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Wang Y, Yang Y, Yuan Q, Li T, Zhou Y, Zong L, Wang M, Xie Z, Ho HC, Gao M, Tong S, Lolli S, Zhang L. Substantially underestimated global health risks of current ozone pollution. Nat Commun 2025; 16:102. [PMID: 39747001 PMCID: PMC11696706 DOI: 10.1038/s41467-024-55450-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 12/11/2024] [Indexed: 01/04/2025] Open
Abstract
Existing assessments might have underappreciated ozone-related health impacts worldwide. Here our study assesses current global ozone pollution using the high-resolution (0.05°) estimation from a geo-ensemble learning model, with key focuses on population exposure and all-cause mortality burden. Our model demonstrates strong performance, achieving a mean bias of less than -1.5 parts per billion against in-situ measurements. We estimate that 66.2% of the global population is exposed to excess ozone for short term (> 30 days per year), and 94.2% suffers from long-term exposure. Furthermore, severe ozone exposure levels are observed in Cropland areas, particularly over Asia. Importantly, the all-cause ozone-attributable deaths significantly surpass previous recognition from specific diseases worldwide. Notably, mid-latitude Asia (30°N) and the western United States show high mortality burden, contributing substantially to global ozone-attributable deaths. Our study highlights current significant global ozone-related health risks and may benefit the ozone-exposed population in the future.
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Affiliation(s)
- Yuan Wang
- School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China
| | - Yuanjian Yang
- School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China
| | - Qiangqiang Yuan
- School of Geodesy and Geomatics, Wuhan University, Wuhan, China.
| | - Tongwen Li
- School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai, China
| | - Yi Zhou
- School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China
| | - Lian Zong
- School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China
| | - Mengya Wang
- School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China
| | - Zunyi Xie
- College of Geography and Environmental Science, Henan University, Kaifeng, China
| | - Hung Chak Ho
- Department of Public and International Affairs, The City University of Hong Kong, Hong Kong, China
| | - Meng Gao
- Department of Geography, Hong Kong Baptist University, Hong Kong, China
| | - Shilu Tong
- School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China
- National Institute of Environmental Health, Chinese Centre for Disease Control and Prevention, Beijing, China
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
| | | | - Liangpei Zhang
- The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China.
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2
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Sun HZ, Tang H, Xiang Q, Xu S, Tian Y, Zhao H, Fang J, Dai H, Shi R, Pan Y, Luo T, Jin H, Ji C, Chen Y, Liu H, Zhao M, Tang K, Ramasamy SN, Loo EXL, Shek LP, Guo Y, Xu W, Bai X. Establishing a Multifaceted Comprehensive Maternity Cohort Facilitates Understanding of How Environmental Exposures Impact Perinatal Health. ENVIRONMENT & HEALTH (WASHINGTON, D.C.) 2024; 2:766-775. [PMID: 39568693 PMCID: PMC11574626 DOI: 10.1021/envhealth.4c00104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Revised: 06/26/2024] [Accepted: 06/27/2024] [Indexed: 11/22/2024]
Abstract
China's "three-child policy", implemented in response to population aging, has made the protection of maternal and infant health an urgent priority. In this environmental and medical big-data era, the Zhejiang Environmental and Birth Health Research Alliance (ZEBRA) maternity cohort was established with the aim of identifying risk factors for perinatal morbidity and mortality from the perspectives of both observational epidemiology and experimental etiology. Compared with conventional birth cohorts, the inclusion of a maternity cohort allows greater scope for research and places an emphasis on maternal health. In particular, it allows us to focus on pregnant women with a history of pregnancy-related illnesses and those planning to have a second or third child. There are currently many pressing issues in perinatal health, including the risk associations between exogenous together with endogenous factors and the occurrence of perinatal abnormalities, pregnancy complications, and adverse pregnancy outcomes. It is crucial to explore the interaction between environmental exposures and genetic factors affecting perinatal health if we are to improve it. It is also worthwhile to assess the feasibility of the early stage prediction of major perinatal abnormalities. We hope to study this in the ZEBRA cohort and also seek nationwide and international collaborations to establish a multicenter cohort consortium, with the ultimate goal of contributing epidemiological evidence to literature and providing evidence-based insights for global maternal and child healthcare.
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Affiliation(s)
- Haitong Zhe Sun
- Centre for Sustainable Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117609, Republic of Singapore
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117609, Republic of Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117609, Republic of Singapore
- Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
- Department of Earth Sciences, University of Cambridge, Cambridge CB2 3EQ, United Kingdom
| | - Haiyang Tang
- Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China
| | - Qingyi Xiang
- Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China
| | - Siyuan Xu
- Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China
| | - Yijia Tian
- Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China
| | - Huan Zhao
- Department of Obstetrics and Gynecology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang 322000, PR China
- Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China
| | - Jing Fang
- Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China
- Lanxi People's Hospital, Jinhua, Zhejiang 321102, PR China
| | - Haizhen Dai
- Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China
| | - Rui Shi
- Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China
| | - Yuxia Pan
- Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China
| | - Ting Luo
- Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China
- Wenling Women's and Children's Hospital, Taizhou, Zhejiang 317500, PR China
| | - Hangbiao Jin
- Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310032, PR China
| | - Chenyang Ji
- Key Laboratory of Pollution Exposure and Health Intervention of Zhejiang Province, Interdisciplinary Research Academy, Zhejiang Shuren University, Hangzhou, Zhejiang 310006, PR China
| | - Yuanchen Chen
- Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310032, PR China
| | - Hengyi Liu
- Institute of Reproductive and Child Health/National Health Commission Key Laboratory of Reproductive Health, School of Public Health, Peking University Health Science Centre, Beijing 100191, PR China
| | - Meirong Zhao
- Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310032, PR China
| | - Kun Tang
- Vanke School of Public Health, Tsinghua University, Beijing 100084, PR China
| | - Sheena Nishanti Ramasamy
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117609, Republic of Singapore
| | - Evelyn Xiu-Ling Loo
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117609, Republic of Singapore
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Drive, Singapore 117609, Republic of Singapore
- Human Potential Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117609, Republic of Singapore
| | - Lynette P Shek
- Centre for Sustainable Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117609, Republic of Singapore
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117609, Republic of Singapore
| | - Yuming Guo
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria 3004, Australia
| | - Wei Xu
- Maternal and Child Health Division, Health Commission of Zhejiang Province, Hangzhou, Zhejiang 310006, PR China
| | - Xiaoxia Bai
- Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China
- Key Laboratory of Women's Reproductive Health, Hangzhou, Zhejiang 310006, PR China
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Wang M, Wei J, Wang X, Luan Q, Xu X. Reconstruction of all-sky daily air temperature datasets with high accuracy in China from 2003 to 2022. Sci Data 2024; 11:1133. [PMID: 39406764 PMCID: PMC11480416 DOI: 10.1038/s41597-024-03980-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Accepted: 10/07/2024] [Indexed: 10/19/2024] Open
Abstract
A high-accuracy, continuous air temperature (Ta) dataset with high spatiotemporal resolution is essential for human health, disease prediction, and energy management. Existing datasets consider factors such as elevation, latitude, and surface temperature but insufficiently address meteorological and spatiotemporal factors, affecting accuracy. Additionally, no high-resolution dataset currently includes daily maximum (Tmax), minimum (Tmin), and mean (Tmean) temperatures generated using a unified methodology. Here, we introduce the four-dimensional spatiotemporal deep forest (4D-STDF) model, integrating 12 multisource factors, encompassing static and dynamic parameters, and six refined spatiotemporal factors to produce Ta datasets. This approach generates three high-accuracy Ta datasets at 1 km spatial resolution covering mainland China from 2003 to 2022. These datasets, in GeoTIFF format with WGS84 projection, comprise daily Tmax, Tmin, and Tmean. The overall RMSE are 1.49 °C, 1.53 °C, and 1.18 °C for the estimates. The 4D-STDF model can also be applied to other regions with sparse meteorological stations.
