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Int. J. Environ. Res. Public Health 2010, 7(8), 3211-3224; doi:10.3390/ijerph7083211

Exploring Variation and Predictors of Residential Fine Particulate Matter Infiltration
Health Canada, 269 Laurier Ave West, Ottawa, Ontario K1A 0K9, Canada
Simon Fraser University, 8888 University Drive, Burnaby, British Columbia V5A 1S6, Canada
University of British Columbia, 2206 East Mall, Vancouver, British Columbia V6T 1Z3, Canada
11568 Woodhollow Ct, Reston, VA 20191, USA
The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada
Environment Canada, 335 River Road, Ottawa, Ontario K1V 1C7, Canada
University of Toronto, 200 College Street, Toronto, Ontario M5S 3E5, Canada
Author to whom correspondence should be addressed.
Received: 30 June 2010; in revised form: 12 August 2010 / Accepted: 13 August 2010 / Published: 16 August 2010


Although individuals spend the majority of their time indoors, most epidemiological studies estimate personal air pollution exposures based on outdoor levels. This almost certainly results in exposure misclassification as pollutant infiltration varies between homes. However, it is often not possible to collect detailed measures of infiltration for individual homes in large-scale epidemiological studies and thus there is currently a need to develop models that can be used to predict these values. To address this need, we examined infiltration of fine particulate matter (PM2.5) and identified determinants of infiltration for 46 residential homes in Toronto, Canada. Infiltration was estimated using the indoor/outdoor sulphur ratio and information on hypothesized predictors of infiltration were collected using questionnaires and publicly available databases. Multiple linear regression was used to develop the models. Mean infiltration was 0.52 ± 0.21 with no significant difference across heating and non-heating seasons. Predictors of infiltration were air exchange, presence of central air conditioning, and forced air heating. These variables accounted for 38% of the variability in infiltration. Without air exchange, the model accounted for 26% of the variability. Effective modelling of infiltration in individual homes remains difficult, although key variables such as use of central air conditioning show potential as an easily attainable indicator of infiltration.
air exchange; air quality; indoor; infiltration; fine particulate matter; PM2.5; residential; sulphur

1. Introduction

Exposure to outdoor particulate matter (PM) has been linked to a wide variety of health effects [13]. However, exposure assessment in epidemiological studies remains a challenge and misclassification limits the power of many studies to find true effects [4,5]. A nearly universal source of exposure misclassification in studies of outdoor air pollution results from estimating exposure based on outdoor levels alone. Indeed, individuals’ true exposures are determined by their time-activity patterns and levels of pollutants in each microenvironment where they spend time—typically indoors at home [6,7]. Surveys of activity patterns for Canada and the United States suggest that adults spend an average of about 88% of their time indoors, 66% of which is in their homes [810]. Exposure misclassification occurs because infiltration of outdoor pollution can vary significantly between residences and also within residences across time [1113].
There are important differences between indoor and outdoor PM composition in addition to differences in concentration. Outdoor generated PM contains relatively more sulphates, nitrates, strong acids, and toxic metals as compared with PM generated in homes, which contains more house dust, endotoxins, mold spores, and fresh combustion products [9]. Consequently, indoor and outdoor generated PM appear to have different health effects. Health effects of indoor PM2.5 have not been thoroughly studied, but have been linked to respiratory problems [1416]. Several studies that measured exposure to both indoor and outdoor PM2.5 have found that outdoor-generated PM generally showed stronger relationships with respiratory [1719] and cardiovascular [18] markers than total or indoor-generated PM2.5 exposure. Therefore, differences in infiltration of outdoor generated PM2.5 across homes could introduce exposure misclassification and may bias health effect estimates [20].
Infiltration differences across homes have the potential to cause differential misclassification, where the degree of misclassification depends on other variables in the analysis (such as disease status). For example, infiltration has been found to depend on characteristics that may also influence disease status, such as socioeconomic status [21,22]. Unlike non-differential misclassification, which generally causes a bias to the null, differential misclassification may cause unexpected effects in health effect estimates by causing a bias toward or away from the null [20].
The infiltration factor (Finf) is defined as the equilibrium proportion of outdoor PM that penetrates indoors and remains suspended [23]. This is determined by the particle penetration efficiency (P, unitless), particle deposition rate (k, h−1), and the air exchange rate (a, h−1) according to the following equation:
F inf = P a a + k
Various studies have estimated Finf for residential homes. Most commonly they have employed sulphur or sulphate as a tracer of outdoor PM2.5 [2428], although indoor and outdoor measures of PM2.5 have also commonly been used to estimate Finf [11,13,21,2931]. Sulphur has few indoor sources and has been demonstrated to be representative of total outdoor infiltrated PM2.5 [9,32,33].
Though a number of studies have estimated Finf in individual homes and some panel studies have considered Finf in epidemiologic analyses [1719], the need to include both indoor and outdoor pollution measurements to estimate Finf has prevented inclusion of infiltration in large-scale epidemiologic studies. The inclusion of infiltration ‘modification’ factors has been limited to air conditioning variables, such as community-wide prevalence of central air conditioning [34,35]. Given the importance of Finf (studies have shown Finf differences can modify indoor exposure rates by a magnitude of 2–4 [11,21,24]), it is important to further examine how Finf varies between homes and what drives these differences. The goal of this study was to determine household factors that influence Finf, as indicated by the sulphur tracer method [27] for homes in Toronto, Ontario.

