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Long-term ambient air pollution and lung function impairment in Chinese children from a high air pollution range area: The Seven Northeastern Cities (SNEC) study
Atmospheric Environment 138 (2016) 144e151
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Long-term ambient air pollution and lung function impairment in Chinese children from a high air pollution range area: The Seven Northeastern Cities (SNEC) study Xiao-Wen Zeng a, Elaina Vivian b, KaheeA. Mohammed b, Shailja Jakhar b, Michael Vaughn c, Jin Huang c, Alan Zelicoff d, Pamela Xaverius b, Zhipeng Bai e, Shao Lin f, Yuan-Tao Hao g, Gunther Paul h, Lidia Morawska i, Si-Quan Wang j, Zhengmin Qian b, **, Guang-Hui Dong a, * a Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yatsen University, Guangzhou 510080, China b Department of Epidemiology, College for Public Health and Social Justice, Saint Louis University, Saint Louis 63104, USA c School of Social Work, College for Public Health and Social Justice, Saint Louis University, Saint Louis, MO 63104, USA d Department of Environmental and Occupational Health, College for Public Health and Social Justice, Saint Louis University, Saint Louis 63104, USA e State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China f Department of Epidemiology and Biostatistics, School of Public Health, State University of New York, Albany, NY 12144-3445, USA g Department of Epidemiology and Biostatistics, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China h Faculty of Health, School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD 4059, Australia i International Laboratory for Air Quality and Health (WHO CC for Air Quality and Health), Australia e China Centre for Air Quality Science and Management, Queensland University of Technology, Brisbane, QLD 4001, Australia j Department of Statistics and Operations Research, Hong Kong Baptist University, Kowloon Tong 999077, Hong Kong
h i g h l i g h t s
g r a p h i c a l a b s t r a c t
The associations between ambient air pollution and lung function are inconsistent. Few studies assessed such associations in children from a high air pollution area. We included a large population of 6740 Chinese children from seven cities of China. Increased odds of lung function impairment associated with exposure to air pollutants. The relationship between pollutants and lung function was modified by gender.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 7 October 2015 Received in revised form
Epidemiological studies have reported inconsistent and inconclusive associations between long-term exposure to ambient air pollution and lung function in children from Europe and America, where air pollution levels were typically low. The aim of the present study is to examine the relationship between
* Corresponding author. Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou 510080, China. ** Corresponding author. Department of Epidemiology, College for Public Health and Social Justice, Saint Louis University, Salus Center/Room 473, 3545 Lafayette Avenue, Saint Louis, MO 63104, USA. E-mail addresses: [email protected] (Z. Qian), [email protected], [email protected] (G.-H. Dong). http://dx.doi.org/10.1016/j.atmosenv.2016.05.003 1352-2310/© 2016 Elsevier Ltd. All rights reserved.
X.-W. Zeng et al. / Atmospheric Environment 138 (2016) 144e151 29 April 2016 Accepted 4 May 2016 Available online 12 May 2016 Keywords: Heavy air pollution Lung function impairment Gender differences Children China
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air pollutants and lung function in children selected from heavily industrialized and polluted cities in northeastern China. During 2012, 6740 boys and girls aged 7e14 years were recruited in 24 districts of seven northeastern cities. Portable electronic spirometers were used to measure lung function. Four-year average concentrations of particulate matter with an aerodynamic diameter 10 mm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3) were measured at monitoring stations in the 24 districts. Two-staged regression models were used in the data analysis, controlling for covariates. Overall, for all subjects, the increased odds of lung function impairment associated with exposure to air pollutants, ranged from 5% (adjusted odds ratio [aOR] ¼ 1.05; 95% confidence interval [CI] ¼ 1.01, 1.10) for FVC < 85% predicted per 46.3 mg/m3 for O3 to 81% (aOR ¼ 1.81; 95%CI ¼ 1.44, 2.28) for FEV1 < 85% predicted per 30.6 mg/m3 for PM10. The linear regression models consistently showed a negative relationship between all air pollutants and lung function measures across subjects. There were significant interaction terms indicating gender differences for lung function impairment and pulmonary function from exposure to some pollutants (P < 0.10). In conclusion, long term exposure to high concentrations of ambient air pollution is associated with decreased pulmonary function and lung function impairment, and females appear to be more susceptible than males. © 2016 Elsevier Ltd. All rights reserved.
1. Introduction
from the SNEC study.
Mounting scientific evidence has indicated the adverse effect of air pollution exposure on human health (WHO, 2014). Children may be more susceptible to the adverse effects of air pollution due to their higher rates of breathing, narrower airways, and developing lungs and immune systems (American Thoracic Society, 1996). Spirometry result, concerning lung development in children, have shown that exposure to air pollutants can predispose individuals to asthma, chronic obstructive lung disease and other disorders (Miller and Marty. 2010). Both cross-sectional and longitudinal studies have shown the link between ambient air pollution exposure and lung function impairment in children in the United States (Gauderman et al., 2004; Darrow et al., 2014; Urman et al., 2014; Gauderman et al., 2015), Canada (Liu et al., 2009), Austria (Horak et al., 2002) and Europe (Barone-Adesi et al., 2015; Spyratos et al., 2015). These studies provided suggestive, but inconclusive results. Most studies have shown adverse effects of particulate air pollutants, particles less than 2.5 mm aerodynamic diameters (PM2.5) and particles less than 10 mm aerodynamic diameters (PM10) on children’s lung function and respiratory symptoms, with the former having stronger effects (Gauderman et al., 2015; Wang et al., 2015). Nitrogen dioxide (NO2) also showed significant effects, while the effects of sulfur dioxide (SO2) and ozone (O3) were not consistent. The association between O3 exposure and respiratory disorders is strong even at low concentrations (Horak et al., 2002; Darrow et al., 2014; Hwang et al., 2015) but no association is observed between O3 exposure and the lung function in children in the US (Gauderman et al., 2004, 2015; Liu et al., 2009). Different ethnic backgrounds, exposure patterns and age may contribute to these inconsistent associations. Despite this, many investigations have indicated an association between air pollutant exposure and development of lung function in children at relatively low levels. Studies concerning this issue in countries that suffer from high concentrations of air pollution are still limited (He et al., 2010; Roy et al., 2012; Dong et al., 2013). Therefore, it is essential to further evaluate the association between lung function and air pollution in children in high air pollution exposure settings. The Seven Northeastern Cities (SNEC) study was conducted to investigate the effect of outdoor air pollution exposure on respiratory health effects in children from the most polluted cities in northeast China (Dong et al., 2013). The present study focused predominantly on the association of long-term exposure of ambient air pollutants and lung function in children using data
2. Methods 2.1. Study cities selection and subject recruitment We conducted a cross-sectional study to examine health outcomes in children based on exposure to ambient air pollutants. Our study participants were selected from a potential pool of 20 million people who were residing in 14 cities in the Liaoning province of Northeastern China. To maximize the inter- and intra-city gradients of the pollutants of interest and minimize the correlation between district-specific ambient pollutants, the seven citiesdShenyang, Dalian, Anshan, Fushun, Benxi, Liaoyang, and Dandong in Liaoning provincedwere selected as study sites according to the mean level of air pollution measured from 2009 to 2011. All urban districts were selected from each of the seven cities. The 24 urban districts selected from the seven cities, included five from Shenyang, four from Dalian, four from Fushun, three from Anshan, three from Benxi, three from Dandong, and two from Liaoyang. There was only one municipal air monitoring station available in each of the 24 study districts. Therefore, we randomly selected one or two classrooms from each grade of one elementary and one middle school within one km of the air monitoring station. The participants were those who lived in the district for at least two years before the start of study and had their homes and schools within one km of an air monitoring station. The final sample size included in this study was 6740 children (n ¼ 3382 boys and n ¼ 3358 girls) with an average age of 11.56 ± 2.07 years (7e14 years). The study was conducted according to World Medical Association Declaration of Helsinki-Ethical Principles for Medical Research Involving Human Subjects and we obtained ethical approval from Human Studies Committee of Sun Yat-sen University. Written informed consent was obtained from each participant and their parents before starting data collection. 2.2. Questionnaire survey We informed the principals of selected schools about the study. Questionnaires with envelopes and forms to record questionnaire distribution and collection were handed out to teachers with verbal and written instructions. Parents were invited to a parent’s night where they voluntarily filled out the questionnaire or took it home and returned it in a sealed envelope. Teachers were responsible for explaining the questionnaire to parents and obtaining the consent
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to participate and were strictly instructed not to urge the parents to participate.
2.3. Ambient air pollution There was a municipal air monitoring station located within one km from the study participants’ homes in each of the 24 selected study districts. In China, children are required to attend the school closest to their home, and national policy forbids selecting transregional schools for children. Therefore, participants’ homes were also within one km from both the monitoring stations and schools. From 2009 to 2012, these stations collected measurements of PM10, SO2, NO2, and O3 concentrations, using uniform methods to ensure quality assurance. Per the US EPA criteria, in China, the ambient air monitoring stations are mandated to be away from major roads, industrial sources, buildings, or residential sources of emissions like the combustion of coal, waste, or oil. Therefore, the monitoring results reflect the background urban air pollution levels in a city rather than from local sources, such as traffic or industrial combustion. These measurements were then used to estimate longterm exposures for study participants. The methodological standards set by the State Environmental Protection Administration of China were strictly followed. Pollutant concentrations were assessed continuously and reported each hour: PM10 by beta-attenuation, SO2 by ultraviolet fluorescence, NO2 by chemiluminescence, and O3 by ultraviolet photometry. After excluding outliers and abnormal values in the hourly data collected at monitoring stations, the final data included measurements from days for which at least 75% of the 1-h values were available. The exposure parameters were set at 4-year averages (i.e. 2009e2012) for concentrations calculated from 10:00 a.m. to 6:00 p.m., 8-h O3 concentrations, and the 24-h PM10, SO2, and NO2 concentrations, in each district. The details regarding quality control of air pollutant monitoring is listed in the Supplementary file.
2.4. Lung function measurement Children’s health examinations were conducted from April 2012 to May 2013. A variety of measures for lung function, including forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), the ratio of FEV1 to FVC, peak expiratory flow rate (PEF), and maximum mid expiratory flow rate (MMEF) were measured utilizing two portable electronic spirometers (Spirolab, MIR, Italy) (Mehrparvar et al., 2014). Two technicians and researchers experienced in the use of electronic spirometers were responsible for collecting lung function data. Once the child participant was able to understand and follow the instructions given to them by the technicians, spirometry was performed according to the American Thoracic Society (ATS) and European Respiratory Society (ERS) standards. Total lung capacity (TLC) and residual volume (RV) were also measured. All measurements were corrected for body temperature and saturated pressure (BTPS). EpiData Entry was utilized for data entry and documentation, to prevent double entry generate a list of ID numbers and a codebook synopsis of the data, and to add dates to backup and enable encryption procedures. Predicted values for lung function were obtained from the reference equations used in our previous studies (Ma et al., 2013). A spirometric pattern was considered to indicate lung function impairment if FEV1% < 85%, FVC < 85%, PEF < 75%, or MMEF < 75% of the predicted value of the respective parameter.
