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RESEARCH ARTICLE
Long-term exposure to ambient air pollutants
and mental health status: A nationwide
population-based cross-sectional study
Jinyoung Shin 1 , Jin Young Park
2 , Jaekyung Choi
1*
1 Department of Family Medicine, Research Institute of Medical Science, Konkuk University School of
Medicine, Konkuk University Medical Center, Seoul, South Korea, 2 Department of Psychiatry, Gangnam
Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
Abstract
There is a suspected but unproven association between long-term exposure to ambient air
pollution and mental health. The aim of this study is to investigate the association between
long-term exposure to ambient air pollution and subjective stress, depressive disorders,
health-related quality of life (QoL) and suicide. We selected 124,205 adults from the Korean
Community Health Survey in 2013 who were at least 19 years old and who had lived in their
current domiciles for > five years. Based on the computer-assisted personal interviews to measure subjective stress in daily life, EuroQoL-5 dimensions, depression diagnosis by a
doctor, suicidal ideation, and suicidal attempts, we evaluated the risk of mental disorders
using multiple logistic regression analysis according to the quartiles of air pollutants, such
as particulate matter <10μm (PM10), nitrogen dioxide (NO2), carbon monoxide (CO), and sulfur dioxide, using yearly average concentration between August 2012 and July 2013. The
prevalence of high stress, poor QoL, depressiveness, diagnosis of depression, and suicide
ideation was positively associated with high concentrations of PM10, NO2, and CO after
adjusting for confounding factors. Men were at increased risk of stress, poor QoL, and
depressiveness from air pollution exposure than were women. The risk of higher stress or
poor QoL in subjects < age 65 increased with air pollution more than did that in subjects � age 65. Long-term exposure to ambient air pollution may be an independent risk factor for
mental health disorders ranging from subjective stress to suicide ideation.
Introduction
Ambient air pollution is composed of a heterogeneous mixture of compounds, including par-
ticulate matter (PM), nitrogen dioxide (NO2), carbon monoxide (CO), and sulfur dioxide
(SO2). These particles are composed of both solid and liquid components that originate from
multiple sources, including vehicle exhaust, road dust, and windblown soil [1].
A growing body of evidence indicates that elevated levels of air pollution are associated
with mental disorders such as depression and suicide. Elderly subjects experienced the aggra-
vation of their depressive symptoms after 3-day exposure to air pollutants [2]. Emergency
PLOS ONE | https://doi.org/10.1371/journal.pone.0195607 April 9, 2018 1 / 12
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OPEN ACCESS
Citation: Shin J, Park JY, Choi J (2018) Long-term
exposure to ambient air pollutants and mental
health status: A nationwide population-based
cross-sectional study. PLoS ONE 13(4): e0195607.
https://doi.org/10.1371/journal.pone.0195607
Editor: Kenji Hashimoto, Chiba Daigaku, JAPAN
Received: November 13, 2017
Accepted: March 26, 2018
Published: April 9, 2018
Copyright: © 2018 Shin et al. This is an open access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: Data are available
from the Korea Centers for Disease Control and
Prevention for the data obtained from the 2013
Community Health Survey and the National
Institute of Environmental Research. Researchers
interested in the data can request access by
sending a proposal to the data access committee at
the following link: https://chs.cdc.go.kr/chs/sub05/
sub05_02.jsp;jsessionid=7Oz5P2fE0QxHWIHlURE
fDtxJ4NBdgVmlzOanpQNiB1QE6XuDMKGPiVOlXa
elhKQn.KCDCWAS02_servlet_PUB2.
Funding: The authors received no specific funding
for this work.
department visits for depressive episode were associated with increased levels of air pollutants
during 0–3 days in 4,985 Korean elderly patients with cardiovascular or respiratory disease [3].
Emergency department visits with depressive disorders and suicide attempts showed associa-
tions with CO, NO2, SO2, and PM10 in 27,047 Canadians [4, 5]. However, these associations
were different according to seasons [6] or represent only sort-term exposure to air pollutants.
Although a few studies have addressed the incidence of mental health disorders in patients
with long-term exposure to air pollution, the association between newly diagnosed major
depressive disorder and PM � 2.5μm has been minimally assessed [7]. A study of increased suicide rate after 4 weeks’ exposures to air pollution did not adjust for known risk factors [8].
Moreover, there is inconsistent evidence that ambient air pollution is associated with depres-
sive symptoms among older adults � 65 years of age living in a metropolitan area of U.S. or
European general population [9, 10].
In other words, there was insufficient evidence to support an association between long-
term exposure to ambient air pollution and mental health status in general population. There-
fore, for this study, we used nationwide population data to investigate the association between
long-term exposure to ambient air pollution and mental health status, including subjective
stress, depressive disorders, health-related quality of life and suicide.
Materials and methods
Study participants
For this study, we evaluated data from the Korean Community Health Survey (KCHS) 2013,
which has been collected by the Korea Centers for Disease Control and Prevention annually
since 2008. We collected the data via computer-assisted personal interviews with 900 people in
each of the 253 community units between August and October in 2013, including 17 metro-
politan areas and provinces. Study participants aged 19 or older in each area who were selected
by the probability proportional sampling method and the systematic sampling method [11].
Among total surveyed 228,781 adults in 2013, we selected subjects who had lived in the same
domicile for > five years. After we matched the domicile code of participants and the location
code of air pollution surveillance station because of using same code system, we ultimately
analyzed 124,205 persons (unweighted number).
Air pollutant variables
We obtained the daily average concentrations of hourly measured particulate matter <10 μm (PM10), NO2, CO, and SO2 as air pollutant variables at nationwide air pollution surveillance
stations from the Korean Air Pollutants Emission Service. We calculated quartiles of air pollut-
ants using yearly average concentration between August 2012 and July 2013. These air pollut-
ant measurements followed the standard reference protocol of the Korean Air Pollutants
Emission Service [12]. PM10 had been measured using beta-ray attenuation method (MEZUS-
610, KENTEK, Daejeon, Korea). NO2 had been measured using chemiluminescence method
(MEZUS-210, KENTEK). CO had been measured using non-dispersive infrared (MEZUS-
310, KENTEK). SO2 had been measured using UV fluorescence (MEZUS-110, KENTEK). We
obtained meteorological data, including temperature, rainfall, and wind speed, from the
National Meteorological Office in the same period [13, 14].
