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. 2026 May 25;14:1827069. doi: 10.3389/fpubh.2026.1827069

Association between short-term air pollution exposure and the risk of fatal recurrence within 1 year in patients with first-episode acute hemorrhagic stroke: a time-stratified case-crossover study

Xiaoyong Gu 1,†, Jianshu Liu 2,†, Hongyu Wang 1, Dong Hou 1, Aihua Li 1,*, Lu Xu 1, Jiajia He 1
PMCID: PMC13243267  PMID: 42267270

Abstract

Objective

To analyze the association between short-term exposure to air pollutants and the risk of fatal recurrence within 1 year in patients with first-episode acute hemorrhagic stroke, to provide scientific basis for health management and risk early warning of patients with hemorrhagic stroke in environmental dimension as well as pollution source control.

Methods

Patients with acute hemorrhagic stroke who had their first onset in Zhenjiang City from 2020 to 2023 and experienced fatal recurrence within 1 year were selected as the study subjects. A time-stratified case-crossover study design was adopted, with each case day (the day of stroke fatal recurrence) matched with the date 1 week before, 2 weeks before, and 1 week after the case day as control days. Through conditional logistic regression analysis, the odds ratio of fatal recurrence risk caused by short-term exposure to air pollutants on case days and control days was compared. Based on the model fit results, statistically significant models were identified, followed by analyses of lag effects, dose–response relationships, and interactions among pollutants. Stratification was performed according to sex, age, and season to identify potential effect modifiers of the corresponding variables.

Results

A total of 1,056 case days and 3,168 control days were included in this study. Using China’s national ambient air quality standards as a reference, the average concentrations of PM2.5, PM10, and O3_8h in Zhenjiang City were close to the first-level concentration limits, while the concentrations of SO2, NO2, and CO were far better than the first-level concentration limits. The conditional logistic regression analysis revealed that short-term exposure to PM2.5, PM10, SO2, and NO2 was associated with an increased risk of fatal recurrence within 1 year in patients with first-episode acute hemorrhagic stroke (p < 0.05), whereas no statistically significant association was observed for CO and O3_8h (p > 0.05). The OR values corresponding to a 1 μg/m3 increase in air pollutant concentration for the 3-day moving average lag (lag02) were as follows: PM2.5 (1.010, 95% CI: 1.007–1.013), PM10 (1.018, 95% CI: 1.011–1.025), SO2 (1.044, 95% CI: 1.012–1.075), and NO2 (1.024, 95% CI: 1.013–1.035); for the 2-day lag (lag2), the OR values were PM10 (1.013, 95% CI: 1.008–1.017) and NO2 (1.017, 95% CI: 1.011–1.023). For each IQR increase in pollutant concentration, the OR value for SO2 (1.137, 95% CI: 1.006–1.274) was the highest among the pollutants in the lag02 model, while the OR value for NO2 (1.123, 95% CI: 1.021–1.224) was the highest among the pollutants in the lag2 model. The dose–response curves of the 3-day moving average lag (lag02) for all four pollutants were statistically significant at low concentration levels (p < 0.05), with the risk of fatal recurrence increasing as the concentration of air pollutants rose. The dose–response curves of PM10 and PM2.5 with a 2-day lag (lag2) was statistically significant at low concentration levels (p < 0.05), with the risk of fatal recurrence first increasing and then decreasing. In contrast, the dose–response curve of NO2 with a 2-day lag (lag2) was statistically significant at high concentration levels (p < 0.05), showing a rapid upward trend in the risk of fatal recurrence. Female sex, age ≥80 years, and autumn were significant effect modifiers, and PM10 attenuated the effect of gaseous pollutants on the risk of fatal recurrence.

Conclusion

Short-term exposure to air PM2.5, PM10, SO2, and NO2, even at low concentrations, can increase the risk of fatal recurrence within 1 year in patients with first-episode acute hemorrhagic stroke. Therefore, further strengthening the control and management of pollution sources and establishing tailored risk warning and control measures for different populations and air pollutants are crucial for the health management of current acute hemorrhagic stroke patients.

Keywords: acute hemorrhagic stroke, first-episode, fatal recurrence within 1 year, short-term exposure, air pollutants

Introduction

Stroke, as the second leading cause of death globally, imposes a disease burden of 143 million disability-adjusted life years annually, with hemorrhagic stroke accounting for approximately 37.6% of all stroke cases (1). Studies indicate that stroke patients have a significantly higher risk of recurrence compared to the general population, with a 30-day recurrence risk of 3.1%, a 1-year recurrence risk of 11.1%, and a 5-year recurrence risk of 26.4% (2). Compared to ischemic stroke, patients with hemorrhagic stroke exhibit a higher risk of hemorrhagic recurrence (3, 4) and mortality (5). The recurrence of hemorrhagic stroke has garnered widespread attention due to its extensive cerebrovascular dysfunction, additional physical and cognitive impairments, and extremely high mortality rates (6, 7). Evidences that long-term exposure to air pollutants increases the risk of diseases such as stroke (8–10) and diabetes (11, 12) were convincing. Comprehensive evidence has proved that exposure to air pollution was positively associated with an increased risk of stroke hospital admission, incidence, and mortality (1, 13, 14), and a prior stroke history may enhance susceptibility to air pollutants (15–18). However, existing research lacks evidence linking the fatal recurrent risks of first-time hemorrhagic stroke patients to environmental pollutants, Moreover, the concentrations and weight values of air pollutants involved in different studies vary. Our study focuses on patients with acute hemorrhagic stroke who have their first onset and experience a fatal recurrence within 1 year, selected Zhenjiang city, which has better air pollution control in the Yangtze River Delta region, employing a case-crossover design to analyze the association between short-term exposure to major air pollutants and recurrence mortality risk, aiming to provide scientific basis for health management and risk early warning of patients with hemorrhagic stroke in environmental dimension as well as pollution source control.

