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Chen S, Wang S, Li T, Zhu H, Liang S, Xu K, Zhang Y, Yuan X, Yang Y, Pan H, Shi X. Effect of PM2.5 on macrosomia in China: A nationwide prospective cohort study. Pediatr Obes 2020; 15:e12584. [PMID: 31689002 DOI: 10.1111/ijpo.12584] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2019] [Revised: 09/07/2019] [Accepted: 09/17/2019] [Indexed: 12/13/2022]
Abstract
BACKGROUND Macrosomia is associated with both neonatal complications and adult diseases (obesity, diabetes mellitus, etc.). Previous studies have reported maternal exposure to PM2.5 might influence metabolism and fetal development and cause adverse pregnancy outcomes. Studies conducted in areas with low PM2.5 concentration have found relationship between gestational PM2.5 exposure and birth weight. However, the impact of air pollution on macrosomia has not been studied, especially in highly polluted areas. OBJECTIVE To evaluate the association between fine particulate matter (PM2.5) exposure during pregnancy and the risk of macrosomia. METHODS Data from preconception health examination and prenatal and postnatal records were collected from 1 January 2010 to 31 December 2012 in the National Free Preconception Health Examination Project. Monthly mean of PM2.5 concentration during pregnancy was estimated from satellite data using an ensemble machine learning model. A newborn with birth weight above 4000 g was defined as macrosomia. Logistic regression models were used to examine the association between maternal exposure to PM2.5 and the risk of macrosomia, after adjusting for maternal age, pre-pregnancy body mass index, parity, neonatal sex, duration of gestation, seasonality, educational level, smoking and drinking habits, past history of diabetes mellitus and hypertension, and family history of diabetes mellitus. Restricted cubic spline models were used to evaluate the dose-response relationship between the risk of macrosomia and PM2.5 concentration. RESULTS Of 177 841 singleton nonlow birth weight newborns included, 14 598 (8.2%) had macrosomia. The mean PM2.5 concentrations were 70.7, 71.5, and 80.9 μg/m3 in the first, second, and third trimesters. In full-adjusted logistic regression models, significant associations were found between increased risk of macrosomia and every 10 μg/m3 increase of PM2.5 concentration over the first (odds ratio [OR]: 1.045; 95% CI, 1.037-1.052), second (OR: 1.035; 95% CI, 1.028-1.043), and third (OR: 1.033; 95% CI, 1.026-1.039) trimesters. There was a nonlinear does-response association between PM2.5 concentration and the risk of macrosomia. CONCLUSIONS Maternal exposure to PM2.5 during pregnancy was associated with an increased risk of macrosomia in China.
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Affiliation(s)
- Shi Chen
- Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC), Beijing, China
| | - Shirui Wang
- Eight-year Program of Clinical Medicine, PUMCH, CAMS & PUMC, Beijing, China
| | - Tiantian Li
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Huijuan Zhu
- Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC), Beijing, China
| | - Siyu Liang
- Eight-year Program of Clinical Medicine, PUMCH, CAMS & PUMC, Beijing, China
| | - Ke Xu
- Department of Endocrinology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yuelun Zhang
- Central Research Laboratory, PUMCH, CAMS & PUMC, Beijing, China
| | - Xianxian Yuan
- Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC), Beijing, China
| | - Yingying Yang
- Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC), Beijing, China
| | - Hui Pan
- Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC), Beijing, China
| | - Xiaoming Shi
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
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Huang K, Bi J, Meng X, Geng G, Lyapustin A, Lane KJ, Gu D, Kinney PL, Liu Y. Estimating daily PM 2.5 concentrations in New York City at the neighborhood-scale: Implications for integrating non-regulatory measurements. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 697:134094. [PMID: 32380602 DOI: 10.1016/j.scitotenv.2019.134094] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2019] [Revised: 08/22/2019] [Accepted: 08/23/2019] [Indexed: 06/11/2023]
Abstract
Previous PM2.5 related epidemiological studies mainly relied on data from sparse regulatory monitors to assess exposure. The introduction of non-regulatory PM2.5 monitors presents both opportunities and challenges to researchers and air quality managers. In this study, we evaluated the advantages and limitations of integrating non-regulatory PM2.5 measurements into a satellite-based daily PM2.5 model at 100 m resolution in New York City in 2015. Two separate machine learning models were developed, one using only PM2.5 data from the US Environmental Protection Agency (EPA), and the other with measurements from both EPA and the New York City Community Air Survey (NYCCAS). The EPA-only model obtained a cross-validation (CV) R2 of 0.85 while the EPA + NYCCAS model obtained a CV R2 of 0.73. With the help of the NYCCAS measurements, the EPA + NYCCAS model predicted distinctly different PM2.5 spatial patterns and more pollution hotspots compared with the EPA model, and its predictions were >15% higher than the EPA model along major roads and in densely populated areas. Our results indicated that satellite AOD and non-regulatory PM2.5 measurements can be fused together to capture neighborhood-scale PM2.5 levels and previous studies may have underestimated the disease burden due to PM2.5 in densely populated areas.