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Affiliation(s)
- Min Wang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, 20742, USA
| | - Xiaodong Wang
- Beijing Visiorld Technology Co., Ltd, 100029, Beijing, China
| | - Qingzu Luan
- Beijing Municipal Climate Center, Beijing Meteorological Bureau, Beijing, 100089, China.
| | - Xinliang Xu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
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4
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Zhu L, Yuan Y, Mayvaneh F, Sun H, Zhang Y, Hu C. Maternal ozone exposure lowers infant's birthweight: A nationwide cohort of over 4 million livebirths in Iran. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 283:116840. [PMID: 39126814 DOI: 10.1016/j.ecoenv.2024.116840] [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: 06/03/2024] [Revised: 07/30/2024] [Accepted: 08/02/2024] [Indexed: 08/12/2024]
Abstract
BACKGROUND Nationwide evidence linking maternal ozone exposure with fetal growth restriction (FGR) was extensively scarce, especially in the Middle East with dry climate and distinct religious culture. METHODS We carried out a national retrospective birth cohort study using registry-based records from 749 hospitals across 31 provinces in Iran from 2013 to 2018. Monthly concentrations of maximum daily average 8-hour (MDA8) ozone at 0.125° × 0.125° resolution were extracted from well-validated spatiotemporal grid dataset. Linear and logistic regression models were employed to evaluate associations of maternal MDA8 ozone exposure with birthweight outcomes. Assuming causality, the comparative risk assessment framework was utilized to estimate the burden of low birthweight (LBW), small for gestational age (SGA), and birthweight loss per livebirth (BLL) attributable to ambient ozone pollution. RESULTS Of 4030383 livebirths included in the study, 264304 (6.6%) were LBW and 484405 (12.0%) were SGA. Each 10-ppb increase in MDA8 ozone exposure was associated with an odds ratio of 1.123 (95% confidence interval [CI]: 1.104 to 1.142) for LBW and 1.210 (95% CI: 1.197 to 1.223) for SGA, and a 30.5-g (95% CI: 29.0 to 32.0) reduction in birthweight. We observed approximately linear exposure-response relationships of maternal MDA8 ozone exposure with LBW (Pnonlinear= 0.786), SGA (Pnonlinear= 0.156), and birthweight reduction (Pnonlinear= 0.104). Under the premise of causal association, we estimated 6.6% (95% CI: 5.7 to 7.5) of LBW, 10.1% (95% CI: 9.6 to 10.6) of SGA, and 18.8 g (95% CI: 17.9 to 19.7) of BLL could be attributable to maternal ozone exposure in Iran. Considerably greater risk and burden of ozone-related FGR were observed among younger, less-educated, and rural-dwelling mothers. CONCLUSIONS Our study provided compelling evidence that maternal ozone exposure was associated with heightened FGR risk and burden, particularly among socioeconomically disadvantaged mothers. These findings underscored the urgent need for government to incorporate socioeconomic factors into future ozone-related health policies, not only to mitigate pollution, but also minimize inequality.
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Affiliation(s)
- Lifeng Zhu
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Yang Yuan
- Shenzhen Bao'an District Songgang People's Hospital, Shenzhen 518100, China
| | - Fatemeh Mayvaneh
- Climatology Research Group, Institute of Landscape Ecology, University of Münster, Münster 48149, Germany
| | - Haitong Sun
- Centre for Atmospheric Science, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK; Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117609, Singapore
| | - Yunquan Zhang
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan 430065, China.
| | - Chengyang Hu
- Department of Humanistic Medicine, School of Humanistic Medicine, Anhui Medical University, Hefei 230032, China; Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei 230032, China.
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5
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Mai Z, Shen H, Zhang A, Sun HZ, Zheng L, Guo J, Liu C, Chen Y, Wang C, Ye J, Zhu L, Fu TM, Yang X, Tao S. Convolutional Neural Networks Facilitate Process Understanding of Megacity Ozone Temporal Variability. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:15691-15701. [PMID: 38485962 DOI: 10.1021/acs.est.3c07907] [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: 08/21/2024]
Abstract
Ozone pollution is profoundly modulated by meteorological features such as temperature, air pressure, wind, and humidity. While many studies have developed empirical models to elucidate the effects of meteorology on ozone variability, they predominantly focus on local weather conditions, overlooking the influences from high-altitude and broader regional meteorological patterns. Here, we employ convolutional neural networks (CNNs), a technique typically applied to image recognition, to investigate the influence of three-dimensional spatial variations in meteorological fields on the daily, seasonal, and interannual dynamics of ozone in Shenzhen, a major coastal urban center in China. Our optimized CNNs model, covering a 13° × 13° spatial domain, effectively explains over 70% of daily ozone variability, outperforming alternative empirical approaches by 7 to 62%. Model interpretations reveal the crucial roles of 2-m temperature and humidity as primary drivers, contributing 16% and 15% to daily ozone fluctuations, respectively. Regional wind fields account for up to 40% of ozone changes during the episodes. CNNs successfully replicate observed ozone temporal patterns, attributing -5-6 μg·m-3 of interannual ozone variability to weather anomalies. Our interpretable CNNs framework enables quantitative attribution of historical ozone fluctuations to nonlinear meteorological effects across spatiotemporal scales, offering vital process-based insights for managing megacity air quality amidst changing climate regimes.