2. Experimental Section

2.1. Data Collection

Sixty homes from the Toronto area were recruited for the study. Homes were randomly selected from among 1,500 owner-occupied homes with secure backyards participating in the population-based Toronto Child Health Evaluation Questionnaire (T-CHEQ) study [36]. The T-CHEQ sample as a whole (n = 5,619) is representative of the population in terms of socioeconomic and housing variability as compared to the 2001 Canada census [36], however, the criteria of home-ownership with backyard biased the current study to a somewhat higher socioeconomic status. Fifty homes were initially recruited to participate in the indoor and outdoor sampling in 2006 (August to November) and an additional 10 homes were recruited to participate in 2007 (July).
Participating households completed a baseline questionnaire before sampling began. The questionnaire was a detailed home assessment, including variables that were anticipated to influence Finf: home age and size [11,37], home value [21], heating and cooling systems used [21,24], presence of air filters [11,17], presence of storm windows [11,24], and number of residents and pets in the home (which have been observed to increase Finf due to increased air exchange) [37,38]. The assessment was completed by trained technicians along with the home owner during an inspection of the home. Unfortunately, it was not possible to collect information on activities in the home during sampling such as cooking, cleaning, window opening and air conditioner use.
Each home was sampled over a five-day period, with approximately 4 homes sampled concurrently. Most inspections began in the middle of the week and included weekdays and weekend days. Indoor measurements were collected at breathing height inside participants’ homes, typically in the family or living room where participants spent the majority of their time. When possible, measurements were collected away from ventilation ducts, fireplaces, and TVs as these may cause elevated concentrations of particles. Outdoor samplers were located in the backyard of the participants’ homes several meters away from the house and away from any combustion sources such as barbeques, automobiles and other localized outdoor sources of air pollution. The outdoor sampling height was approximately 1.5 meters above ground.
Indoor and outdoor PM2.5 mass was sampled over the entire 5-day period using a Chempass Multi-Component Sampling System (Model 3400, R&P/Thermo Scientific, Waltham, MA). The indoor and outdoor systems used a BGI pump with an AC adapter. The sampler target flow rate was 1.8 L/minute and flow rates more than 20% different from the target rate were considered invalid. Flow rates were assessed pre and post sampling using a soap bubble flow meter (AP Buck, Orlando, FL). The samples were collected on 37 mm Teflon™ filters, which were measured gravimetrically following US EPA quality assurance guidelines [39]. Briefly, filters were pre-conditioned for a minimum of 24 h before weighing at a constant air temperature of 21 oC (±0.5 oC) and constant relative humidity (RH) of 40% (±1%).
All samples were analyzed for total sulphur by a Panalytical Epilson 5 energy dispersive x-ray fluorescence (ED-XRF) spectrometer with a 600-watt gadolinium target tube as an excitation source. Micromatter® standard reference samples were used for the quantitative calibration of the system. A calibration check was performed by analyzing NIST standards (SRM1832 and SRM1833). Following the measurement process, data treatment was performed using complex algorithms that corrected for background signal, spectral interferences, and X-ray signal loss due to absorption by the sample mass. Blank filters were used for background subtraction. Typical analytical limits of detection and uncertainty were estimated to be 3.0 ng/cm2 and 5%, respectively.
The indoor temperature and relative humidity were measured using HOBO Temperature Relative Humidity Data Logger (U10-003, Onset Corporation, Bourne, MA, USA) at three-minute time intervals. Outdoor meteorological data were obtained from the National Climate Data and Information Archive of Environment Canada [40].
Air exchange rate (AER) (the rate at which outdoor air replaces indoor air) was measured in the 50 homes monitored in 2006 using a perfluorocarbon tracer (PFT) source emitting a tracer at a constant rate. This was captured by a capillary adsorption tube (CAT), a passive sampler containing a small amount of activated charcoal adsorbing material [41]. Four PFTs were placed in the corners of the floor where the indoor equipment was located, and one CAT was placed near the center of the floor at head height. After exposure, the CAT was shipped to the lab and analyzed by gas chromatography with an electron capture detector (GC/ECD). Air exchange rate was then calculated using the concentration of PFT detected on the CAT and the volume of the home (estimated by multiplying square footage of the home by the technician’s estimate of ceiling height for the home). Air exchange was measured on the main living floor (in family or living room) only as this has been demonstrated to be a relatively accurate estimate of air exchange for the house: Wallace et al. [42] found inter-floor differences of less than 10% in a three-story home measured over a year. However, it was not possible to assess factors that may influence mixing of air in the home, such as closed doors. Closed doors to rooms or whole floors of the home for extended periods of time (e.g., nighttime) would reduce the effective volume of the home and thereby cause an underestimate of air exchange.
In addition to information collected during exposure monitoring, other variables of interest were collected from publicly available tax records [43] and geographic information system (GIS) data. Specifically, information was obtained on home size and market value from the Municipal Property Assessment Corporation (MPAC) of Ontario, as these factors have been observed to influence Finf [21,37]. GIS data were used to determine the distance of each home to the nearest highway. Roadway classifications in the CanMap RouteLogistics dataset were used, including highways and expressways (400 series highways and expressways).