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Affiliation(s)
- Keyong Huang
- Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China; Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Jianzhao Bi
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Xia Meng
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Guannan Geng
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | | | - Kevin J Lane
- Department of Environmental Health, Boston University School of Public Health, Boston, MA, USA
| | - Dongfeng Gu
- Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Patrick L Kinney
- Department of Environmental Health, Boston University School of Public Health, Boston, MA, USA.
| | - Yang Liu
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.
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Sun Z, Yang L, Bai X, Du W, Shen G, Fei J, Wang Y, Chen A, Chen Y, Zhao M. Maternal ambient air pollution exposure with spatial-temporal variations and preterm birth risk assessment during 2013-2017 in Zhejiang Province, China. ENVIRONMENT INTERNATIONAL 2019; 133:105242. [PMID: 31665677 DOI: 10.1016/j.envint.2019.105242] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2019] [Revised: 09/25/2019] [Accepted: 10/02/2019] [Indexed: 06/10/2023]
Abstract
Preterm birth (PTB) can give rise to significant neonatal morbidity and mortality, as well as children's long-term health defects. Many studies have illustrated the associations between ambient air pollution exposure during gestational periods and PTB risks, but most of them only focused on one single air pollutant, such as PM2.5. In this population-based environmental-epidemiology study, we recruited 6275 pregnant mothers in Zhejiang Province, China, and evaluated their gestational exposures to various air pollutants during 2013-2017. Time-to-event logistic regressions were performed to estimate risk associations after adjusting all confounders, and Quasi-AQI model and PCA-GLM analysis were applied to resolve the collinearity issues in multi-pollutant regression models. It was found that gestational exposure to ambient air pollutants was significantly associated with the occurrence of PTB, and SO2 was the largest contributor with a proportion of 29.4%. Three new variables, prime factor (a combination of PM2.5, PM10, SO2, and NO2), carbon factor (CO), and ozone factor (O3), were generated by PCA integration, contributing 63.4%, 17.1%, and 19.5% to PTB risks, respectively. The first and third trimester was the most crucial exposure window, suggesting the pregnant mothers better to avoid severe air pollution exposures during these sensitive periods.
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Affiliation(s)
- Zhe Sun
- Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China; Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Liyang Yang
- Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Xiaoxia Bai
- Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310006, China.
| | - Wei Du
- Ministry of Education Laboratory of Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China; School of Geographical Sciences, East China Normal University, Shanghai 200241, China
| | - Guofeng Shen
- Ministry of Education Laboratory of Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Jie Fei
- Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Yonghui Wang
- Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - An Chen
- College of Information Engineering, China Jiliang University, Hangzhou, Zhejiang 310018, China
| | - Yuanchen Chen
- Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
| | - Meirong Zhao
- Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
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Zhu Q, Xia B, Zhao Y, Dai H, Zhou Y, Wang Y, Yang Q, Zhao Y, Wang P, La X, Shi H, Liu Y, Zhang Y. Predicting gestational personal exposure to PM 2.5 from satellite-driven ambient concentrations in Shanghai. CHEMOSPHERE 2019; 233:452-461. [PMID: 31176908 DOI: 10.1016/j.chemosphere.2019.05.251] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 05/22/2019] [Accepted: 05/27/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND It has been widely reported that gestational exposure to fine particulate matters (PM2.5) is associated with a series of adverse birth outcomes. However, the discrepancy between ambient PM2.5 concentrations and personal PM2.5 exposure would significantly affect the estimation of exposure-response relationship. OBJECTIVE Our study aimed to predict gestational personal exposure to PM2.5 from the satellite-driven ambient concentrations and analyze the influence of other potential determinants. METHOD We collected 762 72-h personal exposure samples from a panel of 329 pregnant women in Shanghai, China as well as their time-activity patterns from Feb 2017 to Jun 2018. We established an ambient PM2.5 model based on MAIAC AOD at 1 km resolution, then used its output as a major predictor to develop a personal exposure model. RESULTS Our ambient PM2.5 model yielded a cross-validation R2 of 0.96. Personal PM2.5 exposure levels were almost identical to the corresponding ambient concentrations. After adjusting for time-activity patterns and meteorological factors, our personal exposure has a CV R2 of 0.76. CONCLUSION We established a prediction model for gestational personal exposure to PM2.5 from satellite-based ambient concentrations and provided a methodological reference for further epidemiological studies.
