Using a Continuous Time Lag to Determine the Associations between Ambient PM2.5 Hourly Levels and Daily Mortality
The authors are interested in understanding the possible association between exposure to short-term fine particulate matter (PM2.5) 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 PM2.5 mass concentration and daily mortality. The functional linear regression model was used to study how hourly PM2.5 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 10μg/m3increase in the hourly PM2.5 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 PM2.5 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.