RESEARCH ARTICLE

The association between ambient air pollution exposure and mentalhealth status in Chinese female college students: a cross-sectional study

Guoyuan Sui1 & Guangcong Liu2& Lianqun Jia1 & Lie Wang3

& Guanlin Yang1

Received: 19 October 2017 /Accepted: 31 July 2018 /Published online: 7 August 2018# Springer-Verlag GmbH Germany, part of Springer Nature 2018

AbstractThe association between exposure to air pollution and mental health has not been adequately studied. Accordingly, this studyaimed to explore the association between exposure to ambient air pollution and mental health status among female collegestudents. We performed a cross-sectional study involving female students attending college located in Anshan, a heavy industrycity in Northeast China. The investigation was performed using electronic questionnaires including the Symptom Checklist-90(SCL-90), Pittsburgh Sleep Quality Index (PSQI), Cornell Medical Index (CMI), and general well-being (GWB) scale betweenMarch and April 2017. The individual daily average of time spent outdoors in each seasonwas used as an indicator of exposure toambient air pollution. The association between mental health status and exposure to ambient air pollution was analyzed usinggeneral linear regression. Of the 412 female participants, 346 (83.98%) submitted valid questionnaires. Multivariate linearregression indicated that GWB was negatively associated with the SCL-90 score, and annual average daily outdoor time andsleep quality were positively associated with the SCL-90 score. This study demonstrated that exposure to ambient air pollutionmay be a risk factor for mental health problems among female college students.

Keywords Air pollution .Mental health . Female college students

Background

The adverse effects of air pollution, especially fine particulatematter (PM2.5) which is a Bhot topic^ in the study of atmo-spheric contaminants, have become a global problem in healthcare in recent decades (World Health Organization 2018).Several epidemiological studies have been performed to as-sess the influence of ambient air pollution on public health. Todate, it has been demonstrated that ambient air pollution can

impair the respiratory, immune, nervous, and circulation sys-tems, and it has also been associated with elevated mortalityrates (Chen and Schwartz 2009; Chowdhury et al. 2018;Jayaraj et al. 2017; Kantipudi et al. 2016; Kelly and Fussell2017; Mortazavi 2018).

Meanwhile, other studies have focused on the psychologi-cal effect of air pollution. Zeigelboim et al. (2015) indicatedthat carbon monoxide may elevate the risk for anxiety anddepression in Brazilian fishermen. Navarro et al. (1987) foundthat air pollution was correlated with trait anxiety in 100 res-idents of Chile. Furthermore, Persson et al. (2007) supportedthis finding by reporting that nitrogen oxide emissions (NOx)were positively correlated with trait anxiety in adults. Power etal. (2015) also indicated a positive association between PM2.5

and anxiety in elderly women, and that recent exposure had astronger association. Rocha et al. (2012) reported a significantassociation between self-reported environmental exposures(including odor and air pollution) and common mental disor-ders in a large general population from Spain, even afteradjusting for all covariables.

Regarding the effect of particulate matter, Lim et al. (2012)suggested that increases in PM10 were a possible risk factorfor depressive symptoms in an elderly population fromKorea.

Responsible editor: Philippe Garrigues

* Guanlin Yangsgy871008@126.com

1 Key Laboratory of Ministry of Education for Traditional ChineseMedicine Viscera-State Theory and Applications, LiaoningUniversity of Traditional Chinese Medicine,110847 Shenyang, Liaoning, People’s Republic of China

2 Shenyang Academy of Environmental Sciences,110167 Shenyang, Liaoning, People’s Republic of China

3 School of Public Health, China Medical University,110013 Shenyang, Liaoning, People’s Republic of China

Environmental Science and Pollution Research (2018) 25:28517–28524https://doi.org/10.1007/s11356-018-2881-6

Kioumourtzoglou et al. (2017) identified a possible associa-tion between PM2.5 and depression among middle age andolder women from the USA, which was similar to the findingsof a study by Pun et al. (2017), in which PM2.5 was associatedwith depressive and anxiety symptoms.