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Affiliation(s)
- Qingyang Zhu
- Key Lab of Health Technology Assessment, National Health Commission of the People's Republic of China, Fudan University, Shanghai, 200032, China; Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai, 200032, China
| | - Bin Xia
- Key Lab of Health Technology Assessment, National Health Commission of the People's Republic of China, Fudan University, Shanghai, 200032, China; Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai, 200032, China
| | - Yingya Zhao
- Key Lab of Health Technology Assessment, National Health Commission of the People's Republic of China, Fudan University, Shanghai, 200032, China; Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai, 200032, China
| | - Haixia Dai
- Key Lab of Health Technology Assessment, National Health Commission of the People's Republic of China, Fudan University, Shanghai, 200032, China; Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai, 200032, China; State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai, 200233, China
| | - Yuhan Zhou
- Key Lab of Health Technology Assessment, National Health Commission of the People's Republic of China, Fudan University, Shanghai, 200032, China; Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai, 200032, China
| | - Ying Wang
- Songjiang Maternity & Child Health Hospital, Shanghai, 201600, China
| | - Qing Yang
- Songjiang Maternity & Child Health Institute, Shanghai, 201600, China
| | - Yan Zhao
- Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, 200126, China
| | - Pengpeng Wang
- Key Lab of Health Technology Assessment, National Health Commission of the People's Republic of China, Fudan University, Shanghai, 200032, China; Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai, 200032, China
| | - Xuena La
- Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai, 200032, China
| | - Huijing Shi
- Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai, 200032, China
| | - Yang Liu
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, 30322, USA.
| | - Yunhui Zhang
- Key Lab of Health Technology Assessment, National Health Commission of the People's Republic of China, Fudan University, Shanghai, 200032, China; Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai, 200032, China.
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Liu F, Chen G, Huo W, Wang C, Liu S, Li N, Mao S, Hou Y, Lu Y, Xiang H. Associations between long-term exposure to ambient air pollution and risk of type 2 diabetes mellitus: A systematic review and meta-analysis. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2019; 252:1235-1245. [PMID: 31252121 DOI: 10.1016/j.envpol.2019.06.033] [Citation(s) in RCA: 129] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 06/10/2019] [Accepted: 06/10/2019] [Indexed: 05/21/2023]
Abstract
Previous meta-analyses on associations between air pollution (AP) and type 2 diabetes mellitus (T2DM) were mainly focused on studies conducted in high-income countries. Evidence should be updated by including more recent studies, especially those conducted in low- and middle-income countries. We therefore conducted a systematic review and meta-analysis of epidemiological studies to conclude an updated pooled effect estimates between long-term AP exposure and the prevalence and incidence of T2DM. We searched PubMed, Embase, and Web of Science to identify studies regarding associations of AP with T2DM prevalence and incidence prior to January 2019. A random-effects model was employed to analyze the overall effects. A total of 30 articles were finally included in this meta-analysis. The pooled results showed that higher levels of AP exposure were significantly associated with higher prevalence of T2DM (per 10 μg/m3 increase in concentrations of particles with aerodynamic diameter < 2.5 μm (PM2.5): odds ratio (OR) = 1.09, 95% confidence interval (95%CI): 1.05, 1.13; particles with aerodynamic diameter < 10 μm (PM10): OR = 1.12, 95%CI: 1.06, 1.19; nitrogen dioxide (NO2): OR = 1.05, 95%CI:1.03, 1.08). Besides, higher level of PM2.5 exposure was associated with higher T2DM incidence (per 10 μg/m3 increase in concentration of PM2.5: hazard ratio (HR) = 1.10, 95%CI:1.04, 1.16), while the associations between PM10, NO2 and T2DM incidence were not statistically significant. The associations between AP exposure and T2DM prevalence showed no significant difference between high-income countries and low- and middle-incomes countries. However, different associations were identified between PM2.5 exposure and T2DM prevalence in different geographic areas. No significant differences were found in associations of AP and T2DM prevalence/incidence between females and males, except for the effect of NO2 on T2DM incidence. Overall, AP exposure was positively associated with T2DM. There still remains a need for evidence from low- and middle-income countries on the relationships between AP and T2DM.
