Interactive Effects of Ambient Ozone and Meteorological Factors on Cerebral Infarction: A Five-Year Time-Series Study
by
and
Yanzhe Chen
1,†, 
Songtai Yang
2,†,
Hanya Que
1,
Jiamin Liu
2,
Zhe Wang
2,
Na Wang
2,
Yunkun Qin
2,
Meng Li
2,*
and
Fang Zhou
2,* 1
Centers for Disease Control and Prevention, Zhengzhou 450001, China
2
Department of Occupational Health, College of Public Health, Zhengzhou University, Zhengzhou 450001, China
*
Authors to whom correspondence should be addressed.
†
Songtai Yang and Yanzhe Chen have contributed equally to this article and should be considered co-first authors.
Toxics 2025, 13(7), 598; https://doi.org/10.3390/toxics13070598
Submission received: 21 May 2025
/
Revised: 12 July 2025
/
Accepted: 14 July 2025
/
Published: 16 July 2025
(This article belongs to the Special Issue Ozone Pollution and Adverse Health Impacts)
Abstract
Objective: Our objective was to investigate the short-term effects of ambient ozone (O3) meteorological factors and their interactions on hospitalizations for cerebral infarction in Zhengzhou, China. Methods: Daily data on air pollutants, meteorological factors, and hospitalization of cerebral infarction patients were collected from 1 January 2019 to 31 December 2023 in Zhengzhou, China. A generalized additive model was constructed to evaluate the association between ambient O3 levels and hospitalization for cerebral infarction. A distributed lag non-linear model was applied to capture lagged and non-linear exposure effects. We further examined the modifying roles of temperature, humidity, wind speed, and atmospheric pressure, and conducted stratified analyses by sex, age, and season. Results: O3 exposure was significantly associated with increased cerebral infarction risk, particularly during the warm season. A bimodal temperature-lag pattern was observed, as follows: moderate temperatures (10–20 °C) were associated with immediate effects, while cold (<10 °C) and hot (>30 °C) temperatures were linked to delayed risks. The association of O3 and hospitalizations for cerebral infarction appeared stronger under high humidity, low wind speed, and low atmospheric pressure. Conclusions: Short-term O3 exposure and adverse meteorological conditions are jointly associated with an elevated risk of cerebral infarction. Integrated air quality and weather-based warning systems are essential for targeted stroke prevention.
1. Introduction
Air pollution has emerged as a significant global public health challenge, with extensive evidence linking air pollution exposure to increased rates of illness and mortality [1,2,3,4]. Extended exposure to air pollutants correlates with elevated disease incidence and impaired pulmonary capacity. Fine particulate matter (PM2.5), nitrogen dioxide (NO2), and sulfur dioxide (SO2) can infiltrate deep respiratory tissues and enter systemic circulation, inducing inflammatory responses and aggravating chronic respiratory conditions. Scientific evidence has further established significant associations between air pollutants and the development of cardiovascular disorders [5,6]. Among these pollutants, PM2.5 and ozone (O3) have become two key indicators for assessing population-level exposure in the global burden of disease studies. In recent years, the concentration of ambient particulate matter (PM) has gradually declined in most Chinese cities, due to effective regulatory efforts, while the levels of ambient O3 have shown a consistent upward trend [7]. O3 is regarded as a secondary pollutant that primarily arises from industrial emissions, vehicular exhaust, and the release of solvents and chemicals. Numerous studies have demonstrated that short-term exposure to O3 is linked to a higher incidence of cardiovascular and cerebrovascular diseases [8,9,10].
Cerebral infarction, commonly known as ischemic stroke, is characterized by the dysfunction of brain cells due to a lack of oxygen and nutrients resulting from inadequate blood supply to the brain. Growing epidemiological evidence has demonstrated a clear link between the incidence of cerebral infarction and various meteorological factors [11,12,13,14,15]. For instance, a study conducted in Japan shows that the incidence of stroke is highest in spring [16]. Similarly, research from Qatar found a significant positive correlation between air temperature and cerebral infarction incidence, whereas a negative correlation was found for relative humidity [17].
Despite these findings, the combined effects and potential interactions between ambient O3 and meteorological factors on the risk of cerebral infarction remain insufficiently explored. Most existing studies have focused on the main effects of single environmental exposures, with limited attention given to the modifying or synergistic effects of meteorological variables on air pollution-related health outcomes. Understanding such interactions is essential for clarifying the environmental determinants of cerebrovascular events, especially under the conditions of climate variability and increasing O3 levels.
Zhengzhou, located in the northern central part of Henan Province, experiences a warm temperate continental climate and has undergone significant industrialization and urban expansion in recent decades. Although the concentration of SO2, NO2, and CO in Zhengzhou has shown a gradual decrease and remains lower than the average level of large- and medium-sized cities across China, the concentration of O3 has remained persistently high, making O3 pollution the primary air quality concern in Zhengzhou City and a significant environmental health risk to its residents.
Given the high incidence of cerebral infarction and persistently elevated ambient O3 concentrations in Zhengzhou, this study aimed to evaluate the short-term effects of ambient O3 exposure and meteorological conditions on hospitalizations for cerebral infarction, using data from 2019 to 2023. A generalized additive model (GAM) was used to quantify the associations between O3 and daily hospital admissions, while a distributed lag non-linear model (DLNM) was used to explore the potential interactions between O3 and meteorological variables. Additionally, stratified analyses were conducted to assess potential differences in sex, age group, and season. It is expected that the results of this study will provide a scientific basis for the development of a theoretical framework for cerebral infarction prevention and control, thereby contributing to reducing the burden of cerebral infarction in the future.
2. Materials and Methods
2.1. Study Population
Hospitalization data were obtained from 4 hospitals located in different districts of Zhengzhou, as follows: Zhengzhou First People’s Hospital, Zhengzhou Second People’s Hospital, Zhengzhou Central Hospital, and Zhengzhou Yi he Hospital. The collected information included admission date, age, sex, and diagnosis codes based on the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10). Patients diagnosed with cerebral infarction between 1 January 2019 and 31 December 2023 were identified based on their ICD-10.
2.2. Exposure Assessment
Air pollutant data were collected from nine fixed air quality monitoring stations operated by the Zhengzhou Environmental Protection Bureau, with the monitoring instruments and their specifications as follows: O3, Thermo Scientific Model 49i ultraviolet photometric O3 analyzer (Thermo Fisher Scientific, Waltham, MA, USA); PM2.5/PM10, Thermo Scientific Model 5030 SHARP synchronous hybrid monitor (Thermo Fisher Scientific, Waltham, MA, USA), SO2/NO2/CO, Ecotech EC9830 gas analyzer (Ecotech, Knoxfield, VIC, Australia). All instruments undergo mandatory quarterly verification by the Provincial Metrology Institute. Annual drift is kept below 2%, and the data missing rate remains under 1%. Missing values were imputed using spatial interpolation based on adjacent stations.