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Affiliation(s)
- Zelin Mai
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Huizhong Shen
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Aoxing Zhang
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Haitong Zhe Sun
- Centre for Atmospheric Science, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, U.K
- Centre for Sustainable Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117609, Republic of Singapore
| | - Lianming Zheng
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Jianfeng Guo
- Shenzhen Ecology and Environment Monitoring Centre of Guangdong Province, Shenzhen 518049, China
| | - Chanfang Liu
- Shenzhen Ecology and Environment Monitoring Centre of Guangdong Province, Shenzhen 518049, China
| | - Yilin Chen
- School of Urban Planning and Design, Peking University, Shenzhen Graduate School, Shenzhen 518055, China
| | - Chen Wang
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Jianhuai Ye
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Lei Zhu
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Tzung-May Fu
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Xin Yang
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Shu Tao
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
- Institute of Carbon Neutrality, Peking University, Beijing 100871, China
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Tang Y, Wang Y, Chen X, Liang J, Li S, Chen G, Chen Z, Tang B, Zhu J, Li X. Diurnal emission variation of ozone precursors: Impacts on ozone formation during Sep. 2019. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 929:172591. [PMID: 38663597 DOI: 10.1016/j.scitotenv.2024.172591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 03/08/2024] [Accepted: 04/17/2024] [Indexed: 04/30/2024]
Abstract
With the issue of ozone (O3) pollution having increasingly gained visibility and prominence in China, the Chinese government explored various policies to mitigate O3 pollution. In some provinces and cities, diurnal regulations of O3 precursor were implemented, such as shifting O3 precursor emission processes to nighttime and offering preferential refueling at night. However, the effectiveness of these policies remains unverified, and their impact on the O3 generation process requires further elucidation. In this study, we utilized a regional climate and air quality model (WRF-Chem, v4.5) to test three scenarios aimed at exploring the impact of diurnal industry emission variation of O3 precursors on O3 formation. Significant O3 variations were observed mainly in urban areas. Shifting volatile organic compounds (VOCs) to nighttime have slight decreased daytime O3 levels while moving nitrogen oxides (NOx) to nighttime elevates O3 levels. Simultaneously moving both to nighttime showed combined effects. Process analysis indicates that the diurnal variation in O3 was mainly attributed to chemical process and vertical mixing in urban areas, while advection becomes more important in non-urban areas, contributing to the changes in O3 and O3 precursors levels through regional transportation. Further photochemical analysis reveals that the O3 photochemical production in urban areas was affected by reduced daytime O3 precursors emissions. Specifically, decreasing VOCs lowered the daytime O3 production by reducing the ROx radicals (ROx = HO + HO˙2 + RO˙2), whereas decreasing NOx promoted the daytime O3 production by weakening ROx radical loss. Our results demonstrate that diurnal regulation of O3 precursors will disrupt the ROx radical and O3 formation in local areas, resulting in a change in O3 concentration and atmospheric oxidation capacity, which should be considered in formulating new relevant policies.
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Affiliation(s)
- Yifan Tang
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China
| | - Yuchen Wang
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China; State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, PR China
| | - Xuwu Chen
- School of Advanced Interdisciplinary Studies, Hunan University of Technology and Business, Changsha 410205, PR China
| | - Jie Liang
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China
| | - Shuai Li
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China
| | - Gaojie Chen
- College of Mathematics and Econometrics, Hunan University, Changsha 410082, PR China
| | - Zuo Chen
- College of Information Science and Technology, Hunan University, Changsha 410082, PR China
| | - Binxu Tang
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China
| | - Jiesong Zhu
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China
| | - Xiaodong Li
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China.
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7
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Guo R, Shi G, Zhang D, Chen Y, Peng C, Zhai C, Yang F. An observed nocturnal ozone transport event in the Sichuan Basin, Southwestern China. J Environ Sci (China) 2024; 138:10-18. [PMID: 38135378 DOI: 10.1016/j.jes.2023.02.054] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 02/26/2023] [Accepted: 02/27/2023] [Indexed: 12/24/2023]
Abstract
The ozone (O3) pollution in China drew lots of attention in recent years, and the Sichuan Basin (SCB) was one of the regions confronting worsening O3 pollution problem. Many previous studies have shown that regional transport is an important contributor to O3 pollution. However, very few features of the O3 profile during transport have been reported, especially in the border regions between different administrative divisions. In this study, we conducted tethered balloon soundings in SCB during the summer of 2020 and captured a nocturnal O3 transport event during the campaign. Vertically, the O3 transport occurred in the bottom of the residual layer, between 200 and 500 m above ground level. Horizontally, the transport pathway was directed from southeast to northwest based on the analysis of the wind field and air mass trajectories. The effect of transport in the residual layer on the surface O3 concentration was related to the spatial distribution of O3. For cities with high O3 concentrations in the upwind region, the transport process would bring clean air masses and abate pollution. For downwind lightly polluted cities, the transport process would slow down the decreasing or even increase the surface O3 concentration during the night. We provided observational facts on the profile features of a transboundary O3 transport event between two provincial administrative divisions, which implicated the importance of joint prevention and control measures. However, the sounding parameters were limited and the quantitative analysis was preliminary, more integrated, and thorough studies of this topic were called for in the future.
