Front. Public Health Frontiers in Public Health Front. Public Health 2296-2565 Frontiers Media S.A. 10.3389/fpubh.2025.1535543 Public Health Original Research Impact of sand and dust storms on mortality in Jinan City, China Shen Chaofan 1 Li Mingjun 2 Wang Qingchang 2 Luan Jinjiao 1 Si Jiliang 1 * Cui Liangliang 3 * 1School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China 2Jinan Municipal Center for Disease Control and Prevention, Jinan, Shandong, China 3Jinan Mental Health Center, Jinan, Shandong, China

Edited by: Arthit Phosri, Mahidol University, Thailand

Reviewed by: Worradorn Phairuang, Chiang Mai University, Thailand

Zhenhua Zhang, Lanzhou University, China

 *Correspondence: Liangliang Cui, cll602@163.com Jiliang Si, sjlsdu@sdu.edu.cn 23 01 2025 2025 13 1535543 27 11 2024 10 01 2025 Copyright © 2025 Shen, Li, Wang, Luan, Si and Cui. 2025 Shen, Li, Wang, Luan, Si and Cui

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 Background

Sand and dust storms (SDSs) cause considerable health risks worldwide. China is a country seriously affected by SDSs, however only few studies researched the risk of SDS in China. The insufficient evidence on SDS hampers effective measures to mitigate its harm.

 Objective

To reveal the mortality risks associated with SDSs in Jinan City and identify sensitive populations vulnerable to these events.

 Methods

For this time-stratified case-crossover study, we collected daily data on all-cause, circulatory, and respiratory deaths, as well as air pollution and meteorological information from Jinan City in China between January 1, 2013, and November 30, 2022. We initially utilized a time-stratified case-crossover design and logistic regression model to examine the short-term relationship between SDSs and mortality risks, adjusting for specific variables such as mean temperature, humidity, wind speeds, and holidays. Subsequently, we conducted stratified analyses by age, gender, and season.

 Results

A total of 53 SDSs were observed, lasting for 88 days during the study period, which accounted for 2% of the study period. The excess mortality risks associated with SDSs were 13% (95% CI: 4–22%), 4% (95% CI: 1–8%), and 3% (95% CI: 1–6%) for respiratory, circulatory, and all-cause death, respectively. Females and people over 65 years of age are vulnerable to respiratory deaths caused by SDSs.

 Conclusion

Short-term exposure to SDSs caused the significantly elevated risks of respiratory, circulatory and all-cause death. Females and individuals over the age of 65 are particularly vulnerable to the effects of SDSs.

 dust storms PM10 mortality case-crossover study logistic regression section-at-acceptance Environmental Health and Exposome Introduction

Sand and dust storms (SDSs) are meteorological events caused by the ongoing release of significant amounts of mineral sand and dust particles into the atmosphere during specific favorable meteorological and synoptic conditions (1, 2). Generally, sand and dust particles were transported from one place to another by wind (3).

Poor air quality caused by SDSs threatens over 150 countries worldwide (4). The prevalence of SDSs has raised significant concern due to their harmful effects on human health (5, 6). Current investigations into the relationship between SDSs and health have primarily concentrated on the impact of SDS events on hospitalization and mortality rates. Research has shown that SDSs were notably linked to hospitalization rates in China (7, 8) and the Canary Islands, Africa (9). Independent studies from North America (10), Europe (11), and Oceania (12) indicated that SDSs increased non-accidental mortality. Several studies in East Asia have revealed that SDSs significantly raised all-cause and circulatory death rates (13–15). A recent study (16) demonstrated that exposure to SDS events was associated with an increased risk of circulatory and respiratory mortality in China, Asia.

Jinan City is located in the eastern part of China that is vulnerable to the effects of SDSs (16), with a population over 9 million. However, there is no study to investigate the effect of SDSs passing through Jinan City on mortality risks. To compensate for the limitation, this study explored the effects of SDSs passing through Jinan City on the risks of respiratory, circulatory, and all-cause death in the population based on a decade of mortality data in the city.