2.5. Assessment of other covariates Using Centers for Disease Control and Prevention BMI growth charts, which uses one-month age intervals and is age and sex specific: we defined overweight if BMI was between the 85th and 95th percentile. Obesity was defined if BMI was greater than or equal to the 95th percentile. BMI was calculated based on a formula (weight (kg)/height (m) squared), and participants were categorized as either normal weight, overweight, or obese. A standardized protocol from the World Health Organization was used to measure height and weight. The children’s families’ annual income was categorized into five categories: <4999 Chinese Yuan Renminbi (RMB), 5000e9999 RMB, 10,000e29,999 RMB, 30,000e100,000 RMB, or >100,000 RMB. Parental education was defined as the highest education level completed by either parent. Passive smoking exposure was defined as living with someone (father, mother and/or other family members) who smokes cigarettes daily in the home. The child was classified as having asthma if the parent/ guardian reported that a doctor had ever diagnosed the child as having asthma. Breastfeeding was defined by asking the mother if she breastfed the child for at least three months. The variable house pet was defined by asking the parents/guardians if they keep any of the following pets in the house: dog, cat, bird, chicken, duck, or goose. Parents were also asked if they use coal in the household for cooking or space heating and if they lived close to a main road.
2.6. Statistical analysis Data were assessed for normality (using ShapiroeWilks W-test) and homogeneity (using Bartlett’s test for unequal variances). Chisquare tests were used to calculate the associations between categorical variables. We evaluated the association of ambient air pollutants with lung function using generalized additive models. We conducted a two-staged multiple regression model to determine the relation between pulmonary function tests (PFT) and ambient air pollutants with children being the first-level units and districts being the second-level units, as described previously (Dong et al., 2013). In brief, at the children level, we modeled the logistic regression to predict impaired lung function for each child using all covariates. At the district level, the random coefficients were regressed on the district-specific pollutant level to explain the variations of the district-specific intercepts and coefficients. In the two-level binary logistic regression model, four ambient air pollutantsdPM10, SO2, NO2, and O3 were considered as the key exposure variables. All analyses were performed using the GLIMMIX procedure in SAS version 9.4 (SAS Institute, Cary, North Carolina). All statistical tests were two-sided. A P-value less than 0.05 was considered statistically significant for main effects and a P-value less than 0.10 was considered statistically significant for interactions between gender and air pollutants.
3. Results 3.1. Characteristics of subjects Study population characteristics are presented in Table 1. The difference of spirometric measures and lung function status was significant when stratified by gender. Females had lower rates of asthma (5.51%), but males had better spirometric measures compared to females (P < 0.05). For example, in all categorical spirometric measures of lung function impairment, except for FEV1, females had more impairment in lung function compared to males (P < 0.05).
X.-W. Zeng et al. / Atmospheric Environment 138 (2016) 144e151
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Table 1 Characteristics of children in 24 districts in northeast of China, stratified by gender. Variables
Males (3382) N (%)
Females (3358) N (%)
Total (6740) N (%)
Age (years), Mean (±SD) Body mass index category Normal weight Obese Overweight Passive smoking exposure Father Mother Other Parental education > high school Breast feeding Family income per year <4999 RMBb 5000e9999 RMBb 10,000e29,999 RMBb 30,000e100,000 RMBb >100,000RMBb Home coal use House pet Asthma House close to main road Exercise time per week (hour), Mean (±SD) Spirometric parameters, Mean (±SD) FVC FEV1 PEF MMEF Lung function status FVC < 85% predicted FEV1 < 85% predicted PEF < 75% predicted
11.59 ± 2.10
11.53 ± 2.03
11.56 ± 2.07
2008 (59.37) 819 (24.22) 555 (16.41)
2510 (74.75) 335 (9.98) 513 (15.28)
4518 (67.03) 1154 (17.12) 1068 (15.85)
1050 (31.05) 300 (8.87) 273 (8.07) 2101 (62.12) 2312 (68.36)
1072 (31.92) 308 (9.17) 278 (8.28) 2110 (62.84) 2439 (72.63)a
2122 (31.48) 608 (9.02) 551 (8.18) 4211 (62.48) 4751 (70.49)
375 (11.09) 431 (12.74) 1197 (35.39) 1250 (36.96) 129 (3.81) 357 (10.56) 694 (20.52) 275 (8.13) 1558 (46.07) 7.85 ± 7.62
383 (11.41) 445 (13.25) 1197 (35.65) 1187 (35.35) 146 (4.35) 319 (9.50) 741 (22.07) 185 (5.51)a 1569 (46.72) 7.35 ± 7.87
758 (11.25) 876 (13.00) 2394 (35.52) 2437 (36.16) 275 (4.08) 676 (10.03) 1435 (21.29) 460 (6.82) 3127 (46.39) 7.60 ± 7.75
2.82 2.63 5.16 3.49
± ± ± ±
0.84 0.77 1.53 1.15
2.43 2.30 4.39 3.21
± ± ± ±
0.59a 0.57a 1.16a 0.91a
2.62 2.46 4.78 3.35
409 (12.18)a 275 (8.19) 272 (8.10)a
350 (10.35) 303 (8.96) 186 (5.50)
± ± ± ±
0.75 0.70 1.41 1.05
759 (11.26) 578 (8.58) 458 (6.80)
Values are n (%). a The difference between males and females is significant at the 0.05 level, using Chi square for categorical variables and t-test for continuous variables. b RMB, Chinese Yuan.
3.2. Air pollution levels
Table 2b Correlation coefficients of air pollutants across 24 districts.
Table 2a presents the distribution of ambient air pollutants during the study period based on the 2009e2012 estimates collected from monitoring stations in the 24 districts in northeast China. Additionally, the correlations between air pollutants among the 24 districts are presented in Table 2b. Overall, between-district correlation coefficients of various air pollutants were relatively low, except for the correlation of PM10 with the other three air pollutants.
3.3. Health effects of air pollution Two-staged binary logistic regression modeling was conducted to calculate adjusted odds ratios and 95% confidence intervals to examine the association of long-term exposure to air pollutants (PM10, SO2, NO2, and O3) and lung function impairment (Table 3). Models were adjusted by age, BMI, smoking exposure, parental education, breastfeeding status, income, home coal use, house pet, asthma status, and distance of home from a main road. Across all
PM10 SO2 NO2 O3
PM10
SO2
NO2
O3
1.00
0.55* 1.00
0.47* 0.28 1.00
0.56* 0.36 0.48* 1.00
Abbreviations: PM10, particles with an aerodynamic diameter 10 mm; SO2, sulfur dioxide; NO2, nitrogen dioxides, O3, ozone. * p < 0.05.
subjects and the four different measures of lung function impairment, long term exposure to ambient air pollution was associated with increased odds of lung function impairment, which ranged from 5% to 81%. There were significant differences between the odds of lung function impairment (measured by FEV1, PEF, and MMEF) for exposure to SO2, between males and females (P < 0.10). Females had higher odds of lung function impairment, with effect estimates ranging from 68% to 78%; as compared to lung function impairment in males, which ranged from 15% to 32%.
Table 2a Range of four years average concentrations of ambient air pollutants in 24 districts, 2009e2012. Air pollutant
Mean ± SD
Median
25th percentile
75th percentile
Minimum
Maximum
PM10 (mg/m3) SO2 (mg/m3) NO2 (mg/m3) O3 (mg/m3)
88.90 ± 21.31 49.75 ± 16.03 36.44 ± 11.10 106.92 ± 165.80
90.38 48.38 35.00 43.75
74.38 38.13 31.75 30.66
104.99 61.50 44.75 76.99
50.00 14.75 10.50 15.00
132.50 79.00 53.25 574.00
Abbreviations: PM10, particles with an aerodynamic diameter 10 mm; SO2, sulfur dioxide; NO2, nitrogen dioxides, O3, ozone.
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Table 3 Adjusted OR and 95% CI for the prevalence of lung function impairment and exposure to the air pollution. Males
FVC < 85% predicted PM10, mg/m3 SO2, mg/m3 NO2, mg/m3 O3, mg/m3 FEV1 < 85% predicted PM10, mg/m3 SO2, mg/m3 NO2, mg/m3 O3, mg/m3 PEF < 75% predicted PM10, mg/m3 SO2, mg/m3 NO2, mg/m3 O3, mg/m3 MMEF < 75% predicted PM10, mg/m3 SO2, mg/m3 NO2, mg/m3 O3, mg/m3
Females
Total
Inter. p-value
aOR
95%CI
aOR
95%CI
aOR
95%CI
1.75 1.24 1.17 1.06
1.41e2.17 0.95e1.62 0.93e1.46 1.01e1.11
1.43 1.48 1.16 1.04
1.17e1.75 1.14e1.92 0.94e1.44 1.00e1.09
1.57 1.36 1.16 1.05
1.32e1.86 1.08e1.71 0.96e1.42 1.01e1.10
0.092 0.189 0.983 0.390
1.83 1.31 1.24 1.03
1.41e2.39 0.94e1.82 0.94e1.63 0.97e1.10
1.79 1.77 1.28 1.06
1.37e2.34 1.26e2.48 0.96e1.69 1.00e1.13
1.81 1.51 1.26 1.05
1.44e2.28 1.12e2.03 0.97e1.62 0.99e1.11
0.862 0.053 0.791 0.265
1.73 1.15 1.33 1.06
1.29e2.33 0.79e1.68 0.99e1.78 0.99e1.13
1.75 1.68 1.43 1.06
1.33e2.29 1.19e2.39 1.09e1.87 0.99e1.12
1.74 1.44 1.39 1.06
1.37e2.22 1.05e1.98 1.08e1.78 1.00e1.12
0.951 0.033 0.596 0.976
1.68 1.32 1.31 1.03
1.29e2.21 0.95e1.82 1.01e1.70 0.96e1.09
1.76 1.78 1.40 1.06
1.36e2.28 1.30e2.45 1.09e1.80 1.00e1.13
1.73 1.55 1.36 1.05
1.37e2.17 1.17e2.05 1.08e1.71 0.99e1.11
0.737 0.054 0.565 0.130
aOR, adjusted odds ratios; CI, confidence intervals. PM10, particles with an aerodynamic diameter 10 mm; SO2, sulfur dioxide; NO2, nitrogen dioxides, O3, ozone. Models for all subjects are adjusted for age, BMI, smoking exposure, parental education, breast feeding status, income, home coal use, house pet, asthma status, exercise time, distance from main road, and districts. Statistical significance for interaction terms was set at p < 0.10, and are bolded. Estimate was scaled to the interquartile range (IQR: Range from 25th to 75th percentile of district-specific concentrations) for each pollutant (30.6 mg/m3 for PM10, 23.4 mg/m3 for SO2, 13.0 mg/m3 for NO2, and 46.3 mg/m3 for O3).