Mental health variables
The KCHS surveyed mental health-related indicators. These indicators were defined as subjec-
tive daily stress, the EuroQol-5 dimensions (EQ-5D) index, the presence or absence of
Ambient air pollutants and mental health status
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Competing interests: The authors have declared
that no competing interests exist.
depressiveness (such as a feeling of sadness or hopelessness lasting more than two consecutive
weeks), physician’s diagnosis of depression, suicidal ideation, or a suicide attempt during the
past year. We assessed subjective stress on a four-point rating scale (“very much,” “a lot,” “a lit-
tle bit,” “rarely”). Ultimately, we defined participants with subjective stress as those who
responded with “very much” or “a lot” of stress. The EQ-5D index is broadly applied to evalu-
ate health-related quality of life in five dimensions (mobility, self-care, usual activities, pain/
discomfort, and anxiety/depression), and each dimension has one of three possible responses
(no problems, some problems, or extreme problems). The EQ-5D index generates a single
value from each dimension using the following weighted health scores: worst possible = 0; best
possible = 1, and a score below zero equates to a health status worse than death [15]. We
defined the fourth quartile of the EQ-5D index (which was 0.913 in this study) as a group with
poor quality of life.
Other variables
We categorized patients as non-smokers, former smokers (smoked at one time but not cur-
rently), or current smokers (smoking daily or intermittently at the time of the survey). We
defined alcohol consumption by drinking frequency of one time per week. We defined physical
activity by intensity and frequency. Active group was doing moderate intense activity � three
times per week or vigorous activity � one time per week. Inactive group was defined when par-
ticipant was not met these criteria. Vigorous physical activity included running (jogging),
climbing, fast biking, fast swimming, soccer, basketball, jumping rope, squash, or singles tennis,
as well as occupational activities such as carrying heavy objects [16]. We also obtained the fol-
lowing demographic information: years of education (< 9, 9–12, or > 12); marital status (mar-
ried/with partner, not married, or divorced/widowed); current employment status (employed
or retired/unemployed); household income (< 7,000,000 won/year or � 7,000,000 won/year);
hours of sleep duration (< 7, 7–9, or > 9); religion (yes or no); residence (rural or urban); and
medical history according to physicians’ diagnoses, including hypertension, diabetes mellitus,
dyslipidemia, stroke, myocardial infarction, ischemic heart disease, asthma, and arthritis. We
divided participants’ length of residence into four groups, 5 � Q1 < 10 years, 10 � Q2 < 15
years, 15 � Q3 < 20 years, or Q4 � 20 years, after excluding those who had lived in their areas
for < 5 years.
Ethical considerations
The institutional review board (IRB) at the Korean Centers for Disease Control and Preven-
tion approved the study protocol, and all of the participants provided written informed con-
sent. The IRB at Gangnam Severance Hospital, Yonsei University College of Medicine
approved this study as well (IRB File Number: 3-2017-0153).
Statistical analyses
We conducted all analyses considering the survey weight. Continuous variables are presented
as means with standard errors, and categorical variables are presented as percentages. We con-
ducted a univariate analysis to find out the association between the characteristics of partici-
pants and mental health status. We then evaluated mental disorder risk using multiple logistic
regression analysis after adjusting for age, sex, smoking, drinking, physical activity, education,
marital status, employment, household income, sleep duration, residence, and medical history
(hypertension, diabetes mellitus, dyslipidemia, stroke, myocardial infarction, ischemic heart
disease, asthma, arthritis). We conducted stratified analyses to investigate the possible effect
modification by sex and age (divided by age 65) in subgroup analysis. EQ-5D index was
Ambient air pollutants and mental health status
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skewed distributed. We showed the meteorological data including mean temperature, rainfall
and wind speed and the level of air pollutant in 2013 in S1 Table. The nationwide values of
ambient air pollutants are also presented as means with standard deviations, medians and
ranges in S1 Table. Therefore, it was analyzed using logarithmic transformation. We con-
ducted all analyses using SAS software 9.4 (SAS Institute Inc., Cary, NC, USA).
Results
The demographic, socioeconomic characteristics, health-related behaviors, and past medical
history of the study population are summarized in Table 1. The mean age was 48.2 years, and
the study population was 50.1% women. Approximately 70% of participants had lived in the
same domicile for > 15 years.
The association between the characteristics of participants and mental health status was
shown in Table 2. Mental health status was associated with various sociodemographic feature,
health-related behaviors and medical factor. The risk of subjective stress decreased older age,
education less than 12 years or unemployed participants. Subjects with current smoking and
alcohol drinking more than one time per week represented a low risk of depressiveness and
depression diagnosis by doctor.
The risk of a mental disorder according to the air pollutant quartile is represented in Fig 1.
After we adjusted for confounding factors, there were positive associations between PM10,
NO2, CO exposure and mental health status except suicidal attempts. The risk of depressive-
ness increased at the third quartile of CO exposure (odds ratio [OR]; 95% confidence interval
[CI]: 1.635(1.497, 1.786)), the highest quartile of NO2 (1.501(1.377, 1.635)) and the third quar-
tile of PM10 (1.335(1.267, 1.408)). There was no association between SO2 exposure and mental
health status.
Compared with women, men had increased prevalence of subjective stress with exposure to
PM10 and prevalence of poor QoL with exposure to CO and SO2 in Table 3. And depressive-
ness in men also increased with exposure to NO2, CO and SO2. The risk of depression diagno-
sis by doctor and suicidal ideation had no difference according to sex (Ps > 0.05). The effect of SO2 was inconsistent according to the quartiles.
The risk of higher stress and poor QoL with PM10 in subjects < age 65 were significantly
increased than that in subjects � age 65 in Table 4. Subjects < age 65 with high quartiles of
PM10, NO2, CO and SO2 had a higher risk of poor QoL than subjects � age 65. In the higher
levels of air pollutants, the risk of depressiveness, depression diagnosis by doctor and suicidal
ideation increased, however, there had no significant difference according to age 65.