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Affiliation(s)
- Ruyue Guo
- Department of Environmental Science and Engineering, Sichuan University, Chengdu 610065, China; College of Carbon Neutrality Future Technology, Sichuan University, Chengdu 610065, China
| | - Guangming Shi
- College of Carbon Neutrality Future Technology, Sichuan University, Chengdu 610065, China; National Engineering Research Center on Flue Gas Desulfurization, Chengdu 610065, China.
| | - Dan Zhang
- Chongqing Academy of Eco-Environmental Science, Chongqing 401147, China
| | - Yang Chen
- Research Center for Atmospheric Environment, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
| | - Chao Peng
- Chongqing Academy of Eco-Environmental Science, Chongqing 401147, China; Key Laboratory for Urban Atmospheric Environment Integrated Observation & Pollution Prevention and Control of Chongqing, Chongqing 401147, China
| | - Chongzhi Zhai
- Chongqing Academy of Eco-Environmental Science, Chongqing 401147, China; Key Laboratory for Urban Atmospheric Environment Integrated Observation & Pollution Prevention and Control of Chongqing, Chongqing 401147, China
| | - Fumo Yang
- College of Carbon Neutrality Future Technology, Sichuan University, Chengdu 610065, China; National Engineering Research Center on Flue Gas Desulfurization, Chengdu 610065, China
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Giang A, Edwards MR, Fletcher SM, Gardner-Frolick R, Gryba R, Mathias JD, Venier-Cambron C, Anderies JM, Berglund E, Carley S, Erickson JS, Grubert E, Hadjimichael A, Hill J, Mayfield E, Nock D, Pikok KK, Saari RK, Samudio Lezcano M, Siddiqi A, Skerker JB, Tessum CW. Equity and modeling in sustainability science: Examples and opportunities throughout the process. Proc Natl Acad Sci U S A 2024; 121:e2215688121. [PMID: 38498705 PMCID: PMC10990085 DOI: 10.1073/pnas.2215688121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/20/2024] Open
Abstract
Equity is core to sustainability, but current interventions to enhance sustainability often fall short in adequately addressing this linkage. Models are important tools for informing action, and their development and use present opportunities to center equity in process and outcomes. This Perspective highlights progress in integrating equity into systems modeling in sustainability science, as well as key challenges, tensions, and future directions. We present a conceptual framework for equity in systems modeling, focused on its distributional, procedural, and recognitional dimensions. We discuss examples of how modelers engage with these different dimensions throughout the modeling process and from across a range of modeling approaches and topics, including water resources, energy systems, air quality, and conservation. Synthesizing across these examples, we identify significant advances in enhancing procedural and recognitional equity by reframing models as tools to explore pluralism in worldviews and knowledge systems; enabling models to better represent distributional inequity through new computational techniques and data sources; investigating the dynamics that can drive inequities by linking different modeling approaches; and developing more nuanced metrics for assessing equity outcomes. We also identify important future directions, such as an increased focus on using models to identify pathways to transform underlying conditions that lead to inequities and move toward desired futures. By looking at examples across the diverse fields within sustainability science, we argue that there are valuable opportunities for mutual learning on how to use models more effectively as tools to support sustainable and equitable futures.
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Affiliation(s)
- Amanda Giang
- Institute for Resources, Environment and Sustainability, University of British Columbia, Vancouver, BCV6T 1Z4, Canada
- Department of Mechanical Engineering, University of British Columbia, Vancouver, BCV6T 1Z4, Canada
| | - Morgan R. Edwards
- La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI53706
- Nelson Institute Center for Sustainability and the Global Environment, University of Wisconsin-Madison, Madison, WI53706
| | - Sarah M. Fletcher
- Department of Civil and Environmental Engineering, Stanford University, Stanford, CA94305
- Woods Institute for the Environment, Stanford University, Stanford, CA94305
| | - Rivkah Gardner-Frolick
- Department of Mechanical Engineering, University of British Columbia, Vancouver, BCV6T 1Z4, Canada
| | - Rowenna Gryba
- Department of Statistics, University of British Columbia, Vancouver, BCV6T 1Z4, Canada
- Department of Geography, University of British Columbia, Vancouver, BCV6T 1Z4, Canada
- Institute for the Oceans and Fisheries, University of British Columbia, Vancouver, BCV6T 1Z4, Canada
| | - Jean-Denis Mathias
- Université Clermont Auvergne, INRAE, UR LISC, Centre de Clermont-Ferrand, AubièreF-63178, France
| | - Camille Venier-Cambron
- Department of Environmental Geography, Instituut voor Milieuvraagstukken, Vrije Universiteit Amsterdam, Amsterdam1081 HV, The Netherlands
| | - John M. Anderies
- School of Sustainability, Arizona State University, Tempe, AZ85287
| | - Emily Berglund
- Department of Civil Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC27695
| | - Sanya Carley
- Kleinman Center for Energy Policy, Stuart Weitzman School of Design, Department of City Planning, University of Pennsylvania, Philadelphia, PA19104
| | - Jacob Shimkus Erickson
- Nelson Institute Center for Sustainability and the Global Environment, University of Wisconsin-Madison, Madison, WI53706
- Department of Agricultural and Applied Economics, University of Wisconsin-Madison, Madison, WI53706
| | - Emily Grubert
- Keough School of Global Affairs, University of Notre Dame, Notre Dame, IN46556
| | - Antonia Hadjimichael
- Department of Geosciences, College of Earth and Mineral Sciences, Pennsylvania State University, University Park, PA16802
- Earth and Environmental Systems Institute, College of Earth and Mineral Sciences, Pennsylvania State University, University Park, PA16802
| | - Jason Hill
- Department of Bioproducts and Biosystems Engineering, University of Minnesota, Minneapolis, MN55455
| | - Erin Mayfield
- Thayer School of Engineering, Dartmouth College, Hanover, NH03755
| | - Destenie Nock
- Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA15213
| | - Kimberly Kivvaq Pikok
- International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, AK99775
| | - Rebecca K. Saari
- Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ONN2L 3G1, Canada
| | - Mateo Samudio Lezcano
- Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA15213
| | - Afreen Siddiqi
- Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Jennifer B. Skerker
- Department of Civil and Environmental Engineering, Stanford University, Stanford, CA94305
| | - Christopher W. Tessum
- Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL61801
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9
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Shen Y, de Hoogh K, Schmitz O, Clinton N, Tuxen-Bettman K, Brandt J, Christensen JH, Frohn LM, Geels C, Karssenberg D, Vermeulen R, Hoek G. Monthly average air pollution models using geographically weighted regression in Europe from 2000 to 2019. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 918:170550. [PMID: 38320693 DOI: 10.1016/j.scitotenv.2024.170550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 01/02/2024] [Accepted: 01/27/2024] [Indexed: 02/12/2024]
Abstract
Detailed spatial models of monthly air pollution levels at a very fine spatial resolution (25 m) can help facilitate studies to explore critical time-windows of exposure at intermediate term. Seasonal changes in air pollution may affect both levels and spatial patterns of air pollution across Europe. We built Europe-wide land-use regression (LUR) models to estimate monthly concentrations of regulated air pollutants (NO2, O3, PM10 and PM2.5) between 2000 and 2019. Monthly average concentrations were collected from routine monitoring stations. Including both monthly-fixed and -varying spatial variables, we used supervised linear regression (SLR) to select predictors and geographically weighted regression (GWR) to estimate spatially-varying regression coefficients for each month. Model performance was assessed with 5-fold cross-validation (CV). We also compared the performance of the monthly LUR models with monthly adjusted concentrations. Results revealed significant monthly variations in both estimates and model structure, particularly for O3, PM10, and PM2.5. The 5-fold CV showed generally good performance of the monthly GWR models across months and years (5-fold CV R2: 0.31-0.66 for NO2, 0.4-0.79 for O3, 0.4-0.78 for PM10, 0.46-0.87 for PM2.5). Monthly GWR models slightly outperformed monthly-adjusted models. Correlations between monthly GWR model were generally moderate to high (Pearson correlation >0.6). In conclusion, we are the first to develop robust monthly LUR models for air pollution in Europe. These monthly LUR models, at a 25 m spatial resolution, enhance epidemiologists to better characterize Europe-wide intermediate-term health effects related to air pollution, facilitating investigations into critical exposure time windows in birth cohort studies.
