Evaluation of the recursive model approach for estimating particulate matter infiltration efficiencies using continuous light scattering data

Fine particulate matter infiltration at Western Montana residences during wildfire season

BACKGROUND/AIMS

Wildfire air pollution is a growing public health concern as wildfires increase in size, intensity, and duration in the United States. The public is often encouraged to stay indoors during wildfire smoke events to reduce exposure. However, there is limited information on how much wildfire smoke infiltrates indoors at residences and what household/behavioral characteristics contribute to higher infiltration. We assessed fine particulate matter (PM2.5) infiltration into Western Montana residences during wildfire season.

METHODS

We measured continuous outdoor and indoor PM2.5 concentrations from July-October 2022 at 20 residences in Western Montana during wildfire season using low-cost PM2.5 sensors. We used paired outdoor/indoor PM2.5 data from each household to calculate infiltration efficiency (Finf; range 0-1; higher values indicate more outdoor PM2.5 infiltration to the indoor environment) using previously validated methods. Analyses were conducted for all households combined and for various household subgroups.

RESULTS

Median (25th percentile, 75th percentile) daily outdoor PM2.5 at the households was 3.7 μg/m3 (2.1, 7.1) during the entire study period and 29.0 μg/m3 (19.0, 49.4) during a 2-week period in September impacted by wildfire smoke. Median daily indoor PM2.5 at the households was 2.5 μg/m3 (1.3, 5.5) overall and 10.4 μg/m3 (5.6, 21.0) during the wildfire period. Overall Finf was 0.34 (95 % Confidence Interval [95%CI]: 0.33, 0.35) with lower values during the wildfire period (0.32; 95%CI: 0.28, 0.36) versus non-wildfire period (0.39; 95%CI: 0.37, 0.42). Indoor PM2.5 concentrations and Finf varied substantially across household subgroups such as household income, age of the home, presence of air conditioning units, and use of portable air cleaners.

CONCLUSIONS

Indoor PM2.5 was substantially higher during wildfire-impacted periods versus the rest of the study. Indoor PM2.5 and Finf were highly variable across households. Our results highlight potentially modifiable behaviors and characteristics that can be used in targeted intervention strategies.

The infiltration of wildfire smoke and its potential dose on pregnant women: Lessons learned from Indonesia wildfires in 2019

The occurrence of wildfires in Indonesia is prevalent during drought seasons. Multiple toxic pollutants emitted from wildfires have deleterious effects on pregnant women. However, the evidence for these on pregnant women was underreported. The study conducted 24-h monitoring of fine particulate matter (PM2.5) concentrations indoors and outdoors in 9 low-income homes in Palangka Raya during the 2019 wildfire season and 6 low-income homes during the 2019 non-wildfire season. A hundred and seventy pregnant women had their PM exposure assessed between July and October 2019 using personal monitors. It was observed that outdoor air pollutant levels were greater than those found indoors without indoor sources. The findings indicate that indoor PM2.5 concentrations were modestly increased by 1.2 times higher than outdoor, suggesting that buildings only partially protected people from exposure during wildfires. The concentrations of PM2.5 were found to be comparatively higher indoors in residential buildings with wood material than in brick houses. The study findings indicate that 8 out of 12 brick houses exhibited a notable RI/O24 h of less than 1 during the wildfires, whereas all I/O24 h ratios during the non-wildfire season were >1, suggesting the influence of indoor sources. Based on the estimation of daily PM2.5 dose, pregnant women received around 21% of their total daily dose during sedentary activity involving cooking. The present research offers empirical support for the view that indoor air quality in low-income households is affected by a complex combination of factors, including wildfire smoke, air tightness, and occupant behaviour. Also, this situation is more likely a potential risk to pregnant women being exposed to wildfire smoke.

Long-Term Indoor-Outdoor PM2.5 Measurements Using PurpleAir Sensors: An Improved Method of Calculating Indoor-Generated and Outdoor-Infiltrated Contributions to Potential Indoor Exposure

Low-cost monitors make it possible now for the first time to collect long-term (months to years) measurements of potential indoor exposure to fine particles. Indoor exposure is due to two sources: particles infiltrating from outdoors and those generated by indoor activities. Calculating the relative contribution of each source requires identifying an infiltration factor. We develop a method of identifying periods when the infiltration factor is not constant and searching for periods when it is relatively constant. From an initial regression of indoor on outdoor particle concentrations, a Forbidden Zone can be defined with an upper boundary below which no observations should appear. If many observations appear in the Forbidden Zone, they falsify the assumption of a single constant infiltration factor. This is a useful quality assurance feature, since investigators may then search for subsets of the data in which few observations appear in the Forbidden Zone. The usefulness of this approach is illustrated using examples drawn from the PurpleAir network of optical particle monitors. An improved algorithm is applied with reduced bias, improved precision, and a lower limit of detection than either of the two proprietary algorithms offered by the manufacturer of the sensors used in PurpleAir monitors.

