Residential Air Exchange Rates Research Articles

Integrating Time-Activity and Air Sensors into Smartphone-based PM2.5 and Ozone Exposure Model for Epidemiologic Studies

BACKGROUND AND AIM: Epidemiologic studies of fine particulate matter (PM2.5) and ozone (O3) often use outdoor concentrations as exposure surrogates, which can induce measurement error. The goal of this study was to improve PM2.5 and O3 exposure assessments for a repeated measurement study with 20 individuals in Philadelphia, Pennsylvania by applying a smartphone-based exposure model called TracMyAir. METHODS: We developed TracMyAir, which is a smartphone (iPhone, Android) application that determines multiple tiers of individual-level exposure metrics in real-time for ambient and non-ambient PM2.5 and O3 using outdoor concentrations, home building characteristics, weather, time-locations, and time-activities. In this study, we extended TracMyAir by including (1) outdoor concentrations from an air quality model (CMAQ), (2) indoor and outdoor PM2.5 concentrations from low-cost air sensors (PurpleAir) and a building infiltration model, (3) a microenvironment model (MicroTrac) based on time-resolved smartphone geolocations, and (4) inhaled ventilation models based on physical activity data from smartphone and smartwatch accelerometers and heart rate sensors. The five tiers of exposure metrics with increasing information needs and model complexity include: residential air exchange rates (AER, Tier 1), infiltration factors (Finf_home, Tier 2), indoor concentrations (Cin_home, Tier 3), exposures (E, Tier 4), and inhaled doses (D, Tier 5). We applied TracMyAir to determine hourly PM2.5 and O3 exposure metrics for three consecutive days for 20 participants consisting of two age groups (18-30 years old, n=10; 55-75 years old, n=10). RESULTS: The TracMyAir predictions showed considerable temporal and house-to-house variability of AER, Finf_home, and Cin_home (Tiers 1-3), and person-to-person variability of E and D (Tiers 4-5). CONCLUSIONS: Our study demonstrates the capability of extending TracMyAir with air pollutant and time-activity sensors and models to estimate individual-level PM2.5 and O3 exposure metrics for an epidemiologic study, in support of improving risk estimation.

Estimating residential air exchange rates in rural Bangladesh using a near field-far field model

Household air pollution due to solid fuel (biomass) combustion is widely prevalent in rural households in the developing world. Providing adequate ventilation can be a potential method to reduce exposures to residents. Previous cookstove studies in rural areas around the world have estimated the ventilation air changes per hour (ACH) values to be of the order of >20. These studies use a one-compartment model to estimate the ACH from the decay of the pollutant released very near to the cookstove. While the one-box compartmental model is appropriate for estimating exposures farther away from emission sources, a multi-compartment (e.g., a 2-box model) may be more appropriate for distinguishing between exposures of the cook versus other occupants in the house, as well as estimating ventilation rates in the house. In the present study, we use a two-compartment model to estimate the ACH. Field based particulate matter measurements were carried out in 40 Bangladesh rural households in kitchen and living room. The overall Geometric Mean (Geometric Standard Deviation) of ACH across households were found to be 0.43 (4.3) in the kitchen and 0.32 (2.7) in the living room. Obtaining the air changes per hour or ventilation rates from the decay curve of concentrations in the near vicinity of the pollutant source will lead to significant over-estimates. Our findings indicate that there is currently a substantial over-estimate based on using an incorrect model to derive the ACH values.