2.2. Statistical Methods

Infiltration was calculated using the ratio of the indoor concentration and the outdoor concentration of sulphur (Equation 2).
F inf S = S in S out
Linear regression was used to examine the association between housing and climatic variables and Finf. Right-skewed variables were log-normally transformed for regression modelling. Ambient temperature was examined as an effect modifier due to its influence on window opening behaviour, use of air conditioners and indoor/outdoor temperature differences. This was included as an interaction term indicating whether outdoor temperature was above or below 18 °C at time of monitoring. Many of the housing and climate characteristics are expected to be correlated and thus bivariate regressions may only indicate an indirect effect. However, variables without a direct effect may still be helpful for predicting Finf in studies with limited data, and are therefore presented along with multiple linear regression models.
To understand the best predictors of Finf, variables that were associated with Finf (p < 0.10) in a simple linear regression were entered into multiple linear regression models. Two sets of models were examined: one set in which air exchange rate was a potential predictor variable and another set in which it was not, as this variable is often not available in larger-scale epidemiology studies. A stepwise procedure was used to select the most appropriate models, with p < 0.10 as the cut-off for inclusion of variables. The guideline of a minimum of 10 observations per degree of freedom was followed to guard against over-fitting. Collinearity between variables was examined using Pearson and Spearman correlation coefficients and t-tests where appropriate, as well as using the “collin” option available for the REG procedure in SAS 9.1 (SAS Institute Inc., Cary, NC, USA).

3. Results

Table 1 provides descriptive data for Finf, pollutant measurements, and home and climate characteristics that were hypothesized a priori to have an effect on Finf. Of the 60 participating homes, 53 were analyzed for sulphur content indoors and outdoors. Of the calculated Finf values, two homes had an indoor/outdoor sulphur ratio greater than one (2.89 and 3.09) and likely indicate an undetected indoor source of sulphur, for example, an unreported humidifier in the home [9]. Only values of Finf between 0 and 1 are physically valid; therefore, these two homes were excluded from further statistical analysis. An additional five homes had a sulphur ratio >0.90 (ranging from 0.93–0.98) without a correspondingly high air exchange rate; these homes were also excluded from further analysis, leaving a total of 46 homes for analysis. As compared with included homes, the excluded homes were more likely to have higher levels of indoor PM2.5 (13.9 ±7.08 μg/m3 vs. 8.17 ± 5.18 μg/m3, p-value = 0.01) and indoor sulphur (1.08 ±0.35 μg/m3 vs. 0.46 ± 0.31 μg/m3, p < 0.001) (consistent with having an indoor source). They did not differ with respect to other analyzed variables. Additional homes were excluded from specific analyses when covariate data was not available; this was most significant for air exchange which was not measured in the 10 homes sampled in 2007. The participating home-owners were of higher socioeconomic status as compared with the general Canadian population, but home values were representative of the Greater Toronto Area, where the average single-family home price is $435,064 [44].
The mean (±standard deviation) Finf among the 46 included homes was 0.52 ± 0.21, ranging from 0.08 to 0.88 with approximately normal distribution. Means were not significantly different based on whether homes were sampled during times when heating might occur: when the outdoor temperature was below 18 °C, the average Finf was 0.54 ± 0.22 as compared with 0.48 ± 0.21 when temperature was above 18 °C. This difference was not statistically significant (p = 0.34).
The mean sulphur concentration was 0.46 ± 0.31 μg/m3 indoors and 0.76 ± 0.36 μg/m3 outdoors. On average, PM2.5 concentrations were slightly higher outdoors (9.72 ± 3.90 μg/m3) than indoors (8.17 ± 5.18 μg/m3).
The results of simple linear regression are presented in Table 2. Since the great majority of homes had air conditioners (96%), a smaller group of “regular users of air conditioning” was also defined: homes that reported using their air conditioner at least 30 days/year. This group consisted of 24 homes (52%). No other information on use of heating or cooling systems was available, including whether they were actually used during sampling.
In multiple linear regression modeling, air exchange rate, use of air conditioning more than 30 days/year, and having forced air heating remained as independent predictors of Finf, predicting 38% of variability (Table 3). Without air exchange, the model was able to predict 26% of variability.

