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Environmental Health Insights
Volume 15: 1–28
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DOI: 10.1177/11786302211018390
Introduction
Dust storms are natural hazards and the most common sources
of natural particles, including very small materials, potential
allergens, and pollutants.1-5 Depending on the nature of the
source of the dust, these materials and substances may include,
quartz, silicon dioxide, oxides of magnesium, calcium, iron, and
aluminum6,7 and sometimes a range of organic matter, anthro-
pogenic pollutants, and salts.8 Dust storms carry millions of
tons of soil into the air each year from thousands of kilometers
away. They can last a few hours or a few days1-5 and distribute
a large number of small particles in the air,9,10 increasing the
amount of particles above the allowable threshold for human
health.11,12 During a dust storm event, the concentration of
PM10 (particles with an aerodynamic diameter <10 µm) and
PM2.5 (particles with an aerodynamic diameter <2.5 µm) par-
ticles are often higher than the normal thresholds recom-
mended by the World Health Organization (PM2.5: 10 µg/m3
annual mean, 25 µg/m3 24-hour mean. PM10: 20 µg/m3 annual
mean, 50 µg/m3 24-hour mean).8,13 It can also exceed 6000 µg/
m3 in seriously strong dust storms.14 According to the Huffman
Classification of dust PM10 range (g/ m3), in dusty air, light
dust storm, dust storm, strong dust storm, and serious strong
dust storm days, levels can be between 50 to 200, 200 to 500,
500 to 2000, 2000 to 5000, and >5000, respectively.15
Dust storms are occurring increasingly frequently in many
desert areas and arid regions around the world,3 causing
extensive damage and emergencies each year.3,16-18 Therefore,
dust storms have attracted increasing attention in recent
Global Health Impacts of Dust Storms:
A Systematic Review
Hamidreza Aghababaeian1,2,3 , Abbas Ostadtaghizadeh1,2,
Ali Ardalan1, Ali Asgary4, Mehry Akbary5, Mir Saeed Yekaninejad6
and Carolyn Stephens7
1Department of Health in Emergencies and Disasters, School of Public Health, Tehran University
of Medical Sciences, Tehran, Iran. 2Center for Air Pollution Research (CAPR), Institute for
Environmental Research (IER), Tehran University of Medical Sciences, Tehran, Iran. 3Department
of Nursing and Emergency, Dezful University of Medical Sciences, Dezful, Iran. 4Disaster and
Emergency Management, School of Administrative Studies, York University, Toronto, Canada.
5Department of Climatology, Faculty of Geographical Sciences, Kharazmi University, Tehran, Iran.
6Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of
Medical Sciences, Tehran, Iran. 7UCL Bartlett Development Planning Unit, London School of
Hygiene & Tropical Medicine, London, UK.
ABSTRACT
BACKGROU ND: Dust storms and their impacts on health are becoming a major public health issue. The current study examines the health
impacts of dust storms around the world to provide an overview of this issue.
METHOD: In this systematic review, 140 relevant and authoritative English articles on the impacts of dust storms on health (up to September
2019) were identified and extracted from 28 968 articles using valid keywords from various databases (PubMed, WOS, EMBASE, and Sco-
pus) and multiple screening steps. Selected papers were then qualitatively examined and evaluated. Evaluation results were summarized
using an Extraction Table.
RE S ULTS : The results of the study are divided into two parts: short and long-term impacts of dust storms. Short-term impacts include mor-
tality, visitation, emergency medical dispatch, hospitalization, increased symptoms, and decreased pulmonar y function. Long-term impacts
include pregnancy, cognitive difficulties, and birth problems. Additionally, this study shows that dust storms have devastating impacts on
health, affecting cardiovascular and respiratory health in particular.
CONCLUSION: The findings of this study show that dust storms have significant public health impacts. More attention should be paid to
these natural hazards to prepare for, respond to, and mitigate these hazardous events to reduce their negative health impacts.
Registration: PROSPERO registration number CRD42018093325
KEYWORDS: Air quality, desert dust, dust storm, health, PM10
RECEIVED: February 16, 2021. ACCEPTED: April 27, 2021.
TYPE: Review
FUNDING: The author(s) disclose d receipt of the following nancial support fo r the
researc h, author ship, and /or publication of this arti cle: This study is a part of PhD
disser tation and was funded and supporte d by School of public health and Center for air
pollution research (CAPR), institute for environment al research (IER), Tehran University of
Medical Sciences, Tehran, Iran, Grant No: 98-03-46 -43717.
DECLARATION OF CONFLICTING INTERESTS: The author(s) declared no potential
conic ts of interest with respect to the research, authorship, and/or publication of this
article.
CORRESPONDING AUTHOR: Abbas Ostadtaghiz adeh, Department of Health in
Emergen cies and D isasters, School of Public Health, Tehran University of Medical
Sciences, Poorsina Avenue, Tehran, Iran. Email: ostadtaghizadeh@gmail.com
1018390EHI0010.1177/11786302211018390Environmental Health InsightsAghababaeian et al
review-article2021
2 Environmental Health Insights
years.16,17,19 Researchers have demonstrated how dust storms
affect various aspects of human life.19 The particles in dust
storms affect weather conditions, agricultural production,
human health, and the ecosystem.20,21 Evidence suggests that
mineral aerosols affect cloud formation and precipitation and
can reduce the acidity of precipitation.22 Moreover, a high
density and diversity of bacteria and plant pollens have been
observed during dust storms.23 In addition to endangering
the ecosystem, dust storms have direct and indirect impacts
on public health and human health.8,20,21,24 Due to their small
sizes, almost all dust storm particles, that is, airborne particles
(PM) can enter the respiratory tract25; larger particles are
often deposited in the upper respiratory tract (nasopharyn-
geal region, tracheobronchial region), while smaller particles
can enter deep lung tissue.26,27 The physical, biological, and
chemical properties of these particles can cause disorders in
the health of the body,8,24,26 and in addition to the respiratory
tract, can damage other systems of the body, including the
cerebral, cardiovascular, skin,8,24,26 blood, and immune
systems.28,29
Research has indicated that exposure to dust particles,
which can remain in the air from hours to days,24 can result in
other problems like conjunctivitis, meningitis, and valley
fe ver.24,26,30 In rare cases, it can even lead to death.26,31
Evidence further suggests that frequent exposure to dust
storms can lead to increased adverse health effects24,32-37 in
people of almost all age groups and genders.3,38,39 People with
a history of diabetes, hypertension, cerebrovascular, or pulmo-
nary disease are also at higher risk.40 Many epidemiological
studies have determined the health effects of dust storms by
comparing outcomes during dust storm periods with out-
comes during non-dust storm periods41-43 and by assessing
the correlation between dust storms or PM10 exposure and
health outcomes.32,44 Many researcher have acknowledged
the existence of a significant association between dust expo-
sure and increased morbidity or mortality, but there is no con-
sensus in this regard to date.45 Pérez etal. stated that increased
PM during dust storms caused a significant increase in mor-
tality rate in Barcelona.46 Chen etal.,47 Kashima etal.,48 and
Delangizan49 also noted that increased PM10 levels during
Asian dust storms increased cardiovascular mortality. Some
studies have reported that Middle Eastern dust storms can
affect inflammation and coagulation markers in young
adults,28,29 have adverse effects on pulmonary function,50 and
increase the number of asthma patients.51-52 Conversely, some
studies have either ruled out the possibility of an increase in
mortality or hospitalizations of patients due to dust storm
exposure or do not consider the increase to be signifi-
cant.43,53-55 For example, in studies conducted in Italy,53
Greece,54 Kuwait,43 and Taipei,55 researchers found no sig-
nificant relationship between dust storms and increased risk
of death. Bell,56 Ueda,57 and Min58 also found that dust
storms did not significantly increase hospitalizations of asth-
matic patients or asthma attacks in Taipei and Japan.56
There are mixed results and a lack of accurate and up-to-
date classified data about the health impacts of dust storms on
humans around the world. Moreover, the causes of dust storm-
related health problems are not yet completely understood.59
Given the importance of the impact of dust storms on human
health as well as the increasing evidence of recurring and nega-
tive impacts of these storms, and because of the lack of system-
atic review studies, the current study conducted an extensive
review of the current literature on the impacts of dust storms
on human health.
Materials and Methods
This systematic review of scientific resources identified articles
related to dust storms and related human health outcomes
published up to 30 September 2019. PubMed, EMBASE,
Scopus, and ISI WoS ( Web of Science) databases were searched
for articles published in relevant journals from the 28th to the
30th of October, 2019. All peer-reviewed articles from English
language journals were discovered in the primary search stage.
Citations and references of all relevant articles were examined
and searched manually to ensure that all relevant articles were
included. The primary search used the following Medical
Subject Headings (MeSH terms) and keywords: Dust* OR
Kosa OR Yellow sand OR Arabian Sand OR Dust Storms
AND Mortality OR Disease* OR Morbidity OR Admission*
OR Health* OR “Adverse affect” OR affect*.
Executive limitations: The main limitations of the current
study were the lack of access to all required databases as well as
the lack of access to the full text of some articles which should
be obtained by correspondence with the authors of those arti-
cles. To resolve this problem, the researchers resorted to using
resources from various universities inside and outside the
country.
Inclusion criteria: All studies that had the full text availa-
ble, that used appropriate methods and data, and that calcu-
lated the impacts of dust storms on health (eg, odds ratio,
relative risk, rate ratio, regression coefficient, percentage change,
excess risk, etc. in health indicators following dust storms);
those in which dust storm was a major problem and those in
which health indicators were analyzed were included in this
study without restrictions on the publication date.
Exclusion criteria: Non-English articles, non-research let-
ters to editors, review studies, case reports, case series, special-
ized articles about microorganisms, animal experiments, in
vitro studies, and dust from volcanic or manmade sources like
stone mines or stone and cement factories were excluded.
Data collection process: The current study followed the
PRISMA guidelines (PRISMA Flow Diagram). EndNote
software was used to manage the retrieved articles. After all
articles were entered into the software, duplicates were identi-
fied and removed. Then, 2 researchers screened the remaining
articles separately based on the inclusion and exclusion criteria
by reading the titles, abstracts, and keywords. After removing
unrelated papers, the full text of the remaining articles were
Aghababaeian et al 3
found and attached, and the quality of each paper in a standard
format related to the type of study was assessed separately by
the 2 researchers using JBI’s critical appraisal tools. In cases of
disagreement between the researchers, the third researcher
helped to select the most relevant items.
Data extraction: The information required for this study
was extracted using a checklist previously reviewed and pre-
pared, which included all the characteristics of the selected
articles, including type of article, publication year, first author’s
name, location of study, study design/methodology, health
effects, PM fraction, and age/gender.
Risk of bias (quality) assessment: For quality assessment of
the included papers, the Critical Appraisal Skills Program
(CASP) checklist was used. The assessment was conducted by
3 independent reviewers. Discrepancies were resolved by 2
other reviewers.
Results
Search results
Out of a total of 35 712 articles searched, 140 articles met the
inclusion criteria (Figure 1). The majority of them were related
to ecological, case crossover, and prospective studies; other
studies included descriptive, retrospective, and Panel studies
and 1 research letter (Table 1).
The current results showed that most data analyses investi-
gated the effects of dust storms on health and used the general-
ized additive model (GAM) with nonlinear Poisson regression
method to analyze the data in ecological and case-crossover
studies.
