Xiaolong Fan1,2, Chen Yang1,2,3, Jinsheng Chen This email address is being protected from spambots. You need JavaScript enabled to view it.1,2, Yuping Chen1,2,3, Gaojie Chen1,2,3, Ziyi Lin1,2,3, Zecong Li2, Yanting Chen1,2, Liqian Yin1,2, Lingling Xu1,2, Naian Xiao This email address is being protected from spambots. You need JavaScript enabled to view it.4,5 

1 Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
2 Fujian Key Laboratory of Atmospheric Ozone Pollution Prevention, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
3 University of Chinese Academy of Sciences, Beijing 100049, China
4 Department of Neurology, The Third Hospital of Xiamen, Fujian 361100, China
5 Department of Neurology and Geriatrics, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fujian 350001, China


Received: August 16, 2023
Revised: November 23, 2023
Accepted: December 6, 2023

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.230195  


Cite this article:

Fan, X., Yang, C., Chen, J., Chen, Y., Chen, G., Lin, Z., Li, Z., Chen, Y., Yin, L., Xu, L., Xiao, N. (2024). Impact of Low PM2.5 Exposure on Asthma Admission: Age-Specific Differences and Evidence from a Low-Pollution Environment in China. Aerosol Air Qual. Res. 24, 230195. https://doi.org/10.4209/aaqr.230195


HIGHLIGHTS

  • Investigate the effects of low-PM2.5 level on asthma admissions in Xiamen.
  • 10 µg m3 PM2.5 increased was associated with 0.49% increase in asthma incidence.
  • Significant health effects in children and the elderly population.
 

ABSTRACT


Many epidemiological studies focus on health research in areas with high concentration of PM2.5. To fill the research gap, our study investigated the acute effects of low PM2.5 level (annual average concentration below 35 µg m–3) on asthma admissions in Xiamen, China. Using a time-stratified case-crossover study design, we examined the association between PM2.5 concentration and asthma admissions from January 2019 to November 2021. The results showed a positive correlation between PM2.5 concentration and the cumulative incidence of asthma, with a lag of 0–7 days. Each 10 µg m3 increased in PM2.5 concentration was associated with a 0.49% increase in asthma incidence. Stratified analysis revealed significant effects in children aged 0–4 years (OR = 2.029, 95% CI = 1.359–3.031) and the elderly population aged over 75 years (OR = 1.399, 95% CI = 1.092–1.793). Distributed lag models demonstrated a hysteresis effect, with significant lagged effects observed in children (lag0–5) and the elderly (lag3–lag5). The multi-pollutant model, considering NO2 and O3, showed consistent results. These findings highlight the age-specific susceptibility to PM2.5 exposure and its impact on asthma admissions, even at lower levels of pollution. Further research is needed to inform environmental protection policies and public health interventions in low-pollution environments.


Keywords: PM2.5, Asthma hospitalization, Health risks Time-stratified


1 INTRODUCTION


According to recent epidemiological research (D'Oliveira et al., 2023; Jiang et al., 2023; Zhu and Lu, 2023), there are strong evidences supporting a correlation between air pollutants and physiological changes that pose health risks. Asthma, a common chronic inflammatory airway disease affecting over 300 million individuals globally (Florence et al., 2023), often leads to persistent and exacerbated symptoms, compromising individuals' quality of life and increasing the risk of premature death. As of 2019, the worldwide prevalence of asthma was reported to be 3,415.5 cases per 100,000 people, with a mortality rate of 5.8 cases per 100,000 people (Safiri et al., 2022). Previous studies have indicated that environmental factors significantly contribute to short-term increases in asthma prevalence (Fan et al., 2016; Paterson et al., 2021; Liu et al., 2022a). In China, the rapid process of industrialization and urbanization has resulted in deteriorating air quality, exacerbating the severity of asthma prevalence.

However, it is worth noting that the majority of research efforts have concentrated on regions with high PM2.5 concentrations, such as Shanghai (Wang et al., 2018, 2019), Beijing (Fan et al., 2021), while cities with low PM2.5 levels have received less attention. Nevertheless, studies have indicated that even at lower levels of pollution, PM2.5 can still have a significant impact on the health of asthma patients (Christidis et al., 2019). Xiamen, situated in southeastern China, is a coastal urbanized city with a population exceeding 3.6 million, neighboring the Yangtze and Pearl River Deltas. From 2016 to 2018, the average concentration of PM2.5 in Xiamen was 2.16% lower than the secondary standard of the Chinese National Ambient Air Quality Standard (CNAAQS), which sets an annual average limit of 35 µg m–3 (Wu et al., 2019). Thus, to investigate the health effects at such low-pollution area will help policymakers take proper preventive measure to control the risk caused by PM2.5 based on epidemiological evidence.