Estimating climate change-related impacts on outdoor air pollution infiltration

BackgroundRising temperatures due to climate change are expected to impact human adaptive response, including changes to home cooling and ventilation patterns. These changes may affect air pollution exposures via alteration in residential air exchange rates, affecting indoor infiltration of outdoor particles. We conducted a field study examining associations between particle infiltration and temperature to inform future studies of air pollution health effects. MethodsWe measured indoor fine particulate matter (PM2.5) in Atlanta in 60 homes (810 sampling-days). Indoor-outdoor sulfur ratios were used to estimate particle infiltration, using central site outdoor sulfur concentrations. Linear and mixed-effects models were used to examine particle infiltration ratio-temperature relationships, based on which we incorporated projected meteorological values (Representative Concentration Pathways intermediate scenario RCP 4.5) to estimate particle infiltration ratios in 20-year future (2046–2065) and past (1981–2000) scenarios. ResultsThe mean particle infiltration ratio in Atlanta was 0.70 ± 0.30, with a 0.21 lower ratio in summer compared to transition seasons (spring, fall). Particle infiltration ratios were 0.19 lower in houses using heating, ventilation, and air conditioning (HVAC) systems compared to those not using HVAC. We observed significant associations between particle infiltration ratios and both linear and quadratic models of ambient temperature for homes using natural ventilation and those using HVAC. Future temperature was projected to increase by 2.1 °C in Atlanta, which corresponds to an increase of 0.023 (3.9%) in particle infiltration ratios during cooler months and a decrease of 0.037 (6.2%) during warmer months. DiscussionWe estimated notable changes in particle infiltration ratio in Atlanta for different 20-year periods, with differential seasonal patterns. Moreover, when stratified by HVAC usage, increases in future ambient temperature due to climate change were projected to enhance seasonal differences in PM2.5 infiltration in Atlanta. These analyses can help minimize exposure misclassification in epidemiologic studies of PM2.5, and provide a better understanding of the potential influence of climate change on PM2.5 health effects.

Modeling of Residential Indoor Concentrations of Ambient PM2.5, Ozone for the Catheterization Genetics (CATHGEN) Study

Air pollution epidemiological studies of ambient fine particulate matter (PM2.5) and ozone (O3) often use outdoor concentrations from central-site monitors as exposure surrogates, which can add bias or uncertainty in health effect estimates. The goal of this study was to improve exposure assessments of ambient PM2.5 and O3 for a 10-year epidemiological study with 2,271 participants with coronary artery disease in central North Carolina called the Catheterization Genetics (CATHGEN) study. We developed an exposure modeling approach to estimate three tiers of individual-level exposure metrics for ambient PM2.5 and O3. We used a hybrid outdoor air quality model (based on satellite- and ground-based air pollution measurements, chemical transport and land-use models) linked to a residential air exchange rate model (based on building characteristics, indoor-outdoor temperatures, wind speed) and mass-balance infiltration model to determine residential air exchange rates (AER, Tier 1), infiltration factors (Finf, Tier 2), and indoor concentrations (Cin, Tier 3). For each of the 2,271 participant homes, we applied the exposure model to determine daily house-specific PM2.5 and O3 exposure metrics (Tiers 1-3) for the 365 days before each participant’s cardiac catheterization date. The daily modeled exposure metrics for all 828,915 participant days showed considerable temporal and house-to-house variability of AER, Finf and Cout (Tiers 1-3). Our study demonstrates the ability to apply an outdoor air quality model linked to a residential infiltration model to determine individual-level ambient PM2.5 and O3 exposure metrics for a large, long-term epidemiological study, in support of improving risk estimation.

Fine-Scale Modeling of Individual Exposures to Ambient PM2.5, EC, NOx, CO for the Coronary Artery Disease and Environmental Exposure (CADEE) Study.

Air pollution epidemiological studies often use outdoor concentrations from central-site monitors as exposure surrogates, which can induce measurement error. The goal of this study was to improve exposure assessments of ambient fine particulate matter (PM2.5), elemental carbon (EC), nitrogen oxides (NOx), and carbon monoxide (CO) for a repeated measurements study with 15 individuals with coronary artery disease in central North Carolina called the Coronary Artery Disease and Environmental Exposure (CADEE) Study. We developed a fine-scale exposure modeling approach to determine five tiers of individual-level exposure metrics for PM2.5, EC, NOx, CO using outdoor concentrations, on-road vehicle emissions, weather, home building characteristics, time-locations, and time-activities. We linked an urban-scale air quality model, residential air exchange rate model, building infiltration model, global positioning system (GPS)-based microenvironment model, and accelerometer-based inhaled ventilation model to determine residential outdoor concentrations (Cout_home, Tier 1), residential indoor concentrations (Cin_home, Tier 2), personal outdoor concentrations (Cout_personal, Tier 3), exposures (E, Tier 4), and inhaled doses (D, Tier 5). We applied the fine-scale exposure model to determine daily 24-h average PM2.5, EC, NOx, CO exposure metrics (Tiers 1–5) for 720 participant-days across the 25 months of CADEE. Daily modeled metrics showed considerable temporal and home-to-home variability of Cout_home and Cin_home (Tiers 1–2) and person-to-person variability of Cout_personal, E, and D (Tiers 3–5). Our study demonstrates the ability to apply an urban-scale air quality model with an individual-level exposure model to determine multiple tiers of exposure metrics for an epidemiological study, in support of improving health risk assessments.