Furthermore, most studies on the impact of dust storms on
health were performed within the last decade (Chart 1).
Most health and dust storm studies included in this study
were undertaken in Japan (n = 29; 20.71%), Taiwan (n = 25;
17.85%), Korea (n = 16; 11.42%), China (n = 10; 7.14%), Spain
(n = 9; 6.42%), and Iran (n = 8; 5.71%), respectively (Figure 2).
In this review, the following adverse health effects of dust
storms emerged as important:
Non-accidental death (mortality due to respiratory, car-
diovascular, or cerebrovascular disease);
Emergency medical dispatch, hospitalization or admis-
sion, and hospital visits due to respiratory or cardiovascu-
lar diseases;
Recordsidentified by searching thethrough databases
(n= PubMed=10603) + (n=Scopus=14314)
(n=WoS= 6460)+ (n=EMBASE= 4287)
Total= 35664
Screening
Included Eligibility noitacifitnedI
All= 35712
Numbers after duplicates were removed
(N= 28968)
Recordsscreened
(N= 353)
Excluded
(N= 120)
Non-English articles
Animal studies
Letterstoeditors
Laboratory studies
Simulation studies
commentaries
Full text study evaluatedfor eligibility
(N= 183) Study excluded
(N=43)
Studies included in synthesis
(N=140)
Records identified by other
sources
(n= 48)
Figure 1. PRISMA ow diagram.
4 Environmental Health Insights
Table 1. Published studies on adverse health effects of dust storms.
REFERENCE FIRST AUTHOR
AND YEAR
STUDY
LOCATION
POPULATION
(AGE, GENDER)
PM
FRACTION
STUDY DESIGN/
METHODOLOGY
HEALTH OUTCOMES RESULTS
All-cause mortality
Al et al.86 Al et al. (2018) Gaziantep/
Tur key Older than
16 ye ars PM10 Retrospective study/
GAM Mortality of
cardiovascular
diseases
Congestive cardiac failure Mortality, OR
0.95 (0.81–1.11)
Acute coronary syndrome mortality, OR
0.40 (0.31–0.50)
Al-Taiar and
Thalib43
Al-Taiar and
Thalib (2014) Kuwait All ages/all
gender PM10 Ecological time series,
GAM All-causes,
respiratory,
cardiovascular
Mortality
Respiratory mortality, RR 0.96 (0.88–1.04)
Cardiovascular mortality, RR 0.98
(0.96–1.012)
All-cause mortality, RR 0.99 (0.97–1.00)
Chan and
Ng38
Chan and Ng
(2 011) Taipei, Taiwan All ages/all
gender PM10 Case-crossover/
conditional logistic
regression models
Non-accidental,
respiratory,
cardiovascular,
deaths
Non-accidental deaths, OR 1.019
(1.003 –1.035)
Above 65 years old, OR 1.025 (1.006–
1.044)
Cardiovascular deaths, OR 1.045
(1.0011–1.081) Respiratory deaths, OR
0.988 (1.038–0.941)
Chen et al.39 Chen et al.
(2004) Taipei, Taiwan All ages/all
gender PM10 Case-crossover/tests
of student Daily mortality Respiratory disease, RR 7.66%
Total deaths, RR 4.92%
Circulatory diseases, RR 2.59%
Crooks et al.3Crooks et al.
(2016) National/United
States All ages/all
gender PM10 Case-crossover/
conditional logistic
regression models
Daily non-accidental
mortality Non-accidental mortality 7.4% (p = 0.011)
Lag2,3 6.7% (p = 0.018)
Lags0–5 2.7% (p = 0.023)
Díaz et al.68 Díaz et al.
(2017) Spain: 9 region All ages/all
gender PM10 Longitudinal
ecological time series/
GAM
Daily mortality Daily mortality values
South-west, 21.20 (20.81–21.59) p < 0.05
South-east, 20.16 (19.88–20.45) p < 0.05
Canary Islands, 17.93 (17.60–18.26)
p < 0.05
Diaz et al.69 Diaz et al.
(2012) Madrid (Spain) All ages/all
gender PM10 Case-crossover
design/Poisson
regression model
Case-specic
mortality Respiratory death, IR 3.34% (0.36, 6.41)
Circulatory causes, IR 4.19% (1.34, 7.13)
Hwang et al.70 Hwang et al.
(2004) Seoul, Korea All ages/ all
gender PM10 Ecological time series
/ GAM Daily non accidental
deaths Non accidental deaths, 1.7% (1.6 5.3)
Aged 65 years and older, 2.2% (3.5 8.3)
Cardiovascular and respiratory, 4.1% ( 3.8
12.6)
Jimenez
et al.71
Jimenez et al.
(2010) Madrid (Spain) Elderly PM10, PM2.5
or PM10–2 .5
Ecological time series/
Poisson regression
models
Mortality PM10
Total mortality, lag3 1.02 (1.01–1.04)
Circulatory, lag3 1.04 (1.01–1.06)
Respiratory, lag1 1.03 (1.00–1.06)
Johnston
et al.72
Johnston et al.
(2 011) Sydney,
Australia All ages/ all
gender PM10 Case crossover /
conditional logistic
regression model
Non-accidental
mortality Non-accidental mortality, lag3, OR 1.16
(1.03–1.3 0)
(Continued)
Aghababaeian et al 5
REFERENCE FIRST AUTHOR
AND YEAR
STUDY
LOCATION
POPULATION
(AGE, GENDER)
PM
FRACTION
STUDY DESIGN/
METHODOLOGY
HEALTH OUTCOMES RESULTS
Kashima
et al.73
Kashima et al.
(2016) South Korea and
Japan >65 years o ld/all
gender
PM10 Ecological time-series
analyses/specic
Poisson regression
models
Cause-specic
mortality All-cause mortality, lag0 RR 1.003 (1.001
1.005)
lag1, 1.001 (1.000 1.003)
Cerebrovascular disease, lag1 RR: 1.006
(1.000 1.011)
Kashima
et al.48
Kashima et al.
(2012) Western Japan Aged 65 or
above l SPM Ecological multi-city
time-series analysis/
Poisson regression
models
Daily all-cause or
cause-specic
mortality
Heart disease, 0.6 (0.1 1.1)
Ischemic heart disease, 0.8 (0.1 1.6)
Arrhythmia, 2.1 (0.3 3.9)
Pneumonia mortality, 0.5 (0.2 0.8)
Khaniabadi
et al.87
Khaniabadi
et al. (2017) Ilam (Iran) –PM10 Ecological time series/
air Q model Respiratory mortality Respiratory Mortality 7.3 (4.9 19.5)
Kim et al.74 Kim et al.
(2012) Seoul, Korea General
population/all
gender
–Ecological time-
series/Poisson
regression analyses
All-cause/
cardiovascular
mortality
The relative risk of total mortality for
general population and over 75 years old
increased on dusty days
Kwon et al.83 Kwon et al.
(2002) Seoul, Korea All ages/all
gender PM10 Ecological time series/
GLM with Poisson
regression
Non accidental
deaths All causes, RR 1.7% (1.6, 5.3)
Persons aged 65 years older, RR 2.2%
(3.5, 8.3)
Cardiovascular and respiratory death, RR
4.1% (3.8, 12.6)
Lee et al.55 Lee et al.
(2014) (Seoul, Korea;
Taipei, Taiwan,
Kitakyushu,
Japan)
All ages/all
gender PM10 Ecological time-series
using/GAM with
Quasi-Poisson
distribution
Mortality Seoul:
Under 65 years old (lag2: 4.44%, lag3: 5%,
and lag4: 4.39%)
Kitakyushu:
Respiratory mortality (lag2: 18.82%)
Total non-accidental mortality (lag0:
−2.77%, lag1: -3.24%)
Taipei:
Over 65 years old (lag0: −3.35%, lag1:
−3.29%)
Respiratory mortality (lag0: −10.62%, lag1:
−9.67%)
Lee et al.75 Lee et al.
(2013) Seven
metropolitan
cities of Korea
All ages/all
gender PM10 Ecological time-
series/GAM with
Quasi-Poisson
regressions
Mortality Lag0
Cardiovascular, 2.91% (0.13, 5.77)
Male: 2.74% (0.74, 4.77)
Lag2 <65 years, 2.52% (0.06, 5.04)
Male 2.4% (0.43, 4.4)
Lag3 <65 years, lag3 2.49% (0.07, 4.97)
Total non-accidental: 1.57% (0.11, 3.06)
Male: 2.24% (0.28, 4.0)
<65 years: 2.43% (0.01, 4.91)
lag5 cardiovascular: 3.7% (0.93, 6.54)
Lee et al.76 Lee et al.
(2007) Seoul, Korea All ages/all
gender PM10 Ecological time-
series, GAM Mortality Total death, IR 0.7 (0.2, 1.3)
(Continued)
Table 1. (Continued)
6 Environmental Health Insights
REFERENCE FIRST AUTHOR
AND YEAR
STUDY
LOCATION
POPULATION
(AGE, GENDER)
PM
FRACTION
STUDY DESIGN/
METHODOLOGY
HEALTH OUTCOMES RESULTS
Mallone
et al.84
Mallone et al.
(2 011) Rome, Italy ⩾35 years/all
gender
PM2.5,
PM2.5 –10, and
PM10
Case-crossover/
Poisson regression
model
Mortality PM2.5 –10 Cardiac mortality, lag 0–2, IR 9.73
(4.25–15.49)
Circulatory system, lag 0 –2, IR 7.93 (3.20
12.88)
PM10 Cardiac mortality, lag 0–2, IR 9.55
(3.81–15.61%)
Perez et al.46 Perez et al.
(2008) Barcelona
(Spain) All ages/all
gender PM2.5 and
PM10-2. 5
Case crossover/linear
regression Daily Mortality PM10-2 .5
Daily mortality, Lag1, OR 1.084 (1.015,
1.15 8)
Perez et al.85 Perez et al.
(2012) Barcelona
(Spain) All ages/all
gender PM1, PM2.5
and PM10
Case–crossover/
conditional logistic
regression
Cause-specic
mortality PM10 -2.5 OR
Cardiovascular mortality, (lag1) 1.085 (1.01
1.15) p < 0.05
Respiratory mortality, (lag 2) 1.109 (0.978,
1.257) p < 0.1
PM2. 5-1 OR
Cardiovascular mortality, (lag1) 1.074
(0.998, 1.156) p < 0.1
Renzi et al.77 Renzi et al.
(2018) Sicily, Italy All ages/all
gender PM10 Ecological time-
series/Poisson
conditional regression
model
Mortality Non-accidental mortality, (lag0–5) IR 3.8%
(3.2, 4.4)
Cardiovascular, IR 4.5% (3.8, 5.3)
Respiratory IR 6.3% (5.4, 7.2)
Pirsaheb
et al.50
Pirsaheb et al.
(2016) Kermanshah,
Iran All ages/all
gender PM10 Descriptive studies/
spearman test Death from
cardiovascular and
respiratory disease
Increased dust concentrations increase
the risk of cardiovascular mortality
Schwartz
et al.88
Schwartz et al.
(1999) Six United
States. cities All ages/all
gender PM10 Ecological/GAM with
Poisson regression Mortality Mortality, RR 0.99 (0.81–1.22)
Sajani et al.53 Sajani et al.