 Materials and methods Study area

This study area, Jinan City, is located in the mid-western of Shandong Province in Eastern China with low north high terrain south. It has a population of 9 million. The geographic position is between 36°01′N ~ 37°32′N and 116°11′E ~ 117°44′E. It belongs to typical warm temperate continental monsoonal climate zone that is characterized by a pronounced monsoon, four distinct seasons, a dry spring with little rain, a warm and rainy summer, a cool and dry autumn and a cold and little snow in winter. The perennial dominant wind direction of the city is from the southeast and east-southeast.

 Data sources

We obtained death records from the China Cause of Deaths Reporting System (CDRS) and categorized causes using the International Classification of Diseases 10th Revision (ICD-10). Our dataset covered death from all-cause, circulatory diseases (ICD-10 codes I00-I99), and respiratory diseases (ICD-10 codes J00-J99).

The assessment of air pollution’s impact on mortality was conducted by analyzing the concentrations of various air pollutants: coarse particulate matter (PM10), fine particulate matter (PM2.5), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and 8-h ozone (O3-8h). There were 28 urban air quality monitoring stations to carry out real-time monitoring of these pollutants. They covered all the areas of Jinan City, whose sites are shown in Supplementary Table S1. Data of air pollutants were from the Jinan Ecological Environmental Protection Bureau website.1

Meteorological information, such as daily mean temperature (T, °C), average relative humidity (RH, %), average air pressure (P, hPa), and average wind speeds (Wind, m/s), was collected from the China Meteorological Science Data Sharing Service Network.2 All data above were from the period between January 1, 2013, and November 30, 2022.

 SDS definition

In this study, referring to the related study, SDS day was defined as day with a daily PM10 concentration exceeding 400 μg/m3 and a PM2.5 to PM10 concentration ratio below 0.4 (16, 17).

 Backward airflow trajectory analysis

We obtained the Global Data Assimilation System (GDAS) meteorological dataset from https://www.ready.noaa.gov/index.php and used MeteoInfoMap software (version 3.7.2; Chinese Academy of Meteorological Sciences; Beijing, China) to calculate 24-h backward airflow trajectories of SDSs. In China, there are three major sources of SDSs affecting population’s health, including the Taklamakan Desert and deserts of Inner Mongolia in China, and deserts of Mongolia, with the Taklamakan Desert affecting its nearby regions (18, 19), the deserts of Inner Mongolia in China and Mongolia contribute mainly to SDSs affecting China’s inland. To align with the airflow trajectories of SDSs impacting Jinan City, we first inputted the GDAS dataset for the days when these SDSs occurred using the MeteoInfoMap software. Next, we filled in the date, longitude, latitude, and sampling point height information in the respective data fields to calculate and fit the trajectories of the SDSs. This method yielded a strong simulation of the various source trajectories of SDSs. SDSs locations were identified based on their passage through Inner Mongolia in China, Mongolia or other areas, and their direction were recognized based on SDSs locations relative to Jinan City (Supplementary Table S2).

 Statistical analyses

Firstly, we conducted descriptive analysis of the data, presenting indicators such as minimum (Min), maximum (Max), median (M), first quartile (P25), and third quartile (P75). Secondly, a time-stratified case-crossover study and logistic regression model was performed to evaluate the association between exposure to SDSs and mortality risks. The specific variables of mean temperature, humidity, wind speeds, and holidays were adjusted in the model. The design principle of a time-stratified case-crossover study is to stratify time, comparing the case phase and control phase within the same month, thus avoiding the confounding effects of long-term temporal trends. The control phase was selected to correspond to the same weekday of the other weeks within the same month and year as the case phase (e.g., if the SDS day occurred on the Wednesday of the 4th week of February 2013, the control days are chosen as the Wednesdays of the 1st, 2nd, and 3rd weeks of February 2013). The logistic regression model is a predictive tool used to estimate the probability of occurrence of the response variable, which varies with the dependent variables. We utilized the Wilcoxon rank-sum test to compare mortality risks between SDS days and non-SDS days.

