Outdoor air pollution in relation to sick building syndrome (SBS) symptoms among residents in Shanghai, China

Accepted Manuscript

Outdoor Air Pollution in Relation to Sick Building Syndrome (SBS) Symptoms among Residents in Shanghai, China Chanjuan Sun , Jialing Zhang , Yuchao Guo , Qingyan Fu , Wei Liu , Jun Pan , Yanmin Huang , Zhijun Zou , Chen Huang PII: DOI: Reference:

S0378-7788(17)33366-2 10.1016/j.enbuild.2018.06.005 ENB 8607

To appear in:

Energy & Buildings

Received date: Revised date: Accepted date:

24 November 2017 3 May 2018 4 June 2018

Please cite this article as: Chanjuan Sun , Jialing Zhang , Yuchao Guo , Qingyan Fu , Wei Liu , Jun Pan , Yanmin Huang , Zhijun Zou , Chen Huang , Outdoor Air Pollution in Relation to Sick Building Syndrome (SBS) Symptoms among Residents in Shanghai, China , Energy & Buildings (2018), doi: 10.1016/j.enbuild.2018.06.005

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Highlights: 

The incidence of sick building syndrome among residents in urban area was higher than that in suburban area in Shanghai.

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Exposure to the residential environment with high concentration of outdoor air pollutants is a risk factor for SBS symptoms. NO2, SO2, PM10 and the weighted mixture of them were significantly in relation

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to part of the SBS symptoms.

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Outdoor Air Pollution in Relation to Sick Building Syndrome (SBS) Symptoms among Residents in Shanghai, China

Chanjuan Suna, Jialing Zhanga, Yuchao Guoa, Qingyan Fub, Wei Liua,c,d, Jun Panc, Yanmin

a

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Huangc, Zhijun Zoua, Chen Huanga,*

School of Environment and Architecture, University of Shanghai for Science and Technology,

Shanghai, China Shanghai Environmental Monitoring Center, Shanghai, China

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Department of Building Science, Tsinghua University, Beijing, China

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Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Beijing, China

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*Corresponding author: Chen Huang. E-mail: [email protected]

Abstract: In order to investigate the relationship between outdoor air pollution and sick building syndrome (SBS) symptoms, the CCHH (China, Children, Home, Health) group in

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Shanghai conducted a cross-sectional survey on the current incidence of SBS symptoms among residents in five districts of Shanghai during April 2011- April 2012. It also collected

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the related outdoor air pollutants data from Shanghai environmental monitoring center (SEMC), including nitrogen dioxide (NO2), sulfur dioxide (SO2) and particles with aerodynamic diameter less than 10 microns (PM10). The logistic regression models were used in this paper

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to study the associations between air pollutants and SBS symptoms. Results were found that the daily mean concentrations of NO2 and SO2 met standard requirement, but that of PM10 exceeded the limit values of the standard in China during this survey. The significant differences were found between the pollutants concentration in urban and suburban area. In addition, the incidences of three categories of SBS symptoms among residents were 79.1% for general symptoms (GS), 65.2% for mucous membrane symptoms (MS) and 35.7% for skin symptoms (SS), respectively. Multiple logistic regression analysis by two case studies illustrated that three outdoor air pollutants, both their concentration quartiles and equal increment, were all associated to and taken as the risk factors for SBS symptoms, for GS

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ORNO2=1.62 (1.18-2.22), MS OR NO2=1.58 (1.21-2.06) and SS OR PM10= 1.21 (1.09-1.35). Furthermore, the new pollutants expressed as the mixtures of their combination were also significantly associated with part of the SBS symptoms. The synthetic air quality indexes of all surveyed residents were calculated as III-level, which represented mild pollution. Therefore, it would be an effective way to decrease the incidence of SBS symptoms in residence to reduce the outdoor air pollution and control the penetration from outdoor to

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indoor environment.

Keywords: Outdoor air pollution, Sick building syndrome, NO2, SO2, PM10

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1. Introduction

Recently, with the rapid development of the economy and society in China, accompanied by the increasing of automobiles and gradually accelerating process of urbanization, the ambient air pollution is becoming more severely. Shanghai, as one of the developed and potential cities in China, is confronted with the increasing concentrations of nitrogen oxide, sulfide, ozone and other air pollutions due to the abundance usage of fuel and vigorous

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development of transportation. Owing to the location along the coast, part of the particles could be taken away by frequent raining, like PM10, however, during 2003~2005, the

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emission of SO2 and NOX in Shanghai was 2-3 times than that in Beijing [1]. Ye et al. [2] also found that the daily mean concentrations of PM10 and PM2.5 were 81.7μg/m3 and 38.6μg/m3, respectively during 2005-2012 in Shanghai. Moreover, Cai et al. [3] mentioned that from

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January 1, 2005 to December 31, 2011, about 2922 days, the daily mean concentrations of NO2, SO2 and PM10 were 60μg/m3, 45μg/m3 and 88μg/m3, respectively in nine districts in

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Shanghai. The concentration of PM10 was higher than the limit value in Ambient Air Quality Standard (AAQS) in China (GB3095-2012) [4].