Building upon this background, our study specifically focuses on Xiamen, a city characterized by low PM2.5 concentration, to investigate the acute effects of such levels on asthma admissions. Employing a time-stratified case-crossover study design, we aim to explore the association between PM2.5 concentration and asthma admissions in Xiamen from January 2019 to November 2021. Examining the relationship between PM2.5 and asthma in Xiamen will provide a comprehensive understanding of the health implications associated with low PM2.5 exposures, contributing valuable insights for governments in formulating environmental protection policies to safeguard public health in low-pollution regions.


2 METHODS


 
2.1 Asthma Admission Data

Xiamen as an urban city with low PM2.5 concentration, which has been selected for this investigation. The asthma admission research data was obtained from the First Affiliated Hospital of Xiamen University, which is internationally recognized by the Joint Commission International and holds a high reputation and authority. In Fig. 1, hospital is about 17 km away from the sampling site. It is worth mentioning that the hospital's outpatient and emergency visit volume, as well as the number of discharged patients, exceeds 30% of the total in Xiamen, ensuring that our sample has good representativeness.

Fig. 1. Location of Xiamen (red dot) in China, sampling sites in black triangle and black dot represents hospital in Xiamen.Fig. 1. Location of Xiamen (red dot) in China, sampling sites in black triangle and black dot represents hospital in Xiamen.

In Xiamen, the departments of Respiratory Medicine, Laboratory Medicine, and Preventive Healthcare demonstrate the strongest clinical and research capabilities. The expertise and experience within these departments could provide robust support for our study. We strictly adhere to the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) code J45 for the classification of asthma cases, ensuring accurate categorization.

To obtain more detailed data, the following information has been extracted: patient IDs, hospital admission dates, gender and age. Additionally, the discharge diagnosis codes revealed the specific conditions diagnosed upon patients' discharge. These detailed pieces of information will assist us in conducting in-depth analysis and understanding the relationship between asthma and environmental factors.


2.2 Air Quality Data

PM2.5 and related gaseous pollutant concentration were measured at Atmospheric Environment Observation Supersite (Sampling site in Fig. 1), located in the Institute of Urban Environment, Chinese Academy of Sciences (IUE, CAS). The other pollutant data from January 1, 2019, to November 30, 2021, was obtained from the website (http://113.108.142.147:20035/emcpublish/) published by the National Environmental Monitoring Center of China (NEMCC). Prior to utilizing this data, quality control measures had been conducted, including averaging the hourly concentrations to obtain daily concentrations. The measurement of PM2.5 concentration was carried out at various monitoring sites according to the technical specifications described in the Chinese Environmental Protection Standard document HJ 655-2013. These techniques include the application of gravimetric oscillation balance method and β absorption method (refer to the link: http://www.cnemc.cn/​jcgf/dqhj). According to Fig. 2 and Fig. S1 in supplementary, the air quality data in sampling site is comparable with the surrounding observation site. Thus, PM2.5 and related gaseous pollutant concentrations in sampling site could well represent the polluted level in Xiamen.

 Fig. 2. Time series of PM2.5 concentration (daily mean concentration) in sampling and surrounding site, coupled with the daily asthma admission in Xiamen, China.Fig. 2. Time series of PM2.5 concentration (daily mean concentration) in sampling and surrounding site, coupled with the daily asthma admission in Xiamen, China.

 
2.3 Meteorological Data

Considering the influence of meteorological conditions, meteorological data were obtained from the National Climate Data Center (NCDC) (ftp://ftp.ncdc.noaa.gov/pub/data/noaa/), including daily mean temperature (T) and relative humidity (RH).

 
2.4 Statistical Analysis

For this study, individuals who were admitted to the hospital with asthma symptoms and received a diagnosis were identified as cases. To establish a comparison, these cases were matched with patients from the same day one week prior or one to two weeks later, serving as their own controls. Cases were assigned a value of 1, while controls were assigned a value of 0. This self-matching design was employed to mitigate the influence of short-term individual differences, such as gender, age, smoking, socioeconomic status, and more. This approach was adopted based on the principles outlined in the study on exposure-response relationships (Han et al., 2016; Chen et al., 2022; He et al., 2022; Liu et al., 2022c). To examine the relationship between asthma events and elemental exposure, the asthma event data has been paired with corresponding elemental exposure data on the case and control days. Conditional logistic regression models were used to construct exposure-response relationships. To ensure smoothness in the associations with wind speed, relative humidity, and temperature, a natural spline smoothing function with three degrees of freedom was applied (Sheffield et al., 2015).

To account for the impact of public holidays, a dichotomous variable was included in the analysis. Adjusted odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) were calculated to estimate exposure-response relationships for asthma admissions associated with incremental changes represented by interquartile ranges (IQRs) of different trace element concentrations.