Meteorological data were sourced from daily reports issued by the Zhengzhou Meteorological Bureau, encompassing metrics such as daily average pressure, temperature, relative humidity, and wind speed. Daily air pollutant concentrations and meteorological data were complete and free of missing values during the study period.
2.3. Statistical Analysis
Spearman rank correlation analysis was conducted to examine the relationship between atmospheric pollutants and meteorological factors. Time-series analysis was then used to assess the association between hospital admissions for cerebral infarction and ambient O3 levels. The data on atmospheric pollution, meteorological variables, and hospital admissions were all continuous daily variables. The distribution of daily hospitalization counts for cerebral infarction followed a quasi-Poisson distribution, satisfying the requirements for time-series analysis. A GAM with a quasi-Poisson link function was applied to analyze the data. Considering the characteristics of the hospital admission data, we controlled for multiple confounding factors, including long-term trends and meteorological variables. This approach allowed us to quantitatively assess the impact of air pollutants on daily hospital admissions and their lagged effects. Excess risk (ER) was calculated to represent the percentage change in daily hospital admissions per 10 µg/m3 increase in atmospheric pollutant concentration. The basic model is defined as follows:
Log[E(Yt)] = α + βZt + s(time, df) + s(temperature, df) + s(atmospheric pressure, df) + s(relative humidity, df) + s(wind speed, df) + as.factor (DOW)+ as.factor (Holiday)
In this model:
E(Yt) represents the expected value of hospital admissions on day t; α is the intercept; Zt is the daily average concentration of each atmospheric pollutant on day t; the β coefficient indicates the relative risk (RR) of daily hospital admissions per unit increase in the concentration of each pollutant (Zt); s() represents a non-parametric smoothing function applied to calendar time, average temperature, average atmospheric pressure, average relative humidity, and average wind speed; DOW is included in the model as a dummy variable to account for the weekday effect; and df denotes the degrees of freedom. Based on previous studies, we selected 7 df/year for calendar time and 3 df for the average temperature, average atmospheric pressure, average relative humidity, and average wind speed [18,19].
To further investigate lagged and non-linear effects, we applied a DLNM, which allows for simultaneous modeling of exposure–response and lag–response relationships for O3 and meteorological variables. This approach captures potential delayed health effects caused by short-term environmental exposures. p-values were corrected for multiple comparisons using the FDR method.
Subgroup analyses were conducted by stratifying the population according to age (≤65 years and >65 years), sex (male and female), and season. Seasons were defined as warm (May to October) and cold (November to April) in accordance with local climate conditions. The z-test was used to evaluate potential differences among these subgroups.
All statistical analyses were conducted using R software (version 4.1.2) with the following packages: “mgcv”, “dlnm”, “splines”, and “ggplot2”. A two-sided p-value of less than 0.05 was deemed statistically significant.
3. Results
3.1. Description Statistics of Daily Hospitalizations for Cerebral Infarction, Air Pollutants, and Meteorological Variables
Given the non-normal distribution of hospitalization, air pollutant, and meteorological data, interannual comparisons are presented as median (P25, P75) values, with distributional differences assessed using the Kruskal–Wallis test. Key findings from Table 1 include the following: cerebral infarction hospitalizations were significantly higher among males than females; individuals aged 18–65 years accounted for the highest proportion of hospitalizations; hospitalization risk was elevated during warm seasons relative to cold seasons; and, with the exception of O3, all pollutants exhibited significant interannual concentration variations (p < 0.05), with median values demonstrating a consistent downward trend over the study period. As shown in Figure 1, concentrations of all pollutants except for O3 exhibited a declining trend annually, while O3 demonstrated distinct seasonal variation, with higher concentrations in warm seasons and lower concentrations in cold seasons.
3.2. Correlation Between Air Pollutants and Meteorological Factors
Spearman correlation analysis results are shown in Table 2. Significant positive correlations were found among all pollutants (r = 0.38–0.80, p < 0.01), except for O3. O3 exhibited correlations with SO2, NO2, CO, PM10, and PM2.5 (r = −0.38 to −0.18, p < 0.01). Furthermore, O3 was positively correlated with temperature and wind speed, but negatively correlated with relative humidity and air pressure (p < 0.05).
3.3. Association Between O3 Levels and Daily Cerebral Infarction Hospitalizations
When the lag period was set to 7 days, a significant positive association was found between O3 levels and hospitalizations for cerebral infarction. As shown in Table 3 and Table 4, the maximum single-day lag effect was 0.11% (95% CI: 0.08–0.14%), while the maximum cumulative lag effect was 0.13% (95% CI: 0.10–0.16%) for every 10 µg/m3 increase in O3 concentration.
Stratified analysis based on sex showed that increased O3 levels significantly elevated hospitalization risk for both men and women, with the strongest effects observed on lag day 0 and day 01. However, no statistically significant difference was found in effect magnitude between sexes. In terms of age classification, O3 exposure was associated with an elevated risk of cerebral infarction across all age groups, with the most pronounced effects in people over 80 years of age. Seasonal analysis shows that the lag effect in the warm season is significantly higher than that in the cold season.
3.4. Influence of Meteorological Factors on Cerebral Infarction
As shown in Figure 2, the analysis revealed a time-varying, non-linear association between ambient temperature and the risk of cerebral infarction. On the day of exposure (lag 0), moderate temperatures (10–20 °C) were associated with the highest relative risk, forming an inverted U-shaped curve. However, at longer lags (lags 2–3), the risk increased under lower temperature conditions, while at lags 4–6, higher temperatures (>30 °C) appeared to be associated with elevated risks, particularly among elderly individuals. These findings suggest a delayed dual-effect of temperature, where both low and high temperatures may influence stroke risk through different lag structures. Stratified analysis showed no substantial difference between males and females, whereas older adults, especially those aged over 80, exhibited greater risk fluctuations over time, reflecting age-specific vulnerability to delayed temperature-related stress.
3.5. Interaction Between Meteorological and Pollutant Factors on Cerebral Infarction
To further study the interaction between O3 and meteorological factors, we used the DLNM model to plot a three-dimensional graph. The results are shown in Figure 3. The risk of cerebral infarction increased with rising O3 levels under various meteorological conditions. The strongest interaction effect was observed between O3 and relative humidity, where hospitalization numbers rose sharply under high humidity (>80%) and high O3 conditions. Similarly, low wind speed (<1.5 m/s) and low atmospheric pressure (<1000 hpa) enhanced the effect of O3 on hospitalization risk. Notably, the combined effect of moderate temperature (10 °C) and high O3 also contributed to increased risk. These findings illustrate a synergistic pattern, whereby specific meteorological environments amplify the harmful impact of O3 on cerebrovascular health.