However, current studies have not yet covered all popula-tion types, given that most of the abovementioned studiesinvolved general or elderly populations. Some special popu-lations, such as college students, have been reported to bemore susceptible to psychological problems (Pacheco et al.2017; Rotenstein et al. 2016; Shi et al. 2015; Shi et al.2016). Therefore, the adverse psychological effect of air pol-lution on these particular populations should be investigated.For example, Luo (2008) found that college students’ psycho-logical health level was lower than the normal standard, andlower in girls, indicating that these issues have not been givensufficient attention. In addition, previous studies have foundsex-related differences in the negative effects of air pollution,and females were more vulnerable to outdoor air pollution(Kan et al. 2008). However, more studies similar to theseremain urgently needed.

Accordingly, this cross-sectional study aimed to explorethe mental health status of Chinese female college students,as well as possible associations between exposure to ambientair pollution and mental health.

Methods

Study protocol

This cross-sectional study was performed between March andApril 2017 in Anshan, China. Anshan is a heavy industry-based city located in the Northeast China, with a populationof > 1 million. The average air quality index (AQI) is 95.9with an average PM2.5 concentration of 65.6 μg/m3 over thethree-year period from 2014 to 2016 (data from theEnvironmental Protection Bureau of China).The recommend-ed annual concentration of PM2.5, based on the World HealthOrganization (WHO), is nomore than 10μg/m3(World HealthOrganization 2016). According to the United StatesEnvironmental Protection Agency (USEPA), a PM2.5 concen-tration of > 55 μg/m3 is considered to be Bunhealthy,^ indicat-ing that participants in the present study were exposed to rel-atively high levels of ambient PM2.5.

All participants were informed about the general informa-tion contained in the questionnaires, and each must provideinformed consent before participating in the investigation. Thesample consisted of first-, second-, and third-year undergrad-uates from 20 randomly selected classes. A total of 412 femalecollege students were investigated. The protocol was ap-proved by the ethical standards of the Committee on HumanExperimentation of China Medical University.

Instruments

Symptom Checklist-90

SymptomChecklist-90 (SCL-90) consists of 90 items and wasused to assess the status of a samples’ mental health (Zhangand Zhang 2013). Each item was assessed using a 5-pointLikert scale, ranging from B1^ (Bnot at all^) to B5^ (extreme-ly), with lower scores reflecting better mental health.

Pittsburgh Sleep Quality Index

The Pittsburgh Sleep Quality Index (PSQI) was used to assessthe women’s sleep quality (Liu et al. 1996). The PSQI is awidely used self-report scale that contains 19 individual items.It has seven components such as subjective sleep quality, sleeplatency, and sleep duration, which can also represent eachaspect of sleep quality. Each component is assigned a scoreof 0–3 interval scale. The total PSQI score, which ranges from0 to 21, was generated by adding the seven component scoresto represent an individual’s sleep quality. The lower the par-ticipant score, the better the sleep quality.

General well-being scale

The general well-being (GWB) scale was used to assess par-ticipants’ general well-being (Wang et al. 1999). The GWB isa self-administered scale that provides a general well-beingindex. Eighteen items were used to assess life satisfactionand level of psychological distress. There are six subscalesmeasuring anxiety, depression, positive well-being, self-con-trol, vitality, and general health. The total score ranges from 0to 110 with lower scores indicating more severe distress.

Cornell Medical Index

The Cornell Medical Index (CMI) was adopted to examinethe self-reported physical symptoms. Taking into account ofthe adverse effects reported by published studies (WorldHealth Organization 2018; Kim et al. 2016), subscales ofthe eye and ear, nervous system, respiratory system, skin,and digestive tract were used to investigate the correspond-ing symptom(s).

Exposure to ambient air pollution

Because most students lived in the dormitory of the samecampus, it was assumed that all participants were exposed tothe same concentrations of ambient air pollution. Therefore,outdoor time was the preferred indicator of air pollution ex-posure, and individual exposure was evaluated by investigat-ing average outdoor time. The average daily outdoor time wasassessed by investigating the time spent outdoors for three

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time periods: before 12:00, between 12:00 and 18:00, andafter 18:00. The average daily exposure time in each seasonwas investigated, as well as the average daily exposure timeduring the past 30 days.