There were also significant differences between males and females for the odds of lung function impairment measured by FVC for exposure to PM10 (P < 0.10). In this scenario, we found males had higher odds of lung function impairment (OR ¼ 1.75, 95% CI ¼ 1.41e2.17), when compared to females (OR ¼ 1.43, 95% CI ¼ 1.17e1.75). Overall, for males, females, and all subjects, the largest effect sizes for odds of lung function impairment, across all measures of impairment, were seen from exposure to PM10 and SO2. Most notably, odds of lung function impairment measured by FEV1, increased by 83% for males exposed to PM10 (95% CI ¼ 1.41e2.39). The smallest effect sizes were seen from exposure to O3 across all measures of lung function for males, females, and all subjects, ranging from 3% to 6%. Table 4 presents the results of a two-staged multiple linear regression analysis to determine the relationship between ambient air pollutants and pulmonary function tests, individually for males and females, and then for all subjects. Overall, the models show a consistent negative relationship between all air pollutants and pulmonary function tests across all subjects. Among males, females, and all subjects, the greatest magnitude of effect was seen from exposure to PM10 (b coefficients range from 59.31 to 389.48, P < 0.05). Additionally, exposure to PM10 indicated a statistically significant difference between males and females in all measures of pulmonary function (P < 0.10). There were also statistically significant differences between males and females in the measurement of pulmonary function, measured by FVC and exposure to O3; and FEV1, and exposure to NO2 and O3.
4. Discussion The present study investigated the association between longterm air pollution exposure and lung function in children living in the most polluted areas in China. Results showed that long term exposure to the four ambient air pollutants investigated was associated with increased odds of lung function impairment, for
which the observed association was more significant in females. Our study, consistent with numerous epidemiological studies in China (He et al., 2010; Roy et al., 2012), Austria (Horak et al., 2002) and the United State (Peters et al., 1999; Gauderman et al., 2004, 2015; Urman et al., 2014), have consistently reported adverse effects of long-term ambient air pollution on lung function in children. In our study, PM10 and SO2 presented the greatest magnitude of adverse associations on lung function across all subjects. For example, an increase of PM10 by 30.6 mg/m3 was associated with reductions in FEV1 of 202.03 mL, 389.48 mL/s in PEF, 309.67 mL/s in MMEF, and 195.54 mL/s in FVC (Table 4). This observation was consistent with other investigations (Oftedal et al., 2008; Urman et al., 2014; Chen et al., 2015). In a cross-sectional study of 1494 non-asthmatic children in Taiwan, Chen et al. (2015) indicated that particulate matter was negatively associated with FVC, FEV1, and MMEF, more than all other pollutants. Gauderman et al. (2015) assessed whether long-term reductions in pollution are associated with improvements in respiratory health among children (over 13 years spanned by the three cohorts) in Southern California, which showed that improvements in 4-year growth of both FEV1 and FVC were associated with declining levels of PM10 (P < 0.001 for FEV1 and FVC). Unlike PM10, the effects of SO2 and O3 on lung function growth and impairment were not consistent. In contrast to other studies (Castro et al., 2009; Hwang et al., 2015), we revealed a high correlation between SO2 and a deficit in spirometric parameters. Our findings are consistent with some recent studies (Frye et al., 2003; Liu et al., 2009; Zebrowska et al., 2010; Gao et al., 2013). In Germany, Frye et al. (2003) found that the percent change of the geometric mean of FVC and FEV1 of children studies (aged 11e14 years) was 4.9% (95%CI: 0.7%e9.3%) and 3.0% (95%CI: 1.1%e7.2%), respectively, for a 100 mg/m3 decrease of SO2. Epidemiological studies with asthmatics have revealed significant, non-threshold relations between SO2 and decrease in FEV1 (Schwela, 2000). It has been shown that short term exposure to O3 causes a decrease in
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Table 4 Regression of pulmonary function test (PFT) on 2009e2012 ambient air pollution. Males
FVC PM10, mg/m3 SO2, mg/m3 NO2, mg/m3 O3, mg/m3 FEV1 PM10, mg/m3 SO2, mg/m3 NO2, mg/m3 O3, mg/m3 PEF PM10, mg/m3 SO2, mg/m3 NO2, mg/m3 O3, mg/m3 MMEF PM10, mg/m3 SO2, mg/m3 NO2, mg/m3 O3, mg/m3
Females
B
SE
116.22 61.55 16.15 14.61
50.21a 54.87 45.03 10.22
132.36 90.28 34.56 14.25
B
Total
Inter P-value
SE
B
SE
59.31 67.51 12.43 7.65
27.67a 27.80a 24.44 5.50
195.54 51.53 46.01 21.87
47.29a 50.12 41.10 9.30a
0.0003 0.633 0.265 0.065
40.61a 45.82a 39.03 8.89
73.53 93.85 21.97 6.73
25.80a 24.02a 24.01 5.53
202.03 90.62 76.37 21.13
41.12a 43.29a 37.18a 8.49a
0.0002 0.911 0.045 0.041
289.09 169.00 61.90 25.51
77.66a 92.43a 78.11 17.89
250.75 219.50 86.76 24.52
49.24a 57.16a 54.85 12.49a
389.48 170.02 102.78 29.69
82.37a 93.13a 78.96 17.89
0.040 0.680 0.606 0.714
254.20 215.48 94.76 25.20
59.74a 68.33a 62.99 14.64
195.45 202.33 74.82 23.99
32.75a 34.36a 41.00 8.86a
309.67 245.07 141.04 23.39
63.47a 68.32a 62.41a 14.15
0.059 0.481 0.151 0.846
B, estimate; SE, standard error. PM10, particles with an aerodynamic diameter 10 mm; SO2, sulfur dioxide; NO2, nitrogen dioxides, O3, ozone. Models for all subjects are adjusted for age, BMI, smoking exposure, parental education, breast feeding status, income, home coal use, house pet, asthma status, exercise time, distance from main road, and districts. Statistical significance was set for interaction terms at P < 0.10, and are bolded. Estimate was scaled to the interquartile range (IQR: Range from 25th to 75th percentile of district-specific concentrations) for each pollutant (30.6 mg/m3 for PM10, 23.4 mg/m3 for SO2, 13.0 mg/m3 for NO2, and 46.3 mg/m3 for O3). a Represents the statistically significant difference at P < 0.05.
lung function (Kopp et al., 2000; Liu et al., 2009; Chang et al., 2012; et al., 2014; Chen et al., 2015). However, we found that ozone Altug had the weakest association with lung function impairment compared to other pollutants. Our observation was consistent with other studies, which showed that long-term exposure of O3 had little effect on lung function (Gauderman et al., 2004; RojasMartinez et al., 2007; Barone-Adesi et al., 2015). In several cohort studies in Southern California, Gauderman et al. (2004; 2015) observed no association between long-term O3 exposure and lung functions in children. It is possible that the effect of short-term exposure to O3 may be more relevant than long-term exposure on lung function impairment. Although epidemiologic studies are supported by toxicology, the underlying mechanisms of the association between air pollution and lung function are not fully elucidated. Multiple animals and in vitro studies have demonstrated that pulmonary inflammation, increase in oxidative stress, cytotoxicity, and a variety of irreversible lung pathologies are mediated by air pollution, especially particulate matter (Andreau et al., 2012; Churg et al., 2003; Huang et al., 2012; Riva et al., 2011). When mucociliary functions and alveolar clearance exceed their limits, particulate matter persist into the lungs leading to a process known as airway remodeling, which is characterized by airway structural changes including hyperplasia of smooth muscle and goblet cells, subepithelial fibrosis, and epithelial alterations, as has been demonstrated in diseases such as asthma and chronic obstructive pulmonary disease (Churg et al., 2003; Riva et al., 2011; Thevenot et al., 2013). On the other hand, many of the air pollutants are either free radicals, such as nitrogen oxide, or have the ability to drive free radical reactions, such as particulate matter. Exposure to free radicals gives rise to oxidative stress within the lung, a key pathway for pulmonary diseases, which interact with other molecules to cause oxidative damage and induce pulmonary inflammatory response (Huang et al., 2012; Thevenot et al., 2013; Evelson and Gonzalez-Flecha. 2000). These mechanisms may explain the lung function findings
in children observed in our study. We observed gender differences in the adverse effects of air pollution on lung function, with females being more vulnerable to exposure. This is similar to other studies which show females’ lung function indices are more susceptible to ambient air pollution than males (Peters et al., 1999; Oftedal et al., 2008; Liu et al., 2009). Oftedal et al. (2008) reported that the association between lifetime exposures to PM10 and NO2 and lower PEF measures was more apparent in females. This could be due to the smaller lung sizes of females, which may compound and amplify the effect of long term exposure to ambient air pollution. However, some studies found that males were more vulnerable to air pollution (He et al., 2010; Gao et al., 2013). There are also other studies showing no gender differences (Gauderman et al., 2004, 2015). Regardless, the effects of gender as a modifier on the association between air pollution and lung function are still unclear. Most of our study subjects were in puberty (11.56 ± 2.07 years), with females usually starting a little earlier than males. It has been reported that age-related trends and smaller airways with greater airway reactivity among females may be linked to gender-different lung function growth rates (Gold et al., 1994; Peters et al., 1999; Clougherty, 2010). The hormonal changes that may be affecting females’ susceptibility to air pollution, needs further investigation (Annesi-Maesano et al., 2003; Oftedal et al., 2008). In addition, female children are usually less physically active than males, which, due to time-activity patterns, could affect lung function development (Clougherty, 2010). Our study has some limitations. First, the cross-sectional nature of the study calls into question the issue of temporality and causation, as evidence that exposure to ambient air pollutants preceded lung function impairment cannot be definitely established. Second, much of the characteristic information about the study population used to adjust the models was collected via survey. Therefore, recall bias and the Hawthorne effect, both types of observation bias, are present concerns for the measurement of covariates that may have resulted in inaccurate information for
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comparison of study subjects. Specifically, asthma is a condition which is often underreported in research utilizing self-report data instead of clinical diagnosis of the condition, which could lead to non-differential misclassification, whereas those with asthma are misclassified as not having asthma. Furthermore, we only collected data from the 24 district-level air pollution stations, which could only represent the average outdoor air quality near the monitoring station. The indoor exposure pattern was not considered in this study, which may also affect lung function in children. 5. Conclusions We found statistical evidence that there is an adverse relationship between air pollutants and pulmonary function, and that relationship was modified by gender, indicating females may be more vulnerable to long term exposure to air pollutants. Therefore, it will be important to take into account gender specific differences in the design of prevention, diagnosis, and treatment strategies for pulmonary diseases in the future. Competing interest The Authors report no conflicts of interest. The Authors are alone responsible for the content and writing of the paper. Acknowledgments The authors acknowledge the cooperation of the seven cities, school principals, teachers, and students and their parents. This research was supported by grants from China Environmental Protection Foundation (CEPF2008-123-1-5), and the Guangdong Province Natural Science Foundation (2014A050503027). The authors report no competing financial interest. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.atmosenv.2016.05.003. References , H., Gaga, E.O., Do €g erog lu, T., Brunekreef, B., Hoek, G., Van Doorn, W., 2014. Altug Effects of ambient air pollution on respiratory tract complaints and airway inflammation in primary school children. Sci. Total. Environ. 479-480, 201e209. American Thoracic Society, 1996. Health effects of outdoor air pollution. Am. J. Respir. Crit. Care. Med. 153, 3e50. Andreau, K., Leroux, M., Bouharrour, A., 2012. Health and cellular impacts of air pollutants: from cytoprotection to cytotoxicity. Biochem. Res. Int. 2012, 493894. Annesi-Maesano, I., Agabiti, N., Pistelli, R., Couilliot, M.F., Forastiere, F., 2003. Subpopulations at increased risk of adverse health outcomes from air pollution. Eur. Respir. J. Suppl. 40, 57se63s. Barone-Adesi, F., Dent, J.E., Dajnak, D., Beevers, S., Anderson, H.R., Kelly, F.J., Cook, D.G., Whincup, P.H., 2015. Long-term exposure to primary traffic pollutants and lung function in children: cross-sectional study and meta-analysis. PloS One 10, e0142565. Castro, H.A., Cunha, M.F., Mendonça, G.A., Junger, W.L., Cunha-Cruz, J., Leon, A.P., 2009. Effect of air pollution on lung function in schoolchildren in Rio de Janeiro, Brazil. Rev. Saude. Publica 43, 26e34. Chang, Y.K., Wu, C.C., Lee, L.T., Lin, R.S., Yu, Y.H., Chen, Y.C., 2012. The short-term effects of air pollution on adolescent lung function in Taiwan. Chemosphere 87, 26e30. Chen, C.H., Chan, C.C., Chen, B.Y., Cheng, T.J., Leon Guo, Y., 2015. Effects of particulate air pollution and ozone on lung function in non-asthmatic children. Environ. Res. 137, 40e48. Churg, A., Brauer, M., del Carmen, Avila-Casado, M., Fortoul, T.I., Wright, J.L., 2003. Chronic exposure to high levels of particulate air pollution and small airway remodeling. Environ. Health. Perspect. 111, 714e718. Clougherty, J.E., 2010. A growing role for gender analysis in air pollution epidemiology. Environ. Health. Perspect. 118, 167e176. Darrow, L.A., Klein, M., Flanders, W.D., Mulholland, J.A., Tolbert, P.E., Strickland, M.J., 2014. Air pollution and acute respiratory infections among children 0-4 years of age: an 18-year time-series study. Am. J. Epidemiol. 180, 968e977.