4. Discussion
This study provides robust evidence that short-term ambient O3 exposure is significantly associated with elevated cerebral infarction risk, with this association substantially modified by meteorological factors. Analysis of five-year hospitalization records from four major Zhengzhou hospitals revealed that heightened stroke risk is correlated not only with increased O3 levels, but also with specific meteorological patterns, including the following: moderate temperatures (10–20 °C) on exposure days, low temperatures (<10 °C) at 2–3-day lag, and high temperatures (>30 °C) at 4–6-day lag, forming a distinct bimodal temperature-lag pattern. Additionally, high relative humidity, low wind speed, and low atmospheric pressure were found to collectively strengthen the association between O3 exposure and cerebrovascular risk. These findings highlight the complex interactions between air pollution and weather in determining stroke risk, especially among older adults and during the warm season.
During the study period, the ambient O3 level in Zhengzhou showed a rising trend. This aligns with national monitoring data from 1341 air quality stations across China, which showed that the annual mean O3 concentration increased from 72.4 μg/m3 in 2015 to 80.9 μg/m3 in 2018 [20].
In addition, the average O3 concentration observed in this study reached 109.02 μg/m3, which was significantly higher than other cities such as Ganzhou [21] (90.52 μg/m3) and Urumqi [22] (67.53 μg/m3), as well as the rest of China. These findings suggest that O3 pollution in Zhengzhou has become a major environmental health concern. Notably, these findings are specifically tied to Zhengzhou’s urban context and may lack generalizability across regions with distinct climatic conditions. We propose focused validation studies in tropical, arid, and cold climate zones.
Consistent with previous studies, we observed a significant positive association between short-term O3 exposure and cerebral infarction hospitalizations. For instance, a time-series study conducted in Shenzhen [9] showed that with a lag time of 0 to 3 days, the risk of hospitalization for cerebral infarction increased by 1.2% for every 10 μg/m3 increase in O3 concentration (95% CI: 0.2% to 2.2%). Similarly, a French [23] study found that O3 exposure was associated with an increased risk of ischemic stroke. Empirical studies from distinct geographical contexts have corroborated O3–stroke associations. Research in Corpus Christi [24] demonstrated a significant positive correlation between ambient O3 levels and severe stroke incidence, paralleled by German [25] epidemiological findings establishing O3 concentration elevation as an independent risk factor for stroke onset. The significant positive associations reported in these studies are generally consistent with those observed in our study. However, studies in London [26] and throughout the United States [27] found no significant correlation between O3 exposure and stroke risk. This notable inconsistency may be attributed to the following factors: 1. There are differences in O3 background concentrations and pollutant synergies between the regions of the prior study and our current research. In environments with high O3 concentrations, human tolerance to pollutant increments may increase, or synergistic effects of other pollutants (such as PM2.5 and NO2) may exacerbate health risks; 2. There are variations in population susceptibility. Individuals with allergic constitutions or chronic diseases exhibit health effects more rapidly after exposure, whereas healthy populations show longer lag times; 3. Research duration: The length of the study period is a non-negligible factor. Long-term studies can observe lag changes caused by pollutant accumulation effects, while short-term studies may overlook cumulative impacts and only reflect immediate effects.
Cerebral infarction shares multiple pathophysiological pathways with other circulatory system diseases. Previous studies have found that men tend to exhibit greater susceptibility to the cardiovascular effects of air pollutants, although the underlying mechanisms remain uncertain [28,29]. Higher smoking prevalence among men may partially explain their heightened risk, as smoking is a known risk factor for both stroke and pollutant-related vascular injury [30]. However, in our stratified analysis, no statistically significant difference was detected between males and females regarding the effect of O3 on cerebral infarction, suggesting that gender may not be a major modifier in this context.
In this study, older adults (>65 years) appeared to have a higher risk of the disease compared to younger adults, which is consistent with the findings of Klompmaker et al. [31] and Lee et al. [32]. However, this contrasts with the findings from Cao Xiuyu et al. [21], who reported that younger people are at higher risk. Such discrepancies may arise from differences in lifestyle, physiological resilience, and exposure behavior across age groups [33]. For example, older adults may have reduced cardiovascular reserve capacity, pre-existing comorbidities, and less ability to adapt to environmental stressors, thereby increasing their vulnerability. These variations highlight the importance of conducting location-specific and population-specific investigations to inform targeted interventions.
While the health effect of O3 on circulatory system diseases has been extensively studied [34,35,36], relatively few studies have focused on the interactive effects between O3 and meteorological factors on cerebral infarction. Under certain meteorological conditions, O3 levels can rise significantly, which may exacerbate their effects on people with underlying medical conditions, such as high blood pressure and diabetes [37]. For instance, high temperatures can increase O3 formation, and at the same time, thermal stress may intensify cardiovascular load, thereby elevating stroke risk [38].
Our analysis indicates that co-occurring high ambient O3 and adverse meteorological conditions (e.g., elevated humidity, diminished wind speed, reduced atmospheric pressure) demonstrate a significant association with heightened cerebral infarction risk. Notably, a bimodal temporal risk pattern emerged, which is as follows: peak hospitalization risk coincided with moderate temperatures (10–20 °C) on exposure days, while low temperatures (<10 °C) correlated with elevated risk at a 2–3-day lag, and high temperatures (>30 °C) showed a delayed association with risk elevation at a 4–6-day lag. This suggests that temperature influences stroke risk via distinct acute and delayed physiological mechanisms, including autonomic dysfunction, hemodynamic instability, dehydration, inflammation, and coagulation changes. These findings suggest that environmental risk management should incorporate the meteorological context when assessing air pollution-related health threats. The observed interactions imply that O3 concentration thresholds for issuing public health warnings may need to be adjusted seasonally or based on concurrent weather conditions. The relevant authorities need to strengthen monitoring of O3 concentrations and issue health warnings during high pollution weather conditions, especially for people at high risk of cardiovascular disease.