Development of TracMyAir Smartphone Application for Modeling Exposures to Ambient PM2.5 and Ozone.

Air pollution epidemiology studies of ambient fine particulate matter (PM2.5) and ozone (O3) often use outdoor concentrations as exposure surrogates. Failure to account for the variability of the indoor infiltration of ambient PM2.5 and O3, and time indoors, can induce exposure errors. We developed an exposure model called TracMyAir, which is an iPhone application (“app”) that determines seven tiers of individual-level exposure metrics in real-time for ambient PM2.5 and O3 using outdoor concentrations, weather, home building characteristics, time-locations, and time-activities. We linked a mechanistic air exchange rate (AER) model, a mass-balance PM2.5 and O3 building infiltration model, and an inhaled ventilation model to determine outdoor concentrations (Tier 1), residential AER (Tier 2), infiltration factors (Tier 3), indoor concentrations (Tier 4), personal exposure factors (Tier 5), personal exposures (Tier 6), and inhaled doses (Tier 7). Using the application in central North Carolina, we demonstrated its ability to automatically obtain real-time input data from the nearest air monitors and weather stations, and predict the exposure metrics. A sensitivity analysis showed that the modeled exposure metrics can vary substantially with changes in seasonal indoor-outdoor temperature differences, daily home operating conditions (i.e., opening windows and operating air cleaners), and time spent outdoors. The capability of TracMyAir could help reduce uncertainty of ambient PM2.5 and O3 exposure metrics used in epidemiology studies.

The impact of air exchange rate on ambient air pollution exposure and inequalities across all residential parcels in Massachusetts.

Individual housing characteristics can modify outdoor ambient air pollution infiltration through air exchange rate (AER). Time and labor-intensive methods needed to measure AER has hindered characterization of AER distributions across large geographic areas. Using publicly available data and regression models associating AER with housing characteristics, we estimated AER for all Massachusetts residential parcels. We conducted an exposure disparities analysis, considering ambient PM2.5 concentrations and residential AERs. Median AERs (h−1) with closed windows for winter and summer were 0.74 (IQR: 0.47–1.09) and 0.36 (IQR: 0.23–0.57), respectively, with lower AERs for single family homes. Across residential parcels, variability of indoor PM2.5 concentrations of ambient origin was twice that of ambient PM2.5 concentrations. Housing parcels above the 90th percentile of both AER and ambient PM2.5 (i.e. the leakiest homes in areas of highest ambient PM2.5) – versus below the 10th percentile – were located in neighborhoods with higher proportions of Hispanics (20.0% vs 2.0%), households with an annual income of less than $20,000 (26.0% vs. 7.5%), and individuals with less than a high school degree (23.2% vs. 5.8%). Our approach can be applied in epidemiological studies to estimate exposure modifiers or to characterize exposure disparities that are not solely based on ambient concentrations.

