With the intensification of urban air pollution, fine particulate matter (PM_2.5) 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 PM_2.5 and its major components on the risk of outpatient visits for insomnia, thereby exploring the public health significance of air pollution on sleep health.

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 PM_2.5 and its 5 components, namely nitrate ion (NO₃^-), sulfate ion (SO₄^2-), 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 PM_2.5 component and risk of insomnia outpatient visits. A weighted quantile sum (WQS) regression model was further applied to assess the mixed exposure effects of PM_2.5 components and to identify the key component.

PM_2.5 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 SO₄^2- (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 PM_2.5 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.

Short-term exposure to PM_2.5 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 PM_2.5 components.

In air pollution prevention and control strategies, the focus should gradually shift from controlling PM_2.5 mass concentration to refined component-based management, with particular attention to source control of secondary inorganic aerosols (NO₃^-, SO₄^2- 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.

Evidence regarding the independent health effects of short-term, acute temperature fluctuations on psoriasis remains scarce. This study aimed to investigate the association pattern, Lagged effects, and population heterogeneity between the temperature change between neighboring days (TCN) and outpatient visits for psoriasis, providing a reference for identifying environmental risk factors for psoriasis.

This was a retrospective time-series study. Data on psoriasis outpatient visits were collected from 2 general hospitals in Chongqing between December 1, 2013 and December 31, 2019, encompassing 24324 patient visits. Concurrent meteorological and air pollutant data were obtained from the local meteorological bureau and the China National Environmental Monitoring Centre. The primary exposure was TCN, with stratification by sex (male/female) and age (<40 years/≥40 years). A distributed Lag nonlinear model (DLNM) was used to analyze the association between TCN and daily outpatient visits. Using the interquartile range (IQR) of TCN as the unit of change and the 25 th percentile as the reference, the relative risk (RR) and its 95% confidence interval (CI) were calculated. Models were adjusted for long-term trends, meteorological factors (wind speed, rainfall, relative humidity), PM_2.5, day of the week, and public holidays. Differences in effects between subgroups were tested using the Z-test.

In the total population, TCN was negatively associated with psoriasis outpatient visits. For each 2.1 ℃ increase in TCN (one IQR), significant single-day Lagged effects were observed at Lag1 and Lag2, with an RR of 0.997 (95%CI: 0.995 to 0.999, P<0.001) at Lag2; cumulative effects were significant from Lag01 to Lag07, with an RR of 0.986 (95%CI: 0.983 to 0.989, P<0.001) at Lag07. Stratified analyses showed significant effects in males and those aged ≥40 years at single-day Lags Lag0 and Lag1 and cumulative Lags from Lag00 to Lag07, with an RR of 0.983 (95%CI: 0.979 to 0.986, P<0.001) at Lag07 for males and an RR of 0.981 (95%CI: 0.977 to 0.985, P<0.001) at Lag07 for those aged ≥40 years. Effects were weaker in females and those aged <40 years, with statistical significance observed only for certain Lag days. Heterogeneity tests indicated significant differences between sexes at Lag0, Lag00, and Lag07, and between age groups at Lag7 and Lag07, suggesting that reductions in healthcare-seeking behavior associated with higher TCN were more pronounced in male patients and those aged ≥40 years.

Higher TCN is statistically associated with a reduction in psoriasis outpatient visits. Sex and age showed suggestive modifying effects, with stronger associations observed in males and individuals aged≥40 years, although the magnitude of the effect differences was very small.

In the future, monitoring acute temperature fluctuations could be integrated into community-based health management systems for chronic skin diseases. Using meteorological warnings, an environmental exposure risk communication mechanism stratified by population and sex can be established to deliver precise recommendations regarding optimal times for outdoor activities to psoriasis patients, thereby optimizing behavioral intervention strategies and guiding patients' decisions on seeking medical care.