Novel methods of characterizing the fate and transport of pollutants in residential and light-commercial buildings

Proposal for Ph.D. Dissertation for: Brent Stephens

The University of Texas at Austin Civil, Architectural and Environmental Engineering

Dissertation Committee Dr. Jeffrey Siegel, Dr. Atila Novoselac, Dr. Richard Corsi, Dr. Ying Xu, Dr. Rodney Ruoff

Abstract Residential and light-commercial buildings are large consumers of energy and represent the environments where humans spend the majority of their time and are most exposed to many airborne pollutants of both indoor and outdoor origin. Current test methods to characterize the fate and transport of pollutants in indoor environments are typically invasive and costly, thus the proposed work herein aims to develop novel methods to do so in a sample of buildings. Three main objectives exist within this proposal, including the development of novel methods to (1) evaluate HVAC systems as pollutant removal devices in residential and light-commercial buildings, (2) infer the nature of outdoor pollutant (particles and reactive gases) penetration into buildings using simple air leakage testing, and (3) measure air exchange rates using simple indoor-outdoor pressure differentials. This research will allow for new tools to evaluate several source and loss mechanisms of indoor pollutants.

1. Introduction

1.1. Background Buildings account for approximately 40% of the total amount of energy consumed in the United States, with essentially equal contributions from both residential and commercial buildings (DOE, 2009a). Low-rise residential and small commercial buildings are especially important because they dominate the existing building stock; over 70% of residential buildings in the U.S. are single-family dwellings (DOE, 2009b) and over 50% of commercial buildings are considered light-commercial with less than 5000 ft2 (465 m2) of floor area (DOE, 2009c). These environments are also important because Americans spend nearly 90% of their time indoors and nearly 75% of their time at home or in an office (Klepeis et al., 2001), thus exposure to airborne pollutants is often greater indoors than outdoors (e.g., Ott and Roberts, 1998; Jones, 1999). A wide variety of airborne pollutants exist inside buildings and have different adverse human health effects associated with their exposure. For example, increased airborne particulate matter is consistently associated with an increased risk of respiratory symptoms, cardiopulmonary mortality, and lung cancer (e.g., Pope et al., 2002; WHO, 2003; Pope and Dockery, 2006; Miller et al., 2007; Ostro et al., 2010). Moreover, a reduction in ambient fine-particulate air pollution (PM2.5: particles less than 2.5 µm in diameter, a criteria pollutant regulated by US EPA) has been linked to increases in life expectancy (Pope et al., 2009). Elevated ultrafine particles (<100 nm in diameter) have also been associated with increased total and cardio-respiratory mortality (e.g., Stölzel et al., 2007). As an example of a gas-phase pollutant, elevated ambient ozone concentrations (O3: an EPA-regulated reactive gas produced outdoors by complex photochemical reactions) have been associated with increases in morbidity and mortality (e.g., Bell et al., 2004) and exposure to byproducts of ozone reactions may also contribute to many adverse health effects (Weschler, 2006). There are also many indoor sources of these pollutants. Particles

1 are generated by cooking, burning cigarettes, candles, and incense, and many other activities (e.g., Afshari et al., 2005; Hussein et al., 2006; Wallace, 2006) and ozone is generated by photocopiers and laser printers (Destaillats et al., 2008) and portable ion generators sold as air cleaners (Weschler, 2000; Waring et al., 2008). To date, associations of these pollutants with adverse health effects have been made in large epidemiological studies using regional outdoor measurements of air pollutants. However, because Americans spend the majority of their time indoors, most of their exposure to these pollutants occurs inside buildings (O3: Weschler, 2006; PM: Meng et al., 2004; Sultan, 2007; Beko et al., 2008). Large cohort studies of adverse health effects from personal exposures (e.g., Cohen et al., 2009) are generally prohibitively expensive, and it has been suggested that appropriate models may be used to more accurately predict indoor exposures to pollutants of outdoor origin using outdoor concentrations made at central monitoring sites (e.g., Hering et al., 2007). Thus, to better predict (and minimize) human exposure to these airborne pollutants and any byproducts, it is important to understand the fate and transport of both indoor and outdoor pollutants within buildings, which is the overall focus of this proposed work. Figure 1 is a simplified schematic of the fate and transport of pollutants (particles and reactive gases, in particular) inside a typical residential or light-commercial building with a recirculating , ventilating, and air-conditioning (HVAC) system, absent of any indoor sources. Occupants are exposed to pollutants of outdoor origin only after they penetrate the via air exchange and overcome any losses by deposition (of particles) or reactions (of reactive gases) within the building shell. Once indoors, pollutant concentrations can be decreased by several mechanisms, including deposition to (or reactions with) indoor surfaces, exfiltration by mechanical ventilation or natural air exchange, or removal by HVAC systems or stand-alone air cleaning devices. These processes provide the foundation for this proposed work and are numbered on Figure 1 according to the proposed research objectives outlined in Section 1.2. Characterizing these various mechanisms in actual buildings can be challenging, thus this work aims to develop novel methods to do so. Other indoor fate and transport mechanisms exist outside of those included in Figure 1 (e.g., particle resuspension and coagulation, homogenous reactions of reactive gases, adsorption and desorption of gases), but are not the focus of this proposed work.

1a

Particles 1b

Reactive Gases 2 3

Figure 1. Simplified model of the fate and transport of pollutants in buildings in the absence of indoor sources. Investigations of the numbered mechanisms proposed in this work include (1a) operational characteristics of HVAC systems, (1b) control of particulate matter by HVAC filters, (2) penetration of ambient outdoor particulate matter and reactive gases, and (3) air and exfiltration by natural air exchange.

This proposed dissertation will be organized as five journal papers, each as a chapter related to at least one of the fate and transport mechanisms identified by the arrows in Figure 1, with introduction and conclusion chapters framing the work. The main thread throughout the work will be the development and

2 refinement of novel test methods for measuring the fate and transport of pollutants inside residential and light-commercial buildings. The first three papers (Objective 1) will focus on methods to measure pollutant removal mechanisms, including the effects of important HVAC system operational parameters (Objective 1a), particle removal efficiency by HVAC filters (Objective 1b), and the combined effects of operational parameters, HVAC filters, filter bypass, and leakage (Objective 1a and 1b). The final two papers will focus on methods to estimate how building envelope structures filter outdoor particles and reactive gases (Objective 2), and the ability to infer air exchange rates from indoor-outdoor pressure differentials (Objective 3).

1.2. Research Objectives The overall objective of this work is to develop and apply novel methods to characterize the transport and control of pollutants in residential and light-commercial buildings, with specific focuses on (1) the overall influence central HVAC systems (divided into two subsections), (2) the penetration of ambient outdoor pollutants, and (3) infiltration/exfiltration via air exchange. These three research objectives are summarized below, and described in further detail in Sections 1.4 and 2. Objective 1. The influence of HVAC systems in real environments. HVAC systems influence both energy use and indoor environmental quality in buildings. Centralized space conditioning has become nearly ubiquitous in U.S. buildings and HVAC systems are often relied upon to deliver clean air to occupied spaces, as the use of higher-efficiency HVAC filters is one way to reduce exposure to respirable particulate matter. HVAC filters are typically tested in laboratory settings but only a few investigations have tested their performance in actual field settings, with even fewer investigations performed in residential and light-commercial buildings. In addition, little is known about how HVAC systems actually operate in real environments. In Objective 1a, I intend to fully characterize a variety of operational characteristics measured using unique test methods in 17 existing residential and light-commercial HVAC systems in Austin, TX (using data collected with various filters installed in occupied buildings from a previous unrelated project). The results will provide insight to building scientists, modelers, and system designers interested in investigating energy consumption and in buildings. The utility of the test methods and data involved is that they can be generalized and applied by others to characterize a wider range of buildings in a wide variety of climates. In Objective 1b, I intend to develop and refine an in situ HVAC filter test method that can quantify the effects of the entire system (filters, filter bypass, ductwork deposition, and duct leakage) on the ability of the system to remove airborne particles. The utility of this method is that other researchers will be able to fully characterize the fate of indoor airborne particles in other similar indoor environments. Additionally, the results from Objective 1a can be combined with the results of Objective 1b to fully characterize the influence of HVAC systems and filters on particle fate and transport in residential and light-commercial buildings. Specific hypotheses in Objective 1 include (1) HVAC system cycling times will significantly diminish particle removal rates relative to continuous operation, (2) duct leakage and filter bypass will significantly alter particle removal rates in real environments, and (3) filter manufacturers under-estimate the useful life spans of HVAC filters. Objective 2. Penetration of ambient outdoor pollutants. Exposure to pollutants of outdoor origin often occurs indoors and the penetration of outdoor pollutants into buildings is governed by several factors, including the nature of leakage pathways in the building shell, air exchange rates (AERs), and both pollutant and building material characteristics. Ambient particle penetration field experiments have been conducted by only a few researchers, and are seldom performed in part because they are non- standardized, difficult, and costly. One opportunity to improve pollutant penetration methodologies is to use calibrated fans to simultaneously measure building air leakage. Standardized pressurization (i.e., blower door) techniques are widely used in residential and light-commercial building energy diagnostics to provide information regarding the amount and shapes of air leakage pathways in the building shell (e.g., ASTM E779). In Objective 2, I hypothesize (1) that significant and positive correlations can be