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Affiliation(s)
- Youchen Shen
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands.
| | - Kees de Hoogh
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands; Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland
| | - Oliver Schmitz
- Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, the Netherlands
| | - Nick Clinton
- Google, Inc, Mountain View, California, United States
| | | | - Jørgen Brandt
- Department of Environmental Science, Aarhus University, Roskilde, Denmark
| | | | - Lise M Frohn
- Department of Environmental Science, Aarhus University, Roskilde, Denmark
| | - Camilla Geels
- Department of Environmental Science, Aarhus University, Roskilde, Denmark
| | - Derek Karssenberg
- Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, the Netherlands
| | - Roel Vermeulen
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands; Julius Centre for Health Sciences and Primary Care, University Medical Centre, Utrecht University, Utrecht, the Netherlands
| | - Gerard Hoek
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
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10
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Sun HZ, Tang H, Fang J, Dai H, Zhao H, Xu S, Xiang Q, Tian Y, Jiao Y, Luo T, Huang M, Shu J, Zang L, Liu H, Guo Y, Xu W, Bai X. A Chinese longitudinal maternity cohort study (2013-2021) on intrahepatic cholestasis phenotypes: Risk associations from environmental exposure to adverse pregnancy outcomes. JOURNAL OF HAZARDOUS MATERIALS 2024; 463:132915. [PMID: 37951168 DOI: 10.1016/j.jhazmat.2023.132915] [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: 08/17/2023] [Revised: 10/30/2023] [Accepted: 10/31/2023] [Indexed: 11/13/2023]
Abstract
Intrahepatic cholestasis of pregnancy (ICP) is an idiopathic disease that occurs during mid-to-late pregnancy and is associated with various adverse pregnancy outcomes, including intrauterine fetal demise. However, since the underlying cause of ICP remains unclear, there is an ongoing debate on the phenotyping criteria used in the diagnostic process. Here, we identified single- and multi-symptomatic ICP (ICP-S and ICP-M) in 104,221 Chinese females from the ZEBRA maternity cohort, with the objective of exploring the risk implications of the two phenotypes on pregnancy outcomes and from environmental exposures. We employed multivariate binary logistic regression to estimate confounder-adjusted odds ratios and found that ICP-M was more strongly associated with preterm birth and low birth weight compared to ICP-S. Throughout pregnancy, incremental exposure to PM2.5, O3, and greenness could alter ICP risks by 17.3%, 12.5%, and -2.3%, respectively, with more substantial associations observed with ICP-M than with ICP-S. The major scientific advancements lie in the elucidation of synergistic risk interactions between pollutants and the protective antagonistic effects of greenness, as well as highlighting the risk impact of preconceptional environmental exposures. Our study, conducted in the context of the "three-child policy" in China, provides epidemiological evidence for policy-making to safeguard maternal and neonatal health.
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Affiliation(s)
- Haitong Zhe Sun
- Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China; Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117609, Republic of Singapore; Centre for Sustainable Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117609, Republic of Singapore; Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK; Department of Earth Sciences, University of Cambridge, Cambridge CB2 3EQ, UK.
| | - Haiyang Tang
- Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China
| | - Jing Fang
- Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China; Lanxi People's Hospital, Jinhua, Zhejiang 321102, PR China
| | - Haizhen Dai
- Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China
| | - Huan Zhao
- Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China; Department of Obstetrics and Gynecology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang 322000, PR China
| | - Siyuan Xu
- Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China
| | - Qingyi Xiang
- Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China
| | - Yijia Tian
- Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China
| | - Yurong Jiao
- Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China
| | - Ting Luo
- Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China
| | - Meishuang Huang
- Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China
| | - Jia Shu
- Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China
| | - Lu Zang
- Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, PR China
| | - Hengyi Liu
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health, School of Public Health, Peking University Health Science Centre, Beijing 100191, PR China
| | - Yuming Guo
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Wei Xu
- Maternal and Child Health Division, Health Commission of Zhejiang Province, Hangzhou, Zhejiang 310006, PR China
| | - Xiaoxia Bai
- Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China; Traditional Chinese Medicine for Reproductive Health Key Laboratory of Zhejiang Province, Hangzhou, Zhejiang 310006, PR China; Zhejiang Provincial Clinical Research Centre for Obstetrics and Gynecology, Hangzhou, Zhejiang 310006, PR China; Key Laboratory of Women's Reproductive Health, Hangzhou, Zhejiang 310006, PR China.
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11
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Wen Y, Zhang S, Wang Y, Yang J, He L, Wu Y, Hao J. Dynamic Traffic Data in Machine-Learning Air Quality Mapping Improves Environmental Justice Assessment. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024. [PMID: 38261755 DOI: 10.1021/acs.est.3c07545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
Air pollution poses a critical public health threat around many megacities but in an uneven manner. Conventional models are limited to depict the highly spatial- and time-varying patterns of ambient pollutant exposures at the community scale for megacities. Here, we developed a machine-learning approach that leverages the dynamic traffic profiles to continuously estimate community-level year-long air pollutant concentrations in Los Angeles, U.S. We found the introduction of real-world dynamic traffic data significantly improved the spatial fidelity of nitrogen dioxide (NO2), maximum daily 8-h average ozone (MDA8 O3), and fine particulate matter (PM2.5) simulations by 47%, 4%, and 15%, respectively. We successfully captured PM2.5 levels exceeding limits due to heavy traffic activities and providing an "out-of-limit map" tool to identify exposure disparities within highly polluted communities. In contrast, the model without real-world dynamic traffic data lacks the ability to capture the traffic-induced exposure disparities and significantly underestimate residents' exposure to PM2.5. The underestimations are more severe for disadvantaged communities such as black and low-income groups, showing the significance of incorporating real-time traffic data in exposure disparity assessment.
