Using a Continuous Time Lag to Determine the Associations between Ambient PM_(2.5) Hourly Levels and Daily Mortality

摘要

The authors are interested in understanding the possible association between exposure to short-term fine particu-late matter (PM_(2.5)) peaks that have changing physical characteristics throughout the day and observable health outcomes (daily mortality). To this end, modern statistical methods are used here that allow for a continuous time lag between hourly PM_(2.5) mass concentration and daily mortality. The functional linear regression model was used to study how hourly PM_(2.5) mass of past days continuously influences the daily mortality count of the current day. Using a Poisson likelihood with the canonical link, the authors found that a 10-μg/m~3 increase in the hourly PM_(2.5) above the hourly average is associated with 1.7% (0.1, 3.4), 2.4% (1.2, 3.7), 1.6% (0.6, 2.7), and 0.8% (-0.2, 1.8) higher risk of mortality on the same day, next day, 2 days, and 3 days later, respectively. The increase in relative risk is statistically significant for lags of 0-2 days, but not at lag 3. The highest association between PM_(2.5) mass concentration and daily mortality was found to occur in the morning when both mass and PM number concentrations peak at approximately 8:00 a.m. (lag of 15, 39, and 63 hr). This morning time interval corresponds to automobile traffic rush hour that coincides with a morning atmospheric inversion that traps high concentrations of nanoparticles.