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Affiliation(s)
- Feifei Liu
- Department of Global Health, School of Health Sciences, Wuhan University, 115# Donghu Road, Wuhan, China
| | - Gongbo Chen
- Department of Global Health, School of Health Sciences, Wuhan University, 115# Donghu Road, Wuhan, China.
| | - Wenqian Huo
- Department of Epidemiology and Biostatistics, School of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Chongjian Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Suyang Liu
- Department of Global Health, School of Health Sciences, Wuhan University, 115# Donghu Road, Wuhan, China
| | - Na Li
- Department of Global Health, School of Health Sciences, Wuhan University, 115# Donghu Road, Wuhan, China
| | - Shuyuan Mao
- Department of Global Health, School of Health Sciences, Wuhan University, 115# Donghu Road, Wuhan, China
| | - Yitan Hou
- Department of Global Health, School of Health Sciences, Wuhan University, 115# Donghu Road, Wuhan, China
| | - Yuanan Lu
- Environmental Health Laboratory, Department of Public Health Sciences, University Hawaii at Manoa, 1960 East West Rd, Biomed Bldg, D105, Honolulu, USA
| | - Hao Xiang
- Department of Global Health, School of Health Sciences, Wuhan University, 115# Donghu Road, Wuhan, China.
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Liang Z, Yang Y, Li J, Zhu X, Ruan Z, Chen S, Huang G, Lin H, Zhou JY, Zhao Q. Migrant population is more vulnerable to the effect of air pollution on preterm birth: Results from a birth cohort study in seven Chinese cities. Int J Hyg Environ Health 2019; 222:1047-1053. [DOI: 10.1016/j.ijheh.2019.07.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 06/27/2019] [Accepted: 07/10/2019] [Indexed: 10/26/2022]
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Song J, Lu M, An Z, Liu Y, Zheng L, Li Y, Chao L, Xu D, Yao S, Wu W. Estimating the acute effects of ambient ozone pollution on the premature rupture of membranes in Xinxiang, China. CHEMOSPHERE 2019; 227:191-197. [PMID: 30986601 DOI: 10.1016/j.chemosphere.2019.04.062] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2018] [Revised: 04/08/2019] [Accepted: 04/08/2019] [Indexed: 06/09/2023]
Abstract
While increasing evidence suggests that ozone (O3) exposure is associated with adverse birth outcomes, only one study has focused on its impact on the premature rupture of membranes (PROM). Therefore, we thus examined the effect of O3 on PROM in Xinxiang, China, using an over-dispersed Poisson generalized additive model. Several confounding factors, including meteorological factors, temporal trends, the day of the week, and public holidays, were considered in the model. We identified a total of 3255 instances of PROM from January 1, 2015 to December 31, 2017, and there was a significant association between the daily maximum 8-h mean concentrations (O3-8h) and PROM. Each 10 μg/m3 increase in the 3-day average concentration (lag02) of O3-8h corresponded to an increment in PROM of 5.42% (95% CI: 1.45-9.39%). Although the results of the stratified analyses were insignificant, a few trending results were observed: stronger associations between O3 and PROM would occur in women with advanced age (≥35) or during the warm season than those in younger women (<35) or during the cool season. Our study indicates that O3 exposure is an important risk factor of PROM and should be considered in its prevention and control in the study area.