This study has several strengths. First, the hospitalization data of patients in this study came from four hospitals in different districts of Zhengzhou City, which can better represent the medical level of citizens. Second, it applies robust and widely accepted statistical models (GAM and DLNM) to capture both non-linear and lagged effects, as well as pollutant–meteorological interactions. Nonetheless, several limitations should be acknowledged. First, our exposure assessment was based on ambient monitoring data, which may not reflect individual-level exposure, particularly considering time spent indoors or in microenvironments. Second, limited by the protection of personal privacy information, we do not collect specific information from hospitalized patients, so we cannot adjust for potential confounding factors, such as lifestyle and medication history. Third, the data used are hospital admission data from 2019–2023. During this period, due to the impact of the COVID-19 epidemic, the number of hospitalizations will fluctuate, which may lead to biases in the data. Although we included the entire 5-year period to limit anomalies, future studies should explicitly address the pandemic’s confounding effects. Despite these limitations, our study provides compelling evidence of the adverse effects of O3 exposure on cerebral infarction and highlights the amplifying role of specific meteorological conditions. This emphasizes the need for multifactorial environmental health assessments and meteorologically informed air quality management strategies in urban settings.
5. Conclusions
This study observed a significant association between short-term O3 exposure and elevated risk of hospitalization for cerebral infarction. Notably, this association was modified by the following meteorological conditions: 1. The bimodal temperature effect: The highest risk occurs at moderate temperatures (10–20 °C) on the exposure day; significant risk increases are observed with a 2–3-day lag at low temperatures (<10 °C) and a 4–6-day lag at high temperatures (>30 °C); 2. The synergistic amplification mechanism: High humidity (>80%), low wind speed (<1.5 m/s), and low atmospheric pressure (<1000 hpa) collectively enhance the cerebrovascular hazards of O3; 3. Vulnerable populations and seasons: Individuals aged over 80 and the warm season (May–October) exhibit particularly pronounced risks. Thus, this study emphasizes the need to integrate air quality and meteorological indicators to establish a warning system for cerebral infarction, with a focus on protecting elderly populations with pronounced risks.
Author Contributions
Y.C. and S.Y.: conceptualization, methodology, formal analysis, and writing—original draft. H.Q.: visualization. J.L.: formal analysis. Z.W.: validation. N.W.: writing—review and editing. Y.Q.: writing—review. M.L.: funding acquisition, conceptualization, methodology, and supervision. F.Z.: funding acquisition, conceptualization, and methodology. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported by the National Key R&D Program of China (Ministry of Science and Technology of China, Grant No. 2022YFC3803201), the Science and Technology Research Project of Henan Province (Department of Science and Technology of Henan Province, Grant No. 232102310496), and the Natural Science Foundation of Henan Province (Department of Science and Technology of Henan Province, Grant No. 222300420537)
Institutional Review Board Statement
The study has been approved by the Ethics Committee of Zhengzhou University (No. ZZUIRB2024-200).
Informed Consent Statement
Consent was not required, as the study analyzed anonymized data.
Data Availability Statement
Due to privacy protection restrictions, the data generated by this study are not yet made public. However, under the condition of providing a reasonable research plan and obtaining ethical approval, the relevant data can be requested for access by contacting the corresponding author.
Conflicts of Interest
The authors declare no conflicts of interest.
References
- Young, S.S. Air Pollution and Mortality in the Medicare Population. JAMA 2018, 319, 2135. [Google Scholar] [CrossRef] [PubMed]
- Lanzinger, S.; Schneider, A.; Breitner, S.; Stafoggia, M.; Erzen, I.; Dostal, M.; Pastorkova, A.; Bastian, S.; Cyrys, J.; Zscheppang, A.; et al. Ultrafine and Fine Particles and Hospital Admissions in Central Europe. Results from the UFIREG Study. Am. J. Respir. Crit. Care Med. 2016, 194, 1233–1241. [Google Scholar] [CrossRef] [PubMed]
- Kingdon, C. Air pollution is the largest environmental risk to public health and children are especially vulnerable. BMJ 2023, 381, 1037. [Google Scholar] [CrossRef] [PubMed]
- Xin, Y.; Li, J. Air pollution and cardiovascular diseases in young adults. Eur. Heart J. 2021, 42, 4192. [Google Scholar] [CrossRef] [PubMed]
- He, M.; Ding, W.; Lai, K. Special Issue: Air Pollutant Exposure and Respiratory Diseases. Toxics 2025, 13, 441. [Google Scholar] [CrossRef] [PubMed]
- Kausar, S.; Cao, X.; Yadoung, S.; Wongta, A.; Zhou, K.; Kosashunhanan, N.; Hongsibsong, S. Associations Between Individual Health Risk Perceptions and Biomarkers of PAH Exposure Before and After PM2.5 Pollution in the Suburbs of Chiang Mai Province. Toxics 2025, 13, 491. [Google Scholar] [CrossRef] [PubMed]
