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

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Research on the associations between PM2.5 and total respiratory diseases (RD) in Lanzhou is limited. We investigated the short-term impact of PM2.5 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 PM2.5 exposure metrics (DECH (DECH PM2.5), daily mean concentration (Mean PM2.5), and hourly peak concentration (Peak PM2.5)) and RD hospitalizations. Subgroup analyses and sensitivity analyses were also performed. We found similar effects on RD hospitalizations using DECH PM2.5 and Mean PM2.5, but relatively weak associations observed using Peak PM2.5. The cumulative lag effect increased daily. Subgroup analyses showed that females and children aged 0-17years were more susceptible to PM2.5 pollution and that the association was enhanced during the cold season. Our research strengthened the evidence that exposure to ambient PM2.5 increases the risk of RD. This study revalidated the reliability of the new metrics and confirmed that DECH PM2.5 effect estimates for exposure-disease were more accurate than the Mean PM2.5.