Modeling individual exposures to ambient PM2.5 in the diabetes and the environment panel study (DEPS)

Air pollution epidemiology studies of ambient fine particulate matter (PM2.5) often use outdoor concentrations as exposure surrogates, which can induce exposure error. The goal of this study was to improve ambient PM2.5 exposure assessments for a repeated measurements study with 22 diabetic individuals in central North Carolina called the Diabetes and Environment Panel Study (DEPS) by applying the Exposure Model for Individuals (EMI), which predicts five tiers of individual-level exposure metrics for ambient PM2.5 using outdoor concentrations, questionnaires, weather, and time-location information. Using EMI, we linked a mechanistic air exchange rate (AER) model to a mass-balance PM2.5 infiltration model to predict residential AER (Tier 1), infiltration factors (Finf_home, Tier 2), indoor concentrations (Cin, Tier 3), personal exposure factors (Fpex, Tier 4), and personal exposures (E, Tier 5) for ambient PM2.5. We applied EMI to predict daily PM2.5 exposure metrics (Tiers 1–5) for 174 participant-days across the 13 months of DEPS. Individual model predictions were compared to a subset of daily measurements of Fpex and E (Tiers 4–5) from the DEPS participants. Model-predicted Fpex and E corresponded well to daily measurements with a median difference of 14% and 23%; respectively. Daily model predictions for all 174 days showed considerable temporal and house-to-house variability of AER, Finf_home, and Cin (Tiers 1–3), and person-to-person variability of Fpex and E (Tiers 4–5). Our study demonstrates the capability of predicting individual-level ambient PM2.5 exposure metrics for an epidemiological study, in support of improving risk estimation.

Probabilistic estimation of residential air exchange rates for population-based human exposure modeling.

Residential air exchange rates (AERs) are a key determinant in the infiltration of ambient air pollution indoors. Population-based human exposure models using probabilistic approaches to estimate personal exposure to air pollutants have relied on input distributions from AER measurements. An algorithm for probabilistically estimating AER was developed based on the Lawrence Berkley National Laboratory Infiltration model utilizing housing characteristics and meteorological data with adjustment for window opening behavior. The algorithm was evaluated by comparing modeled and measured AERs in four US cities (Los Angeles, CA; Detroit, MI; Elizabeth, NJ; and Houston, TX) inputting study-specific data. The impact on the modeled AER of using publically available housing data representative of the region for each city was also assessed. Finally, modeled AER based on region-specific inputs was compared with those estimated using literature-based distributions. While modeled AERs were similar in magnitude to the measured AER they were consistently lower for all cities except Houston. AERs estimated using region-specific inputs were lower than those using study-specific inputs due to differences in window opening probabilities. The algorithm produced more spatially and temporally variable AERs compared with literature-based distributions reflecting within- and between-city differences, helping reduce error in estimates of air pollutant exposure.

Modification of ambient air pollution health associations by residential air exchange rates in multiple US cities

Introduction. Ambient air pollutant infiltration indoors, which varies with air exchange rate (AER), is a key factor affecting the contribution of ambient concentrations to total personal exposure ...

Air Pollution Exposure Model for Individuals (EMI) in Health Studies: Evaluation for Ambient PM2.5 in Central North Carolina.

Air pollution health studies of fine particulate matter (diameter ≤2.5 μm, PM2.5) often use outdoor concentrations as exposure surrogates. Failure to account for variability of indoor infiltration of ambient PM2.5 and time indoors can induce exposure errors. We developed and evaluated an exposure model for individuals (EMI), which predicts five tiers of individual-level exposure metrics for ambient PM2.5 using outdoor concentrations, questionnaires, weather, and time-location information. We linked a mechanistic air exchange rate (AER) model to a mass-balance PM2.5 infiltration model to predict residential AER (Tier 1), infiltration factors (Tier 2), indoor concentrations (Tier 3), personal exposure factors (Tier 4), and personal exposures (Tier 5) for ambient PM2.5. Using cross-validation, individual predictions were compared to 591 daily measurements from 31 homes (Tiers 1-3) and participants (Tiers 4-5) in central North Carolina. Median absolute differences were 39% (0.17 h(-1)) for Tier 1, 18% (0.10) for Tier 2, 20% (2.0 μg/m(3)) for Tier 3, 18% (0.10) for Tier 4, and 20% (1.8 μg/m(3)) for Tier 5. The capability of EMI could help reduce the uncertainty of ambient PM2.5 exposure metrics used in health studies.

Estimation of bias with the single-zone assumption in measurement of residential air exchange using the perfluorocarbon tracer gas method.