(2 011) Emilia-Romagna
(Italy) All ages/all
gender PM10 Case crossover/
conditional logistic
regression
Mortality Respiratory mortality, OR 22.0 (4.0–43.1)
Natural, OR 1.04 (0.99–1.09)
Cardiovascular mortality, OR 1.04
(0 .96 –1.12)
Stafoggia
et al.78
Stafoggia et al.
(2016) Southern
European
cities-Spain,
France, Italy,
Greece
All ages/all
gender PM10 Case-crossover/
Poisson regression
models
Mortality Natural mortality lag0–1, IR 0.65% (0.24–
1.06)
Shahsavani
et al.79
Shahsavani
et al. (2019) Tehran and
Ahvaz, IRAN All ages/all
gender PM10 Case crossover/
conditional Poisson
regression models
Mortality Daily mortality 3.28 (2.42–4.15)
Tobias et al.80 Tobias et al.
(2 011) Madrid (Spain) All ages/all
gender PM2.5 and
PM10–2.5 Case-crossover/
conditional logistic
regression models
Mortality PM10 –2. 5
Each increase of 10 g/m3 of PM10–2 .5
increased
Total mortality, 2.8% (P = 0.01)
(Continued)
Table 1. (Continued)
Aghababaeian et al 7
REFERENCE FIRST AUTHOR
AND YEAR
STUDY
LOCATION
POPULATION
(AGE, GENDER)
PM
FRACTION
STUDY DESIGN/
METHODOLOGY
HEALTH OUTCOMES RESULTS
Wang and
Lin81
Wang and Lin
(2015) Metropolitan
Taipei All ages/all
gender PM10 Ecological time series/
distributed lag
non-linear model
Mortality All-cause mortality lag0-5, RR 1.10
(1.04–1.17)
Elders 1.10 (1.02–1.18)
Elderly circulatory Mortality lag0 -5, RR 1.21
(1.02–1.44)
Samoli et al.54 Samoli et al.
(2 011) Athens, Greece All ages/all
gender PM10 Ecological time series/
Poisson regression
models
Mortality Mortality 0.71% (0.40 0.99)
Neophytou
et al.82
Neophytou
et al. (2013) Nicosia, Cyprus All ages/all
gender PM10 Ecological time-
series/GAM Mortality Total nom accidental, IR 0.13% (1.03, 1.30)
Cardiovascular mortality, IR 2.43 (0.53–
4.37)
Respiratory mortality, IR 0.79 (4.69, 3.28)
Goto et al.60 Goto et al.
(2010) Western Japan All ages/all
gender –Ecological time-
series/Spearman’s
rank correlation
Bronchial asthma
mortality Asthma mortality (r = 0.268, n = 8, P > 0.05)
Achilleos
et al.41
Achilleos et al.
(2019) Kuwait All ages/all
gender Poor
visibility
(AOD >0.4)
Ecological time-
series/generalized
additive model (GAM)/
Poisson regression
models
Mortality Rate ratio: 1.02, (1.00–1.04)
Emergency dispatch or air medical retrieval service
Holyoak
et al.90
Holyoak et al.
(2 011) Queensland,
Australia – – Ecological
retrospective review/
simple t-test
Air medical retrieval
service for
respiratory and injury
cases
Respiratory cases 62.5% increased
Injury cases 13.3% increased
Aghababaeian
et al.42
Aghababaeian
et al. (2019) Iran/dezful All ages/all
gender PM10 Ecological time-series
/GAM Emergency dispatch
of cardiovascular,
respiratory and trafc
accident missions
RR of Emergency dispatch
Lag2 1.008 (1.001–1.016)/
female/18–60 years/>60 years
Lag3 1.008 (1.00 1.01)
Lag4 1.008 (1.00–1.01)
Lag5 1.008 (1.00–1.01)
Lag6 1.007 (1.00–1.01)
Lag7 1.006 (1.000–1.01)
Lag0 -7 1.06 (1.01–1.12)
Lag0 -14 1.0 9 (1.01–1.17)
>60 years 1.28 (1.08–1.52)
Cardiovascular Problems Lag0-14 1.3 3
(1.17–1.50)
Respiratory problems Lag0-14 1.13
(0.93–1.38)
Trafc Accident Trauma Lag0-14 1.03
(0 .94–1.13)
(Continued)
Table 1. (Continued)
8 Environmental Health Insights
REFERENCE FIRST AUTHOR
AND YEAR
STUDY
LOCATION
POPULATION
(AGE, GENDER)
PM
FRACTION
STUDY DESIGN/
METHODOLOGY
HEALTH OUTCOMES RESULTS
Kashima
et al.89
Kashima et al.
(2014) Okayama,
Japan Elderly people SPM Ecological time-
series/Poisson
regression with GAM
Emergency
ambulance calls All causes, Lag 0 1.009 (1.002–1.017)
Cardiovascular, lag0-3 1.02 (1.00–1.03)
Cardiovascular, Lag0 1.016 (1.001–1.032)
Cerebrovascular, Lag0 1.028 (1.007–1.049)
Pulmonary, Lag0 1.005 (0.986–1.025)
Ueda et al.61 Ueda et al.
(2012) Nagasaki, Japan All ages/all
gender SPM Case-crossover/
conditional logistic
regression
Emergency
ambulance
dispatches
All causes lag0–3 12.1% (2.3–22.9)
Cardiovascular diseases 20.8% (3.5–40.9)
Visits
Akpinar-Elci
et al.137
Akpinar-Elci
et al. (2015) Grenada,
Caribbean All ages/all
gender –Ecological/regression
analysis Asthma visits Asthma (R 2 = 0.036, p < 0. 0 01)
Cadelis
et al.138
Cadelis et al.
(2014) Guadeloupe
(Caribbean) Children/all
gender PM10,
PM2. 5-10
Case-crossover/t-test
and Mann-Whitney Visits of children due
to asthmatic
conditions
PM10
Lag0 IR 9.1% (7.1–11.1)
Lag0 –1 IR 5.1% (1.8–7.7)
PM2.5 –10
Lag0 IR 4.5% (3.3–5)
Lag0 –1 IR: 4.7% (2.5–6.5)
Carlsen
et al.142
Carlsen et al.
(2015) Reykjavík,
Iceland All ages/all
gender PM10 Ecological time-series
study/generalized
additive regression
model
Emergency hospital
visits Emergency hospital visits 5.8% (p = 0.02)
Chan et al.143 Chan et al.
(2008) Taipei, Taiwan All ages/all
gender PM10 Ecological time-
series/Poisson
regression model and
paired t-test
Emergency visits Cardiovascular visits 1.5 (0.3–2.6)
Ischemic heart diseases visits 0.7 (0.1–1.4)
Cerebrovascular visits 0.7 (0.1–1.3)
Chronic obstructive pulmonary disease
(COPD) visits 0.9 (0.1–1.7)
Chien et al.144 Chien et al.
(2014) Taipei, Taiwan Children PM10 Ecological studies/
structural additive
regression modeling
Conjunctivitis clinic
visits Conjunctivitis visits
Preschool children 1.48% (0.79, 2.17)
Schoolchildren. 9.48% (9.03, 9.93)
Chien et al.146 Chien et al.
(2012) Taipei, Taiwan Children PM10 Ecological/STAR
model and
autoregressive
correlation
Respiratory diseases
visits Respiratory visits
Preschool children 2.54% (2.43, 2.66)
Schoolchildren 5.03% (4.87, 5.20)
Hefin et al.147 Hefin et al.
(1994) Washington,
United States All ages/all
gender PM10 Ecological/
multivariable analysis
using generalized
estimating equations
Emergency room
visits for respiratory
disorders
Daily number of emergency visits for
bronchitis, IR 3.5%
Daily Number of emergency room visits, IR
4.5%
Lin et al.148 Lin et al. (2016) Taipei, Taiwan All ages/all
gender PM10 Ecological time series/
DLNM Emergency room
visits All causes visits, RR 1.10 (1.07, 1.13)
Respiratory visits, RR 1.14 (1.08, 1.21)
(Continued)
Table 1. (Continued)
Aghababaeian et al 9
REFERENCE FIRST AUTHOR
AND YEAR
STUDY
LOCATION
POPULATION
(AGE, GENDER)
PM
FRACTION
STUDY DESIGN/
METHODOLOGY
HEALTH OUTCOMES RESULTS
Liu and Liao149 Liu and Liao
(2017) Taiwan All ages/all
gender PM2.5 Case-crossover/
conditional logistic
regression
Emergency visits Cardiovascular, OR 2.92 (1.22–5.08)
Respiratory, OR 1.86 (1.30–2.91)
Merrield
et al.141
Merrield et al.
(2013) Sydney,
Australia All ages/all
gender PM10 Ecological time-
series/distributed-lag
Poisson generalized
models
Emergency visits Asthma visits, RR 1.23, (p < 0.01)
All visits, R 1.04, (p < 0.01)
Respiratory visits, RR 1.20, (p < 0.01)
Cardiovascular visits, RR 0.91, (p = 0.09)
Nakamura
et al.139
Nakamura
et al. (2016) Nagasaki, Japan children aged
0–15 years/all
gender
SPM Case-crossover/
conditional logistic
models
Pediatric emergency
visits for respiratory
diseases
School children
Bronchial asthma visits, lag3 OR 1.83
(1.212– 2.786)
Lag4 1.8 29 (CI, 1.179–2 .806)
Preschool children
Respiratory visit, lag0, OR 1.244 (1.128–
1.373)
Lag day 1, OR 1.314 (1.189–1.452)
Lag day 2, OR 1.273 (1.152–1.408)
Park et al.62 Park et al.
(2015) Chuncheon,
Gangwon-do,
Korea
All ages/all
gender PM10 Ecological
retrospective study/
Poisson regression
model
Hospital visits for
airway diseases Asthma visits, RR 1.10 (P < 0.05)
COPD visits, RR 1.29 (P < 0. 05)
Wang et al.63 Wang et al.
(2016) Minqin, China All ages/all
gender –Ecological time series/
generated regression
model
Pulmonary
tuberculosis (PTB)
visits
PTB visits, R2 = 0.685
Park et al.140 Park et al.
(2016) Seoul and
Incheon, Korea 11–20, 51–70
and 49 0 years/all
gender
PM10 Case-crossover/T-
tests and Poisson
regression model
Asthma exacerbation Asthma related visits
Lag0, RR 0.96 (0.95–0.98)
Lag1, RR 1.27 (1. 25–1.29)
Lag2, RR 1.12 (1.10 –1.14)
Lag3, RR 1. 25 (1.23 –1. 26)
Lag4, RR 1.13(1.12–1.15)
Lag5, RR 1.06 (1.04–1.07)
Lag6, RR 0.82 (0.81–0.81)
Yu et al.12 Yu et al. (2012) Taipei (Taiwan) Children PM10 Ecological studies/
STAR model/
generalized additive
mode
Children’s respiratory
health risks All children
Lag0 −3.66
Lag1 −2.05
Lag2 1.78
Lag3 2.40
Lag4 0.66
Lag5 1.74
Lag6 −1.01
Lag7 2.26
(Continued)
Table 1. (Continued)
10 Environmental Health Insights
REFERENCE FIRST AUTHOR
AND YEAR
STUDY
LOCATION
POPULATION
(AGE, GENDER)
PM
FRACTION
STUDY DESIGN/
METHODOLOGY
HEALTH OUTCOMES RESULTS
Yan g145 Yang (2006) Taipe, Taiwan All ages/all
gender PM10 Case-crossover/
Poisson regression
model
Conjunctivitis visit Lag0 RR 1.02 (0.88–7.99)
Lag1 RR 0.99 (0.86–7.46)
Lag2 RR 0.95 (0.83–6.93)
Lag3 RR 0.97 (0.85–7.11)
Lag4 RR 1.11 (0.97–9.41)
Lag5 RR 0.95 (0.84–6.86)
Lorentzou
et al.122
Lorentzou et al.