Partitioning indoor-generated and outdoor-generated PM2.5 from real-time residential measurements in urban and peri-urban Beijing

Limited number of projects have attempted to partition and quantify indoor- and outdoor-generated PM_2.5 (PM_2.5^ig and PM_2.5^og) where strong indoor sources (e.g., solid fuel, tobacco smoke, or kerosene) exist. This study aimed to apply and refine a previous recursive model used to derive infiltration efficiency (F_inf) to additionally partition pollution concentrations into indoor and outdoor origins within residences challenged by elevated ambient and indoor combustion-related sources. During the winter of 2016 and summer of 2017 we collected residential measurements in 72 homes in urban and peri-urban Beijing, 12 of which had additional paired residential outdoor measurements during the summer season. Local ambient measurements were collected throughout. We then compared the calculated PM_2.5^ig and using (i) outdoor and (ii) ambient measurements as model inputs. The results from outdoor and ambient measurements were not significantly different, which suggests that ambient measurements can be used as a model input for pollution origin partitioning when paired outdoor measurements are not available. From the results calculated using ambient measurements, the mean percentage contribution of indoor-generated PM_2.5 was 19 % (σ = 22 %), and 7 % (11 %) of the total indoor PM_2.5 for peri-urban and urban homes respectively during the winter; and 18 % (18 %) and 6 % (10 %) of the total indoor PM_2.5 during the summer. Partitioning pollution into PM_2.5^ig and PM_2.5^og is important to allow investigation of distinct associations between health outcomes and particulate mixes, often with different physiochemical composition and toxicity. It will also inform targeted interventions that impact indoor and outdoor sources of pollution (e.g., domestic fuel switching vs. power generation), which are typically radically different in design and implementation.

Indoor Air Quality Considerations for Laboratory Animals in Wildfire-Impacted Regions-A Pilot Study

Wildfire events are increasing across the globe. The smoke generated as a result of this changing fire landscape is potentially more toxic than air pollution from other ambient sources, according to recent studies. This is especially concerning for populations of humans or animals that live downwind of areas that burn frequently, given that ambient exposure to wildfire smoke cannot be easily eliminated. We hypothesized that a significant indoor air pollution risk existed for laboratory animal facilities located proximal to fire-prone areas. Here, we measured real time continuous outdoor and indoor air quality for 28 days at a laboratory animal facility located in the Rocky Mountain region. We demonstrated that during a wildfire event, the indoor air quality of this animal facility is influenced by ambient smoke events. The daily average indoor fine particulate matter value in an animal room exceeded the Environmental Protection Agency's ambient annual standard 14% of the time and exceeded the World Health Organization's ambient annual guideline 71% of the time. We further show that specialized cage filtration systems are capable of mitigating air pollution penetrance and could improve an animal's microenvironment. The potential effects for laboratory animal physiology that occur in response to the exposure levels and durations measured in this study remain to be determined; yet, even acute wildfire exposure events have been previously correlated with significant differences in gene regulatory and metabolic processes in vivo. We believe these findings warrant consideration for indoor laboratory animal facility air quality monitoring and development of smoke exposure prevention and response protocols, especially among facilities located downwind of fire-prone landscapes.

Outdoor and indoor fine particulate matter at skilled nursing facilities in the western United States during wildfire and non-wildfire seasons

Wildfire activity is increasing in parts of the world where extreme drought and warming temperatures contribute to fireprone conditions, including the western United States. The elderly are among the most vulnerable, and those in long-term care with preexisting conditions have added risk for adverse health outcomes from wildfire smoke exposure. In this study, we report continuous co-located indoor and outdoor fine particulate matter (PM_2.5 ) measurements at four skilled nursing facilities in the western United States. Throughout the year 2020, over 8000 h of data were collected, which amounted to approximately 300 days of indoor and outdoor sampling at each facility. The highest indoor 24 h average PM_2.5 recorded at each facility was 43.6 µg/m³ , 103.2 µg/m³ , 35.4 µg/m³ , and 202.5 µg/m³ , and these peaks occurred during the wildfire season. The indoor-to-outdoor PM_2.5 ratio and calculated infiltration efficiencies indicated high variation in the impact of wildfire events on Indoor Air Quality between the four facilities. Notably, infiltration efficiency ranged from 0.22 to 0.76 across the four facilities. We propose that this variability is evidence that PM_2.5 infiltration may be impacted by modifiable building characteristics and human behavioral factors, and this should be addressed in future studies.