Demographic factors

Demographic factors included the following: name, age,height, weight, birth date, average monthly expenses, al-cohol use, smoking, green tea intake, black tea intake,coffee intake, carbonated beverage intake, drinking watersource, soy food intake, fruit intake, physical exercise,time of physical exercise, average daily duration of cellphone use, average daily duration of calling using cellphone, average daily duration of computer use, layingthe laptop on the lap, bedroom odor(s), odors in otherusual places, blood pressure, blood glucose level, historyof disease, protection against air pollution, father’s educa-tion level, father’s occupation, father’s smoking, father’salcohol use, mother’s education level, mother’s occupation,mother’s smoking, and mother’s alcohol use.

All questions were posted on the Internet with the help ofthe Sojump website (https://www.sojump.com/), so that theparticipants could answer them online. Sojump is a websitethat is specially designed for online investigations. Allquestions were entered into the system to generate anelectronic questionnaire, and also generated a uniformresource locator (URL) that linked to the questionnaire. Thewebsite would check the status of answers automatically. Onlyquestionnaires in which all questions were answered could besubmitted, which ensured the integrity of the answers.

Statistical analysis

Independent sample t test and one-way analysis of variance(ANOVA) were used to examine the distributions of mentalhealth factors among the categorical variables. Univariate lin-ear regression was performed to examine the relationshipsbetween continuous variables and SCL-90 scores among fe-male college students. Subsequently, multivariate linear re-gression was performed to explore the adjusted associationbetween ambient air pollution exposure, other potential fac-tors, and SCL-90 scores. The analyses were performed usingSPSS 19.0; all figures were created by GraphPad Prism 5 andR statistic (version 3.5.1, the comprehensive R ArchiveNetwork, http://cran.r-project.org/); a two-tailed p < 0.05 wasconsidered to be statistically significant.

Results

Demographic factors

Of the 412 participants, 346 valid questionnaires were collect-ed. The mean age of the participants was (19.87 ± 1.46) years,with a mean body mass index (BMI) of 20.45 ± 2.81 kg/m2.Of the 346 female students, 23.99% (n = 83) spent < 1000Yuan per month, 54.05% (n = 187) spent 1000–1500 Yuanper month, and 21.96% (n = 76) spent more than 1500 Yuanper month. Few of the women had alcohol (n = 10) orsmoking (n = 9) habits, and 64.16% (n = 222) drank mineralwater or purified water as their main source of drinking water.The proportion of subjects who drank black tea, green tea, andcoffee was 15.03% (n = 59), 15.61% (n = 54), and 21.10%

Fig. 1 Average monthly expenses(Yuan), smoking (cigarettes/week), alcohol use, green teaintake, black tea intake, coffeeintake, carbonated beverageintake, drinking water source, andsoy food intake (times/week)

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(n = 73), respectively. There were 194 (56.7%) women whoused their cell phones for > 5 h/day, whereas 73.99% (n = 256)used their cell phones for calls < 0.5 h/day. Average computeruse among 294 women (84.97%) was < 1 h/day.

Regarding characteristics of the father, 42.20% (n = 146)smoked, 45.38% (n = 189) used alcohol, and 60.40% (n =209) had a high school or above education level. As formothers, 2.89% (n = 10) smoked, 1.73% (n = 6) used alcohol,and 65.38% (n = 220) had high school or above education level.Detailed demographic information is shown in Figs. 1, 2, and 3.

Exposure to ambient air pollution

The mean daily exposure to air pollution in the past 30 dayswas 2.47 ± 1.92 h. The mean daily exposure in the spring(March to May), summer (June to August), autumn(September to November), and winter (December toFebruary) was 2.37 ± 1.58, 1.93 ± 1.49, 1.90 ± 1.48, and1.69 ± 1.44 h, respectively. The annual average daily exposuretime was 1.97 ± 1.27 h. Details of daily exposure to ambientair pollution are presented in Fig. 4.