3 drawn between blower door test results and experimentally determined size-resolved particle penetration factors, and (2) that a previously developed particle penetration test methodology can also be expanded to reactive gases (e.g., ozone) and that both leakage characteristics and building envelope construction materials can be correlated with gas-phase penetration factors. I am aware of no such application of these methods reported in the literature. The utility of both of these correlations is that they can be used to infer the nature of ambient particle and reactive gas penetration indoors from easy and ubiquitous blower door test results and building envelope material characteristics, which may allow epidemiological studies to adopt more specific population exposures from ambient pollutant data. Additionally, the methods may be used to assess changes in the ability of a building to protect indoor environments from outdoor pollution after undergoing retrofit measures. Objective 3. Development of a novel air exchange rate test method. The infiltration of outdoor air into and out of buildings represents a concern for excessive energy use and can significantly impact concentrations of indoor pollutants. Tracer gas techniques (e.g., ASTM E741) provide the only direct measure of air exchange rates (AERs), but these tests require expensive gas sampling equipment and a high level of invasiveness, which makes long-term monitoring in occupied buildings challenging. Most short-term air leakage characterizations are made with flow meters and calibrated fans (i.e., blower doors) that estimate leakage at much larger pressure differences than those encountered during normal operation. In Objective 3, I intend to develop a novel method of monitoring long-term AERs in residential and light- commercial buildings. I hypothesize that long-term AERs caused by infiltration can be inferred by combining blower door data with continuously monitored indoor-outdoor static pressure differences, external surface pressures on a building, and climatic conditions (e.g., wind speed and direction and indoor-outdoor temperature differences). The utility of this method is that continuous long-term AER measurements will be able to be predicted from only a few short-term tests. Summary of research objectives. Research Objectives 1-3 are all related in some form to characterizing the fate and transport of pollutants inside buildings. Objective 1 mostly targets removal processes of indoor pollutants (specifically particles) by aiming to characterize the influence of HVAC systems on particle removal rates. Objective 2 targets a source of indoor air pollution (outdoor air pollution) by aiming to more easily characterize how both particles and reactive gases are transported indoors. Objective 3 targets both a source and removal mechanism of indoor pollutants by aiming to more easily measure long-term AERs (air exchange can both transport outdoor pollutants indoors and dilute indoor pollutants by transporting them outdoors). The work outlined in this proposal will add substantially to the body of knowledge and available test methodologies regarding the fate and transport of pollutants inside residential and light-commercial buildings.

1.3. Literature Review This section provides a review of literature relevant to the proposed work. It is divided into four sections. The first three sections follow the order of Research Objectives 1-3 and the fourth section describes how the proposed work furthers knowledge of, and methodologies related to, the fate and transport of pollutants in buildings.

1.3.1. Impacts of residential and light-commercial HVAC systems on energy and IAQ

1.3.1.1. HVAC System Operation Centralized space conditioning has become ubiquitous in U.S. buildings. Over 60% of existing and approximately 90% of newly constructed residential buildings in the U.S. use central forced air distribution systems for air-conditioning purposes (HUD, 2007) and approximately 20-25% of all light- commercial buildings in the U.S. use the same style of central air-conditioning systems as found in residences (EIA, 2006). The design and operation of HVAC systems greatly affects both energy consumption and indoor air quality; however, little is known about how residential and light-commercial HVAC systems actually operate in real environments.

4 Several studies have suggested that the field performance of air-conditioning systems often fails to compare to laboratory performance at standard conditions (e.g., James et al., 1997; Parker et al., 1997; Proctor, 1997; Proctor, 1998; Withers and Cummings, 1998; Downey and Proctor, 2002). Shortcomings in the field performance of air-conditioning systems has implications for both energy consumption and indoor air quality, as central HVAC systems are often relied upon to deliver clean air to occupied spaces. In addition, indoor air quality model input parameters such as airflow rates, temperatures, and operation times often come from ideal or design conditions (or are simply assumed) and may not accurately describe real environments (e.g., Thornburg et al., 2001; Riley et al., 2002; Siegel and Nazaroff, 2003; Klepeis and Nazaroff, 2006; Zhao et al., 2007; Zuraimi et al., 2007; Waring and Siegel, 2008). Previous field studies of smaller air-conditioning systems have been limited mostly to residential systems but have provided valuable insight to some operational characteristics, including indoor air-handler unit and outdoor unit operation, duct leakage, system operating pressures, and (to a lesser extent) fractional runtimes. Airflow and recirculation rates. The performance of an air-conditioning system is in part dependent on the airflow rate through the system. Low airflow rates result in degradation of cooling capacity and increased energy consumption (Parker et al., 1997). Excessive airflow rates result in inadequate moisture removal and decreased indoor environmental performance (Li and Deng, 2007). Manufacturer-recommended airflow rates for smaller systems are typically 169 to 193 m3 hr-1 per kW of capacity (350-400 CFM ton-1), although a wide range of airflow rates (usually lower than manufacturer- recommended rates) have been measured in real installations (e.g., Parker et al., 1997; Proctor, 1997; Stephens et al., 2010). One key parameter for indoor air quality purposes is the recirculation rate, which is defined as the HVAC volumetric airflow rate divided by space volume. The product of the recirculation rate and in-duct air cleaner efficiency can be directly compared to other loss mechanisms including air exchange rates and deposition to surfaces or homogeneous reactions. Recirculation rates are a function of system airflow rates, house volume, and fractional operation times, and values used in models and experiments in the literature range from 4 to 24 hr-1 (Thornburg et al., 2001; Riley et al., 2002; Klepeis and Nazaroff, 2006; Zuraimi et al., 2007; Waring and Siegel, 2008). More information is needed on airflow rates and recirculation rates in residential and light-commercial buildings. Duct leakage. Ductwork is often located outside of conditioned space and unintentional duct leaks can have large associated energy penalties (e.g., Jump et al., 1996; Siegel et al., 2000; O’Neal et al., 2002; Francisco et al., 2006). Return duct leaks may be sources of indoor air pollution, operating under negative pressure and sucking in pollutants from outdoor spaces. Parker et al. (1993) simulated air distribution systems in residential buildings and estimated that the combination of air leakage and in ductwork located in unconditioned attics could increase summertime peak electricity consumption more than 30%. In one field study, Jump et al. (1996) reported an average decrease in HVAC energy use of 18% in houses that were tested before and after duct retrofitting (ranging from 5% to 57%). Although the energy impacts of duct leakage have been modeled and measured, the indoor air quality impacts are not currently well characterized. Fractional operation times. Residential and light-commercial air-conditioning systems typically cycle on and off to meet the cooling load of the building and the frequency of system runtime affects both energy and IAQ. Longer fractional operation times (i.e., duty cycle) can remove more indoor pollutants by increasing the amount of contact time that indoor air has with in-duct air cleaners. However, the longer a system operates the more energy it consumes. Little is known about how often cycling systems actually operate in real environments. Previous investigations have traditionally either assumed values for fractional operation times (Thornburg et al., 2001; Klepeis and Nazaroff, 2006; Waring and Siegel, 2008) or estimated them from energy models (Macintosh et al., 2010). Thornburg et al. (2004) is the only recently published study of which I am aware that measured the fractional runtime of residential heating and cooling systems. They summarized 182 days worth of heating and cooling operation data from a study of 26 homes in North Carolina and 33 days worth of cooling operation data from a study of 9 homes in Florida. Average air-conditioner duty cycles were 6% (std. dev. 5%) and 21% (std. dev. 11%) in the NC and FL homes, respectively. It was not clear whether duty cycles were typically high enough to

5 effectively decrease indoor pollutant levels and that additional data are needed to characterize ranges of fractional operation times.

1.3.1.2. HVAC filter test methodologies One key pollutant removal mechanism in buildings is the HVAC filter, which is typically tested for particle removal efficiency only in laboratory settings. Hanley et al. (1994) first reported on a laboratory apparatus and test procedure to quantify the fractional filtration efficiency of in-duct air cleaners. The work was later adapted by the American Society for Heating, Refrigerating and Air- Conditioning Engineers (ASHRAE) as a basis for the most widely used standard for measuring filter efficiency in the United States, ASHRAE Standard 52.2-2007 (ASHRAE, 2007). ASHRAE Standard 52.2 details a test procedure to determine two air-cleaner performance characteristics of importance: size- resolved particle removal efficiency and resistance to airflow (pressure drop), and assigns a Minimum Efficiency Reporting Value (MERV) to the tested air-cleaner based on the results. The test procedure uses an aerosol generator to generate polydisperse solid-phase particles from an aqueous potassium chloride (KCl) solution and measures particle concentrations (in 12 size ranges between 0.3-10 µm in diameter) upstream and downstream of the air cleaner using either optical or aerodynamic particle counters. Size- resolved particle removal efficiency is calculated by subtracting the average ratio of downstream-to- upstream particle concentrations from unity. Standard 52.2 acknowledges that the test method involves particle concentrations and compositions, airflow rates, pressure drops, and temperature and levels that are almost certain to be different from those that the air cleaner will encounter when installed in a real system, which raises questions about how HVAC filters actually perform in real buildings. In addition, particle removal efficiency has been shown to change in time with filter dust loading (Hanley et al., 1994; Hanley et al., 1999). Only a few recent studies have reported HVAC filter performance in actual field settings and have used a variety of metrics. The in situ performance of air-cleaners (and other HVAC components that may remove particles) has been quantified in two primary ways: 1) by measuring concentrations upstream and downstream of the air-cleaner or component in question, or 2) by measuring the overall particle loss rate with and without the addition of an operating air-cleaner or component. Upstream-Downstream Methods. Burroughs and Kinzer (1998) tested filter efficiency by measuring 0.3-10 µm particles (in six bins) upstream and downstream of HVAC filters using laser diffraction counters in 3 homes with central air-conditioning units. Fugler et al. (2000) determined the upstream/downstream removal efficiency of 10 commercially available HVAC filters (from ordinary filters to electrostatic (ESP) and HEPA filters) in six occupied houses using laser particle counters measuring 0.5-5+ µm particles. Clean air delivery rates (CADRs) were calculated by multiplying removal efficiency by the measured airflow rates. Jamriska et al. (2000) investigated the effects of filtration and ventilation on the reduction of submicron (16-626 nm) particle concentrations upstream and downstream of the coil, filter, and several places in the ducts and main zones in a large office building using a scanning mobility particle sizer (SMPS) with an electrostatic classifier and a condensation nucleus counter. ASHRAE Guideline 26 (2008) provides instructions for a test procedure that measures the in situ size-resolved upstream/downstream removal efficiency (of 0.3-5 µm particles) and pressure drop of HVAC filtration devices, components, and the entire system (ASHRAE, 2008). The guideline recommends using optical particle counters because they are currently the most convenient and most commonly used instruments for these measurements. Whole-house Methods. Offermann et al. (1992) evaluated the performance of six in-duct air cleaners (no filter, followed by two types of coarse fiber filters, two types of extended surface filters, and two types of ESPs) in a forced-air HVAC system using a whole-house methodology in an IAQ research house. Cigarette smoke was used as a test aerosol in order to elevate indoor particle concentrations and the whole-house decay rates were measured with and without an air-cleaner installed in the air-handling unit (AHU). Total particle concentrations (0.01-0.3 µm) were measured with a condensation nucleus counter in conjunction with an electrostatic classifier and 0.1-3.0 µm particles were measured with an optical particle counter. The authors calculated an effective cleaning rate (ECR, similar to a CADR) for