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Affiliation(s)
- Yifan Wen
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
| | - Shaojun Zhang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
- Beijing Laboratory of Environmental Frontier Technologies, Beijing 100084, P. R. China
- Laboratory of Transport Pollution Control and Monitoring Technology, Transport Planning and Research Institute, Ministry of Transport, Beijing 100028, P. R. China
| | - Yuan Wang
- Department of Earth System Science, Stanford University, Stanford, California 94305, United States
| | - Jiani Yang
- Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California 91125, United States
| | - Liyin He
- Department of Global Ecology, Carnegie Institution for Science, Stanford, California 94305, United States
| | - Ye Wu
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
- Beijing Laboratory of Environmental Frontier Technologies, Beijing 100084, P. R. China
- Laboratory of Transport Pollution Control and Monitoring Technology, Transport Planning and Research Institute, Ministry of Transport, Beijing 100028, P. R. China
| | - Jiming Hao
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
- Beijing Laboratory of Environmental Frontier Technologies, Beijing 100084, P. R. China
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12
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Wu G, Guan K, Ainsworth EA, Martin DG, Kimm H, Yang X. Solar-induced chlorophyll fluorescence captures the effects of elevated ozone on canopy structure and acceleration of senescence in soybean. JOURNAL OF EXPERIMENTAL BOTANY 2024; 75:350-363. [PMID: 37702411 DOI: 10.1093/jxb/erad356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 09/11/2023] [Indexed: 09/14/2023]
Abstract
Solar-induced chlorophyll fluorescence (SIF) provides an opportunity to rapidly and non-destructively investigate how plants respond to stress. Here, we explored the potential of SIF to detect the effects of elevated O3 on soybean in the field where soybean was subjected to ambient and elevated O3 throughout the growing season in 2021. Exposure to elevated O3 resulted in a significant decrease in canopy SIF at 760 nm (SIF760), with a larger decrease in the late growing season (36%) compared with the middle growing season (13%). Elevated O3 significantly decreased the fraction of absorbed photosynthetically active radiation by 8-15% in the middle growing season and by 35% in the late growing stage. SIF760 escape ratio (fesc) was significantly increased under elevated O3 by 5-12% in the late growth stage due to a decrease of leaf chlorophyll content and leaf area index. Fluorescence yield of the canopy was reduced by 5-11% in the late growing season depending on the fesc estimation method, during which leaf maximum carboxylation rate and maximum electron transport were significantly reduced by 29% and 20% under elevated O3. These results demonstrated that SIF could capture the elevated O3 effect on canopy structure and acceleration of senescence in soybean and provide empirical support for using SIF for soybean stress detection and phenotyping.
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Affiliation(s)
- Genghong Wu
- Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois Urbana Champaign, Urbana, IL 61801, USA
- Department of Natural Resources and Environmental Sciences, College of Agricultural, Consumers, and Environmental Sciences, University of Illinois Urbana Champaign, Urbana, IL 61801, USA
- Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, Jülich 52425, Germany
| | - Kaiyu Guan
- Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois Urbana Champaign, Urbana, IL 61801, USA
- Department of Natural Resources and Environmental Sciences, College of Agricultural, Consumers, and Environmental Sciences, University of Illinois Urbana Champaign, Urbana, IL 61801, USA
- National Center for Supercomputing Applications, University of Illinois Urbana Champaign, Urbana, IL 61801, USA
| | - Elizabeth A Ainsworth
- Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois Urbana Champaign, Urbana, IL 61801, USA
- Department of Plant Biology, University of Illinois Urbana Champaign, Urbana, IL 61801, USA
- USDA-ARS, Global Change and Photosynthesis Research Unit, Urbana, IL 61801, USA
| | - Duncan G Martin
- Department of Plant Biology, University of Illinois Urbana Champaign, Urbana, IL 61801, USA
| | - Hyungsuk Kimm
- Department of Natural Resources and Environmental Sciences, College of Agricultural, Consumers, and Environmental Sciences, University of Illinois Urbana Champaign, Urbana, IL 61801, USA
- Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 08826, South Korea
| | - Xi Yang
- Department of Environmental Sciences, University of Virginia, Charlottesville, VA 22903, USA
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13
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Wang Y, Li Q, Luo Z, Zhao J, Lv Z, Deng Q, Liu J, Ezzati M, Baumgartner J, Liu H, He K. Ultra-high-resolution mapping of ambient fine particulate matter to estimate human exposure in Beijing. COMMUNICATIONS EARTH & ENVIRONMENT 2023; 4:451. [PMID: 38130441 PMCID: PMC7615407 DOI: 10.1038/s43247-023-01119-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 11/16/2023] [Indexed: 12/23/2023]
Abstract
With the decreasing regional-transported levels, the health risk assessment derived from fine particulate matter (PM2.5) has become insufficient to reflect the contribution of local source heterogeneity to the exposure differences. Here, we combined the both ultra-high-resolution PM2.5 concentration with population distribution to provide the personal daily PM2.5 internal dose considering the indoor/outdoor exposure difference. A 30-m PM2.5 assimilating method was developed fusing multiple auxiliary predictors, achieving higher accuracy (R2 = 0.78-0.82) than the chemical transport model outputs without any post-simulation data-oriented enhancement (R2 = 0.31-0.64). Weekly difference was identified from hourly mobile signaling data in 30-m resolution population distribution. The population-weighted ambient PM2.5 concentrations range among districts but fail to reflect exposure differences. Derived from the indoor/outdoor ratio, the average indoor PM2.5 concentration was 26.5 μg/m3. The internal dose based on the assimilated indoor/outdoor PM2.5 concentration shows high exposure diversity among sub-groups, and the attributed mortality increased by 24.0% than the coarser unassimilated model.
