An association between air pollution and increased cardiovascular disease (CVD) mortality has been reported, but underlying mechanisms are unknown. The authors examined short-term associations between ambient pollutants (particulate matter less than 10 microm in aerodynamic diameter (PM10), ozone, carbon monoxide, nitrogen dioxide, and sulfur dioxide) and cardiac autonomic control using data from the fourth cohort examination (1996-1998) of the population-based Atherosclerosis Risk in Communities Study. For each participant, the authors calculated PM10 and gaseous pollutant exposures as 24-hour averages and ozone exposure as an 8-hour average 1 day prior to the randomly allocated examination date. They calculated 5-minute heart rate variability indices and used logarithmically transformed data on high-frequency (0.15-0.40 Hz) and low-frequency (0.04-0.15 Hz) power, standard deviation of normal R-R intervals, and mean heart rate. Linear regression was used to adjust for CVD risk factors and demographic, socioeconomic, and meteorologic variables. Regression coefficients for a one-standard-deviation increase in PM10 (11.5 microg/m3) were -0.06 ms2 (standard error (SE), 0.018), -1.03 ms (SE, 0.31), and 0.32 beats/minute (SE, 0.158) for log-transformed high-frequency power, standard deviation of normal R-R intervals, and heart rate, respectively. Similar results were found for gaseous pollutants. These cross-sectional findings suggest that higher ambient pollutant concentrations are associated with lower cardiac autonomic control, especially among persons with existing CVD, and highlight a putative mechanism through which air pollution is associated with CVD.

Spatial estimations are increasingly used to estimate geocoded ambient particulate matter (PM) concentrations in epidemiologic studies because measures of daily PM concentrations are unavailable in most U.S. locations. This study was conducted to a) assess the feasibility of large-scale kriging estimations of daily residential-level ambient PM concentrations, b) perform and compare cross-validations of different kriging models, c) contrast three popular kriging approaches, and d ) calculate SE of the kriging estimations. We used PM data for PM with aerodynamic diameter ≤10 μm (PM10) and aerodynamic diameter ≤ 2.5 μm (PM2.5) from the U.S. Environmental Protection Agency for the year 2000. Kriging estimations were performed at 94,135 geocoded addresses of Women’s Health Initiative study participants using the ArcView geographic information system. We developed a semiautomated program to enable large-scale daily kriging estimation and assessed validity of semivariogram models using prediction error (PE), standardized prediction error (SPE), root mean square standardized (RMSS), and SE of the estimated PM. National- and regional-scale kriging performed satisfactorily, with the former slightly better. The average PE, SPE, and RMSS of daily PM10 semivariograms using regular ordinary kriging with a spherical model were 0.0629, −0.0011, and 1.255 μg/m3, respectively; the average SE of the estimated residential-level PM10 was 27.36 μg/m3. The values for PM2.5 were 0.049, 0.0085, 1.389, and 4.13 μg/m3, respectively. Lognormal ordinary kriging yielded a smaller average SE and effectively eliminated out-of-range predicted values compared to regular ordinary kriging. Semiautomated daily kriging estimations and semivariogram cross-validations are feasible on a national scale. Lognormal ordinary kriging with a spherical model is valid for estimating daily ambient PM at geocoded residential addresses.

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