized the oxidative potential (OP) of indoor PM2.5 of ambient origin in an unoccupied apartment unit with doors and windows closed and found that the intrinsic (mass-normalized) OP was higher for indoor PM samples than for concurrent outdoor PM samples and that the extent of enhancement of intrinsic OP was correlated with differences in indoor and outdoor temperature and relative humidity (RH) (Zeng et al., 2021). Natural ventilation (airflow through open windows) has also recently been shown to alter the composition of indoor PM, for example by providing more PM surface area (from increased PM introduction from outdoors) for partitioning of semi-volatile compounds onto indoor PM (Fortenberry et al., 2019) and by temporarily altering SVOC removal processes (Kristensen et

Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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al., 2019). Recent work has also demonstrated that SVOCs from material sources directly partition to settled dust, which in turn affects SVOC exposure in resuspended particles (W. Bi et al., 2021), and that particles emitted from indoor sources (e.g., candles) enhance partitioning of gas-phase SVOCs to indoor particles, affecting the particle composition and enhancing surface off-gassing (Kristensen et al., 2023). The dynamics of partitioning of SVOCs to PM in indoor air is important since indoor environments tend to be much richer in specific SVOCs with known adverse health effects (e.g., endocrine disruption, cancer) than are found outdoors. These SVOCs include plasticizers, flame retardants, some pesticides, and per- and polyfluoroalkyl substances (PFAS) used to provide stain resistance on many indoor materials as well as use in other consumer products. Understanding the interaction of airborne and settled PM with these SVOCs and the impacts of such interactions on human exposure to these chemicals is important as new SVOCs are substituted for those being phased out.

Also worth noting, the chemical composition, pH, and surrounding RH conditions of human respiratory droplets (or surrogates of respiratory droplets) have also been shown to affect the viability of airborne viruses contained within PM (Ahlawat et al., 2022; Huynh et al., 2022; Lin and Marr, 2020; Lin et al., 2020). Such transformation processes are clearly important for influencing indoor PM properties but remain challenging to empirically assess in real-world environments.

SOCIOECONOMIC DISPARITIES IN INDOOR PARTICLE DYNAMICS

The extent to which socioeconomic disparities in individual source, sink, and transformation processes contribute to disparities in indoor PM exposure has not been explored in much depth in the literature, but there are several logical ways in which known socioeconomic differences in buildings and their occupants and their activities likely contribute to such disparities.

First, the concentration and composition of outdoor PM varies geographically, and such differences have been shown to be associated with socioeconomic status, age, and race/ethnicity. For example, concentrations of ambient PM2.5 (Miranda et al., 2011) and many of its chemical constituents (Bell and Ebisu, 2012) are higher in non-Hispanic Black populations than in White populations. Such racial disparities in ambient PM2.5 concentrations have been demonstrated at all income levels (Paolella et al., 2018) and, as noted in Chapter 3, can lead directly to similar disparities in exposure to indoor PM2.5 of ambient origin, holding all other factors constant.

Second, there are known differences in primary building characteristics that plausibly contribute to disparities in indoor PM2.5 sources, sinks, and transformations. For example, lower-cost homes tend to have lower airtightness (i.e., they have leakier building envelopes), which means they allow greater amounts of outdoor air infiltration (Chan et al., 2005, 2013) and thus greater amounts of outdoor pollutant entry (Stephens, 2015). Along these lines, one study found that higher predicted infiltration air change rates in residences were associated with increased risks of emergency department visits for asthma and wheeze associated with outdoor PM2.5 when ambient PM2.5 concentrations were below a certain level (Sarnat et al., 2013). Another study found that “variability in factors that influence the fraction of ambient PM2.5 that infiltrates and persists indoors (such as the air change rate) could possibly bias health effect estimates in study designs for which a spatiotemporal comparison of exposure effects across subjects is conducted” (Hodas et al., 2013, p. 573). As another example, larger homes have been associated with lower indoor particle concentrations (Klepeis et al., 2017), and home size scales with income (U.S.

Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Census Bureau, 2022). Similarly, one 2021 study observed that renters in multifamily housing units experienced a higher proportion of indoor PM2.5 concentrations from indoor sources than homeowners in either single-family or multi-family housing, suggesting that differences in indoor sources had less to do with housing type and more to do with socioeconomic factors (Chu et al., 2021). And of course, occupants of multi-family housing units can experience transport from adjacent units, whereas occupants of single-family housing units cannot (and lower income occupants are more likely to live in multi-family housing). Conversely, occupants of single-family housing units may have more entry points for ambient PM to infiltrate. Moreover, a higher prevalence of central air conditioning, which is also more prevalent in higher-income groups, has also been associated with a lower risk of mortality associated with ambient PM2.5 (Franklin et al., 2007).

Third, there are also known differences in human activities that plausibly contribute to disparities in indoor PM2.5 sources, sinks, and transformations. For example, window opening frequency has been shown to be an important predictor of the amount of ambient PM2.5 that enters and persists indoors (Allen et al., 2012). Until very recently, few studies of window opening behavior in homes had been conducted, with limited geographic coverage (El Orch et al., 2014; Johnson and Long, 2005). The first known nationwide survey of window opening behavior in U.S. homes was published in 2022 (Morrison et al., 2022); it found that approximately 44 percent of respondents said that at least one window was open prior to taking the survey. Greater window-opening frequency was associated with having a lower income, living in attached homes or apartments, renting, lack of air conditioning, and being Asian or Hispanic. Window-opening frequency was also different by geographic region; people living in the western and northern parts of the United States reported opening windows more frequently than those in the southeastern United States. Such rich information does not yet exist for schools, although there are more robust datasets available for offices, especially internationally (Fabi et al., 2012). Better understanding window-opening behaviors could lead to a better understanding of how window opening acts as a source of ambient PM and a loss for PM of indoor origin. Additionally, more frequent kitchen range hood use, which can lower occupant exposures to indoor PM from cooking sources, has been associated with higher income and education levels (Zhao et al., 2020). A recent nationally representative sample of residential range hood use in Canada found that only 30% of respondents who had mechanical ventilation devices over their cooktop surfaces reported regularly using their devices; more frequent use was associated with the device being vented to the outdoors (approximately two-thirds of the devices vented outdoors), having quiet operation or multiple fan speed settings, covering over half the cooktop, and having higher perceived effectiveness (Sun and Singer, 2023).

There are other plausible links between socioeconomic factors and the source and composition of fine PM indoors. For example, research suggests that smoking rates are higher in lower-income and lower-education populations (CDC, 2023) and exposure may be further exacerbated in these populations due to such factors as inadequate ventilation, poor building condition and maintenance, overcrowded living spaces, and lack of access to or information on air filtration and other technological and behavioral means of limiting PM. Such links, however, remain to be explored in depth.

Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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OBSERVATIONS AND RECOMMENDATIONS

There are two primary reasons for wanting to learn more about individual source, sink, and transformation processes in indoor environments. The first is to be able to understand and model population exposures to indoor PM at scale to extend the types of epidemiology studies that can be conducted. To do this, a broad and deep understanding of the presence and magnitude of many specific indoor source, sink, and transformation processes across the building stock is needed, akin to how ambient air quality models have advanced to yield estimates of local ambient PM concentrations at very high spatial resolution (Di et al., 2016, 2017; van Donkelaar et al., 2016). Such an understanding would make it possible to target appropriate practical mitigation measures for different contexts, and, specifically, to use practical mitigation to address exposure disparities.

The second reason is to be able to understand results from investigations of practical mitigation interventions within the context of the other mechanistic impacts on indoor PM that might exist in a study population. The aforementioned example of a building with an air cleaner operating and with windows open illustrates this need; results from an air cleaning intervention study in such a building under those operational conditions would lead to the conclusion that air cleaning did not have a significant impact on reducing indoor PM or on reducing health effects. However, the lack of impact would be due to competition from ventilation or the introduction of non-monitored outdoor air pollutants. To overcome such limitations, the research community needs to adopt a more “building-aware” epidemiological approach whereby research characterizing the effects of a practical mitigation approach provides the context of the mechanisms that affect fate, transport, and transformations of indoor PM (e.g., if an air cleaner intervention is done in homes/locations with other competing mechanisms like high air change rates/windows wide open, was that characterized and how?). In order to make this contextualization possible, clear, practical, and relatively low-cost monitoring approaches will be needed to identify and quantify important parameters that potentially affect the effectiveness of practical mitigation measures.

