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Li W, Li Y, Xu W, Chen Z, Gao Y, Liu Z, Li Q, Jiang M, Liu H, Luo B, Zhan Y, Dai L. Maternal PM 2.5 exposure and hypospadias risk in Chinese offspring: Insights from a nationwide surveillance-based study. JOURNAL OF HAZARDOUS MATERIALS 2024; 472:134503. [PMID: 38718509 DOI: 10.1016/j.jhazmat.2024.134503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 04/23/2024] [Accepted: 04/30/2024] [Indexed: 05/30/2024]
Abstract
Research on the association between maternal PM2.5 exposure and hypospadias risk in male offspring, particularly in highly polluted areas, has been limited and inconsistent. This study leveraged data from China's National Population-based Birth Defects Surveillance System spanning the years 2013 to 2019, and employed sophisticated machine learning models to estimate daily PM2.5 levels and other pollutants for mothers at a 1-km resolution and a 6-km buffer surrounding maternal residences. Multivariate logistic regression analyses were performed to evaluate the relationship between PM2.5 exposure and hypospadias risk. For sensitivity analyses, stratification analysis was conducted, and models for one-pollutant and two-pollutants, as well as distributed lag nonlinear models, were constructed. Of the 1194,431 boys studied, 1153 cases of hypospadias were identified. A 10 μg/m3 increase in maternal PM2.5 exposure during preconception and the first trimester was associated with an elevated risk of isolated hypospadias, with Odds Ratios (ORs) of 1.102 (95% CI: 1.023-1.188) and 1.089 (95% CI: 1.007-1.177) at the 1-km grid, and 1.122 (95% CI: 1.034-1.218) and 1.143 (95% CI: 1.048-1.246) within the 6-km buffer. Higher quartiles of PM2.5 exposure were associated with increased odds ratios compared to the lowest quartile. These findings highlight a significant association between PM2.5 exposure during the critical conception period and an elevated risk of isolated hypospadias in children, emphasizing the need for targeted interventions to reduce PM2.5 exposure among expectant mothers.
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Affiliation(s)
- Wenyan Li
- National Center for Birth Defects Monitoring, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610041, China; Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, Sichuan 610041, China
| | - Yanhua Li
- Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610041, China; West China School of Nursing, Sichuan University, Chengdu, Sichuan 610041, China
| | - Wenli Xu
- National Center for Birth Defects Monitoring, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610041, China; Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, Sichuan 610041, China
| | - Zhiyu Chen
- National Center for Birth Defects Monitoring, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610041, China; Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, Sichuan 610041, China
| | - Yuyang Gao
- National Center for Birth Defects Monitoring, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610041, China; Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, Sichuan 610041, China
| | - Zhen Liu
- National Center for Birth Defects Monitoring, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610041, China; Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, Sichuan 610041, China
| | - Qi Li
- National Center for Birth Defects Monitoring, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610041, China; Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, Sichuan 610041, China
| | - Ming Jiang
- Department of Epidemiology and Health Statistics, West China School of Public Health, Sichuan University, Chengdu, Sichuan 610041, China
| | - Hanmin Liu
- NHC Key Laboratory of Chronobiology, Sichuan University, Chengdu, Sichuan 610041, China
| | - Biru Luo
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, Sichuan 610041, China; Department of Nursing Management, West China Second University, Sichuan University, Chengdu, Sichuan 610041, China.
| | - Yu Zhan
- College of Carbon Neutrality Future Technology, Sichuan University, Chengdu, Sichuan 610041, China.
| | - Li Dai
- National Center for Birth Defects Monitoring, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610041, China; Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, Sichuan 610041, China; Med-X Center for Informatics, Sichuan University, Chengdu, Sichuan 610041, China.