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Based on the current situation of air pollution, report on air pollution in 2012 published by World Health Organization (WHO) mentioned that about seven million people were killed by ambient air pollution [5]. There are a variety of outdoor air pollution, including solid substances, like air particles, and gaseous substances, like nitrogen oxide, sulfide, ozone and other complex compounds. Many studies indicated that the outdoor air pollutions both solid substances and gaseous substances have a serious harmful effect on human and animals’ health. Shan et al. conducted a survey on the self-assessment of health status, outpatient and inpatient service requirements of patients with hypertension, heart disease and stroke in 62 cities of 17

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provinces, as well as annual inhalable particulate matter (PM10), sulfur dioxide (SO2) and nitrogen dioxide (NO2) concentration to assess the possible impact of air pollutants on the self-assessment of health and health services utilization. Results showed that air pollution may have an impact on the demand for health services in patients with hypertension, cardiovascular and cerebrovascular diseases, and the different levels of air pollutants may have different effects on health [6]. Cormier et al. collected concentrations of PM10 and PM2.5 over an eight-year period and identified increased exposure to these two particulate matters was associated with an increased short-term risk of outpatient and emergency visits

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for coronary heart disease [7]. Stankovic et al. surveyed 1136 subjects (residents between

the ages of 18 and 70 living in the same area for more than 5 years with different degrees of air pollution) and measured the concentrations of black smoke and sulfur dioxide from 2001 to 2011. They found that exposure and air pollution increase the risk of high blood pressure [8]. During 2002-2006, 1,500 pregnant women in Nancy and Poitiers in France were

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surveyed and monitored for NO2 and PM10. This study found short-term associations

between air pollution and body temperature and blood pressure among pregnant women [9]. Exposure to nitrogen dioxide or sulfur dioxide and other air pollutants increased the frequency of asthma, allergic rhinitis and chronic obstructive pulmonary disease (COPD) reported in literatures [10, 11]. In addition, researchers stated that air pollution were associated with different diseases and physiological or psychological defect. A case-control

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study in Denmark indicated that NO2 was found to be associated with Parkinson's disease and it was taken as a risk factor [12]. In Japan, a study about air pollution and suicide rate

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investigated that high concentration of air pollution was positively correlated with gradually increasing suicide rate [13]. Huang et al. proposed that the prevalence of influenza cases had a uptrend with the concentration of NO2 increasing trough a cross-sectional survey in

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Nanjing [14]. Bell and Lavigne [15, 16] have found that air pollution, PM10, PM2.5 and sulfur dioxide, was also associated with low birth weight, premature birth and other birth defects.

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Moreover, several studies have proposed that exposures to ambient sulphur dioxide (SO2), nitrogen dioxide (NO2) and particulate matter with an aerodynamic diameter <= 10 μm (PM10) could induce new-onset of childhood asthma and exacerbate symptoms in the

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asthmatic patients [17-19]. Therefore, the ambient air pollution should be paid more attention for health outcomes. Sick building syndromes (SBS) symptoms, as series of common and certain symptoms, are caused by building and ambient environment [20]. Generally, there are three categories of these symptoms, which are general symptoms, mucous symptoms and skin symptoms respectively. There is no confirmed clinical definition on it now and no definite pathological theory for the pathogenic etiopathogenesis of it. Since it was defined by WHO in 1983 [21], most of the studies on SBS were about the indoor environment and the relationship between them [22-24].

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A total of 26,992 papers from 79 countries (with "room" as the search term) from 1978 to 2014 were summarized. Statistical results showed that indoor air quality is related to health [25]. In the history of research on indoor air quality and health, it is proposed that most diseases related to environmental exposure are caused by exposure to indoor air [26]. Multivariate logistic regression analysis found that adults with SBS symptoms were related to family environmental factors through questionnaires of 5,299 parents of 3-6 years old [27]. A correlation between indoor dampness and SBS symptoms was proposed by several studies. By investigating 429 adults randomly selected, it was found that the dampness and

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molds in workplace are associated with SBS symptoms [28]. Engvall et al stratified random sample selection of 609 multi-family dwellings in Stockholm and assessed the weekly

symptoms by mail questionnaire. Multivariate logistic regression analysis found that there was an overall increase in all wetness scores [29]. Sahlberg randomly recruited 1,000

Swedish ordinary people and distributed a self-administered questionnaire that found

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residential dampness or indoor mold was a risk factor for new-onset SBS symptoms [30, 31]. Finnegan et al conducted a questionnaire-based survey of seven buildings and found that, six SBS symptoms had a higher incidence in air-conditioned buildings than the natural ventilated buildings [32]. Similarly, a questionnaire was used to compare the feelings of employees in air-conditioned buildings and natural ventilation shops. It was found that the SBS in air-conditioned buildings should be statistically significant compared with the

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naturally ventilated shops [33]. Harrison J et al. found that bacteria, fungi are SBS pathogenic factors [34]. Hodgson et al. found that VOCS concentration and light were pathogenic factors

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of SBS [35]. Lin, Z et al. found that air drying is a causative factor in SBS [36]. Engvall et al. found that multi-storey buildings have a positive correlation with SBS [29]. Bing-Ling Wang found that formaldehyde is a causative factor in SBS [37]. They all mentioned that indoor air

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quality is closely related to human health. The outdoor and indoor air connects tightly with each other [38, 39]. There is a very close

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connection between outdoor and indoor air and the former has a great impact on the quantity and distribution of indoor air pollutants, so the indoor air quality was affected by

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outdoor air pollution [40, 41]. The variation trend of outdoor and indoor air pollution is consistent for normal air tightness [42]. Research has confirmed that the gradual worsening of indoor air quality is a risk factor for the incidence of SBS symptoms [27, 36, 43, 44]. These findings verified the possibility of the effects of outdoor air pollution on the incidence of SBS symptoms and could be also a risk factor. However, few studies conducted the analysis on the associations between outdoor air pollution and SBS symptoms [45]. A study performed by Lu, C et al. connected outdoor air pollution, meteorological parameters and selected indoor exposure and building characteristics at home and weekly SBS symptoms by a standardized questionnaire among 3485 randomly selected adults in Changsha, China, although they found that no significant associations with SBS were observed for outdoor air

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pollutants. Moreover, the situation of air pollution in Shanghai is severe, so it is necessary to conduct the association study. This paper aimed to investigate the effects of several outdoor air pollutants in Shanghai on the incidence of SBS symptoms among residents (parents of the preschool children).