Referring to the model in the related studies (16, 17), we determined the main model (Equation 1) in this study. It was as follows:

log E Y t = α + β Z t + ns T e m p t , d f + ns R H t , d f + ns Win d t , d f + factor stratum + factor holiday

The definition of each variable in the model is shown in Supplementary material.

The following (Equation 2) calculated odds ratio (OR) for mortality associated with SDS events basing on the estimated β coefficients:

OR = e β Stratified analyses

Moreover, stratified analyses were conducted based on season (spring and winter), age (<65 and ≥ 65), and gender (males and females). Statistical differences between stratified estimates were estimated by two-sample Z-tests with the following formula (Equation 3):

β 1 − β 2 / S E 1 2 + S E 2 2

β1 and β2 are regression coefficients specific to two subgroups. SE1 and SE2 are their corresponding standard errors.

 Definition of lag days

We investigated the delayed impact of 31 days after the SDS, and observed that the risks of all-cause and circulatory death ceased by the 6th day after SDSs (lag 6), while the risk of respiratory death diminished at lag 3. Therefore, the lag days for both conditions were consistently identified as 6 days.

 Sensitivity analyses

Sensitivity analyses were conducted by adjusting the degrees of freedom for the temperature variable (df = 7, 8, 9) and using different degrees of freedom for relative humidity and wind speed variables (df = 4, 5, 6) in spline functions (16). In addition, three alternative definitions of SDSs were tested by altering the PM2.5 to PM10 concentration ratio (0.35 and 0.45) or considering only PM10 concentration (16).

Statistical analyses were performed using Rstudio software (version 4.2.3; Posit Inc., MA, United States). All tests were two-sided with statistical significance set at a p-value less than 0.05.

 Results Summary statistics for SDSs and mortality due to SDSs

During the 10-year study period, 53 SDSs were recorded, with a duration of 88 days, representing 2% of the total study period. Figure 1 showed the annual emergence number of SDSs which primarily transpired from March to May and from November to January of the subsequent year.

The yearly and monthly emergence number of sand and dust storms from 2013 to 2022 in Jinan City, China.

The demographic characteristics of deaths, meteorological factors, and air pollutants during SDS days and non-SDS days were displayed in Table 1. The number of deaths and the PM10 concentrations significantly increased during the study period (Figure 2). During SDSs, the concentrations of PM10, PM2.5, SO2, NO2, and CO were notably elevated compared to non-SDS days, whereas levels of O3 significantly decreased (Supplementary Table S3). Additionally, the daily death counts of all-cause, circulatory, and respiratory showed a significant elevation on SDSs days in comparison to non-SDS days (Supplementary Table S4).

Summary statistics of mortality of all-cause, circulatory and respiratory, meteorological and air pollutants variables during SDSs and non-SDS days from 2013 to 2022 in Jinan city, China.