(2019) Heraklion in
Crete Island,
Greece
All ages/all
gender PM10 Ecological
retrospective analysis/
one-way ANOVA and
Pearson Correlation
Emergency
department visits Correlation
All cases 0.313 p = 0.1 28
Allergy cases 0.929 p = 0.000
Dyspnea cases 0.464 p = 0.041
Trianti et al.52 Trianti et al.
(2017) Athens, Greece Age d 18 years
and
Upper/all gender
PM10 Ecological study/
mixed Poisson model Respiratory
morbidity/emergency
room visits
Respiratory visits, IR 1.95% (0.02, 3.91)
Asthma visits, IR 38% (p < 0 .0 01)
COPD visits, IR 57% (p < 0. 00 1)
Respiratory infections visits, IR 60%
(p < 0.0 01)
Yang et al.150 Yang et al.
(2015) Wuwei, China All ages/ all
gender PM2.5 Ecological time-
series/GAM Respiratory and
cardiovascular
outpatient visits
Respiratory outpatient
Male, RR 1.217 (1.08, 1.606)
Female, RR 1.175 (1.025, 1.347)
Cardiovascular outpatient
Male, RR 1.146 (1.056, 1.243)
Female, RR 1.105 (1.017, 1.201)
Long-term health effects
Altindag
et al.32
Altindag et al.
(2017) Korea Infant PM10 Cohort/linear
regression models Birth weight, a binary
indicator of low
birthweight,
gestation, premature
birth, and fetal
growth
Birth Weight, _0.232 (P = 0 .10 )
Low birth weight, 0.0001 (P = 0.000)
Gestation −0.001 (P = 0.001)
Prematurity, 0.0001 (P = 0.000)
Growth, −0.005 (P = 0.003)
Dadvand
et al.169
Dadvand et al.
(2 011) Barcelona/Spain Pregnant woman PM10 Cohort/linear
regression models-
logistic regression
model
Pregnancy
complications Birth weight −2.1 (−5.8, 1.7)
Gestation 0.5 (0.4, 0.6)
Preeclampsia 0.98 (0.91, 1.07)
Li et al.33 Li et al. (2018) Between
northern and
southern China.
Aged 10 –
15 years, all
gender
–Cohort/xed-effect
model Children’s cognitive
function Reduction in word scores, 0.20 (0.06, 0.35)
Reduction in mathematics scores 0.18
(0.10, 0.25)
Viel et al.34 Viel et al.
(2019) Guadeloupe
(French West
Indies)
909 pregnant
women PM10 Cohort/multivariate
logistic regression
models
Preterm births OR 1.40, (1.08–1.81)
(Continued)
Table 1. (Continued)
Aghababaeian et al 11
REFERENCE FIRST AUTHOR
AND YEAR
STUDY
LOCATION
POPULATION
(AGE, GENDER)
PM
FRACTION
STUDY DESIGN/
METHODOLOGY
HEALTH OUTCOMES RESULTS
Tong et al.36 Tong et al.
(2017) Southwestern
United States All ages/all
gender PM10 Research letter/
correlation coefcient Valley fever Correlation coefcient
Maricopa, 0.51
Pima, 0.36–0.41
Ma et al.44 Ma et al. (2017) Western China All ages/ all
gender TSP, PM10 Ecological time series/
Pearson correlation
coefcient
Measles incidence The correlation coefcient for TSP
Entire Lanzhou city, 0.291
Downtown Lanzhou, 0.346
The correlation coefcient for PM10
Entire Lanzhou city, 0.260
Downtown Lanzhou, 0.342
Dust events, Excess measles
Zhangye, 39.1 (17.3–87.6)
Lanzhou, 149.9 (7.1–413.4)
Jiuquan, 31.3 (20.6–63.5)
Hospitalization or admission
Aili and
Oanh91
Aili and Oanh
(2015) China/
Taklimakan
Desert
All ages/all
gender TSP Ecological time series/
GAM Daily number of
outpatients
Daily number of
inpatients
Respiratory outpatients, RR 1.01 (1.00–1.02)
Respiratory inpatients, RR 0.99 (0.99 –1.00)
Digestion outpatients, RR 1.005 (0.99–1.01)
Digestion inpatients, RR 1.001 (0.999–1.002)
Circulatory outpatients, RR 1.010 (1.003–
1.016)
Circulatory inpatients, RR 1.001 (0.999–
1.002)
Gynecology outpatients, RR 1.008
(1.002 –1.014)
Gynecology inpatients, RR 0.999 (0.997–
1.0 01)
Pediatrics outpatients, RR 1.010 (1.002–
1.018)
Pediatrics Inpatients, RR 1.001 (0.999–
1.002)
ENT outpatients, RR, 1.007 (1.002–1.012)
ENT inpatients, RR, 1.002 (0.998–1.004)
Al et al.86 Al et al. (2018) Gaziantep/
Tur key Older than
16 ye ars PM10 Retrospective study/
GAM Morbidity of
cardiovascular
diseases admitted to
emergency
department
Congestive cardiac failure admission, OR
1.003 (0.972–1.036)
Hospitalization, OR 2.209 (2.069–2.359)
Acute coronary syndrome admission, OR
1.15 0 (1.135 –1.16 6)
Hosp italization, O R 1. 304 (1.273–1.336)
Alangari
et al.126
Alangari et al.
(2015) Riyadh, Saudi
Arabia Children
2–12 yea rs PM10 Ecological/correlation
coefcient Patient presented to
the emergency
department (ED) with
acute asthma
Acute asthma, r = –0.14, (P = 0.45)
Admission rate, r = −0.08, (P = 0.65)
(Continued)
Table 1. (Continued)
12 Environmental Health Insights
REFERENCE FIRST AUTHOR
AND YEAR
STUDY
LOCATION
POPULATION
(AGE, GENDER)
PM
FRACTION
STUDY DESIGN/
METHODOLOGY
HEALTH OUTCOMES RESULTS
Alessandrini
et al.92
Alessandrini
et al. (2013) Rome, Italy Less than
14 years or
35 years or more
PM2.5,
PM2. 5-10
and
PM10
Ecological time-
series/GAM Respiratory, cardiac
and cerebrovascular
hospitalizations
PM2.5
Cardiac diseases, lag0–1 2.41 (−0.21, 5.09)
Cerebrovascular diseases, lag0 −2.14
(−4.73, 0.53)
Respiratory diseases, lag0–5 −0.52 (−5.33,
4.53)
Respiratory diseases0–14 −2.14 (−9.09, 5.35)
PM2.5 –10 (IR)
Cardiac diseases, lag0–1 3.93 (1.58, 6.34)
Cerebrovascular diseases, lag0 1.68
(− 0.70, 4.11)
Respiratory Diseases, lag0–5 4.77 (−0.57,
10.40)
Respiratory diseases lag0–14 −1.20 (−8.52,
6.71)
PM10
Cardiac diseases, lag0–1 3.37 (1.11, 5.68)
Cerebrovascular diseases, lag0 2.64 (0.06,
5.29)
Respiratory Diseases, lag0–5 3. 59 (0 .18 , 7.12)
Respiratory diseases, lag0–14 −0.04 (−4.64,
4.78)
Al-Hemoud
et al.93
Al-Hemoud
et al. (2018) Kuwait All ages/all
gender PM10 Ecological time series/
GAM Daily morbidity Bronchial asthma, r = 0.292
Respiratory infection
Lower, r = 0.737
upper, r = 0.8 39
Al -Ta iar51 A l -Taiar (2012) Kuwait All ages/all
gender PM10 Ecological time series
generalized/GAM Daily emergency
admissions due to
asthma and
respiratory causes
Asthma admission, RR 1.07 (1.02–1.12)
Respiratory admission, RR 1.06 (1.04–
1.08)
Barnett127 Barnett (2012) Brisbane,
Australia All ages/all
gender PM10 Ecological time series/
Poisson regression
model
Emergency
admissions to
hospital
Emergency admissions 39% (5, 81%)
Bell et al.56 Bell et al.
(2008) Taipei, Taiwan All ages/all
gender PM10 Ecological time-
series/Poisson
time-series model
Cause-specic
hospital admissions Ischemic heart disease, Lag2 16.17 (1.17,
33.39)
Chan et al.135 Chan et al.
(2018) Nationwide/
Taiwan All ages/all
gender Tot al
atmospheric
PM
Ecological time-
series/autoregressive
model-ARMAX
regression
Diabetes
hospitalization Diabetes lag1 27.41 (p = 0.04)
Chen and
Yan g 94
Chen and Yang
(2005) Taipei, Taiwan All ages/all
gender PM10 Case-crossover/tests
of student Daily hospital
admissions for
cardiovascular
disease (CVD)
CVD, lag1 RR (3.65%) P > 0.05
(Continued)
Table 1. (Continued)
Aghababaeian et al 13
REFERENCE FIRST AUTHOR
AND YEAR
STUDY
LOCATION
POPULATION
(AGE, GENDER)
PM
FRACTION
STUDY DESIGN/
METHODOLOGY
HEALTH OUTCOMES RESULTS
Cheng et al.119 Cheng et al.
(2008) Taipei, Taiwan All ages/all
gender PM10 Case-crossover/
Poisson regression
models
Daily pneumonia
hospital admissions Pneumonia admissions
lag0 RR 1.03 (0.98–1.08)
lag1 RR 1.04 (1.00–1.09)
lag2 RR 1.04 (0.99–1.09)
lag3 RR 1.03 (0.99–1.08)
Chiu et al.121 Chiu et al.
(2008) Taipei, Taiwan All ages/all
gender PM10 Case-crossover/
Poisson regression
models
COPD admissions COPD, Lag3, RR 1.057; (0.982–1.138)
Dong et al.59 Dong et al.
(2007) large cities of
Korea All ages/all
gender PM10 Ecological/correlation
coefcients Hospitalization Seoul 0.652
Busan 0.377
Daegu 0.681
Incheon 0.736
Kwangju 0.481
Daejeon 0.652
Uisan 0.702
Jeju-do 0.129
Ebenstein
et al.107
Ebenstein et al.
(2015) Israel,
Jerusalem and
Tel Aviv
All ages/all
gender PM10 Ecological/IV
methodology/Poisson
regression approach
Respiratory hospital
admissions Respiratory admissions IR 0.8%
COPD 0.01 (0.003)
Asthma 0.008 (0.003)
Respiratory abnormalities 0.006 (0.002)
Ebrahimi
et al.92
Ebrahimi et al.
(2014) Sanandaj, Iran All ages/ all
gender PM10 Ecological/Pearson’s
correlation coefcient,
linear regression
model
Emergency
admissions for
cardiovascular and
respiratory diseases
Cardiovascular 0.48 (P < 0.05)
Respiratory patients 0.19 (P > 0.0 5)
Ebrahimi
et al.64
Geravandi
et al. (2017) Ahvaz/Iran All ages/all
gender PM10 Ecological/non-
parametric Mann-
Whitney U test/
correlation coefcients
Hospital admissions
for Respiratory
diseases
Respiratory diseases (r = 0.53)
Grineski
et al.11
Grineski et al.