2. Methods and Materials 2.1

Cross-sectional survey

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This study was part of the study “China, Children, Home, Health” (CCHH) – Phase I (crosssectional survey) in which the relationship between ambient environment and children

health was to be established. It was conducted from 2011-2012. 15266 participants from

five districts (three urban and two suburban) in Shanghai were randomly selected to join in this research. They are parents of the preschool children from 72 kindergartens in these five districts. They reported the information on demographic, building characteristics,

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environmental exposures of their residence, lifestyle behaviors, as well as their health status by questionnaires. Questionnaire in details have been described in previous studies [46]. Gender, home dampness exposures, environmental tobacco smoke (ETS), history of family atopy, and ownership, location of the current residence were all included [47]. This study focused on health status of adults, which were also the participants, and questions referring

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to sick building syndrome (SBS) symptoms are listed as below [48]:

difficulty.

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(1) General symptoms: Fatigue, Heavy head, Headache, Dizziness, Concentration

(2) Mucous membrane symptoms: Cough, Irritation in throat, Irritation in nose,

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Irritation in eyes.

(3) Skin symptoms: Dry facial skin, Itchy ears, Dry hands

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They were asked to report that whether one or more symptoms bothered them during last three months [37]. The answers were provided as “Yes, often (every week)”, “Yes, sometimes” and “No, never”. As long as they had at least one of the certain symptoms in

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three major categories, then they were considered being bothered by this kind of symptoms. This might overestimate the effects of environmental factors on SBS symptoms, but on the other side, it was taken as the optimum method to reflect the effects on a class of symptoms and has been used in some previous studies until now [49-51]. This study was approved by the ethical committee in the School of Public Health, Fudan University in Shanghai, China.

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2.2

Air pollutants data collection

Outdoor air pollution including nitrogen dioxide (NO2), sulfur dioxide (SO2) and atmospheric PM10 were taken as objects in this study. The daily mean value of these air pollution from five local monitoring stations in five districts as described above were collected by Shanghai environmental monitoring center (SEMC) during 2011 to June 30th 2012. It covered from three months before this survey to the end of this survey. For example, the NO.176 of the residents, who lived in Baoshan district and was surveyed on December 18, 2011. In

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response, the data of NO2, SO2 and PM10 were collected from September 18 to December 18, 2011. The concentrations of these three air pollutants for 90 days were all recorded and calculated the mean values ± standard deviation (SD). The repetitive process was performed for every case. In addition, the air pollutants, which were obtained by weighted-mixing two or three of these air pollutions, were defined as new air pollutants in this study.

2.3 Data analysis

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The mean values and standard deviation of three air pollutants, NO2, SO2 and PM10, during

90 days before reporting day were calculated based on the original data from SEMC where the abnormal data have been excluded [52, 53]. These three air pollutants and the combined objects according to the independent impacts of these pollutants were then classified as four categories by quartiles (25%, 50% and 75%). Frequency statistics, crosstab

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and Pearson chi-square test were used to analyze the incidence of SBS symptoms and the difference among different levels of air pollutants. Then the correlation coefficients among

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pairs of outdoor air pollutants were investigated by Spearman’s correlation analyses. Pvalue<0.05 indicated significance in all statistical analysis. Binary logistic regression models were performed to analyze the associations between SBS (both “frequently (weekly)’’ and

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“sometimes’’) and concentration of different air pollutants with adjustment of confounding factors for gender, history of family atopy, ownership and location of current residence (whether the distance to traffic trunk is within 200 meters), household dampness exposures

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(water damage, condensation on window pane, damp on clothing /beddings, mildew odor, damp stains and mould stains, when one of these presented) and environmental tobacco

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smoke, ETS (smoking of family member, usage of mosquito-repellent incense and usage of incense, when one of these presented). These confounding factors were depended on the significant associations with SBS symptoms. In order to explore the effects of the increasing air pollutants concentration on the higher incidence of the SBS symptoms, there were two cases proposed in this study where binary logistic regression models were used. Case a): the adjustment factors were considered as described before. Case b): Besides the factors adjusted in Case a, the air pollutant which was not included in the mixture pollutant was also adjusted in this case, because of the significant association between different air pollutants. The associations and dose- response relationship of air pollutants concentration and SBS symptoms were further analyzed.

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3. Results In this study, 15266 valid questionnaires were obtained and 13335 of them were reported by parents of 4-6 year-old preschool children. These samples were mainly taken to conduct the current analysis.

3.1 Demographic information of the surveyed residents On account of the vague distinction between responses “Yes, Often” and “Yes, Sometimes”

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of SBS symptoms, as well as the small sample (less than 5% for most symptoms, except for Fatigue, about 20%) of “Yes, often (every week)”, the answers “Yes, often (every week)” and “Yes, sometimes” were merged together as “Yes” in this study. Table 1 lists the personal information of the participants, including gender, atopy history, and part of possible risk

factors on SBS symptoms. Female subjects were more than male in this study and 76.1% of

all reported atopy experience in their family. Among them, 63.2% had the ownership of their

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current residence. Household dampness indicators were reported in 79.9% of these

residences while over half (56.5%) of these families were exposed under ETS. In addition, 40.6% of these participants lived within 200 meters of traffic trunk.

