The relationship between daily cardiovascular mortality and daily ambient concentrations of particulate pollutants (sulfur, arsenic, selenium, and mercury) and daily source contributions from coal power plants and smelters (individually, combined, and with interaction) in Phoenix, AZ, 1995–1998: A multipollutant approach to acute, time-series air pollution epidemiology: I

Abstract

The objective of this paper is to estimate the increase in risk of daily cardiovascular mortality due to an increase in the daily ambient concentration of the individual particulate pollutants sulfur (S), arsenic (As), selenium (Se), and mercury (Hg) using single-pollutant models (SPMs) and to compare this risk to the combined increase in risk due to an increase in all four pollutants by including all four pollutants in the same model (multipollutant model, MPM) and to the risks from source contributions from power plants and smelters. Individual betas in a multipollutant model (MPM) were summed to give a combined beta. Interaction was investigated with a pollutant product term. SPMs (controlling for time trends, temperature, and relative humidity), for an interquartile range (IQR) increase in the pollutant concentration on lag day 0, gave these percent excess risks (±95% confidence levels): S, 6.9% (1.3–12%); As, 2.9% (0.4–5.5%); Se, 1.4% (–1.7 to 4.6); Hg, 9.6% (4.8–14.6%). The SPM beta for S (as sulfate) was higher than found in other studies. The SPM beta for Hg gave the largest t-statistic and beta per unit mass of any pollutant studied. An (IQR) increase in all four pollutants gave an excess risk of 15.4% (7.5–23.8%), slightly smaller than the combination of S and Hg, 16.7% (9.1–24.9%). The combined beta was 71% of the sum of the four individual SPM betas, indicating a reduction in confounding among pollutants in the combined model. As and Se were shown to be noncausal; their SPM betas could be explained as confounding by S.Implications: The combined effect of several pollutants can be estimated by including the appropriate pollutants in the same statistical model, summing their individual betas to give a combined beta, and using a variance–covariance matrix to obtain the standard error. This approach identifies and reduces confounding among the species in the multipollutant model and can be used to identify confounded species that have no independent relationship with mortality. The effect of several pollutants acting together may be higher than that of one pollutant. Further work is needed to understand the strong relationship of mortality with particulate mercury and sulfate.