(2 011) El Paso, Texas,
United Stats All ages/all
gender PM2.5 Case-crossover/-
conditional logistic
regression
Hospital admissions
for Asthma and Acute
bronchitis
Asthma 1.11 (0. 96–1.28)
All ages 1.23 (0.99–1.55)
Kamouchi
et al.131
Kamouchi et al.
(2012) Fukuoka, Japan 20 years and
older/all gender –Case-crossover/
conditional logistic
regression
Ischemic stroke Overall///Atherothrombotic
Z D7
lag0 –1, OR 1.07 0.93–1.23///1.44 1.08–1.91
lag0–2, O R 1.04 0.97–1.18///1.4 8 1.14–1.93
lag0–3, OR 1.02 0.90–1.15///1.37 1.06–1.76
lag0–4, OR 1.02 0.90–1.14///1.35 1.06–1.73
lag0–5, OR 1.02 0.91–1.15///1.35 1.06–1.72
Kanatani
et al.115
Kanatani et al.
(2010) Toyama, Japan Children –Case-crossover/
generalized estimating
equations logistic and
Conditional logistic
regression
Asthma
hospitalization OR 1.88 (1.04–3.41; P 5 0.037)
(Continued)
Table 1. (Continued)
14 Environmental Health Insights
REFERENCE FIRST AUTHOR
AND YEAR
STUDY
LOCATION
POPULATION
(AGE, GENDER)
PM
FRACTION
STUDY DESIGN/
METHODOLOGY
HEALTH OUTCOMES RESULTS
Kang et al.120 Kang et al.
(2012) Taipei, Taiwan All ages/all
gender PM10 Ecological time series/
Kruskal–Wallis test/
auto-regressive
integrated moving
average (ARIMA)
method
Pneumonia
hospitalization Pneumonia admissions (P = 0.001)
Kang et al.132 Kang et al.
(2013) Taiwan All ages/all
gender PM Ecological time series/
ARIMA method
(auto-regressive
integrated moving
average)
Stroke hospitalization Stroke admissions (239.6), post-DS days
(249.2) (p < 0.0 01)
Kashima
et al.40
Kashima et al.
(2017) Okayama,
Japan Elderly SPM Case-crossover/
conditional logistic
regression analyses
Susceptibility of the
elderly to disease Respiratory OR: 1.09 (1.00, 1.19)
Cardiovascular OR: 0.99 (0.97, 1.01)
Cerebrovascular OR: 1.15 (1.01, 1.31)
Khaniabadi
et al.87
Khaniabadi
et al. (2017) Khorramabad
(Iran) All ages/all
gender PM10 Ecological time series/
AirQ model Hospitalizations for
chronic obstructive
pulmonary disease
(COPD)
COPD, ER, 7.3% (4.9, 19.5)
Khaniabadi
et al.95
Khaniabadi
et al. (2017) Ilam, Iran All ages/all
gender PM10 Ecological time series/
AirQ model Cardiovascular and
respiratory
admissions
Respiratory diseases 4.7% (3.2–6.7%)
Cardiovascular diseases, 4.2% (2.6–5.8%)
Ko et al.136 Ko et al. (2016) Fukuok- western
Japan Men, Women
ratio 30,15 Age,
49.6 ± 22.7
–Cohort design/t-test Acute conjunctivitis Conjunctivitis scores P < 0.05
Kojima et al.98 Kojima et al.
(2017) Kumamoto,
Japan 20 years of age
or older/all
gender
PM2.5 Case-crossover/
conditional logistic
regression model
Acute myocardial
infarction (AMI) AMI OR, 1.46 (1.09–1.95)
Non ST-segment OR 2.03 (1.30–3.15)
Lai and
Cheng109
Lai and Cheng
(2008) Taipei, Taiwan All ages/all
gender PM10 Case-control/Z test Respiratory
admissions Elderly RR 3.44; (0.03–380.1)
All age RR 1.04; (0.30–3.16)
Pre-school RR, 1.01 (0.26–3.89)
Lee and Lee117 Lee and Lee
(2014) Seoul, Korea All ages/all
gender PM10 Ecological time series
patterns/paired t-test Daily asthma patients Lag0, 3.79 p = 0.4
Lag1, 4.85 p = 0.3
Lag2, 11.02 p = 0 .1
Lag3, 15.46 p = 0.06
Lag4, 18.05 p = 0.03
Lag5, 17.76 p = 0.02
Lag6, 18.18 p = 0.01
Lorentzou
et al.122
Lorentzou et al.
(2019) Heraklion in
Crete Island,
Greece
All ages/all
gender PM10 Ecological/one-way
ANOVA and Pearson
correlation
COPD morbidity COPD exacerbations, 3.0 (0.8–5.2)
Dyspnea admissions, 0.71 (p = 0.001)
COPD admissions, 0.813 p = 0.000
(Continued)
Table 1. (Continued)
Aghababaeian et al 15
REFERENCE FIRST AUTHOR
AND YEAR
STUDY
LOCATION
POPULATION
(AGE, GENDER)
PM
FRACTION
STUDY DESIGN/
METHODOLOGY
HEALTH OUTCOMES RESULTS
Matsukawa
et al.99
Matsukawa
et al. (2014) Fukuoka, Japan Patients aged
⩾20 years/all
gender
SPM Case-crossover/
conditional logistic
regression model
Incidence of acute
myocardial infarction AMI
Lag4 OR 1.33 (1.05–1.69)
Lag0- 4 OR 1.20 (1.02 –1.40)
Menendez
et al.128
Menendez
et al. (2017) Gran Canaria,
Spain Adults (age
14–80 year s) and
>80/all gender
PM10 Epidemiological
survey/(ANOVA) and
Spearman correlation
coefcients ()
Health condition of
the allergic
population
ρ (p-values)
Pneumony 0.2 (0.5)
Asthma 0.8 (0.0)
COPD 0.0 (1.0)
Meng and
Lu96
Meng and Lu
(2007) Minqin, China All ages/all
gender –Ecological time-
series/GAM Daily hospitalization
for respiratory and
cardiovascular
diseases
Respiratory hospitalization, lag3 RR
Male 1.14 (1.01–1.29)
Female 1.18 (1.0 0 –1.41)
Respiratory infection, Male, RR 1.28
(1.04–1.59
Pneumonia, Lag6 Males, RR 1.17 (1.00–
1.38)
Hypertension, Lag3 Males, RR 1.30 (1.03,
1.64)
Middleton
et al.97
Middleton et al.
(2008) Nicosia, Cyprus All ages/all
gender PM10 Ecological time-
series/GAM Respiratory and
cardiovascular
morbidity
All-cause 4.8% (0.7, 9.0)
Cardiovascular 10.4% (−4.7, 27.9)
Nakamura
et al.103
Nakamura
et al. (2015) All-Japan All ages/all
gender SPM Case-crossover/
conditional logistic
models
Out-of-hospital
cardiac arrests Cardiac arrests, lag1 OR
Model 1 1.00 (0.97–1.19)
Model 2 1.08 (0.97–1.20)
Nastos,
et al.104
Nastos, et al.
(2 011) Crete Island,
Greece All ages/all
gender –Ecological time
series-HYSPLIT 4
model of air resources
laboratory of NOAA
Cardiovascular and
respiratory
syndromes
Respiratory ve-fold increased
Cardiovascular didn’t increased signicant
Pirsaheb
et al.50
Pirsaheb et al.
(2016) Kermanshah,
Iran All ages/all
gender PM10 Ecological/regression Respiratory disease Respiratory infection P ⩽ 0.05
Chronic pulmonary disease P ⩽ 0.05
COPD P > 0.0 5
Angina P > 0.05
Asthma P > 0.05
Prospero
et al.129
Prospero et al.
(2008) Caribbean Aged 18 years
and
under/all gender
–Ecological time series/
Mann–Whitney
rank-sum test,
two-tailed
Pediatric asthma Pediatric asthma, P > 0.05
Radmanesh
et al.133
Radmanesh
et al. (2019) Abadan, Iran All ages/all
gender PM10 Ecological studies/
Pearson coefcient Hospital admission
for cerebral ischemic
attack, epilepsy and
headaches
Cerebral ischemic attack, r: 0.113 p = 0.3
Epilep sy, r: 0.492 p = 0.03
Headaches, r: 0.009 p = 0.9
Reyes et al.110 Reyes et al.
(2014) Madrid (Spain) All ages/all
gender PM10-2.5 Ecological time series/
conditional logistic
regression model
Hospital admissions Respiratory admissions, Lag7 RR 1.031
(1.002 1.060)
(Continued)
Table 1. (Continued)
16 Environmental Health Insights
REFERENCE FIRST AUTHOR
AND YEAR
STUDY
LOCATION
POPULATION
(AGE, GENDER)
PM
FRACTION
STUDY DESIGN/
METHODOLOGY
HEALTH OUTCOMES RESULTS
Rutherford,
et al.118
Rutherford,
et al. (1999) Brisbane,
Australia All ages/all
gender TSP Cross sectional/paired
two-tailed t-tests Impact on asthma
severity Asthma severity, P ⩽ 0.0 5
In General P > 0.05
Stafoggia
et al.78
Stafoggia et al.
(2016) Southern
European
cities-Spain,
France, Italy,
Greece
All ages/all
gender PM10 Case-
crossover/”Poisson
regression models
Hospital admissions Admissions, IR
Cardiovascular, age ⩾15 0.32 (–0.24, 0.89)
Respiratory, age ⩾15 0.70 (–0.45, 1.87)
Respiratory, age 0–14 2.47 (0.22, 4.77)
Tam et al.101 Tam et al.
(2012) Hong Kong All ages/all
gender PM10-2.5 Case-crossover/t-test/
Poisson regression
model
Daily emergency
admissions for
cardiovascular
diseases
PM10–2.5
Ischemic heart disease, RR 1.04 (1.00,
1.08)
Tao et al.111 Tao et al.
(2012) Lanzhou, China All ages/all
gender PM10 Ecological/Poisson
regression model into
GAM model
Respiratory diseases
admissions Respiratory hospitalizations, RR
Male, 1.148 P > 0.0 5
Female 1.144 P > 0.05
Teng et al.100 Teng et al.
(2016) Taipei, Taiwan All ages/all
gender PM10 Ecological time series/
autoregressive with
exogenous variables
model
Daily acute
myocardial infarction
hospital admissions
AMI hospitalizations, 3.2 more
Thalib and
Al -Ta iar51
Thalib and
Al -Ta iar (2012) Kuwait All ages/all
gender PM10 Ecological time series
study/GAM Asthma admissions Asthma, RR 1.07 (1.02–1.12)
Respiratory admission, RR 1.06 (1.04–
1.08)
Ueda et al.57 Ueda et al.
(2010) Fukuoka, Japan children under
12 years of age/
all gender
SPM Case-crossover/
conditional logistic
regression
Hospitalization for
asthma Asthma hospitalization, lag2,3 OR 1.041
(1.013 –1.070)
Vodonos
et al.123
Vodonos et al.
(2014) Be’er Sheva,
Israel All ages/all
gender PM10 Ecological time series/
GAM Hospitalizations due
to exacerbation of
COPD
COPD exacerbation: IR 1.16 (p < 0.0 01)
Vodonos
et al.2
Vodonos et al.
(2015) Be’er Sheva,
Israel Above 18 years
old/all gender PM10 Case crossover/GAM Cardiovascular
Morbidity Acute coronary syndrome (lag1);
OR = 1.007 (1.002–1.012).