Variable SDS days Non-SDS days
n (%) Min P25 M P75 Max n (%) Min P25 M P75 Max
All-cause death counts 10,572 (100) 76 104 116 132 211 407,090 (100) 62 99 111 126 225
<65 year 2,794 (26) 17 26 31 36 55 104,295 (26) 8 25 29 34 54
≥65 year 7,778 (74) 44 75 86 97 178 302,795 (74) 37 72 82 96 178
Male 5,817 (55) 36 57 66 73 103 227,293 (56) 31 55 63 71 127
Female 4,755 (45) 33 45 52 60 108 179,797 (44) 24 42 49 58 119
Circulatory death counts 5,846 (100) 33 57 64 73 123 216,915 (100) 24 50 59 70 143
<65 year 1,069 (18) 3 9 12 14 23 37,649 (17) 2 8 10 13 26
≥65 year 4,777 (82) 23 48 52 61 113 179,266 (83) 17 41 48 58 118
Male 2,940 (50) 13 28 32 39 56 111,130 (51) 8 25 30 37 79
Female 2,906 (50) 16 28 32 38 68 105,785 (49) 9 23 29 35 73
Respiratory death counts 907 (100) 2 7 10 13 38 32,882 (100) 0 6 8 12 31
<65 year 88 (10) 0 0 1 2 4 3,246 (10) 0 0 1 1 7
≥65 year 819 (90) 1 6 9 12 34 29,576 (90) 0 5 8 11 28
Male 485 (53) 1 3 5 7 18 17,690 (54) 0 3 5 7 19
Female 422 (47) 0 3 5 6 20 15,192 (46) 0 2 4 6 17
Meteorological
RH (%) 88 (−) 18 33 47 65 97 3,533 (−) 15 41 55 70 100
Mean.T. (°C) 88 (−) -3 3 13 19 33 3,533 (−) -12 6 17 25 34
Pressure (hPa) 88 (−) 981 992 997 1,003 1,013 3,533 (−) 975 988 997 1,004 1,022
Wind (m/s) 88 (−) 1 2 2 3 8 3,533 (−) 0 2 2 3 8
Air pollution
PM10 (μg/m3) 88 (−) 199 244 332 456 798 3,533 (−) 5 75 111 158 399
PM2.5 (μg/m3) 88 (−) 76 88 104 264 443 3,533 (−) 4 33 51 83 280
SO2 (μg/m3) 88 (−) 7 37 66 149 429 3,533 (−) 5 12 21 42 382
CO (μg/m3) 88 (−) 391 1,033 1,426 3,381 6,555 3,533 (−) 277 707 925 1,232 5,102
NO2 (μg/m3) 88 (−) 15 46 59 92 165 3,533 (−) 9 29 40 54 137
O3 (μg/m3) 88 (−) 11 27 84 112 238 3,533 (−) 7 62 100 149 282
PM2.5/PM10 88 (−) 0.2 0.4 0.4 0.6 0.8 3,533 (−) 0.1 0.4 0.5 0.6 0.9

SDSs, sand and dust storms; Min, Minimum; P25, 25th percentile; M, Median; P75, 75th percentile; Max, Maximum; Mean.T., Mean Temperature; RH, Relative humidity; PM2.5, Fine particulate matter; PM10, Coarse particulate matter; SO2, Sulfur dioxide; CO-Carbon monoxide; NO2, Nitrogen dioxide; O3, Ozone.

Temporal trends of death due to all-cause, circulatory, and respiratory with the concentration of PM10 during SDSs from 2013 to 2022 in Jinan City, China. Red Points represent the SDSs days; Black line represents PM10 concentration; Orange line represents all-cause death; Blue line represents circulatory death; Green line represents respiratory death. SDSs = sand and dust storms; PM10 = coarse particulate matter.

After calculating the backward airflow trajectories of SDSs passing through Jinan City, the study classified the source locations of SDSs into Mongolia (9, 17%); Inner Mongolia in China (18, 34%); Inner Mongolia in China and Mongolia (16, 30%); and other regions (10, 19%). The transportation routes identified were northwest (41, 77%); northeast (8, 15%); southwest (2, 4%); and west (2, 4%) (Supplementary Figure S1).

 Association between SDSs and mortality due to SDSs

A significant increase in the risks of respiratory, circulatory and all-cause death are shown in Figure 3, with the highest death risk observed at lag0 [odds ratio (OR) = 1.13, 95% confidence interval (CI): 1.04, 1.22], lag0 (OR = 1.04, 95% CI: 1.01, 1.08), lag5 (OR = 1.03, 95% CI: 1.01, 1.06), respectively.

Summary of lag effect of sand and dust storms on the risks of all-cause, circulatory and respiratory death from 2013 to 2022 in Jinan City, China. The “*” represents statistically significant.

 Stratified analyses results

In subgroups analysis of age, we observed that the risk of respiratory death associated with SDSs in the age group ≥65 was higher than that in the age group <65, with the maximum lag effect in the age group ≥65 emerged on lag2 (OR = 1.25, 95% CI: 0.98, 1.60), and that in the age group <65 occurred on lag0 (OR = 1.12, 95% CI: 1.03, 1.22). The risks of all-cause death were notably increased in both age groups, with the maximum lag effect in the age group ≥65 appeared on lag2 (OR = 1.05, 95% CI: 1.01, 1.10), and that in the age group <65 occurred on lag5 (OR = 1.03, 95% CI: 1.01, 1.06), but their group differences were not significant. Meanwhile, the risks of circulatory death were significantly elevated in both age groups, with the maximum lag effect in the age group ≥65 occurred on lag4 (OR = 1.07, 95% CI: 1.01, 1.15), and that in the age group <65 emerged on lag0 (OR = 1.04, 95% CI: 1.01, 1.08), but their group differences were not significant (Figure 4).