To account for potential non-linear cumulative effects, a distributed hysteresis model was employed for each trace element. This involved the use of a cross-basis matrix and natural splines to capture hysteresis responses, considering effects from 0 to 7 days sequentially.

All statistical analyses were performed using the "mgcv" package in R Statistical Software version 3.2.4.

 
3 RESULTS


Asthma admission data were investigated and out of the total 2,207 asthma patients included, 55% (1214) were male, while 45% (993) were female. The distribution of patients based on age groups was as follows: 21.5% (475) aged 0–14, 71.2% (1571) aged 15–75, and 7.3% (161) aged over 75.

Table 1 summarizes the descriptive statistics of air quality data and meteorological parameters in Jimei District, Xiamen. During the study period, the daily levels of PM2.5 ranged from 1.2–77.0 µg m–3 with an annual mean of 18.5 µg m–3. The annual mean PM2.5 concentration is 47% lower than the Grade II Annual PM2.5 Standard of CNAAQS (35.0 µg m-3) and 1.9 times of the WHO guideline of annual mean PM2.5 (10.0 µg m–3). The daily level of gaseous pollutants ranged from 1.0–94.0 µg m–3 (annual mean, 24.5 µg m-3) for NO2, and 1.6–156.5 µg m–3 (annual mean 58.5 µg m–3) for O3. The average daily temperature during the study period ranged from 7.3 to 34.3°C (annual mean, 23.2°C). Meanwhile, the average daily humidity ranged from 30.8 to 93.6% (annual mean, 70.9%).

Table 1. Descriptive statistics of air quality data and meteorological parameters in Jimei District, Xiamen.

Fig. 2 illustrates the variations in air pollutant levels and daily admissions for asthma. Specifically, Fig. 2 depicts the temporal pattern of PM2.5 concentration in sampling and surrounding observation site (daily mean values). It is observed that PM2.5 concentration tended to be higher during colder seasons and lower during warmer seasons. On the other hand, Fig. 2 represents the daily count of asthma admissions. It is worth noting that the frequency of admissions for asthma remained in steady level during the entire study period (Fig. S2).

Fig. 3 presents the findings indicating a positive correlation between PM2.5 concentration and the cumulative incidence of asthma, with a lag of 0–7 days. Specifically, each 10 µg m–3 increase in PM2.5 concentration was associated with a 0.49% increase in the incidence of asthma (odds ratio [OR] = 1.059, 95% confidence interval [CI] = 1.007–1.113). Stratifying the results by age revealed varying impacts of PM2.5 on different age groups. The most significant effect was observed in children aged 0–4 years, with a 2.029-fold increase in the odds of developing asthma (95% CI = 1.359–3.031) within 0–7 days after exposure. Similarly, the elderly population aged over 75 years exhibited a heightened risk of asthma (OR = 1.399, 95% CI = 1.092–1.793). However, no significant association was found between PM2.5 and the cumulative incidence of asthma in the age groups of 0–14 years (OR = 1.175, 95% CI = 0.996–1.387) and 14–75 years (OR = 1.042, 95% CI = 0.980–1.108). These results highlight the age-specific susceptibility to PM2.5 exposure and its impact on asthma development.

Fig. 3. Overall cumulative OR of PM2.5 exposure in different age groups with a lag of 0–7 days from 2019 to 2021 in Xiamen.Fig. 3. Overall cumulative OR of PM2.5 exposure in different age groups with a lag of 0–7 days from 2019 to 2021 in Xiamen.

For distributed lag model analysis, a hysteresis effect of PM2.5 on asthma admissions across different age groups in Xiamen has been shown in Fig. 4. Specifically, for the 0–4-year-old population, the lagged effect of PM2.5 exhibited significance at lag0–5, with the highest impact observed on the first day (OR = 1.134, 95% CI = 1.037–1.240). However, no significant effect was found in the population aged 0–14 and 15–75. For individuals over 75 years old, the lag effect of PM2.5 displayed significance at lag3–lag5, with the odds ratio showing an upward trend. Specifically, at lag5, the odds ratio was 1.049 (95% CI = 1.008–1.092). These findings emphasize the age-specific variations in the lagged effect of PM2.5 and its influence on asthma admissions.

Fig. 4. Odds ratio from current (lag0) to lag7 of exposure to PM2.5 to asthma admissions in different age groups during study period in Xiamen.Fig. 4. Odds ratio from current (lag0) to lag7 of exposure to PM2.5 to asthma admissions in different age groups during study period in Xiamen.