Residential air exchange rates (AERs) are vital in understanding the temporal and spatial drivers of indoor air quality (IAQ). Several methods to quantify AERs have been used in IAQ research, often with the assumption that the home is a single, well-mixed air zone. Since 2005, Health Canada has conducted IAQ studies across Canada in which AERs were measured using the perfluorocarbon tracer (PFT) gas method. Emitters and detectors of a single PFT gas were placed on the main floor to estimate a single-zone AER (AER1z). In three of these studies, a second set of emitters and detectors were deployed in the basement or second floor in approximately 10% of homes for a two-zone AER estimate (AER2z). In total, 287 daily pairs of AER2z and AER1z estimates were made from 35 homes across three cities. In 87% of the cases, AER2z was higher than AER1z. Overall, the AER1z estimates underestimated AER2z by approximately 16% (IQR: 5–32%). This underestimate occurred in all cities and seasons and varied in magnitude seasonally, between homes, and daily, indicating that when measuring residential air exchange using a single PFT gas, the assumption of a single well-mixed air zone very likely results in an under prediction of the AER.

Modeling spatial and temporal variability of residential air exchange rates for the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS).

Air pollution health studies often use outdoor concentrations as exposure surrogates. Failure to account for variability of residential infiltration of outdoor pollutants can induce exposure errors and lead to bias and incorrect confidence intervals in health effect estimates. The residential air exchange rate (AER), which is the rate of exchange of indoor air with outdoor air, is an important determinant for house-to-house (spatial) and temporal variations of air pollution infiltration. Our goal was to evaluate and apply mechanistic models to predict AERs for 213 homes in the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS), a cohort study of traffic-related air pollution exposures and respiratory effects in asthmatic children living near major roads in Detroit, Michigan. We used a previously developed model (LBL), which predicts AER from meteorology and questionnaire data on building characteristics related to air leakage, and an extended version of this model (LBLX) that includes natural ventilation from open windows. As a critical and novel aspect of our AER modeling approach, we performed a cross validation, which included both parameter estimation (i.e., model calibration) and model evaluation, based on daily AER measurements from a subset of 24 study homes on five consecutive days during two seasons. The measured AER varied between 0.09 and 3.48 h−1 with a median of 0.64 h−1. For the individual model-predicted and measured AER, the median absolute difference was 29% (0.19 h‑1) for both the LBL and LBLX models. The LBL and LBLX models predicted 59% and 61% of the variance in the AER, respectively. Daily AER predictions for all 213 homes during the three year study (2010–2012) showed considerable house-to-house variations from building leakage differences, and temporal variations from outdoor temperature and wind speed fluctuations. Using this novel approach, NEXUS will be one of the first epidemiology studies to apply calibrated and home-specific AER models, and to include the spatial and temporal variations of AER for over 200 individual homes across multiple years into an exposure assessment in support of improving risk estimates.

Refined ambient PM2.5 exposure surrogates and the risk of myocardial infarction

Using a case-crossover study design and conditional logistic regression, we compared the relative odds of transmural (full-wall) myocardial infarction (MI) calculated using exposure surrogates that account for human activity patterns and the indoor transport of ambient PM(2.5) with those calculated using central-site PM(2.5) concentrations to estimate exposure to PM(2.5) of outdoor origin (exposure to ambient PM(2.5)). Because variability in human activity and indoor PM(2.5) transport contributes exposure error in epidemiologic analyses when central-site concentrations are used as exposure surrogates, we refer to surrogates that account for this variability as "refined" surrogates. As an alternative analysis, we evaluated whether the relative odds of transmural MI associated with increases in ambient PM(2.5) is modified by residential air exchange rate (AER), a variable that influences the fraction of ambient PM(2.5) that penetrates and persists indoors. Use of refined exposure surrogates did not result in larger health effect estimates (ORs=1.10-1.11 with each interquartile range (IQR) increase), narrower confidence intervals, or better model fits compared with the analysis that used central-site PM(2.5). We did observe evidence for heterogeneity in the relative odds of transmural MI with residential AER (effect-modification), with residents of homes with higher AERs having larger ORs than homes in lower AER tertiles. For the level of exposure-estimate refinement considered here, our findings add support to the use of central-site PM(2.5) concentrations for epidemiological studies that use similar case-crossover study designs. In such designs, each subject serves as his or her own matched control. Thus, exposure error related to factors that vary spatially or across subjects should only minimally impact effect estimates. These findings also illustrate that variability in factors that influence the fraction of ambient PM(2.5) in indoor air (e.g., AER) could possibly bias health effect estimates in study designs for which a spatiotemporal comparison of exposure effects across subjects is conducted.