Fig. 2 Fruit intake (times/week),physical exercise, time ofphysical exercise (h/day), cellphone use (h/day), calling usingcell phone (h/day), computer use(h/day), laying the laptop on thelap, bedroom odor(s) and odors inother usual places

Fig. 3 Blood pressure, bloodglucose level, history of disease,air pollution protection, father’seducation level, father’soccupation, father’s smoking,father’s alcohol use, mother’seducation level, mother’soccupation, mother’s smoking,mother’s alcohol use

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Results of the SCL-90, PSQI, GWB, and CMI

The mean total score of the SCL-90 among the women was128.32 ± 40.51, and mean scores of all ten subscales rangedfrom 8.28 to 18.65, indicating that general mental healthamong the samples was relatively good. The results of theCMI demonstrated that the eyes, respiratory system, skin, ner-vous and digestive systems were in good health. The meanscore of the PSQI among women was 3.81 ± 2.80, suggestingthat general sleep quality was good. The mean score of theGWB was 74.69 ± 13.05, indicating that women had a strongsense of well-being. The results of the SCL-90, PSQI, GWB,and CMI are summarized in Fig. 5.

Statistical results

Independent sample t test and one-way ANOVA

There was no difference in the SCL-90 score among thegroups according to mother’s occupations. There was no dif-ference in the SCL-90 score according to father’s educationlevel. However, the SCL-90 score differed significantly ac-cording to mother’s educational level (p = 0.010).

No differences were found in the SCL-90 score accordingto average monthly expenses, main drinking water source,green tea, carbonated beverage, or black tea intake. But, theSCL-90 score was significantly lower in those who reportedstimulus odor(s) in their bedrooms (p = 0.022).

Linear regression

According to univariate regression analysis, the average dailyexposure time during the past year and during the past 30 dayswas correlated with the SCL-90 score (B = 4.27, p = 0.013;B = 2.29, p = 0.044, respectively), as was sleep quality (B =6.98, p < 0.001). BMI or average monthly expenses or thetime of physical exercise was not correlated with the SCL-90 score. Average daily computer (B = 18.83, p < 0.001) andcell phone (B = 4.72, p < 0.001) use was associated with theSCL-90 score; however, the average daily duration of callsusing a cell phone was not significant. Symptoms involvingthe eyes and ears, nervous, digestive and respiratory systems,and skin from the CMI were all associated with the SCL-90score. Table 1 summarizes the results of the univariate regres-sion analysis in detail.

Multivariate regression showed that, after adjusting forthe confounding factors, nervous system symptom score,general well-being, sleep quality, and annual average dai-ly exposure were all significantly associated with theSCL-90 score (Table 2).

Discussion

This study investigated the mental health status of femalecollege students. We found that mental health status was sim-ilar to normal standards, but scores in somatization, hostility,and depression were significantly higher (Tong 2010; Zhangand Luo 1998), which was consistent with a previous study(Luo 2008). This suggested that Chinese female college stu-dents may have worse mental health status.

Fig. 4 Daily exposure to ambient air pollution. (1) The past 30 days. (2)March to May. (3) June to August. (4) September to November. (5)December to February. (6) The whole year

Fig. 5 Scores of SCL, PSQI, GWB, and CMI SCL-90 SymptomChecklist-90, CMI Cornell Medical Index, PSQI Pittsburgh SleepQuality Index, GWB general well-being. (1) SCL-90 score. (2)

Somatization. (3) Obsessive symptoms. (4) Interpersonal sensitivity. (5)Depression. (6) Anxiety. (7) Hostility. (8) Fear. (9) Stubborn. (10)Psychotic. (11) Others. (12) GWB

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The results showed that the SCL-90 score was negativelyaffected by stimulus odor(s) in bedrooms, indicating that long-term exposure to indoor stimulus odor may deteriorate mentalhealth. The most common indoor odor in China is from dec-orations or furniture, which commonly contain formaldehyde.Formaldehyde is a type of irritating gas that can cause cough,skin redness, and blurred vision. The physical symptomscaused by a long-term exposure to formaldehyde may inducefurther psychological problems.