Methods

Data sources

The incidence data of acute hemorrhagic stroke were sourced from the Zhenjiang Acute Cardiovascular and Cerebrovascular Event Monitoring Database. This monitoring program covered all districts of the city, with the monitoring subjects being local residents aged 18 and above who had resided in the area for at least 6 months. All secondary and above hospitals in the city were required to report cases of acute cerebrovascular events through the Zhenjiang Chronic Disease Network Management Information System within 15 days of discovery. The county/district CDCs conducted quality control reviews of the report card information within 7 days and verified the data monthly against the stroke mortality cases in the China CDC Cause of Death Registry System (19), also supplementary case report work for mortality incidents were conducted. The medical classification of acute hemorrhagic stroke was based on the International Classification of Diseases, 10th Edition (ICD-10), selecting recurrent cases diagnosed with subarachnoid hemorrhage (I60), intracerebral hemorrhage (I61), and other non-traumatic intracranial hemorrhages (I62). Disease diagnosis criteria or definitions refer to appendix 5 in the “China Resident Acute Cardiovascular and Cerebrovascular Event Incidence Monitoring Report” (20). Recurrent cases excluded those that were inactive or in the recovery period, cases of cerebral arteriosclerosis, or cases of non-acute episodes such as outpatient medication or regular hospitalization for “maintenance.” Each episode of the same type was recorded as a case within a 28-day period (21). If another acute episode consistent with diagnostic criteria occurred more than 28 days after onset, it was reported as a recurrent case. If different types of cerebrovascular events occurred within 28 days, they were reported as two separate incidents. Given that the risk of death in stroke patients significantly increases with disease progression (22), this study only included data from hemorrhagic stroke patients who experienced their fatal recurrence within 1 year after the initial onset between 2020 and 2023 for analysis. All fatal episode occurred on the day of recurrence and were verified through the China CDC Cause of Death Registry reporting system, all out-of-hospital deaths were reported as supplementary cases. As this study was an observational study utilizing routine surveillance data, and all patient identifiers had been de-identified prior to data acquisition by the research team, all data used has been anonymized and requires no approval. Therefore, the informed consent process was waived.

During the study, meteorological data were sourced from the Zhenjiang Meteorological Bureau, collecting temperature, relative humidity, and other meteorological data from all monitoring stations in Zhenjiang City, and calculating the arithmetic mean of meteorological data for each jurisdiction. The temperature values were recorded as the atmospheric temperature above 2 meters above ground, with the average temperature calculated at eight time points: 2:00,5:00,8:00,11:00,14:00,17:00,20:00, and 23:00 daily. The concentration data of major air pollutants were obtained from the Air Quality Historical Data Query Platform1, collecting 24-h average concentrations of sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), fine particulate matter (PM2.5), and inhalable particulate matter (PM10), as well as 8-h average ozone concentration (O3_8h) from all monitoring stations in Zhenjiang City. Meteorological and air pollutant data are complete and continuous, with no missing values. The arithmetic mean of daily pollutant concentrations for each jurisdiction was calculated. The meteorological data and air pollutant concentration level of the local area on the study day and the control day were used as the exposure level of study object.

Statistical analysis

Continuously variable data with normal distribution were expressed as mean ± standard deviation, while non-normally distributed variables were represented by median and quartiles. Analysis was considered statistically significant when p < 0.05. A quality control analysis was conducted using an EXCEL data sheet to organize and analyze the case data of first-episode acute hemorrhagic stroke patients who experienced fatal recurrence within 1 year from 2020 to 2023. The data variables included sex, age, recurrence date, and residential area. A time-stratified case-crossover study design was employed to better control for individual characteristics and potential confounding factors (23). We focused on the acute event-triggering effect of short-term exposure, when conducting temporal stratification, we also considered the fact that excessively long time intervals may lead to significant variations in the distribution of pollutant concentrations and the levels of meteorological factors. For each case day (the day of stroke fatal recurrence), the exposure levels of air pollutants and meteorological factors were matched with the exposure levels of three control days (the day 1 week prior the case day, the day 2 weeks prior the case day, and the day 1 week after the case day, respectively), we also controlled the seasonal, weekday, and temporal trend effects. A total of 3,168 control days were matched for 1,056 study subjects. Spearman correlation analysis was performed to summarize pairwise correlations between air pollutants. Subsequently, a conditional logistic regression model was constructed to estimate the odds ratio (OR) and the 95% confidence interval (95% CI) of risk for fatal recurrence within 1 year for first-episode acute hemorrhagic stroke patients for each specific increase in air pollutant concentration. Temperature, relative humidity, weekday effects, and public holidays were included as covariates in the model, both temperature and relative humidity variables were in the form of natural spline functions [both the degrees of freedom (df) and Knots values were set to 3]. When plotting the dose–response curve, the natural spline function degrees of freedom (df) were set to 3. According to the model fit results, The single—day lag effect of pollutants peaks in the 2—day lag model (lag2), while the cumulative lag effect reaches its maximum in the 3—day moving average lag model (lag02). Both demonstrated statistically significant results. Therefore, this study focused on the analysis of lag2 and lag02 model fit results.

Stratified analysis was conducted by sex (male and female), age (18–44 years, 45–64 years, 65–79 years, and ≥80 years), and season (spring: March–May, summer: June–August, autumn: September–November, winter: December–February) to identify potential effect modifiers of the associated variables. Based on pairwise correlation analysis results and conditional logistic regression analysis results, statistically significant pollutants with low or moderate correlations (R ≤ 0.6) were selected to construct multiple two-pollutant models, sensitivity analysis was conducted by separately adjusting covariate effects such as temperature and relative humidity, nonlinear effects, and public holidays. Conditional logistic regression analysis was performed using the “survival” package in R software (version 4.5.1).

Results

Basic information

This study included a total of 1,056 case days and 3,168 control days. The mean age at fatal recurrence for the 1,056 study subjects was 75.56 ± 12.55 years, with 559 males (52.90%) and 497 females (47.10%) (Table 1). The average daily concentrations for each pollutant on case days were as follows: PM2.5 is 38.45 ± 24.30 μg/m3, PM10 is 59.17 ± 34.35 μg/m3, SO2 is 6.58 ± 2.60 μg/m3, CO is 0.65 ± 0.18 mg/m3, NO2 is 29.83 ± 13.81 μg/m3, and O3_8h is 105.41 ± 48.44 μg/m3. The average daily concentrations for each pollutant on control days were as follows: PM2.5 is 37.51 ± 24.46 μg/m3, PM10 is 57.90 ± 35.36 μg/m3, SO2 is 6.50 ± 2.54 μg/m3, CO is 0.65 ± 0.24 mg/m3, NO2 is 29.63 ± 13.93 μg/m3, and O3_8h is 104.02 ± 48.11 μg/m3. Taking China’s national ambient air quality standards (24) as a reference, the first—level concentration limit for 24-h average concentration of PM2.5, PM10, SO2, NO2, CO and O3_8h are as follows: 35 μg/m3, 50μg/m3, 50μg/m3, 80μg/m3, 4mg/m3, 100μg/m3, and the second—level concentration limit for 24-h average concentration are 75 μg/m3, 150 μg/m3, 150 μg/m3, 80 μg/m3, 4 mg/m3, 160 μg/m3, respectively. The average concentrations of PM2.5, PM10, and O3_8h in Zhenjiang City are close to the first—level concentration limits, which are the standards for nature reserves, scenic spots, and other areas requiring special protection. Meanwhile, the concentrations of SO2, NO2, and CO are far better than the first—level concentration limits. The air pollutant concentrations and meteorological data on case days and control days are detailed in Table 2. Pairwise correlations among the six air pollutants were observed, and the details are showed in Supplementary Table S1.