Indoor particle dynamics in a school office: determination of particle concentrations, deposition rates and penetration factors under naturally ventilated conditions

Recently, the problem of indoor particulate matter pollution has received much attention. An increasing number of epidemiological studies show that the concentration of atmospheric particulate matter has a significant effect on human health, even at very low concentrations. Most of these investigations have relied upon outdoor particle concentrations as surrogates of human exposures. However, considering that the concentration distribution of the indoor particulate matter is largely dependent on the extent to which these particles penetrate the building and on the degree of suspension in the indoor air, human exposures to particles of outdoor origin may not be equal to outdoor particle concentration levels. Therefore, it is critical to understand the relationship between the particle concentrations found outdoors and those found in indoor micro-environments. In this study, experiments were conducted using a naturally ventilated office located in Qingdao, China. The indoor and outdoor particle concentrations were measured at the same time using an optical counter with four size ranges. The particle size distribution ranged from 0.3 to 2.5 μm, and the experimental period was from April to September, 2016. Based on the experimental data, the dynamic and mass balance model based on time was used to estimate the penetration rate and deposition rate at air exchange rates of 0.03-0.25 h^-1. The values of the penetration rate and deposition velocity of indoor particles were determined to range from 0.45 to 0.82 h^-1 and 1.71 to 2.82 m/h, respectively. In addition, the particulate pollution exposure in the indoor environment was analyzed to estimate the exposure hazard from indoor particulate matter pollution, which is important for human exposure to particles and associated health effects. The conclusions from this study can serve to provide a better understanding the dynamics and behaviors of airborne particle entering into buildings. And they will also highlight effective methods to reduce exposure to particles in office buildings.

Estimation of residential fine particulate matter infiltration in Shanghai, China

Ambient concentrations of fine particulate matter (PM_2.5) concentration is often used as an exposure surrogate to estimate PM_2.5 health effects in epidemiological studies. Ignoring the potential variations in the amount of outdoor PM_2.5 infiltrating into indoor environments will cause exposure misclassification, especially when people spend most of their time indoors. As it is not feasible to measure the PM_2.5 infiltration factor (F_inf) for each individual residence, we aimed to build models for residential PM_2.5F_inf prediction and to evaluate seasonal F_inf variations among residences. We repeated collected paired indoor and outdoor PM_2.5 filter samples for 7 continuous days in each of the three seasons (hot, cold and transitional seasons) from 48 typical homes of Shanghai, China. PM_2.5-bound sulfur on the filters was measured by X-ray fluorescence for PM_2.5F_inf calculation. We then used stepwise-multiple linear regression to construct season-specific models with climatic variables and questionnaire-based predictors. All models were evaluated by the coefficient of determination (R²) and root mean square error (RMSE) from a leave-one-out-cross-validation (LOOCV). The 7-day mean (±SD) of PM_2.5F_inf across all observations was 0.83 (±0.18). F_inf was found higher and more varied in transitional season (12-25 °C) than hot (>25 °C) and cold (<12 °C) seasons. Air conditioning use and meteorological factors were the most important predictors during hot and cold seasons; Floor of residence and building age were the best transitional season predictors. The models predicted 60.0%-68.4% of the variance in 7-day averages of F_inf, The LOOCV analysis showed an R² of 0.52 and an RMSE of 0.11. Our finding of large variation in residential PM_2.5F_inf between seasons and across residences within season indicated the important source of outdoor-generated PM_2.5 exposure heterogeneity in epidemiologic studies. Our models based on readily available data may potentially improve the accuracy of estimates of the health effects of PM_2.5 exposure.