Discussion
In this study, we used Korean nationwide population-based data to identify associations
between long-term exposure to ambient air pollutants and mental health status. After consid-
ering mental health-related confounding factors such as socioeconomic status, health-related
behavior and medical history, air pollutants may be an independent predictor of mental health
status, ranging from subjective stress level to suicidal ideation.
Our results were similar to those of a previous Korean study in which emergency depart-
ment visits for depressive episodes in patients with a past history of depressive disorder were
associated with recent air pollutant levels [3]. However, our study findings confirmed the asso-
ciations between subjective stress in daily life or suicide attempts in the general population and
long-term exposure to ambient air pollutants. In a three-year study from the National Health
Insurance database, there was an association between major depressive disorder and PM2.5 [7].
However, we additionally assessed the effects of SO2, NO2, and CO on mental health status,
Ambient air pollutants and mental health status
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Table 1. Baseline characteristics of study population.
Variables Total Men Women
Age, years 48.2±0.04 47.0±0.06 49.4±0.05 Smoking
Never 61.2 41.6 80.7
Former 16.1 30.9 1.3
Current 22.7 27.5 18.0
Alcohol intake
Never or less than one time per week 87.1 85.4 88.8
More than one time per week 12.9 15.6 11.2
Physical activity
Active 44.5 46.2 42.8
Inactive 55.5 53.8 57.2
Education
< 9 years 22.4 19.1 25.7
9–12 years 31.8 33.0 30.6
> 12 years 45.9 47.9 43.7
Marital status
Married/with partner 65.0 67.3 62.7
Not married 22.9 26.9 18.9
Divorced/widowed 12.1 5.8 18.4
Employment
Employed 62.8 77.5 48.2
Retired/unemployed 37.2 22.5 51.8
Household income
< 7,000,000 won/year 70.0 69.0 70.9
�7,000,000 won/year 30.0 31.0 29.1
Sleep time, hours
< 7 hours 48.7 47.6 49.8
7–9 hours 48.1 48.7 47.5
> 9 hours 3.2 3.7 2.7
Religion, yes 28.2 20.6 35.7
Residence of urban 79.7 79.6 79.8
Hypertension 19.5 19.3 19.7
Diabetes mellitus 7.4 8.0 6.9
Dyslipidemia 11.2 10.7 11.7
Stroke 1.4 1.6 1.3
Myocardial infarction 1.0 1.2 0.8
Ischemic heart disease 1.4 1.3 1.5
Asthma 2.4 2.1 2.7
Arthritis 9.6 4.1 15.0
Length of residence
5–10 years 14.4 13.9 14.9
10–15 years 13.7 13.2 14.2
15–20 years 11.0 10.9 11.1
� 20 years 60.8 62.0 59.8
Subjective stress 27.5 32.0 23.0
Poor quality of life 21.7 18.4 32.9
Depressiveness 6.2 4.2 8.0
(Continued)
Ambient air pollutants and mental health status
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and thereby, we confirmed the associations between long-term exposure to ambient air pollut-
ants including PM10, NO2, and CO and subjective stress, poor QoL, depressiveness, and sui-
cide ideation.
In this study, we found no clear linear correlation between the risk of mental health disor-
ders and the air pollutant concentration quartile. We believe that the reason for this finding is
a threshold effect at low levels of air pollutants; if the concentration is above a certain cut-off
value, a significant effect may be similar. We also identified a weak association between
Table 1. (Continued )
Variables Total Men Women
Depression diagnosis 2.5 1.3 3.7
Suicidal ideation 8.8 6.6 11.3
Suicide attempt 0.4 0.4 0.5
Data was shown by mean and standard error or percentage. Physical active group was defined as moderate intense
activity � 3 times per week or vigorous activity �1 time per week. Inactive group was not met these criteria. Length
of residence with same domicile was counted. Medical history was defined as a physician’s diagnosis. The subjects
with subjective stress were defined as those responding with “very much” or “a lot” of stress. The fourth quartile of
the EuroQol-5 dimensions index was defined as a group with poor quality of life.
https://doi.org/10.1371/journal.pone.0195607.t001
Table 2. Univariate analysis for the association between the characteristics of participant and mental health status.
Subjective stress Poor quality of life Depressiveness Depression diagnosis by doctor Suicidal ideation Suicide attempt
Age 0.991 (0.990,0.992) 1.049(1.048,1.051) 1.010 (1.009,1.012) 1.021 (1.019,1.024) 1.024 (1.022,1.025) 1.007 (1.001,1.013)
Women 1.005 (0.977,1.034) 2.325 (2.256,2.396) 1.926 (1.819,2.039) 2.765 (2.523,3.029) 1.731 (1.654,1.811) 1.301 (1.059,1.600)
Current smoking 1.591 (1.539,1.646) 1.713 (1.645,1.784) 0.916 (0.857,0.980) 0.783 (0.707,0.867) 0.996 (0.944,1.051) 2.090 (1.699,2.573)
Alcohol (�1/week) 1.303 (1.259,1.348) 1.619 (1.555,1.686) 0.880 (0.822,0.943) 0.632 (0.566,0.705) 0.951 (0.900,1.004) 1.570 (1.254,1.967)
Physically inactive 1.101 (1.068,1.134) 1.757 (1.700,1.815) 1.122 (1.061,1.187) 1.381 (1.270,1.502) 1.323 (1.264,1.386) 1.454 (1.179,1.974)
Education,
� 12 years
0.939 (0.911,0.967) 2.818 (2.720,2.921) 1.594 (1.502,1.692) 2.280 (2.070,2.511) 2.273 (2.155,2.398) 3.100 (2.414,3.980)
Divorced/widowed 1.091 (1.057,1.125) 1.324 (1.282,1.368) 1.405 (1.330,1.485) 1.395 (1.284,1.516) 1.309 (1.250,1.371) 1.589 (1.297,1.947)
Unemployed 0.728 (0.706,0.750) 2.949 (2.858,3.044) 1.759 (1.665,1.859) 2.784 (2.566,3.020) 1.736 (1.659,1.816) 2.010 (1.638,2.468)
Household income
< 7,000,000