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Zhu R, Luo W, Grieneisen ML, Zuoqiu S, Zhan Y, Yang F. A novel approach to deriving the fine-scale daily NO 2 dataset during 2005-2020 in China: Improving spatial resolution and temporal coverage to advance exposure assessment. ENVIRONMENTAL RESEARCH 2024; 249:118381. [PMID: 38331142 DOI: 10.1016/j.envres.2024.118381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 01/22/2024] [Accepted: 01/30/2024] [Indexed: 02/10/2024]
Abstract
Surface NO2 pollution can result in serious health consequences such as cardiovascular disease, asthma, and premature mortality. Due to the extensive spatial variation in surface NO2, the spatial resolution of a NO2 dataset has a significant impact on the exposure and health impact assessment. There is currently no long-term, high-resolution, and publicly available NO2 dataset for China. To fill this gap, this study generated a NO2 dataset named RBE-DS-NO2 for China during 2005-2020 at 1 km and daily resolution. We employed the robust back-extrapolation via a data augmentation approach (RBE-DA) to ensure the predictive accuracy in back-extrapolation before 2013, and utilized an improved spatial downscaling technique (DS) to refine the spatial resolution from 10 km to 1 km. Back-extrapolation validation based on 2005-2012 observations from sites in Taiwan province yielded an R2 of 0.72 and RMSE of 10.7 μg/m3, while cross-validation across China during 2013-2020 showed an R2 of 0.73 and RMSE of 9.6 μg/m3. RBE-DS-NO2 better captured spatiotemporal variation of surface NO2 in China compared to the existing publicly available datasets. Exposure assessment using RBE-DS-NO2 show that the population living in non-attainment areas (NO2 ≥ 30 μg/m3) grew from 376 million in 2005 to 612 million in 2012, then declined to 404 million by 2020. Unlike this national trend, exposure levels in several major cities (e.g., Shanghai and Chengdu) continued to increase during 2012-2020, driven by population growth and urban migration. Furthermore, this study revealed that low-resolution dataset (i.e., the 10 km intermediate dataset before the downscaling) overestimated NO2 levels, due to the limited specificity of the low-resolution model in simulating the relationship between NO2 and the predictor variables. Such limited specificity likely biased previous long-term NO2 exposure and health impact studies employing low-resolution datasets. The RBE-DS-NO2 dataset enables robust long-term assessments of NO2 exposure and health impacts in China.
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Affiliation(s)
- Rongxin Zhu
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan, 610065, China; College of Carbon Neutrality Future Technology, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Wenfeng Luo
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Michael L Grieneisen
- Department of Land, Air, and Water Resources, University of California, Davis, CA, 95616, United States
| | - Sophia Zuoqiu
- Pittsburgh Institute, Sichuan University, Chengdu, Sichuan, 610207, China
| | - Yu Zhan
- College of Carbon Neutrality Future Technology, Sichuan University, Chengdu, Sichuan, 610065, China.
| | - Fumo Yang
- College of Carbon Neutrality Future Technology, Sichuan University, Chengdu, Sichuan, 610065, China
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Liu R, Ma Z, Gasparrini A, de la Cruz A, Bi J, Chen K. Integrating Augmented In Situ Measurements and a Spatiotemporal Machine Learning Model To Back Extrapolate Historical Particulate Matter Pollution over the United Kingdom: 1980-2019. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:21605-21615. [PMID: 38085698 DOI: 10.1021/acs.est.3c05424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2023]
Abstract
Historical PM2.5 data are essential for assessing the health effects of air pollution exposure across the life course or early life. However, a lack of high-quality data sources, such as satellite-based aerosol optical depth before 2000, has resulted in a gap in spatiotemporally resolved PM2.5 data for historical periods. Taking the United Kingdom as an example, we leveraged the light gradient boosting model to capture the spatiotemporal association between PM2.5 concentrations and multi-source geospatial predictors. Augmented PM2.5 from PM10 measurements expanded the spatiotemporal representativeness of the ground measurements. Observations before and after 2009 were used to train and test the models, respectively. Our model showed fair prediction accuracy from 2010 to 2019 [the ranges of coefficients of determination (R2) for the grid-based cross-validation are 0.71-0.85] and commendable back extrapolation performance from 1998 to 2009 (the ranges of R2 for the independent external testing are 0.32-0.65) at the daily level. The pollution episodes in the 1980s and pollution levels in the 1990s were also reproduced by our model. The 4-decade PM2.5 estimates demonstrated that most regions in England witnessed significant downward trends in PM2.5 pollution. The methods developed in this study are generalizable to other data-rich regions for historical air pollution exposure assessment.