4. Discussion

Infiltration is rarely measured in studies of air pollution and health because it is generally not feasible to do so for a large number of homes. However, it is an important determinant of personal exposures to outdoor air pollution. Variability between and within homes can be significant, for example, Allen et al. [11] and Wallace et al. [27] found approximately a three- to four-fold range in Finf estimates between individual homes, ranging from 0.24 to 1.00 and 0.26 to 0.87, respectively. Our estimates of Finf show a ten-fold difference across homes, ranging from 0.08 to 0.88. Simple measures of PM2.5 concentrations inside the home are insufficient to address these differences in exposure, since recent work suggest that outdoor particulate matter may be more detrimental to health than particulate matter generated in homes [9,18]. However, activities in homes such as cooking have also been found to contribute significant amounts of potentially toxic PM [45]. Development of reliable and efficient methods for modelling infiltration among large populations is therefore an important goal for improving air pollution health studies.
Toronto experiences relatively hot summers (average daily highs of 27 °C in July) and cold winters (average daily lows of −11 °C in January) [46]. These temperatures necessitate ensuring that buildings are well sealed from outdoor air and typically homes in Canada use mechanical heating/cooling for occupant comfort. It is therefore expected that Toronto homes would have relatively low infiltration as compared with homes in more moderate climates. Indeed, the mean infiltration estimate of 0.52 for homes in Toronto is lower than generally observed in milder climates, such as 0.65 in Seattle [11], 0.62 in Victoria, BC [21] (both averaged over heating and non-heating seasons) and 0.70 in Riverside, CA (measured in fall) [29]. Regions with similar temperature extremes to Toronto that have also conducted measurements across heating and non-heating seasons have found similar estimates: 0.45 and 0.55 in North Carolina [27,31]; and 0.59 in Helsinki [37].
Another seasonally important source of unexplained variability is likely to be window opening behaviour. Meng et al. [47] observed that infiltration was highest when outdoor temperatures were close to 20 °C and decreased at higher and lower temperatures, likely due to window opening. Open windows significantly increase air exchange rates (by an estimated 1.1 air changes per hour as measured by Wallace et al. 2002 [42]) and thereby also increased Finf [11,42,48].
Regular use of air conditioning was a consistent predictor of infiltration, despite a lack of information on actual use during sampling. Homes that reported using air conditioning on more than 30 days per year had significantly lower infiltration than homes that either did not have air conditioning or used it less than 30 days per year. Air conditioned homes are expected to have lower infiltration due to being more tightly sealed as well as experiencing deposition resulting from the ventilation system. This has been demonstrated in a number of studies including Dockery and Spengler [24], Suh et al. [25], Long et al. [49], and Meng et al. [12,47]. In addition, epidemiological studies have found that air conditioning prevalence accounts for some of the variability observed in the health effects of particulate matter across different cities. Janssen et al. [35] and Medina-Ramon et al. [50] found that across cities in the US, higher prevalence of central air conditioning attenuated the acute effects of PM10. A meta-analysis of 19 studies of particulate matter in the US also found that mortality was modified by central air conditioning prevalence: mortality increased by 0.57% (95% CI: 0.39–0.74%) per 10 μg/m3 increase in ambient PM10 in communities with central air conditioning prevalence greater than 30% vs. 0.76% (0.54–0.98%) with prevalence less than 30% [51]. A recent study by Bell et al. [52] confirmed the findings of attenuating effects of central air conditioning use on cardiovascular and respiratory diseases as well as mortality. Differences in central air conditioning prevalence across communities accounted for 17% of the variability in cardiovascular hospitalizations.
Publicly available property assessment data from MPAC was of limited utility in predicting infiltration in these Toronto homes. Hystad et al. [21] also examined the usefulness of property assessment data in Seattle, WA and Victoria, BC for predicting infiltration and found that the value of the structure, also known as improved value, served as an effective surrogate for a number of other housing variables and predicted Finf. Homes with structure value below the median had 15% higher infiltration than homes above the median. However, total market value did not predict Finf in Seattle, nor did it in Toronto. Unlike data sources in BC and WA, MPAC does not separate the home value into structure value and land value, preventing further examination of this relationship.
Of the other variables obtained from MPAC, only presence of forced air heating predicted infiltration, accounting for 7% of the variability. Although presence of central air conditioning was available through MPAC, comparison with home owner report suggests that this variable is not well maintained in the database; of the 39 homes who reported having central air conditioning, only 24 were classified as such in the MPAC database. Therefore, questionnaire data was relied on to classify homes for air conditioning presence.
Home age has previously been found to influence infiltration, though inconsistently across different studies. This is not unexpected as different building methods influencing tightness of the homes differ across regions and over time, and may be altered by home renovations (such as updated windows and insulation). There was a significant linear relationship with older homes having higher infiltration, although this relationship did not remain significant in multivariate models. A trend of lower infiltration in newer homes was also observed by Lachenmyer and Hidy [30], Hanninen et al. [37], and Hystad et al. [21], while Allen et al. [11] found that older homes had lower infiltration in a sample of Seattle homes. No effect of home age was observed by Meng et al. [47].
Distance to expressways was not associated with Finf. It was hypothesized that homes near busy roads may have lower infiltration due to windows being closed to reduce noise, however it is important to note that only 5 (11%) of study homes were within 500 m of expressways and that previous research has shown simple distance-based metrics to be only moderate predictors of noise [53].