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Affiliation(s)
- Yongyue Wang
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Qiwei Li
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Zhenyu Luo
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Junchao Zhao
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Zhaofeng Lv
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Qiuju Deng
- Centre for Clinical and Epidemiologic Research, Beijing An Zhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing 100029, China
| | - Jing Liu
- Centre for Clinical and Epidemiologic Research, Beijing An Zhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing 100029, China
| | - Majid Ezzati
- School of Public Health, Imperial College London, London SW72AZ, UK
| | - Jill Baumgartner
- School of Population and Global Health, McGill University, Montréal, QC H3A0G4, Canada
| | - Huan Liu
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Kebin He
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
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14
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Wei J, Li Z, Chen X, Li C, Sun Y, Wang J, Lyapustin A, Brasseur GP, Jiang M, Sun L, Wang T, Jung CH, Qiu B, Fang C, Liu X, Hao J, Wang Y, Zhan M, Song X, Liu Y. Separating Daily 1 km PM 2.5 Inorganic Chemical Composition in China since 2000 via Deep Learning Integrating Ground, Satellite, and Model Data. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:18282-18295. [PMID: 37114869 DOI: 10.1021/acs.est.3c00272] [Citation(s) in RCA: 65] [Impact Index Per Article: 32.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Fine particulate matter (PM2.5) chemical composition has strong and diverse impacts on the planetary environment, climate, and health. These effects are still not well understood due to limited surface observations and uncertainties in chemical model simulations. We developed a four-dimensional spatiotemporal deep forest (4D-STDF) model to estimate daily PM2.5 chemical composition at a spatial resolution of 1 km in China since 2000 by integrating measurements of PM2.5 species from a high-density observation network, satellite PM2.5 retrievals, atmospheric reanalyses, and model simulations. Cross-validation results illustrate the reliability of sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), and chloride (Cl-) estimates, with high coefficients of determination (CV-R2) with ground-based observations of 0.74, 0.75, 0.71, and 0.66, and average root-mean-square errors (RMSE) of 6.0, 6.6, 4.3, and 2.3 μg/m3, respectively. The three components of secondary inorganic aerosols (SIAs) account for 21% (SO42-), 20% (NO3-), and 14% (NH4+) of the total PM2.5 mass in eastern China; we observed significant reductions in the mass of inorganic components by 40-43% between 2013 and 2020, slowing down since 2018. Comparatively, the ratio of SIA to PM2.5 increased by 7% across eastern China except in Beijing and nearby areas, accelerating in recent years. SO42- has been the dominant SIA component in eastern China, although it was surpassed by NO3- in some areas, e.g., Beijing-Tianjin-Hebei region since 2016. SIA, accounting for nearly half (∼46%) of the PM2.5 mass, drove the explosive formation of winter haze episodes in the North China Plain. A sharp decline in SIA concentrations and an increase in SIA-to-PM2.5 ratios during the COVID-19 lockdown were also revealed, reflecting the enhanced atmospheric oxidation capacity and formation of secondary particles.
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Affiliation(s)
- Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland 20742, United States
| | - Zhanqing Li
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland 20742, United States
| | - Xi Chen
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Chi Li
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Yele Sun
- State Key Laboratory of Atmospheric Boundary Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Jun Wang
- Department of Chemical and Biochemical Engineering, Iowa Technology Institute, University of Iowa, Iowa 52242, United States
| | - Alexei Lyapustin
- Laboratory for Atmospheres, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, United States
| | - Guy Pierre Brasseur
- Max Planck Institute for Meteorology, Hamburg 20146, Germany
- National Center for Atmospheric Research, Boulder, Colorado 80307, United States
| | - Mengjiao Jiang
- Max Planck Institute for Meteorology, Hamburg 20146, Germany
- School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China
| | - Lin Sun
- College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
| | - Tao Wang
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
| | - Chang Hoon Jung
- Department of Health Management, Kyungin Women's University, Incheon 21041, Korea
| | - Bing Qiu
- Civil Aviation Medical Center, Civil Aviation Administration of China, Beijing 100123, China
| | - Cuilan Fang
- Jiulongpo Center for Disease Control and Prevention, Chongqing 400039, China
| | - Xuhui Liu
- Taiyuan Center for Disease Control and Prevention, Taiyuan 030015, China
| | - Jinrui Hao
- Taiyuan Center for Disease Control and Prevention, Taiyuan 030015, China
| | - Yan Wang
- Harbin Center for Disease Control and Prevention, Harbin 150010, China
| | - Ming Zhan
- Pudong Center for Disease Control and Prevention, Shanghai 200120, China
| | | | - Yuewei Liu
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
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15
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Sun HZ, Zhao J, Liu X, Qiu M, Shen H, Guillas S, Giorio C, Staniaszek Z, Yu P, Wan MW, Chim MM, van Daalen KR, Li Y, Liu Z, Xia M, Ke S, Zhao H, Wang H, He K, Liu H, Guo Y, Archibald AT. Antagonism between ambient ozone increase and urbanization-oriented population migration on Chinese cardiopulmonary mortality. Innovation (N Y) 2023; 4:100517. [PMID: 37822762 PMCID: PMC10562756 DOI: 10.1016/j.xinn.2023.100517] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 09/17/2023] [Indexed: 10/13/2023] Open
Abstract
Ever-increasing ambient ozone (O3) pollution in China has been exacerbating cardiopulmonary premature deaths. However, the urban-rural exposure inequity has seldom been explored. Here, we assess population-scale O3 exposure and mortality burdens between 1990 and 2019 based on integrated pollution tracking and epidemiological evidence. We find Chinese population have been suffering from climbing O3 exposure by 4.3 ± 2.8 ppb per decade as a result of rapid urbanization and growing prosperity of socioeconomic activities. Rural residents are broadly exposed to 9.8 ± 4.1 ppb higher ambient O3 than the adjacent urban citizens, and thus urbanization-oriented migration compromises the exposure-associated mortality on total population. Cardiopulmonary excess premature deaths attributable to long-term O3 exposure, 373,500 (95% uncertainty interval [UI]: 240,600-510,900) in 2019, is underestimated in previous studies due to ignorance of cardiovascular causes. Future O3 pollution policy should focus more on rural population who are facing an aggravating threat of mortality risks to ameliorate environmental health injustice.