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[Insert Table 1 Demographic information of the participants and building characteristics]

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3.2 Incidence of SBS symptoms of the participants Table 2 lists the incidence of 13 kinds of SBS symptoms in the last 3 months reported by the

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participants. The highest incidence of them was 76.9% for “Fatigue”. Incidence of “Dry hands” was relatively low. For three major categories described above, 79.1% of these participants suffered from general symptoms, 65.2% for mucous symptoms and 35.7% for

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skin symptoms.

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[Insert Table 2 Incidence of SBS symptoms among the residents in Shanghai] Figure 1 displays the incidence of SBS symptoms among residents in five districts in Shanghai. It could be seen that the incidence of the residents in Jing’an district (urban area of Shanghai) was the highest for all these three symptoms. Generally speaking, the incidence of GS, MS and SS among the residents in urban area were all significantly (t-test, p<0.05) higher than that in the suburban area.

[Insert Figure 1 Incidence of SBS symptoms among residents in different districts, Shanghai]

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3.3 Concentration level of outdoor air pollutants Table 3 lists the concentrations of three air pollutants including the mean value, standard deviation (SD), maximum, minimum, quartiles and inter-quartile range. It demonstrates that during three months before the survey day, the mean concentration of NO2, SO2 and PM10 were 58.7μg/m3, 37.1μg/m3 and 62.7μg/m3 respectively. Among these three air pollutants, the concentration of NO2 varied largely during the past 3 months. The highest mean value

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was 62.7μg/m3 for PM10, and the inter-quartile was also the largest. Both the mean value ± SD and inter-quartile of new mixed air pollutants were larger than those of the original air

pollutants. The Spearman's correlation coefficients of different air pollutants were listed in table 4, and as it could be seen, the associations in statistically presented among them.

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[Insert Table 3 Concentration of different air pollutants (μg/m3)]

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[Insert Table 4 Correlation coefficients between two air pollutants]

Figure 2 shows the mean value ± SD of different air pollutants concentration in five districts

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in Shanghai. For NO2, the highest concentration occurred in Zhabei district (Urban area) and concentration of it in urban area was overall higher than that in suburban area, where the lowest one was in Fengxian district which is suburban area and close to the sea. For SO2 and

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PM10, the highest concentration occurred in Baoshan district (Suburban area), 50.2μg/m3 and 72.8μg/m3 respectively, which were significantly higher than those in other districts. The

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concentration of these two air pollutants in Jing’an district (Urban area) was second high.

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[Insert Figure 2 Mean value ± SD of concentration for NO2, SO2 and PM10 in 5 districts of Shanghai]

3.4 Associations between concentration of outdoor air pollutants and SBS symptoms (in different levels) Table 5 lists the associations between the concentrations of outdoor air pollutants in different levels and SBS symptoms by binary logistic regression analysis. The concentrations were categorized by quartiles (<25%, 25-50%, 50-75% and >75%) and the concentration of

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first quartile was taken as the reference category. It displays that the incidence of three major categories of SBS symptoms increased significantly in statistics with the increasing of air pollution concentration (p<0.05). The significant positive correlations between NO2 concentration and general symptoms were found without confounding factors adjustment. After adjusting confounding factors (gender, history of family atopy, home dampness exposures, distance to traffic trunk and environmental tobacco smoking) adjusted, the positive correlations were not found. The significant positive correlations between NO2 concentration and Mucous membrane symptoms were also found. However, after adjusting

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the confounding factors which involved the non-mixture pollutants in the same stage, the

associations were no longer significant. Therefore, the confounding factors certainly played an important role on increasing the incidence of SBS symptoms and the correlation between different air pollutants was further testified. In addition, high concentration of NO2 was

negatively correlated with skin symptoms. Interesting is that in comparisons of three cases,

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except for the significant positive correlations between general symptoms and

concentration of SO2 in the unadjusted case, the negative correlations in statistically were found between lower concentration of SO2 and three kinds of SBS symptoms in the lower group. Moreover, higher concentrations of SO2 were positively correlated to MS and SS.

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PM10 was negatively correlated to GS while positively to SS significantly in case (a).

[Insert Table 5 Associations between the concentrations of outdoor air pollutants in

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different levels and SBS symptoms by binary logistic regression]

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3.5 Dose-response relationship between outdoor air pollutants and SBS symptoms

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Table 6 lists the dose- response relationship of three outdoor air pollutants and SBS symptoms of the residents. The increments of NO2, SO2 and PM10 were 10μg/m3, 5μg/m3 and 15μg/m3 respectively according to the inter-quartile range. It displays that the dose

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response relationships with slope above 1 were between NO2 concentration and GS, MS, as well as between PM10 and MS, SS. For the concentration of SO2, only the positive relation with SS was significant (AOR, 95% CI: 1.06, 1.02-1.10). On account of the different influences of these three air pollutants, taking the AORs as the reference, the weighted factors were given to each air pollutant to generate the new mixtures. The weighted concentrations were used to evaluate the mixed impact of air pollutant on SBS. Referring to the mixture, the increments were 15μg/m3, 20μg/m3, 15μg/m3 and 18μg/m3 respectively for SO2 & NO2, SO2 & PM10, NO2 & PM10 and SO2 & NO2 & PM10. The significant dose relationship could be found between these mixture concentrations and

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three SBS symptoms except for SO2& NO2 (p > 0.05), although AORs were above 1. The negative correlation was presented between SO2, PM10 and GS, but not for all three cases.