5. Conclusions

This study, as well as others, has found that a large portion of the variability in infiltration cannot be explained using housing and climate characteristics. While information on air conditioning use and outdoor temperature can serve as proxy measures of window opening, they do not capture many important factors, such as the number of open windows, the size of the opening(s), and the duration that windows are left open, which could influence infiltrating particles. These variables were unavailable for this study to be able to identify their contribution.
Finf estimates found in Toronto homes are comparable to previous estimates from similar climates and homes and confirm that typically there is a high degree of variation in infiltration across different homes. Exposure to outdoor pollution can be markedly different even for individuals living in close proximity and experiencing similar outdoor levels. Effective modelling of Finf in individual homes remains difficult, although key variables such as use of central air conditioning show potential as an easily attainable indicator of Finf. These are important considerations when investigating the health effects of air pollution in large epidemiological studies especially when considering providing guidance on high air pollution days to vulnerable populations.


The authors would like to acknowledge the generosity of the families who participated in this study and provided valuable data. We would like to thank Robin Coombs and Kelly Sabaliauskas for their careful data collection. Appreciation is expressed to Keith Van Ryswyk, Hongyu You and Ryan Kulka for their help with training and data management. The authors are grateful to Dave Stieb for his guidance and suggestions regarding the design of the study. The GIS data were kindly provided by Michael Jerrett, Bernie Beckerman and Ketan Shankardass. Thank you to David Mathieu for his assistance with the XRF analyses. Funding was provided by Health Canada under the Clean Air Regulatory Agenda.


