Research Paper-Two Parts SECOND PART-3pages Third Part 4 pages 7 pages total

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

* cjk@kuh.ac.kr

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

PLOS ONE | https://doi.org/10.1371/journal.pone.0195607 April 9, 2018 2 / 12

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.

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