Subgroups analysis of lag effect of sand and dust storms on the risks of all-cause, circulatory and respiratory death from 2013 to 2022 in Jinan City, China. The “*” represents statistically significant.

Additionally, it was observed that the risk of respiratory death was higher in females compared to males, with the highest risk in females occurring at lag2 (OR = 1.19, 95% CI: 1.06, 1.34) and in males at lag0 (OR = 1.16, 95% CI: 1.04, 1.29). There was a significant increase in the risks of all-cause death in both genders, with the highest risk in males at lag5 (OR = 1.05, 95% CI: 1.02, 1.08) and in females at lag0 (OR = 1.04, 95% CI: 1.01, 1.07), although the differences between the groups were not significant. Additionally, the risks of circulatory death significantly rose in both genders, with the highest risk in males at lag5 (OR = 1.05, 95% CI: 1.01, 1.10) and in females at lag4 (OR = 1.04, 95% CI: 1.00, 1.09), but the group differences were not significant (Figure 4).

In a stratified analysis of the seasons, we observed that SDSs in the two seasons notably increased risks of all-cause, circulatory, respiratory death, but the differences of these groups were not significant. The maximum lag effect of all-cause death in the spring appeared on lag0 (OR = 1.05, 95% CI: 1.01, 1.08), while that in the winter occurred on lag5 (OR = 1.06, 95% CI: 1.02, 1.10). The maximum lag effect of circulatory death in the spring appeared on lag0 (OR = 1.08, 95% CI: 1.04, 1.13), while that in the winter occurred on lag5 (OR = 1.06, 95% CI: 1.01, 1.11). The maximum lag effect of respiratory death in the spring appeared on lag1 (OR = 1.18, 95% CI: 1.05, 1.31), while that in the winter occurred on lag0 (OR = 1.19, 95% CI: 1.05, 1.36) (Figure 5).

Summary of lag effect of sand and dust storms during the different seasons on the risks of all-cause, circulatory and respiratory death from 2013 to 2022 in Jinan City, China. The “*” represents statistically significant.

 Sensitive analyses results

The sensitivity analyses showed that the main findings remained nearly unchanged, suggesting that the main model had a good fit and produced stable results (Supplementary Figures S2, S3).

 Discussion

We conducted a retrospective analysis to explore the association between SDSs passing through Jinan City and mortality risks over the past decade. We observed that SDSs passing through Jinan City originate from Inner Mongolia in China, Mongolia, or other regions. Meanwhile, Jinan City is a region prone to the impact of SDSs (16). Our findings indicated a notable rise in the risks of respiratory, circulatory, and all-cause death linked with SDSs. This is consistent with the study by Pouri et al. who observed that SDSs resulted in a 18%, 25%, and 16% elevated risk of respiratory, circulatory, and all-cause death, respectively (20). A previous study in China also demonstrated that SDSs lead to an elevated excess mortality risk from circulatory and respiratory diseases. They found an 8.9% elevated excess mortality risk for respiratory death due to SDSs (16), which was lower than the result of our study in Jinan City, suggesting that SDSs passing through Jinan City were even more dangerous and needed attention.

In line with a previous study (20), our study revealed that the older adult are more vulnerable to respiratory death due to SDSs. The increased vulnerability of the older adult to air pollution can be attributed to the natural deterioration of the immune system with age (21). This decline in immune function reduces their ability to resist environmental hazards effectively (22–24). In addition, older people are more prone to chronic diseases, which can worsen their current diseases and even cause mortality (25). Older adult individuals with chronic obstructive pulmonary disease (COPD) faced increased mortality rates after exposure to outdoor air pollution (26, 27).

