{"title":"学龄儿童和青少年颗粒物暴露变异性与体重指数之间的城乡差异:来自中国的证据","authors":"Xiaofeng Sun, Fangying Liu, Feng Cui, Jingyi Zhao, Qian Zhang, Xiaowei Hao, Meng Zhang, Qing Wang","doi":"10.1007/s11869-025-01748-x","DOIUrl":null,"url":null,"abstract":"<div><p>An increasing number of epidemiological studies have suggested that ambient exposure to particular matter (PM) contributes to child obesity. However, whether variations in PM influence body mass index remains unknown. This study aimed to estimate the association between variability in ambient PM exposure and body mass index and the related urban-rural disparities, independent of air pollution exposure. This research utilized a cohort data set from the National School-aged Children and Adolescents Health Monitoring Project of China, encompassing over 1 million school-aged children and adolescents aged 6 to 19 from 2013 to 2021. Pollution levels were defined by assigning each student the nearest grid according to the longitude and latitude of each student’s school. Subsequently, the monthly coefficients of variation in PM over 30 days preceding the physical examination date were calculated. Panel multilevel and panel quantile regression models were applied. In panel multilevel model, inverse probability weighting was introduced to adjust for bias in our study sample. After controlling for pollution concentration, a 1% increase in the monthly variability of PM<sub>2.5</sub> and PM<sub>10</sub> was associated with 0.0025 (95% CI: 0.22, 0.27) and 0.0034 (95% CI: 0.32, 0.36) increases in body mass index, respectively. The effect sizes of variability of PM<sub>2.5</sub> and PM<sub>10</sub> were 0.0022 (95% CI: 0.20, 0.25) and 0.0031 (95% CI: 0.29, 0.33) in urban areas, respectively. In contrast, in rural areas, the corresponding BMI increases were 0.0035 (95% CI: 0.30, 0.40) and 0.0043 (95% CI: 0.40, 0.47). Similar results were reported adjusting for weights and across quantile levels. Moreover, the effects were more pronounced for children and adolescents with a higher body mass index. The urban-rural disparities remained at different quantiles. To our knowledge, this is the first study revealing the positive relationship between air pollution variations and child obesity and the related urban-rural disparities. Environmental health policies accounting for variations in pollution exposure should be implemented, with particular focuses in rural areas.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 7","pages":"2007 - 2019"},"PeriodicalIF":2.9000,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Urban-rural disparities in the association between variability in particulate matter exposure and body mass index in school-aged children and adolescents: evidence from China\",\"authors\":\"Xiaofeng Sun, Fangying Liu, Feng Cui, Jingyi Zhao, Qian Zhang, Xiaowei Hao, Meng Zhang, Qing Wang\",\"doi\":\"10.1007/s11869-025-01748-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>An increasing number of epidemiological studies have suggested that ambient exposure to particular matter (PM) contributes to child obesity. However, whether variations in PM influence body mass index remains unknown. This study aimed to estimate the association between variability in ambient PM exposure and body mass index and the related urban-rural disparities, independent of air pollution exposure. This research utilized a cohort data set from the National School-aged Children and Adolescents Health Monitoring Project of China, encompassing over 1 million school-aged children and adolescents aged 6 to 19 from 2013 to 2021. Pollution levels were defined by assigning each student the nearest grid according to the longitude and latitude of each student’s school. Subsequently, the monthly coefficients of variation in PM over 30 days preceding the physical examination date were calculated. Panel multilevel and panel quantile regression models were applied. In panel multilevel model, inverse probability weighting was introduced to adjust for bias in our study sample. After controlling for pollution concentration, a 1% increase in the monthly variability of PM<sub>2.5</sub> and PM<sub>10</sub> was associated with 0.0025 (95% CI: 0.22, 0.27) and 0.0034 (95% CI: 0.32, 0.36) increases in body mass index, respectively. The effect sizes of variability of PM<sub>2.5</sub> and PM<sub>10</sub> were 0.0022 (95% CI: 0.20, 0.25) and 0.0031 (95% CI: 0.29, 0.33) in urban areas, respectively. In contrast, in rural areas, the corresponding BMI increases were 0.0035 (95% CI: 0.30, 0.40) and 0.0043 (95% CI: 0.40, 0.47). Similar results were reported adjusting for weights and across quantile levels. Moreover, the effects were more pronounced for children and adolescents with a higher body mass index. The urban-rural disparities remained at different quantiles. To our knowledge, this is the first study revealing the positive relationship between air pollution variations and child obesity and the related urban-rural disparities. Environmental health policies accounting for variations in pollution exposure should be implemented, with particular focuses in rural areas.