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Think Tank for Public Health | Publishing Language: Chinese | Open Access

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

Qianqian LI1Yanni FAN2Xianmei FU1Kexue WANG1Yuefei LI1Yi WANG1Qiuqian LIU1Yumeng ZHOU1( )Tongjian CAI1( )
Department of Disease Prevention and Control, State Key Laboratory of Trauma and Chemical Poisoning, Key Laboratory of Chongqing Education Commission for Hospital Infection Monitoring and Control of China, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing
Department of Information, Second Affiliated Hospital (Tangdu Hospital), Air Force Medical University, Xi'an, Shaanxi, China
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Abstract

Objective

With 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.

Methods

This 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.

Results

PM2.5 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.

Conclusion

Short-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.

Countermeasures

In 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.

CLC number: R195; R741; X513 Document code: A

References

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Journal of Army Medical University
Pages 1420-1432

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Cite this article:
LI Q, FAN Y, FU X, et al. 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. Journal of Army Medical University, 2026, 48(10): 1420-1432. https://doi.org/10.16016/j.2097-0927.202603110

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Received: 25 March 2026
Revised: 05 April 2026
Published: 30 May 2026
© 2026 Journal of Army Medical University

This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).