- Wang, Y.; Gao, W.; Wang, S.; Song, T.; Gong, Z.; Ji, D.; Wang, L.; Liu, Z.; Tang, G.; Huo, Y.; et al. Contrasting trends of PM2.5 and surface-ozone concentrations in China from 2013 to 2017. Natl. Sci. Rev. 2020, 7, 1331–1339. [Google Scholar] [CrossRef] [PubMed]
- Zhu, L.; Ding, S.; Xu, L.; Wu, Z. Ozone treatment alleviates brain injury in cerebral ischemic rats by inhibiting the NF-κB signaling pathway and autophagy. Cell Cycle 2022, 21, 406–415. [Google Scholar] [CrossRef] [PubMed]
- Guo, Y.; Xie, X.; Lei, L.; Zhou, H.; Deng, S.; Xu, Y.; Liu, Z.; Bao, J.; Peng, J.; Huang, C. Short-term associations between ambient air pollution and stroke hospitalisations: Time-series study in Shenzhen, China. BMJ Open 2020, 10, e032974. [Google Scholar] [CrossRef] [PubMed]
- Dzhambov, A.M.; Dikova, K.; Georgieva, T.; Panev, T.I.; Mukhtarov, P.; Dimitrova, R. Short-term effects of air pollution on hospital admissions for cardiovascular diseases and diabetes mellitus in Sofia, Bulgaria (2009–2018). Arch. Ind. Hyg. Toxicol. 2023, 74, 48–60. [Google Scholar] [CrossRef] [PubMed]
- Ma, C.; Yang, J.; Nakayama, S.F.; Iwai-Shimada, M.; Jung, C.-R.; Sun, X.-L.; Honda, Y. Cold Spells and Cause-Specific Mortality in 47 Japanese Prefectures: A Systematic Evaluation. Environ. Health Perspect. 2021, 129, 67001. [Google Scholar] [CrossRef] [PubMed]
- Lei, L.; Bao, J.; Guo, Y.; Wang, Q.; Peng, J.; Huang, C. Effects of diurnal temperature range on first-ever strokes in different seasons: A time-series study in Shenzhen, China. BMJ Open 2020, 10, e033571. [Google Scholar] [CrossRef] [PubMed]
- Li, Z.; Li, G.; Li, Y.; Chen, Y.; Li, J.; Chen, H. Flow field around bubbles on formation of air embolism in small vessels. Proc. Natl. Acad. Sci. USA 2021, 118, e2025406118. [Google Scholar] [CrossRef] [PubMed]
- Katsuki, M.; Narita, N.; Ishida, N.; Watanabe, O.; Cai, S.; Ozaki, D.; Sato, Y.; Kato, Y.; Jia, W.; Nishizawa, T.; et al. Preliminary development of a prediction model for daily stroke occurrences based on meteorological and calendar information using deep learning framework (Prediction One; Sony Network Communications Inc., Japan). Surg. Neurol. Int. 2021, 12, 31. [Google Scholar] [CrossRef] [PubMed]
- Fukunaga, A.; Koyama, H.; Fuse, T.; Haraguchi, A. The onset of cerebral infarction may be affected by differences in atmospheric pressure distribution patterns. Front. Neurol. 2023, 14, 1230574. [Google Scholar] [CrossRef] [PubMed]
- Turin, T.C.; Kita, Y.; Murakami, Y.; Rumana, N.; Sugihara, H.; Morita, Y.; Tomioka, N.; Okayama, A.; Nakamura, Y.; Abbott, R.D.; et al. Higher stroke incidence in the spring season regardless of conventional risk factors: Takashima Stroke Registry, Japan, 1988–2001. Stroke 2008, 39, 745–752. [Google Scholar] [CrossRef] [PubMed]
- Salam, A.; Kamran, S.; Bibi, R.; Korashy, H.M.; Parray, A.; Al Mannai, A.; Al Ansari, A.; Kanikicharla, K.K.; Gashi, A.Z.; Shuaib, A. Meteorological Factors and Seasonal Stroke Rates: A Four-year Comprehensive Study. J. Stroke Cerebrovasc. Dis. 2019, 28, 2324–2331. [Google Scholar] [CrossRef] [PubMed]
- Liu, L.; Wang, B.; Qian, N.; Wei, H.; Yang, G.; Wan, L.; He, Y. Association between ambient PM2.5 and outpatient visits of children’s respiratory diseases in a megacity in Central China. Front. Public Health 2022, 10, 952662. [Google Scholar]
- Lian, X.; Xi, L.; Zhang, Z.; Yang, L.; Du, J.; Cui, Y.; Li, H.; Zhang, W.; Wang, C.; Liu, B.; et al. Impact of air pollutants on influenza-like illness outpatient visits under COVID-19 pandemic in the subcenter of Beijing, China. J. Med. Virol. 2023, 95, e28514. [Google Scholar] [CrossRef] [PubMed]
- Maji, K.J.; Sarkar, C. Spatio-temporal variations and trends of major air pollutants in China during 2015–2018. Environ. Sci. Pollut. Res. 2020, 27, 33792–33808. [Google Scholar] [CrossRef] [PubMed]
- Cao, X.; You, X.; Wang, D.; Qiu, W.; Guo, Y.; Zhou, M.; Chen, W.; Zhang, X. Short-term effects of ambient ozone exposure on daily hospitalizations for circulatory diseases in Ganzhou, China: A time-series study. Chemosphere 2023, 327, 138513. [Google Scholar] [CrossRef] [PubMed]
- Nie, Y.; Yang, Z.; Lu, Y.; Bahani, M.; Zheng, Y.; Tian, M.; Zhang, L. Interaction between air pollutants and meteorological factors on pulmonary tuberculosis in northwest China: A case study of eight districts in Urumqi. Int. J. Biometeorol. 2024, 68, 691–700. [Google Scholar] [CrossRef] [PubMed]
- Henrotin, J.-B.; 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, 1990–1996. [Google Scholar] [CrossRef] [PubMed]
- Wing, J.J.; Sánchez, B.N.; Adar, S.D.; Meurer, W.J.; Morgenstern, L.B.; Smith, M.A.; Lisabeth, L.D. Synergism of Short-Term Air Pollution Exposures and Neighborhood Disadvantage on Initial Stroke Severity. Stroke 2017, 48, 3126–3129. [Google Scholar] [CrossRef] [PubMed]
- Liao, M.; Zhang, S.; He, C.; Breitner, S.; Cyrys, J.; Naumann, M.; Braadt, L.; Traidl-Hoffmann, C.; Hammel, G.; Peters, A.; et al. Air pollution and stroke: Short-term exposure’s varying effects on stroke subtypes. Ecotoxicol. Environ. Saf. 2025, 298, 118296. [Google Scholar] [CrossRef] [PubMed]
- Butland, B.K.; Atkinson, R.W.; Crichton, S.; Barratt, B.; Beevers, S.; Spiridou, A.; Hoang, U.; Kelly, F.J.; Wolfe, C.D. Air pollution and the incidence of ischaemic and haemorrhagic stroke in the South London Stroke Register: A case–cross-over analysis. J. Epidemiol. Community Health 2017, 71, 707–712. [Google Scholar] [CrossRef] [PubMed]