A method to measure the ozone penetration factor in residences under infiltration conditions: application in a multifamily apartment unit

Recent experiments have demonstrated that outdoor ozone reacts with materials inside residential building enclosures, potentially reducing indoor exposures to ozone or altering ozone reaction byproducts. However, test methods to measure ozone penetration factors in residences (P) remain limited. We developed a method to measure ozone penetration factors in residences under infiltration conditions and applied it in an unoccupied apartment unit. Twenty-four repeated measurements were made, and results were explored to (i) evaluate the accuracy and repeatability of the new procedure using multiple solution methods, (ii) compare results from 'interference-free' and conventional UV absorbance ozone monitors, and (iii) compare results against those from a previously published test method requiring artificial depressurization. The mean (±s.d.) estimate of P was 0.54 ± 0.10 across a wide range of conditions using the new method with an interference-free monitor; the conventional monitor was unable to yield meaningful results due to relatively high limits of detection. Estimates of P were not clearly influenced by any indoor or outdoor environmental conditions or changes in indoor decay rate constants. This work represents the first known measurements of ozone penetration factors in a residential building operating under natural infiltration conditions and provides a new method for widespread application in buildings.

Indoor particulate matter in rural, wood stove heated homes

Ambient particulate matter (PM) exposures have adverse impacts on public health, but research evaluating indoor PM concentrations in rural homes in the United States using wood as fuel for heating is limited. Our objectives were to characterize indoor PM mass and particle number concentrations (PNCs), quantify infiltration of outdoor PM into the indoor environment, and investigate potential predictors of concentrations and infiltration in 96 homes in the northwestern US and Alaska using wood stoves as the primary source of heating. During two forty-eight hour sampling periods during the pre-intervention winter of a randomized trial, we assessed PM mass (<2.5μm) and PNCs (particles/cm(3)) in six size fractions (0.30-0.49, 0.50-0.99, 1.00-2.49, 2.5-5.0, 5.0-10.0, 10.0+μm). Daily mean (sd) PM2.5 concentrations were 28.8 (28.5)μg/m(3) during the first sampling period and 29.1 (30.1)μg/m(3) during the second period. In repeated measures analyses, household income was inversely associated with PM2.5 and smaller size fraction PNCs, in particular. Time of day was a significant predictor of indoor and outdoor PM2.5 concentrations, and infiltration efficiency was relatively low (Finf (sd)=0.27 (0.20)). Our findings demonstrate relatively high mean PM concentrations in these wood burning homes and suggest potential targets for interventions for improving indoor air quality and health in rural settings.

A new exposure metric for traffic-related air pollution? An analysis of determinants of hopanes in settled indoor house dust

BACKGROUND

Exposure to traffic-related air pollution (TRAP) can adversely impact health but epidemiologic studies are limited in their abilities to assess long-term exposures and incorporate variability in indoor pollutant infiltration.

METHODS

In order to examine settled house dust levels of hopanes, engine lubricating oil byproducts found in vehicle exhaust, as a novel TRAP exposure measure, dust samples were collected from 171 homes in five Canadian cities and analyzed by gas chromatography-mass spectrometry. To evaluate source contributions, the relative abundance of the highest concentration hopane monomer in house dust was compared to that in outdoor air. Geographic variables related to TRAP emissions and outdoor NO2 concentrations from city-specific TRAP land use regression (LUR) models were calculated at each georeferenced residence location and assessed as predictors of variability in dust hopanes.

RESULTS

Hopanes relative abundance in house dust and ambient air were significantly correlated (Pearson's r=0.48, p<0.05), suggesting that dust hopanes likely result from traffic emissions. The proportion of variance in dust hopanes concentrations explained by LUR NO2 was less than 10% in Vancouver, Winnipeg and Toronto while the correlations in Edmonton and Windsor explained 20 to 40% of the variance. Modeling with household factors such as air conditioning and shoe removal along with geographic predictors related to TRAP generally increased the proportion of explained variability (10-80%) in measured indoor hopanes dust levels.

CONCLUSIONS

Hopanes can consistently be detected in house dust and may be a useful tracer of TRAP exposure if determinants of their spatiotemporal variability are well-characterized, and when home-specific factors are considered.