6 each air-cleaner as the observed increase in particle decay rate with the air cleaner operating, multiplied by the volume of indoor air. Total HVAC system efficiency was calculated by dividing the ECR by the airflow rate through the AHU. More recently, Howard-Reed et al. (2003) measured the effects of HVAC filters on decay rates of 0.3-10 µm particles in an occupied townhouse and an unoccupied test house using an optical particle counter. Particle concentrations of different sizes were elevated by cooking with a gas stove, burning a citronella candle, and pouring cat litter into the HVAC return. The subsequent decay of particle concentrations was measured with the HVAC fan off and with the fan on during three HVAC filter test conditions (no filter, a standard furnace filter installed, and an ESP installed). A time-varying number balance equation was used to estimate total size-resolved particle decay rates caused by infiltration, deposition, and filtration combined. Air exchange rates were measured independently using a tracer gas and subtracted from total particle decay rates to estimate the loss rate by deposition and filtration alone. The authors did not report upstream/downstream efficiencies, nor did they attempt to calculate whole-house removal efficiencies as in Offermann et al. (1992). Wallace et al. (2004) extended the work of Howard-Reed et al. (2003) by investigating the deposition rates of fine and ultrafine particles (0.01-2.5 µm) in an occupied townhouse using an aerodynamic particle sizer and a scanning mobility particle sizer with a differential mobility analyzer linked to a condensation particle counter. Optical particle counters were also used to determine the single-pass efficiency of the filters (for 0.3 to 10+ µm particles) by measuring upstream and downstream concentrations in the duct. The authors did not compare their findings to any filter ratings and did not attempt to calculate efficiency from the measured decay rates. Most recently, Macintosh et al. (2008) developed a test method for determining the whole house particle removal rates and CADRs for both in-duct and portable air-cleaning systems. They tested four in- duct cleaners (a conventional MERV 2 filter, a MERV 8 filter, a conventional electrostatic air cleaner, and an intense field dielectric electrostatic air cleaner) and three portable cleaners (two HEPA models and an ion generator) in a manufactured test house. Particle concentrations were elevated by aerosolizing a fine test dust standard with an acoustical generator and the subsequent decay of 0.3-20 µm particles was measured using both optical particle counters and an aerodynamic particle sizer installed in a central location. Number and mass balance equations were fitted using a first-order decay function in order to estimate particle removal rates with the air cleaners installed (similar to Howard-Reed et al., 2003, and others). Upstream-downstream particle removal efficiencies were also measured by sampling upstream at the AHU return and downstream at a supply register. Advantages and disadvantages of upstream-downstream methods. Measuring filter efficiency by the upstream-downstream method has its advantages: it is a relatively quick procedure to perform and it isolates the effects of the filter alone. It can also be extended to other sections of the HVAC system to measure the removal efficiency of other components. However, some challenges exist in accurately performing the test method. First, either two particle counters are required to measure upstream and downstream concentrations (which requires accurate calibration of the instruments), or a switching valve must be used (which may introduce additional sampling losses). Second, physical access to locations immediately upstream and downstream of the filter in a real HVAC system is not always easy or even possible. Access constraints may also increase the requirements for sampling line length, which can increase particle losses before reaching the counters. Third, isokinetic sampling is not trivial to achieve inside of an HVAC system; non-isokinetic sampling can lead to inaccurate representations of different particle sizes. Fourth, the sampling location along a duct or AHU can lead to errors because of unmixed flow and large amounts of turbulence located near blower fans. Finally, the upstream-downstream method does not provide any further information about particle interactions in a real environment (e.g., deposition to surfaces). Advantages and disadvantages of whole-house decay test methods. Advantages of whole- house test methods are that they can capture the effects of the entire HVAC system (including filters, deposition to ducts and other components, losses and gains by duct leakage, and filter bypass airflow) and can fully characterize indoor particle dynamics in an environment. However, attempts to perform accurate whole-house decay methods are met with some challenges. First, whole-house number or mass balance

7 decay approaches have generally required assumptions of complete mixing in the environment (an assumption that is rarely tested) and a negligible influence of outdoor particles. Second, multiple instrumentation is required to measure both particles and tracer gas at the same time. Third, if one wishes to calculate filter or system efficiency from removal rates (in units of inverse time), both the airflow rate through the air-cleaner and the volume of the space must be known. Measuring HVAC airflow rates is a nontrivial test in itself. Offermann et al. (1992) estimated system airflow rates for each filter condition indirectly by measuring the pressure drop across the fan and referencing the manufacturer’s fan calibration curve. Howard-Reed et al. (2003) measured duct air velocities using a hot wire anemometor and converted the values to a volumetric airflow rate by multiplying by the area of the ductwork. MacIntosh et al. (2008) simply assumed that airflow rates remained constant with each filter. Fourth, the whole-house method is generally time intensive. Single test durations were 4-5 hours in Offermann et al. (1992) and were reduced to approximately 2 hours in MacIntosh et al. (2008). Previous investigations on the effects of HVAC systems and filters on indoor particle dynamics often remain limited, due in part to the difficulty, duration, and expense of test methods. In addition, researchers often have not included all parameters of interest. Whole-house deposition rates are often reported without filter efficiencies (either measured upstream and downstream or back-calculated), comparisons are seldom made to manufacturer-reported test values (which also change in time with filter standards), system airflow rates are seldom measured accurately, and duct leakage or filter bypass is seldom well characterized.

1.3.2. Penetration of ambient outdoor pollutants Outdoor pollutants are transported indoors via air leakage through both intentional and unintentional openings in the building envelope (Siren, 1993). Particles and reactive gases can deposit on or react with materials within the building envelope, but the process is dependent on several factors including the geometry of openings, the amount of airflow through the openings, particle size, gas-phase pollutant reactivity, and the reactivity and surface characteristics of the envelope materials. The next sections describe relevant literature first on particle penetration, then on reactive gas penetration. 1.3.2.1. Particulate matter Particles of different sizes interact with cracks in the building envelope in different ways. Ultrafine particles (<0.1 µm) deposit on crack walls by Brownian motion, coarse particles (>1 µm) tend to impact or settle by gravitation within envelopes, and mid-sized accumulation mode particles (0.1-1 µm) are generally not dominated by either force (Liu and Nazaroff, 2001). Investigations on the penetration of outdoor airborne particulate matter have generally occurred in four forms: modeling efforts, laboratory measurements, measurement of indoor-outdoor concentration ratios, and more recently, specific particle infiltration methods. A recently published paper presents an extensive review of many of these studies, as well as a few others (Chen and Zhao, 2011). Modeling. Liu and Nazaroff (2001) modeled size-resolved particle penetration through building envelope structures. They predicted that 0.1-1.0 µm particles would have the highest penetration efficiency through idealized smooth rectangular cracks in a structure, while the envelope would significantly filter super-micron (>1 µm) and ultrafine (<100 nm) particles by means of gravitational settling and Brownian diffusion, respectively. The magnitude of penetration depends on pressure differences across the crack and crack geometry. For wall cavities, they predicted that fiberglass insulation would act as a perfectly efficient particle filter for all particles regardless of pressure difference. Laboratory. Mosley et al. (2001) performed particle penetration experiments in a two- compartment chamber that was separated by a partition with idealized horizontal slits cut into a window to simulate leakage paths. Particles (0.05-5 µm) were generated in one compartment and transported to the other compartment via airflow induced by an applied pressure differential. At a pressure of 2 Pa, only 2% of 2 µm particles and 0.1% of 5 µm particles penetrated the building shell. At 10 Pa, 85% of 2 µm particles and <1% of 5 µm particles penetrated the envelope structure. Liu and Nazaroff (2003) performed similar experiments of 0.02-7 µm particle penetration through rectangular slots cut into common building materials (aluminum, brick, concrete, plywood, lumber, and oriented strand board). They found that

8 particle size and crack height were the two main factors that governed particle penetration. The penetration factor was near 1 for 0.1-1.0 µm particles for > 0.25 mm crack heights and pressure differences > 4 Pa. However, idealized laboratory penetration tests are only moderately useful as particle penetration is likely to diminish for non-ideal cracks in real buildings with significant surface roughness and irregular geometry. Indoor-outdoor ratios. Several researchers have measured indoor and outdoor particle concentrations in real buildings using a variety of instruments to measure number or mass concentrations of a variety of particle sizes, often simultaneously with AERs. A basic time-varying mass or number balance on an indoor space with no indoor sources is shown in Equation 1. Some used a physical- statistical model on steady-state data measured during periods of no indoor sources in order to estimate either infiltration factors (IFs, which take into account penetration, AER, and particle deposition rates, as shown in Equation 2) or penetration factors (e.g., Fogh et al., 1997; Abt et al., 2000; Long et al., 2001; Williams et al., 2003; Bhangar et al., 2010). dCi,in = P ! ! !Ci,out "(! + ")Ci,in (1) dt