Table 1.

Characteristics of first-episode acute hemorrhagic stroke patients with fatal recurrence within 1 year in Zhenjiang City from 2021 to 2023.

Variable N = 1,056 Percent (%)
Sex
Male 559 52.90
Female 497 47.10
Age_group
18–44 years 21 2.00
45–64 years 156 14.80
65–79 years 436 41.30
≥80 years 443 42.00
Season
Spring 260 24.60
Summer 254 24.10
Autumn 246 23.30
Winter 296 28.00
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Table 2.

Summary of air pollutant concentrations and meteorological data for case days and control days.

Variable Mean SD Percentile IQR
P25 P50 P75
On case days (n = 1,056)
Air pollutant
PM2.5 (μg/m3) 38.45 24.30 22.00 32.00 48.00 26.00
PM10 (μg/m3) 59.17 34.35 35.00 51.00 76.00 41.00
SO2 (μg/m3) 6.58 2.60 5.00 6.00 8.00 3.00
CO (mg/m3) 0.65 0.18 0.50 0.60 0.80 0.30
NO2 (μg/m3) 29.83 13.81 19.00 26.00 37.00 18.00
O3_8h (μg/m3) 105.41 48.44 70.00 97.00 136.00 66.00
Meteorological condition
Temprature (°C) 16.77 9.29 8.78 17.00 25.24 16.46
Relative humidity (%) 71.19 16.10 59.28 70.54 83.97 24.69
On control days (n = 3,168)
Air pollutant
PM2.5 (μg/m3) 37.51 24.46 21.00 31.00 47.00 26.00
PM10 (μg/m3) 57.90 35.36 34.00 50.00 74.00 40.00
SO2 (μg/m3) 6.50 2.54 4.00 6.00 8.00 4.00
CO (mg/m3) 0.65 0.24 0.50 0.60 0.80 0.30
NO2 (μg/m3) 29.63 13.93 19.00 26.00 36.00 17.00
O3_8h (μg/m3) 104.02 48.11 68.25 94.00 133.00 64.75
Meteorological condition
Temprature (°C) 16.79 9.34 8.73 17.03 25.35 16.62
Relative humidity (%) 71.06 16.43 58.75 71.38 84.00 25.25
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Lag effects of short-term exposure to air pollutants on mortality risk

Conditional logistic regression analysis revealed that short-term exposure to PM2.5, PM10, SO2, and NO2 was associated with an increased risk of fatal recurrence within 1 year in patients with first-episode acute hemorrhagic stroke (p < 0.05), whereas such associations were not statistically significant for CO and O3_8h (p > 0.05). The 3-day moving average lag (lag02) model fitting results showed that the OR values for each 1 μg/m3 increase in pollutant concentration were: PM2.5 1.010 (95% CI: 1.007–1.013), PM10 1.018 (95% CI: 1.011–1.025), SO2 1.044 (95% CI: 1.012–1.075), and NO2 1.024 (95% CI: 1.013–1.035). The OR values for each interquartile (IQR) increase in pollutant concentration were as follows: PM2.5 (1.103, 95% CI: 1.029–1.177), PM10 (1.113, 95% CI: 1.012–1.219), SO2 (1.137, 95% CI: 1.006–1.274), and NO2 (1.112, 95% CI: 1.008–1.214). SO2 exhibited the highest effect among the different pollutants. The 2-day lag model analysis showed that the OR values for each 1 μg/m3 increase in pollutant concentration were: PM2.5 is 1.003 (95% CI: 1.000–1.007), PM10 is 1.013 (95% CI: 1.008–1.017), SO2 is 1.032 (95% CI: 0.999–1.066), and NO2 is 1.017 (95% CI: 1.011–1.023). The OR values for each IQR increase in pollutant concentration were: PM2.5 (1.089, 95% CI: 0.995–1.191), PM10 (1.111, 95% CI: 1.016–1.216), SO2 (1.100, 95% CI: 0.998–1.213), and NO2 (1.123, 95% CI: 1.021–1.224). NO2 exhibited the highest effect among the different pollutants. Details are showed in Figures 1, 2 and Supplementary Table S2.

Figure 1.

Six-panel figure displaying odds ratios (ORs) and 95 percent confidence intervals for each increase in unit concentration of CO, NO2, O3_8h, PM10, PM2.5, and SO2 across different lag models and averaging periods, with a red dashed reference line at OR =1.

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OR values and their 95% CI for the risk of fatal recurrence within 1 year in patients with first-episode acute hemorrhagic stroke for each 1 μg/m3 increase in air pollutant concentration.

Figure 2.

Six-panel figure displaying odds ratios (ORs) and 95 percent confidence intervals for each IQR increase in concentration of CO, NO2, O3_8h, PM10, PM2.5, and SO2 across different lag models and averaging periods, with a red dashed reference line at OR =1.

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OR values and their 95% CI for the risk of fatal recurrence within 1 year in patients with first-episode acute hemorrhagic stroke for each 1 IQR increase in air pollutant concentration.

Dose–response curve of short-term exposure

The 3-day moving average lag (lag02) exposure dose–response curves for air pollutants revealed that as pollutant concentrations increased, the NO2 dose–response curve exhibited an upward trend at low concentrations, followed by a relatively stable level. The SO2 dose–response curve initially declined and then gradually increased. The PM2.5 and PM10 dose–response curves showed rapid increases at low concentrations, followed by a downward trend. All four pollutants’ dose–response curves demonstrated statistically significant at low concentrations in the 3—day moving average (lag02) model (p < 0.05). The 2-day lag (lag2) exposure dose–response curves indicated that as pollutant concentrations increased, the effect of PM10 and PM2.5 first rose and then declined, while the NO2 curve initially increased, stabilized, and then rose rapidly. The 2-day lag (lag2) exposure dose–response curves of PM10 and PM2.5 showed statistically significant effects at low concentrations (p < 0.05), whereas NO2 curve exhibited statistical significance at high concentrations (p < 0.05), details are showed in Figure 3).