Our findings suggest that females face a heightened risk of respiratory death related to SDS events. The study of Pouri et al. (20) also revealed that SDSs notably elevated respiratory mortality in females. Several studies have proved that air pollution is more likely to have severe influences on females (28–31). These may be explained by gender variances in physiological structures that females have narrower airway dimensions and higher breathing rates (32). One study showed that females have a faster respiratory rate than males (33), which could be a possible reason why women are more susceptible to the effects of air pollution than men.

China is a country significantly affected by SDSs. With increasing awareness of the dangers posed by SDSs, various strategies have been proposed to mitigate the health risks associated with air pollution events, including SDSs (34–37).

 Conclusion

Short-term exposure to SDSs caused the significantly elevated risks of respiratory, circulatory and all-cause death. Females and people over 65 years of age are vulnerable to respiratory deaths caused by SDSs. This study, conducted in Jinan City, offers new evidence regarding the adverse effects of SDSs on the risks of respiratory, circulatory, and all-cause mortality through a time-stratified case-crossover analysis.

 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 authors.

 Author contributions

CS: Writing – original draft, Conceptualization, Data curation, Formal analysis. ML: Conceptualization, Writing – original draft. QW: Data curation, Writing – original draft. JL: Data curation, Writing – original draft. JS: Writing – review & editing. LC: Writing – review & editing.

 Funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the 34th Batch of Jinan Science and Technology Innovation Development Plan (Clinical Medicine Science and Technology Innovation Plan) Projects (grant no. 202134008), and special funds for High-Level Talents in Jinan Healthcare Industry. This project was funded by Cheeloo College of Medicine, Shandong University (grant no. qlyxjy-202346).

 Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

 Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2025.1535543/full#supplementary-material