[Insert Table 6 Dose-response relationship between outdoor air pollutants and SBS

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symptoms]

4. Discussion

Based on the large sample size and high response rate of this survey, the statistical results

could represent the incidence of SBS symptoms among these adults, who were the parents of preschool children in Shanghai, from urban to suburban areas. The selective bias could be

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eliminated by large sample size. In addition, two cases were analyzed in order to distinguish the effects of different confounding factors on the associations between risk factors and SBS symptoms. Besides, two methods, illustrated by increments and percentiles were used in this study to investigate the associations between outdoor air pollutants and SBS symptoms from different perspectives.

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An interesting finding is the incidence of GS, MS and SS among the residents in urban area were all higher than that in the suburban area. The highest ones were in Jing’an district which was the old town of Shanghai. With regard to the air pollutants, it displayed that the

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concentrations of NO2 and SO2 in Fengxian district (the suburban area) was obviously lower than those in other districts, including three in urban area and one in suburban area. It likely

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attributes to the location of Fengxian district which is near the East China Sea. Part of the air pollutants are taken away by the sea wind. Compared with the suburban area, the traffic trunk is more crowded and the amount of automobile is larger in urban area which results in

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the larger exhaust emissions. That is why the concentrations of NO2 and SO2 in this region were higher. In addition, the air pollution in Baoshan district, a famous heavy industrial base,

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is serious and the concentrations of three air pollutants were relatively higher, especially for PM10, of which concentration was higher than that in other districts during this survey. The daily average concentrations of outdoor air pollutants observed in this study were all beyond the limited mean values (NO2: 40μg/m3, SO2: 20μg/m3, PM10: 50μg/m3) for 24 hours in Air Quality Standard [54] published by World Health Organization (WHO). In addition, the concentration of PM10 was above the limit value (40μg/m3) in Ambient Air Quality Standard (AAQS) in China [4]. Besides, outdoor air pollution plays an unnegligible role on affecting indoor air quality. According to the air tightness of windows, the penetration coefficients of outdoor particulate matters (PMs) were 0.3~0.98 based on our previous measurements.

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Considering the worst case, the penetration coefficient was taken as 1. Furthermore, people spend 80% of the time in the indoor environment [55]. The synthetic air quality index (AQI) which takes count of the highest pollution sub-index and the average index simultaneously could be calculated as follows [56].

 C C C I   max | 1 , 2 ,..., n S1 S 2 Sn 

 1 C |   i Si  n

  

(1)

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Where I is the synthetic air quality index which was scaled up to 5 levels. I (<=0.49): clean,

suitable for human life; II (0.50~0.99): not contaminated, all the environmental factors are not beyond the limit; III (1.00~1.49): mild pollution, at least one of the air pollutants is

beyond the limit. Generally, there will not be acute or chronic toxicosis except the sensitive group; IV (1.50~1.99): moderate pollution, two to three environmental factors are generally beyond the limit. The health of people is damaged obviously and the sensitive persons are

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damaged severely. V (>=2.00): severe pollution, three to four environmental factors are

beyond the limit. People health is damaged severely and sensitive persons might be dead. Ci means the concentration of some air pollutant; Si means the limit value of this air pollutant in standard. Based on the daily average concentration for three air pollutants investigated in this paper, 58.7μg/m3, 37.1μg/m3 and 62.7μg/m3 for NO2, SO2 and PM10, respectively, the

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synthetic AQI was 1.01 with regard to limit values of 80μg/m3, 50μg/m3 and 40μg/m3 in AAQS. The distribution of the synthetic air quality index for all 13335 samples was displayed in figure 3. It could be seen most of these families were exposed in the III- level environment

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where the air was mild polluted and a few families were exposed in the moderate polluted environment. Of course, the actual situation was complicated and the penetration

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coefficient was not equal to 1. The incidence of SBS symptoms among the adults sampled from the parents of preschool children was higher than that surveyed from Chongqing [51], Changsha [45] and Urumuqi [57]. Furthermore, the results from logistic regression illustrated

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that three air pollutants, NO2, SO2, and PM10, were significantly associated with three SBS symptoms, meanwhile the dose relationships in statistically were observed.

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The results from statistical analysis showed that the significant associations were found between the concentration of NO2 and three SBS symptoms. With the increasing of the concentration, the incidence of GS and MS increased. It is strange that the highest level of NO2 concentration was considered as a protect factor according to this logistic regression analysis (AOR< 1, p <0.05). The protection consciousness of resident may be a possible reason for this phenomenon. When the concentrations of outdoor air pollutants increase and up to a certain extent, people would reduce the outdoor activities or close the windows to avoid the direct contact to pollution from outdoor air. The highest concentration of NO2 in this study was over 66.4μg/m3, which is higher than the limit value of daily mean value -

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Level 2 standard [4]. Besides, NO2 or the mixture with NO2 were positively correlated with GS and MS which further illustrated that NO2 was a risk factor for GS and MS of adults. It was also stated by Zhang et al. [58] that with the concentration increasing by 10%, the incidence of MS increased 13%. They were positively associated with each other. It confirmed that the symptoms would be relieved when the objects left the contaminated area. In Case b, the lower concentration of NO2 was significantly associated with GS and their association was effected by confounding factors. Under certain conditions, it means the lower outdoor air pollution was more dangerous for interior residents than higher. Liu et al [59] have

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presented that exposure on NO2 pollution was correlated with allergic rhinitis and a risk

factor on respiratory health among children. Besides, the respondents, indoor dampness indicators, environmental tobacco exposure and so on may also affect the association

between NO2 and MS due to the positively correlation cannot be found after adjusting the confounding factors for the same stage of contaminants.