Dong, G.H., Qian, Z.M., Liu, M.M., Wang, D., Ren, W.H., Bawa, S., Fu, J., Wang, J., Lewis, R., Zelicoff, A., Simckes, M., Trevathan, E., 2013. Breastfeeding as a modifier of the respiratory effects of air pollution in children. Epidemiology 24, 387e394. Evelson, P., Gonzalez-Flecha, B., 2000. Time course and quantitative analysis of the adaptive responses to 85% oxygen in the rat lung and heart. Biochim. Biophys. Acta 1523, 209e216. Frye, C., Hoelscher, B., Cyrys, J., Wjst, M., Wichmann, H.E., Heinrich, J., 2003. Association of lung function with declining ambient air pollution. Environ. Health. Perspect 111, 383e387. Gao, Y., Chan, E.Y.Y., Li, L.P., He, Q.Q., Wong, T.W., 2013. Chronic effects of ambient air pollution on lung function among Chinese children. Arch. Dis. Child. 98, 128e135. Gauderman, W.J., Avol, E., Gilliland, F., Vora, H., Thomas, D., Berhane, K., McConnell, R., Kuenzli, N., Lurmann, F., Rappaport, E., Margolis, H., Bates, D., Peters, J., 2004. The effect of air pollution on lung development from 10 to 18 years of age. N. Engl. J. Med. 351, 1057e1067. Gauderman, W.J., Urman, R., Avol, E., Berhane, K., McConnell, R., Rappaport, E., Chang, R., Lurmann, F., Gilliland, F., 2015. Association of improved air quality with lung development in children. N. Engl. J. Med. 372, 905e913. Gold, D.R., Wypij, D., Wang, X., Speizer, F.E., Pugh, M., Ware, J.H., Ferris Jr., B.G., Dockery, D.W., 1994. Gender- and race-specific effects of asthma and wheeze on level and growth of lung function in children in six U.S. cities. Am. J. Respir. Crit. Care. Med. 149, 1198e1208. Horak, F., Studnicka, M., Gartner, C., Spengler, J.D., Tauber, E., Urbanek, R., Veiter, A., Frischer, T., 2002. Particulate matter and lung function growth in children: a 3yr follow-up study in Austrian schoolchildren. Eur. Respir. J. 19, 838e845. He, Q.Q., Wong, T.W., Du, L., Jiang, Z.Q., Gao, Y., Qiu, H., Liu, W.J., Wu, J.G., Wong, A., Yu, T.S., 2010. Effects of ambient air pollution on lung function growth in Chinese school children. Respir. Med. 104, 1512e1520. Huang, W., Wang, G., Lu, S.E., Kipen, H., Wang, Y., Hu, M., Lin, W., Rich, D., OhmanStrickland, P., Diehl, S.R., Zhu, P., Tong, J., Gong, J., Zhu, T., Zhang, J., 2012. Inflammatory and oxidative stress responses of healthy young adults to changes in air quality during the Beijing Olympics. Am. J. Respir. Crit. Care. Med. 186, 1150e1159. Hwang, B.F., Chen, Y.H., Lin, Y.T., Wu, X.T., Leo, Lee, Y., 2015. Relationship between exposure to fine particulates and ozone and reduced lung function in children. Environ. Res. 137, 382e390. Kopp, M.V., Bohnet, W., Frischer, T., Ulmer, C., Studnicka, M., Ihorst, G., Gardner, C., Forster, J., Urbanek, R., Kuehr, J., 2000. Effects of ambient ozone on lung function in children over a two-summer period. Eur. Respir. J 16, 893e900. Liu, L., Poon, R., Chen, L., Frescura, A.M., Montuschi, P., Ciabattoni, G., Wheeler, A., Dales, R., 2009. Acute effects of air pollution on pulmonary function, airway inflammation, and oxidative stress in asthmatic children. Environ. Health. Perspect. 117, 668e674. Ma, Y.N., Wang, J., Dong, G.H., Liu, M.M., Wang, D., Liu, Y.Q., Zhao, Y., Ren, W.H., Lee, Y.L., Zhao, Y.D., He, Q.C., 2013. Predictive equations using regression analysis of pulmonary function for healthy children in Northeast China. PloS One 8, e63875. Mehrparvar, A.H., Sakhvidi, M.J., Mostaghaci, M., Davari, M.H., Hashemi, S.H., Zare, Z., 2014. Spirometry values for detecting a restrictive pattern in occupational health settings. Tanaffos 13, 27e34. Miller, M.D., Marty, M.A., 2010. Impact of environmental chemicals on lung development. Environ. Health. Perspect. 118, 1155e1164. Oftedal, B., Brunekreef, B., Nystad, W., Madsen, C., Walker, S.-E., Nafstad, P., 2008. Residential outdoor air pollution and lung function in schoolchildren. Epidemiology 19, 129e137. Peters, J.M., Avol, E., Gauderman, W.J., Linn, W.S., Navidi, W., London, S.J., Margolis, H., Rappaport, E., Vora, H., Gong, H., Thomas, D.C., 1999. A study of twelve Southern California communities with differing levels and types of air pollution. II. Effects on pulmonary function. Am. J. Respir. Crit. Care. Med. 159, 768e775. Riva, D.R., Magalhaes, C.B., Lopes, A.A., Lanças, T., Mauad, T., Malm, O., Valenca, S.S., Saldiva, P.H., Faffe, D.S., Zin, W.A., 2011. Low dose of fine particulate matter (PM2.5) can induce acute oxidative stress, inflammation and pulmonary impairment in healthy mice. Inhal. Toxicol. 23, 257e267. Rojas-Martinez, R., Perez-Padilla, R., Olaiz-Fernandez, G., Mendoza-Alvarado, L., Moreno-Macias, H., Fortoul, T., McDonnell, W., Loomis, D., Romieu, I., 2007. Lung function growth in children with long-term exposure to air pollutants in Mexico City. Am. J. Respir. Crit. Care. Med. 176, 377e384. Roy, A., Hu, W., Wei, F., Korn, L., Chapman, R.S., Zhang, J.J., 2012. Ambient particulate matter and lung function growth in Chinese children. Epidemiology 23, 464e472. Spyratos, D., Sioutas, C., Tsiotsios, A., Haidich, A.B., Chloros, D., Triantafyllou, G., Sichletidis, L., 2015. Effects of particulate air pollution on nasal and lung function development among Greek children: a 19-year cohort study. Int. J. Environ. Health. Res. 25, 480e489. Thevenot, P.T., Saravia, J., Jin, N., Giaimo, J.D., Chustz, R.E., Mahne, S., Kelley, M.A., Hebert, V.Y., Dellinger, B., Dugas, T.R., Demayo, F.J., Cormier, S.A., 2013. Radicalcontaining ultrafine particulate matter initiates epithelial-to-mesenchymal transitions in airway epithelial cells. Am. J. Respir. Cell. Mol. Biol. 48, 188e197. Urman, R., McConnell, R., Islam, T., Avol, E.L., Lurmann, F.W., Vora, H., Linn, W.S., Rappaport, E.B., Gilliland, F.D., Gauderman, W.J., 2014. Associations of children’s lung function with ambient air pollution: joint effects of regional and nearroadway pollutants. Thorax 69, 540e547.
X.-W. Zeng et al. / Atmospheric Environment 138 (2016) 144e151 Wang, M., Gehring, U., Hoek, G., Keuken, M., Jonkers, S., Beelen, R., Eeftens, M., Postma, D.S., Brunekreef, B., 2015. Air pollution and lung function in Dutch children: a comparison of exposure estimates and associations based on land use regression and dispersion exposure modeling approaches. Environ. Health. Perspect. 123, 847e851. WHO, 2014. Air Quality Deteriorating in Many of the World’s Cities [cited 2015].
151
Available from: http://www.who.int/mediacentre/news/releases/2014/airquality/en/. Zebrowska, A., Mankowski, R., 2010. Effects of long-term exposure to air pollution on respiratory function and physical efficiency of pre-adolescent children. Eur. J. Med. Res. Suppl 2, 224e228.
