A comparative analysis of three PM2.5 exposure metrics and their impact on respiratory disease hospitalizations in Lanzhou, China.

Abstract

Research on the associations between PM_2.5 and total respiratory diseases (RD) in Lanzhou is limited. We investigated the short-term impact of PM_2.5 on total RD hospitalizations in Lanzhou (2015-2019) using various exposure metrics. We collected data on hospitalizations, daily air pollutant concentrations, and meteorological factors during the study period. Daily excessive concentration hours (DECH) were calculated according to the World Health Organization's air quality guidelines. A distributional lag nonlinear model (DLNM) based on a generalized additive model (GAM) was used to comparatively analyze the association between three PM_2.5 exposure metrics (DECH (DECH PM_2.5), daily mean concentration (Mean PM_2.5), and hourly peak concentration (Peak PM_2.5)) and RD hospitalizations. Subgroup analyses and sensitivity analyses were also performed. We found similar effects on RD hospitalizations using DECH PM_2.5 and Mean PM_2.5, but relatively weak associations observed using Peak PM_2.5. The cumulative lag effect increased daily. Subgroup analyses showed that females and children aged 0-17 years were more susceptible to PM_2.5 pollution and that the association was enhanced during the cold season. Our research strengthened the evidence that exposure to ambient PM_2.5 increases the risk of RD. This study revalidated the reliability of the new metrics and confirmed that DECH PM_2.5 effect estimates for exposure-disease were more accurate than the Mean PM_2.5.