In addition to the single-pollutant model for PM2.5, the multi-pollutant model has been examined and investigated the effect of other pollutants, including ozone (O3) and nitrogen dioxide (NO2) on the model. Fig. 5 illustrates the 7-day cumulative odds ratio risk associated with PM2.5 exposure among the 0–4-year-old population. The analysis accounts for the impact of gaseous pollutants, namely NO2 and O3, through adjustment. After adjusting for these pollutants and considering all included factors, the OR were calculated as follows: OR = 2.040 (95% CI = 1.217–3.420), OR = 2.057 (95% CI = 1.215–3.483), and OR = 2.052 (95% CI = 1.204–3.499).

 Fig. 5. After adjustment for multiple pollutants, cumulative odds ratio from current (lag0) to lag7 of exposure to PM2.5 to asthma admissions in 0–4 age groups from 2019 to 2021 in Xiamen, China.Fig. 5. After adjustment for multiple pollutants, cumulative odds ratio from current (lag0) to lag7 of exposure to PM2.5 to asthma admissions in 0–4 age groups from 2019 to 2021 in Xiamen, China.

 
4 DISCUSSIONS


Our study provides empirical evidence regarding the association between short-term exposure (0–7 days) to PM2.5 and the risk of asthma exacerbation in Xiamen, China. Specifically, our findings indicate a substantial risk of asthma in two vulnerable populations: children aged 0–4 years and older adults aged 75 and above. Moreover, the impact of PM2.5 on asthma differs significantly between these age groups, with children being more susceptible compared to the elderly. Notably, children face a significant risk of asthma exacerbation following 1–4 days of exposure to PM2.5. On the other hand, the elderly population primarily experiences an effect with a delayed onset, typically occurring 3–5 days after exposure to ambient environment.

Several recent studies have examined the short-term exacerbation of asthma in response to PM2.5 exposure in different locations, including Adelaide, Korea, and Taipei (Chen et al., 2016; Chang et al., 2017; Kim et al., 2017). According to our research findings, our result further confirms that there is a significant correlation between an increase of 10 µg m–3 in PM2.5 concentration and a 0.49% increase in the incidence of asthma (OR = 1.059, 95% CI = 1.007–1.113). Multiple studies have demonstrated that metal ions and organic compounds present in PM2.5 particles can induce oxidative stress reactions, leading to cell damage, inflammatory responses, and increased airway hyperresponsiveness, further exacerbating asthma symptoms (Riva et al., 2011; Bates et al., 2015; Piao et al., 2021; Liu et al., 2022a). Recent research has also indicated that chemicals within PM2.5 particles can stimulate airway receptors and nerve endings, disrupting the normal functioning of the autonomic nervous system. This may result in increased airway smooth muscle contraction and mucus production, further intensifying asthma symptoms (Liu et al., 2022b). Furthermore, these chemicals can disrupt the normal functioning of the immune system, leading to abnormal activation of immune cells and increased release of inflammatory mediators, thereby exacerbating airway inflammation and asthma symptoms (Hodge et al., 2021; Piao et al., 2021; Xu et al., 2023). Additionally, studies have shown that PM2.5 particles can directly damage respiratory epithelial cells, compromising the integrity of the airway epithelial barrier. This makes the airways more susceptible to infection and inflammation, further exacerbating asthma symptoms (Zhao et al., 2020; Celebi Sozener et al., 2022).

Our study found a significant association between the risk of admission for childhood asthma (0–4 years old) and PM2.5 (OR = 2.029, 95% CI = 1.359–3.031). Previous research has indicated that children are more susceptible to PM-related diseases due to higher respiratory rates, narrower airways, immature lung tissue, and longer exposure to outdoor air (Kim, 2004; Bateson and Schwartz, 2007). Similar effects have been observed in studies conducted in other regions (Norris et al., 1999; Chen et al., 2016; Wu et al., 2019). For instance, a study conducted in Seattle, Washington, revealed that for every 11 µg m–3 increased in PM2.5 concentration, the odds ratio for childhood asthma was 1.15 (95% CI: 1.08–1.23). Additionally, our results show a higher risk of PM2.5-related asthma in Xiamen compared to the United States, particularly in areas with lower PM2.5 levels. There is evidence suggesting that the health impact of environmental PM2.5 depends not only on its total mass but also on its chemical composition. The carbonaceous components of PM2.5, such as black carbon (BC) and organic matter (OM), have been associated with childhood asthma/wheezing (Khreis et al., 2017). Recent studies have shown that PM2.5-bound trace elements might increase neutrophil infiltration by increasing tumor necrosis factor alpha and interferon gamma excretion based on the Murine asthma models and high levels of Cu and Fe may induce oxidative stress and chronic inflammation in asthma cases (Huang et al., 2016; Mao et al., 2018). Although PM2.5 levels in Xiamen are lower than in other cities, the BC concentration in Xiamen is comparable to moderate polluted Chinese cities such as Beijing, Guangzhou, and Nanjing (Deng et al., 2020).