Wang et al.113 Wang et al.
(2014) Taiwan, All ages/all
gender PM10 Ecological time series/
ARIMAX regression
model
Asthma admissions Asthma, Lag1-3 average of 17–20 (p < 0.05)
more hospitalized
Wang et al.125 Wang et al.
(2015) Minqin County,
China Above 40 year s
old/all gender –Case-control/
comparison/Student’s
t test
Human respiratory
system Chronic rhinitis, OR 3.14 (1.77–5.55)
Chronic bronchitis, OR 2.46 (1.42–4.28)
Chronic cough, OR 1.78 (1.24–2.56)
Watanabe
et al.114
Watanabe
et al. (2014) Western Japan Aged À18 years
old/all gender SPM Descriptive/telephone
survey/t-test. Multiple
regression analysis
Worsening asthma Worsening asthma 11–22%
Pulmonary function of asthma patients
−0.367 p = 0.0 03
(Continued)
Table 1. (Continued)
Aghababaeian et al 17
REFERENCE FIRST AUTHOR
AND YEAR
STUDY
LOCATION
POPULATION
(AGE, GENDER)
PM
FRACTION
STUDY DESIGN/
METHODOLOGY
HEALTH OUTCOMES RESULTS
Yang et al.134 Yang et al.
(2005) Taipei, Taiwan All ages/all
gender PM10 Case-crossover/
Poisson regression
model
Stroke admissions Hemorrhagic stroke, Lag3 RR 1.15
(1.01–10.10)
Yang et al.102 Yang et al.
(2009) Taipei, Taiwan All ages/all
gender PM10 Case-crossover/
Poisson regression
model
Hospital admissions
for congestive heart
failure
CHF, Lag1 RR 1.114 (0.993 –1.250)
Yang et al.116 Yang et al.
(2005) Taipei, Taiwan All ages/all
gender PM10 Case-crossover
studies/Poisson
regression model
Daily admissions for
asthma Asthma lag2 8% (p > 0.05)
Al et al.86 Al et al. (2018) Gaziantep,
Tur key All ages/all
gender PM10 Retrospective study/
GAM Cardiovascular
diseases admitted to
ED
Cardiac failure, OR
Admission 1.003 (0.972–1.036) P = 0.833
Hospitalization 2.209 (2.069–2.359)
P = 0.001
Gyan et al.112 Gyan et al.
(2005) Caribbean
island of
Tri n idad
Patients aged
15 years and
under
–Ecological/Poisson
regression model Pediatric asthma
accident and
emergency
admissions
Admission rate increased 7.8–9.25
Bennett
et al.105
Bennett et al.
(2006) Lower Fraser
Valley, British
Columbia,
Canada
All ages/all
gender PM10 Ecological time-
series/Chi-squared Hospital admissions hospitalizations Respiratory 0.89, 2 = 0.71
Cardiac 0.91, 2 = 0.54)
Cheng et al.65 Cheng et al.
(2008) Taipei, Taiwan All ages/all
gender PM10 Case-crossover/
Poisson regression
model
Daily pneumonia
hospital admissions Pneumonia admissions, RR 1.032
(0.980–1.086)
Lag1 1.049 (1.002–1.098)
Lag2 1.044 (0.999–1.092)
Lag3 1.037 (0.993–1.084)
Wilson et al.124 Wilson et al.
(2012) Hong Kong All ages/all
gender PM10 Case-crossover/
Poisson regression
model
Daily emergency
admissions for
respiratory diseases
COPD, lag2 RR 1.05 (1.01–1.09)
Wiggs et al.130 Wiggs et al.
(2003) Karakalpakstan,
Uzbekistan Children/all
gender PM10 Ecological Respiratory health Decreased the rate of respiratory health
problems
Pulmonary function
Hong et al.162 Hong et al.
(2010) Seoul, Korea Children/all
gender PM2.5 and
PM10
Prospective/linear
mixed-effects mode Pulmonary function
of school children PM2.5 (P > 0.05)
PM10 (P > 0. 05)
Kurai et al.157 Kurai et al.
(2017) Yonago, Tottori,
western Japan School children/
adults PM2.5 Descriptive/
longitudinal /Linear
mixed models
Respiratory function Lag0, −1.76 (−3.30, −0.21)
Lag0 –1, −1.54 (−2.84, −0.25)
Lag0–2, −1.05 (−2.21, 0.11)
Lag0–3, −1.09 (−2.18, −0.01)
(Continued)
Table 1. (Continued)
18 Environmental Health Insights
REFERENCE FIRST AUTHOR
AND YEAR
STUDY
LOCATION
POPULATION
(AGE, GENDER)
PM
FRACTION
STUDY DESIGN/
METHODOLOGY
HEALTH OUTCOMES RESULTS
Watanabe
et al.161
Watanabe
et al. (2016) western Japan Schoolchildren SPM A panel study/linear
mixed models Pulmonary function Peak expiratory ow (PEF) −3.62 (−4.66,
−2.59)
Watanabe
et al.66
Watanabe
et al. (2015) western Japan Schoolchildren SPM Longitudinal follow-up
study/linear mixed
models
Pulmonary function PEF
2012
−8.17 (−11.40, −4.93)
2013
−1.17 (−4.07, 1.74)
Yoo et al.160 Yoo et al.
(2008) Seoul, Korea Children PM10 Prospective/Pearson
correlation tests/
paired t-test
Respiratory
symptoms and peak
expiratory ow
PEF decreased (p < 0.05)
Watanabe
et al.158
Watanabe
et al. (2016) Western Japan Aged 18 years SPM Panel study/linear
mixed models Pulmonary function PEF, in allergic patients with Asthma _16.3
(_32.9, 0.4) P = 0.06
Rhinitis _7.0 (_19.5, 5.5) P = 0.27
Conjunctivitis _3.9 (_38.8, 30.9) P = 0.83
Dermatitis _5.6 (_21.3, 10.2) P = 0.49
Food allergy 0.4 (_23.3, 23.9) P = 0.9 8)
Watanabe
et al.159
Watanabe
et al. (2015) Western Japan Aged >18 year s SPM Panel study study/
linear regression
analysis
Pulmonary function
in adult with asthma PEF 0.01 ( −0.62, 0.11)
Park et al.163 Park et al.
(2005) Incheon, Korea Ages of 16 and
75 years/ all
gender
PM10 Cohort/t-test/GAM
with Poisson log-linear
regression
Peak expiratory ow
rates and respiratory
symptoms of
asthmatics
PEF 1.05 (0.89–1.24)
O’Hara
et al.166
O’Hara et al.
(2001) Karakalpakstan,
Uzbekistan Children aged 7
to 11 PM10 Cross-sectional
survey/multivariate
regression model
Lung function There was an inverse relationship between
dust event and Lung function
Other impacts
Lee et al.168 Lee et al.
(2019) Korean national All ages/all
gender PM10 Case-crossover/
conditional logistic
regression
Risk of suicide Suicide risk, 13.1% (4.5–22.4) P = 0.00 2
Soy et al.106 Soy et al.
(2016) Mardin, Turkey All gender/18 to
65 years PM10 Prospective study/
pairs t-test Quality of life(QoL) in
patients with or
without asthma
QoL, AR 2.5-fold higher
SF-36, AR 1.9-fold higher
Islam et al.167 Islam et al.
(2019) Saudi Arabia All ages/all
gender – Ecological/panel
regression models Road trafc
accidents P ⩽ 0.05
Mu et al.117 Mu et al. (2010) Choyr City,
Mongolia 44.2 ± 17. 3 /a l l
gender
–Cross-sectional/
student’s t-test/
multiple regression
analysis
Health-related
Quality of Life Decreased HRQL P < 0.05
(Continued)
Table 1. (Continued)
Aghababaeian et al 19
REFERENCE FIRST AUTHOR
AND YEAR
STUDY
LOCATION
POPULATION
(AGE, GENDER)
PM
FRACTION
STUDY DESIGN/
METHODOLOGY
HEALTH OUTCOMES RESULTS
Sing and symptom
Higashi
et al.151
Higashi et al.
(2014) Japan Aged 23–
84 years
all gender
PM2.5 Panel study/logistic
regression with a
generalized estimating
equation
Daily cough
occurrence in
patients with chronic
cough
Grade 1, 1.111 (0.995, 1.239)
Grade 2, 1.171 (1.006, 1.363)
Grade 3, 1.357 (1.029, 1.788)
Grade 4, 1.414 (0.983, 2.036)
Higashi
et al.152
Higashi et al.
(2014) Kanazawa,
Japan Between 23 and
84 TSP Cohort study,
McNamara’s test Cough and allergic
symptoms in adult
with chronic cough
Cough p = 0.02
Watanabe
et al.67
Watanabe
et al. (2012) Japan Age 63.4 ± 15. 2/
all gender
SPM Descriptive telephone
survey/multivariate
logistic regression
analysis
Lower respiratory
tract symptoms in
asthma patients
Exacerbation 4%
Unaffected 48%
Otani et al.153 Otani et al.
(2 011) Yonago, Japan all gender/mean
age of
36.2 ± 12.5 ye ar s
SPM Ecological Time-
series/t test/Pearson’s
correlation coefcient
Daily symptoms All symptoms (p = 0.020)
Skin symptom (p < 0. 00 1)
Onishi et al.154 Onishi et al.
(2012) Yonago, Japan All gender/mean
age-SD:
36 .2 –12.5 yea rs
SPM Prospective/
Wilcoxon’s rank test Symptom nasal/
ocular/respiratory/
throat /skin
symptoms
All symptom increased
Mu et al.35 Mu et al. (2011) Mongolia 35–44/all gender –Descriptive studies/
cross-sectional study/
multiple logistic
regression analysis
Eye and respiratory
system symptoms Itchy eye P = 0. 3
Bloodshot eye P = 0.02
Lacrimation P = 0.001
Respiratory system P > 0.05
Majbauddin
et al.155
Majbauddin
et al. (2016) Yonago, Japan Mean age of
33.57 ± 1/all
gender
SPM Prospective web-
based survey/
student’s t-test
Daily symptoms Ocular, r = 0.47 (P < 0 .0 1)
Nasal, r = 0.61 (P < 0. 00 1)
Skin, r = 0.445 (P < 0.05)
Kanatani
et al.164
Kanatani et al.
(2016) Kyoto, Tottori,
Toyama, Japan Pregnant women SPM Observational study/
Cohort/conditional
logistic regression
analysis
Allergic symptoms Allergic symptoms, OR 1.10 (1.04–1.18)
Yoo et al.160 Yoo et al.
(2008) Seoul, Korea Children PM10 Prospective/Pearson
correlation tests/
paired t-test
Respiratory
symptoms in children
with mild asthma
Cough 42.9 ± 20.8 (p < 0.05)
Runny/stuffed nose 53.8 ± 19.2 ( p < 0.05)
Sore throat 24.2 ± 13.5 ( p < 0 .05)
Eye irritation 24.5 ± 18.1 ( p < 0.05)
Limited physical activity 16.2 ± 12. 5
Nocturnal awakening 15.7 ± 14.1
Shortness of breath 20.1 ± 13.8 (p < 0.05)
Wheeze 16.7 ± 7. 1 ( p < 0.05)
(Continued)
Table 1. (Continued)
20 Environmental Health Insights
REFERENCE FIRST AUTHOR
AND YEAR
STUDY
LOCATION
POPULATION
(AGE, GENDER)
PM
FRACTION
STUDY DESIGN/
METHODOLOGY
HEALTH OUTCOMES RESULTS
Watanabe
et al.159
Watanabe
et al. (2015) Western Japan Aged >18 year s SPM Panel study/linear
regression analysis Respiratory
symptoms in adult
patients with asthma
All symptom 0.04 (0.03, 0.05)
Park et al.163 Park et al.