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High concentration of SO2 was positively correlated with MS and SS. The quartiles increment of SO2 concentration was significantly associated with SS, but no dose-response relationship was found with MS. This finding was consistent with the study conducted by Zhang, et al [58]. The negative correlation between concentration distribution of SO2 and GS was found in Case a, which means the confounding factors including the characteristics of subjects and

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the indoor environmental factors had an impact on the associations. Research on the children in Taiwan campus found that long term exposure to SO2 would increase the incidence of allergic rhinitis[60]. One study on the associations of air pollution and chronic

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respiratory disease illustrated that the incidence of wheeze was strongly correlated with SO2 [61, 62].

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In addition, positive correlation between PM10 and SS were found by concentration levels analysis. The association between PM10 and MS was found in high concentration of PM10

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which represented that PM10 was also a risk factor on MS and SS. The studies on students in school [63] also stated that PM10 was correlated with cough and other mucosal symptoms. Furthermore, PM10 was confirmed to be significantly positively associated with incidence of

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mucosal symptoms and respiratory diseases by many studies [61, 63, 64]. Skin, as well as lung tissue, is the contact surface to the ambient atmosphere and no doubt to the air pollution. They are most susceptible to outdoor air pollutions and statements in literature showed that the airborne particulates could aggravate the skin diseases (eczema and dermatitis) and respiratory diseases (dry cough, asthma and croup). The dose - response relationship between the mixed air pollution and SBS symptoms revealed that the combined effect of PM10 and SO2 was negative on GS. This phenomenon might be due to both the PM10 and SO2 were negatively correlated with GS which further resulted in the negative correlation found in the mixture. Except for this, other combined

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effects were all positively correlated with MS and SS. Qian et al. [65] have conducted research on the air pollution in Wuhan, Guangzhou and so on, four cities in China, and they presented that the mixture air pollution have an adverse effect on children’s health. It was positively associated with the high incidence of cough and wheeze. The combined effect of SO2 and NO2 was positive on GS and MS, but with the concentration increasing, the correlation disappeared. It might attribute to the negative correlation between high concentration of SO2 and GS. The information missing resulted by the simple classification method of concentration levels or the incomprehensive selection of confounding factors

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may lead to this result. Furthermore, the significant associations were not found between

SO2 and MS which indirectly had an impact on the association analysis between the mixtures (PM10 & SO2, and NO2 & PM10) and the symptoms (MS and SS). After adding the SO2, the

association between the mixtures with MS was weakened and that with SS was significant.

The mixtures of SO2, NO2 and PM10 were also associated with MS and SS, not with GS, which concentration and the incidence of GS.

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could attribute to the dose-response relationship between the increments of NO2

In conclusion, referring to the associations between the concentration level and increments of outdoor air pollution and SBS symptoms, the definition was purposed as follow: among all the calculated AORs, if there was no less than 50% (Five) of them represented the

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significance in statistically, in other words AOR was above 1 and p <0.05, then it was considered that this kind of air pollutant was associated with one kind of SBS symptoms. For the weighted mixture, the results listed in table 6 were taken as the reference. The

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determination results were listed in table 7.

[Insert Table 7 Summative evaluation on the associations between air pollutants and SBS

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symptoms]

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There were still some limitations of this paper. Firstly, only three outdoor air pollutions were discussed in this paper and the effects of indoor environmental parameters, such as temperature and humidity, and other air pollutions, such as PM2.5, carbon monoxide (CO) or ozone (O3), on the incidence of SBS symptoms were not investigated. The mixtures in this paper constituted by simply accumulating the concentration of two or three air pollutants were instead of the actual mixed air pollutants which may not reflect the exact effects of these pollutions. Actually, the influence factors on SBS symptoms were complicated and synthetic. They included the outdoor and indoor environmental factors, especially indoor environment, so it is not enough to study part of these parameters. However, from this study, the significant associations between three outdoor air pollutants and SBS symptoms

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were found. Therefore, the findings in this study could be the primary results to reflect the effects of ambient pollution on the incidence of SBS symptoms. Secondly, the SBS symptoms were based on subjective responses to the questionnaire by parents. Errors and bias may exist, decreasing the validity of these results [66, 67]. Thirdly, among these respondents, the females (70.2%) were largely more than males (24%) which may result in selective bias. The report errors and recall bias were very common in subjective survey. Setting questions to validate the questionnaire and increasing the sample size could be the effective ways to avoid this bias. In this study, the invalid samples were excluded and the total sample size

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was large, so although these limitations accompanied with the survey method, the large size samples could reflect primarily the effects of certain outdoor air pollutants by two analysis methods used in this paper. It could be avoided and acceptable.

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5. Conclusion

The daily mean concentrations of air pollutants during 2011-2012 in five districts in Shanghai, China were obtained and the incidence of SBS symptoms among residents was surveyed in this paper. Following conclusions could be drawn.

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(1) The mean concentrations of NO2, SO2 and PM10 during this survey were 58.7μg/m3, 37.1μg/m3 and 62.7μg/m3 respectively. The concentrations of NO2 and

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SO2 in Fengxian (suburban area) were obviously lower than that in other districts. (2) The incidences of three categories of SBS symptoms among residents who were

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the parents of 4-6 years old preschool children in Shanghai were 79.1%, 65.2% and 35.7% separately. The incidences of these symptoms in Jing’an district (urban

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area) was the highest.