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Long-term ambient air pollution and lung function impairment in Chinese children from a high air pollution range area: The Seven Northeastern Cities (SNEC) study Xiao-Wen Zeng a, Elaina Vivian b, KaheeA. Mohammed b, Shailja Jakhar b, Michael Vaughn c, Jin Huang c, Alan Zelicoff d, Pamela Xaverius b, Zhipeng Bai e, Shao Lin f, Yuan-Tao Hao g, Gunther Paul h, Lidia Morawska i, Si-Quan Wang j, Zhengmin Qian b, **, Guang-Hui Dong a, * a Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yatsen University, Guangzhou 510080, China b Department of Epidemiology, College for Public Health and Social Justice, Saint Louis University, Saint Louis 63104, USA c School of Social Work, College for Public Health and Social Justice, Saint Louis University, Saint Louis, MO 63104, USA d Department of Environmental and Occupational Health, College for Public Health and Social Justice, Saint Louis University, Saint Louis 63104, USA e State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China f Department of Epidemiology and Biostatistics, School of Public Health, State University of New York, Albany, NY 12144-3445, USA g Department of Epidemiology and Biostatistics, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China h Faculty of Health, School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD 4059, Australia i International Laboratory for Air Quality and Health (WHO CC for Air Quality and Health), Australia e China Centre for Air Quality Science and Management, Queensland University of Technology, Brisbane, QLD 4001, Australia j Department of Statistics and Operations Research, Hong Kong Baptist University, Kowloon Tong 999077, Hong Kong
h i g h l i g h t s
g r a p h i c a l a b s t r a c t
The associations between ambient air pollution and lung function are inconsistent. Few studies assessed such associations in children from a high air pollution area. We included a large population of 6740 Chinese children from seven cities of China. Increased odds of lung function impairment associated with exposure to air pollutants. The relationship between pollutants and lung function was modified by gender.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 7 October 2015 Received in revised form
Epidemiological studies have reported inconsistent and inconclusive associations between long-term exposure to ambient air pollution and lung function in children from Europe and America, where air pollution levels were typically low. The aim of the present study is to examine the relationship between
* Corresponding author. Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou 510080, China. ** Corresponding author. Department of Epidemiology, College for Public Health and Social Justice, Saint Louis University, Salus Center/Room 473, 3545 Lafayette Avenue, Saint Louis, MO 63104, USA. E-mail addresses: [email protected] (Z. Qian), [email protected], [email protected] (G.-H. Dong). http://dx.doi.org/10.1016/j.atmosenv.2016.05.003 1352-2310/© 2016 Elsevier Ltd. All rights reserved.
X.-W. Zeng et al. / Atmospheric Environment 138 (2016) 144e151 29 April 2016 Accepted 4 May 2016 Available online 12 May 2016 Keywords: Heavy air pollution Lung function impairment Gender differences Children China
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air pollutants and lung function in children selected from heavily industrialized and polluted cities in northeastern China. During 2012, 6740 boys and girls aged 7e14 years were recruited in 24 districts of seven northeastern cities. Portable electronic spirometers were used to measure lung function. Four-year average concentrations of particulate matter with an aerodynamic diameter 10 mm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3) were measured at monitoring stations in the 24 districts. Two-staged regression models were used in the data analysis, controlling for covariates. Overall, for all subjects, the increased odds of lung function impairment associated with exposure to air pollutants, ranged from 5% (adjusted odds ratio [aOR] ¼ 1.05; 95% confidence interval [CI] ¼ 1.01, 1.10) for FVC < 85% predicted per 46.3 mg/m3 for O3 to 81% (aOR ¼ 1.81; 95%CI ¼ 1.44, 2.28) for FEV1 < 85% predicted per 30.6 mg/m3 for PM10. The linear regression models consistently showed a negative relationship between all air pollutants and lung function measures across subjects. There were significant interaction terms indicating gender differences for lung function impairment and pulmonary function from exposure to some pollutants (P < 0.10). In conclusion, long term exposure to high concentrations of ambient air pollution is associated with decreased pulmonary function and lung function impairment, and females appear to be more susceptible than males. © 2016 Elsevier Ltd. All rights reserved.
1. Introduction
from the SNEC study.
Mounting scientific evidence has indicated the adverse effect of air pollution exposure on human health (WHO, 2014). Children may be more susceptible to the adverse effects of air pollution due to their higher rates of breathing, narrower airways, and developing lungs and immune systems (American Thoracic Society, 1996). Spirometry result, concerning lung development in children, have shown that exposure to air pollutants can predispose individuals to asthma, chronic obstructive lung disease and other disorders (Miller and Marty. 2010). Both cross-sectional and longitudinal studies have shown the link between ambient air pollution exposure and lung function impairment in children in the United States (Gauderman et al., 2004; Darrow et al., 2014; Urman et al., 2014; Gauderman et al., 2015), Canada (Liu et al., 2009), Austria (Horak et al., 2002) and Europe (Barone-Adesi et al., 2015; Spyratos et al., 2015). These studies provided suggestive, but inconclusive results. Most studies have shown adverse effects of particulate air pollutants, particles less than 2.5 mm aerodynamic diameters (PM2.5) and particles less than 10 mm aerodynamic diameters (PM10) on children’s lung function and respiratory symptoms, with the former having stronger effects (Gauderman et al., 2015; Wang et al., 2015). Nitrogen dioxide (NO2) also showed significant effects, while the effects of sulfur dioxide (SO2) and ozone (O3) were not consistent. The association between O3 exposure and respiratory disorders is strong even at low concentrations (Horak et al., 2002; Darrow et al., 2014; Hwang et al., 2015) but no association is observed between O3 exposure and the lung function in children in the US (Gauderman et al., 2004, 2015; Liu et al., 2009). Different ethnic backgrounds, exposure patterns and age may contribute to these inconsistent associations. Despite this, many investigations have indicated an association between air pollutant exposure and development of lung function in children at relatively low levels. Studies concerning this issue in countries that suffer from high concentrations of air pollution are still limited (He et al., 2010; Roy et al., 2012; Dong et al., 2013). Therefore, it is essential to further evaluate the association between lung function and air pollution in children in high air pollution exposure settings. The Seven Northeastern Cities (SNEC) study was conducted to investigate the effect of outdoor air pollution exposure on respiratory health effects in children from the most polluted cities in northeast China (Dong et al., 2013). The present study focused predominantly on the association of long-term exposure of ambient air pollutants and lung function in children using data
2. Methods 2.1. Study cities selection and subject recruitment We conducted a cross-sectional study to examine health outcomes in children based on exposure to ambient air pollutants. Our study participants were selected from a potential pool of 20 million people who were residing in 14 cities in the Liaoning province of Northeastern China. To maximize the inter- and intra-city gradients of the pollutants of interest and minimize the correlation between district-specific ambient pollutants, the seven citiesdShenyang, Dalian, Anshan, Fushun, Benxi, Liaoyang, and Dandong in Liaoning provincedwere selected as study sites according to the mean level of air pollution measured from 2009 to 2011. All urban districts were selected from each of the seven cities. The 24 urban districts selected from the seven cities, included five from Shenyang, four from Dalian, four from Fushun, three from Anshan, three from Benxi, three from Dandong, and two from Liaoyang. There was only one municipal air monitoring station available in each of the 24 study districts. Therefore, we randomly selected one or two classrooms from each grade of one elementary and one middle school within one km of the air monitoring station. The participants were those who lived in the district for at least two years before the start of study and had their homes and schools within one km of an air monitoring station. The final sample size included in this study was 6740 children (n ¼ 3382 boys and n ¼ 3358 girls) with an average age of 11.56 ± 2.07 years (7e14 years). The study was conducted according to World Medical Association Declaration of Helsinki-Ethical Principles for Medical Research Involving Human Subjects and we obtained ethical approval from Human Studies Committee of Sun Yat-sen University. Written informed consent was obtained from each participant and their parents before starting data collection. 2.2. Questionnaire survey We informed the principals of selected schools about the study. Questionnaires with envelopes and forms to record questionnaire distribution and collection were handed out to teachers with verbal and written instructions. Parents were invited to a parent’s night where they voluntarily filled out the questionnaire or took it home and returned it in a sealed envelope. Teachers were responsible for explaining the questionnaire to parents and obtaining the consent
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to participate and were strictly instructed not to urge the parents to participate.
2.3. Ambient air pollution There was a municipal air monitoring station located within one km from the study participants’ homes in each of the 24 selected study districts. In China, children are required to attend the school closest to their home, and national policy forbids selecting transregional schools for children. Therefore, participants’ homes were also within one km from both the monitoring stations and schools. From 2009 to 2012, these stations collected measurements of PM10, SO2, NO2, and O3 concentrations, using uniform methods to ensure quality assurance. Per the US EPA criteria, in China, the ambient air monitoring stations are mandated to be away from major roads, industrial sources, buildings, or residential sources of emissions like the combustion of coal, waste, or oil. Therefore, the monitoring results reflect the background urban air pollution levels in a city rather than from local sources, such as traffic or industrial combustion. These measurements were then used to estimate longterm exposures for study participants. The methodological standards set by the State Environmental Protection Administration of China were strictly followed. Pollutant concentrations were assessed continuously and reported each hour: PM10 by beta-attenuation, SO2 by ultraviolet fluorescence, NO2 by chemiluminescence, and O3 by ultraviolet photometry. After excluding outliers and abnormal values in the hourly data collected at monitoring stations, the final data included measurements from days for which at least 75% of the 1-h values were available. The exposure parameters were set at 4-year averages (i.e. 2009e2012) for concentrations calculated from 10:00 a.m. to 6:00 p.m., 8-h O3 concentrations, and the 24-h PM10, SO2, and NO2 concentrations, in each district. The details regarding quality control of air pollutant monitoring is listed in the Supplementary file.
2.4. Lung function measurement Children’s health examinations were conducted from April 2012 to May 2013. A variety of measures for lung function, including forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), the ratio of FEV1 to FVC, peak expiratory flow rate (PEF), and maximum mid expiratory flow rate (MMEF) were measured utilizing two portable electronic spirometers (Spirolab, MIR, Italy) (Mehrparvar et al., 2014). Two technicians and researchers experienced in the use of electronic spirometers were responsible for collecting lung function data. Once the child participant was able to understand and follow the instructions given to them by the technicians, spirometry was performed according to the American Thoracic Society (ATS) and European Respiratory Society (ERS) standards. Total lung capacity (TLC) and residual volume (RV) were also measured. All measurements were corrected for body temperature and saturated pressure (BTPS). EpiData Entry was utilized for data entry and documentation, to prevent double entry generate a list of ID numbers and a codebook synopsis of the data, and to add dates to backup and enable encryption procedures. Predicted values for lung function were obtained from the reference equations used in our previous studies (Ma et al., 2013). A spirometric pattern was considered to indicate lung function impairment if FEV1% < 85%, FVC < 85%, PEF < 75%, or MMEF < 75% of the predicted value of the respective parameter.