Figure 3.

Grouped line graphs compare the odds ratio with ninety-five percent confidence intervals for PM2.5, PM10, SO2, and NO2 pollutant concentrations, across two conditions labeled Lag02 and Lag2, demonstrating non-linear trends and increasing uncertainty at higher concentrations.

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Dose–response curves of short-term exposure to air pollutants (μg/m3) and the risk of fatal recurrence within 1 year in patients with first-episode acute hemorrhagic stroke.

Stratified analysis results of short-term exposure

Stratified analysis by sex, age, and season revealed that in the 3-day moving average lag (lag02) model, the increased risk of fatal recurrence within 1 year in patients with first-episode acute hemorrhagic stroke was statistically associated with short-term exposure to PM10, SO2, and NO2 in autumn (p < 0.05), with short-term exposure to PM2.5 and PM10 for women (p < 0.05), and with short-term exposure to SO2 in the 45–69 years and ≥80 years age groups (p < 0.05). In the 2-day lag (lag2) model, the increased risk of fatal recurrence within 1 year in patients with first-episode acute hemorrhagic stroke was statistically associated with short-term exposure to NO2 in autumn (p < 0.05), with short-term exposure to PM10 and NO2 for women (p < 0.05), and with short-term exposure to SO2 and NO2 in the ≥80 years age group (p < 0.05), details are showed in Table 3.

Table 3.

Stratified analysis results of the association between short-term exposure to air pollutants and the risk of fatal recurrence.

Variable PM2.5 PM10 SO2 NO2
Lag02 OR (95%CI) p-value OR (95%CI) p-value OR (95%CI) p-value OR (95%CI) p-value
Sex
Male 1.002 (0.996,1.008) 0.535 1.002 (0.998,1.006) 0.406 1.054 (0.997,1.115) 0.065 1.007 (0.997,1.017) 0.194
Female 1.017 (1.007,1.028) 0.028* 1.023 (1.011,1.034) 0.013* 1.032 (0.973,1.095) 0.293 1.028 (0.997,1.059) 0.175
Age group
18–44 years 1.004 (0.974,1.034) 0.808 0.999 (0.982,1.017) 0.911 1.009 (0.772,1.320) 0.945 0.998 (0.939,1.062) 0.960
45–64 years 1.008 (0.998,1.018) 0.138 1.004 (0.997,1.011) 0.212 1.112 (1.004,1.232) 0.042* 1.018 (0.998,1.039) 0.080
65–79 years 1.013 (0.996,1.030) 0.359 1.001 (0.997,1.006) 0.526 0.995 (0.933,1.061) 0.872 1.001 (0.989,1.012) 0.908
≥80 years 1.014 (0.997,1.031) 0.269 1.018 (0.999,1.036) 0.079 1.069 (1.004,1.138) 0.037* 1.027 (0.999,1.055) 0.073
Season
Spring 1.001 (0.991,1.011) 0.825 0.999 (0.994,1.005) 0.781 0.990 (0.918,1.066) 0.785 0.990 (0.970,1.010) 0.333
Summer 0.998 (0.973,1.024) 0.870 0.997 (0.981,1.013) 0.719 1.039 (0.915,1.180) 0.552 1.000 (0.963,1.038) 0.997
Autumn 1.014 (0.999,1.028) 0.072 1.019 (1.011,1.028) 0.025* 1.097 (1.030,1.164) 0.021* 1.034 (1.016,1.051) 0.008*
Winter 1.002 (0.997,1.007) 0.480 1.002 (0.998,1.006) 0.350 1.029 (0.963,1.100) 0.391 1.002 (0.992,1.012) 0.695
Lag2
Sex
Male 1.002 (0.998,1.007) 0.359 1.002 (0.999,1.005) 0.132 1.036 (0.991,1.083) 0.115 1.006 (0.998,1.014) 0.160
Female 1.005 (0.999,1.010) 0.077 1.014 (1.005,1.024) 0.023* 1.028 (0.980,1.079) 0.259 1.019 (1.010,1.028) 0.032*
Age group
18–44 years 1.014 (0.982,1.047) 0.389 1.002 (0.990,1.013) 0.756 0.997 (0.809,1.228) 0.979 1.006 (0.953,1.063) 0.823
45–64 years 1.004 (0.996,1.013) 0.324 1.003 (0.998,1.008) 0.270 1.032 (0.949,1.123) 0.460 1.009 (0.993,1.025) 0.268
65–79 years 1.003 (0.998,1.008) 0.309 1.002 (0.998,1.005) 0.286 1.001 (0.952,1.053) 0.964 1.003 (0.994,1.012) 0.520
≥80 years 1.003 (0.998,1.008) 0.261 1.015 (0.999,1.031) 0.053 1.067 (1.014,1.122) 0.012* 1.031 (1.021,1.041) 0.017*
Season
Spring 1.001 (0.994,1.009) 0.727 1.001 (0.997,1.004) 0.745 0.980 (0.923,1.041) 0.520 0.997 (0.982,1.013) 0.735
Summer 0.994 (0.975,1.013) 0.527 0.995 (0.983,1.007) 0.431 1.008 (0.915,1.110) 0.876 1.006 (0.979,1.034) 0.682
Autumn 1.004 (0.994,1.014) 0.472 1.005 (0.998,1.012) 0.145 1.057 (0.979,1.141) 0.158 1.022 (1.010,1.034) 0.028*
Winter 1.003 (0.999,1.007) 0.190 1.016 (0.999,1.033) 0.080 1.050 (0.996,1.107) 0.069 1.005 (0.997,1.013) 0.202
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*p < 0.05.

Sensitivity analysis

Based on the results of pollutant correlation analysis, pollutant combinations with low or moderate correlations (R ≤ 0.6) were selected to construct two-pollutant models of 3-day moving average lag (lag02) and 2-day lag (lag2), which was used to test model stability. Since O3_8h and CO were not statistically significant in the model, they were excluded from the sensitivity analysis. After adjusting for temperature, humidity, non-linearity, and week effects in the pollutant model, the OR values for the risk of fatal recurrence within 1 year in patients with first-episode acute hemorrhagic stroke due to short-term exposed to the air pollutants showed no significant changes. The effects of the two-pollutant model were nearly consistent with those of the single-pollutant model. However, it should be noted that adjusting for PM10 concentration in the model weakens the effect of gaseous pollutants, which is consistent in both the 3-day moving average lag (lag02) and 2-day lag (lag2) models, details are showed in Supplementary Table S3.