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Affiliation(s)
- Jie Song
- School of Public Health, Xinxiang Medical University, Xinxiang, 453003, China; Henan International Collaborative Laboratory for Air Pollution Health Effects and Intervention, Xinxiang, 453003, China.
| | - Mengxue Lu
- Xinxiang Medical University, Xinxiang, 453003, China
| | - Zhen An
- School of Public Health, Xinxiang Medical University, Xinxiang, 453003, China; Henan International Collaborative Laboratory for Air Pollution Health Effects and Intervention, Xinxiang, 453003, China
| | - Yue Liu
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Liheng Zheng
- Hebei Chest Hospital, Shijiazhuang, 050041, China
| | - Yuchun Li
- School of Public Health, Xinxiang Medical University, Xinxiang, 453003, China
| | - Ling Chao
- School of Public Health, Xinxiang Medical University, Xinxiang, 453003, China
| | - Dongqun Xu
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Sanqiao Yao
- School of Public Health, Xinxiang Medical University, Xinxiang, 453003, China; Henan International Collaborative Laboratory for Air Pollution Health Effects and Intervention, Xinxiang, 453003, China
| | - Weidong Wu
- School of Public Health, Xinxiang Medical University, Xinxiang, 453003, China; Henan International Collaborative Laboratory for Air Pollution Health Effects and Intervention, Xinxiang, 453003, China
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Li Z, Yuan X, Fu J, Zhang L, Hong L, Hu L, Liu L. Association of ambient air pollutants and birth weight in Ningbo, 2015-2017. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2019; 249:629-637. [PMID: 30933760 DOI: 10.1016/j.envpol.2019.03.076] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Revised: 02/28/2019] [Accepted: 03/18/2019] [Indexed: 06/09/2023]
Abstract
Previous studies have suggested a change of birth weight linked with elevated ambient air pollutant concentrations during the pregnancy. However, investigations of the influence of higher pollutant levels on birth weight change are limited. The goal of this study is to evaluate whether the air pollution of Ningbo is associated with birth weight, and which trimester could be a window period for maternal exposure to air pollution. A total of 170,008 live births were selected in the Ningbo city of Zhejiang, China, from 2015 to 2017. We estimated the association between the decreased birth weight and the increased air pollutant concentrations in the three trimesters and full gestation. The effects of interaction among pollutants were identified using a co-pollutant adjustment model. An interquartile range increases in PM2.5 (10.55 μg/m3), SO2(4.6 μg/m3), CO (125.59 μg/m3), and O3 (14.54 μg/m3) concentrations during the entire gestation were associated with 3.65 g (95% confidence interval: -6.02 g, -1.29 g), 5.02 g (-6.89 g, -3.14 g), 2.64 g (-4.65 g, -0.63 g) and 2.9 g (-4.8 g, 1 g) decreases, respectively, in birth weight. With each interquartile range increment in NO2 concentration was associated with an 8.05 g (6.24 g, 9.85 g) increase in birth weight. In the first trimester, only the PM2.5 exposure seemed to be associated with the greatest decline in birth weight. After adjustment for co-pollutant, both PM2.5 and SO2 were still associated with birth weight, except for CO for O3 adjustment, O3 for SO2 adjustment, and O3 for NO2 adjustment. Maternal exposure to air pollution may be associated with a decrease of birth weight, but the contribution of various pollutants is necessary to verify by future research.
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Affiliation(s)
- Zhen Li
- Department of Preventative Medicine, Medicine School of Ningbo University, 818 Fenghua Road, Ningbo, Zhejiang Province 315211, People's Republic of China
| | - Xiaoqi Yuan
- Pediatric Surgery Ward, Ningbo Women and Children Hospital, Ningbo, Zhejiang Province 315012, People's Republic of China
| | - Jianfei Fu
- Department of Medical Records and Statistics, Ningbo First Hospital, Ningbo, Zhejiang Province 315010, People's Republic of China
| | - Lingyun Zhang
- Department of Preventative Medicine, Medicine School of Ningbo University, 818 Fenghua Road, Ningbo, Zhejiang Province 315211, People's Republic of China
| | - Lixia Hong
- Department of Preventative Medicine, Medicine School of Ningbo University, 818 Fenghua Road, Ningbo, Zhejiang Province 315211, People's Republic of China
| | - Lingjie Hu
- Department of Preventative Medicine, Medicine School of Ningbo University, 818 Fenghua Road, Ningbo, Zhejiang Province 315211, People's Republic of China
| | - Liya Liu
- Department of Preventative Medicine, Medicine School of Ningbo University, 818 Fenghua Road, Ningbo, Zhejiang Province 315211, People's Republic of China.