Prospective study of particulate air pollution exposures, subclinical atherosclerosis, and clinical cardiovascular disease: The Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air)

The Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) was initiated in 2004 to investigate the relation between individual-level estimates of long-term air pollution exposure and the progression of subclinical atherosclerosis and the incidence of cardiovascular disease (CVD). MESA Air builds on a multicenter, community-based US study of CVD, supplementing that study with additional participants, outcome measurements, and state-of-the-art air pollution exposure assessments of fine particulate matter, oxides of nitrogen, and black carbon. More than 7,000 participants aged 45-84 years are being followed for over 10 years for the identification and characterization of CVD events, including acute myocardial infarction and other coronary artery disease, stroke, peripheral artery disease, and congestive heart failure; cardiac procedures; and mortality. Subcohorts undergo baseline and follow-up measurements of coronary artery calcium using computed tomography and carotid artery intima-medial wall thickness using ultrasonography. This cohort provides vast exposure heterogeneity in ranges currently experienced and permitted in most developed nations, and the air monitoring and modeling methods employed will provide individual estimates of exposure that incorporate residence-specific infiltration characteristics and participant-specific time-activity patterns. The overarching study aim is to understand and reduce uncertainty in health effect estimation regarding long-term exposure to air pollution and CVD.

Statistical methods used to test for agreement of medical instruments measuring continuous variables in method comparison studies: a systematic review

Background

Accurate values are a must in medicine. An important parameter in determining the quality of a medical instrument is agreement with a gold standard. Various statistical methods have been used to test for agreement. Some of these methods have been shown to be inappropriate. This can result in misleading conclusions about the validity of an instrument. The Bland-Altman method is the most popular method judging by the many citations of the article proposing this method. However, the number of citations does not necessarily mean that this method has been applied in agreement research. No previous study has been conducted to look into this. This is the first systematic review to identify statistical methods used to test for agreement of medical instruments. The proportion of various statistical methods found in this review will also reflect the proportion of medical instruments that have been validated using those particular methods in current clinical practice.

Methodology/Findings

Five electronic databases were searched between 2007 and 2009 to look for agreement studies. A total of 3,260 titles were initially identified. Only 412 titles were potentially related, and finally 210 fitted the inclusion criteria. The Bland-Altman method is the most popular method with 178 (85%) studies having used this method, followed by the correlation coefficient (27%) and means comparison (18%). Some of the inappropriate methods highlighted by Altman and Bland since the 1980s are still in use.

Conclusions

This study finds that the Bland-Altman method is the most popular method used in agreement research. There are still inappropriate applications of statistical methods in some studies. It is important for a clinician or medical researcher to be aware of this issue because misleading conclusions from inappropriate analyses will jeopardize the quality of the evidence, which in turn will influence quality of care given to patients in the future.

Modeling Of In-Vehicle Human Exposure to Ambient Fine Particulate Matter

A method for estimating in-vehicle PM(2.5) exposure as part of a scenario-based population simulation model is developed and assessed. In existing models, such as the Stochastic Exposure and Dose Simulation model for Particulate Matter (SHEDS-PM), in-vehicle exposure is estimated using linear regression based on area-wide ambient PM(2.5) concentration. An alternative modeling approach is explored based on estimation of near-road PM(2.5) concentration and an in-vehicle mass balance. Near-road PM(2.5) concentration is estimated using a dispersion model and fixed site monitor (FSM) data. In-vehicle concentration is estimated based on air exchange rate and filter efficiency. In-vehicle concentration varies with road type, traffic flow, windspeed, stability class, and ventilation. Average in-vehicle exposure is estimated to contribute 10 to 20 percent of average daily exposure. The contribution of in-vehicle exposure to total daily exposure can be higher for some individuals. Recommendations are made for updating exposure models and implementation of the alternative approach.

Validation of continuous particle monitors for personal, indoor, and outdoor exposures