(3) The significant associations were found between these three air pollutants and the

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weighted mixture of them and part of the SBS symptoms, especially the MS and SS, among residents analyzed by two Case studies. They were the definitely risk factors for these SBS symptoms.

6. Acknowledgements This work was supported by the National Key Research and Development Program of China [grant numbers 2017YFC0702700], the National Natural Science Foundation of China [grant numbers 51708347] and the Shanghai Sailing Program [grant numbers 17YF1412800].

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Conflict of Interest

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The authors declare that they have no conflicts of interests.

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Tables:

Table 1 Demographic information of subjects and building characteristics. Frequency, %

3108 9082

25.5 74.5

8831 5158

63.1 36.9

4833 7076

40.6 59.4

9837 3097

76.1 23.9

6495 1633

79.9 20.1

7319 5637

56.5 43.5

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Subjects Male Female Ownership of current residence Yes No Distance to traffic trunk Within 200m Above 200m Atopic symptoms, ever Yes No Indoor dampness exposures Yes No ETSb Yes No

Sample numbera, n

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Factors

a

Total number of the survey is 13335, but due to the default value, the number of each

item is different and less than 13335. b

represents environmental tobacco smoking

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Table 2 Incidence of SBS symptoms among the residents in Shanghai Number, n (percent, %)

Factors

Number, n (percent, %)

Fatigue

9499 (76.9)

Cough

5355 (46.0)

Heavy head

4212 (36.8)

Dry facial skin

3093 (27.2)

Headache

5103 (43.8)

Itchy eyes

2137 (19.0)

1826 (16.2)

Dry hands

1086 (9.6)

3707 (32.6)

Arthralgia

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Dizziness

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Factors

Concentration difficulty

2582 (22.5)

2848 (24.9)

a

General symptoms

8665 (79.1)

Irritation in nose

4065 (35.5)

Mucous symptomsb

7114 (65.2)

Irritation in throat

5218 (45.3)

Skin symptomsc

3970 (35.7)

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Irritation on eyes

a General symptoms: Fatigue, Heavy head, Headache, Dizziness, Concentration difficulty,

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at least one of them. b Mucous membrane symptoms: Cough, Irritation in throat, Irritation in nose, Irritation in

eyes, at least one of them. c Skin symptoms: Dry facial skin, Itchy ears, Dry hands, at least one of them

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Table 3 Concentration of different air pollutants (μg/m3) Percentiles

Air pollutants items (μg/m3)

Mean value ± SD

Minimum

NO2

58.7±10.2

SO2 PM10

Maximum

Inter-quartile range

50%

75%

38.8

56.3

63.0

66.4

72.3

10.1

37.1 ± 6.6

28.8

32.1

35.8

37.3

52.5

5.2

62.7 ± 8.1

49.9

56.0

62.4

70.6

96.4

14.6

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25%

Table 4 Correlation coefficients between two air pollutants SO2

SO2

1

NO2

0.340*

PM10

0.572*

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*represents significant level p-value < 0.01

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NO2

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Correlation coefficients (r)

PM10

-

-

1

-

-0.116*

1

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Table 5 Associations between the concentrations of outdoor air pollutants in different levels and SBS symptoms by binary logistic regression.

Prevale (μg/m ) nce, % 3

OR, 95%CI

Mucous symptoms

Preval AOR, 95%CIa AOR, 95%CIb ence, %

OR, 95%CI

NO2 <56.3

76.6

1.00

1.00

1.00

61.1

Skin symptoms

Prevale nce, %

OR, 95%CI

35.6

1.00

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General symptoms

Air pollutants

1.00

AOR, 95%CIa AOR, 95%CIb

1.00

1.00

AOR, 95%CIa AOR, 95%CIb

1.00

1.00

80.2 1.24,1.10-1.40 1.10,0.92-1.32 1.62,1.18-2.22 68.5 1.38,1.24-1.54 1.40,1.19-1.63 1.58,1.21-2.06 38.4 1.13,1.02-1.25 1.14,0.98-1.33 1.12,0.87-1.45

63.0-66.4

80.2 1.11,1.04-1.19 1.08,0.98-1.20 1.12,0.98-1.27 66.7 1.13,1.06-1.20 1.11,1.02-1.21 1.10,0.99-1.22 37.3 1.04,0.98-1.10 1.07,0.98-1.16 1.00,0.90-1.11

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56.3-63.0

SO2 <32.1

80.0

1.00

1.00

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79.6** 1.06,1.02-1.11 1.07,1.00-1.15 1.10,0.99-1.23 64.1# 1.04,1.01-1.08 1.06,1.01-1.13 1.05,0.96-1.15 31.1# 0.94,0.90-0.97

>66.4

1.00

64.3

1.00

1.00

1.00

34.5

1.00

0.95,0.900.99.

1.02,0.94-1.11

1.00

1.00

77.7 1.11,1.05-1.17 0.78,0.64-0.95 0.86,0.70-1.05 62.8 1.04,0.99-1.09 0.92,0.78-1.08 0.91,0.77-1.07 35.4 0.94,0.90-0.99 1.06,0.91-1.25 1.03,0.88-1.22

35.8-37.3

80.4 1.01,0.95-1.08 1.01,0.91-1.11 1.02,0.91-1.14 67.6 1.08,1.02-1.14 1.09,1.00-1.18 1.06,0.97-1.16 35.1 1.01,0.96-1.07 1.02,0.94,1.10 1.03,0.94-1.12

>37.3

78.1* 0.96,0.92-1.01 0.94,0.88-0.99 1.02,0.90-1.15 66.1** 1.03,0.99-1.07 1.06,1.01-1.12 1.11,1.01-1.23 38.0* 1.05,1.01-1.09 1.07,1.02-1.13 1.03,0.94-1.13