</p></div>\",\"PeriodicalId\":49109,\"journal\":{\"name\":\"Air Quality Atmosphere and Health\",\"volume\":\"18 7\",\"pages\":\"2007 - 2019\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2025-05-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Air Quality Atmosphere and Health\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s11869-025-01748-x\",\"RegionNum\":4,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Air Quality Atmosphere and Health","FirstCategoryId":"93","ListUrlMain":"https://link.springer.com/article/10.1007/s11869-025-01748-x","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Urban-rural disparities in the association between variability in particulate matter exposure and body mass index in school-aged children and adolescents: evidence from China
An increasing number of epidemiological studies have suggested that ambient exposure to particular matter (PM) contributes to child obesity. However, whether variations in PM influence body mass index remains unknown. This study aimed to estimate the association between variability in ambient PM exposure and body mass index and the related urban-rural disparities, independent of air pollution exposure. This research utilized a cohort data set from the National School-aged Children and Adolescents Health Monitoring Project of China, encompassing over 1 million school-aged children and adolescents aged 6 to 19 from 2013 to 2021. Pollution levels were defined by assigning each student the nearest grid according to the longitude and latitude of each student’s school. Subsequently, the monthly coefficients of variation in PM over 30 days preceding the physical examination date were calculated. Panel multilevel and panel quantile regression models were applied. In panel multilevel model, inverse probability weighting was introduced to adjust for bias in our study sample. After controlling for pollution concentration, a 1% increase in the monthly variability of PM2.5 and PM10 was associated with 0.0025 (95% CI: 0.22, 0.27) and 0.0034 (95% CI: 0.32, 0.36) increases in body mass index, respectively. The effect sizes of variability of PM2.5 and PM10 were 0.0022 (95% CI: 0.20, 0.25) and 0.0031 (95% CI: 0.29, 0.33) in urban areas, respectively. In contrast, in rural areas, the corresponding BMI increases were 0.0035 (95% CI: 0.30, 0.40) and 0.0043 (95% CI: 0.40, 0.47). Similar results were reported adjusting for weights and across quantile levels. Moreover, the effects were more pronounced for children and adolescents with a higher body mass index. The urban-rural disparities remained at different quantiles. To our knowledge, this is the first study revealing the positive relationship between air pollution variations and child obesity and the related urban-rural disparities. Environmental health policies accounting for variations in pollution exposure should be implemented, with particular focuses in rural areas.
期刊介绍:
Air Quality, Atmosphere, and Health is a multidisciplinary journal which, by its very name, illustrates the broad range of work it publishes and which focuses on atmospheric consequences of human activities and their implications for human and ecological health.
It offers research papers, critical literature reviews and commentaries, as well as special issues devoted to topical subjects or themes.
International in scope, the journal presents papers that inform and stimulate a global readership, as the topic addressed are global in their import. Consequently, we do not encourage submission of papers involving local data that relate to local problems. Unless they demonstrate wide applicability, these are better submitted to national or regional journals.
Air Quality, Atmosphere & Health addresses such topics as acid precipitation; airborne particulate matter; air quality monitoring and management; exposure assessment; risk assessment; indoor air quality; atmospheric chemistry; atmospheric modeling and prediction; air pollution climatology; climate change and air quality; air pollution measurement; atmospheric impact assessment; forest-fire emissions; atmospheric science; greenhouse gases; health and ecological effects; clean air technology; regional and global change and satellite measurements.
This journal benefits a diverse audience of researchers, public health officials and policy makers addressing problems that call for solutions based in evidence from atmospheric and exposure assessment scientists, epidemiologists, and risk assessors. Publication in the journal affords the opportunity to reach beyond defined disciplinary niches to this broader readership.