Similarly, individuals aged 75 and above who are exposed to PM2.5 have been found are more susceptible to asthma compared to adults (OR = 1.399, 95% CI = 1.092–1.793). Studies have shown that aging brings about physiological and morphological changes in the lungs, which may impact the development of asthma in the elderly (Estenne et al., 1985). Moreover, significant age-related physiological and immune changes complicate the presentation, diagnosis, and treatment of asthma in the elderly population. Furthermore, the pathological physiology and treatment characteristics of asthma in elderly patients are not as well-defined as in younger individuals and children (Pate et al., 2021). There is evidence suggesting that elderly asthma patients are more likely to be underdiagnosed and undertreated. For example, inhaled corticosteroids (ICS), which are a cornerstone of chronic asthma treatment, are not fully utilized in elderly patients (Burrows et al., 1991; Tu and Sin, 2001). With the aging population, the number of elderly asthma patients is expected to increase, necessitating further research to better understand the underlying pathophysiology in this population.

Our findings revealed significant effects of PM2.5 exposure on children within 0–5 days of exposure, whereas for the elderly, the effects became significant after a lag of 3–5 days. Children typically have higher respiratory rates and narrower airways, rendering them more vulnerable to the direct impact of PM2.5 (Were et al., 2020). On the other hand, older adults may undergo age-related alterations in their lung physiology and immune system, resulting in a comparatively long response time to PM2.5 (Liu et al., 2021). Further investigation into these disparities would contribute to a more comprehensive comprehension of the health effects of air pollution across different age groups and offer guidance for relevant public health interventions.

Our study has several noteworthy limitations that should be considered. Firstly, the generalizability of our findings may be limited due to the focus on a specific geographic area (Xiamen). The air pollution characteristics and population demographics in other regions may differ, and caution should be exercised when extrapolating the results to different settings. Replication of the study in diverse locations would enhance the external validity and strengthen the broader applicability of the findings. Secondly, although we made efforts to account for potential confounding factors, the presence of residual confounding cannot be completely ruled out. There may be additional unmeasured or unknown factors that influence the relationship between PM2.5 and asthma admissions. Future studies could consider the inclusion of more comprehensive data on relevant confounders to further mitigate potential confounding effects. Additionally, our study focused solely on the association between PM2.5 and asthma admissions, without exploring other health outcomes or underlying mechanisms. Considering the multifaceted nature of air pollution and its potential impacts on various health conditions, future research should investigate a broader range of health outcomes and explore the underlying biological pathways to provide a more comprehensive understanding of the topic. Despite these limitations, our study contributes valuable insights into the relationship between PM2.5 and asthma admissions, within the specific context of Xiamen. The findings should be interpreted considering these limitations, and further research is warranted to address these gaps and strengthen the evidence base in this field.

 
5 CONCLUSIONS


In conclusion, this study examined the impact of low levels of PM2.5 exposure on the risk of asthma admissions. In contrast to previous studies that primarily focused on areas with high PM2.5 concentration, this study investigated asthma admissions in a low PM2.5 environment. The findings revealed the significant impact of low PM2.5 concentration on the risk of asthma admissions, addressing a research gap in this area. The study specifically considered the differences among age groups, with a particular focus on vulnerable populations such as children and older adults who are more susceptible to the effects of air pollution. The results demonstrated distinct age-specific responses to PM2.5 exposure, with a notable increase in asthma risk observed in children within 1–4 days of exposure and a delayed response in the elderly population, typically occurring 3–5 days after exposure. This finding contributes to a better understanding of the health effects of air pollution across different age groups. These findings have important implications for addressing the health effects of air pollution in low-pollution regions and informing relevant policy and intervention strategies.

 
ACKNOWLEDGEMENTS


This work was supported by the National Key Research and Development Program (2022YFC3700304), the National Natural Science Foundation of China (No. U22A20578), the Science and Technology Department of Fujian Province (No. 2022J05097), Xiamen Atmospheric Environment Observation and Research Station of Fujian Province, and Fujian Key Laboratory of Atmospheric Ozone Pollution Prevention (Institute of Urban Environment, Chinese Academy of Sciences).


