Abstract
Air pollution is widely understood to be capable of exacerbating asthma symptoms. Here we examined the association between traffic-related air pollution and development of asthma in school children. Subjects were 10,069 school children in their first through third years of compulsory education (6–9-year old). The main outcome was incidence of asthma as determined from the questionnaire. Follow-up surveys were conducted every year up to 4 years after the end of the study. To evaluate individual level of exposure to traffic-related air pollution, we used a simulation model that accounted for exposure level both at home and at school. As surrogates of traffic-related air pollution, the estimation target was the annual average individual exposure of automobile exhaust-originating nitrogen oxides (NOx) and elemental carbon (EC). Confounding factors were adjusted using a discrete-time logistic regression model. We found a positive association between exposure to EC and incidence of asthma. The odds ratio (OR) (95% confidence interval) for asthma incidence was 1.07 (1.01–1.14) for each 0.1 μg/m3 EC and 1.01 (0.99–1.03) for each 1 p.p.b. NOx. Traffic-related air pollution is associated with development of asthma in school children.
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Acknowledgements
This study was founded by the Japanese Ministry of the Environment. We thank the external review committee: Tominaga S (chair; Aichi Cancer Center, Japan), Akiba S (Kagoshima University, Japan), Fukuchi Y (Juntendo University, Japan), Kasahara M (Chubu University, Japan), Ota K (Teikyo University, Japan), Morikawa A (Gunma University, Japan), Shirai M (Waseda University, Japan), Yanagisawa Y (The University of Tokyo, Japan), and Yoshimura I (Tokyo University of Science, Japan). We also thank members of the Environmental Health Affairs Office in the Japanese Ministry of the Environment (who teamed as coordinating officers) and CIMIC Co. Ltd, Tokyo, Japan (a data center of this study) (Appendix IV).
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Appendices
Appendix I
Appendix I: Method for estimating individual exposure level of traffic-related air pollutants
A variety of numerical models have been developed for estimating individual exposure concentrations for pollutants, such as interpolation, LUR and dispersion models.22 However, distinction between model evaluation and model construction is ambiguous, because in the construction process of LUR models results of site-specific field measurement are used to determine the regression parameters. In contrast, the dispersion model that was used in this study is based on site-independent methods of emission estimates and physical dispersion mechanisms, except in the determination of the adjustment concentrations where the field measurement results were used.15 We estimated annual average outdoor concentration levels (OCLs) of elemental carbon (EC) and nitrogen oxides (NOx) for houses and schools of all subjects using a dispersion model.15 This model was composed of three types of dispersion models in different spatial scales, from a few meters to several kilometers. To estimate the fine structure of spatial distribution of OCLs near roads, we applied a recently developed building-resolving dispersion model.17 Model performance was confirmed by field measurement at permanent and temporary observatory stations for air quality. This model explained 67% and 78% of the variability in 2007 annual average concentrations for EC and NOx, respectively.15
Using the following regression models, we estimated indoor concentration levels (ICLs) in houses and schools from the OCLs estimated above:
ICL of EC at a house=0.57 × OCL of EC at the house+0.33 (μg/m3)
ICL of EC at a school=0.92 × OCL of EC at the school+17.6 (μg/m3)
ICL of NOx at a house=0.72 × OCL of NOx at the house+0.83 (p.p.b.)
ICL of NOx at a school=0.73 × OCL of NOx at the school+4.3 (p.p.b.)
The regression models were determined by the test of fitting using selected 131 subjects who were investigated for real ICL and real OCL. Using this regression model, we calculated concentrations of EC and NOx at both places (home and school) for each subject.
We then determined via questionnaire the daily amount of time per subject spent at home, school, and outdoors over a 24-h period. We multiplied ICL at school by amount of time at school, ICL at home by amount of time at home, mean value of OCL at home and OCL at school by amount of time at school, and average value of OCL at home in children attending the same school by amount of time at home (Appendix Table A1). Individual exposure level was the summation of the variables multiplied above for each subject. This model explained >60% of the variability, except for variation in NOx concentrations during winter, as ICLs during that period were affected by air pollutants emitted from indoor heating facilities.
Annual individual exposure level was estimated as follows:
(X1Y1+X2Y2+X3Y3+X4Y4) × 200+[(24−X4)Y2+X4Y4] × 165
Appendix II
Appendix II: Definition of respiratory symptoms
Prevalence of wheeze was defined as follows: a yes answer to the question “Has this child’s chest ever sounded wheezy or whistling when having cold?” and a yes answer to the question “Has this child’s chest sounded wheezy or whistling for at least two times in the past 2 years?” Prevalence of persistent cough was defined as follows: the answers to several cough-related questions indicate that the study child has coughed for at least 3 months per year either along with or apart from cold. Prevalence of persistent phlegm was defined as follows: the answers to several phlegm-related questions indicate that the study child has brought up phlegm or mucus from the chest for at least 3 months per year either along with or apart from cold. Prevalence of chest illnesses was defined as follows: a yes answer to the question “During the past 3 years, has this child had any chest illness that has kept him/her from his/her usual activities for as much as 3 days?” and a yes answer to the question “Did he/she bring up more phlegm or seem more congested than usual with any of these illness?”16
Appendix III
Appendix III: Other general characteristics
Appendix IV
Appendix IV: Organization of the study on respiratory disease and automobile exhaust (SORA)
The external review committee:
Tominaga S (chair; Aichi Cancer Center), Akiba S (Kagoshima University), Fukuchi Y (Juntendo University), Kasahara M (Chubu University), Ota K (Teikyo University), Morikawa A (Gunma University), Shirai M (Waseda University), Yanagisawa Y (The University of Tokyo), and Yoshimura I (Tokyo University of Science).
The exposure assessment committee:
Nitta H (chair; National Institute for Environmental Studies), Hasegawa S (Center for Environmental Science in Saitama), Nakai S (Yokohama National University), Ohara T (National Institute for Environmental Studies), Sakamoto K (Saitama University), Shima M (Hyogo College of Medicine), Tamura K, (National Institute for Environmental Studies), and Yokota H (Tokyo Metropolitan Research Institute).
Coordinating officers:
The members of the Environmental Health Affairs Office in the Ministry of the Environment.
Data center:
CIMIC Co. Ltd, Tokyo, Japan.
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Yamazaki, S., Shima, M., Nakadate, T. et al. Association between traffic-related air pollution and development of asthma in school children: Cohort study in Japan. J Expo Sci Environ Epidemiol 24, 372–379 (2014). https://doi.org/10.1038/jes.2014.15
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DOI: https://doi.org/10.1038/jes.2014.15
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