Continuous monitors can be used to supplement traditional filter-based methods of determining personal exposure to air pollutants. They have the advantages of being able to identify nearby sources and detect temporal changes on a time scale of a few minutes. The Windsor Ontario Exposure Assessment Study (WOEAS) adopted an approach of using multiple continuous monitors to measure indoor, outdoor (near-residential) and personal exposures to PM₂.₅, ultrafine particles and black carbon. About 48 adults and households were sampled for five consecutive 24-h periods in summer and winter 2005, and another 48 asthmatic children for five consecutive 24-h periods in summer and winter 2006. This article addresses the laboratory and field validation of these continuous monitors. A companion article (Wheeler et al., 2010) provides similar analyses for the 24-h integrated methods, as well as providing an overview of the objectives and study design. The four continuous monitors were the DustTrak (Model 8520, TSI, St. Paul, MN, USA) and personal DataRAM (pDR) (ThermoScientific, Waltham, MA, USA) for PM₂.₅; the P-Trak (Model 8525, TSI) for ultrafine particles; and the Aethalometer (AE-42, Magee Scientific, Berkeley, CA, USA) for black carbon (BC). All monitors were tested in multiple co-location studies involving as many as 16 monitors of a given type to determine their limits of detection as well as bias and precision. The effect of concentration and electronic drift on bias and precision were determined from both the collocated studies and the full field study. The effect of rapid changes in environmental conditions on switching an instrument from indoor to outdoor sampling was also studied. The use of multiple instruments for outdoor sampling was valuable in identifying occasional poor performance by one instrument and in better determining local contributions to the spatial variation of particulate pollution. Both the DustTrak and pDR were shown to be in reasonable agreement (R² of 90 and 70%, respectively) with the gravimetric PM₂.₅ method. Both instruments had limits of detection of about 5 μg/m³. The DustTrak and pDR had multiplicative biases of about 2.5 and 1.6, respectively, compared with the gravimetric samplers. However, their average bias-corrected precisions were <10%, indicating that a proper correction for bias would bring them into very good agreement with standard methods. Although no standard methods exist to establish the bias of the Aethalometer and P-Trak, the precision was within 20% for the Aethalometer and within 10% for the P-Trak. These findings suggest that all four instruments can supply useful information in environmental studies.

Infiltration of outdoor ultrafine particles into a test house

Ultrafine particles (UFP) (<100 nm) have been related to adverse human health effects such as oxidative stress and cardiovascular mortality. However, human exposure to particles of outdoor origin is heavily dependent on their infiltration into homes. The infiltration factor (Finf) and its variation as a function of several factors becomes of enormous importance in epidemiological studies. The objective of this study is to investigate the transport of UFP into a residential building and to determine the functional dependence of infiltration on particle size and air change rate. A secondary objective was to estimate the values of the penetration coefficient P and composite deposition rate kcomp that enter into the definition of Finf. Using continuous measurements of indoor and outdoor concentrations of size-resolved particles ranging from 5 to 100 nm in a manufactured test house, particle penetration through the building, composite deposition, and the resulting value of Finf were calculated for two cases: closed windows and one window open 7.5 cm. Finf ranged from close to 0 (particles<10 nm) to 0.3 (particles>80 nm) with windows closed and from 0 to 0.6 with one window open. The penetration coefficient (closed windows) increased from about 0.2 for 10-nm particles to an asymptote near 0.6 for particles from 30-100 nm. Open window penetration coefficients were higher, ranging from 0.6 to 0.8. Closed-window composite deposition rates, which included losses to the furnace filter and to the ductwork as well as to interior surfaces, monotonically decreased from levels of about 1.5 h(-1) for 10-nm particles to 0.3 h(-1) for 100-nm particles. For the open-window case, composite deposition rates were higher for particles<20 nm, reaching values of 3.5 h(-1). Mean standard errors associated with estimates of P, kcomp, and Finf for two series of measurements ranged from 1.0% to 4.4%.

Modeling residential fine particulate matter infiltration for exposure assessment

Individuals spend the majority of their time indoors; therefore, estimating infiltration of outdoor-generated fine particulate matter (PM(2.5)) can help reduce exposure misclassification in epidemiological studies. As indoor measurements in individual homes are not feasible in large epidemiological studies, we evaluated the potential of using readily available data to predict infiltration of ambient PM(2.5) into residences. Indoor and outdoor light scattering measurements were collected for 84 homes in Seattle, Washington, USA, and Victoria, British Columbia, Canada, to estimate residential infiltration efficiencies. Meteorological variables and spatial property assessment data (SPAD), containing detailed housing characteristics for individual residences, were compiled for both study areas using a geographic information system. Multiple linear regression was used to construct models of infiltration based on these data. Heating (October to February) and non-heating (March to September) season accounted for 36% of the yearly variation in detached residential infiltration. Two SPAD housing characteristic variables, low building value, and heating with forced air, predicted 37% of the variation found between detached residential infiltration during the heating season. The final model, incorporating temperature and the two SPAD housing characteristic variables, with a seasonal interaction term, explained 54% of detached residential infiltration. Residences with low building values had higher infiltration efficiencies than other residences, which could lead to greater exposure gradients between low and high socioeconomic status individuals than previously identified using only ambient PM(2.5) concentrations. This modeling approach holds promise for incorporating infiltration efficiencies into large epidemiology studies, thereby reducing exposure misclassification.