Ci,in P ! ! IF = = (2) Ci,out ! + " 3 where Ci,in = indoor concentration of particles of diameter i (mass or number per m ), Ci,out = outdoor concentration of particles of diameter i (mass or number per m3), P = penetration factor (-), λ = air exchange rate (hr-1), and β = particle deposition rate (hr-1). Abt et al. (2000) reported that IFs showed wide variability between 4 homes but generally decreased with increasing particle size, ranging from 0.38 to 0.94 for 0.02-0.5 µm particles and from 0.12 to 0.53 for 0.7-10 µm particles. Long et al. (2001) reported that the penetration efficiency of 0.02-10 µm particles in 9 homes ranged from ~0.2 to >0.9 and depended on both particle size, season, and home characteristics, and that it may not be reasonable to assume one infiltration factor in a population exposure assessment since building characteristics, seasonal home dynamics, and ambient conditions may affect the ability of particles to penetrate indoors and remain suspended. Williams et al. (2003) analyzed particulate matter data (PM2.5 mass) from 37 residences and reported an average penetration efficiency of 0.72 (std. dev. 0.21) across all homes, but with considerable variability (from 0.11-1.0) across individual homes. Bennett and Koutrakis (2006) reported IFs of seven Boston-area homes ranging from 0.24 to 0.86, depending on the home and particle size of interest. Bhangar et al. (2010) reported IFs of ultrafine particles (6-100 nm) in 7 California residences ranging from 0.11 to 0.47, with operating HVAC systems (and filters) lowering IFs by a factor of 2-4. Other researchers have used well-mixed mass or number balance models to estimate penetration factors from time-varying concentration data (e.g., Vette et al., 2001; Chao et al., 2003; Zhu et al., 2005; Hussein et al., 2006). AERs were always measured in order to account for exfiltration losses in their models. Vette et al. (2001) reported that penetration factors ranged from ~0.5 to 0.9 for 0.01-2.5 µm particles at a single occupied residence, using nighttime data assumed to be source-free periods. Chao et al. (2003) estimated penetration factors in six naturally ventilated high-rise apartments using a steady- state number balance, reporting average penetration factors across the six apartments ranging from ~0.6 (std. dev. ~0.3) for 0.02-1.0 µm particles, to ~0.7-0.8 (std. dev. ~0.2) for 0.5-2.5 µm particles, to ~0.5 (std. dev. ~0.3) for 2.6-10 µm particles. Zhu et al. (2005) reported relatively constant penetration factors of ~0.5 for 0.02-0.2 µm particles at four apartments, with declining average penetration factors for smaller particles, down to <0.2 for <0.01 µm particles. Hussein et al. (2006) estimated size-resolved penetration factors and deposition rates during periods of no indoor sources at an occupied house without a mechanical ventilation system. Average penetration factors ranged from 0.4 for ~0.015 µm particles to ~0.8 for 0.1-0.3 µm particles, and showed similar results between measurements taken in the kitchen and the living room. Wallace and Williams (2005) performed a sulfur-based method (Sarnat et al., 2002) to calculate infiltration factors for penetration of PM2.5 mass into a sample of 36 homes. Median IFs were 0.55

9 (ranging from 0.26 to 0.87) across all homes, and was lowest in the summer when air-conditioning was in use. However, the sulfur-based method was thought to over-predict infiltration factors for PM2.5 because sulfur particle are <0.5 µm and the upper range of PM2.5 is likely to have smaller penetration efficiencies. Targeted penetration methods. Thatcher and Layton (1995) measured size-resolved particle concentrations (0.3-25+ µm in diameter) indoors and outdoors at a two-story residence, while simultaneously measuring AER with tracer gas decay. They determined size-resolved deposition rates of particles by artificially elevating indoor aerosol concentrations and solving for the subsequent decay rate, and subtracting out the AER. Size-resolved penetration factors were estimated using steady-state indoor- outdoor concentration ratios, measured AER, and the previously estimated decay rate in conjunction with Equation 2. Penetration factors for this house were approximately 1 for all particle sizes reported. Thatcher et al. (2003) performed size-resolved particle measurements (0.1-10 µm) indoors and outdoors at two houses, while simultaneously measuring AER by tracer gas decay. A three-part experimental approach was used to 1) measure the decay rate of particles after an artificial concentration elevation (particle elevation was achieved by igniting a natural gas burner and performing vigorous resuspension activities), 2) rapidly reduce particle concentrations below background levels by introducing HEPA-filtered outdoor air, and 3) measure the subsequent particle concentration rebound period in order to obtain the whole-house penetration factor. Estimated penetration factors ranged from ~1 for 0.1 µm particles to ~0.3 for 10 µm particles. Most recently, Rim et al. (2010) measured IFs, penetration factors, and deposition rates of ultrafine particles (<0.1 µm) at an unoccupied test house during two conditions: 1) closed windows and 2) with a window open approximately 7.5 cm. The penetration factor increased from ~0.2 for 0.01 µm particles to an asymptote of ~0.6 for 0.03-0.1 µm particles with closed windows and ranged from 0.6 to 0.8 across all particle sizes with the window open. These two studies provide much of the motivation for the proposed work to correlate particle penetration to air leakage tests in buildings, as detailed further in Section 2.1. All of the previously mentioned studies are summarized in Table 1.

Table 1. Summary of particle penetration efficiencies (P) and infiltration factors (IF) from residential studies Source Location P or IF Value (-) Particle Sizes Thatcher and Layton (1995) 2 homes P ~1.0 0.1-10 µm 0.38-0.94 0.02-0.5 µm Abt et al. (2000) 4 homes IF 0.12-0.53 0.7-10 µm Long et al. (2001) 9 homes P ~0.2-0.9+ 0.02-10 µm Vette et al. (2001) 1 home P ~0.5-0.9 0.01-2.5 µm 0.6 ± 0.3 0.02-1.0 µm Chao et al. (2003) 6 apartments P 0.7 ± 0.2 0.5-2.5 µm 0.5 ± 0.3 2.6-10 µm ~0.7-1.0 0.2-1 µm Thatcher et al. (2003) 2 homes P ~0.3-0.9 3-10 µm

Williams et al. (2003) 37 homes P 0.72 ± 0.21 PM2.5 mass <0.2 <0.01 µm Zhu et al. (2005) 4 apartments P 0.5 0.02-0.2 µm 0.49 0.02-0.03 µm Bennett and Koutrakis (2006) 7 homes IF 0.76 0.2-0.3 µm 0.32 4-6 µm 0.4 0.015 µm Hussein et al. (2006) 1 home P ~0.8 0.1-0.3 µm ~0.2 0.01 µm Rim et al. (2010) 1 home P ~0.6 0.03-0.1 µm 0.6-0.8* 0.01-0.1 µm Bhangar et al. (2010) 7 homes IF 0.11-0.47 0.006 - 0.1 µm *Penetration efficiency increased for all particle sizes with a window purposefully opened.

10 1.3.2.2. Reactive gases (ozone) Much less work fundamental work has been done on the penetration of reactive gases into indoor environments. Some modeling has been done and indoor-outdoor ratios and indoor deposition losses have been measured (Lee et al., 1999), but I am not aware of any experimental work performed specifically on the penetration of reactive gases indoors performed in the laboratory or in actual buildings. Modeling. Liu and Nazaroff (2001) modeled reactive gas penetration through idealized rectangular cracks and fiberglass insulation-filled cavities in building envelopes and found penetration to depend on both crack geometry and the reactivity of crack surfaces, as parameterized by a material’s reaction probability with the gas. They predicted that ozone penetration through fiberglass insulation could vary from >90% to ~10-40%, depending on the reactivity of the fiberglass fibers. Walker et al. (2010) applied the modeling methods of Liu and Nazaroff (2001) to assess the impacts of different ventilation systems on indoor ozone concentrations and reported that mechanical ventilation systems in conjunction with common building envelopes should typically reduce indoor ozone concentrations by 80- 90% relative to outdoors. Indoor-outdoor measurements. Indoor ozone concentrations are a function of AER, penetration through the building envelope, the overall ozone decay rate (i.e., the average deposition velocity to interior surfaces), and any indoor sources or homogenous reactions. Weschler (2000) summarized most of the known measured indoor-outdoor ozone concentration ratios reported in the literature from various types of buildings. Indoor-outdoor ratios range from 0.05 in tightly sealed buildings or in those that use activated carbon HVAC filtration to 0.85 in mechanically ventilated buildings with very high air exchange rates. Limiting reported I/O ratios to large residential studies, Avol et al. (1998) and Romieu et al. (1998) found average I/O ratios of 0.37 ± 0.25 and 0.20 ± 0.18 in 126 homes in the U.S. and 145 homes in Mexico, respectively. In a smaller study, Zhang and Lioy (1994) reported average I/O ratios ranging from 0.22 ± 0.09 to 0.62 ± 0.11 in six homes in New Jersey, depending on ventilation rates and indoor gas combustion. In the absence of indoor sources and homogenous reactions, steady-state estimates of I/O ratios can be made using Equation 2, but replacing P with an ozone-specific penetration factor and β with an overall ozone deposition rate to surfaces. Weschler (2006) reported that P is usually assumed to be equal to 1, but that the assumption remains largely untested. Lee et al. (1999) artificially elevated indoor ozone concentrations in 43 homes in the U.S. and solved a mass-balance on the subsequent concentration decay period, finding average ozone removal rates (β) of 2.8 ± 1.3 hr-1. The wide spread in measured I/O ratios and deposition velocities of ozone may be attributed to differences in a combination of AERs, furnishings, and building envelopes, although the penetration of ozone or any other reactive gases remains largely untested in any setting.

1.3.3. Air exchange rate methodologies Residential and light-commercial buildings in the U.S. are seldom mechanically ventilated and typically rely on infiltration of outdoor air through unintentional openings and cracks in the building envelope to provide air exchange. AERs can have competing effects on energy consumption and indoor air quality. High AERs introduce more unconditioned outdoor air that may require more energy to heat or cool, but if outdoor air is clean relative to indoor air, greater amounts of outdoor air can dilute indoor pollutant concentrations. AER is one of the most basic and important parameters required to be able to quantify the amount of air infiltration into or exfiltration out of buildings for most testing purposes. Tracer gas techniques generally provide the only direct measure of actual AERs, but these tests require expensive and invasive gas sampling equipment. Most leakage measurements in buildings are made with blower-doors that estimate leakage at much larger pressure differences than those encountered during normal building operation (Sherman, 1995). Large databases exist on the inter-home variability of blower-door test results (Chan et al., 2005). However, problems usually exist in extrapolating high- pressure leakage data to the low operating pressures of interest. Large databases also exist on the inter- home variability of AERs (Murray and Burmaster, 1995), but much less is known about long-term intra- home variations in AERs.