Regarding the most important finding of this study, multivar-iate linear regression revealed that general well-being, sleep qual-ity, and exposure to ambient air pollution were all correlated withthe SCL-90 score after fully adjusting for potential covariates.This was the first study to find an association between mentalhealth status and air pollution exposure among female collegestudents, which was consistent with previous studies involvingother populations (Kioumourtzoglou et al. 2017; Lim et al. 2012;Pun et al. 2017; Persson et al. 2007; Power et al. 2015; Rocha etal. 2012; Vert et al. 2017). In China, the main pollutants inambient air include PM2.5, PM10, ozone, NOx, sulfur dioxide(SO2), and carbonmonoxide. PM2.5 andNOx have been reportedto be associated with anxiety, depressive symptoms, and other

psychological symptoms (Kioumourtzoglou et al. 2017; Lim etal. 2012; Pun et al. 2017; Persson et al. 2007; Power et al. 2015;).

The mechanism of air pollution’s effect on mental healthremains unclear; however, current studies have already pro-vided clues. Block et al. (2004) reported that diesel-exhaustparticles can produce proinflammatory factors and reactiveoxygen species. Moreover, PM2.5 has been shown to increaseneuroinflammation, oxidative stress, cerebrovascular damage,and neurodegeneration (Block and Calderon-Garciduenas2009; MohanKumar et al. 2008). General air pollution alsohas been associated with increases in proinflammatorymarkers in the blood (Fonken et al. 2011; Thompson et al.2010). PM2.5 can also increase the levels of glucocorticoidactivity markers and levels of the stress hormone cortisol(Thomson et al. 2013). Moreover, air pollution exposure hasalso been found to increase systemic oxidative stress (Kelly2003; Risom et al. 2005), and there is evidence that oxidativepathways can enhance the risk for depression (Ng et al. 2008).Previous studies have reported similar results regarding theassociation between mental health status and sleep quality.Hayashino et al. (2010) found that sleep quality was associat-ed with depression, and a recently published meta-analysis onthis topic, which synthesized the results from 3069 individualsconsolidated this association (Becker et al. 2017).

Limitations of this investigation must be addressed. First,this was not a multi-center study; therefore, extrapolation ofour findings may not be as persuasive as those of multi-centerstudies. Second, due to the study design, cross-sectional studiescannot determine the causality between ambient air pollutionand mental health status. However, limitations of this studyshould not deny the findings of this study because, for the firsttime, we have provided possible evidence that air pollution canaffect the mental health of female college students.

Table 1 Univariate linearregression on the associatedfactors of the SCL-90 score

No. Variables B SD P value

1 Annual average daily exposure 4.27 1.70 0.013*

2 Average daily exposure in the past 30 days 2.29 1.13 0.044*

3 General well-being − 2.17 0.12 0.000**

4 Sleep quality 6.98 0.68 0.000**

5 BMI 0.22 0.78 0.776

6 Eye symptom score 7.46 1.12 0.000**

7 Respiratory symptom score 6.68 0.72 0.000**

8 Skin score 13.87 1.74 0.000**

9 Nerve system symptom score 10.31 0.90 0.000**

10 Digestive system symptom score 3.49 0.55 0.000**

11 Average daily duration of cell phone use 4.72 2.23 0.035*

12 Average daily duration of calling using cell phone 4.43 3.16 0.162

13 Average daily duration of computer use 18.83 4.42 0.000**

14 Time of physical exercise 0.69 2.96 0.815

15 Average monthly expenses 1.10 2.74 0.690

SCL-90: Symptom Checklist-90; BMI body mass index; *:p < 0.05, **:p < 0.01

Table 2 Multivariate linear regression on the associated factors of theSCL-90 score

No. Variables B SD P value

1 Annual average daily exposure 3.03 1.49 0.043*

2 Sleep quality 2.14 0.68 0.002**

3 General well-being − 1.56 0.15 0.000**

4 Neutral system symptom score 2.97 1.19 0.013*

SCL-90, Symptom Checklist 90; *:p < 0.05, **:p < 0.01

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Conclusion

This was the first study to report an association between men-tal health status and air pollution exposure among female col-lege students. We found that ambient air pollution exposuremay be a risk factor for mental health problems among femalecollege students. And stimulus odor(s) in bedroom, generalwell-being, and sleep quality were also found to be associatedwith mental health.

Compliance with ethical standards

The protocol was approved by the ethical standards of the Committee onHuman Experimentation of China Medical University.

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