PM 2.5 of Ambient Origin: Estimates and Exposure Errors Relevant to PM Epidemiology

Department of Environmental Sciences, Rutgers, The State University of New Jersey, Нью-Брансуик, New Jersey, United States
Environmental Science and Technology (Impact Factor: 5.33). 08/2005; 39(14):5105-12. DOI: 10.1021/es048226f
Source: PubMed


Epidemiological studies routinely use central-site particulate matter (PM) as a surrogate for exposure to PM of ambient (outdoor) origin. Below we quantify exposure errors that arise from variations in particle infiltration to aid evaluation of the use of this surrogate, rather than actual exposure, in PM epidemiology. Measurements from 114 homes in three cities from the Relationship of Indoor, Outdoor and Personal Air (RIOPA) study were used. Indoor PM2.5 of outdoor origin was calculated as follows: (1) assuming a constant infiltration factor, as would be the case if central-site PM were a "perfect surrogate" for exposure to outdoor particles; (2) including variations in measured air exchange rates across homes; (3) also incorporating home-to-home variations in particle composition, and (4) calculating sample-specific infiltration factors. The final estimates of PM2.5 of outdoor origin take into account variations in building construction, ventilation practices, and particle properties that result in home-to-home and day-to-day variations in particle infiltration. As assumptions became more realistic (from the first, most constrained model to the fourth, least constrained model), the mean concentration of PM2.5 of outdoor origin increased. Perhaps more importantly, the bandwidth of the distribution increased. These results quantify several ways in which the use of central site PM results in underestimates of the ambient PM2.5 exposure distribution bandwidth. The result is larger uncertainties in relative risk factors for PM2.5 than would occur if epidemiological studies used more accurate exposure measures. In certain situations this can lead to bias.

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    • "One possible explanation for this observation is that the use of sulfur as a tracer does not account for changes in PM 2.5 properties that result from indoor– outdoor transport (Meng et al., 2005; Polidori et al., 2006). In particular, sulfur would not reflect losses of volatile or semivolatile components of PM that tend to partition to the gas-phase when entering an indoor environment (Lunden et al., 2003). "
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    ABSTRACT: Unlabelled: Indoor fine particles (FPs) are a combination of ambient particles that have infiltrated indoors, and particles that have been generated indoors from activities such as cooking. The objective of this paper was to estimate the infiltration factor (Finf ) and the ambient/non-ambient components of indoor FPs. To do this, continuous measurements were collected indoors and outdoors for seven consecutive days in 50 non-smoking homes in Halifax, Nova Scotia in both summer and winter using DustTrak (TSI Inc) photometers. Additionally, indoor and outdoor gravimetric measurements were made for each 24-h period in each home, using Harvard impactors (HI). A computerized algorithm was developed to remove (censor) peaks due to indoor sources. The censored indoor/outdoor ratio was then used to estimate daily Finfs and to determine the ambient and non-ambient components of total indoor concentrations. Finf estimates in Halifax (daily summer median = 0.80; daily winter median = 0.55) were higher than have been reported in other parts of Canada. In both winter and summer, the majority of FP was of ambient origin (daily winter median = 59%; daily summer median = 84%). Predictors of the non-ambient component included various cooking variables, combustion sources, relative humidity, and factors influencing ventilation. This work highlights the fact that regional factors can influence the contribution of ambient particles to indoor residential concentrations. Practical implications: Ambient and non-ambient particles have different risk management approaches, composition, and likely toxicity. Therefore, a better understanding of their contribution to the indoor environment is important to manage the health risks associated with fine particles (FPs) effectively. As well, a better understanding of the factors Finf can help improve exposure assessment and contribute to reduced exposure misclassification in epidemiologic studies.
    Indoor Air 12/2013; 24(4). DOI:10.1111/ina.12084 · 4.90 Impact Factor
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    • "where (1/P) and (k/P) can be regarded as the intercept and the slope, respectively, in the context of a regression analysis. A least trimmed square robust regression was conducted to examine the association between 1/F INF and 1/a for the 114 RIOPA homes with home-specific F INF (Meng et al., 2005). A positive association (P-value < 0.001) between 1/F INF and 1/a was seen (Figure 1), and the population average P (0.83) and k (0.06 h −1 ), derived from the regression intercept and slope, are similar to what we found previously for the overall RIOPA study (Turpin et al, 2007). "
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    ABSTRACT: Effects of physical/environmental factors on fine particle (PM(2.5)) exposure, outdoor-to-indoor transport and air exchange rate (AER) were examined. The fraction of ambient PM(2.5) found indoors (F(INF)) and the fraction to which people are exposed (alpha) modify personal exposure to ambient PM(2.5). Because F(INF), alpha, and AER are infrequently measured, some have used air conditioning (AC) as a modifier of ambient PM(2.5) exposure. We found no single variable that was a good predictor of AER. About 50% and 40% of the variation in F(INF) and alpha, respectively, was explained by AER and other activity variables. AER alone explained 36% and 24% of the variations in F(INF) and alpha, respectively. Each other predictor, including Central AC Operation, accounted for less than 4% of the variation. This highlights the importance of AER measurements to predict F(INF) and alpha. Evidence presented suggests that outdoor temperature and home ventilation features affect particle losses as well as AER, and the effects differ.Total personal exposures to PM(2.5) mass/species were reconstructed using personal activity and microenvironmental methods, and compared to direct personal measurement. Outdoor concentration was the dominant predictor of (partial R(2) = 30-70%) and the largest contributor to (20-90%) indoor and personal exposures for PM(2.5) mass and most species. Several activities had a dramatic impact on personal PM(2.5) mass/species exposures for the few study participants exposed to or engaged in them, including smoking and woodworking. Incorporating personal activities (in addition to outdoor PM(2.5)) improved the predictive power of the personal activity model for PM(2.5) mass/species; more detailed information about personal activities and indoor sources is needed for further improvement (especially for Ca, K, OC). Adequate accounting for particle penetration and persistence indoors and for exposure to non-ambient sources could potentially increase the power of epidemiological analyses linking health effects to particulate exposures.
    Atmospheric Environment 11/2009; 43(36):5750-5758. DOI:10.1016/j.atmosenv.2009.07.066 · 3.28 Impact Factor
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    • "have used F inf to derive separate estimates of ambient and non-ambient components of PM 2.5 . Meng et al. (2005) examined F inf and the contributions of ambient PM 2.5 to residential indoor PM 2.5 concentrations. They found that the use of central site PM 2.5 as a surrogate for exposure to PM 2.5 of ambient origin significantly underestimates the distribution of exposures, resulting in larger uncertainties in reported relative risks. "
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    ABSTRACT: 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.
    Journal of Exposure Science and Environmental Epidemiology 09/2008; 19(6):570-9. DOI:10.1038/jes.2008.45 · 3.19 Impact Factor
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