2.5. Assessment of other covariates Using Centers for Disease Control and Prevention BMI growth charts, which uses one-month age intervals and is age and sex specific: we defined overweight if BMI was between the 85th and 95th percentile. Obesity was defined if BMI was greater than or equal to the 95th percentile. BMI was calculated based on a formula (weight (kg)/height (m) squared), and participants were categorized as either normal weight, overweight, or obese. A standardized protocol from the World Health Organization was used to measure height and weight. The children’s families’ annual income was categorized into five categories: <4999 Chinese Yuan Renminbi (RMB), 5000e9999 RMB, 10,000e29,999 RMB, 30,000e100,000 RMB, or >100,000 RMB. Parental education was defined as the highest education level completed by either parent. Passive smoking exposure was defined as living with someone (father, mother and/or other family members) who smokes cigarettes daily in the home. The child was classified as having asthma if the parent/ guardian reported that a doctor had ever diagnosed the child as having asthma. Breastfeeding was defined by asking the mother if she breastfed the child for at least three months. The variable house pet was defined by asking the parents/guardians if they keep any of the following pets in the house: dog, cat, bird, chicken, duck, or goose. Parents were also asked if they use coal in the household for cooking or space heating and if they lived close to a main road.
2.6. Statistical analysis Data were assessed for normality (using ShapiroeWilks W-test) and homogeneity (using Bartlett’s test for unequal variances). Chisquare tests were used to calculate the associations between categorical variables. We evaluated the association of ambient air pollutants with lung function using generalized additive models. We conducted a two-staged multiple regression model to determine the relation between pulmonary function tests (PFT) and ambient air pollutants with children being the first-level units and districts being the second-level units, as described previously (Dong et al., 2013). In brief, at the children level, we modeled the logistic regression to predict impaired lung function for each child using all covariates. At the district level, the random coefficients were regressed on the district-specific pollutant level to explain the variations of the district-specific intercepts and coefficients. In the two-level binary logistic regression model, four ambient air pollutantsdPM10, SO2, NO2, and O3 were considered as the key exposure variables. All analyses were performed using the GLIMMIX procedure in SAS version 9.4 (SAS Institute, Cary, North Carolina). All statistical tests were two-sided. A P-value less than 0.05 was considered statistically significant for main effects and a P-value less than 0.10 was considered statistically significant for interactions between gender and air pollutants.
3. Results 3.1. Characteristics of subjects Study population characteristics are presented in Table 1. The difference of spirometric measures and lung function status was significant when stratified by gender. Females had lower rates of asthma (5.51%), but males had better spirometric measures compared to females (P < 0.05). For example, in all categorical spirometric measures of lung function impairment, except for FEV1, females had more impairment in lung function compared to males (P < 0.05).
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Table 1 Characteristics of children in 24 districts in northeast of China, stratified by gender. Variables
Males (3382) N (%)
Females (3358) N (%)
Total (6740) N (%)
Age (years), Mean (±SD) Body mass index category Normal weight Obese Overweight Passive smoking exposure Father Mother Other Parental education > high school Breast feeding Family income per year <4999 RMBb 5000e9999 RMBb 10,000e29,999 RMBb 30,000e100,000 RMBb >100,000RMBb Home coal use House pet Asthma House close to main road Exercise time per week (hour), Mean (±SD) Spirometric parameters, Mean (±SD) FVC FEV1 PEF MMEF Lung function status FVC < 85% predicted FEV1 < 85% predicted PEF < 75% predicted
11.59 ± 2.10
11.53 ± 2.03
11.56 ± 2.07
2008 (59.37) 819 (24.22) 555 (16.41)
2510 (74.75) 335 (9.98) 513 (15.28)
4518 (67.03) 1154 (17.12) 1068 (15.85)
1050 (31.05) 300 (8.87) 273 (8.07) 2101 (62.12) 2312 (68.36)
1072 (31.92) 308 (9.17) 278 (8.28) 2110 (62.84) 2439 (72.63)a
2122 (31.48) 608 (9.02) 551 (8.18) 4211 (62.48) 4751 (70.49)
375 (11.09) 431 (12.74) 1197 (35.39) 1250 (36.96) 129 (3.81) 357 (10.56) 694 (20.52) 275 (8.13) 1558 (46.07) 7.85 ± 7.62
383 (11.41) 445 (13.25) 1197 (35.65) 1187 (35.35) 146 (4.35) 319 (9.50) 741 (22.07) 185 (5.51)a 1569 (46.72) 7.35 ± 7.87
758 (11.25) 876 (13.00) 2394 (35.52) 2437 (36.16) 275 (4.08) 676 (10.03) 1435 (21.29) 460 (6.82) 3127 (46.39) 7.60 ± 7.75
2.82 2.63 5.16 3.49
± ± ± ±
0.84 0.77 1.53 1.15
2.43 2.30 4.39 3.21
± ± ± ±
0.59a 0.57a 1.16a 0.91a
2.62 2.46 4.78 3.35
409 (12.18)a 275 (8.19) 272 (8.10)a
350 (10.35) 303 (8.96) 186 (5.50)
± ± ± ±
0.75 0.70 1.41 1.05
759 (11.26) 578 (8.58) 458 (6.80)
Values are n (%). a The difference between males and females is significant at the 0.05 level, using Chi square for categorical variables and t-test for continuous variables. b RMB, Chinese Yuan.
3.2. Air pollution levels
Table 2b Correlation coefficients of air pollutants across 24 districts.
Table 2a presents the distribution of ambient air pollutants during the study period based on the 2009e2012 estimates collected from monitoring stations in the 24 districts in northeast China. Additionally, the correlations between air pollutants among the 24 districts are presented in Table 2b. Overall, between-district correlation coefficients of various air pollutants were relatively low, except for the correlation of PM10 with the other three air pollutants.
3.3. Health effects of air pollution Two-staged binary logistic regression modeling was conducted to calculate adjusted odds ratios and 95% confidence intervals to examine the association of long-term exposure to air pollutants (PM10, SO2, NO2, and O3) and lung function impairment (Table 3). Models were adjusted by age, BMI, smoking exposure, parental education, breastfeeding status, income, home coal use, house pet, asthma status, and distance of home from a main road. Across all
PM10 SO2 NO2 O3
PM10
SO2
NO2
O3
1.00
0.55* 1.00
0.47* 0.28 1.00
0.56* 0.36 0.48* 1.00
Abbreviations: PM10, particles with an aerodynamic diameter 10 mm; SO2, sulfur dioxide; NO2, nitrogen dioxides, O3, ozone. * p < 0.05.
subjects and the four different measures of lung function impairment, long term exposure to ambient air pollution was associated with increased odds of lung function impairment, which ranged from 5% to 81%. There were significant differences between the odds of lung function impairment (measured by FEV1, PEF, and MMEF) for exposure to SO2, between males and females (P < 0.10). Females had higher odds of lung function impairment, with effect estimates ranging from 68% to 78%; as compared to lung function impairment in males, which ranged from 15% to 32%.
Table 2a Range of four years average concentrations of ambient air pollutants in 24 districts, 2009e2012. Air pollutant
Mean ± SD
Median
25th percentile
75th percentile
Minimum
Maximum
PM10 (mg/m3) SO2 (mg/m3) NO2 (mg/m3) O3 (mg/m3)
88.90 ± 21.31 49.75 ± 16.03 36.44 ± 11.10 106.92 ± 165.80
90.38 48.38 35.00 43.75
74.38 38.13 31.75 30.66
104.99 61.50 44.75 76.99
50.00 14.75 10.50 15.00
132.50 79.00 53.25 574.00
Abbreviations: PM10, particles with an aerodynamic diameter 10 mm; SO2, sulfur dioxide; NO2, nitrogen dioxides, O3, ozone.
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Table 3 Adjusted OR and 95% CI for the prevalence of lung function impairment and exposure to the air pollution. Males
FVC < 85% predicted PM10, mg/m3 SO2, mg/m3 NO2, mg/m3 O3, mg/m3 FEV1 < 85% predicted PM10, mg/m3 SO2, mg/m3 NO2, mg/m3 O3, mg/m3 PEF < 75% predicted PM10, mg/m3 SO2, mg/m3 NO2, mg/m3 O3, mg/m3 MMEF < 75% predicted PM10, mg/m3 SO2, mg/m3 NO2, mg/m3 O3, mg/m3
Females
Total
Inter. p-value
aOR
95%CI
aOR
95%CI
aOR
95%CI
1.75 1.24 1.17 1.06
1.41e2.17 0.95e1.62 0.93e1.46 1.01e1.11
1.43 1.48 1.16 1.04
1.17e1.75 1.14e1.92 0.94e1.44 1.00e1.09
1.57 1.36 1.16 1.05
1.32e1.86 1.08e1.71 0.96e1.42 1.01e1.10
0.092 0.189 0.983 0.390
1.83 1.31 1.24 1.03
1.41e2.39 0.94e1.82 0.94e1.63 0.97e1.10
1.79 1.77 1.28 1.06
1.37e2.34 1.26e2.48 0.96e1.69 1.00e1.13
1.81 1.51 1.26 1.05
1.44e2.28 1.12e2.03 0.97e1.62 0.99e1.11
0.862 0.053 0.791 0.265
1.73 1.15 1.33 1.06
1.29e2.33 0.79e1.68 0.99e1.78 0.99e1.13
1.75 1.68 1.43 1.06
1.33e2.29 1.19e2.39 1.09e1.87 0.99e1.12
1.74 1.44 1.39 1.06
1.37e2.22 1.05e1.98 1.08e1.78 1.00e1.12
0.951 0.033 0.596 0.976
1.68 1.32 1.31 1.03
1.29e2.21 0.95e1.82 1.01e1.70 0.96e1.09
1.76 1.78 1.40 1.06
1.36e2.28 1.30e2.45 1.09e1.80 1.00e1.13
1.73 1.55 1.36 1.05
1.37e2.17 1.17e2.05 1.08e1.71 0.99e1.11
0.737 0.054 0.565 0.130
aOR, adjusted odds ratios; CI, confidence intervals. PM10, particles with an aerodynamic diameter 10 mm; SO2, sulfur dioxide; NO2, nitrogen dioxides, O3, ozone. Models for all subjects are adjusted for age, BMI, smoking exposure, parental education, breast feeding status, income, home coal use, house pet, asthma status, exercise time, distance from main road, and districts. Statistical significance for interaction terms was set at p < 0.10, and are bolded. Estimate was scaled to the interquartile range (IQR: Range from 25th to 75th percentile of district-specific concentrations) for each pollutant (30.6 mg/m3 for PM10, 23.4 mg/m3 for SO2, 13.0 mg/m3 for NO2, and 46.3 mg/m3 for O3).