REFERENCES


  1. Bates, J.T., Weber, R.J., Abrams, J., Verma, V., Fang, T., Klein, M., Strickland, M.J., Sarnat, S.E., Chang, H.H., Mulholland, J.A. (2015). Reactive oxygen species generation linked to sources of atmospheric particulate matter and cardiorespiratory effects. Environ. Sci. Technol. 49, 13605–13612. https://doi.org/10.1021/acs.est.5b02967

  2. Bateson, T.F., Schwartz, J. (2007). Children's response to air pollutants. J. Toxicol. Environ. Health Part A 71, 238–243. https://doi.org/10.1080/15287390701598234

  3. Burrows, B., Barbee, R.A., Cline, M.G., Knudson, R.J., Lebowitz, M.D. (1991). Characteristics of asthma among elderly adults in a sample of the general population. Chest 100, 935–942. https://doi.org/10.1378/chest.100.4.935

  4. Celebi Sozener, Z., Ozdel Ozturk, B., Cerci, P., Turk, M., Gorgulu Akin, B., Akdis, M., Altiner, S., Ozbey, U., Ogulur, I., Mitamura, Y. (2022). Epithelial barrier hypothesis: effect of the external exposome on the microbiome and epithelial barriers in allergic disease. Allergy 77, 1418–1449. https://doi.org/10.1111/all.15240

  5. Chang, J.H., Hsu, S.C., Bai, K.J., Huang, S.K., Hsu, C.W. (2017). Association of time-serial changes in ambient particulate matters (PMs) with respiratory emergency cases in Taipei's Wenshan District. PLoS One 12, e0181106. https://doi.org/10.1371/journal.pone.0181106

  6. Chen, K., Glonek, G., Hansen, A., Williams, S., Tuke, J., Salter, A., Bi, P. (2016). The effects of air pollution on asthma hospital admissions in Adelaide, South Australia, 2003–2013: time-series and case–crossover analyses. Clin. Exp. Allergy 46, 1416–1430. https://doi.org/10.1111/cea.​12795

  7. Chen, Y., Kong, D., Fu, J., Zhang, Y., Zhao, Y., Liu, Y., Chang, Z., Liu, Y., Liu, X., Xu, K., Jiang, C., Fan, Z. (2022). Associations between ambient temperature and adult asthma hospitalizations in Beijing, China: a time-stratified case-crossover study. Respir. Res. 23, 38. https://doi.org/​10.1186/s12931-022-01960-8

  8. Christidis, T., Erickson, A.C., Pappin, A.J., Crouse, D.L., Pinault, L.L., Weichenthal, S.A., Brook, J.R., Van Donkelaar, A., Hystad, P., Martin, R.V., Tjepkema, M., Burnett, R.T., Brauer, M. (2019). Low concentrations of fine particle air pollution and mortality in the Canadian Community Health Survey cohort. Environ. Health 18, 84. https://doi.org/10.1186/s12940-019-0518-y

  9. D’Oliveira, A., Dominski, F.H., De Souza, L.C., Branco, J.H.L., Matte, D.L., Da Cruz, W.M., Andrade, A. (2023). Impact of air pollution on the health of the older adults during physical activity and sedentary behavior: A systematic review. Environ. Res. 234, 116519. https://doi.org/10.1016/j.​envres.2023.116519

  10. Deng, J., Zhao, W., Wu, L., Hu, W., Ren, L., Wang, X., Fu, P. (2020). Black carbon in Xiamen, China: temporal variations, transport pathways and impacts of synoptic circulation. Chemosphere 241, 125133. https://doi.org/10.1016/j.chemosphere.2019.125133

  11. Estenne, M., Yernault, J.C., De Troyer, A. (1985). Rib cage and diaphragm-abdomen compliance in humans: effects of age and posture. J. Appl. Physiol. 59, 1842–1848. https://doi.org/10.1152/​jappl.1985.59.6.1842

  12. Fan, J., Li, S., Fan, C., Bai, Z., Yang, K. (2016). The impact of PM2.5 on asthma emergency department visits: a systematic review and meta-analysis. Environ. Sci. Pollut. Res. 23, 843–850. https://doi.org/10.1007/s11356-015-5321-x

  13. Fan, M.Y., Zhang, Y.L., Lin, Y.C., Cao, F., Sun, Y., Qiu, Y., Xing, G., Dao, X., Fu, P. (2021). Specific sources of health risks induced by metallic elements in PM2.5 during the wintertime in Beijing, China. Atmos. Environ. 246, 118112. https://doi.org/10.1016/j.atmosenv.2020.118112

  14. Florence, S., Nicolas, B., Catherine, M., Mare, S., Renaud, L. (2023). Cytokine-targeted therapies for asthma and copd. Eur. Respir. Rev. 32, 220193. https://doi.org/10.1183/16000617.0193-2022

  15. Han, L., Pisani, M.A., Araujo, K.L.B., Allore, H.G. (2016). Use of self-matching to control for stable patient characteristics while addressing time-varying confounding on treatment effect: a case study of older intensive care patients. Int. J. Stat. Med. Res. 5, 8–16. https://doi.org/10.6000/​1929-6029.2016.05.01.2