Discussion

This study employed a time-stratified case-crossover design to analyze the association between short-term exposure to air pollutants and the risk of fatal recurrence within 1 year in patients with first-episode acute hemorrhagic stroke. The mean age of death in the study subjects was 75.56 ± 12.55 years, indicating that the key population at risk for fatal recurrence in acute hemorrhagic stroke patients is the older population. This finding is consistent with our stratified analysis results, which demonstrated that short-term exposure to SO2 and NO2 significantly increased the risk of fatal recurrence within 1 year in patients with first-episode acute hemorrhagic stroke aged 80 years and above. This may be related to the heightened sensitivity of old hemorrhagic stroke patients to air pollutants (25–27).

The conditional logistic regression analysis revealed that short-term exposure to PM2.5, PM10, SO2, and NO2 was associated with an increased risk of fatal recurrence within 1 year in patients with first-episode acute hemorrhagic stroke. Notably, statistical significance was only observed for PM2.5 and SO2 in the 3-day moving average lag model, while PM10 and NO2 showed statistical significance in both the 3-day moving average lag model and the 2-day lag model. These findings align with previous studies investigating the association between short-term exposure to air pollutants and the incidence or mortality of hemorrhagic stroke (28–30). Numerous studies (31–34) have reported a positive correlation between the risk of hemorrhagic stroke and concentrations of PM10 and NO2. Although this differs from the outcome event of fatal recurrence within 1 year in our study, which focuses on first-episode acute hemorrhagic stroke patients, we consider the potential pathogenic mechanisms of PM10 and NO2 in hemorrhagic stroke—namely, their ability to cause direct ischemic damage to blood vessels, contribute to atherosclerosis and thereby increase the risk of cerebral vascular rupture, and induce vasoconstriction leading to elevated blood pressure (35)—as supporting evidence for our conclusions. Although studies have demonstrated a significant association between short—term exposure to CO and ozone and the risk of hemorrhagic stroke (33–38), no statistically significant association between ozone and CO exposure and fatal recurrence risk was observed in this study. This discrepancy may be attributed to the heterogeneity of the study subjects and the different outcome events being examined. However, we should also note that, Benefiting from effective air pollution control measures, the concentration of air pollutants in our city has remained at a low level, this may have weakened the association between pollutants exposure and the risk of fatal recurrence in patients with acute hemorrhagic stroke. This is also reflected in the fact that the lower limits of the 95% CIs for the OR values of the short-term lag effects of the other five air pollutants, excluding CO, are all close to 1. However, in the lag2 and lag02 models, the statistically significant association we found between short—term exposure to relevant pollutants and an increased risk of fatal recurrence indicates that air pollutants can still exert an impact on hemorrhagic stroke conditions even at low concentrations.

The dose–response curve of short-term pollutant exposure demonstrated that the risk of fatal recurrence within 1 year in patients with first-episode acute hemorrhagic stroke progressively increased with elevated concentrations of PM2.5, PM10, SO2, and NO2 in the air. However, three air pollutants showed statistically significant effects only at low concentrations, which was consistent with previous studies (39). In contrast, NO2 exhibited statistical significance only at high concentrations, given that this critical concentration (>40 μg/m3) corresponds to the 75th percentile of NO2 exposure levels in the study subjects, so the actual number of exposure-response relationship observations is relatively sufficient, this conclusion holds practical significance. Previous research (40–42) reported no statistical association between short-term NO2 exposure and the risk of stroke, particularly hemorrhagic stroke, which may be related to its health mechanisms of action. Current effective air pollution control has significantly reduced NO2 concentrations, making it insufficient to exert health hazards at low levels. As NO2 concentrations rise, its role in increasing the risk of fatal recurrence in acute hemorrhagic stroke patients may become more pronounced. This finding underscores the importance of strengthening emission control measures for transportation, a major source of NO2 pollution, and highlights the need for further validation in subsequent studies investigating the association between NO2 exposure and stroke risk. However, due to the low concentration of pollutants being controlled in this region, it inevitably resulted in fewer observed cases of exposure to high pollutant concentrations. Therefore, the accuracy of the exposure-response relationship under high pollutant concentrations requires further research to confirm Figure 3.

Stratified analysis revealed that the risk of fatal recurrence within 1 year for patients with first-episode acute hemorrhagic stroke who were short-term exposed to air pollutants exhibited sex, age, and seasonal differences, which have been reported in related studies (30, 39, 43, 44). Although the outcome events and exposure lag patterns differed, the disease categories and pollutant types involved remained consistent, indirectly validating the conclusion that we found. This also corroborates the individual-specific differences in this risk (45). Overall, among patients with first-episode acute hemorrhagic stroke, female patients exposed to PM2.5, PM10, and NO2 in the short term, as well as patients aged ≥80 years exposed to SO2 and NO2 in the short term, exhibited an increased risk of fatal recurrence within 1 year. This may be attributed to differences in the intensity of inflammatory responses (46, 47) induced by air pollution exposure between female and elderly populations, leading to distinct health outcomes.

The stratified analysis also suggests that short-term exposure to PM10, SO2, and NO2 in autumn increases the risk of fatal recurrence within 1 year. A study on seasonal differences between air pollutants and stroke in Changsha, China, which has similar climate conditions to Zhenjiang (48), found that the concentrations of air PM10, SO2, and NO2 in autumn were significantly associated with the risk of hemorrhagic stroke, this study is consistent with ours in terms of region, disease type, and time. This seasonal difference can be explained by the theory regarding the correlation between diurnal temperature variations and human activity intensity (49), the cool autumn climate in this region directly promotes increased frequency and duration of outdoor activities among residents, thereby elevating exposure intensity to air particulate matter and NO2, important pollutants in the Yangtze River Delta region (50), combined with SO2, under the influence of facilitating factors such as autumn fog and low efficiency of post-growth air phytofiltration (51), leading to health hazards. The analysis revealed that after adjusting for PM10 concentration, the effects of SO2 and NO2, two gaseous pollutants, weakened, this may be attributed to multicollinearity or shared emission sources among pollutants. We should also consider that under different socioeconomic determinants (52), differences in the composition of air pollutants across regions may lead to varying health effects. However, there is no consensus on whether there is an interaction between particulate pollutants and gaseous pollutants in relevant studies (53–55), the interpretation of this effect requires further evaluation based on robust evidence.