1.077 (1.038,1.117) 1.714 (1.643,1.788) 1.453 (1.349,1.564) 1.728 (1.542,1.936) 1.608 (1.509,1.713) 2.312 (1.693,3.158)
Sleep time
<7,or �9
1.518 (1.474,1.564) 1.443 (1.399,1.488) 1.493 (1.412,1.579) 1.586 (1.461,1.721) 1.509 (1.443,1.579) 1.952 (1.584,2.405)
Residence of urban 1.030 (0.989,1.073) 0.795 (0.761,0.829) 1.083 (1.005,1.167) 1.069 (0.949,1.204) 0.847 (0.797,0.900) 0.888 (0.686,1.149)
Hypertension 0.962 (0.918,1.018) 2.905 (2.810,3.003) 1.381 (1.299,1.469) 1.883 (1.731,2.049) 1.793 (1.708,1.882) 1.559 (1.246.1.950)
Diabetes Mellitus 1.023 (0.969,1.079) 2.754 (2.625,2.888) 1.512 (1.390,1.645) 2.056 (1.829,2.310) 1.956 (1.828,2.093) 2.205 (1.636,2.973)
Dyslipidemia 1.175 (1.125,1.228) 2.170 (2.082,2.261) 1.658 (1.546,1.779) 2.747 (2.511,3.006) 1.776 (1.674,1.885) 1.614 (1.260.2.068)
Stroke 1.316 (1.182,1.465) 9.142 (8.160,10.243) 2.523 (2.172,2.931) 3.513 (2.941,4.197) 3.402 (3.024,3.828) 4.156 (2.718,6.353)
Myocardial infarction 1.158 (1.020,1.316) 4.391 (3.903,4.940) 2.297 (1.938,2.723) 2.730 (2.158,3.453) 2.733 (2.354,3.172) 3.237 (1.847,5.675)
Ischemic heart disease 1.142 (1.025,1.272) 4.276 (3.879,4.713) 2.195 (1.874,2.571) 3.885 (3.267,4.621) 2.712 (2.397,3.069) 2.344 (1.357,4.049)
Asthma 1.495 (1.368,1.635) 2.844 (2.615,3.094) 2.629 (2.327,2.970) 3.073 (2.594,3.642) 2.763 (2.498,3.055) 2.554 (1.681,3.882)
Arthritis 1.219 (1.165,1.276) 7.433 (7.114,7.767) 2.440 (2.279,2.613) 3.747 (3.434,4.088) 2.949 (2.789,3.118) 2.405 (1.876,3.083)
Physically inactive group was defined when participant was doing moderate intense activity < three times per week or vigorous activity < one time per week; years of
education (� 12, or > 12); marital status (married/with partner, not married, or divorced/widowed); current employment status (employed or retired/unemployed);
household income (< 7,000,000 won/year or � 7,000,000 won/year); hours of sleep duration (7–9, and <7 or > 9); residence (rural or urban); and medical history
according to physicians’ diagnoses, including hypertension, diabetes mellitus, dyslipidemia, stroke, myocardial infarction, ischemic heart disease, asthma, and arthritis.
https://doi.org/10.1371/journal.pone.0195607.t002
Ambient air pollutants and mental health status
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depression diagnosis by a physician and ambient air pollution quartile. This association may
decrease after adjustment of the known risk factors for depression diagnosis, but a strong asso-
ciation between air pollution and parameters of other mental health status was maintained.
Therefore, air pollution may be an unknown risk factor in other mental health parameters. In
addition, undiagnosed depressive patients may have other risk factors. In generally, it was
known that the risk of mental health disorder was higher in women and the elderly, but air pol-
lution may be an important risk factor for men or persons < 65 years old because these groups
may be exposed to air pollution more frequently with high activity [17, 18]. Except the rate of
subjective stress, women’s mental health status showed more poor than men in this study,
though the rates of suicide attempt were similar. It has been proposed that men’s mental health
status may be masked by alcohol and physical violence, and their diagnosis of depression may
be underdiagnosed [19]. Accordingly, known confounding factors may be correlated with
women’s diagnosed depression from the previous studies [19]. Therefore, air pollutants, as a
new association factor of mental health status, may be found out an independent risk factor and
enhanced the risk for men. Further research is needed to support any such causal relationship
or biological difference. In this study, there was no association between suicide attempts and air
pollution exposure. Suicide attempts represent acute symptom worsening, which may be more
influenced by short-term rather than long-term exposure to ambient air pollutants [5, 20].
Air pollutants may be strong inflammatory agents in psycho-endocrine-immune connec-
tions through an inflammatory process; cyclooxygenase-2, interleukin-1β and particulate- matter–associated lipopolysaccharides [21]. Exposure to air pollutants leads to elevated hippo-
campal pro-inflammatory cytokine expression, and in addition, there are architectural changes
in the dendrites of the hippocampus that can increase depressive-like behaviors in animal
models [22]. Neuroinflammation caused by exposure to air pollution can alter innate immune
responses and even influence human neurodegenerative disease [21].
Fig 1. The odds ratios and 95% confidence intervals of a mental health disorder according to the air pollutant quartile. (A) PM10 (B) NO2 (C) CO (D) SO2.
https://doi.org/10.1371/journal.pone.0195607.g001
Ambient air pollutants and mental health status
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Particulate and gaseous pollutants coexist in the air and may induce adverse health effects.
PM rarely exists by itself within the ambient environment because gaseous, semi-volatile, and
volatile compounds (i.e., aldehydes and polycyclic aromatic hydrocarbons) are constantly
changing and interacting. Many vapor-phase compounds attach to the surface of PM and/or
by themselves form secondary aerosolized particles [1]. The concentrations of PM10 and NO2
Table 3. Air pollution and mental health status according to sex.
