Correcting for the effects of location and atmospheric conditions on air pollution exposures in a case–crossover study

A limitation of most air pollution health effects studies is that they rely on monitoring data averaged over one or more ambient monitors to represent daily air pollution exposures for individuals. Such data analyses therefore implicitly require the assumption of a homogeneous spatial distribution for particulate matter (PM). This assumption may be suspected in the Pacific Northwest because of its hilly topography and local variations in wood burning. To examine the bias from substituting regional PM (i.e., the average of three ambient monitor measurements) for individual PM exposure, we conducted an exposure substudy to identify the influence of location factors, specifically urban versus suburban classification and topographic features (“upstream” versus “downstream”), on local ambient measurements. Using nephelometer measurements collected over 1 year in four locations, we developed regression models to predict local PM as a function of regional PM, atmospheric stagnation, temperature, and location. We found a significant interaction between atmospheric stagnation and topography, with the most upstream site having reduced PM levels on high stagnation days after controlling for regional PM. We also found a significant interaction with temperature at one downstream site thought to be heavily exposed to wood smoke in the winter. These results are consistent with the physics of surface radiation inversions. The interactions reordered the index versus referent exposures in a case–crossover analysis of out-of-hospital primary cardiac arrest for subjects living in specific locations, but did not meaningfully change the associations with PM from the analysis using regional PM as the exposure. The lack of change in these results may be due to limitations in the data used to correct the exposure estimates or to the absence of a PM effect among persons without prior heart disease who experienced a primary cardiac arrest.

[1]  Thomas Lumley,et al.  Bias in the case – crossover design: implications for studies of air pollution , 2000 .

[2]  Peter Wallbrink,et al.  Hydrologic characteristics and modelling of a small forested catchment in southeastern new South Wales. Pre-logging condition , 1986 .

[3]  V. Hasselblad,et al.  Assessment of human exposure to ambient particulate matter. , 1999, Journal of the Air & Waste Management Association.

[4]  Thomas Lumley,et al.  Referent Selection in Case-Crossover Analyses of Acute Health Effects of Air Pollution , 2001, Epidemiology.

[5]  L. Sheppard,et al.  Estimating short-term PM effects accounting for surrogate exposure measurements from ambient monitors , 2000 .

[6]  K. Beven,et al.  A physically based, variable contributing area model of basin hydrology , 1979 .

[7]  T. Raghunathan,et al.  Dietary intake and cell membrane levels of long-chain n-3 polyunsaturated fatty acids and the risk of primary cardiac arrest. , 1995, JAMA.

[8]  E. O'Loughlin.  Prediction of Surface Saturation Zones in Natural Catchments by Topographic Analysis , 1986 .

[9]  L. Sheppard,et al.  Asthma aggravation, combustion, and stagnant air , 2000, Thorax.

[10]  H Checkoway,et al.  A Case-Crossover Analysis of Particulate Matter Air Pollution and Out-of-Hospital Primary Cardiac Arrest , 2001, Epidemiology.

[11]  J. Koenig,et al.  Simultaneous indoor and outdoor particle light-scattering measurements at nine homes using a portable nephelometer , 1998 .

[12]  L Sheppard,et al.  Effects of ambient air pollution on nonelderly asthma hospital admissions in Seattle, Washington, 1987-1994. , 1999, Epidemiology.

[13]  A case-crossover analysis of fine particulate matter air pollution and out-of-hospital sudden cardiac arrest. , 2000, Research report.