Subjects in our study were sourced from the city-wide acute cardiovascular and cerebrovascular event surveillance system, ensuring good representativeness and effectively reflecting the 1-year fatal recurrence of first-episode acute hemorrhagic stroke patients across the city. However, our study has limitations, we used regional average levels of meteorological and air pollutant monitoring data as the expose level of study subjects. The lack of individual exposure data may lead to dilution bias in exposure—response relationships, resulting in either overestimation or underestimation of the corresponding exposure effects. Consequently, the spatial heterogeneity of pollutants and the exposure risks of susceptible populations with specific characteristics cannot be accurately reflected. Future studies should integrate Geographic Information Systems (GIS) and employ high-resolution exposure assessment techniques to obtain precise regional meteorological environmental distribution data. Long-term follow-up tracking of patient health outcomes across different geographical regions should also be conducted. This will provide scientific evidence for stroke patient health management and promote the establishment of an interdepartmental early–warning mechanism for the risk of acute events in stroke patients.

Acknowledgments

We would like to express our gratitude to all the investigators who participated in the Zhenjiang Acute Cardiovascular and Cerebrovascular Event Monitoring program, as well as to the engineers who were responsible for the maintenance of the registration and reporting system. Precisely because they take their responsibility for the monitoring work seriously, the high quality of the monitoring data is guaranteed. We also would like to thank the technical support from Guiding Scientific and Technological Project for Social Development (Grant No. FZ2025104) of Zhenjiang Municipal Science and Technology Bureau.

Funding Statement

The author(s) declared that financial support was not received for this work and/or its publication.

Edited by: Carla Martins, New University of Lisbon, Portugal

Reviewed by: Susanne Breitner, Ludwig Maximilian University of Munich, Germany

Corda Mariana O., NOVA University of Lisbon, Portugal

Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

XYG: Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. JL: Funding acquisition, Project administration, Resources, Supervision, Validation, Writing – review & editing. HYW: Data curation, Investigation, Project administration, Supervision, Writing – review & editing. DH: Data curation, Formal analysis, Investigation, Project administration, Validation, Writing – review & editing. AHL: Conceptualization, Data curation, Formal analysis, Methodology, Resources, Validation, Visualization, Writing – original draft, Writing – review & editing. LX: Data curation, Investigation, Writing – review & editing. JJH: Conceptualization, Data curation, Investigation, Writing – review & editing.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Correction note

A correction has been made to this article. Details can be found at: 10.3389/fpubh.2026.1898980.

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Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2026.1827069/full#supplementary-material

Data_Sheet_1.docx (22.7KB, docx)