To date, there is a relatively strong body of literature and a deep fundamental understanding of the types of mechanistic processes that influence the fate, transport, and transformations of indoor PM. It is often more economically or practically feasible to model such processes than to measure them because of the significant requirements for equipment and labor to conduct field measurements, although the gap between measurements and models is closer for some processes than others. Models enable extrapolations from measurements that necessarily must occur in a limited number of buildings and conditions, and also enable simulated experimentation to assess possible impacts of mitigation efforts or other factors on exposures. The research community has also demonstrated the ability to observe previously unobserved mechanisms and to learn to quantify those mechanisms that are expected to exist. It is important for the research community to continue to build and maintain capacity for identifying, quantifying, and measuring new mechanisms for sources, sinks, and transformations of indoor PM as they arise and to subsequently understand the potential impacts of such mechanisms on the toxicity of indoor PM.

There is a narrower understanding of the magnitude and range of many individual source, sink, and transformation processes across the building stock and different types of buildings. Measurement approaches are often complicated, cumbersome, or invasive or require specialized (and expensive) equipment, so sample sizes are often very limited. Moreover, while a broad

Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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.
×

characterization of every mechanism across the building stock is not feasible or necessary, it remains to be understood what minimal information of indoor PM dynamics is needed to meaningfully improve understanding of the health effects of indoor PM exposure (e.g., by modeling exposures across the building stock) and the impacts of practical mitigation measures (e.g., by measuring their impacts in intervention studies).

In summary, then, the committee offers the following recommendations:

The indoor air research community should:

  • continue to build and maintain capacity for identifying, quantifying, and measuring new mechanisms for sources, sinks, and transformations of indoor PM as they arise and to subsequently understand the potential impacts of such mechanisms on the toxicity of indoor PM. This recommendation echoes two recommendations offered in the Why Indoor Chemistry Matters report: 6 – “[r]esearchers who study toxicology and epidemiology and their funders should prioritize resources toward understanding indoor exposures to contaminants, including those of outdoor origin that undergo subsequent transformations indoors” and 7 – “[r]searchers and their funders should devote resources to creating emissions inventories specific to building types and to identifying indoor transformations that impact outdoor air quality” (NASEM, 2022; p. 7). Such work is needed to gain a more complete understanding of the chemical complexity of the indoor environment and its attendant health implications. It should be noted, though, it is not the sole province of EPA—some falls under the responsibility of agencies like the Consumer Product Safety Commission or falls into a regulatory void where responsibility for action is unclear.
  • come to consensus on what minimal information on indoor PM dynamics is needed to meaningfully improve understanding of the health effects of indoor PM exposure, for example, by modeling exposures across the building stock for use in epidemiology studies.
  • adopt a more building-aware epidemiology approach whereby research characterizing the effects of a practical mitigation approach would need to also provide the context of the mechanisms (i.e., source, sinks, and transformations) that affect indoor PM. In order to enable this contextualization, research should explore what minimal information on indoor PM dynamics is needed to meaningfully improve understanding of practical mitigation measures for indoor PM. To do so, there is a specific need for clear, practical, and relatively low-cost monitoring approaches to identify and quantify important parameters that potentially affect the effectiveness of practical mitigation strategies.
  • identify the subsets of building types and locations that may be particularly vulnerable to high indoor PM exposures for occupants based on our understanding of the characteristics that influence the fate, transport, and transformation of indoor PM. The same reasoning that is used to specific susceptible populations of individuals for inclusion in a health or mitigation study could be applied to such identification of vulnerable building types and locations.
Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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Suggested Citation:"4 Particle Dynamics and Building Characteristics that Influence 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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 Health Risks of Indoor Exposure to Fine Particulate Matter and Practical Mitigation Solutions
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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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