Quantitative estimation of excess mortality for drivers and passengers exposed to particulate matters in long-distance buses

Abstract

The purpose of this study was to estimate quantitatively the excess mortality for driver/passenger in long-distance buses in terms of long driving time and inhaled particulate matters (PMs) concentrations. This study used an area under the curve (AUC) approach integrating the driving time and a predicted single pulsed PM concentration to estimate the fluctuating PM exposures in long-distance buses. Different peak functions were used to fit a unique fluctuating PM dataset adopted from previous study in Taiwan. We showed that gamma distribution had a best-fitting performance with the minimum values of coefficient of variation (CV) for PM 2.5 and PM 10 of 2.9% and 11.7%. The results also indicated that the predicted CV values for PM 2.5 (5.3%) and PM 10 (14.0%) from fitted normal distributions were also agreeable compared with the original dataset. The results indicated that the PM 2.5 -associated excess mortality estimates ranged from 0.64 to 1.04 and 4103–6833 individuals per 10 5 population for passengers under short-term and drivers under long-term PM exposures. Moreover, the interquartile ranges of the excess mortality estimate in the proposed model were 2.5–5.6 times less than that in the original dataset. We concluded that our AUC-based model may successfully reduce the variations in PM exposure estimates, and thereby provide more accurate values for improving risk estimation of future excess mortality attributable to traffic-related air pollutants. ► AUC approach can estimate fluctuating PM exposure in long-distance buses. ► AUC model could reduce the variations in PM exposure estimates. ► Drivers had 1000-fold higher than passengers of PM 2.5 -associated excess mortality.