Thirdhand cigarette smoke in an experimental chamber: evidence of surface deposition of nicotine, nitrosamines and polycyclic aromatic hydrocarbons and de novo formation of NNK

BackgroundA growing body of evidence shows that secondhand cigarette smoke undergoes numerous chemical changes after it is released into the air: it can adsorb to indoor surfaces, desorb back into...

Identifying housing and meteorological conditions influencing residential air exchange rates in the DEARS and RIOPA studies: development of distributions for human exposure modeling

Appropriate prediction of residential air exchange rate (AER) is important for estimating human exposures in the residential microenvironment, as AER drives the infiltration of outdoor-generated air pollutants indoors. AER differences among homes may result from a number of factors, including housing characteristics and meteorological conditions. Residential AER data collected in the Detroit Exposure and Aerosol Research Study (DEARS) and the Relationships of Indoor, Outdoor and Personal Air (RIOPA) study were analyzed to determine whether the influence of a number of housing and meteorological conditions on AER were consistent across four cities in different regions of the United States (Detroit MI, Elizabeth NJ, Houston TX, Los Angeles, CA). Influential factors were identified and used as binning variables for deriving final AER distributions for the use in exposure modeling. In addition, both between-home and within-home variance in AER in DEARS were quantified with the goal of identifying reasonable AER resampling frequencies for use in longitudinal exposure modeling efforts. The results of this analysis indicate that residential AER is depended on ambient temperature, the presence (or not) of central air conditioning, and the age of the home. Furthermore, between-home variability in AER accounted for the majority (67%) of the total variance in AER for Detroit homes, indicating lower within-home variability. These findings are compared with other previously published AER distributions, and the implications for exposure modeling are discussed.

Influence of human activity patterns, particle composition, and residential air exchange rates on modeled distributions of PM2.5 exposure compared with central-site monitoring data

Central-site monitors do not account for factors such as outdoor-to-indoor transport and human activity patterns that influence personal exposures to ambient fine-particulate matter (PM(2.5)). We describe and compare different ambient PM(2.5) exposure estimation approaches that incorporate human activity patterns and time-resolved location-specific particle penetration and persistence indoors. Four approaches were used to estimate exposures to ambient PM(2.5) for application to the New Jersey Triggering of Myocardial Infarction Study. These include: Tier 1, central-site PM(2.5) mass; Tier 2A, the Stochastic Human Exposure and Dose Simulation (SHEDS) model using literature-based air exchange rates (AERs); Tier 2B, the Lawrence Berkeley National Laboratory (LBNL) Aerosol Penetration and Persistence (APP) and Infiltration models; and Tier 3, the SHEDS model where AERs were estimated using the LBNL Infiltration model. Mean exposure estimates from Tier 2A, 2B, and 3 exposure modeling approaches were lower than Tier 1 central-site PM(2.5) mass. Tier 2A estimates differed by season but not across the seven monitoring areas. Tier 2B and 3 geographical patterns appeared to be driven by AERs, while seasonal patterns appeared to be due to variations in PM composition and time activity patterns. These model results demonstrate heterogeneity in exposures that are not captured by the central-site monitor.

An Apparatus for Generating Aged Cigarette Smoke for Controlled Human Exposure Studies

Research has shown that cigarette smoke changes chemically and physically after it is released into indoor air, that these changes can increase secondhand smoke (SHS) toxicity, and that acute exposures to even low levels of SHS increase the risk of cardiopulmonary disease. We designed a system to reproduce realistic SHS exposures in the laboratory for use in controlled human exposure studies. We generated cigarette smoke with a smoking machine, diluted it and conducted it through a 6 m3 stainless steel flow reactor at rates equivalent to the upper ranges of normal residential air exchange rates, to create aged cigarette smoke as a model for secondhand cigarette smoke. We observed that approximately 50% of the particle mass deposited within the system and that particle deposition percentage was higher when absorbent materials were placed within the system. The particle size ranges and deposition percentages, coefficients and velocities observed for this smoke aerosol are in good agreement with published va...