Subjective stress Poor quality of life Depressiveness Depression diagnosis by
doctor
Suicidal ideation
Men Women Men Women Men Women Men Women Men Women
PM10
Q4 1.121(1.062,
1.183)
1.089
(1.036,1.145)
1.256
(1.175,1.337)
1.236(1.137,
1.344)
1.342
(1.158,1.556)
1.385
(1.248,1.536)
0.978
(0.758,1.263)
1.022
(0.886,1.179)
1.241
(1.109,1.388)
1.256
(1.154,1.368)
Q3 1.078
(1.028,1.131)
1.033(0.981,
1.087)
1.360
(1.278,1.446)
1.338(1.233,
1.451)
1.442
(1.250,1.662)
1.409
(1.275,1.557)
1.076
(0.849,1.363)
1.101
(0.960,1.262)
1.246
(1.117,1.391)
1.175
(1.081,1.277)
Q2 1.005
(0.955,1.058)
0.998(0.945,
1.053)
1.088
(1.020,1.159)
1.078(0.989,
1.175)
1.208
(1.040,1.403)
1.184
(1.066,1.314)
1.034
(0.804,1.331)
1.026
(0.890,1.182)
1.013
(0.899,1.141)
1.051
(0.965,1.144)
Q1 1 1 1 1 1 1 1 1 1 1
p-inter
action
0.009 0.593 0.741 0.969 0.583
NO2
Q4 1.205(1.140,
1.274)
1.161
(1.104,1.220)
1.587(1.458,
1.727)
1.518
(1.426,1.617)
1.707
(1.479,1.970)
1.389
(1.252,1.542)
1.280(1.010,
1.623)
1.223
(1.066,1.403)
1.319
(1.177,1.478)
1.402
(1.286,1.529)
Q3 1.119(1.057,
1.184)
1.108
(1.053,1.167)
1.140(1.042,
1.274)
1.057
(0.989,1.129)
1.440
(1.238,1.675)
1.158
(1.038,1.291)
1.125(0.879,
1.440)
1.241
(1.069,1.441)
1.074
(0.957,1.205)
1.134
(1.031,1.247)
Q2 1.139(1.079,
1.202)
1.072
(1.021,1.126)
1.054(0.972,
1.142)
0.996
(0.937,1.059)
1.146
(0.991,1.325)
1.128
(1.017,1.251)
1.005(0.783,
1.290)
1.059
(0.929,1.208)
1.045
(0.943,1.158)
1.197
(1.098,1.304)
Q1 1 1 1 1 1 1 1 1 1 1
p-inter
action
0.054 0.391 0.011 0.765 0.205
CO
Q4 1.123(1.064,
1.186)
1.091
(1.038,1.147)
1.196
(1.123,1.274)
1.135(1.045,
1.232)
1.535
(1.338,1.663)
1.524
(1.375,1.689)
1.204(0.946,
1.533)
1.100
(0.964,1.256)
1.318
(1.178,1.476)
1.162
(1.069,1.263)
Q3 1.111(1.051,
1.173)
1.085
(1.032,1.140)
1.433
(1.346,1.526)
1.186(1.091,
1.290)
1.697
(1.465,1.966)
1.584
(1.424,1.763)
1.091(0.852,
1.397)
1.197
(1.039,1.378)
1.290(1.148,
1.450)
1.152(1.057,
1.256)
Q2 1.089(1.032,
1.150)
1.079
(1.027,1.134)
1.212
(1.139,1.290)
1.065(0.980,
1.158)
1.389
(1.176,1.593)
1.369
(1.112,1.643)
0.999(0.776,
1.286)
1.086
(0.951,1.241)
1.163(1.034,
1.307)
1.061(0.975,
1.155)
Q1 1 1 1 1 1 1 1 1 1 1
p-inter
action
0.580 <0.001 0.045 0.633 0.324
SO2
Q4 0.944(0.894,
0.997)
0.973
(0.925,1.023)
0.909
(0.852,0.970)
0.905
(0.835,0.981)
1.146
(0.933,1.323)
0.986
(0.891,1.091)
0.944(0.738,
1.208)
0.804
(0.701,0.922)
0.879
(0.785,0.984)
0.861
(0.791,0.938)
Q3 1.042(0.986,
1.101)
1.059
(1.008,1.114)
1.145
(1.074,1.220)
1.129
(1.037,1.229)
1.345
(1.162,1.557)
1.171
(1.059,1.294)
1.082(0.848,
1.379)
0.977
(0.849,1.125)
1.091
(0.972,1.224)
1.045
(0.959,1.139)
Q2 0.939(0.890,
0.991)
0.962
(0.917,1.010)
0.972
(0.912,1.035)
0.851
(0.783,0.925)
1.013
(0.876,1.172)
1.131
(1.025,1.248)
1.018(0.797,
1.302)
0.834
(0.729,0.955)
0.909
(0.812,1.018)
0.962
(0.885,1.047)
Q1 1 1 1 1 1 1 1 1 1 1
p-inter
action
0.738 0.017 0.002 0.527 0.488
Bold characteristics means P–value <0.05 among the values with p-interaction < 0.05. EQ-5D index was analyzed by logarithmic transformation. Adjustment for age, smoking, drinking, physical activity, education, marital status, employment, household income, sleep duration, residence and medical history (hypertension, diabetes
mellitus, dyslipidemia, stroke, myocardial infarction, ischemic heart disease, asthma, arthritis).
https://doi.org/10.1371/journal.pone.0195607.t003
Ambient air pollutants and mental health status
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are highly correlated because they share the same pathway as that for depression and neuro-
logic disorders [23]; in contrast, NO2 and SO2 may have different physiologic influences on
human health. Although both are gaseous molecules, NO2 is poorly soluble in water, whereas
SO2 is highly water soluble [24].
Table 4. Air pollution and mental health status according to age.