11 Wallace et al. (2002) measured AERs in an occupied townhouse for one year with a tracer gas method and demonstrated that AERs correlated with window openings, exhaust fan operation, indoor- outdoor temperature differences, and wind speed and direction (all of which can manifest themselves as a pressure difference). Figure 2 shows the cumulative distribution of the approximately 4,600 tracer gas decay measurements taken in Wallace et al. (2002). The average AER measured in the townhouse was 0.65 hr-1 with a standard deviation of 0.56 hr-1 (and a range from nearly 0 to over 4 hr-1). Indoor-outdoor temperature differences played a much more important role in determining AERs in this townhouse than wind, but this may have been because of house construction or local wind shielding impacts. Many researchers have fundamentally investigated the interaction of climatic conditions on AERs in buildings, often focusing on accurately modeling AERs from input parameters such as indoor-outdoor temperature differences (which drive pressure differences by the buoyant ), wind speed and direction (which drive pressure differences on the exterior of the building), and the spatial distribution of leakage sites. Sherman and Modera (1986) compared AERs measured using tracer decay tests to those predicted by an infiltration model in 15 houses; the model yielded correct results to within approximately 20%. Walker and Wilson (1993) Figure 2. Cumulative distribution of AERs measured in summarized the accuracy of several existing a townhouse by Wallace et al. (2002). infiltration models using hourly-averaged tracer gas tests in two test houses, isolating the data into periods of stack- and wind-dominated AERs. The best performing models predicted actual AERs within approximately 10% on average. Wang et al. (2009) conducted blower-door depressurization tests in conjunction with multiple AER tests using a tracer decay method under a wide range of weather conditions in 16 detached houses. They compared measured AERs to those predicted by two single-zone models (AIM-2 and LBNL) that rely on meteorological conditions and an estimation of a house’s leakage distribution; average errors were within 25% for both models. Most recently, Breen et al. (2010) used questionnaires about window openings by occupants and meteorological data to incorporate information into infiltration models and predicted residential AERs to within 40-50% (absolute differences). Unfortunately, most of these models rely on specific inputs that cannot be measured directly, such as the height of neutral pressure, height of the highest and lowest leaks in the envelope, fractions of leakage areas in the floor, ceiling, and walls, and shielding coefficients and terrain parameters. All of these methods rely in some form on accurately estimating the pressure differentials introduced by temperature differentials and wind velocity, but I am not aware of any attempts to correlate measured indoor-outdoor pressure differentials directly with AERs as I plan to do in Objective 3.

1.3.4. How the proposed work furthers knowledge of the fate and transport of indoor pollutants In general, previous work on the fate and transport of pollutants inside residential and light- commercial buildings has been difficult and costly and many studies have neglected important building details. For example, very few outdoor pollutant penetration studies have been performed due to their experimental challenges, and researchers focused on particulate matter or gas-phase pollutants have often neglected to investigate the influence of building characteristics. Similarly, researchers that investigate building energy and air leakage characteristics often neglect to investigate individual pollutant behavior in detail. This work aims to close some of those gaps. The work in Objective 1 will characterize the operation of HVAC systems in which filters are installed (Objective 1a), develop and refine an HVAC filter test method that will allow for comparisons between field tests and lab tests, as well as the investigation of previously uncharacterized particle transport pathways such as duct leakage and filter bypass airflow (Objective 1b). Objective 2 will more fundamentally approach pollutant penetration across

12 building envelopes that may allow for the application of air leakage details to infer pollutant penetration of the entire existing building stock. Finally, the work in Objective 3 will develop a method to assess the long-term variations in AERs in buildings without the need for invasive tracer decay tests.

1.4. Results to Date To date, investigations into Objectives 1a and 1b have been performed, while preliminary work has been done for the ideas in Objectives 2 and 3. This section provides a brief review of the major results obtained from Objective 1 thus far and the subsequent section (Section 2) describes methodology for future research into Objectives 2 and 3.

1.4.1. Influence of HVAC systems on energy and IAQ (Objective 1)

1.4.1.1. Residential and light-commercial HVAC operation (Objective 1a) The work to date in Objective 1a used a previously collected dataset to characterize a variety of operational parameters measured in 17 existing residential and light-commercial air-conditioning systems in Austin, Texas, operating only in the cooling mode. Eight of the systems were located in occupied residences and nine were located in occupied light-commercial buildings. The air-conditioning systems were previously monitored in order to identify the potential energy implications of higher efficiency air filtration; generally, the energy impacts of filters were minimal and a wide variety of climate conditions and occupant settings heavily influenced the results (Stephens et al., 2010). The test sites were visited once a month for a year, during which time three categories of filtration efficiency typically used in residential and light-commercial systems were installed. Approximately 114 days (totaling over 3100 hours) of air-conditioning operation were recorded and several parameters were characterized from the data and discussed in the work in Objective 1a. Comparisons were made between actual operation and design or manufacturer-recommended values, as well as between measured parameters and literature values. For example, measured airflow rates were lower than manufacturer-recommended values in the majority of the systems. Interestingly, they did not generally diminish drastically with three months of filter loading, contrary to what filter manufacturers suggest. The average recirculation rate across all systems was approximately 1.5 hr-1 after accounting for average fractional operation time (only 21%), which is still enough to make HVAC airflow competitive with the air exchange rate as a loss mechanism in most homes (with an average AER of 0.5 hr-1), depending on filter efficiency. Outdoor condenser- unit power draws increased 1.8 ± 0.8% per °C increase in outdoor temperature, on average, and cooling capacities were consistently more than 30% lower than rated capacities. The median increase in fractional runtimes (i.e., duty cycles) was approximately 6% per °C increase in hourly indoor-outdoor temperature differential and the median thermostat setting was approximately 25-26°C, with an interquartile range of approximately 23-27°C. Increased system runtimes were most associated with lower average indoor endpoint temperatures and greater fractions of supply duct leakage. The results of this work provide insight into the ranges of actual residential and light-commercial HVAC operational parameters for other building scientists, modelers, and system designers to use. Of particular interest, Figure 3 shows the average hourly fractional operation times measured in both residential and light-commercial buildings during each day of cooling operation by the test systems, along with corresponding average hourly outdoor temperatures. Operational times generally trend with outdoor temperature as the systems respond to meet the cooling load. Operational times were similar between residential and light-commercial systems from 5 PM to 7 AM, but the light-commercial systems ran up to 30-150% more often than residential systems during typical business hours (10-30% more in absolute time). Thus, from this albeit limited sample, HVAC systems and filters may be doing more to protect building occupants from exposure to airborne particles at work than at home. A manuscript from the work from Objective 1a has been prepared for submission to Building and Environment, entitled “Operational characteristics of residential and light-commercial air-conditioning systems in Texas.” A current draft is attached in Appendix 1 of this proposal.

13 1.0 34°

0.8 32°

30° 0.6

28° 0.4

26° 0.2 Fractional Operation Time Operation Fractional

24°(°C) Temperature Outdoor Average 0.0

22° 12 AM 6 AM 12 PM 6 PM 12 AM Hour of Day

Residential (Oper.) Light-Commercial (Oper.) Residential (Temp.) Light-Commercial (Temp.)

Figure 3. Mean fractional operation time of 8 residential and 9 light-commercial systems. Error bars represent standard deviations; dashed lines represent average outdoor temperatures.

1.4.1.2. Development of an in-situ HVAC filter test method (Objective 1b) A whole-house air-cleaner test methodology (Offermann et al., 1992; MacIntosh et al., 2008) was refined and applied with readily available filters in a manufactured test house for the work in Objective 1b. The method first artificially elevates particle concentrations and measures the subsequent concentration decay with and without an air-cleaner installed in the operating HVAC system. Air exchange rates are measured simultaneously by tracer gas decay. Therefore, the total particle deposition rate (to surfaces, ducts, and/or filters) can be calculated. The refined methodology differs from previous work in four distinct ways: 1) HEPA- and activated-carbon-filtered outdoor air is supplied to the house in an attempt to maintain positive pressurization with respect to outdoors, which should eliminate the infiltration of outdoor particles, diminish the potential effects of secondary organic aerosol formation from reactions of ozone and unsaturated organic compounds (e.g., Weschler and Shields, 1999), and shorten the test duration; 2) system airflow rates are measured during each test with a more accurate flow plate device; 3) several mixing fans are operated in an attempt to achieve reasonably well-mixed conditions to satisfy the well-mixed reactor model as accurately as possible; and 4) a nonlinear least- squares regression is performed on the resulting data to ensure accurate estimates of particle loss rates, even if a particle source exists. Finally, whole-house deposition rates are used to estimate the size- resolved particle removal efficiency of each filter, and those estimates are compared to those measured upstream and downstream of the filters. The test method relies on solving a well-mixed size-resolved number balance of particles (of diameter i) in the space, assuming no indoor sources of particles, as shown in Equation 3.

dC Q # Q i,in = OAS (1"# )C " $C " %C " i,HVAC HVAC C (3) dt V i,OAS i,out i,in i,in V i,in

-3 where Ci,in is the indoor particle concentration (# m ), t is time (hr), QOAS is the airflow rate of the 3 -1 HEPA- and activated-carbon-filtered outdoor air supply (m hr ), ηi,OAS is the size-resolved particle ! removal efficiency of the outdoor air supply HEPA filter (dimensionless), Ci,out is the outdoor particle concentration (# m-3), λ is the air exchange rate (hr-1), β is the deposition rate of particles to indoor -1 surfaces (hr ), ηi,HVAC is the size-resolved particle removal efficiency of the HVAC system 3 -1 (dimensionless), QHVAC is the airflow rate through the HVAC system (m hr ), and V is the volume of the building (m3). Equation 3 accounts for the change in indoor particle concentration of diameter i in time

14 due to the addition of ambient particles by the filtered outdoor air supply and losses of particles by air exchange to the outdoors, deposition to surfaces, and losses by airflow through the HVAC system (assuming that it is operating). All of the loss mechanisms (λ, β, and ηi,HVACQHVAC/V) can be combined into one loss term, L (hr-1), in the number balance and the test procedure can be repeated for three basic conditions: (1) with no filter installed and the HVAC system off (i.e., background decay); (2) with the HVAC system operating with no filter installed; and (3) with the HVAC system operating with a test filter installed. Because air exchange rates are measured, λ can be subtracted from the total loss rate (L) to determine the effects of surface and ductwork deposition alone (in the no filter case) or the combined effects of surface and ductwork deposition and removal by a filter (in the filter installed case). Then the particle loss rates estimated from regressions of Equation 3 can be used against each other to determine the relative contribution of a filter or HVAC system condition. A comparison of the loss terms of each of the three operations systems is shown in Table 2.