  16. He, C., Liu, C., Chen, R., Meng, X., Wang, W., Ji, J., Kang, L., Liang, J., Li, X., Liu, Y., Yu, X., Zhu, J., Wang, Y., Kan, H. (2022). Fine particulate matter air pollution and under-5 children mortality in China: A national time-stratified case-crossover study. Environ. Int. 159, 107022. https://doi.org/​10.1016/j.envint.2021.107022

  17. Hodge, M.X., Henriquez, A.R., Kodavanti, U.P. (2021). Adrenergic and glucocorticoid receptors in the pulmonary health effects of air pollution. Toxics 9, 132. https://doi.org/10.3390/toxics​9060132

  18. Huang, X., Xie, J., Cui, X., Zhou, Y., Wu, X., Lu, W., Shen, Y., Yuan, J., Chen, W. (2016). Association between concentrations of metals in urine and adult asthma: a case-control study in Wuhan, China. PLoS One 11, e0155818. https://doi.org/10.1371/journal.pone.0155818

  19. Jiang, X., Wang, R., Chang, T., Zhang, Y., Zheng, K., Wan, R., Wang, X. (2023). Effect of short-term air pollution exposure on migraine: a protocol for systematic review and meta-analysis on human observational studies. Environ. Int. 174, 107892. https://doi.org/10.1016/j.envint.​2023.107892

  20. Khreis, H., Kelly, C., Tate, J., Parslow, R., Lucas, K., Nieuwenhuijsen, M. (2017). Exposure to traffic-related air pollution and risk of development of childhood asthma: a systematic review and meta-analysis. Environ. Int. 100, 1–31. https://doi.org/10.1016/j.envint.2016.11.012

  21. Kim, H., Kim, H., Park, Y.H., Lee, J.T. (2017). Assessment of temporal variation for the risk of particulate matters on asthma hospitalization. Environ. Res. 156, 542–550. https://doi.org/​10.1016/j.envres.2017.04.012

  22. Kim, J.J. (2004). Ambient air pollution: health hazards to children. Pediatrics 114, 1699–1707. https://doi.org/10.1542/peds.2004-2166

  23. Liu, K., Hua, S., Song, L. (2022a). PM2.5 exposure and asthma development: the key role of oxidative stress. Oxid. Med. Cell. Longevity 2022, 3618806. https://doi.org/10.1155/2022/​3618806

  24. Liu, M., Jia, X., Liu, H., He, R., Zhang, X., Shao, Y. (2022b). Role of TRPV1 in respiratory disease and association with traditional Chinese medicine: A literature review. Biomed. Pharmacother. 155, 113676. https://doi.org/10.1016/j.biopha.2022.113676

  25. Liu, W., Wei, J., Cai, M., Qian, Z., Long, Z., Wang, L., Vaughn, M.G., Aaron, H.E., Tong, X., Li, Y., Yin, P., Lin, H., Zhou, M. (2022c). Particulate matter pollution and asthma mortality in China: A nationwide time-stratified case-crossover study from 2015 to 2020. Chemosphere 308, 136316. https://doi.org/10.1016/j.chemosphere.2022.136316

  26. Liu, Y., Wang, J., Huang, Z., Liang, J., Xia, Q., Xia, Q., Liu, X. (2021). Environmental pollutants exposure: a potential contributor for aging and age-related diseases. Environ. Toxicol. Pharmacol. 83, 103575. https://doi.org/10.1016/j.etap.2020.103575

  27. Mao, S., Wu, L., Shi, W. (2018). Association between trace elements levels and asthma susceptibility. Respir. Med. 145, 110–119. https://doi.org/10.1016/j.rmed.2018.10.028

  28. Norris, G., YoungPong, S.N., Koenig, J.Q., Larson, T.V., Sheppard, L., Stout, J.W. (1999). An association between fine particles and asthma emergency department visits for children in seattle. Environ. Health Perspect. 107, 489–493. https://doi.org/10.1289/ehp.99107489

  29. Pate, C.A., Zahran, H.S., Qin, X., Johnson, C., Hummelman, E., Malilay, J. (2021). Asthma surveillance — United States, 2006–2018. MMWR Surveill. Summ. 70, 1–32. https://doi.org/​10.15585/mmwr.ss7005a1

  30. Paterson, C.A., Sharpe, R.A., Taylor, T., Morrissey, K. (2021). Indoor PM2.5, VOCs and asthma outcomes: A systematic review in adults and their home environments. Environ. Res. 202, 111631. https://doi.org/10.1016/j.envres.2021.111631

  31. Piao, C.H., Fan, Y., Nguyen, T.V., Shin, H.S., Kim, H.T., Song, C.H., Chai, O.H. (2021). PM2.5 exacerbates oxidative stress and inflammatory response through the Nrf2/NF-κB signaling pathway in OVA-induced allergic rhinitis mouse model. Int. J. Mol. Sci. 22, 8173. https://doi.org/​10.3390/ijms22158173