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Affiliation(s)
- Riyang Liu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, Jiangsu 210023, People's Republic of China
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut 06520, United States
- Yale Center on Climate Change and Health, Yale School of Public Health, New Haven, Connecticut 06520, United States
| | - Zongwei Ma
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, Jiangsu 210023, People's Republic of China
| | - Antonio Gasparrini
- Environment & Health Modelling (EHM) Lab, Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, London WC1H 9SH, United Kingdom
| | - Arturo de la Cruz
- Environment & Health Modelling (EHM) Lab, Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, London WC1H 9SH, United Kingdom
| | - Jun Bi
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, Jiangsu 210023, People's Republic of China
| | - Kai Chen
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut 06520, United States
- Yale Center on Climate Change and Health, Yale School of Public Health, New Haven, Connecticut 06520, United States
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Zhao Z, Lu Y, Zhan Y, Cheng Y, Yang F, Brook JR, He K. Long-term spatiotemporal variations in surface NO 2 for Beijing reconstructed from surface data and satellite retrievals. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 904:166693. [PMID: 37657553 DOI: 10.1016/j.scitotenv.2023.166693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 08/14/2023] [Accepted: 08/28/2023] [Indexed: 09/03/2023]
Abstract
Remote sensing data from the Ozone Monitoring Instrument (OMI) and the TROPOspheric Monitoring Instrument (TROPOMI) play important roles in estimating surface nitrogen dioxide (NO2), but few studies have compared their differences for application in surface NO2 reconstruction. This study aims to explore the effectiveness of incorporating the tropospheric NO2 vertical column density (VCD) from OMI and TROPOMI (hereafter referred to as OMI and TROPOMI, respectively, for conciseness) for deriving surface NO2 and to apply the resulting data to revisit the spatiotemporal variations in surface NO2 for Beijing over the 2005-2020 period during which there were significant reductions in nitrogen oxide emissions. In the OMI versus TROPOMI performance comparison, the cross-validation R2 values were 0.73 and 0.72, respectively, at 1 km resolution and 0.69 for both at 100 m resolution. The comparisons between satellite data sources indicate that even though TROPOMI has a finer resolution it does not improve upon OMI for deriving surface NO2 at 1 km resolution, especially for analyzing long-term trends. In light of the comparison results, we used a hybrid approach based on machine learning to derive the spatiotemporal distribution of surface NO2 during 2005-2020 based on OMI. We had novel, independent passive sampling data collected weekly from July to September of 2008 for hindcasting validation and found a spatiotemporal R2 of 0.46 (RMSE = 7.0 ppb). Regarding the long-term trend of surface NO2, the level in 2008 was obviously lower than that in 2007 and 2009, as expected, which was attributed to pollution restrictions during the Olympic Games. The NO2 level started to steadily decline from 2015 and fell below 2008's level after 2017. Based on OMI, a long-term and fine-resolution surface NO2 dataset was developed for Beijing to support future environmental management questions and epidemiological research.
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Affiliation(s)
- Zixiang Zhao
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan 610065, China
| | - Yichen Lu
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan 610065, China
| | - Yu Zhan
- College of Carbon Neutrality Future Technology, Sichuan University, Chengdu, Sichuan 610065, China.
| | - Yuan Cheng
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Fumo Yang
- College of Carbon Neutrality Future Technology, Sichuan University, Chengdu, Sichuan 610065, China
| | - Jeffrey R Brook
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Kebin He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
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