References

  • 1.GBD 2019 stroke collaborators . Global, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the global burden of disease study 2019. Lancet Neurol. (2021) 20:795–820. doi: 10.1016/S1474-4422(21)00252-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Mohan KM, Wolfe CD, Rudd AG, Heuschmann PU, Kolominsky-Rabas PL, Grieve AP. Risk and cumulative risk of stroke recurrence: a systematic review and meta-analysis. Stroke. (2011) 42:1489–94. doi: 10.1161/STROKEAHA.110.602615 [DOI] [PubMed] [Google Scholar]
  • 3.Bailey RD, Hart RG, Benavente O, Pearce LA. Recurrent brain hemorrhage is more frequent than ischemic stroke after intracranial hemorrhage. Neurology. (2001) 56:773–7. doi: 10.1212/wnl.56.6.773, [DOI] [PubMed] [Google Scholar]
  • 4.Nakase T, Yoshioka S, Sasaki M, Suzuki A. Clinical features of recurrent stroke after intracerebral hemorrhage. Neurol Int. (2012) 4:e10. doi: 10.4081/ni.2012.e10, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Feng W, Hendry RM, Adams RJ. Risk of recurrent stroke, myocardial infarction, or death in hospitalized stroke patients. Neurology. (2010) 74:588–93. doi: 10.1212/WNL.0b013e3181cff776 [DOI] [PubMed] [Google Scholar]
  • 6.Feigin VL, Krishnamurthi RV, Parmar P, Norrving B, Mensah GA, Bennett DA, et al. Update on the global burden of ischemic and hemorrhagic stroke in 1990-2013: the GBD 2013 study. Neuroepidemiology. (2015) 45:161–76. doi: 10.1159/000441085, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Weimar C, Ziegler A, König IR, Diener HC. Predicting functional outcome and survival after acute ischemic stroke. J Neurol. (2002) 249:888–95. doi: 10.1007/s00415-002-0755-8 [DOI] [PubMed] [Google Scholar]
  • 8.Huang K, Liang F, Yang X, Liu F, Li J, Xiao Q, et al. Long term exposure to ambient fine particulate matter and incidence of stroke: prospective cohort study from the China-PAR project. BMJ. (2019) 367:l6720. doi: 10.1136/bmj.l6720, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Chen G, Wang A, Li S, Zhao X, Wang Y, Li H, et al. Long-term exposure to air pollution and survival after ischemic stroke. Stroke. (2019) 50:563–70. doi: 10.1161/STROKEAHA.118.023264, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Lipsett MJ, Ostro BD, Reynolds P, Goldberg D, Hertz A, Jerrett M, et al. Long-term exposure to air pollution and cardiorespiratory disease in the California teachers study cohort. Am J Respir Crit Care Med. (2011) 184:828–35. doi: 10.1164/rccm.201012-2082OC, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Liu F, Chen G, Huo W, Wang C, Liu S, Li N, et al. Associations between long-term exposure to ambient air pollution and risk of type 2 diabetes mellitus: a systematic review and meta-analysis. Environ Pollut. (2019) 252:1235–45. doi: 10.1016/j.envpol.2019.06.033, [DOI] [PubMed] [Google Scholar]
  • 12.Yang BY, Fan S, Thiering E, Seissler J, Nowak D, Dong GH, et al. Ambient air pollution and diabetes: a systematic review and meta-analysis. Environ Res. (2020) 180:108817. doi: 10.1016/j.envres.2019.108817, [DOI] [PubMed] [Google Scholar]
  • 13.Fu P, Guo X, Cheung FMH, Yung KKL. The association between PM2.5 exposure and neurological disorders: a systematic review and meta-analysis. Sci Total Environ. (2019) 655:1240–8. doi: 10.1016/j.scitotenv.2018.11.218 [DOI] [PubMed] [Google Scholar]
  • 14.Niu Z, Liu F, Yu H, Wu S, Xiang H. Association between exposure to ambient air pollution and hospital admission, incidence, and mortality of stroke: an updated systematic review and meta-analysis of more than 23 million participants. Environ Health Prev Med. (2021) 26:15. doi: 10.1186/s12199-021-00937-1, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Oudin A, Forsberg B, Jakobsson K. Air pollution and stroke. Epidemiology. (2012) 23:505–6. doi: 10.1097/EDE.0b013e31824ea667 [DOI] [PubMed] [Google Scholar]
  • 16.Kettunen J, Lanki T, Tiittanen P, Aalto PP, Koskentalo T, Kulmala M, et al. Associations of fine and ultrafine particulate air pollution with stroke mortality in an area of low air pollution levels. Stroke. (2007) 38:918–22. doi: 10.1161/01.STR.0000257999.49706.3b, [DOI] [PubMed] [Google Scholar]
  • 17.Henrotin JB, Zeller M, Lorgis L, Cottin Y, Giroud M, Béjot Y. Evidence of the role of short-term exposure to ozone on ischaemic cerebral and cardiac events: the Dijon vascular project (DIVA). Heart. (2010) 96:1990–6. doi: 10.1136/hrt.2010.200337, [DOI] [PubMed] [Google Scholar]
  • 18.Oudin A, Strömberg U, Jakobsson K, Stroh E, Björk J. Estimation of short-term effects of air pollution on stroke hospital admissions in southern Sweden. Neuroepidemiology. (2010) 34:131–42. doi: 10.1159/000274807, [DOI] [PubMed] [Google Scholar]
  • 19.Zhou M, Wang H, Zeng X, Yin P, Zhu J, Chen W, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990-2017: a systematic analysis for the global burden of disease study 2017. Lancet. (2019) 394:1145–58. doi: 10.1016/S0140-6736(19)30427-1, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.China Center for Disease Control and Prevention, chronic noncommunicable disease prevention and control center". In: China Residents 'Acute Cardiovascular and Cerebrovascular Events Incidence Monitoring Report (2014–2020). Beijing: People's Medical Publishing House; (2023) [Google Scholar]
  • 21.Yan L, Hou L, Cai X, Long Z, Chen X, Jing W. Analysis of the incidence and mortality characteristics of acute myocardial infarction in China residents from 2015 to 2019 [J]. China Circulation Journal. (2024) 39:968–75. doi: 10.3969/j.issn.1000-3614.2024.10.003 [DOI] [Google Scholar]
  • 22.Stahmeyer JT, Stubenrauch S, Geyer S, Weissenborn K, Eberhard S. The frequency and timing of recurrent stroke: an analysis of routine health insurance data. Dtsch Arztebl Int. (2019) 116:711–7. doi: 10.3238/arztebl.2019.0711, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Sun S, Stewart JD, Eliot MN, Yanosky JD, Liao D, Tinker LF, et al. Short-term exposure to air pollution and incidence of stroke in the Women's Health Initiative. Environ Int. (2019) 132:105065. doi: 10.1016/j.envint.2019.105065, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Ministry of Ecological Environment of the people’s republic of China. Available online at: https://www.mee.gov.cn/ywgz/fgbz/bz/bzwb/dqhjbh/dqhjzlbz/201203/t20120302_224165.shtml (Accessed February 29, 2012).
  • 25.Berginer VM, Goldsmith J, Batz U, Vardi H, Shapiro Y. Clustering of strokes in association with meteorologic factors in the Negev Desert of Israel: 1981-1983. Stroke. (1989) 20:65–9. doi: 10.1161/01.str.20.1.65, [DOI] [PubMed] [Google Scholar]
  • 26.Hess KL, Wilson TE, Sauder CL, Gao Z, Ray CA, Monahan KD. Aging affects the cardiovascular responses to cold stress in humans. J Appl Physiol. (1985) 107:1076–82. doi: 10.1152/japplphysiol.00605.2009, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Keatinge WR, Coleshaw SR, Cotter F, Mattock M, Murphy M, Chelliah R. Increases in platelet and red cell counts, blood viscosity, and arterial pressure during mild surface cooling: factors in mortality from coronary and cerebral thrombosis in winter. Br Med J (Clin Res Ed). (1984) 289:1405–8. doi: 10.1136/bmj.289.6456.1405, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Guo Y, Luo C, Cao F, Liu J, Yan J. Short-term environmental triggers of hemorrhagic stroke. Ecotoxicol Environ Saf. (2023) 265:115508. doi: 10.1016/j.ecoenv.2023.115508, [DOI] [PubMed] [Google Scholar]