There were also significant differences between males and females for the odds of lung function impairment measured by FVC for exposure to PM10 (P < 0.10). In this scenario, we found males had higher odds of lung function impairment (OR ¼ 1.75, 95% CI ¼ 1.41e2.17), when compared to females (OR ¼ 1.43, 95% CI ¼ 1.17e1.75). Overall, for males, females, and all subjects, the largest effect sizes for odds of lung function impairment, across all measures of impairment, were seen from exposure to PM10 and SO2. Most notably, odds of lung function impairment measured by FEV1, increased by 83% for males exposed to PM10 (95% CI ¼ 1.41e2.39). The smallest effect sizes were seen from exposure to O3 across all measures of lung function for males, females, and all subjects, ranging from 3% to 6%. Table 4 presents the results of a two-staged multiple linear regression analysis to determine the relationship between ambient air pollutants and pulmonary function tests, individually for males and females, and then for all subjects. Overall, the models show a consistent negative relationship between all air pollutants and pulmonary function tests across all subjects. Among males, females, and all subjects, the greatest magnitude of effect was seen from exposure to PM10 (b coefficients range from 59.31 to 389.48, P < 0.05). Additionally, exposure to PM10 indicated a statistically significant difference between males and females in all measures of pulmonary function (P < 0.10). There were also statistically significant differences between males and females in the measurement of pulmonary function, measured by FVC and exposure to O3; and FEV1, and exposure to NO2 and O3.
4. Discussion The present study investigated the association between longterm air pollution exposure and lung function in children living in the most polluted areas in China. Results showed that long term exposure to the four ambient air pollutants investigated was associated with increased odds of lung function impairment, for
which the observed association was more significant in females. Our study, consistent with numerous epidemiological studies in China (He et al., 2010; Roy et al., 2012), Austria (Horak et al., 2002) and the United State (Peters et al., 1999; Gauderman et al., 2004, 2015; Urman et al., 2014), have consistently reported adverse effects of long-term ambient air pollution on lung function in children. In our study, PM10 and SO2 presented the greatest magnitude of adverse associations on lung function across all subjects. For example, an increase of PM10 by 30.6 mg/m3 was associated with reductions in FEV1 of 202.03 mL, 389.48 mL/s in PEF, 309.67 mL/s in MMEF, and 195.54 mL/s in FVC (Table 4). This observation was consistent with other investigations (Oftedal et al., 2008; Urman et al., 2014; Chen et al., 2015). In a cross-sectional study of 1494 non-asthmatic children in Taiwan, Chen et al. (2015) indicated that particulate matter was negatively associated with FVC, FEV1, and MMEF, more than all other pollutants. Gauderman et al. (2015) assessed whether long-term reductions in pollution are associated with improvements in respiratory health among children (over 13 years spanned by the three cohorts) in Southern California, which showed that improvements in 4-year growth of both FEV1 and FVC were associated with declining levels of PM10 (P < 0.001 for FEV1 and FVC). Unlike PM10, the effects of SO2 and O3 on lung function growth and impairment were not consistent. In contrast to other studies (Castro et al., 2009; Hwang et al., 2015), we revealed a high correlation between SO2 and a deficit in spirometric parameters. Our findings are consistent with some recent studies (Frye et al., 2003; Liu et al., 2009; Zebrowska et al., 2010; Gao et al., 2013). In Germany, Frye et al. (2003) found that the percent change of the geometric mean of FVC and FEV1 of children studies (aged 11e14 years) was 4.9% (95%CI: 0.7%e9.3%) and 3.0% (95%CI: 1.1%e7.2%), respectively, for a 100 mg/m3 decrease of SO2. Epidemiological studies with asthmatics have revealed significant, non-threshold relations between SO2 and decrease in FEV1 (Schwela, 2000). It has been shown that short term exposure to O3 causes a decrease in
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Table 4 Regression of pulmonary function test (PFT) on 2009e2012 ambient air pollution. Males
FVC PM10, mg/m3 SO2, mg/m3 NO2, mg/m3 O3, mg/m3 FEV1 PM10, mg/m3 SO2, mg/m3 NO2, mg/m3 O3, mg/m3 PEF PM10, mg/m3 SO2, mg/m3 NO2, mg/m3 O3, mg/m3 MMEF PM10, mg/m3 SO2, mg/m3 NO2, mg/m3 O3, mg/m3
Females
B
SE
116.22 61.55 16.15 14.61
50.21a 54.87 45.03 10.22
132.36 90.28 34.56 14.25
B
Total
Inter P-value
SE
B
SE
59.31 67.51 12.43 7.65
27.67a 27.80a 24.44 5.50
195.54 51.53 46.01 21.87
47.29a 50.12 41.10 9.30a
0.0003 0.633 0.265 0.065
40.61a 45.82a 39.03 8.89
73.53 93.85 21.97 6.73
25.80a 24.02a 24.01 5.53
202.03 90.62 76.37 21.13
41.12a 43.29a 37.18a 8.49a
0.0002 0.911 0.045 0.041
289.09 169.00 61.90 25.51
77.66a 92.43a 78.11 17.89
250.75 219.50 86.76 24.52
49.24a 57.16a 54.85 12.49a
389.48 170.02 102.78 29.69
82.37a 93.13a 78.96 17.89
0.040 0.680 0.606 0.714
254.20 215.48 94.76 25.20
59.74a 68.33a 62.99 14.64
195.45 202.33 74.82 23.99
32.75a 34.36a 41.00 8.86a
309.67 245.07 141.04 23.39
63.47a 68.32a 62.41a 14.15
0.059 0.481 0.151 0.846
B, estimate; SE, standard error. PM10, particles with an aerodynamic diameter 10 mm; SO2, sulfur dioxide; NO2, nitrogen dioxides, O3, ozone. Models for all subjects are adjusted for age, BMI, smoking exposure, parental education, breast feeding status, income, home coal use, house pet, asthma status, exercise time, distance from main road, and districts. Statistical significance was set for interaction terms at P < 0.10, and are bolded. Estimate was scaled to the interquartile range (IQR: Range from 25th to 75th percentile of district-specific concentrations) for each pollutant (30.6 mg/m3 for PM10, 23.4 mg/m3 for SO2, 13.0 mg/m3 for NO2, and 46.3 mg/m3 for O3). a Represents the statistically significant difference at P < 0.05.
lung function (Kopp et al., 2000; Liu et al., 2009; Chang et al., 2012; et al., 2014; Chen et al., 2015). However, we found that ozone Altug had the weakest association with lung function impairment compared to other pollutants. Our observation was consistent with other studies, which showed that long-term exposure of O3 had little effect on lung function (Gauderman et al., 2004; RojasMartinez et al., 2007; Barone-Adesi et al., 2015). In several cohort studies in Southern California, Gauderman et al. (2004; 2015) observed no association between long-term O3 exposure and lung functions in children. It is possible that the effect of short-term exposure to O3 may be more relevant than long-term exposure on lung function impairment. Although epidemiologic studies are supported by toxicology, the underlying mechanisms of the association between air pollution and lung function are not fully elucidated. Multiple animals and in vitro studies have demonstrated that pulmonary inflammation, increase in oxidative stress, cytotoxicity, and a variety of irreversible lung pathologies are mediated by air pollution, especially particulate matter (Andreau et al., 2012; Churg et al., 2003; Huang et al., 2012; Riva et al., 2011). When mucociliary functions and alveolar clearance exceed their limits, particulate matter persist into the lungs leading to a process known as airway remodeling, which is characterized by airway structural changes including hyperplasia of smooth muscle and goblet cells, subepithelial fibrosis, and epithelial alterations, as has been demonstrated in diseases such as asthma and chronic obstructive pulmonary disease (Churg et al., 2003; Riva et al., 2011; Thevenot et al., 2013). On the other hand, many of the air pollutants are either free radicals, such as nitrogen oxide, or have the ability to drive free radical reactions, such as particulate matter. Exposure to free radicals gives rise to oxidative stress within the lung, a key pathway for pulmonary diseases, which interact with other molecules to cause oxidative damage and induce pulmonary inflammatory response (Huang et al., 2012; Thevenot et al., 2013; Evelson and Gonzalez-Flecha. 2000). These mechanisms may explain the lung function findings
in children observed in our study. We observed gender differences in the adverse effects of air pollution on lung function, with females being more vulnerable to exposure. This is similar to other studies which show females’ lung function indices are more susceptible to ambient air pollution than males (Peters et al., 1999; Oftedal et al., 2008; Liu et al., 2009). Oftedal et al. (2008) reported that the association between lifetime exposures to PM10 and NO2 and lower PEF measures was more apparent in females. This could be due to the smaller lung sizes of females, which may compound and amplify the effect of long term exposure to ambient air pollution. However, some studies found that males were more vulnerable to air pollution (He et al., 2010; Gao et al., 2013). There are also other studies showing no gender differences (Gauderman et al., 2004, 2015). Regardless, the effects of gender as a modifier on the association between air pollution and lung function are still unclear. Most of our study subjects were in puberty (11.56 ± 2.07 years), with females usually starting a little earlier than males. It has been reported that age-related trends and smaller airways with greater airway reactivity among females may be linked to gender-different lung function growth rates (Gold et al., 1994; Peters et al., 1999; Clougherty, 2010). The hormonal changes that may be affecting females’ susceptibility to air pollution, needs further investigation (Annesi-Maesano et al., 2003; Oftedal et al., 2008). In addition, female children are usually less physically active than males, which, due to time-activity patterns, could affect lung function development (Clougherty, 2010). Our study has some limitations. First, the cross-sectional nature of the study calls into question the issue of temporality and causation, as evidence that exposure to ambient air pollutants preceded lung function impairment cannot be definitely established. Second, much of the characteristic information about the study population used to adjust the models was collected via survey. Therefore, recall bias and the Hawthorne effect, both types of observation bias, are present concerns for the measurement of covariates that may have resulted in inaccurate information for
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comparison of study subjects. Specifically, asthma is a condition which is often underreported in research utilizing self-report data instead of clinical diagnosis of the condition, which could lead to non-differential misclassification, whereas those with asthma are misclassified as not having asthma. Furthermore, we only collected data from the 24 district-level air pollution stations, which could only represent the average outdoor air quality near the monitoring station. The indoor exposure pattern was not considered in this study, which may also affect lung function in children. 5. Conclusions We found statistical evidence that there is an adverse relationship between air pollutants and pulmonary function, and that relationship was modified by gender, indicating females may be more vulnerable to long term exposure to air pollutants. Therefore, it will be important to take into account gender specific differences in the design of prevention, diagnosis, and treatment strategies for pulmonary diseases in the future. Competing interest The Authors report no conflicts of interest. The Authors are alone responsible for the content and writing of the paper. Acknowledgments The authors acknowledge the cooperation of the seven cities, school principals, teachers, and students and their parents. This research was supported by grants from China Environmental Protection Foundation (CEPF2008-123-1-5), and the Guangdong Province Natural Science Foundation (2014A050503027). The authors report no competing financial interest. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.atmosenv.2016.05.003. References , H., Gaga, E.O., Do €g erog lu, T., Brunekreef, B., Hoek, G., Van Doorn, W., 2014. Altug Effects of ambient air pollution on respiratory tract complaints and airway inflammation in primary school children. Sci. Total. Environ. 479-480, 201e209. American Thoracic Society, 1996. Health effects of outdoor air pollution. Am. J. Respir. Crit. Care. Med. 153, 3e50. Andreau, K., Leroux, M., Bouharrour, A., 2012. Health and cellular impacts of air pollutants: from cytoprotection to cytotoxicity. Biochem. Res. Int. 2012, 493894. Annesi-Maesano, I., Agabiti, N., Pistelli, R., Couilliot, M.F., Forastiere, F., 2003. Subpopulations at increased risk of adverse health outcomes from air pollution. Eur. Respir. J. Suppl. 40, 57se63s. Barone-Adesi, F., Dent, J.E., Dajnak, D., Beevers, S., Anderson, H.R., Kelly, F.J., Cook, D.G., Whincup, P.H., 2015. Long-term exposure to primary traffic pollutants and lung function in children: cross-sectional study and meta-analysis. PloS One 10, e0142565. Castro, H.A., Cunha, M.F., Mendonça, G.A., Junger, W.L., Cunha-Cruz, J., Leon, A.P., 2009. Effect of air pollution on lung function in schoolchildren in Rio de Janeiro, Brazil. Rev. Saude. Publica 43, 26e34. Chang, Y.K., Wu, C.C., Lee, L.T., Lin, R.S., Yu, Y.H., Chen, Y.C., 2012. The short-term effects of air pollution on adolescent lung function in Taiwan. Chemosphere 87, 26e30. Chen, C.H., Chan, C.C., Chen, B.Y., Cheng, T.J., Leon Guo, Y., 2015. Effects of particulate air pollution and ozone on lung function in non-asthmatic children. Environ. Res. 137, 40e48. Churg, A., Brauer, M., del Carmen, Avila-Casado, M., Fortoul, T.I., Wright, J.L., 2003. Chronic exposure to high levels of particulate air pollution and small airway remodeling. Environ. Health. Perspect. 111, 714e718. Clougherty, J.E., 2010. A growing role for gender analysis in air pollution epidemiology. Environ. Health. Perspect. 118, 167e176. Darrow, L.A., Klein, M., Flanders, W.D., Mulholland, J.A., Tolbert, P.E., Strickland, M.J., 2014. Air pollution and acute respiratory infections among children 0-4 years of age: an 18-year time-series study. Am. J. Epidemiol. 180, 968e977.
