Air conditioning and source-specific particles as modifiers of the effect of PM(10) on hospital admissions for heart and lung disease.

Self Cite

Although many studies report that exposure to air pollution harms health, few have examined associations between pollution sources and health outcomes. We hypothesized that pollution originating in different locations has different associations with heart rate variability (HRV) among 497 men from the Normative Aging Study in Boston, Massachusetts. We identified the paths that air masses traveled ('back-trajectories') before arriving in Boston on the days the men were examined. Next, we classified these trajectories into six clusters. We examined whether the association of measured air pollutants with HRV (standard deviation of normal-to-normal intervals, high-frequency power (HF) and low-frequency power (LF), and LF/HF ratio) differed by cluster. We also examined whether the clusters alone (not considering air pollution measurements) showed different associations with HRV. The effects of black carbon (BC) on all HRV measures were strongest on days with southwest trajectories. Subjects who were examined on days where air parcels came from west had the strongest associations with ozone. All particle pollutants (particulate matter o2.5 mm in aerodynamic diameter (PM 2.5 ), BC, and sulfates) were associated with increased LF/HF ratio on days with relatively short trajectories, which are related to local, slow-moving air masses. We also observed significant increases in LF/HF in days where air came from the northwest and west, compared to north trajectory days. Health effects associated with exposure to air pollution can be evaluated using pollutant concentrations as well as aspects of the pollution mixture captured by identifying locations where air masses originate. Independent effects of both these indicators of pollution exposure were seen on cardiac autonomic function.

Section: Discussionmentioning

confidence: 99%

Source location of air pollution and cardiac autonomic function: Trajectory cluster analysis for exposure assessment

Park

OʼNeill

Stunder

et al. 2007

Self Cite

“…Differences would be expected to be greater restricting the analysis to working adults and weekdays. Recent analyses have indicated that housing characteristics such as air conditioning reduce risks of morbidity and mortality associated with outdoor air pollution (Levy et al, 2000(Levy et al, , 2005Janssen et al, 2002), suggesting that this type of environment is in fact protective. In addition, in some instances, advice to reduce the intensity and duration of outdoor physical activity is warranted both in relation to extreme heat and smog, which often coincide.…”

Section: Discussionmentioning

confidence: 99%

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

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.

“…Since it is likely that a it varies over time and across populations, it will be essential to better characterize this parameter. For instance, Janssen et al (2002) discuss how city-specific estimates of ab vary by prevalence of air conditioning use, an important determinant of population-average ambient attenuation.…”

Section: Implications Of the Measurement Error Model In Time Series Smentioning

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.