  1. Pope, C. Epidemiology of Fine Particulate Air Pollution and Human Health: Biologic Mechanisms and Who’s at Risk? Environ. Health Perspect 2000, 108, 713–723. [Google Scholar]
  2. Peters, A; Dockery, DW; Muller, JE; Mittleman, MA. Increased Particulate Air Pollution and the Triggering of Myocardial Infarction. Circulation 2001, 103, 2810–2815. [Google Scholar]
  3. McCreanor, J; Cullinan, P; Nieuwenhuijsen, MJ; Stewart-Evans, J; Malliarou, E; Jarup, L; Harrington, R; Svartengren, M; Han, IK; Ohman-Strickland, P; et al. Respiratory Effects of Exposure to Diesel Traffic in Persons with Asthma. N. Engl. J. Med 2007, 357, 2348–2358. [Google Scholar]
  4. Shy, CM; Kleinbaum, DG; Morgenstern, H. The Effect of Misclassification of Exposure Status in Epidemiological Studies of Air Pollution Health Effects. Bull. N. Y. Acad. Med 1978, 54, 1155–1165. [Google Scholar]
  5. Navidi, W; Lurmann, F. Measurement Error in Air Pollution Exposure Assessment. J. Expo. Anal. Environ. Epidemiol 1995, 5, 111–124. [Google Scholar]
  6. Nethery, E; Leckie, SE; Teschke, K; Brauer, M. From Measures to Models: An Evaluation of Air Pollution Exposure Assessment for Epidemiologic Studies of Pregnant Women. Occup. Environ. Med 2008, 65, 579–586. [Google Scholar]
  7. Spengler, JD; Treitman, RD; Tosteson, TD; Mage, DT; Soczek, ML. Personal Exposures to Respirable Particulates and Implications for Air Pollution Epidemiology. Environ. Sci. Technol 1985, 19, 700–707. [Google Scholar]
  8. Leech, JA; Wilby, K; McMullen, E; Laporte, K. The Canadian Human Activity Pattern Survey: Report of Methods and Population Surveyed. Chronic Dis. Can 1996, 17, 118–123. [Google Scholar]
  9. Wilson, WE; Brauer, M. Estimation of Ambient and Non-Ambient Components of Particulate Matter Exposure from a Personal Monitoring Panel Study. J. Expo. Sci. Env. Epid 2006, 16, 264–274. [Google Scholar]
  10. Klepeis, NE; Nelson, WC; Ott, WR; Robinson, JP; Tsang, AM; Switzer, P; Behar, JV; Hern, SC; Engelmann, WH. The National Human Activity Pattern Survey (NHAPS): A Resource for Assessing Exposure to Environmental Pollutants. J. Expo. Anal. Environ. Epidemiol 2001, 11, 231–252. [Google Scholar]
  11. Allen, R; Larson, T; Sheppard, L; Wallace, L; Liu, LJS. Use of Real-Time Light Scattering Data to Estimate the Contribution of Infiltrated and Indoor-Generated Particles to Indoor Air. Environ. Sci. Technol 2003, 37, 3484–3492. [Google Scholar]
  12. Meng, QY; Turpin, BJ; Polidori, A; Lee, JH; Weisel, C; Morandi, M; Colome, S; Stock, T; Winer, A; Zhang, J. PM2.5 of Ambient Origin: Estimates and Exposure Errors Relevant to PM Epidemiology. Environ. Sci. Technol 2005, 39, 5105–5112. [Google Scholar]
  13. Long, CM; Suh, HH; Catalano, PJ; Koutrakis, P. Using Time-and Size-Resolved Particulate Data to Quantify Indoor Penetration and Deposition Behavior. Environ. Sci. Technol 2001, 35, 2089–2099. [Google Scholar]
  14. Schwarze, P; Ovrevik, J; Lag, M; Refsnes, M; Nafstad, P; Hetland, R; Dybing, E. Particulate Matter Properties and Health Effects: Consistency of Epidemiological and Toxicological Studies. Hum. Exp. Toxicol 2006, 25, 559–579. [Google Scholar]
  15. Smit, L; Heederik, D; Doekes, G; Blom, C; van Zweden, I; Wouters, I. Exposure-Response Analysis of Allergy and Respiratory Symptoms in Endotoxin-Exposed Adults. Eur. Respir. J 2008, 31, 1241–1248. [Google Scholar]
  16. Mitchell, CS; Zhang, JJ; Sigsgaard, T; Jantunen, M; Lioy, PJ; Samson, R; Karol, MH. Current State of the Science: Health Effects and Indoor Environmental Quality. Environ. Health Perspect 2007, 115, 958–964. [Google Scholar]
  17. Koenig, JQ; Mar, TF; Allen, RW; Jansen, K; Lumley, T; Sullivan, JH; Trenga, CA; Larson, TV; Liu, LJS. Pulmonary Effects of Indoor-and Outdoor-Generated Particles in Children with Asthma. Environ. Health Perspect 2005, 113, 499–503. [Google Scholar]
  18. Ebelt, ST; Wilson, WE; Brauer, M. Exposure to Ambient and Nonambient Components of Particulate Matter: A Comparison of Health Effects. Epidemiology 2005, 16, 396–405. [Google Scholar]
  19. Allen, RW. Changes in Lung Function and Airway Inflammation among Asthmatic Children Residing in a Woodsmoke-Impacted Urban Area. Inhal. Toxicol 2008, 20, 423–433. [Google Scholar]
  20. Rothman, KJ; Greenland, S; Lash, TL. Modern Epidemiology; Lippincott Williams & Wilkins: Philadelphia, PA, USA, 2008. [Google Scholar]
  21. Hystad, PU; Setton, EM; Allen, RW; Keller, PC; Brauer, M. Modeling Residential Fine Particulate Matter Infiltration for Exposure Assessment. J. Expo. Sci. Env. Epid 2008, 19, 570–579. [Google Scholar]
  22. Mielck, A; Reitmeir, P; Wjst, M. Severity of Childhood Asthma by Socioeconomic Status. Int. J. Epidemiol 1996, 25, 388–393. [Google Scholar]
  23. Wilson, WE; Mage, DT; Grant, LD. Estimating Separately Personal Exposure to Ambient and Nonambient Particulate Matter for Epidemiology and Risk Assessment: Why and How. J. Air Waste Manage. Assoc 2000, 50, 1167–1183. [Google Scholar]