(2005) Incheon, Korea Ages of 16 and
75 years/all
gender
PM10 Prospective
study/t-test/GAM with
Poisson log-linear
regression
Respiratory
symptoms of
asthmatics
Nighttime symptoms RR 1.05 (0.99–1.17)
O’Hara
et al.166
O’Hara et al.
(2001) Karakalpakstan,
Uzbekistan Children aged 7
to 11 PM10 Descriptive studies/
cross-sectional
survey/multivariate
regression model
Respiratory
symptoms and lung
function
There is an apparent inverse relationship
between total dust exposure and
respiratory health
Watanabe
et al.165
Watanabe
et al. (2011) Western Japan At least 18 years
old SPM Cross-sectional
telephone survey/
multivariate logistic
regression analysis
Worsening asthma Aggravated lower respiratory tract
symptoms in asthma patients
Meo et al.156 Meo et al.
(2013) Riyadh, Saudi
Arabia Age
28.6 ± 3.14 years/
all gender
–Descriptive studies /
Chi square test General health
complaints OR
Wheeze 4.18 (2.36–7.41)
Cough 4.13 (2.28–7.46)
Acute asthmatic attack 6.7 (4.09–10.99)
Psychological disturbances 3.72 (2.48–
5.57)
Eye irritation/redness 7.89 (4.4–14.16)
Headache 4.17 (2.8–6.2)
Body ache 1.24 (0.82–1.88)
Sleep disturbance 4.16 (2.77–6.22)
Runny nose 31.9 (14.33–70.96)
Abbreviations: , Spearman correlation coefficients; AOD, aerosol optical depth; AMI, acute myocardial infarction; ACS acute coronary syndrome; CHF, congestive heart failure; COPD, chronic obstructive pulmonary disease;
GAM, generalized additive model; IHD; ischemic heart diseases; IR, increase risk; OR, odds ratio; PM, particulate matter; PM10, particles less than 10 m in aerodynamic diameter; PM2.5, particles less than 2.5 m in
aerodynamic diameter; PM2.5-10, particles with an aerodynamic diameter >2.5 µm and <10 µm; PTB, pulmonary tuberculosis; QoL, quality of life; RR, relative risk; SPM, suspended particulate matter; TSP, total suspended
particulate.
Table 1. (Continued)
Aghababaeian et al 21
Daily symptoms such as nasopharyngeal, skin, or ocular
symptoms, and decreased pulmonary function Table 1).
The current analysis indicated that the effects of dust storms
on health can be divided into 2 general sections: short- and
long-term effects. Short-term effects have been defined herein
as human health problems that occurred during or immediately
after a dust storm, and long-term effects are defined as human
health problems that occurred after a long exposure to several
periods of dust storms.
Short-Term Health Effects
The short-term effects included all-cause mortality, emergency
dispatch or air medical retrieval service, hospitalization or
admission, healthcare visits, daily symptoms, decreased pulmo-
nary function, and other problems.
Mortality
Thirty-three articles from almost all regions discussed mortal-
ity due to dust storms by means of different health problems,
such as increased total non-accidental deaths,3,38,39,41,46,53-55,68-82
cardiovascular deaths,3,38,39,48,50,53,70,74,77,82-85 mortality due to
acute coronary syndrome (ACS),3,70,81,86 and respiratory mor-
tality.48,53,55,77,87 Some studies reported, however, that the num-
ber of cases was not increased significantly for all-causes,43,88
respiratory,38,43 cardiovascular,43 or cerebrovascular mortality.69
Neophytou etal.82 in Nicosia reported that associations for res-
piratory mortality was −0.79 (−4.69, 3.28) on dust storm days.
Lee etal.55 in Taipei found that dust storms have a protective
effect on non-accidental deaths, respiratory deaths, and death
in people >65 years of age.
Emergency dispatch or air medical retrieval service
Four articles discussed the emergency medical services required
due to dust storm, focusing on different health problems. This
review observed an increased relative risk of all medical emer-
gency dispatches and a significant increase in cardiovascular
dispatches,42 increased daily ambulance calls due to respiratory,
cardiovascular, and all causes,89 and an increase in emergency
12
01112
5
23
10
1
6
13
18
6
14 13
15
13
5
8
0
2
4
6
8
10
12
14
16
18
20
1994 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Number of studies in different years
Chart 1. Number of studies of the impact of dust storms on health in different years.
Figure 2. Locations of dust storms and health impact research, 1994–2019.
22 Environmental Health Insights
dispatches due to cardiovascular, respiratory, injury and all
causes.90
Hospitalization or admission
Sixty-two articles from almost all regions discussed hospitali-
zation or admission due to dust storms by means of different
health problems or diseases. The results indicated that in many
studies, dust storms were associated with an increased risk of
hospital admission due to cardiovascular, cerebrovascular, and
respiratory diseases, among others.
Cardiovascular disease (CVD) hospitalizations or admissions. In
relation to cardiovascular diseases and the effect of dust storms,
17 studies stated that dust storms can increase: (1) the risk of
circulatory outpatients and inpatients91; (2) odds ratio of
admission and hospitalization due to congestive cardiac fail-
ure86 and acute coronary syndrome2,86; (3) effects on cardiac
diseases92; (4) risk of CVD hospitalization or admission40,78,93-97;
(5) emergency admissions for CVD92; (6) the impacts on acute
myocardial infarction (AMI)98-100; (7) emergency hospital
admissions for ischemic heart diseases (IHD)101; (8) hospital
admissions for congestive heart failure (CHF)102; and (9) inpa-
tient hospitalization due to cardiac failure.86 However, some
studies reported non-significant results, such as no association
between dust storms and out-of-hospital cardiac arrests103 and
no significant changes in admissions concerning cardiovascular
syndromes.104 Also, some reported no significant association
between increased dust particles and angina.50 Bennett etal.105
reported that the dust storms were not associated with an
excess of CVD hospitalizations.
Respiratory disease hospitalizations or admissions. Regarding
respiratory diseases related to dust storms, 35 studies stated
that dust storms can increase the risk of respiratory outpa-
tients,91 respiratory disease hospitalizations or admis-
sions,11,40,43,51,57,78,92,93,96,104,106-114 cases of bronchial asthma,93
asthma-related hospitalizations or admissions,51,57,112-116 cases
of aggravated asthma disease,117,118 daily pneumonia admis-
sions,119,120 hospital admissions for chronic obstructive pulmo-
nary disease (COPD),50,87,121-123 emergency hospital admissions
for COPD,124 emergency admissions for respiratory diseases,92
admitted patients suffering from respiratory infection,50 and
the prevalence of chronic bronchitis, cough, and rhinitis.125
Surprisingly, several studies did not find any link between
dust storms and negative health outcomes, such as no signifi-
cant effect on asthma exacerbations in Riyadh,126 no significant
change in the risk of emergency admission in dust events,127 and
no association between sandstorms and risk of hospital admis-
sion for asthma or pneumonia patients.56 Moreover, some stud-
ies reported no statistically significant relationship between
increased dust levels and pulmonary function, allergic disease,
emergency admission, or drug use128; no significant relationship
between increased risk of chronic obstructive pulmonary
disease, asthma, and angina and increased concentration of dust
storms50,129; And no excess risk of respiratory hospitaliza-
tions.105 Only two studies found a decrease in respiratory prob-
lems after dust storms, like a decreased risk of respiratory
inpatients in Taklimakan Desert,91 and a lower rate of respira-
tion problems among children in areas with higher levels of dust
deposition as reported by Wiggs etal.130
Cerebrovascular diseases hospitalizations or admissions. Regard-
ing the correlation between cerebrovascular diseases and dust
storms, 6 studies stated that dust storms can increase the risk of
cerebrovascular diseases,40,92 the incidence of athero-throm-
botic brain infarction,131 stroke admission rates,132 hospital
admissions for epilepsy problems, cerebral ischemic attacks,
and various types of headaches,133 and daily intracerebral hem-
orrhagic (ICH) stroke admissions.134 Bell et al.,56 however,
reported that sandstorms have no significant relationship with
the risk of admission to cerebrovascular patients. Moreover,
Yang et al.134 stated that there is no significant association
between the risk of ischemic stroke and dust storms.
Other diseases hospitalizations or admissions. Aili etal.91 reported
that the risk of digestion outpatients and inpatients, gynecol-
ogy outpatients, pediatrics outpatients and inpatients, and
ENT outpatients and inpatients was increased during dust
storms. Chan etal.135 also stated that dust storms were signifi-
cantly associated with diabetes admissions for females. Fur-
thermore, Ko etal.137 stated that dust storms can increase the
risk of conjunctivitis.
Healthcare visits
Nineteen articles studied the daily number of healthcare visits
due to dust storms for different health problems. Except for 1
article, all others reported that dust storms are associated with
an increased daily number of healthcare visits due to asthma-
related health problems137-141 cardiac, respiratory, and stroke
diagnoses,142 emergency healthcare visits for IHD, CVD, and
COPD,143 conjunctivitis clinic visits,144,145 children clinic visits
for respiratory problems,139,146 healthcare visits for respiratory
diseases,52,139,146,147 healthcare visits for all causes, circulatory,
and respiratory diseases,148 and for cardiovascular and respira-
tory problems.149,150 Lorentzou et al.122 also reported a large
increase in emergency visits related to dyspnea during dust
storms; however, no clinically significant increase was observed
in the total number of emergency visits.
Daily symptoms
Twenty articles studied the daily symptoms resulting from dust
storms. In 2 studies, Higashi etal.151,152 showed the effects of
Kosa on cough. Otani etal.153 found that the scores for symp-
toms (nasopharyngeal, ocular, respiratory, and skin) were sig-
nificantly higher when related to dust storms. Onishi etal.154
Aghababaeian et al 23
reported that all symptoms (nasal, ocular, respiratory, throat,
and skin) increased after exposure to dust storms. Mu etal.35
also reported that an increased risk of eye lacrimation occur-
rence is associated with dust events. Majbauddin et al.155
reported a positive correlation between the increased concen-
tration of dust storms and ocular, nasal, and skin symptoms.
Similarly Meo etal.156 observed that sandstorms can increase
complaints of sleep and psychological disturbances as well as
other problems like eye irritation, cough, wheeze, headache,
and runny nose.
Pulmonary function
Nine articles discussed pulmonary function in relation to dust
storms, and the evidence is conflicting. Kurai etal.,157 Watanabe
etal.,158,159 Yoo etal.160 and Watanabe etal.161 all found that
dust storms have a significant, negative effect on pulmonary
function. Other studies, including Hong etal.,162 Watanabe
et al.159 and Park et al.163 found no significant relationship
between pulmonary function and dust storms. Kanatani etal.
found that dust storms can increase the risk of allergic symp-
toms in pregnant women.164 Yoo etal.,160 reported a significant
increase in respiratory symptoms during dust storms, and
Watanabe etal.159 reported that sand and dust storms are sig-
nificantly associated with respiratory symptoms. Moreover,
Park etal.163 reported a relationship between nighttime symp-
toms and particular matter levels during dust storms. Watanabe
etal.165 also stated that dust storms worsen respiratory symp-
toms in asthmatic patients, but some studies like O’Hara
etal.166 reported that pulmonary function was better in chil-
dren who were more exposed to dust storms than in those with
low exposure to dust.