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Liang Z, Yang Y, Qian Z, Ruan Z, Chang J, Vaughn MG, Zhao Q, Lin H. Ambient PM 2.5 and birth outcomes: Estimating the association and attributable risk using a birth cohort study in nine Chinese cities. ENVIRONMENT INTERNATIONAL 2019; 126:329-335. [PMID: 30825752 DOI: 10.1016/j.envint.2019.02.017] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Revised: 02/04/2019] [Accepted: 02/05/2019] [Indexed: 05/05/2023]
Abstract
BACKGROUND Previous studies have reported that maternal exposure to particles with aerodynamic diameter <2.5 μm (PM2.5) is associated with birth outcomes. However, a multicity birth cohort study has not been conducted in China, and the attributable fraction of adverse birth outcomes due to PM2.5 exposure remains unknown. METHODS We examined associations in a birth cohort of 1,455,026 mother-and-live-birth pairs who were followed up from the first hospital visit for pregnancy until the birth of the baby during 2014-2017 in nine cites of the Pearl River Delta (PRD) region, China. The PM2.5 exposures were estimated based on the air pollution concentrations of the nearby monitors. Cox proportional hazards regressions were employed to examine the associations. RESULTS We found 1% (HR = 1.01; 95% CI: 1.00, 1.02), 6% (HR = 1.06; 95% CI: 1.05, 1.07), and 7% (HR = 1.07; 95% CI: 1.06, 1.08) increases in risk of PTB and 20% (HR = 1.20; 95% CI: 1.18, 1.22), 18% (HR = 1.18; 95% CI: 1.15, 1.20), and 20% (HR = 1.20; 95% CI: 1.17, 1.23) increases in risk of LBW, with each 10 μg/m3 increase in PM2.5 from trimester 1 to trimester 3, respectively. For PTB, highest HRs were observed during trimester 3, as for LBW, stronger effect were observed during trimester 1 and trimester 3. We further estimated that 7.84% (95% CI: 6.21%, 9.50%) of PTB and 14.85% (95% CI: 13.00%, 16.61%) of the LBW cases could be attributable to PM2.5 exposure during the third trimester. CONCLUSION The results indicate that maternal PM2.5 exposure is a risk factor for both LBW and PTB, and responsible for considerable burdens of PTB and LBW in the Pearl River Delta region.
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Affiliation(s)
- Zhijiang Liang
- Department of Public Health, Guangdong Women and Children Hospital, 521 Xingnan Road, Panyu District, Guangzhou 511442, China
| | - Yin Yang
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Zhengmin Qian
- Department of Epidemiology and Biostatistics, College for Public Health & Social Justice, Saint Louis University, Saint Louis, MO 63104, United States of America
| | - Zengliang Ruan
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Jenjen Chang
- Department of Epidemiology and Biostatistics, College for Public Health & Social Justice, Saint Louis University, Saint Louis, MO 63104, United States of America
| | - Michael G Vaughn
- School of Social Work, College for Public Health & Social Justice, Saint Louis University, St. Louis. MO 63103, United States of America
| | - Qingguo Zhao
- Epidemiological Research Office of Key Laboratory of Male Reproduction and Genetics, Family Planning Research Institute of Guangdong Province, Guangzhou, China; Epidemiological Research Office of Key Laboratory of Male Reproduction and Genetics (National Health and Family Planning Commission), Family Planning Special Hospital of Guangdong Province, Guangzhou, China.
| | - Hualiang Lin
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China.
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60
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Xie P, Zhao C, Huang W, Yong T, Chung ACK, He K, Chen X, Cai Z. Prenatal exposure to ambient fine particulate matter induces dysregulations of lipid metabolism in adipose tissue in male offspring. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 657:1389-1397. [PMID: 30677905 DOI: 10.1016/j.scitotenv.2018.12.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2018] [Revised: 11/29/2018] [Accepted: 12/02/2018] [Indexed: 06/09/2023]
Abstract
Prenatal exposure to ambient fine particles (diameter < 0.25 μm, PM2.5) has been found to be associated with abnormal growth and development in offspring. However, the effects of PM2.5 on the lipid metabolism of adipose tissue in offspring are unclear. In the present study, we established a mouse model of prenatal exposure to PM2.5 by intratracheal instillation to pregnant C57BL/6 female mice with PM2.5 suspension or normal saline. We found that prenatal exposure to PM2.5 of a mouse model reduced body weight in adult male offspring after 6 weeks old. Histological analysis showed that the adipocyte size was significantly reduced in epididymal adipose tissue (eWAT) in male offspring, but not in brown adipose tissue. The expression levels of genes related to fatty acid synthesis (ACC1, ACSL1) and oxidation (PPARα) in eWAT were also significantly decreased. In addition, downregulation of pro-inflammatory cytokines (TNFα, IL-1β, IL-6) was also observed. Lipidomics analysis of eWAT demonstrated that prenatal exposure of PM2.5 reduced lysophosphatidylcholines (LPC), phosphatidylcholines (PC), phosphatidylethanolamines (PE), sphingomyelins (SM), and ceramides (Cer), indicating that metabolic pathways, including SM-Cer signaling and glycerophospholipids remodeling, were disrupted. In summary, prenatal exposure to PM2.5 was associated with the dysregulations in lipid metabolism of eWAT and pro-inflammatory response in male offspring.