  • 29.Orellano P, Reynoso J, Quaranta N, Bardach A, Ciapponi A. Short-term exposure to particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), and ozone (O3) and all-cause and cause-specific mortality: systematic review and meta-analysis. Environ Int. (2020) 142:105876. doi: 10.1016/j.envint.2020.105876, [DOI] [PubMed] [Google Scholar]
  • 30.Xu R, Wang Q, Wei J, Lu W, Wang R, Liu T, et al. Association of short-term exposure to ambient air pollution with mortality from ischemic and hemorrhagic stroke. Eur J Neurol. (2022) 29:1994–2005. doi: 10.1111/ene.15343, [DOI] [PubMed] [Google Scholar]
  • 31.Qian Y, Zhu M, Cai B, Yang Q, Kan H, Song G, et al. Epidemiological evidence on association between ambient air pollution and stroke mortality. J Epidemiol Community Health. (2013) 67:635–40. doi: 10.1136/jech-2012-201096 [DOI] [PubMed] [Google Scholar]
  • 32.Tsai SS, Goggins WB, Chiu HF, Yang CY. Evidence for an association between air pollution and daily stroke admissions in Kaohsiung. Taiwan Stroke. (2003) 34:2612–6. doi: 10.1161/01.STR.0000095564.33543.64, [DOI] [PubMed] [Google Scholar]
  • 33.Yorifuji T, Kashima S, Tsuda T, Ishikawa-Takata K, Ohta T, Tsuruta K, et al. Long-term exposure to traffic-related air pollution and the risk of death from hemorrhagic stroke and lung cancer in Shizuoka. Japan Sci Total Environ. (2013) 443:397–402. doi: 10.1016/j.scitotenv.2012.10.088, [DOI] [PubMed] [Google Scholar]
  • 34.Xiang H, Mertz KJ, Arena VC, Brink LL, Xu X, Bi Y, et al. Estimation of short-term effects of air pollution on stroke hospital admissions in Wuhan, China. PLoS One. (2013) 8:e61168. doi: 10.1371/journal.pone.0061168, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Yorifuji T, Kawachi I, Sakamoto T, Doi H. Associations of outdoor air pollution with hemorrhagic stroke mortality. J Occup Environ Med. (2011) 53:124–6. doi: 10.1097/JOM.0b013e3182099175, [DOI] [PubMed] [Google Scholar]
  • 36.Han MH, Yi HJ, Ko Y, Kim YS, Lee YJ. Association between hemorrhagic stroke occurrence and meteorological factors and pollutants. BMC Neurol. (2016) 16:59. doi: 10.1186/s12883-016-0579-2, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Hua Q, Meng X, Gong J, Qiu X, Shang J, Xue T, et al. Ozone exposure and cardiovascular disease: a narrative review of epidemiology evidence and underlying mechanisms. Fundam Res. (2024) 5:249–63. doi: 10.1016/j.fmre.2024.02.016, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Ho AFW, Lim MJR, Zheng H, Leow AS, Tan BY, Pek PP, et al. Association of ambient air pollution with risk of hemorrhagic stroke: a time-stratified case crossover analysis of the Singapore stroke registry. Int J Hyg Environ Health. (2022) 240:113908. doi: 10.1016/j.ijheh.2021.113908, [DOI] [PubMed] [Google Scholar]
  • 39.Chen S, Lin X, Du Z, Zhang Y, Zheng L, Ju X, et al. Potential causal links between long-term ambient particulate matter exposure and cerebrovascular mortality: insights from a large cohort in southern China. Environ Pollut. (2023) 328:121336. doi: 10.1016/j.envpol.2023.121336, [DOI] [PubMed] [Google Scholar]
  • 40.Byrne CP, Bennett KE, Hickey A, Kavanagh P, Broderick B, O'Mahony M, et al. Short-term air pollution as a risk for stroke admission: a time-series analysis. Cerebrovasc Dis. (2020) 49:404–11. doi: 10.1159/000510080, [DOI] [PubMed] [Google Scholar]
  • 41.Qi X, Wang Z, Guo X, Xia X, Xue J, Jiang G, et al. Short-term effects of outdoor air pollution on acute ischaemic stroke occurrence: a case-crossover study in Tianjin. China Occup Environ Med. (2020) 77:862–7. doi: 10.1136/oemed-2019-106301, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Li M, Edgell RC, Wei J, Li H, Qian ZM, Feng J, et al. Air pollution and stroke hospitalization in the Beibu gulf region of China: a case-crossover analysis. Ecotoxicol Environ Saf. (2023) 255:114814. doi: 10.1016/j.ecoenv.2023.114814, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Cai M, Lin X, Wang X, Zhang S, Qian ZM, McMillin SE, et al. Ambient particulate matter pollution of different sizes associated with recurrent stroke hospitalization in China: a cohort study of 1.07 million stroke patients. Sci Total Environ. (2023) 856:159104. doi: 10.1016/j.scitotenv.2022.159104, [DOI] [PubMed] [Google Scholar]
  • 44.Gaines B, Kloog I, Zucker I, Ifergane G, Novack V, Libruder C, et al. Particulate air pollution exposure and stroke among adults in Israel. Int J Environ Res Public Health. (2023) 20:1482. doi: 10.3390/ijerph20021482, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Gabet S, Puy L. Current trend in air pollution exposure and stroke. Curr Opin Neurol. (2025) 38:54–61. doi: 10.1097/WCO.0000000000001331, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.He S, Klevebro S, Baldanzi G, Pershagen G, Lundberg B, Eneroth K, et al. Ambient air pollution and inflammation-related proteins during early childhood. Environ Res. (2022) 215:114364. doi: 10.1016/j.envres.2022.114364, [DOI] [PubMed] [Google Scholar]
  • 47.Pope CA, 3rd, Bhatnagar A, McCracken JP, Abplanalp W, Conklin DJ, O'Toole T. Exposure to fine particulate air pollution is associated with endothelial injury and systemic inflammation. Circ Res. (2016) 119:1204–14. doi: 10.1161/CIRCRESAHA.116.309279, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Zhong H, Shu Z, Zhou Y, Lu Y, Yi B, Tang X, et al. Seasonal effect on association between atmospheric pollutants and hospital emergency room visit for stroke. J Stroke Cerebrovasc Dis. (2018) 27:169–76. doi: 10.1016/j.jstrokecerebrovasdis.2017.08.014, [DOI] [PubMed] [Google Scholar]
  • 49.Shen Z, Shi H, Jiang Y, Sun Z. Diurnal variation in the urban thermal environment and its relationship to human activities in China: a Tencent location-based service geographic big data perspective. Environ Sci Pollut Res Int. (2024) 31:14218–28. doi: 10.1007/s11356-023-31789-7, [DOI] [PubMed] [Google Scholar]
  • 50.Liu M, Xiao S, Wang Y, Li L, Mi J, Wang S. Synergistic analysis of atmospheric pollutants NO2 and PM2.5 based on land use regression models: a case study of the Yangtze River Delta, China. Environ Monit Assess. (2023) 195:1048. doi: 10.1007/s10661-023-11637-4, [DOI] [PubMed] [Google Scholar]
  • 51.Zhang BJ, Zhou Y, Pawełkowicz M, Sadłos A, Żurkowski M, Małecka-Przybysz M, et al. Autumn and winter air phytofiltration—are plants able to biofilter air during peak pollutant emissions? J Environ Manag. (2025) 373:124027. doi: 10.1016/j.jenvman.2025.124027, [DOI] [PubMed] [Google Scholar]
  • 52.Zhu M, Guo J, Zhou Y, Cheng X. Exploring the spatiotemporal evolution and socioeconomic determinants of PM2.5 distribution and its hierarchical management policies in 366 Chinese cities. Front. Public Health. (2022) 10:843862. doi: 10.3389/fpubh.2022.843862, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Ito K, Thurston GD, Silverman RA. Characterization of PM2.5, gaseous pollutants, and meteorological interactions in the context of time-series health effects models. J Expo Sci Environ Epidemiol. (2007) 17:S45–60. doi: 10.1038/sj.jes.7500627, [DOI] [PubMed] [Google Scholar]
  • 54.Dockery DW, Schwartz J, Spengler JD. Air pollution and daily mortality: associations with particulates and acid aerosols. Environ Res. (1992) 59:362–73. doi: 10.1016/s0013-9351(05)80042-8, [DOI] [PubMed] [Google Scholar]
  • 55.Sarnat JA, Brown KW, Schwartz J, Coull BA, Koutrakis P. Ambient gas concentrations and personal particulate matter exposures: implications for studying the health effects of particles. Epidemiology. (2005) 16:385–95. doi: 10.1097/01.ede.0000155505.04775.33 [DOI] [PubMed] [Google Scholar]

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