Estimating Separately Personal Exposure to Ambient and Nonambient Particulate Matter for Epidemiology and Risk Assessment: Why and How

We evaluated the impact on personal exposure to air pollutants of following advice which typically accompanies air quality advisories and indices. Scripts prescribed the time, location, duration and nature of activities intended to simulate daily activity patterns for adults and children. Scripts were paired such that one individual would proceed with usual activities (base scenario), whereas the other (intervention scenario) would alter activities as if following advice. Other than commuting, where the intervention group walked or used public transportation rather than riding in personal vehicles, this group generally spent less time outdoors. Ultra-fine particles (UFPs), particulate matter of median aerodynamic diameter less than 2.5 mm (PM 2.5 ) and total volatile organic compounds (VOCs) were measured using samplers carried by individuals during the course of daily activities. During daytime activities (e.g., work, daycare) constituting the largest share of sampling time (approximately 6 h per day), the intervention group experienced a 14% reduction in exposure to UFPs (P ¼ 0.01), a 21% reduction in exposure to PM 2.5 (P ¼ 0.08), and an 86% increase in exposure to VOCs (P ¼ 0.02). Other findings included an 89% increase in exposure to UFPs (P ¼ 0.02) and a threefold increase in exposure to VOCs (P ¼ 0.08) in the intervention group during evening cooking. Following smog advisory advice results in reduced exposures to some pollutants, while at the same time increasing exposure to others. Advice needs to be refined giving consideration to overall personal exposure.

Section: Discussionsupporting

confidence: 89%

A scripted activity study of the impact of protective advice on personal exposure to ultra-fine and fine particulate matter and volatile organic compounds

Stieb

Evans

Sabaliauskas

et al. 2007

“…Note that X it C is ambient concentration at individual i's spatial location (and only equals ambient source exposure if the individual spends all their time outdoors), and a it is the degree of attenuation from the ambient concentration (Ott et al, 2000;Sheppard and Damian, 2000;Wilson et al, 2000). We further assume that X it N is independent of X it A and a it is independent of X it C .…”

Section: Personal Exposure Modelmentioning

confidence: 99%

Exposure and measurement contributions to estimates of acute air pollution effects

Sheppard

Slaughter

Schildcrout

et al. 2004

Air pollution health effect studies are intended to estimate the effect of a pollutant on a health outcome. The definition of this effect depends upon the study design, disease model parameterization, and the type of analysis. Further limitations are imposed by the nature of exposure and our ability to measure it. We define a plausible exposure model for air pollutants that are relatively nonreactive and discuss how exposure varies. We discuss plausible disease models and show how their parameterizations are affected by different exposure partitions and by different study designs. We then discuss a measurement model conditional on ambient concentrations and incorporate this into the disease model. We use simulation studies to show the impact of a range of exposure model assumptions on estimation of the health effect in the ecologic time series design. This design only uses information from the timevarying ambient source exposure. When ambient and nonambient sources are independent, exposure variation due to nonambient source exposures behaves like Berkson measurement error and does not bias the effect estimates. Variation in the population attenuation of ambient concentrations over time does bias the estimates with the bias being either positive or negative depending upon the association of this parameter with ambient pollution. It is not realistic to substitute measured average personal exposures into time series studies because so much of the variation in personal exposures comes from nonambient sources that do not contribute information in the time series design. We conclude that general statements about the implications of measurement error need to be conditioned on the health effect study design and the health effect parameter to be estimated.

“…Several studies have used tracer species and recursive models (RMs) to estimate F inf and separate exposure into its ambient and non-ambient component (Wilson et al, 2000;Allen et al, 2003;Strand et al, 2006Strand et al, , 2007Wilson and Brauer, 2006). However, although F inf has now been incorporated in a small number of health panel studies (Ebelt et al, 2005;Koenig et al, 2005;Strand et al, 2006;Allen et al, 2008), the effort and cost involved in collecting particle concentration data inside and outside homes have prevented large epidemiology studies from incorporating F inf in their exposure assessments.…”

Section: Introductionmentioning

confidence: 99%

Modeling residential fine particulate matter infiltration for exposure assessment

Hystad

Setton

Allen

et al. 2008

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.