@article{LI2026, 
author = {Qianqian LI and Yanni FAN and Xianmei FU and Kexue WANG and Yuefei LI and Yi WANG and Qiuqian LIU and Yumeng ZHOU and Tongjian CAI},
title = {Nitrate (NO3-) may be the key component driving insomnia risk elevation from short-term PM2.5 exposure: time-series-based public health evidence and refined management recommendations},
year = {2026},
journal = {Journal of Army Medical University},
volume = {48},
number = {10},
pages = {1420-1432},
keywords = {particulate matter, insomnia, environmental exposure},
url = {https://www.sciopen.com/article/10.16016/j.2097-0927.202603110},
doi = {10.16016/j.2097-0927.202603110},
abstract = {ObjectiveWith the intensification of urban air pollution, fine particulate matter (PM2.5) may affect sleep health, but the specific roles of its components in insomnia risk remain unclear. This study aims to evaluate the impacts of short-term exposure to PM2.5 and its major components on the risk of outpatient visits for insomnia, thereby exploring the public health significance of air pollution on sleep health.MethodsThis study was a time-series ecological study. Insomnia outpatient data from the Second Affiliated Hospital (Tangdu Hospital) of Air Force Medical University in Xi'an between January 1, 2015, and December 31, 2019 were collected. Concentrations of PM2.5 and its 5 components, namely nitrate ion (NO3-), sulfate ion (SO42-), ammonium ion (NH4+), organic matter (OM), and black carbon (BC), were obtained from the Tracking Air Pollution in China (TAP) dataset. A generalized additive model (GAM) was constructed to evaluate the association between exposure to single PM2.5 component and risk of insomnia outpatient visits. A weighted quantile sum (WQS) regression model was further applied to assess the mixed exposure effects of PM2.5 components and to identify the key component.ResultsPM2.5 and the 5 components exhibited significant strong correlations (r=0.88 to 0.98). After adjusting for covariates, all components were significantly positively associated with insomnia risk on the exposure day (lag0). Single-day lag effects peaked at lag3, with NO3- showing the largest effect size (IRR=1.05, 95%CI: 1.02 to 1.08). Cumulative lag effects showed an increasing trend over time, reaching the maximum at lag06, with the largest effect sizes observed for NO3- (IRR=1.08, 95%CI: 1.04 to 1.12), NH4+ (IRR=1.07, 95%CI: 1.03 to 1.11), and SO42- (IRR=1.06, 95%CI: 1.02 to 1.10). Stratified analyses revealed no significant differences across sex, age, or season. The WQS model showed that mixed exposure to PM2.5 components was significantly positively associated with insomnia risk (RR=1.075, 95%CI: 1.049 to 1.102, P<0.01), with NO3- accounting for the largest relative weight (weight=0.841). This effect did not differ significantly across age or sex groups.ConclusionShort-term exposure to PM2.5 components significantly increases the risk of insomnia outpatient visits, with evident lag and cumulative effects. NO3- is the key component driving insomnia risk elevation in mixed exposures to PM2.5 components.CountermeasuresIn air pollution prevention and control strategies, the focus should gradually shift from controlling PM2.5 mass concentration to refined component-based management, with particular attention to source control of secondary inorganic aerosols (NO3-, SO42- and NH4+), thereby providing a basis for developing more targeted public health interventions. Precision mitigation measures targeting traffic emissions, industrial exhaust, coal combustion, and agricultural activities should be promoted to reduce emissions of nitrate precursors.}
}