Air pollution mixture complexity and its effect on PM2.5-related mortality: A multicountry time-series study in 264 cities.

Background: Fine particulate matter (PM_2.5) occurs within a mixture of other pollutant gases that interact and impact its composition and toxicity. To characterize the local toxicity of PM_2.5, it is useful to have an index that accounts for the whole pollutant mix, including gaseous pollutants. We consider a recently proposed pollutant mixture complexity index (PMCI) to evaluate to which extent it relates to PM_2.5 toxicity.

Methods: The PMCI is constructed as an index spanning seven different pollutants, relative to the PM_2.5 levels. We consider a standard two-stage analysis using data from 264 cities in the Northern Hemisphere. The first stage estimates the city-specific relative risks between daily PM_2.5 and all-cause mortality, which are then pooled into a second-stage meta-regression model with which we estimate the effect modification from the PMCI.

Results: We estimate a relative excess risk of 1.0042 (95% confidence interval: 1.0023, 1.0061) for an interquartile range increase (from 1.09 to 1.95) of the PMCI. The PMCI predicts a substantial part of within-country relative risk heterogeneity with much less between-country heterogeneity explained. The Akaike information criterion and Bayesian information criterion of the main model are lower than those of alternative meta-regression models considering the oxidative capacity of PM_2.5 or its composition.

Conclusions: The PMCI represents an efficient and simple predictor of local PM_2.5-related mortality, providing evidence that PM_2.5 toxicity depends on the surrounding gaseous pollutant mix. With the advent of remote sensing for pollutants, the PMCI can provide a useful index to track air quality.