Table 2. Comparison of loss terms of each of the three HVAC and filter operating conditions Operating Condition Loss term, L (hr-1) = Losses to: (1) HVAC off " + # Surfaces $ Q (2) HVAC on, no filter " + # + i,ducts HVAC Surfaces and ductwork V $ Q (3) HVAC on, filter! " + # + i,ducts+ filter HVAC Surfaces, ductwork, and filter V

The size-resolved! particle removal efficiency of the HVAC ductwork alone can be estimated by comparing the loss rates of conditions (2) and (1), as shown in Equation 4. ! V (#2 $ #1) "i,ducts = (4) QHVAC The size-resolved particle removal efficiency of the combination of the HVAC ductwork and filter can be estimated by comparing the loss rates of conditions (3) and (1), as shown in Equation 5. V (# $ # ) 3 1 ! "i,ducts+ filter = (5) QHVAC Finally, because the filter and ductwork in series, as shown in Equation 6, filter efficiency can be estimated by Equation 7.

"i,ducts+ filter =1# (1#"i,ducts)(1#"i, filter ) (6) ! 1#"i,ducts+ filter "i, filter =1# (7) 1#"i,ducts ! To validate the whole-house test method, experiments were performed in an instrumented test house, a three-bedroom two-bathroom manufactured home located at the Pickle Research Campus (described in Novoselac and Siegel, 2009). The house has a floor area of 110 m2, a volume of ! approximately 250 m3, and contains two identical air-handling units (one with ductwork installed in the crawlspace and one with ductwork installed in the attic roofline). Only the downflow unit with ductwork in the crawlspace was used in these experiments. The HVAC system was operated in the fan-only mode during all of the experiments (no cooling or heating). Reasonably well-mixed conditions were achieved by the use of several oscillating fans throughout the house. In an investigation of the well-mixed assumption, average percentage differences across all particle size bins between the master bedroom, dining room, and small bedroom relative to those measured near the HVAC system return plenum ranged from -8% to +26%. Although the well-mixed assumption introduces some error (larger in some size bins and locations than others), overall average spatial differences in particle concentrations are near an arbitrary “acceptable” range of 10% generally used in the literature (Klepeis, 1999).

15 A fan and filter combination was installed in a window frame in a bedroom in order to supply HEPA- and activated-carbon-filtered outdoor air to the space. To obtain an initial elevated concentration of a range of particle sizes, particles were generated by burning six sticks of incense in three locations for several minutes, followed by shaking a vacuum cleaner bag into the HVAC return system in the absence of a filter for approximately 15 seconds. A TSI AeroTrak 8220 handheld optical particle counter was installed near the central return for the downflow HVAC system and set to log particle number concentrations in six bins (0.3-0.5 µm, 0.5-0.7 µm, 0.7-1.0 µm, 1.0-3.0 µm, 3.0-5.0 µm, and >5 µm) at one-minute intervals. Once sufficiently elevated particle levels were achieved (at least twice background, but usually an order of magnitude higher or more, depending on particle size and quality of incense), the incense sticks were extinguished. The vacuum cleaner bag was shaken just before leaving the test house because the rapid deposition rates of the larger particles emitted by the process. Air exchange rates were measured using CO2 as a tracer gas in accordance with ASTM E741. At the same time that particle concentrations were being elevated, CO2 was injected into each room of the house from a cylinder and mass flow controllers until the CO2 concentration in each room was at least 500 ppm or greater above background. Some results of the estimated loss rates (β or β + ηi,HVACQHVAC/V, excluding AERs) are summarized across five conditions in Figure 4: background with HVAC off, HVAC on with no filter, and HVAC on with low, medium, and high efficiency filters (MERV <5, 7, and 11 as defined by ASHRAE Standard 52.2). Deposition rates include the total loss term (L) estimated from nonlinear regressions of the solution to Equation 3, minus the air exchange rate (λ).

10 0.3-0.5 µm 0.5-0.7 µm 0.7-1.0 µm 1.0-3.0 µm 3.0-5.0 µm 5.0+ µm 9 8 7 6 5 4 3 Deposition Rate, 1/hr 2 1 0 BG NF <5 7 11 BG NF <5 7 11 BG NF <5 7 11 BG NF <5 7 11 BG NF <5 7 11 BG NF <5 7 11 Figure 4. Box plot HVACof size -Offresolved particleNo Filter depositionMERV rates (excluding<5 MERVAER) measured7 MERV in the 11test house during replicate tests at five filter conditions: “BG” = Background w/ HVAC off, “NF” = No Filter, “<5” = MERV <5, “7” = MERV 7, and “11” = MERV 11. Deposition rates are split into six particle size bins as indicated by the dashed vertical lines.

Particle loss rates generally increased as particle sizes increased and as filter efficiency increased. The widest ranges in deposition rates attributed to different rated filter removal efficiency generally occurred for 0.5-5 µm particles, as the classifications by ASHRAE Standard 52.2 should reflect. On average, high-efficiency (MERV 11) filters increased particle loss rates in the test house by 2.4 to 6.1 hr-1

16 relative to background deposition rates measured with the HVAC system off and the difference increased with increasing particle size. However, for the largest and smallest particle size bins, filters did not always increase loss rates very much over simply running the HVAC system without a filter. For example, medium-efficiency filters did not increase loss rates of 0.3-0.5 µm particles and increased average loss rates of particles greater than 5 µm only 15%, which suggests that primary loss mechanisms of those size particles may be deposition onto ductwork or exfiltration through duct leakage. To further investigate the utility of this whole-house filter test method, a comparison of estimated removal efficiencies using all four test methods is shown in Figure 5. 120% 120% 100% a) MERV <5 100% b) MERV 7 80% 80%

60% 60%

40% 40% Removal Efficiency (%) Efficiency Removal 20% (%) Efficiency Removal 20%

0% 0% 0.1 1 10 0.1 1 10 Geometric Mean Diameter (!m) Geometric Mean Diameter (!m)

120% 120% 100% c) MERV 11 Manufacturer Reported 80% 100% Lab Tests 60% Upstream/Downstream 40% 80% Whole-house

Removal Efficiency (%) Efficiency Removal 20%

0% 60% 0.1 1 10 Geometric Mean Diameter (!m) Figure 5. Comparison of size-resolved particle removal40% efficiency of three filters (MERV <5, MERV 7,

MERV 11) tested by four methods: ASHRAE 52.2 test results (%) Efficiency Removal reported by the manufacturer, results from the initial loading stage of ASHRAE 52.2 lab tests on the filters20% after being used in the test house, results from upstream/downstream measurements in the test house, and results calculated from the whole-house methods in the test house. Removal efficiency is plotted versus the geometric mean diameter taken from each particle size bin of the instruments (12 bins for the 52.2 tests and0% 6 bins for the test house measurements). 0.1 1 10 In general, each test method (lab tests by the manufacturer, lab tests by anGeometric independent Mean Diameteragency, (!m) upstream-downstream measurements using two particle counters, and the refined whole-house method using one central particle counter) resulted in similar values of particle removal efficiency. The whole- house method tended to over predict removal efficiency for larger particles relative to the upstream- downstream method, especially for higher-efficiency filters. The opposite effect was observed for smaller particles with the same higher-efficiency filters. The completed research in Objective 1b to date generally shows that all four methods provide reasonable agreement with each other and that more steps should be taken to reduce the uncertainty in the whole-house method. A manuscript for this work has been prepared for submission to Aerosol Science and Technology, entitled “Comparison of In-situ Test Methods for Determining Particle Removal Efficiency of HVAC Filters in a Test House.” A current draft is attached in Appendix 2 of this proposal.

17 2. Methodology for future research

This section describes the methodology to be performed for future work to meet the remaining proposed research objectives. The majority of the work in Objective 1 has already been completed; thus this section focuses on the largely unfinished objectives of this proposal (Objectives 2 and 3). Section 2.1 and 2.2 describes the basis for the need for the proposed pollutant penetration and air exchange rate test methods, respectively. Section 2.3 describes the proposed research methods.