  24. Dockery, DW; Spengler, JD. Indoor-Outdoor Relationships of Respirable Sulfates and Particles. Atmos. Environ. (1967) 1981, 15, 335–343. [Google Scholar]
  25. Suh, HH; Spengler, JD; Koutrakis, P. Personal Exposures to Acid Aerosols and Ammonia. Environ. Sci. Technol 1992, 26, 2507–2517. [Google Scholar]
  26. Leaderer, B; Naeher, L; Jankun, T; Balenger, K; Holford, T; Toth, C; Sullivan, J; Wolfson, J; Koutrakis, P. Indoor, Outdoor, and Regional Summer and Winter Concentrations of PM10, PM2.5, SO42−, H+, NH4+, NO3−, NH3, and Nitrous Acid in Homes with and without Kerosene Space Heaters. Environ. Health Perspect 1999, 107, 223–231. [Google Scholar]
  27. Wallace, L; Williams, R. Use of Personal-Indoor-Outdoor Sulfur Concentrations to Estimate the Infiltration Factor and Outdoor Exposure Factor for Individual Homes and Persons. Environ. Sci. Technol 2005, 39, 1707–1714. [Google Scholar]
  28. Molnár, P; Johannesson, S; Boman, J; Barregård, L; Sällsten, G. Personal Exposures and Indoor, Residential Outdoor, and Urban Background Levels of Fine Particle Trace Elements in the General Population. J. Environ. Monitor 2006, 8, 543–551. [Google Scholar]
  29. Ozkaynak, H; Xue, J; Spengler, J; Wallace, L; Pellizzari, E; Jenkins, P. Personal Exposure to Airborne Particles and Metals: Results from the Particle TEAM Study in Riverside, California. J. Expo. Anal. Environ. Epidemiol 1996, 6, 57–78. [Google Scholar]
  30. Lachenmyer, C; Hidy, G. Urban Measurements of Outdoor-Indoor PM2.5 Concentrations and Personal Exposure in the Deep South. Part I. Pilot Study of Mass Concentrations for Nonsmoking Subjects. Aerosol Sci. Tech 2000, 32, 34–51. [Google Scholar]
  31. Williams, R; Suggs, J; Rea, A; Sheldon, L; Rodes, C; Thornburg, J. The Research Triangle Park Particulate Matter Panel Study: Modeling Ambient Source Contribution to Personal and Residential PM Mass Concentrations. Atmos. Environ 2003, 37, 5365–5378. [Google Scholar]
  32. Sarnat, JA; Long, CM; Koutrakis, P; Coull, BA; Schwartz, J; Suh, HH. Using Sulfur as a Tracer of Outdoor Fine Particulate Matter. Environ. Sci. Technol 2002, 36, 5305–5314. [Google Scholar]
  33. Martuzevicius, D; Grinshpun, SA; Reponen, T; Górny, RL; Shukla, R; Lockey, J; Hu, S; McDonald, R; Biswas, P; Kliucininkas, L. Spatial and Temporal Variations of PM2.5 Concentration and Composition Throughout an Urban Area with High Freeway density—The Greater Cincinnati Study. Atmos. Environ 2004, 38, 1091–1105. [Google Scholar]
  34. Franklin, M; Zeka, A; Schwartz, J. Association between PM2.5 and all-Cause and Specific-Cause Mortality in 27 US Communities. J. Expo. Sci. Env. Epid 2006, 17, 279–287. [Google Scholar]
  35. Janssen, NAH; Schwartz, J; Zanobetti, A; Suh, HH. Air Conditioning and Source-Specific Particles as Modifiers of the Effect of PM10 on Hospital Admissions for Heart and Lung Disease. Environ. Health Perspect 2002, 110, 43–49. [Google Scholar]
  36. Dell, SD; Foty, RG; Gilbert, NL; Jerret, M; To, T; Walter, SD; Stieb, DM. Asthma and Allergic Disease Prevalence in a Diverse Sample of Toronto School Children: Results from the Toronto Child Health Evaluation Questionnaire (T-CHEQ) Study. Can. Respir. J 2010, 17, e1–6. [Google Scholar]
  37. Hänninen, O; Lebret, E; Ilacqua, V; Katsouyanni, K; Künzli, N; Srám, R; Jantunen, M. Infiltration of Ambient PM2.5 and Levels of Indoor Generated Non-ETS PM2.5 in Residences of Four European Cities. Atmos. Environ 2004, 38, 6411–6423. [Google Scholar]
  38. Sarnat, SE; Coull, BA; Ruiz, PA; Koutrakis, P; Suh, HH. The Influences of Ambient Particle Composition and Size on Particle Infiltration in Los Angeles, CA, Residences. J. Air Waste Manag. Assoc 2006, 56, 186–196. [Google Scholar]
  39. Human Exposure and Atmospheric Sciences Division. Monitoring PM2.5 in Ambient Air Using Designated Reference or Class 1 Equivalent Methods, Quality Assurance Guidance Document 2.12. Available online: (accessed on 1 July 2010).
  40. Environment Canada. Canadian Daily Climate Data. 2008. Available online: (accessed on 1 November 2009).
  41. Dietz, RN; Goodrich, RW; Cote, EA; Wieser, RF. Detailed Description and Performance of a Passive Perfluorocarbon Tracer System for Building Ventilation and Air Exchange Measurements. In Measured Air Leakage of Buildings: A Symposium; American Society for Testing and Materials: Ann Arbor, MI, USA, 1986; p. 203. [Google Scholar]
  42. Wallace, L; Emmerich, S; Howard-Reed, C. Continuous Measurements of Air Change Rates in an Occupied House for 1 Year: The Effect of Temperature, Wind, Fans, and Windows. J. Expo. Anal. Environ. Epidemiol 2002, 12, 296–306. [Google Scholar]
  43. MPAC. PropertyLine. 2008. Available online: (accessed on 1 November 2009).
  44. The Canadian Real Estate Association. MLS Statistics. Available online: (accessed on 1 July 2010).
  45. Evans, G; Peers, A; Sabaliauskas, K. Particle Dose Estimation from Frying in Residential Settings. Indoor Air 2008, 18, 499–510. [Google Scholar]