- Mazidi, M.; Speakman, J.R. Impact of Obesity and Ozone on the Association Between Particulate Air Pollution and Cardiovascular Disease and Stroke Mortality Among US Adults. J. Am. Heart Assoc. 2018, 7, e008006. [Google Scholar] [CrossRef] [PubMed]
- Henrotin, J.B.; Besancenot, J.P.; Bejot, Y.; Giroud, M. Short-term effects of ozone air pollution on ischaemic stroke occurrence: A case-crossover analysis from a 10-year population-based study in Dijon, France. Occup. Environ. Med. 2007, 64, 439–445. [Google Scholar] [CrossRef] [PubMed]
- Kim, I.-S.; Yang, P.-S.; Lee, J.; Yu, H.T.; Kim, T.-H.; Uhm, J.-S.; Pak, H.-N.; Lee, M.-H.; Joung, B. Long-term exposure of fine particulate matter air pollution and incident atrial fibrillation in the general population: A nationwide cohort study. Int. J. Cardiol. 2019, 283, 178–183. [Google Scholar] [CrossRef] [PubMed]
- Piedra, L.M.; Andrade, F.C.D.; Hernandez, R.; Perreira, K.M.; Gallo, L.C.; González, H.M.; Gonzalez, S.; Cai, J.; Chen, J.; Castañeda, S.F.; et al. Association of Subjective Social Status with Life’s Simple 7s Cardiovascular Health Index Among Hispanic/Latino People: Results From the HCHS/SOL. J. Am. Heart Assoc. 2021, 10, e012704. [Google Scholar] [CrossRef] [PubMed]
- Klompmaker, J.O.; Hart, J.E.; James, P.; Sabath, M.B.; Wu, X.; Zanobetti, A.; Dominici, F.; Laden, F. Air pollution and cardiovascular disease hospitalization—Are associations modified by greenness, temperature and humidity? Environ. Int. 2021, 156, 106715. [Google Scholar] [CrossRef] [PubMed]
- Lee, D.-W.; Han, C.-W.; Hong, Y.-C.; Oh, J.-M.; Bae, H.-J.; Kim, S.; Lim, Y.-H. Short-term exposure to air pollution and hospital admission for heart failure among older adults in metropolitan cities: A time-series study. Int. Arch. Occup. Environ. Health 2021, 94, 1605–1615. [Google Scholar] [CrossRef] [PubMed]
- Potter, T.; Tannous, J.; Vahidy, F.S. A Contemporary Review of Epidemiology, Risk Factors, Etiology, and Outcomes of Premature Stroke. Curr. Atheroscleros. Rep. 2022, 24, 939–948. [Google Scholar] [CrossRef] [PubMed]
- Guo, J.; Zhou, J.; Han, R.; Wang, Y.; Lian, X.; Tang, Z.; Ye, J.; He, X.; Yu, H.; Huang, S.; et al. Association of Short-Term Co-Exposure to Particulate Matter and Ozone with Mortality Risk. Environ. Sci. Technol. 2023, 57, 15825–15834. [Google Scholar] [CrossRef] [PubMed]
- Niu, Y.; Zhou, Y.; Chen, R.; Yin, P.; Meng, X.; Wang, W.; Liu, C.; Ji, J.S.; Qiu, Y.; Kan, H.; et al. Long-term exposure to ozone and cardiovascular mortality in China: A nationwide cohort study. Lancet Planet. Health 2022, 6, e496–e503. [Google Scholar] [CrossRef] [PubMed]
- Xu, R.; Sun, H.; Zhong, Z.; Zheng, Y.; Liu, T.; Li, Y.; Liu, L.; Luo, L.; Wang, S.; Lv, Z.; et al. Ozone, Heat Wave, and Cardiovascular Disease Mortality: A Population-Based Case-Crossover Study. Environ. Sci. Technol. 2024, 58, 171–181. [Google Scholar] [CrossRef] [PubMed]
- Liu, X.; Li, Z.; Zhang, J.; Guo, M.; Lu, F.; Xu, X.; Deginet, A.; Liu, M.; Dong, Z.; Hu, Y.; et al. The association between ozone and ischemic stroke morbidity among patients with type 2 diabetes in Beijing, China. Sci. Total Environ. 2022, 818, 151733. [Google Scholar] [CrossRef] [PubMed]
- Li, J.; Huang, J.; Cao, R.; Yin, P.; Wang, L.; Liu, Y.; Pan, X.; Li, G.; Zhou, M. The association between ozone and years of life lost from stroke, 2013–2017: A retrospective regression analysis in 48 major Chinese cities. J. Hazard. Mater. 2021, 405, 124220. [Google Scholar] [CrossRef] [PubMed]
Figure 1.
Time-series of the six criteria pollutants concentrations, 2019–2023.
Figure 2.
Three-dimensional surface plots of the lagged effects of ambient temperature on cerebral infarction across sex and age subgroups. (A) Total; (B) Male; (C) Female; (D) Age: 18–65 years; (E) Age: 66–80 years; (F) Age: >80 years. Abbreviation: RR, relative risk.
Figure 2.
Three-dimensional surface plots of the lagged effects of ambient temperature on cerebral infarction across sex and age subgroups. (A) Total; (B) Male; (C) Female; (D) Age: 18–65 years; (E) Age: 66–80 years; (F) Age: >80 years. Abbreviation: RR, relative risk.
Figure 3.
Interaction between meteorological and O3 on cerebral infarction. Hospitalizations, hospitalizations for cerebral infarction. (A) Temperature–O3 Interaction and Cerebral Infarction; (B) Relative humidity–O3 Interaction and Cerebral Infarction; (C) Air pressure–O3 Interaction and Cerebral Infarction; (D) Wind speed–O3 Interaction and Cerebral Infarction. Abbreviations: T, temperature; RH, relative humidity; AP, air pressure; WS, wind speed.
Figure 3.
Interaction between meteorological and O3 on cerebral infarction. Hospitalizations, hospitalizations for cerebral infarction. (A) Temperature–O3 Interaction and Cerebral Infarction; (B) Relative humidity–O3 Interaction and Cerebral Infarction; (C) Air pressure–O3 Interaction and Cerebral Infarction; (D) Wind speed–O3 Interaction and Cerebral Infarction. Abbreviations: T, temperature; RH, relative humidity; AP, air pressure; WS, wind speed.
Table 1.
Summary statistics of air pollutants, meteorological factors, and cerebral infarction hospitalizations in Zhengzhou, China from 2019 to 2023.
Table 1.
Summary statistics of air pollutants, meteorological factors, and cerebral infarction hospitalizations in Zhengzhou, China from 2019 to 2023.