  32. Riva, D.R., Magalhães, C.B., Lopes, A.A., Lanças, T., Mauad, T., Malm, O., Valença, S.S., Saldiva, P.H., Faffe, D.S., Zin, W.A. (2011). Low dose of fine particulate matter (PM2.5) can induce acute oxidative stress, inflammation and pulmonary impairment in healthy mice. Inhalation Toxicol. 23, 257–267. https://doi.org/10.3109/08958378.2011.566290

  33. Safiri, S., Carson-Chahhoud, K., Karamzad, N., Sullman, M.J., Nejadghaderi, S.A., Taghizadieh, A., Bell, A.W., Kolahi, A.-A., Ansarin, K., Mansournia, M.A. (2022). Prevalence, deaths, and disability-adjusted life-years due to asthma and its attributable risk factors in 204 countries and territories, 1990-2019. Chest 161, 318–329. https://doi.org/10.1016/j.chest.2021.09.042

  34. Sheffield, P.E., Zhou, J., Shmool, J.L., Clougherty, J.E. (2015). Ambient ozone exposure and children's acute asthma in New York City: A case-crossover analysis. Environ. Health 14, 25. https://doi.org/10.1186/s12940-015-0010-2

  35. Tu, J.V., Sin, D.D. (2001). Underuse of inhaled steroid therapy in elderly patients with asthma. Chest 119, 720–725. https://doi.org/10.1378/chest.119.3.720

  36. Wang, Y., Zu, Y., Huang, L., Zhang, H., Wang, C., Hu, J. (2018). Associations between daily outpatient visits for respiratory diseases and ambient fine particulate matter and ozone levels in Shanghai, China. Environ. Pollut. 240, 754–763. https://doi.org/10.1016/j.envpol.2018.05.029

  37. Wang, Y., Shi, Z., Shen, F., Sun, J., Huang, L., Zhang, H., Chen, C., Li, T., Hu, J. (2019). Associations of daily mortality with short-term exposure to PM2.5 and its constituents in Shanghai, China. Chemosphere 233, 879–887. https://doi.org/10.1016/j.chemosphere.2019.05.249

  38. Were, F.H., Wafula, G.A., Lukorito, C.B., Kamanu, T.K.K. (2020). Levels of PM10 and PM2.5 and respiratory health impacts on school-going children in Kenya. J. Health Pollut. 10, 200912. https://doi.org/10.5696/2156-9614-10.27.200912

  39. Wu, J., Zhong, T., Zhu, Y., Ge, D., Lin, X., Li, Q. (2019). Effects of particulate matter (PM) on childhood asthma exacerbation and control in Xiamen, China. BMC Pediatr. 19, 194. https://doi.org/10.1186/s12887-019-1530-7

  40. Xu, S., Karmacharya, N., Woo, J., Cao, G., Guo, C., Gow, A., Panettieri, R.A., Jr., Jude, J.A. (2023). Starving a cell promotes airway smooth muscle relaxation: inhibition of glycolysis attenuates excitation-contraction coupling. Am. J. Respir. Cell Mol. Biol. 68, 39–48. https://doi.org/​10.1165/rcmb.2021-0495OC

  41. Zhao, C., Wang, Y., Su, Z., Pu, W., Niu, M., Song, S., Wei, L., Ding, Y., Xu, L., Tian, M., Wang, H. (2020). Respiratory exposure to PM2.5 soluble extract disrupts mucosal barrier function and promotes the development of experimental asthma. Sci. Total Environ. 730, 139145. https://doi.org/10.1016/j.scitotenv.2020.139145

  42. Zhu, J., Lu, C. (2023). Air quality, pollution perception, and residents’ health: evidence from China. Toxics 11, 591. https://doi.org/10.3390/toxics11070591 


Share this article with your colleagues 

 

Subscribe to our Newsletter 

Aerosol and Air Quality Research has published over 2,000 peer-reviewed articles. Enter your email address to receive latest updates and research articles to your inbox every second week.

8.3
2023CiteScore
 
 
79st percentile
Powered by
Scopus
 
   SCImago Journal & Country Rank

2023 Impact Factor: 2.5
5-Year Impact Factor: 2.8

Aerosol and Air Quality Research partners with Publons

CLOCKSS system has permission to ingest, preserve, and serve this Archival Unit
CLOCKSS system has permission to ingest, preserve, and serve this Archival Unit

Aerosol and Air Quality Research (AAQR) is an independently-run non-profit journal that promotes submissions of high-quality research and strives to be one of the leading aerosol and air quality open-access journals in the world. We use cookies on this website to personalize content to improve your user experience and analyze our traffic. By using this site you agree to its use of cookies.