  46. Environment Canada. Canadian Climate Normals or Averages 1971–2000. Available online: (accessed on 1 May 2010).
  47. Meng, Q; Spector, D; Colome, S; Turpin, B. Determinants of Indoor and Personal Exposure to PM2.5 of Indoor and Outdoor Origin during the RIOPA Study. Atmos. Environ 2009, 43, 5750–5758. [Google Scholar]
  48. Howard-Reed, C; Wallace, LA; Ott, WR. The Effect of Opening Windows on Air Change Rates in Two Homes. J. Air Waste Manag. Assoc 2002, 52, 147–159. [Google Scholar]
  49. Long, CM; Sarnat, JA. Indoor-Outdoor Relationships and Infiltration Behavior of Elemental Components of Outdoor PM 2.5 for Boston-Area Homes. Aerosol Sci. Tech 2004, 38, 91–104. [Google Scholar]
  50. Medina-Ramon, M; Zanobetti, A; Schwartz, J. The Effect of Ozone and PM10 on Hospital Admissions for Pneumonia and Chronic Obstructive Pulmonary Disease: A National Multicity Study. Am. J. Epidemiol 2006, 163, 579–588. [Google Scholar]
  51. Levy, J; Hammitt, J; Spengler, J. Estimating the Mortality Impacts of Particulate Matter: What Can Be Learned from Between-Study Variability? Environ. Health Perspect 2000, 108, 109–117. [Google Scholar]
  52. Bell, ML; Ebisu, K; Peng, RD; Dominici, F. Adverse Health Effects of Particulate Air Pollution: Modification by Air Conditioning. Epidemiology 2009, 20, 682–686. [Google Scholar]
  53. Allen, RW; Davies, H; Cohen, MA; Mallach, G; Kaufman, JD; Adar, SD. The Spatial Relationship between Traffic-Generated Air Pollution and Noise in 2 US Cities. Environ. Res 2009, 109, 334–342. [Google Scholar]
Table 1. Baseline and measured characteristics of 46 homes in Toronto. For household characteristics that were not obtained via questionnaire, the data source is indicated in brackets.
Table 1. Baseline and measured characteristics of 46 homes in Toronto. For household characteristics that were not obtained via questionnaire, the data source is indicated in brackets.
Measurement CharacteristicsTotal NMean (standard deviation)
Finf460.52 ± 0.21
PM2.5 indoors468.17 ± 5.18 μg/m3
PM2.5 outdoors469.72 ± 3.90 μg/m3
Sulphur indoors460.46 ± 0.31 μg/m3
Sulphur outdoors460.76 ± 0.36 μg/m3
Air exchange350.22 ± 0.15/h
Indoor temperature4622.0 ± 2.1 °C
Indoor relative humidity4652.0 ± 7.5 %
Outdoor temperature4614.6 ± 6.2 °C
Outdoor relative humidity4672.6 ±7.7%
Household CharacteristicsMedian (quartile range)
Number of people in the home444 (4–5)
Year home built (MPAC)441948 (1925–1967)
Market value of home (MPAC)44$437,000 ($330,000–558,000)
Distance to expressway (GIS)461.85 km (1.3–2.6 km)
Frequency (percentage)
Forced air heating (MPAC)4633 (72%)
Have air conditioner (central or window unit)4644 (96%)
Have central air conditioner4639 (85%)
Use air conditioning > 30 days/year4624 (52%)
Wood burning fireplace4620 (43%)
Air cleaning filter on furnace4534 (76%)
Premium air cleaning filter on furnace4616 (36%)
Dog or cat in the home4617 (37%)
Storm windows4610 (22%)
Table 2. Simple linear regression of infiltration (Finf) with housing and climate characteristics predicted to influence Finf. Bold indicates regression with a p-value under 0.10.
Table 2. Simple linear regression of infiltration (Finf) with housing and climate characteristics predicted to influence Finf. Bold indicates regression with a p-value under 0.10.
Independent VariableNRegression Coefficientp-valueStandard ErrorR2
Ln of Air exchange350.1390.0030.0430.24
Absolute temperature difference between indoors and outdoors (°C)46−0.0030.6990.0070.00
Number of people in the home440.0030.9330.0370.00
Year home built44−0.0030.0110.0010.14
Market value of home ($100,000)44−0.0020.8490.0120.00
Distance to expressway (km)460.0230.3810.0260.02
Use air conditioning >30 days/year46−0.2010.0010.0560.23
Forced air heating (0/1)46−0.1300.0780.0720.07
Wood burning fireplace (0/1)460.1200.0560.0610.08
Air cleaning filter on furnace (0/1)45−0.0970.1980.0740.04
Premium filter on furnace (0/1)450.0830.2180.0670.04
Dog or cat in the home (0/1)460.0290.6560.0660.00
Storm windows (0/1)460.1210.1130.0750.06
Table 3. Multivariate models predicting infiltration factor (Finf) using climate and housing characteristics.
Table 3. Multivariate models predicting infiltration factor (Finf) using climate and housing characteristics.
Independent VariablesModel NaRegression CoefficientStandard ErrorP-valueAdjusted Model R2
Including air exchange as potential predictor
1 Intercept
 Ln of air exchange
 Use air conditioning > 30 d/yr
 Forced air heating (0/1)

Excluding air exchange as potential predictor
2 Intercept
 Use air conditioning > 30 d/yr
 Forced air heating (0/1)

3 Intercept
 Use air conditioning > 30 d/yr
 Forced air heating (0/1)

aModels 1 and 2 are performed on the same homes for direct comparability, while Model 3 includes additional homes for which air exchange rate was not available.
Int. J. Environ. Res. Public Health EISSN 1660-4601 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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