2.1. Basis for proposed pollutant penetration research methods

2.1.1.1. Basis for particulate matter work In a recent review of essentially all known work on I/O ratios, infiltration factors, and penetration factors of particulate matter, Chen and Zhao (2011) proposed that the particle penetration factor was the most relevant parameter for characterizing the penetration mechanism through cracks and leaks in the building envelope. Knowledge of particle penetration factors and indoor deposition rates have been identified as critical missing pieces of information in explaining human exposure to particles indoors (Thatcher et al., 2001). Thatcher et al. (2003) provided a specific method of determining size-resolved particle penetration efficiencies and validated it with particle measurements indoors and outdoors at two houses, while simultaneously measuring AER by tracer gas decay. They used a three-part experimental approach to 1) measure the decay rate of particles after an artificial concentration elevation, 2) rapidly reduce particle concentrations below background levels by introducing outdoor air filtered by a high efficiency particulate (HEPA filter), and 3) measure the subsequent particle concentration rebound period in order to obtain the whole-house penetration efficiency. Thatcher et al. (2003) also happened to report results of blower door air leakage tests in the two homes in Richmond, CA and Clovis, CA. The leakier Richmond home had an estimated leakage area (ELA, as defined by ASTM E779) of 148 cm2 and the tighter Clovis home had an ELA of 87 cm2. Penetration efficiencies in the leakier Richmond home were greater than or equal to the tighter Clovis home for every particle size. The ratios of penetration efficiencies between the two homes were approximately equivalent to the ELA ratios (148 cm2/87 cm2 ≈ 1.7) for three particle sizes: 0.23 µm, 0.35 µm, and 2.9 µm in diameter, as shown in Figure 6. 3.0

ELA Ratio 2.5 OPC APS

2.0

1.5 P(Richmond)/P(Clovis) P(Richmond)/P(Clovis) 1.0

0.5 0.1 1 10 Particle Diameter (!m)

Figure 6. Ratio of size-resolved penetration factors measured at two test homes (“Richmond” and “Clovis”) in Thatcher et al. (2003). The horizontal dashed gray line represents the ratio between estimated leakage areas measured in the Richmond and Clovis home (~1.7). Error bars represent standard deviations from the measurements in Thatcher et al. (2003) added in quadrature.

18 The results in Figure 6 suggest that there may be a fundamental correlation between building air leakage characteristics and size-resolved particle penetration efficiency. Accumulation mode particles (0.1-1 µm) are likely to have the highest penetration efficiencies regardless of air leakage because they are least affected by any deposition mechanisms (Brownian motion, gravitational settling, impaction, and interception). Losses of larger particles within building envelopes are typically dominated by gravitational settling or impaction. Because leakier buildings typically have large openings, the surface area of cracks available for particles to deposit onto the envelope of a leaky building is likely much less than in a tight building. Thus, the much greater penetration efficiencies for large particles (> 3 µm) into the leakier Richmond house in Figure 6 may be explained by significantly less surface area of cracks for deposition. However, it is difficult to draw any conclusions from the data at these two homes and I am not aware of any literature investigating these relationships. Much more fundamental work in this area is warranted. Most recently, Rim et al. (2010) measured size-resolved penetration efficiencies of ultrafine particles (<100 nm) with a similar method as Thatcher et al. (2003) at an unoccupied test house during two conditions: 1) with closed windows and 2) with a window open approximately 7.5 cm. The window opening increased penetration efficiencies for every particle size measured (10-100 nm), and also minimized the variation in penetration between particle sizes. Much of the ambient particle laden outdoor air must have entered through the window opening, providing fewer bends and cracks for particle removal by the building envelope. Although not reported in detail in Rim et al. (2010), the open window would have affected the size and nature of air leakage pathways in the envelope of their test house. These two studies provide the motivation for this proposal to use detailed pollutant penetration experiments to investigate a hypothesized correlation between penetration efficiency and building air leakage characteristics. Leakage characteristics are described relatively easily using large calibrated blower door fans, which measure the air tightness of a building by pressurizing or depressurizing to a known indoor-outdoor pressure differential and measuring the air flow rate through the fan (ASTM E779). The resultant air supply or exhaust through the fan is made up from leakage pathways in the building envelope. Blower door measurements are performed at a variety of pressure differentials in order to establish a relationship between the flow through the building, Q (m3 s-1), and the indoor-outdoor pressure difference, ΔP (Pa), as shown in Equation 8. Q = C"P n (8) where C is a flow coefficient (m3 s-1 Pa-n) and n is a pressure exponent (-). The flow coefficient, C, is directly correlated to the total leakage area in a building envelope. The pressure exponent, n, is limited to values between 0.5 and 1, and is often found to be near 0.65 for the typical combined leakage ! pathways in buildings (Sherman and Chan, 2004). A pressure exponent of 0.5 describes short leaks with high flow rates and high Reynolds numbers that can be treated as orifice flow with negligible frictional losses. Low flow rates and low Reynolds numbers are dominated by laminar frictional losses and correspond to an exponent of 1. Leakage characteristics are often reported as the flow at a pressure difference of 50 Pa (Q50) or the relationship in Equation 8 is often used to determine an estimated leakage area, ELA (the area with a discharge coefficient of 1 that would have the same flow at some specified reference pressure, m2), as shown in Equation 9 (ASTM E779). 2P Q = ELA ref (9) " -3 where Pref = reference pressure (usually 4 Pa) and ρ = air density (kg m ). Each of these methods can be used to directly compare inter-home variability in air tightness, or intra-home variability in air tightness before and after building retrofits. Because penetration efficiency is in part a function of the ! geometry of openings and the amount of airflow through the openings, I hypothesize that penetration efficiencies will be significantly and positively correlated with these parameters from air leakage tests (Q, ELA, C, and/or some multivariate function of C and n). If a significant correlation between air leakage characteristics and ambient particle penetration proves to exist, the results of this proposed study of a relatively small sample of buildings could allow for immediate characterization of the protective ability of

19 thousands of buildings for which datasets of blower door results exist (e.g., Chan et al., 2005) and may improve accuracy in relating outdoor concentrations to indoor exposure for large populations.

2.1.1.2. Basis for reactive gas work Much less work fundamental work has been done on the penetration of reactive gases into indoor environments. Liu and Nazaroff (2001) reported that ozone penetration efficiency would vary with the nature of building leakage paths and the reaction probabilities of materials in the building envelope. Reaction probabilities of common building materials range approximately four orders of magnitude, from 2.2×10-4 (brick) to 5.5×10-8 (aluminum), which suggests that building envelopes made of more reactive materials may act as a stronger buffer between indoor environments and outdoor ozone. Reported average indoor-outdoor ozone concentration ratios in homes have ranged from 0.2 to 0.4, with considerable variability between 0 and 1 (Avol et al., 1998; Romieu et al., 1998). A Monte Carlo analysis of predicted steady-state ozone penetration efficiencies (using Equation 2), using averages and standard deviations for indoor-outdoor ozone ratios (Avol et al., 1998), ozone deposition rates (Lee et al. 1999), and air exchange rates (Breen et al., 2010), predicts average (± std. dev.) penetration efficiencies of ozone in U.S. homes of 0.54 ± 0.28. I am not aware of any experimental work specifically on the penetration of reactive gases indoors performed in the laboratory or in actual buildings, but this screening analysis suggests that ozone penetration through residential building envelopes should be investigated further and that the assumption that P is equal to 1 should certainly be tested in real environments.

2.2. Basis for proposed air exchange rate research methods As mentioned in Section 1.3.3, many researchers have focused on successfully predicting air exchange rates in residential and light-commercial buildings from meteorological data. The goal of most previous research has been to predict AERs from indoor-outdoor temperature differences, wind speed, and building operational characteristics (e.g., frequency of window openings). However, all of the previous models essentially rely on manifesting those three conditions into pressure differences between indoors and outdoors and leakage areas in the envelope. Because long-term AER testing with tracer gas decay is invasive, costly, and time consuming, I aim to find an easier way to measure AERs. I hypothesize that AERs can be inferred by working backwards from previous work and simply measuring indoor-outdoor pressure differentials. I recently began to test his hypothesis in the test house by measuring indoor-outdoor pressure static differentials (measuring indoors at a central location and outdoors by exposing a tube to the ambient air) and AERs by tracer gas decay simultaneously. A relationship for the currently limited amount of measurements of these two variables is shown in Figure 7.

0.8 -1 2 0.7 AER (hr ) = C1!P + C2 -1 -2 C1 = 0.011 ± 0.003 (hr Pa ) 0.6 -1 C2 = 0.179 ± 0.027 (hr ) 0.5

0.4

0.3

Air ExchangeAir Rate (1/hr) 0.2

0.1

0.0 0 1 2 3 4 5 6 I/O Pressure Difference (Pa) Figure 7. Relationship between AER and indoor-outdoor pressure differential measured at the test house

20 The AERs in Figure 7 represent CO2 tracer gas decay tests performed in the unoccupied test house for periods ranging from approximately 2 to 16 hours. The data were plotted and a quadratic trend was noticed, thus a nonlinear regression was performed on the data using the empirical relationship AER 2 = C1∆P + C2, where C2 would be the minimum AER rate at essentially zero indoor-outdoor pressure differential. Many more data points should be collected in order to investigate this potential relationship. If this method can lead to accurate estimations of AERs, researchers would be enabled to perform a relatively short series of intensive tracer tests across a wide range of indoor-outdoor pressure differentials (i.e., range of weather conditions), then leave noninvasive pressure sensors in place to infer real-time long-term AERs.

2.3. Future Experiments and Methods My experimental plan is to first conduct further pilot studies of Objectives 2 and 3 in an unoccupied test house at the Pickle Research Campus, then scale up to a set of field studies in real buildings as necessary.

2.3.1. Pollutant penetration methods: particles and ozone (Objective 2) I propose to perform the methods of Thatcher et al. (2003) to estimate the penetration efficiency of particles in a sample of approximately 20 homes and light-commercial buildings. No method currently exists to measure the penetration efficiency of ozone, so I have recently developed an ozone penetration method that involves characterizing the ozone decay rate in a building, followed by simultaneous measurements of steady state I/O ozone concentrations and AERs. These penetration efficiency experiments will be performed during approximately one day of testing in each building (a sample of convenience, tested while unoccupied or mostly unoccupied). The general experimental procedure will be as follows: 1. Install several oscillating fans throughout the building to achieve adequate mixing. 2. Install particle and ozone monitoring instrumentation at a central indoor location and outdoors at a convenient place near the building (or place one sampling instrument and alternating switching valves in a central indoor location and install equal length sampling lines to sample both indoors and outdoors). 3. Simultaneously elevate indoor concentrations of particles by burning incense, shaking used vacuum cleaner bags, shuffling feet on carpet, cooking, or any other particle generating activities (Thatcher et al., 2003), ozone by opening doors and windows to achieve elevated indoor concentrations during periods of high outdoor concentrations (Lee et al., 1999), and an inert tracer gas (CO2) by direct injection or exposing dry ice (for CO2) in a central location (ASTM E741). Operate HVAC system without a filter to increase mixing. 4. Turn off HVAC system, close all doors and windows, and measure the decay of all pollutants and tracer gas by the combined effects of air exchange and deposition to surfaces (or reactions). Because I will simultaneously measure AER with tracer gas, decay rates can be computed from the time-varying measurements. 5. Install high-efficiency particle air (HEPA) filtration and activated carbon filtration on an outdoor air supply fan at a window or door opening and supply clean outdoor air to reduce indoor concentrations to as close to zero as possible. Begin injection of tracer gas decay again. 6. Monitor the concentration rebound period. In the absence of indoor sources of particles and ozone in the unoccupied building, the concentration rebound will occur by the penetration of outdoor pollutants alone. Penetration efficiency can be solved for during the time-varying rebound period or the subsequent steady-state period, if achieved.