Setting maximum emission rates from ozone emitting consumer appliances in the United States and Canada

A Monte Carlo analysis of indoor ozone levels in four cities was applied to provide guidance to regulatory agencies on setting maximum ozone emission rates from consumer appliances. Measured distributions of air exchange rates, ozone decay rates and outdoor ozone levels at monitoring stations were combined with a steady-state indoor air quality model resulting in emission rate distributions (mg h−1) as a function of % of building hours protected from exceeding a target maximum indoor concentration of 20 ppb. Whole-year, summer and winter results for Elizabeth, NJ, Houston, TX, Windsor, ON, and Los Angeles, CA exhibited strong regional differences, primarily due to differences in air exchange rates. Infiltration of ambient ozone at higher average air exchange rates significantly reduces allowable emission rates, even though air exchange also dilutes emissions from appliances. For Houston, TX and Windsor, ON, which have lower average residential air exchange rates, emission rates ranged from −1.1 to 2.3 mg h−1 for scenarios that protect 80% or more of building hours from experiencing ozone concentrations greater than 20 ppb in summer. For Los Angeles, CA and Elizabeth, NJ, with higher air exchange rates, only negative emission rates were allowable to provide the same level of protection. For the 80th percentile residence, we estimate that an 8-h average limit concentration of 20 ppb would be exceeded, even in the absence of an indoor ozone source, 40 or more days per year in any of the cities analyzed. The negative emission rates emerging from the analysis suggest that only a zero-emission rate standard is prudent for Los Angeles, Elizabeth, NJ and other regions with higher summertime air exchange rates. For regions such as Houston with lower summertime air exchange rates, the higher emission rates would likely increase occupant exposure to the undesirable products of ozone reactions, thus reinforcing the need for zero-emission rate standard.

Application of Refined Exposure Surrogates in Ambient PM2.5 Epidemiology Studies: Accounting for Temporal/Spatial Variations in Particulate Matter Infiltration

S-29A4-3 Background/Aims: In particulate matter (PM) epidemiology, outdoor fine PM (PM2.5) mass/species concentrations are frequently used as surrogates of exposure to outdoor-generated PM2.5. The penetration of outdoor particles into indoor spaces and their persistence indoors varies due to variations in air exchange rate/housing stock/ventilation practices and variations in particle properties/source mix. Exposure to outdoor-generated PM also depends on time-activity patterns. We hypothesized that accounting for human activity patterns and outdoor-to-indoor particle transport would result in less exposure error and bias than the use of central site PM2.5 alone, resulting in larger risk estimates, narrower confidence intervals, and better model fits. Methods: To test this hypothesis, we used case-crossover methods to estimate the risk of myocardial infarction associated with the ambient PM2.5 concentration in the previous 24 hours. Here, we compare results from epidemiological analyses where PM2.5 was measured at a central site (Tier 1) to those where population mean PM2.5 exposures were estimated using the Stochastic Human Exposure and Dose Simulation model (Tier 2), a probabilistic model that introduces temporal variability in exposure estimates by accounting for time-activity patterns and seasonal differences in residential air exchange rates. Results: In Tier 1 analyses, each interquartile increase in PM2.5 concentration in the previous 24 hours was associated with a significantly increased risk of a transmural myocardial infarction (odds ratio = 1.10; 95% confidence interval = 1.00, 1.20). In Tier 2 analyses, each interquartile increase was associated with a similarly sized effect (odds ratio = 1.09; 1.00, 1.19). AIC values for each model were also similar. Conclusion: Future work will further refine exposure estimates to account for spatial and temporal variability in residential air exchange rates. Comparisons between Tier 1 and 2 models within a cohort study estimating the risk of a newborn infant being small for gestational age associated with mean first and third trimester PM2.5 concentrations will also be evaluated and presented.