Subjective stress Poor quality of life Depressiveness Depression diagnosis by
doctor
Suicidal ideation
Age � 65 Age < 65 Age � 65 Age < 65 Age � 65 Age < 65 Age � 65 Age < 65 Age � 65 Age < 65
PM10
Q4 0.940(0.872,
1.014)
1.150
(1.101,1.201)
0.949(0.867,
1.038)
1.338
(1.277,1.456)
1.329
(1.137,1.555)
1.383
(1.246,1.535)
0.866
(0.707,1.059)
1.061
(0.910,1.237)
1.192(1.067,
1.331)
1.269
(1.165,1.382)
Q3 0.986(0.918,
1.060)
1.062
(1.018,1.108)
1.101(1.012,
1.198)
1.473
(1.383,1.569)
1.327
(1.142,1.542)
1.446
(1.310,1.600)
0.907
(0.752,1.049)
1.154
(0.996,1.338)
1.211(1.094,
1.341)
1.206
(1.108,1.312)
Q2 0.919(0.854,
0.988)
1.025
(0.979,1.073)
1.006(0.926,
1.094)
1.122
(1.049,1.201)
1.189
(1.028,1.376)
1.200
(1.079,1.334)
1.027
(0.856,1.232)
1.022
(0.874,1.194)
1.054(0.945,
1.175)
1.033
(0.945,1.130)
Q1 1 1 1 1 1 1 1 1 1 1
p-inter
action
<0.001 <0.001 0.688 0.218 0.765
NO2
Q4 1.182
(1.095,1.277)
1.171
(1.120,1.225)
1.203
(1.101,1.314)
1.706
(1.598,1.821)
1.550
(1.341,1.792)
1.478
(1.337,1.633)
1.071
(0.884,1.299)
1.289
(1.107,1.501)
1.319
(1.177,1.478)
1.702
(1.286,1.529)
Q3 1.189
(1.101,1.284)
1.093
(1.044,1.144)
0.843
(0.771,0.923)
1.207
(1.127,1.294)
1.334
(1.144,1.556)
1.223
(1.098,1.362)
1.123
(0.929,1358)
1.231
(1.049,1.444)
1.074
(0.957,1.205)
1.134
(1.031,1.247)
Q2 1.096
(1.023,1.174)
1.109
(1.061,1.160)
0.932
(0.861,1.009)
1.068
(0.999,1.141)
1.101
(0.963,1.258)
1.139
(1.027,1.262)
0.974
(0.818,1.153)
1.080
(0.929,1.254)
1.045
(0.943,1.158)
1.197
(1.098,1.304)
Q1 1 1 1 1 1 1 1 1 1 1
p-inter
action
0.060 <0.001 0.662 0.893 0.181
CO
Q4 1.091
(1.014,1.173)
1.111
(1.063,1.162)
0.971
(0.890,1.061)
1.249
(1.172,1.332)
1.626
(1.398,1.891)
1.463
(1.322,1.618)
1.213
(1.006,1.462)
1.110
(0.961,1.284)
1.115(0.999,
1.244)
1.255
(1.152,1.368)
Q3 1.060
(0.984,1.142)
1.098
(1.051,1.148)
0.999
(0.916,1.090)
1.470
(1.379,1.567)
1.600
(1.368,1.872)
1.635
(1.475,1.811)
1.094
(0.898,1.331)
1.187
(1.020,1.382)
1.145(1.027,
1.277)
1.229
(1.125,1.343)
Q2 1.097
(1.019,1.182)
1.069
(1.025,1.116)
0.997
(0.913,1.089)
1.234
(1.157,1.316)
1.473
(1.260,1.721)
1.504
(1.359,1.666)
1.103
(0.905,1.343)
1.050
(0.905,1.218)
1.027(0.920,
1.147)
1.131
(1.035,1.236)
Q1 1 1 1 1 1 1 1 1 1 1
p-inter
action
0.758 <0.001 0.329 0.602 0.265
SO2
Q4 0.994
(0.921,1.072)
0.955
(0.914,0.998)
0.934
(0.855,1.022)
0.902
(0.846,0.961)
1.272
(1.094,1.479)
0.987
(0.894,1.089)
0.977
(0.804,1.187)
0.901
(0.775,1.043)
0.947
(0.850,1.056)
0.839
(0.771,0.914)
Q3 1.041
(0.962,1.126)
1.047
(1.002,1.095)
1.025
(0.934,1.125)
1.181
(1.109,1.258)
1.341
(1.149,1.563)
1.203
(1.091,1.326)
0.987
(0.817,1.191)
1.195
(0.944,1.271)
1.022
(0.916,1.141)
1.074
(0.986,1.169)
Q2 0.957
(0.889,1.030)
0.947
(0.907,0.989)
0.995
(0.910,1.086)
0.908
(0.851,0.969)
1.193
(1.027,1.386)
1.066
(0.968,1.175)
0.954
(0.781,1.166)
0.935
(0.805,1.085)
0.934
(0.841,1.038)
0.942
(0.865,1.026)
Q1 1 1 1 1 1 1 1 1 1 1
p-inter
action
0.500 <0.001 0.092 0.825 0.104
Bold characteristics means P–value <0.05 among the values with p-interaction < 0.05. EQ-5D index was analyzed by logarithmic transformation. Adjustment for sex, smoking, drinking, physical activity, education, marital status, employment, household income, sleep duration, residence and medical history (hypertension, diabetes
mellitus, dyslipidemia, stroke, myocardial infarction, ischemic heart disease, asthma, arthritis)
https://doi.org/10.1371/journal.pone.0195607.t004
Ambient air pollutants and mental health status
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This study has a number of strengths. For one, we used large, heterogeneous, nationwide
population-based data. We also considered a wide range of known covariates related to depres-
sion, including socioeconomic status [25]. Therefore, we were able to determine the effects of
ambient air pollution as an independent risk factor for poor mental health. In particular, previ-
ously known risk factors were related to women and the elderly, but this study confirmed that
air pollution was a risk factor for mental health disorders in men and individuals < 65 years
old.
This study also has several limitations. First, it was not possible to establish causality
between air pollution and mental health disorders in this cross-sectional study. Second, we
matched the community and air pollutant levels using participants’ home territories. There-
fore, if participants worked far from their domiciles, the matching would not have accurately
reflected air pollutant levels at their dwellings. Third, we focused on each pollutant and its
respective effect on mental health status. However, the adverse effects of pollution on mental
health may be caused by unmeasured pollutants, such as PM2.5 or ozone [26], or combinations
of multiple pollutants [6], which the effects of complex mixtures of constituent toxins on men-
tal health cannot be explained in this study. Additionally, KCHS did not include the surveyed
day to protect personal information, which linked each other. Weather condition may affect
the level of air pollution and human activity, however, we cannot adjust them [13]. However,
KCHS surveyed during three months, which had stable weather conditions from August to
October without monsoon. Therefore, the weather effect would be relatively minimized.
Lastly, the prevalence of depression in this study (2.49%) was lower than that found in previous
studies of Koreans (3.7%) and Canadians (3.9%) [27, 28]. Therefore, we may have underesti-
mated the prevalence of mental health disorders in these nationwide population-based KCHS
data.