| Variables | 2019 | 2020 | 2021 | 2022 | 2023 |
|---|---|---|---|---|---|
| Hospitalized cases | |||||
| Total | 21.0 (17.0, 27.0) | 20.0 (16.0, 25.0) | 22.0 (16.5, 28.0) | 17.0 (9.5, 22.0) | 19.0 (15.0, 25.0) |
| Gender | |||||
| Male | 13.0 (9.0, 16.0) | 12.0 (9.0, 15.0) | 13.0 (10.0, 17.0) | 10.0 (6.0, 14.0) | 12.0 (9.0, 16.0) |
| Female | 9.0 (6.0, 11.0) | 8.0 (6.0, 11.0) | 9.0 (6.0, 11.0) | 6.0 (3.0, 9.0) | 7.0 (5.0, 10.0) |
| Age (years) | |||||
| 18–65 | 9.0 (6.0, 12.0) | 9.0 (6.0, 12.0) | 10.0 (7.0, 13.0) | 7.0 (4.0, 10.0) | 8.0 (6.0, 11.0) |
| 65–80 | 8.0 (5.5, 10.0) | 7.0 (5.0, 10.0) | 8.0 (5.5, 11.0) | 6.0 (3.0, 8.0) | 8.0 (5.0, 10.0) |
| >80 | 4.0 (3.0, 6.0) | 4.0 (2.0, 5.0) | 4.0 (2.0, 5.0) | 3.0 (1.0, 4.0) | 3.0 (2.0, 5.0) |
| Season a | |||||
| Warm | 21.0 (17.0, 27.0) | 22.0 (18.0, 27.0) | 21.0 (16.0, 26.0) | 18.5 (9.0, 25.0) | 19.0 (16.0, 25.0) |
| Cold | 21.0 (16.0, 27.0) | 18.0 (14.0, 23.0) | 24.0 (19.0, 28.0) | 15.0 (10.0, 20.0) | 19.0 (14.0, 24.0) |
| Air pollutants b | |||||
| O3 (μg/m3) | 104.1 (68.3, 150.2) | 102.1 (70.7, 149.9) | 95.0 (65.0, 138.0) | 106.0 (63.0, 157.5) | 106.0 (64.75, 145.0) |
| NO2 (μg/m3) | 41.0 (31.0, 54.0) | 35.0 (25.7, 48.0) | 29.0 (20.0, 41.0) | 24.6 (17.7, 33.6) | 26.0 (18.0, 37.0) |
| CO (mg/m3) | 0.8 (0.7, 1.1) | 0.7 (0.6, 0.9) | 0.7 (0.6, 0.8) | 0.6 (0.5, 0.8) | 0.6 (0.5, 0.7) |
| SO2 (μg/m3) | 9.0 (6.0, 12.0) | 8.0 (5.0, 11.0) | 8.0 (5.0, 10.0) | 7.6 (5.7, 9.4) | 6.3 (4.6, 8.5) |
| PM10 (μg/m3) | 91.0 (67.5, 130.0) | 81.0 (58.0, 108.0) | 73.0 (50.0, 108.0) | 70.5 (51.1, 102.4) | 65.1 (44.5, 106.9) |
| PM2.5 (μg/m3) | 39.0 (28.0, 69.5) | 37.0 (25.7, 60.0) | 32.0 (22.0, 54.0) | 32.7 (22.3, 55.1) | 31.3 (21.0, 51.8) |
| Meteorological factors | |||||
| Temperature (°C) | |||||
| Warm | 26.1 (21.7, 28.7) | 25.7 (22.1, 27.8) | 25.8 (21.2, 28.5) | 17.1 (12.4, 28.3) | 26.2 (20.0, 29.2) |
| Cold | 7.2 (3.0, 13.5) | 7.2 (3.5, 14.1) | 9.3 (5.5, 13.5) | 11.3 (3.0, 16.7) | 7.1 (3.3, 13.4) |
| Relative Humidity (%) | |||||
| Warm | 58.0 (45.2, 69.0) | 69.0 (50.0, 81.0) | 74.0 (57.5, 87.0) | 62.0 (46.0, 74.0) | 66.0 (54.0, 74.0) |
| Cold | 55.0 (42.0, 69.5) | 57.5 (42.0, 74.2) | 50.0 (32.0, 69.0) | 57.0 (43.0, 73.5) | 53.0 (38.5, 66.0) |
| Wind speed (m/s) | |||||
| Warm | 1.7 (1.4, 2.1) | 1.7 (1.3, 2.1) | 1.8 (1.3, 2.2) | 1.7 (1.3, 2.2) | 1.3 (1.0, 1.9) |
| Cold | 1.5 (1.2, 2.1) | 1.6 (1.2, 2.2) | 1.8 (1.3, 2.5) | 1.5 (1.1, 1.9) | 1.6 (1.2, 2.3) |
| Air pressure (hpa) | |||||
| Warm | 995 (991, 998) | 994 (991, 1001) | 994 (991, 1000) | 1005 (994, 1011) | 996 (991, 1002) |
| Cold | 1010 (1004, 1015) | 1011 (1006, 1015) | 1009 (999, 1013) | 1009 (1002, 1013) | 1010 (1003, 1016) |
Note: a warm season: May to October; cold season: November to April. b O3: maximum daily 8-h average; other pollutants: 24-h average.
Table 2.
Spearman’s correlation coefficients between air pollutants and meteorological.
| O3 | NO2 | CO | SO2 | PM10 | PM2.5 | T | RH | WD | AP | |
|---|---|---|---|---|---|---|---|---|---|---|
| O3 | 1.00 | |||||||||
| NO2 | −0.32 ** | 1.00 | ||||||||
| CO | −0.37 ** | 0.58** | 1.00 | |||||||
| SO2 | −0.26 ** | 0.67 ** | 0.40 ** | 1.00 | ||||||
| PM10 | −0.18 ** | 0.45 ** | 0.38 ** | 0.43 ** | 1.00 | |||||
| PM2.5 | −0.38 ** | 0.61 ** | 0.80 ** | 0.49 ** | 0.66 ** | 1.00 | ||||
| T | 0.73 ** | −0.37 ** | −0.42 ** | −0.44 ** | −0.23 ** | −0.50 ** | 1.00 | |||
| RH | −0.15 ** | −0.08 ** | 0.33 ** | −0.44 ** | −0.21 ** | 0.13 ** | 0.046 | 1.00 | ||
| WD | 0.04 * | −0.35 ** | −0.25 ** | −0.10 ** | −0.02 | −0.20 ** | 0.09 ** | −0.02 ** | 1.00 | |
| AP | −0.64 ** | 0.33 ** | 0.31 ** | 0.41 ** | 0.15 ** | 0.37 ** | −0.89 ** | −0.10 ** | −0.12 ** | 1.00 |
Abbreviations: T, temperature; RH, relative humidity; WD, wind speed; AP, air pressure; PM2.5, fine particulate matter; PM10, inhalable particulate matter; SO2, sulfur dioxide; NO2, nitrogen dioxide; CO, carbon monoxide; O3, ozone. * p < 0.05; ** p < 0.01.
Table 3.
Single-day lag effects of ozone on hospitalization risk for cerebral infarction (ER, 95% CI).
Table 3.