1http://fb.sdem.org.cn:8801/airdeploy.web/AirQuality/MapMain.aspx

2http://data.cma.cn/

 References Taghavi F Owlad E Ackerman SA. Enhancement and identification of dust events in the south-west region of Iran using satellite observations. J Earth Syst Sci. (2017) 126:28. doi: 10.1007/s12040-017-0808-0 Milinevsky G Evtushevsky O Klekociuk A Wang Y Grytsai A Shulga V . Early indications of anomalous behaviour in the 2019 spring ozone hole over Antarctica. Int J Remote Sens. (2020) 41:7530–40. doi: 10.1080/2150704x.2020.1763497 Song X Zhou T Zhang J Su Y Zhou H He L. Preparation and application of molecularly imprinted monolithic extraction column for the selective microextraction of multiple macrolide antibiotics from animal muscles. Polymers. (2019) 11:1109. doi: 10.3390/polym11071109, PMID: 31266161 ESCAPU. Sand and dust storms in Asia and the Pacific: opportunities for regional cooperation and action. (2018). Chen Q Wang M Sun H Wang X Wang Y Li Y . Enhanced health risks from exposure to environmentally persistent free radicals and the oxidative stress of PM2.5 from Asian dust storms in Erenhot, Zhangbei and Jinan, China. Environ Int. (2018) 121:260–8. doi: 10.1016/j.envint.2018.09.012, PMID: 30223202 Li X Cai H Ren X He J Tang J Xie P . Sandstorm weather is a risk factor for mortality in ischemic heart disease patients in the Hexi corridor, northwestern China. Environ Sci Pollut R. (2020) 27:34099–106. doi: 10.1007/s11356-020-09616-0, PMID: 32557065 Ma Y Zhang H Zhao Y Zhou J Yang S Zheng X . Short-term effects of air pollution on daily hospital admissions for cardiovascular diseases in western China. Environ Sci Pollut R. (2017) 24:14071–9. doi: 10.1007/s11356-017-8971-z, PMID: 28409432 Tao Y An XQ Sun ZB Hou Q Wang Y. Association between dust weather and number of admissions for patients with respiratory diseases in spring in Lanzhou. Sci Total Environ. (2012) 423:8–11. doi: 10.1016/j.scitotenv.2012.01.064, PMID: 22386996 Dominguez-Rodriguez A Baez-Ferrer N Rodriguez S Avanzas P Abreu-Gonzalez P Terradellas E . Saharan dust events in the Dust Belt -Canary Islands- and the observed association with in-hospital mortality of patients with heart failure. J Clin Med. (2020) 9:376. doi: 10.3390/jcm9020376, PMID: 32019177 Crooks JL Cascio WE Percy MS Reyes J Neas LM Hilborn ED. The association between dust storms and daily non-accidental mortality in the United States, 1993-2005. Environ Health Perspect. (2016) 124:1735–43. doi: 10.1289/ehp216, PMID: 27128449 Renzi M Forastiere F Calzolari R Cernigliaro A Madonia G Michelozzi P . Short-term effects of desert and non-desert PM10 on mortality in Sicily, Italy. Environ Int. (2018) 120:472–9. doi: 10.1016/j.envint.2018.08.016 Johnston F Hanigan I Henderson S Morgan G Bowman D. Extreme air pollution events from bushfires and dust storms and their association with mortality in Sydney, Australia 1994-2007. Environ Res. (2011) 111:811–6. doi: 10.1016/j.envres.2011.05.007 Chan CC Ng HC. A case-crossover analysis of Asian dust storms and mortality in the downwind areas using 14-year data in Taipei. Sci Total Environ. (2011) 410-411:47–52. doi: 10.1016/j.scitotenv.2011.09.031, PMID: 21995878 Wang YC Lin YK. Mortality associated with particulate concentration and Asian dust storms in metropolitan Taipei. Atmos Environ. (2015) 117:32–40. doi: 10.1016/j.atmosenv.2015.06.055 Kojima S Michikawa T Ueda K Sakamoto T Matsui K Kojima T . Asian dust exposure triggers acute myocardial infarction. Eur Heart J. (2017) 38:3202–8. doi: 10.1093/eurheartj/ehx509, PMID: 29020374 Zhang C Yan ML Du H Ban J Chen C Liu YY . Mortality risks from a spectrum of causes associated with sand and dust storms in China. Nat Commun. (2023) 14:6867. doi: 10.1038/s41467-023-42530-w, PMID: 37891159 Jung J Lee EM Myung W Kim H Kim H Lee H. Burden of dust storms on years of life lost in Seoul, South Korea: a distributed lag analysis. Environ Pollut. (2022) 296:118710. doi: 10.1016/j.envpol.2021.118710 Xu CQ Guan QY Lin JK Luo HP Yang LQ Tan Z . Spatiotemporal variations and driving factors of dust storm events in northern China based on high-temporal-resolution analysis of meteorological data (1960-2007). Environ Pollut. (2020) 260:114084. doi: 10.1016/j.envpol.2020.114084, PMID: 32041033 Chen SY Huang JP Li JX Jia R Jiang NX Kang LT . Comparison of dust emissions, transport, and deposition between the Taklimakan Desert and Gobi Desert from 2007 to 2011. Sci China-Earth