Conclusions
Long-term exposure to ambient air pollution was a risk factor of a wide range of potential
mental health disorders. Future investigations must not only include more studies to deter-
mine the mechanisms of action but also examine the effect of demographic characteristics.
This information is helpful to make a policy to control air pollution and to understand the
action of air pollutant on the body correctly.
Supporting information
S1 Table. The ambient air pollutants and meteorological data in Korea. Particulate matter
<10 μm (PM10); Sulfur dioxide (SO2); Nitrogen dioxide (NO2); Carbon monoxide (CO). Tem- perature, rainfall and wind speed were shown at Seoul (Lat.(N) 37˚34´, Long.(E) 126˚57´).
Korea Meteorological Administration, Seoul, Korea (Aug. 2012- July. 2013) http://www.kma.
go.kr/repositary/sfc/pdf/sfc_ann_2013.pdf.
(DOCX)
Acknowledgments
The authors would like to thank the Korea Centers for Disease Control and Prevention for the
data obtained from the 2013 Community Health Survey and the National Institute of Environ-
mental Research.
Author Contributions
Conceptualization: Jinyoung Shin, Jin Young Park, Jaekyung Choi.
Ambient air pollutants and mental health status
PLOS ONE | https://doi.org/10.1371/journal.pone.0195607 April 9, 2018 10 / 12
Data curation: Jinyoung Shin.
Formal analysis: Jinyoung Shin.
Investigation: Jinyoung Shin, Jaekyung Choi.
Methodology: Jinyoung Shin, Jin Young Park.
Supervision: Jin Young Park, Jaekyung Choi.
Validation: Jin Young Park.
Visualization: Jin Young Park.
Writing – original draft: Jinyoung Shin.
Writing – review & editing: Jin Young Park, Jaekyung Choi.
References 1. Sun Q, Hong X, Wold LE. Cardiovascular effects of ambient particulate air pollution exposure. Circula-
tion. 2010; 121(25):2755–65. https://doi.org/10.1161/CIRCULATIONAHA.109.893461 PMID:
20585020; PubMed Central PMCID: PMCPMC2924678.
2. Lim YH, Kim H, Kim JH, Bae S, Park HY, Hong YC. Air pollution and symptoms of depression in elderly
adults. Environmental health perspectives. 2012; 120(7):1023–8. Epub 2012/04/20. https://doi.org/10.
1289/ehp.1104100 PMID: 22514209; PubMed Central PMCID: PMCPMC3404652.
3. Cho J, Choi YJ, Suh M, Sohn J, Kim H, Cho SK, et al. Air pollution as a risk factor for depressive episode
in patients with cardiovascular disease, diabetes mellitus, or asthma. Journal of affective disorders.
2014; 157:45–51. Epub 2014/03/04. https://doi.org/10.1016/j.jad.2014.01.002 PMID: 24581827.
4. Szyszkowicz M, Rowe BH, Colman I. Air pollution and daily emergency department visits for depres-
sion. International journal of occupational medicine and environmental health. 2009; 22(4):355–62.
Epub 2009/01/01. https://doi.org/10.2478/v10001-009-0031-6 PMID: 20197262.
5. Szyszkowicz M, Willey JB, Grafstein E, Rowe BH, Colman I. Air pollution and emergency department
visits for suicide attempts in vancouver, Canada. Environmental health insights. 2010; 4:79–86. Epub
2010/11/17. https://doi.org/10.4137/EHI.S5662 PMID: 21079694; PubMed Central PMCID:
PMCPMC2978939.
6. Oudin A, Astrom DO, Asplund P, Steingrimsson S, Szabo Z, Carlsen HK. The association between
daily concentrations of air pollution and visits to a psychiatric emergency unit: a case-crossover study.
Environmental health: a global access science source. 2018; 17(1):4. Epub 2018/01/13. https://doi.org/
10.1186/s12940-017-0348-8 PMID: 29321054; PubMed Central PMCID: PMCPMC5763570.
7. Kim KN, Lim YH, Bae HJ, Kim M, Jung K, Hong YC. Long-Term Fine Particulate Matter Exposure and
Major Depressive Disorder in a Community-Based Urban Cohort. Environmental health perspectives.
2016; 124(10):1547–53. Epub 2016/04/30. https://doi.org/10.1289/EHP192 PMID: 27129131; PubMed
Central PMCID: PMCPMC5047772 interests.
8. Kim Y, Myung W, Won HH, Shim S, Jeon HJ, Choi J, et al. Association between air pollution and suicide
in South Korea: a nationwide study. PloS one. 2015; 10(2):e0117929. Epub 2015/02/19. https://doi.org/
10.1371/journal.pone.0117929 PMID: 25693115; PubMed Central PMCID: PMCPMC4333123.
9. Wang Y, Eliot MN, Koutrakis P, Gryparis A, Schwartz JD, Coull BA, et al. Ambient air pollution and
depressive symptoms in older adults: results from the MOBILIZE Boston study. Environmental health
perspectives. 2014; 122(6):553–8. Epub 2014/03/13. https://doi.org/10.1289/ehp.1205909 PMID:
24610154; PubMed Central PMCID: PMCPMC4050499.
10. Zijlema WL, Wolf K, Emeny R, Ladwig KH, Peters A, Kongsgard H, et al. The association of air pollution
and depressed mood in 70,928 individuals from four European cohorts. International journal of hygiene
and environmental health. 2016; 219(2):212–9. Epub 2015/12/20. https://doi.org/10.1016/j.ijheh.2015.
11.006 PMID: 26682644.
11. Seo J, Choi B, Kim S, Lee H, Oh D. The relationship between multiple chronic diseases and depressive
symptoms among middle-aged and elderly populations: results of a 2009 korean community health sur-
vey of 156,747 participants. BMC public health. 2017; 17(1):844. Epub 2017/10/27. https://doi.org/10.
1186/s12889-017-4798-2 PMID: 29070021; PubMed Central PMCID: PMCPMC5657127.
12. Sun Taek Kim. Quality control of Air pollution Monitoring System and establishment of Data Evaluation
Scheme(I). Korean Air Pollutants Emission Service2015.