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PM10

PT

32.1-35.8

<56.0

80.4

1.00

1.00

1.00

64.4

1.00

1.00

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1.00

31.6

1.00

1.00

1.00

79.3 0.93,0.82-1.07 1.00,0.82-1.24 0.81,0.52-1.24 63.2 0.95,0.85-1.06 0.96,0.81-1.13 1.08,0.77-1.51 34.2 1.13,1.01-1.26 1.11,0.94-1.31 0.90,0.65-1.25

62.4-70.6

78.4 0.94,0.88-1.00 0.88,0.79-0.97 0.99,0.81-1.22 66.0 1.04,0.98-1.10 1.00,0.92-1.09 1.15,0.97-1.36 38.1 1.16,1.09-1.22 1.13,1.04-1.22 1.00,0.85-1.18

>70.6

78.3 0.96,0.92-1.00 0.92,0.86-0.99 0.95,0.81-1.08 67.1* 1.04,1.00-1.08 1.06,1.00-1.12 1.07,0.96-1.20 39.0# 1.11,1.07-1.16 1.14,1.08-1.20 1.21,1.09-1.35

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56.0-62.4

Numbers in bold means the significance in chi-square test or binary logistic regression analysis (p<0.05); * represents 0.01 ≤ p<0.05, **, 0.001 ≤ p<0.01, #, p<0.001; OR means odds ratio without adjusting the confounding factors. a

Case a), in which the confounding factors were participants, history of atopy, ownership of residence, indoor dampness exposure indicators, within or

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Case b), in which the confounding factors included all the items in Case a) and the air pollutants that were not mixed.

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b

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without traffic trunk in 200m and environmental tobacco exposures.

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Table 6 Dose-response relationship between outdoor air pollutants and SBS symptoms General symptoms OR, 95%CI

a

AOR, 95%CI

Mucous symptoms b

AOR, 95%CI

OR, 95%CI

AOR, 95%CI

a

Skin symptoms

b

AOR, 95%CI

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Increment of pollutants (μg/m3)

OR, 95%CI

AOR, 95%CIa

AOR, 95%CIb

NO2 (10)

1.10,1.05-1.16 1.06,0.98-1.15 1.15,1.05-1.27 1.12,1.07-1.17 1.11,1.04-1.19 1.09,1.01-1.18 1.00,0.96-1.05 1.01,0.95-1.08 0.94,0.84-1.01

SO2 (5)

0.97,0.94-1.00 0.95,0.91-0.99 0.94,0.88-0.99 1.02,0.99-1.05 1.04,0.99-1.08 0.98,0.93-1.03 1.04,1.01-1.07 1.06,1.02-1.10 1.03,0.98-1.08

PM10 (15)

1.02,0.93-1.12 0.86,0.75-0.99 0.87,0.72-1.05 1.24,1.14-1.34 1.20,1.07-1.35 1.17,1.01-1.37 1.27,1.18-1.37 1.26,1.12-1.40 1.25,1.08-1.44

M

Mix. (SO2, NO2) 1.03,0.97-1.09 1.03,0.94-1.12 1.05,0.96-1.15 1.05,0.99-1.10 1.03,0.96-1.11 1.01,0.94-1.08 1.06,1.01-1.12 1.09,1.02-1.18 1.00,0.91-1.10 (15) 1.00,0.92-1.09 0.90,0.80-1.02 0.85,0.74-0.97 1.23,1.14-1.33 1.22,1.09-1.36 1.18,1.05-1.33 1.27,1.18-1.37 1.26,1.13-1.40 1.30,1.16-1.46

Mix. (NO2, PM10) (15)

1.04,0.95-1.14 0.94,0.82-1.08 1.05,0.87-1.26 1.19,1.11-1.28 1.16,1.05-1.29 1.21,1.06-1.38 1.28,1.18-1.38 1.25,1.12-1.39 1.22,1.07-1.41

PT

Mix. (SO2, NO2, 1.03,0.95-1.12 0.93,0.83-1.05 PM10)(18)

ED

Mix. (SO2, PM10) (20)

1.19,1.11-1.28 1.15,1.04-1.27

1.24,1.15-1.33 1.23,1.11-1.36

Numbers in bold means the significance in chi-square test or binary logistic regression analysis (p<0.05); a

Case a), in which the confounding factors were participants, history of atopy, ownership of residence, indoor dampness exposure indicators, within or

AC

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without traffic trunk in 200m and environmental tobacco exposures.

b

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Case b), in which the confounding factors included all the items in Case a) and the air pollutants that were not mixed.

AC

CE

PT

ED

M

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Mix.( ) represents the community effects of two or three kinds of air pollutants as a new mixture.

ACCEPTED MANUSCRIPT Table 7 Summative evaluation on the associations between air pollutants and SBS symptoms General symptoms

Mucous symptoms

Skin symptoms

NO2

√

√

X

SO2

X

X

X

PM10

X

X

√

Mix. (SO2, NO2)

√

X

X

Mix. (NO2, PM10)

X

√

√

Mix. (SO2, PM10)

X

√

Mix. (SO2, NO2, PM10)

X

√

CR IP T

Air pollutants items

√ √

AN US

There were nine AOR values for the analysis on the association between the mixed air pollutants and SBS symptoms, so the correlation was judged based on more than five AOR values above 1;

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“√” represents the significant association, “X”, no association.

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AN US

CR IP T

Figure 2

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Figure 3