Other impacts
Some articles explored the relationship of dust storms with
road traffic accidents, risk of suicide, placental abruption, and
health-related quality of life. Islam etal.167 found that sand-
storms and the number of vehicles were significantly responsi-
ble for road traffic accidents. Soy etal.106 reported that dust
storms can have adverse effects on the quality of life of patients
with asthma and allergies. Mu et al.117 reported that dust
storms can decrease health-related quality of life in everyone
exposed to them. Lee etal.168 reported that exposure to dust
storms was associated with an increased risk of suicide (13.1%;
p = 0.002).
Long-Term Health Effects
Six articles discussed the long-term adverse health effects
caused by dust storms by means of different outcomes, like
reduced birth weight, baby’s birth weight <2.5 Kg, gestation/
gestational age >37 weeks and premature birth,32 and decreased
cognitive function in children.33 Preterm births34 were corre-
lated with Valley fever incidences36 and increased spring
measles incidence.44 Only one article was observed to indicate
no significant effect of desert dust storms on pregnancy
consequences.169
Discussion
In this study, the majority of valid scientific databases were
searched to find articles and studies related to the health effects
of dust storms. Other similar studies have used fewer scientific
databases in their search. The final number of articles included
in this study is higher than that in all previous studies.24,26 The
current results showed that the model most used to evaluate
the health effects of dust storms was the GAM method. In this
regards, Ramsay 2003 stated, “Such methods eliminate the need to
specify a parametric form for secular trends and allow a greater
degree of robustness against model misspecification.”170 The results
of the current study also showed that most dust storm studies
have been carried out in Japan, Taiwan, and South Korea,
which may be due to the large number of dust storms occurring
in Northeast Asia. This area is exposed to yellow dust storms
caused by strong winds on the Loos Plateau and the Gobi and
Talkmanistan Deserts, and as yellow dust storms became so
prevalent in that area within the last two decades, researchers in
the area have studied their health effects.152,171
The review results showed that most studies around the
world confirm the adverse effects of dust storms on health. The
relevant health problems were categorized into long-term and
short-term impacts. Few studies were found that focused on
the long-term impacts of dust storms on human and public
health; however, those studies found showed that dust storms
may increase the risks for problems in pregnancy and child-
birth, children’s cognitive problems, and infectious diseases. In
line with the risks of birth as well as cognitive problems in
children, animal studies have shown that the fetal brain is easily
exposed to air pollutants, because in the human fetus, the
blood-brain barrier has not yet developed; therefore, the fetal
brain is exposed to pollutants and is sensitive to blood changes
caused by them.1-3 Furthermore, new research on humans has
shown that environmental pollutants can possibly create
inflammation, oxidative stress, and vascular damage to the fetal
brain after passing through the placenta.4-7 Researchers have
studied the effects of PM from dust storms on maternal health
during pregnancy and birth problems, and they refer to varia-
tions in maternal host-defense mechanisms, maternal-placen-
tal exchanges, oxidative pathways, and endocrine dysfunction
as possible causes of these problems.8 Ultimately, the evidence
from infectious diseases shows that pathogenic microorgan-
isms are abundant in dust storms,9 and dust storms can spread
these microorganisms over a large area. Therefore, it can be
argued that microorganisms that are suspended or attached to
dust particles can be transferred from one part to another and
may induce infectious diseases at various destinations by dust
storms.10,11 More studies have been conducted on the short-
term impacts of dust storms. The majority of these studies
indicate the effects of dust storms on important body systems,
24 Environmental Health Insights
including the cardiovascular, respiratory and cerebral systems,
which lead to the increased incidence of clinical symptoms and
severity of symptoms; increased emergency visits, ambulance
dispatches, and hospitalizations or admissions; decreased lung
capacity; and eventually death.
Most studies show that dust storms increase the risk of car-
diovascular problems, the number of cardiovascular emergency
medical dispatches, cardiovascular visits, the number of cardio-
vascular symptoms among patients referring to the hospital,
cardiovascular admissions or hospitalizations, and deaths due
to cardiovascular disease. Although the exact mechanism for
the effects of dust storms on heart problems has not yet been
determined,12 studies show that fine particles in dust storms
can enter lung tissue and the bloodstream through chemical
interactions,13 causing a thrombolytic and inflammatory pro-
cess and the secretion of cytokines in the body.14,15 Moreover,
the toxicity of some of these substances in the body reduces the
contractibility of the heart, increases vasoconstriction, and
increases blood pressure.14,18-20 Therefore, the above cases may
confirm the effects of dust storms on cardiovascular health.
The results of the current study showed that according to
most articles, the risk of death following respiratory problems;
the risk of admission and hospitalization due to respiratory dis-
orders like pneumonia, asthma, and chronic obstructive pulmo-
nary disease and other respiratory problems; respiratory
symptoms; and healthcare visits associated with dust storms
have increased. Other results showed that dust storms reduce
lung capacity and function.
The results of studies have shown that 1 mechanism of dust
storms is that small particulates in dust storms are likely to
trigger an innate immune response by T-lymphocytes in the
body and respiratory system, which can cause chronic inflam-
mation and advanced COPD.22-25 PM can also play a signifi-
cant role in respiratory oxidative stress, increase pulmonary
inflammation, increase atopic responses and Immunoglobulin
E production in respiratory problems (especially asthma), and
exacerbate symptoms.26 Another mechanism that may cause
respiratory illnesses following a dust storm is the presence of
pathogens such as microorganisms and fungi37 as well as some
minerals such as silica in some of these storms. These particles
enter the airway after dust storms and exacerbate the disease or
cause respiratory problems in people at risk.22 For example,
neutrophilic pulmonary inflammation may be caused by bacte-
rial and fungal debris in dust particles to which individuals are
exposed. Some of this debris includes lipopolysaccharide
(LPS), a cell wall glycolipid of gram-negative bacteria, and -
glucan, which is the most important constituent of the fungal
wall. Both of them are clearly observed in dust storms along
with dust particles.22,38,39 Although the precise mechanisms for
pneumonia are yet to be found, some studies have suggested
that high amounts of particles in dust storms can cause oxida-
tive stress, induce inflammation, increase blood clotting, dis-
rupt defense cells, and cause immune system fluctuations,
ultimately inducing alveolar inflammation and exacerbating
lung disease.3,40,41
In 2009, Calderon Garosia stated that pollutants in dust
storms can cause problems such as cardiovascular, respiratory,
liver, and skin toxicity through systemic inflammation42 and
may induce a pre-inflammatory systemic response in cytokines,
which may disrupt the HPA axis and ultimately cause mood
swings and psychological problems, including suicidal
thoughts.42-44 In addition, chemical components found in dust
storms can enter the brain through the mucosa and olfactory
system.42 After entering the nervous system, they may accumu-
late in the anterior cortex of the brain and cause problems in
emotional regulation and impulse control.45 Some researchers
also suggest that some mechanisms are associated with placen-
tal abruption due to dust storms, such as microbiological and
chemical substances in dust storms that induce an inflamma-
tory response in the body.46,47 Inflammation and ischemia
increase the risk of decidual bleeding, followed by hematoma
formation and placental abruption.48,49 There is also some
speculation that as lipopolysaccharide has been found in Asian
dust storms, the activity of this endotoxin in the body may lead
to premature birth due to chorioamnionitis, which is also asso-
ciated with placental abruption.50,51
The current review shows that some studies have also linked
dust storms with some other health problems, such as increased
road accidents, increased suicide risk, increased premature placen-
tal abruption, ocular problems, and reduced quality of life. These
issues could be further studied in areas prone to dust storms.
Islam11 stated that the reduced field of vision, the lack of dust
storm warning systems, and traffic due to dust and sand storms
can be considered as reasons for the recent increase in number of
road accidents. Dust particulates in these storms can also cause
acute ocular problems such as tears and conjunctivitis in people
due to their inflammatory effects.52 I In terms of the quality of
life, Mu53 stated that an increase in health problems and clinical
symptoms that are associated with allergens and ocular problems
such as conjunctivitis dust storms reduce the quality of life.
Despite all the significant effects of dust storms on health,
this review found some studies that presented no significant
association between dust storms and health problems includ-
ing all-cause and respiratory mortality,43,88 cardiovascular,103-105
cerebral,134 and respiratory problems.127-129 Moreover, some
studies reported that dust storms may have a protective effect
against non-accidental and respiratory death55 and other pul-
monary problems.91,130,166
However, O’Hara stated that although the lack of matching
of exposed and non-exposed groups in nutritional, economic,
and social problems may play a role in the insignificance of the
effects of dust storms on health, the chemical and physical
nature of the particles in dust storms are of more importance
than their total amounts.55,166 Differences in the chemical and
physical nature of particulate matters may cause different
health outcomes in varying regions.55 Another reason for the
Aghababaeian et al 25
difference may be the use of rapid early warning systems in
some countries. Lee justified the protective effects of dust
storms on death, stating that in Taipei, a complex rapid early
warning system is used for dust storms, and the use of this
system may produce protective effects of dust storms on mor-
tality.55 Finally, almost all of the reviewed articles reported on a
group of diseases or deaths that were studied, while dust storms
may not affect all diseases and deaths.22 This may be another
reason for these differing results.
Conclusion
This systematic review presents an accurate and comprehensive
study of all aspects of human health in relation to dust storms.
For the first time in the world, this in-depth and unique study
was conducted to summarize the short-term and long-term
effects of dust storms. To date, this amount of reliable data on
this issue has never been investigated. As the results showed,
despite the short-term effects dust storms have on human
health (including adverse effects on the respiratory, skin, ocular,
cardiovascular, and cerebral systems as well as increased mor-
tality and morbidity) that may occur immediately after each
dust storm, the frequency of dust storms in an area is also an
important factor. In addition to exacerbating short-term health
effects, they may also cause long-term health effects, which
may include health problems for pregnant mothers, fetuses and
infants, in the cognitive function of children, and increases in
some infectious diseases. Therefore, as climate change and
drought have caused this phenomenon to endanger the lives of
many people around the world, and as the health and well-
being of people is a main priority in any country, it is recom-
mended that more studies be conducted in countries exposed
to dust storms to examine the health effects of these storms in
order to better understand the mechanisms through which
dust storms impact human and public health and to develop a
better strategy for preparing for, preventing, and mitigating the
destructive effects of these storms.
Acknowledgements
Many thanks to Institute for School of public health and
Environmental Research (IER) of Tehran University of
Medical Sciences (TUMS), for supported current study.
Author Contributions
“HA and AOT designed the study; HA collected the data; HA
and AOT analyzed and interpreted the data. HA, AOT, A
Ardalan, MA, MY, CS and A Asgary prepared the manuscript.
All authors contributed to the drafting and final review of the
manuscript. The author (s) read and approved the final
manuscript.”
Ethical Approval
Current study was approved by the Ethics Committee of
Tehran University of Medical Sciences (TUMS) Ethics Code:
IR.TUMS.SPH.REC.1399.004, and also all methods were
performed in accordance with the relevant guidelines and
regulations.
ORCID iD
Hamidreza Aghababaeian https://orcid.org/0000-0003-3339
-5507
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