Figure 8 shows a qualitative timeline of a typical experiment to be performed in each building, using a time-varying concentration profile. These approximately daylong experiments will be used to characterize the decay rate of particles and ozone (“AER + Decay” using Equation 1), followed by the penetration efficiency of particles and ozone (“Penetration rebound” using Equation 1 and/or 2). I will

21 also perform air leakage tests using standardized blower door methods during the field visits. Again, I hypothesize that “tighter” buildings will result in lower indoor concentrations of particles and ozone in the absence of indoor sources, as shown in Figure 8. These experiments (in particular, the particle experiments) will contribute significantly to gaps in our current knowledge of particle penetration factors and deposition rates in real environments, which are two critical parameters identified as missing from our knowledge of exposure to particulate matter of outdoor origin in Thatcher et al. (2001).

Elevation AER + Decay

Indoor Penetration rebound Leakier Building PM and HEPA Tighter Building O3 supply

Time

Figure 8. Timeline of experiments and sampling to be performed in each home

The succession of experiments will be repeated as necessary (up to six times in each building) to improve accuracy and minimize uncertainty. I have access to all appropriate instrumentation for these experiments in our laboratory at UT-Austin. Because I will be performing these experiments in actual buildings, I will rely on portable instrumentation. I will measure particle concentrations using a TSI AeroTrak optical particle counter (size-resolved particles, 0.3-10 µm), a TSI P-Trak condensation particle counter (total ultrafine particles, 0.01-1 µm), and a TSI DustTrak or SidePak light-scattering nephelometer (which infers PM2.5 mass). I will measure AERs using CO2 injection and a Telaire Series 7000 infrared absorption CO2 monitor (0-2500 ppm CO2, ±50 ppm), and ozone using a 2B Technologies Model 202 Ozone Monitor using UV-absorbance (0-100 ppm O3, ±1.5 ppb). Statistical correlations between each pollutant penetration efficiency variable measured (size-resolved particles or ozone) and measured blower door results (and building material characteristics) will be drawn initially using Spearman rank correlation coefficients, a nonparametric test statistic that can provide significance at the 95% confidence level with small samples of even fewer than 20 (Daniel, 1990). More sophisticated multi- parameter models will be built if necessary. Uncertainty during each daylong experiment will be addressed using the larger of instrumentation uncertainties, standard errors of nonlinear regressions from Equation 1, and standard deviations of average measurements (Equation 2) added in quadrature (ASHRAE, 2005). All analysis, including correlation of penetration efficiency and building leakage parameters (and associated uncertainties) will be conducted in a statistical computing software package (Stata) that I have used extensively for previous projects (Stephens et al., 2010a; Stephens et al., 2010b).

2.3.2. Measuring air exchange rates with indoor-outdoor pressure differentials (Objective 3) I plan to continue the work from section 2.2 on measuring AERs simultaneously with indoor- outdoor static pressure differentials at the test house facility. I will expand the work to measure surface pressures on each side of the house (Modera and Wilson, 1990), as well as indoor-outdoor temperature differences and wind speed in order to distinguish between stack- and wind-dominated infiltration. If a strong empirical correlation continues to exist, I will investigate correlations between the model parameters and parameters from blower door air leakage tests in an attempt to determine if the number of simultaneous tracer gas tests can be minimized by simply using blower door coefficients. These tests will all made with the same leakage area of the test house with windows and doors closed, but I will be able to expand on the work by intentionally creating larger openings (e.g., by opening

22 windows several centimeters) to determine how model parameters change with different flow patterns and leakage areas. The ability of the model to withstand changes in air leakage pathways will be crucial to the utility of this method in occupied buildings where doors and windows can be opened freely. Once the air exchange methodology is verified in the test house, I will be able to perform validation of the same tests on a small subset of homes from a sample of convenience recruited from the UT-Austin faculty, staff, and student populations, as well as from other friends and colleagues in the community.

3. Organization of Dissertation and Future Publications

Much of the work proposed in Objective 1 has already been completed. A full-length manuscript on characterizing residential and light-commercial HVAC operational parameters (Objective 1a) has been prepared for submission to Building and Environment (attached in Appendix 1). Work to date on HVAC filter test methodologies (Objective 1b) has been accepted to the Indoor Air conference in Austin in 2011 (accepted abstract) and a full-length manuscript is currently in preparation for submission to Aerosol Science and Technology (draft attached in Appendix 2). The work in Objective 1b will be extended to another manuscript to describe the combined effects of HVAC filters, filter bypass, and duct leakage on particle removal rates in residential and light-commercial buildings, likely for submission to HVAC&R Research, Atmospheric Environment, or Building and Environment. One or two conference papers of this ongoing work will likely be prepared for an ASHRAE annual meeting or Healthy Buildings 2012. Full-length manuscripts describing the completed results of the proposed work in Objective 2 (novel assessment of pollutant penetration) and Objective 3 (novel assessment of air exchange rates) will likely be targeted for submission to Environmental Science and Technology and Atmospheric Environment, respectively. An abstract for a portion of Objective 2 (assessing ozone penetration efficiencies) has already been accepted to the Indoor Air 2011 conference as an abstract submission.

4. Project Timeline

The proposed schedule for this work is outlined in Table 3. The work in Objective 2 (pollutant penetration methodologies) has been proposed as an EPA STAR proposal as well as a larger proposal to the U.S. Department of Housing and Urban Development to investigate changes in pollutant penetration after weatherization retrofits in low-income homes. Thus, the proposed schedule may be subject to the outcome of those grant proposals. The tentative schedule herein most closely aligns with my EPA STAR proposal. I propose to complete all work and defend my dissertation by August 2012. At least three months of my time will be spent on an internship as a requirement of the NSF IGERT program in which I am currently enrolled, and I have tentatively planned that for late spring and early summer of 2011.

Table 3. Tentative schedule for completion of proposed work 2011 2012 Tasks J F M A M J J A S O N D J F M A M J J A IGERT internship Pilot tests in test house Recruitment of test buildings Field and full test house experiments Analysis and manuscript preparation Defense

Major project tasks include the following: • Pilot tests for Objectives 2 and 3 will occur first in a test house facility on the Pickle Research Campus at UT (3 months).

23 • My IGERT internship will probably take place in late spring and early summer of 2011, likely at Lawrence Berkeley National Lab where I have already established contact with some researchers (3 months). There remains a possibility to conduct some of my dissertation research on my internship. • Once back in Austin, the recruitment of buildings for field testing will be an ongoing process until approximately 20 residential and light-commercial buildings are recruited (3 months, potentially longer). • Field-testing will take place during the majority of my remaining time as a PhD student. I will attempt to conduct the majority of field visits during the summer and early fall months when ambient ozone levels are highest (up to 6 tests in 20 buildings = 120 tests maximum, 11 months). • Data analysis and manuscript preparation (papers and dissertation chapters) will take place during the final 6 months of my time as a PhD student. • I plan to defend my dissertation as early as August 2012. This date is subject to change according to funding opportunities and the pace and success of research work.

5. Summary

Three main cohesive objectives exist within this proposal, all related to characterizing the fate and transport of pollutants inside residential and light-commercial buildings. I propose to develop novel ways to fully characterize the effects of HVAC systems as pollutant removal devices (Objective 1), including operational characteristics (Objective 1a) and in-situ particle removal efficiency of HVAC filters (Objective 1b), infer the nature of outdoor pollutant (particles and reactive gases) penetration into buildings using simple air leakage testing and building material characteristics (Objective 2), and measure air exchange rates using simple indoor-outdoor pressure differentials (Objective 3). Objective 1 generally targets indoor pollutant removal processes by aiming to characterize the influence of HVAC systems on particle removal rates. Objective 2 targets a source of indoor air pollution (outdoor air pollution) by aiming to more easily characterize how both particles and reactive gases are transported indoors. Objective 3 targets both a source and removal mechanism of indoor pollutants by aiming to more easily measure long-term AERs (air exchange can both bring outdoor pollutants indoors and dilute indoor pollutants by transporting them outdoors). The work outlined in this proposal will add substantially to the body of knowledge and available test methodologies regarding the fate and transport of pollutants inside residential and light-commercial buildings. The utility of these test methods and data involved is that they can be generalized and applied by others to characterize a wider range of buildings in a wide variety of climates. Much of the work in this proposal represents a novel endeavor. Objective 1 will be the first attempt of which I am aware to measure the effects of filter loading, duct leakage, filter bypass, and system operation on particle removal in real environments. Objective 2 will be the first attempt of which I am aware to correlate ambient pollutant penetration and building air leakage tests. The utility of these correlations is that they may be used to infer the nature of ambient particle (and reactive gas) penetration indoors from easy and ubiquitous blower door test results (and building envelope material characteristics), which will allow epidemiological studies to adopt more specific population exposures from ambient pollutant data. Additionally, the methods in Objective 2 may be used to assess changes in the ability of a building to protect indoor environments from outdoor pollution after undergoing weatherization retrofit measures. Finally, Objective 3 will be the first investigation of which I am aware to infer air exchange rates from indoor-outdoor pressure differences and will allow other researchers to more easily characterize meaningful AERs during their investigations using relatively simple measurements. Overall, this work aims to close gaps that exist between researchers that often investigate indoor pollutants and building characteristics independently.

24 6. References

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