Ambient air pollutants and mental health status
PLOS ONE | https://doi.org/10.1371/journal.pone.0195607 April 9, 2018 11 / 12
13. Choi Y, Ghim YS. Assessment of the clear-sky bias issue using continuous PM10 data from two AERO-
NET sites in Korea. Journal of environmental sciences (China). 2017; 53:151–60. Epub 2017/04/05.
https://doi.org/10.1016/j.jes.2016.02.020 PMID: 28372739.
14. Ayanlade A, Oyegbade EF. Influences of wind speed and direction on atmospheric particle concentra-
tions and industrially induced noise. SpringerPlus. 2016; 5(1):1898. Epub 2016/11/16. https://doi.org/
10.1186/s40064-016-3553-y PMID: 27843755; PubMed Central PMCID: PMCPMC5084142.
15. Lee YK, Nam HS, Chuang LH, Kim KY, Yang HK, Kwon IS, et al. South Korean time trade-off values for
EQ-5D health states: modeling with observed values for 101 health states. Value Health. 2009; 12
(8):1187–93. https://doi.org/10.1111/j.1524-4733.2009.00579.x PMID: 19659703.
16. Kim J, Kim H. Demographic and Environmental Factors Associated with Mental Health: A Cross-Sec-
tional Study. Int J Environ Res Public Health. 2017; 14(4). https://doi.org/10.3390/ijerph14040431
PMID: 28420189; PubMed Central PMCID: PMCPMC5409632.
17. Hong J, Zhong T, Li H, Xu J, Ye X, Mu Z, et al. Ambient air pollution, weather changes, and outpatient
visits for allergic conjunctivitis: A retrospective registry study. Scientific reports. 2016; 6:23858. Epub
2016/04/02. https://doi.org/10.1038/srep23858 PMID: 27033635; PubMed Central PMCID:
PMCPMC4817244.
18. Mannucci PM, Franchini M. Health Effects of Ambient Air Pollution in Developing Countries. Int J Envi-
ron Res Public Health. 2017; 14(9). Epub 2017/09/13. https://doi.org/10.3390/ijerph14091048 PMID:
28895888; PubMed Central PMCID: PMCPMC5615585.
19. Seidler ZE, Dawes AJ, Rice SM, Oliffe JL, Dhillon HM. The role of masculinity in men’s help-seeking for
depression: A systematic review. Clin Psychol Rev. 2016; 49:106–18. Epub 2016/10/26. https://doi.org/
10.1016/j.cpr.2016.09.002 PMID: 27664823.
20. Kim C, Jung SH, Kang DR, Kim HC, Moon KT, Hur NW, et al. Ambient particulate matter as a risk factor
for suicide. The American journal of psychiatry. 2010; 167(9):1100–7. Epub 2010/07/17. https://doi.org/
10.1176/appi.ajp.2010.09050706 PMID: 20634364.
21. Calderon-Garciduenas L, Solt AC, Henriquez-Roldan C, Torres-Jardon R, Nuse B, Herritt L, et al. Long-
term air pollution exposure is associated with neuroinflammation, an altered innate immune response,
disruption of the blood-brain barrier, ultrafine particulate deposition, and accumulation of amyloid beta-
42 and alpha-synuclein in children and young adults. Toxicol Pathol. 2008; 36(2):289–310. https://doi.
org/10.1177/0192623307313011 PMID: 18349428.
22. Fonken LK, Xu X, Weil ZM, Chen G, Sun Q, Rajagopalan S, et al. Air pollution impairs cognition, pro-
vokes depressive-like behaviors and alters hippocampal cytokine expression and morphology. Molecu-
lar psychiatry. 2011; 16(10):987–95, 73. Epub 2011/07/06. https://doi.org/10.1038/mp.2011.76 PMID:
21727897; PubMed Central PMCID: PMCPMC3270364.
23. Maes M, Kubera M, Obuchowiczwa E, Goehler L, Brzeszcz J. Depression’s multiple comorbidities
explained by (neuro)inflammatory and oxidative & nitrosative stress pathways. Neuro endocrinology let-
ters. 2011; 32(1):7–24. Epub 2011/03/17. PMID: 21407167.
24. Greenberg N, Carel RS, Derazne E, Tiktinsky A, Tzur D, Portnov BA. Modeling long-term effects attrib-
uted to nitrogen dioxide (NO2) and sulfur dioxide (SO2) exposure on asthma morbidity in a nationwide
cohort in Israel. Journal of toxicology and environmental health Part A. 2017; 80(6):326–37. Epub 2017/
06/24. https://doi.org/10.1080/15287394.2017.1313800 PMID: 28644724.
25. Pun VC, Manjourides J, Suh H. Association of Ambient Air Pollution with Depressive and Anxiety Symp-
toms in Older Adults: Results from the NSHAP Study. Environmental health perspectives. 2017; 125
(3):342–8. Epub 2016/08/16. https://doi.org/10.1289/EHP494 PMID: 27517877; PubMed Central
PMCID: PMCPMC5332196.
26. Kioumourtzoglou MA, Power MC, Hart JE, Okereke OI, Coull BA, Laden F, et al. The Association
Between Air Pollution and Onset of Depression Among Middle-Aged and Older Women. American jour-
nal of epidemiology. 2017; 185(9):801–9. Epub 2017/04/04. https://doi.org/10.1093/aje/kww163 PMID:
28369173; PubMed Central PMCID: PMCPMC5411676.
27. Patten SB, Williams JV, Lavorato DH, Wang JL, McDonald K, Bulloch AG. Descriptive epidemiology of
major depressive disorder in Canada in 2012. Canadian journal of psychiatry Revue canadienne de
psychiatrie. 2015; 60(1):23–30. Epub 2015/04/18. https://doi.org/10.1177/070674371506000106
PMID: 25886546; PubMed Central PMCID: PMCPMC4314053.
28. Oh DH, Kim SA, Lee HY, Seo JY, Choi BY, Nam JH. Prevalence and correlates of depressive symp-
toms in korean adults: results of a 2009 korean community health survey. J Korean Med Sci. 2013; 28
(1):128–35. https://doi.org/10.3346/jkms.2013.28.1.128 PMID: 23341723; PubMed Central PMCID:
PMCPMC3546091.
Ambient air pollutants and mental health status
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