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Affiliation(s)
- Peisi Xie
- State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong, SAR, China
| | - Chao Zhao
- State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong, SAR, China
| | - Wei Huang
- State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong, SAR, China
| | - Ting Yong
- State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong, SAR, China
| | - Arthur C K Chung
- State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong, SAR, China; HKBU Institute for Research and Continuing Education, Shenzhen, China
| | - Kaiwu He
- Key Laboratory of Modern Toxicology of Shenzhen, Shenzhen Center for Disease Control and Prevention, Shenzhen 518055, China
| | - Xiangfeng Chen
- Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China
| | - Zongwei Cai
- State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong, SAR, China; HKBU Institute for Research and Continuing Education, Shenzhen, China.
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61
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Meng X, Hand JL, Schichtel BA, Liu Y. Space-time trends of PM 2.5 constituents in the conterminous United States estimated by a machine learning approach, 2005-2015. ENVIRONMENT INTERNATIONAL 2018; 121:1137-1147. [PMID: 30413295 DOI: 10.1016/j.envint.2018.10.029] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 10/14/2018] [Accepted: 10/15/2018] [Indexed: 05/12/2023]
Abstract
Particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5) is a complex mixture of chemical constituents emitted from various emission sources or through secondary reactions/processes; however, PM2.5 is regulated mostly based on its total mass concentration. Studies to identify the impacts on climate change, visibility degradation and public health of different PM2.5 constituents are hindered by limited ground measurements of PM2.5 constituents. In this study, national models were developed based on random forest algorithm, one of machine learning methods that is of high predictive capacity and able to provide interpretable results, to predict concentrations of PM2.5 sulfate, nitrate, organic carbon (OC) and elemental carbon (EC) across the conterminous United States from 2005 to 2015 at the daily level. The random forest models achieved high out-of-bag (OOB) R2 values at the daily level, and the mean OOB R2 values were 0.86, 0.82, 0.71 and 0.75 for sulfate, nitrate, OC and EC, respectively, over 2005-2015. The long-term temporal trends of PM2.5 sulfate, nitrate, OC and EC predictions agreed well with their corresponding ground measurements. The annual mean of predicted PM2.5 sulfate and EC levels across the conterminous United States decreased substantially from 2005 to 2015; while concentrations of predicted PM2.5 nitrate and OC decreased and fluctuated during the study period. The annual prediction maps captured the characterized spatial patterns of the PM2.5 constituents. The distributions of annual mean concentrations of sulfate and nitrate were generally regional in the extent that sulfate decreased from east to west smoothly with enhancement in California and nitrate had higher concentration in Midwest, Metro New York area, and California. OC and EC had regional high concentrations in the Southeast and Northwest as well as localized high levels around urban centers. The spatial patterns of PM2.5 constituents were consistent with the distributions of their emission sources and secondary processes and transportation. Hence, the national models developed in this study could provide supplementary evaluations of spatio-temporal distributions of PM2.5 constituents with full time-space coverages in the conterminous United States, which could be beneficial to assess the impacts of PM2.5 constituents on radiation budgets and visibility degradation, and support exposure assessment for regional to national health studies at county or city levels to understand the acute and chronic toxicity and health impacts of PM2.5 constituents, and consequently provide scientific evidence for making targeted and effective regulations of PM2.5 pollution.
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Affiliation(s)
- Xia Meng
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Jenny L Hand
- Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA
| | - Bret A Schichtel
- National Park Service, Air Resources Division, Lakewood, CO, USA
| | - Yang Liu
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
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