Sci. (2017) 60:1338–55. doi: 10.1007/s11430-016-9051-0 Pouri N Karimi B Kolivand A Mirhoseini SH. Ambient dust pollution with all-cause, cardiovascular and respiratory mortality: a systematic review and meta-analysis. Sci Total Environ. (2023) 912:168945. doi: 10.1016/j.scitotenv.2023.168945, PMID: 38042201 Dalle S Rossmeislova L Koppo K. The role of inflammation in age-related sarcopenia. Front Physiol. (2017) 8:8. doi: 10.3389/fphys.2017.01045, PMID: 29311975 Eckel SP Louis TA Chaves PHM Fried LP Margolis HG. Modification of the association between ambient air pollution and lung function by frailty status among older adults in the cardiovascular health study. Am J Epidemiol. (2012) 176:214–23. doi: 10.1093/aje/kws001, PMID: 22811494 Zhang Z Zhang G Bin S. The spatial impacts of air pollution and socio-economic status on public health: empirical evidence from China. Socioecon Plan Sci. (2022) 83:101167. doi: 10.1016/j.seps.2021.101167 Ayyamperumal R Banerjee A Zhang Z Nazir N Li F Zhang C . Quantifying climate variation and associated regional air pollution in southern India using Google earth engine. Sci Total Environ. (2024) 909:168470. doi: 10.1016/j.scitotenv.2023.168470, PMID: 37951269 Liu S Yan Z Liu Y Yin Q Kuang L. Association between air pollution and chronic diseases among the elderly in China. Nat Hazards. (2017) 89:79–91. doi: 10.1007/s11069-017-2955-7 Bentayeb M Simoni M Baiz N Norback D Baldacci S Maio S . Adverse respiratory effects of outdoor air pollution in the elderly. Int J Tuberc Lung D. (2012) 16:1149–61. doi: 10.5588/ijtld.11.0666 Sinharay R Gong J Barratt B Ohman-Strickland P Ernst S Kelly FJ . Respiratory and cardiovascular responses to walking down a traffic-polluted road compared with walking in a traffic-free area in participants aged 60 years and older with chronic lung or heart disease and age-matched healthy controls: a randomised, crossover study. Lancet. (2018) 391:339–49. doi: 10.1016/s0140-6736(17)32643-0, PMID: 29221643 Chen R Huang W Wong C-M Wang Z Thuan Quoc T Chen B . Short-term exposure to sulfur dioxide and daily mortality in 17 Chinese cities: the China air pollution and health effects study (CAPES). Environ Res. (2012) 118:101–6. doi: 10.1016/j.envres.2012.07.003, PMID: 22831556 Chen R Kan H Chen B Huang W Bai Z Song G . Association of Particulate air Pollution with Daily Mortality. Am J Epidemiol. (2012) 175:1173–81. doi: 10.1093/aje/kwr425, PMID: 22510278 Zhang Z Zhao M Zhang Y Feng Y. How does urbanization affect public health? New evidence from 175 countries worldwide. Front. Public Health. (2023) 10:10. doi: 10.3389/fpubh.2022.1096964, PMID: 36684862 Pu W Zhang A Zhang Z Qin S Xia Q. Can urban land market reform mitigate industrial emissions? Environmental evidence from 257 prefecture-level cities in China. Environ Res. (2023) 236:116707. doi: 10.1016/j.envres.2023.116707, PMID: 37479211 Yunginger JW Reed CE Oconnell EJ Melton LJ Ofallon WM Silverstein MD. A community-based study of the epidemiology of asthma: incidence rates, 1964–1983. Am Rev Respir Dis. (1992) 146:888–94. doi: 10.1164/ajrccm/146.4.888, PMID: 1416415 Pichard LE Patil SP Gladmon E Smith PL Schwartz AR Schneider H . Women have a greater ventilatory responses to upper airway obstruction than men. Conf Proc IEEE Eng Med Biol Soc. (2004) 2004:3878–80. doi: 10.1109/IEMBS.2004.1404085 Zhang Z Zhang Y Zhao M Muttarak R Feng Y. What is the global causality among renewable energy consumption, financial development, and public health? New perspective of mineral energy substitution. Resour Policy. (2023) 85:104036. doi: 10.1016/j.resourpol.2023.104036, PMID: 39798718 Zhang Z Liu Q Chen Y Shao S Tang Y. The evolution of central environmental protection inspection policy attention in China: An investigation based on inspection reports. Chin J Popul Resour. (2023) 21:203–11. doi: 10.1016/j.cjpre.2023.11.001, PMID: 39798718 Jiang Y Xiao Y Zhang Z Zhao S. How does central-local interaction affect local environmental governance? Insights from the transformation of central environmental protection inspection in China. Environ Res. (2024) 243:117668. doi: 10.1016/j.envres.2023.117668, PMID: 38007082 Zhang Z Shang Y Zhang G Shao S Fang J Li P . The pollution control effect of the atmospheric environmental policy in autumn and winter: evidence from the daily data of Chinese cities. J Environ Manag. (2023) 343:118164. doi: 10.1016/j.jenvman.2023.118164, PMID: 37224689