Dong, G.H., Qian, Z.M., Liu, M.M., Wang, D., Ren, W.H., Bawa, S., Fu, J., Wang, J., Lewis, R., Zelicoff, A., Simckes, M., Trevathan, E., 2013. Breastfeeding as a modifier of the respiratory effects of air pollution in children. Epidemiology 24, 387e394. Evelson, P., Gonzalez-Flecha, B., 2000. Time course and quantitative analysis of the adaptive responses to 85% oxygen in the rat lung and heart. Biochim. Biophys. Acta 1523, 209e216. Frye, C., Hoelscher, B., Cyrys, J., Wjst, M., Wichmann, H.E., Heinrich, J., 2003. Association of lung function with declining ambient air pollution. Environ. Health. Perspect 111, 383e387. Gao, Y., Chan, E.Y.Y., Li, L.P., He, Q.Q., Wong, T.W., 2013. Chronic effects of ambient air pollution on lung function among Chinese children. Arch. Dis. Child. 98, 128e135. Gauderman, W.J., Avol, E., Gilliland, F., Vora, H., Thomas, D., Berhane, K., McConnell, R., Kuenzli, N., Lurmann, F., Rappaport, E., Margolis, H., Bates, D., Peters, J., 2004. The effect of air pollution on lung development from 10 to 18 years of age. N. Engl. J. Med. 351, 1057e1067. Gauderman, W.J., Urman, R., Avol, E., Berhane, K., McConnell, R., Rappaport, E., Chang, R., Lurmann, F., Gilliland, F., 2015. Association of improved air quality with lung development in children. N. Engl. J. Med. 372, 905e913. Gold, D.R., Wypij, D., Wang, X., Speizer, F.E., Pugh, M., Ware, J.H., Ferris Jr., B.G., Dockery, D.W., 1994. Gender- and race-specific effects of asthma and wheeze on level and growth of lung function in children in six U.S. cities. Am. J. Respir. Crit. Care. Med. 149, 1198e1208. Horak, F., Studnicka, M., Gartner, C., Spengler, J.D., Tauber, E., Urbanek, R., Veiter, A., Frischer, T., 2002. Particulate matter and lung function growth in children: a 3yr follow-up study in Austrian schoolchildren. Eur. Respir. J. 19, 838e845. He, Q.Q., Wong, T.W., Du, L., Jiang, Z.Q., Gao, Y., Qiu, H., Liu, W.J., Wu, J.G., Wong, A., Yu, T.S., 2010. Effects of ambient air pollution on lung function growth in Chinese school children. Respir. Med. 104, 1512e1520. Huang, W., Wang, G., Lu, S.E., Kipen, H., Wang, Y., Hu, M., Lin, W., Rich, D., OhmanStrickland, P., Diehl, S.R., Zhu, P., Tong, J., Gong, J., Zhu, T., Zhang, J., 2012. Inflammatory and oxidative stress responses of healthy young adults to changes in air quality during the Beijing Olympics. Am. J. Respir. Crit. Care. Med. 186, 1150e1159. Hwang, B.F., Chen, Y.H., Lin, Y.T., Wu, X.T., Leo, Lee, Y., 2015. Relationship between exposure to fine particulates and ozone and reduced lung function in children. Environ. Res. 137, 382e390. Kopp, M.V., Bohnet, W., Frischer, T., Ulmer, C., Studnicka, M., Ihorst, G., Gardner, C., Forster, J., Urbanek, R., Kuehr, J., 2000. Effects of ambient ozone on lung function in children over a two-summer period. Eur. Respir. J 16, 893e900. Liu, L., Poon, R., Chen, L., Frescura, A.M., Montuschi, P., Ciabattoni, G., Wheeler, A., Dales, R., 2009. Acute effects of air pollution on pulmonary function, airway inflammation, and oxidative stress in asthmatic children. Environ. Health. Perspect. 117, 668e674. Ma, Y.N., Wang, J., Dong, G.H., Liu, M.M., Wang, D., Liu, Y.Q., Zhao, Y., Ren, W.H., Lee, Y.L., Zhao, Y.D., He, Q.C., 2013. Predictive equations using regression analysis of pulmonary function for healthy children in Northeast China. PloS One 8, e63875. Mehrparvar, A.H., Sakhvidi, M.J., Mostaghaci, M., Davari, M.H., Hashemi, S.H., Zare, Z., 2014. Spirometry values for detecting a restrictive pattern in occupational health settings. Tanaffos 13, 27e34. Miller, M.D., Marty, M.A., 2010. Impact of environmental chemicals on lung development. Environ. Health. Perspect. 118, 1155e1164. Oftedal, B., Brunekreef, B., Nystad, W., Madsen, C., Walker, S.-E., Nafstad, P., 2008. Residential outdoor air pollution and lung function in schoolchildren. Epidemiology 19, 129e137. Peters, J.M., Avol, E., Gauderman, W.J., Linn, W.S., Navidi, W., London, S.J., Margolis, H., Rappaport, E., Vora, H., Gong, H., Thomas, D.C., 1999. A study of twelve Southern California communities with differing levels and types of air pollution. II. Effects on pulmonary function. Am. J. Respir. Crit. Care. Med. 159, 768e775. Riva, D.R., Magalhaes, C.B., Lopes, A.A., Lanças, T., Mauad, T., Malm, O., Valenca, S.S., Saldiva, P.H., Faffe, D.S., Zin, W.A., 2011. Low dose of fine particulate matter (PM2.5) can induce acute oxidative stress, inflammation and pulmonary impairment in healthy mice. Inhal. Toxicol. 23, 257e267. Rojas-Martinez, R., Perez-Padilla, R., Olaiz-Fernandez, G., Mendoza-Alvarado, L., Moreno-Macias, H., Fortoul, T., McDonnell, W., Loomis, D., Romieu, I., 2007. Lung function growth in children with long-term exposure to air pollutants in Mexico City. Am. J. Respir. Crit. Care. Med. 176, 377e384. Roy, A., Hu, W., Wei, F., Korn, L., Chapman, R.S., Zhang, J.J., 2012. Ambient particulate matter and lung function growth in Chinese children. Epidemiology 23, 464e472. Spyratos, D., Sioutas, C., Tsiotsios, A., Haidich, A.B., Chloros, D., Triantafyllou, G., Sichletidis, L., 2015. Effects of particulate air pollution on nasal and lung function development among Greek children: a 19-year cohort study. Int. J. Environ. Health. Res. 25, 480e489. Thevenot, P.T., Saravia, J., Jin, N., Giaimo, J.D., Chustz, R.E., Mahne, S., Kelley, M.A., Hebert, V.Y., Dellinger, B., Dugas, T.R., Demayo, F.J., Cormier, S.A., 2013. Radicalcontaining ultrafine particulate matter initiates epithelial-to-mesenchymal transitions in airway epithelial cells. Am. J. Respir. Cell. Mol. Biol. 48, 188e197. Urman, R., McConnell, R., Islam, T., Avol, E.L., Lurmann, F.W., Vora, H., Linn, W.S., Rappaport, E.B., Gilliland, F.D., Gauderman, W.J., 2014. Associations of children’s lung function with ambient air pollution: joint effects of regional and nearroadway pollutants. Thorax 69, 540e547.
X.-W. Zeng et al. / Atmospheric Environment 138 (2016) 144e151 Wang, M., Gehring, U., Hoek, G., Keuken, M., Jonkers, S., Beelen, R., Eeftens, M., Postma, D.S., Brunekreef, B., 2015. Air pollution and lung function in Dutch children: a comparison of exposure estimates and associations based on land use regression and dispersion exposure modeling approaches. Environ. Health. Perspect. 123, 847e851. WHO, 2014. Air Quality Deteriorating in Many of the World’s Cities [cited 2015].
151
Available from: http://www.who.int/mediacentre/news/releases/2014/airquality/en/. Zebrowska, A., Mankowski, R., 2010. Effects of long-term exposure to air pollution on respiratory function and physical efficiency of pre-adolescent children. Eur. J. Med. Res. Suppl 2, 224e228.