Single-day lag effects of ozone on hospitalization risk for cerebral infarction (ER, 95% CI).
| Lag Day | Gender (%) | Age (%) | Season (%) | |||||
|---|---|---|---|---|---|---|---|---|
| Total | Male | Female | 18–65 | 66–80 | >80 | Warm | Cold | |
| 0 | 0.11 (0.08, 0.14) | 0.12 (0.07, 0.16) | 0.12 (0.06, 0.16) | 0.12 (0.07, 0.16) | 0.08 (0.03, 0.13) | 0.13 (0.06, 0.20) | 0.08 (0.03, 0.12) | 0.01 (−0.06, 0.05) |
| 1 | 0.08 (0.05, 0.10) | 0.09 (0.03, 0.14) | 0.10 (0.05, 0.14) | 0.07 (0.03, 0.12) | 0.08 (0.03, 0.12) | 0.07 (0.01, 0.14) | 0.03 (0.00, 0.06) | 0.01 (−0.04, 0.06) |
| 2 | 0.08 (0.05, 0.11) | 0.07 (0.04, 0.11) | 0.10 (0.05, 0.14) | 0.08 (0.04, 0.12) | 0.10 (0.06, 0.15) | 0.06 (0.00, 0.13) | 0.02 (0.00, 0.05) | 0.03 (−0.01, 0.09) |
| 3 | 0.07 (0.04, 0.10) | 0.06 (0.03, 0.10) | 0.08 (0.04, 0.13) | 0.06 (0.02, 0.10) | 0.09 (0.04, 0.13) | 0.07 (0.01, 0.13) | −0.01(−0.04, 0.01) | 0.06 (0.02, 0.12) |
| 4 | 0.08 (0.05, 0.10) | 0.09 (0.05, 0.12) | 0.06 (0.01, 0.10) | 0.08 (0.04, 0.12) | 0.09 (0.04, 0.13) | 0.04 (0.00, 0.10) | 0.01 (−0.03, 0.04) | 0.03 (−0.01, 0.08) |
| 5 | 0.06 (0.03, 0.09) | 0.06 (0.02, 0.09) | 0.06 (0.01, 0.10) | 0.06 (0.02, 0.10) | 0.05 (0.01, 0.10) | 0.09 (0.02, 0.15) | −0.01 (−0.04, 0.02) | 0.01 (−0,04, 0.03) |
| 6 | 0.05 (0.02, 0.08) | 0.05 (0.02, 0.09) | 0.05 (0.01, 0.10) | 0.04 (0.01, 0.02) | 0.07 (0.03, 0.12) | 0.06 (0.00, 0.12) | −0.01 (−0.04, 0.03) | −0.01 (−0.07, 0.03) |
| 7 | 0.09 (0.06, 0.11) | 0.09 (0.06, 0.13) | 0.07 (0.03, 0.12) | 0.07 (0.03, 0.11) | 0.11 (0.07, 0.15) | 0.07 (0.01, 0.13) | 0.03 (0.01, 0.06) | −0.01 (−0.06, 0.04) |
Note: The statistically significant estimates are bolded (p < 0.05).
Table 4.
Cumulative lag effects of ozone on hospitalization risk for cerebral infarction (ER, 95% CI).
Table 4.
Cumulative lag effects of ozone on hospitalization risk for cerebral infarction (ER, 95% CI).
| Lag Day | Gender (%) | Age (%) | Season (%) | |||||
|---|---|---|---|---|---|---|---|---|
| Total | Male | Female | 18–65 | 66–80 | >80 | Warm | Cold | |
| 0–1 | 0.13 (0.10, 0.16) | 0.11 (0.07, 0.15) | 0.11 (0.07, 0.16) | 0.12 (0.08, 0.17) | 0.08 (0.03, 0.13) | 0.16 (0.08, 0.23) | 0.08 (0.05, 0.12) | −0.01 (−0.06, 0.05) |
| 0–2 | 0.11 (0.08, 0.14) | 0.09 (0.03, 0.15) | 0.10 (0.05, 0.14) | 0.07 (0.03, 0.12) | 0.07 (0.03, 0.12) | 0.13 (0.06, 0.20) | 0.02 (−0.05, 0.06) | 0.00 (−0.04, 0.06) |
| 0–3 | 0.07 (0.05, 0.10) | 0.07 (0.03, 0.11) | 0.09 (0.05, 0.14) | 0.08 (0.04, 0.12) | 0.10 (0.05, 0.14) | 0.07 (0.00, 0.14) | 0.01 (−0.01, 0.05) | 0.03 (−0.01, 0.09) |
| 0–4 | 0.08 (0.05, 0.11) | 0.06 (0.02, 0.09) | 0.08 (0.03, 0.12) | 0.06 (0.02, 0.10) | 0.08 (0.04, 0.13) | 0.06 (0.00, 0.12) | −0.01 (−0.05, 0.02) | 0.06 (0.02, 0.12) |
| 0–5 | 0.07 (0.04, 0.10) | 0.08 (0.05, 0.12) | 0.05 (0.01, 0.09) | 0.08 (0.04, 0.12) | 0.08 (0.03, 0.12) | 0.06 (0.00, 0.12) | 0.01 (−0.04, 0.04) | 0.02 (−0.02, 0.08) |
| 0–6 | 0.05 (0.03, 0.08) | 0.05 (0.02, 0.08) | 0.05 (0.01, 0.09) | 0.05 (0.01, 0.09) | 0.05 (0.00, 0.09) | 0.03 (0.00, 0.10) | −0.01 (−0.04, 0.02) | 0.00 (−0,04, 0.05) |
| 0–7 | 0.05 (0.02, 0.07) | 0.05 (0.01, 0.08) | 0.04 (0.00, 0.09) | 0.03 (0.00, 0.07) | 0.07 (0.02, 0.11) | 0.08 (0.02, 0.14) | −0.01 (−0.04, 0.01) | −0.02 (−0.07, 0.03) |
Note: The statistically significant estimates are bolded (p < 0.05).
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Chen, Y.; Yang, S.; Que, H.; Liu, J.; Wang, Z.; Wang, N.; Qin, Y.; Li, M.; Zhou, F. Interactive Effects of Ambient Ozone and Meteorological Factors on Cerebral Infarction: A Five-Year Time-Series Study. Toxics 2025, 13, 598. https://doi.org/10.3390/toxics13070598
AMA Style
Chen Y, Yang S, Que H, Liu J, Wang Z, Wang N, Qin Y, Li M, Zhou F. Interactive Effects of Ambient Ozone and Meteorological Factors on Cerebral Infarction: A Five-Year Time-Series Study. Toxics. 2025; 13(7):598. https://doi.org/10.3390/toxics13070598
Chicago/Turabian StyleChen, Yanzhe, Songtai Yang, Hanya Que, Jiamin Liu, Zhe Wang, Na Wang, Yunkun Qin, Meng Li, and Fang Zhou. 2025. "Interactive Effects of Ambient Ozone and Meteorological Factors on Cerebral Infarction: A Five-Year Time-Series Study" Toxics 13, no. 7: 598. https://doi.org/10.3390/toxics13070598
APA StyleChen, Y., Yang, S., Que, H., Liu, J., Wang, Z., Wang, N., Qin, Y., Li, M., & Zhou, F. (2025). Interactive Effects of Ambient Ozone and Meteorological Factors on Cerebral Infarction: A Five-Year Time-Series Study. Toxics, 13(7), 598. https://doi.org/10.3390/toxics13070598
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