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Affiliation(s)
- Haitong Zhe Sun
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
- Department of Earth Sciences, University of Cambridge, Cambridge CB2 3EQ, UK
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Junchao Zhao
- State Key Joint Laboratory of ESPC, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, School of Environment, Tsinghua University, Beijing 100084, China
| | - Xiang Liu
- School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
| | - Minghao Qiu
- Department of Earth System Science, Stanford University, Stanford, CA 94305, USA
| | - Huizhong Shen
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Serge Guillas
- Department of Statistical Science, University College London, London WC1E 6BT, UK
- The Alan Turing Institute, London NW1 2DB, UK
| | - Chiara Giorio
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Zosia Staniaszek
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Pei Yu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Michelle W.L. Wan
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Man Mei Chim
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Kim Robin van Daalen
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge CB2 0BD, UK
- Barcelona Supercomputing Center, Department of Earth Sciences, 08034 Barcelona, Spain
| | - Yilin Li
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Zhenze Liu
- School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Mingtao Xia
- Department of Mathematics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Shengxian Ke
- State Key Laboratory of New Ceramics and Fine Processing, Key Laboratory of Advanced Materials of Ministry of Education, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
| | - Haifan Zhao
- Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK
| | - Haikun Wang
- School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
| | - Kebin He
- State Key Joint Laboratory of ESPC, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, School of Environment, Tsinghua University, Beijing 100084, China
| | - Huan Liu
- State Key Joint Laboratory of ESPC, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, School of Environment, Tsinghua University, Beijing 100084, China
| | - Yuming Guo
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Alexander T. Archibald
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
- National Centre for Atmospheric Science, Cambridge CB2 1EW, UK
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16
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Gibson JM, Osman KK, Conroy-Ben O, Giang A. Environmental Science for the Betterment of All. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:13267-13269. [PMID: 37498804 DOI: 10.1021/acs.est.3c05429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
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17
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Kalbande R, Kumar B, Maji S, Yadav R, Atey K, Rathore DS, Beig G. Machine learning based quantification of VOC contribution in surface ozone prediction. CHEMOSPHERE 2023; 326:138474. [PMID: 36958496 DOI: 10.1016/j.chemosphere.2023.138474] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 03/14/2023] [Accepted: 03/20/2023] [Indexed: 06/18/2023]
Abstract
The prediction of surface ozone is essential attributing to its impact on human and environmental health. Volatile organic compounds (VOCs) are crucial in driving ozone concentration; particularly in urban areas where VOC limited regimes are prominent. The limited measurements of VOCs, however, hinder assessing the VOC-ozone relationship. This work applies machine learning (ML) algorithms for temporal forecasting of surface ozone over a metropolitan city in India. The availability of continuous VOCs measurement data along with meteorology and other pollutants during 2014-2016 makes it possible to deduce the influence of various input parameters on surface ozone prediction. After evaluating the best ML model for ozone prediction, simulations were carried out using varied input combinations. The combination with isoprene, meteorology, NOx, and CO (Isop + MNC) was the best with RMSE 4.41 ppbv and MAPE 6.77%. A season-wise comparison of simulations having all data, only meteorological data and Isop + MNC as input showed that Isop + MNC simulation gives the best results during the summer season (RMSE: 5.86 ppbv, MAPE: 7.05%). This shows the increased ability of the model to capture ozone peaks (high ozone during summer) relatively better when isoprene data is used. The overall results highlight that using all available data doesn't necessarily give best prediction results; also critical thinking is essential when evaluating the model results.
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Affiliation(s)
- Ritesh Kalbande
- Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India; Mohanlal Sukhadia University, Udaipur, India.
| | - Bipin Kumar
- Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India.
| | - Sujit Maji
- Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India.
| | - Ravi Yadav
- Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India; NUS Environmental Research Insitute, National University of Singapore, Singapore.
| | - Kaustubh Atey
- Indian Institute of Science Education and Research, Pune, India.
| | | | - Gufran Beig
- National Institute of Advanced Studies, Indian Institute of Science Campus, Bangalore, India.
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18
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Guo B, Wu H, Pei L, Zhu X, Zhang D, Wang Y, Luo P. Study on the spatiotemporal dynamic of ground-level ozone concentrations on multiple scales across China during the blue sky protection campaign. ENVIRONMENT INTERNATIONAL 2022; 170:107606. [PMID: 36335896 DOI: 10.1016/j.envint.2022.107606] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 10/25/2022] [Accepted: 10/26/2022] [Indexed: 06/16/2023]
Abstract
Surface ozone (O3), one of the harmful air pollutants, generated significantly negative effects on human health and plants. Existing O3 datasets with coarse spatiotemporal resolution and limited coverage, and the uncertainties of O3 influential factors seriously restrain related epidemiology and air pollution studies. To tackle above issues, we proposed a novel scheme to estimate daily O3 concentrations on a fine grid scale (1 km × 1 km) from 2018 to 2020 across China based on machine learning methods using hourly observed ground-level pollutant concentrations data, meteorological data, satellite data, and auxiliary data including digital elevation model (DEM), land use data (LUD), normalized difference vegetation index (NDVI), population (POP), and nighttime light images (NTL), and to identify the difference of influential factors of O3 on diverse urbanization and topography conditions. Some findings were achieved. The correlation coefficients (R2) between O3 concentrations and surface net solar radiation (SNSR), boundary layer height (BLH), 2 m temperature (T2M), 10 m v-component (MVW), and NDVI were 0.80, 0.40, 0.35, 0.30, and 0.20, respectively. The random forest (RF) demonstrated the highest validation R2 (0.86) and lowest validation RMSE (13.74 μg/m3) in estimating O3 concentrations, followed by support vector machine (SVM) (R2 = 0.75, RMSE = 18.39 μg/m3), backpropagation neural network (BP) (R2 = 0.74, RMSE = 19.26 μg/m3), and multiple linear regression (MLR) (R2 = 0.52, RMSE = 25.99 μg/m3). Our China High-Resolution O3 Dataset (CHROD) exhibited an acceptable accuracy at different spatial-temporal scales. Additionally, O3 concentrations showed decreasing trend and represented obviously spatiotemporal heterogeneity across China from 2018 to 2020. Overall, O3 was mainly affected by human activities in higher urbanization regions, while O3 was mainly controlled by meteorological factors, vegetation coverage, and elevation in lower urbanization regions. The scheme of this study is useful and valuable in understanding the mechanism of O3 formation and improving the quality of the O3 dataset.
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Affiliation(s)
- Bin Guo
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China.
| | - Haojie Wu
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China
| | - Lin Pei
- School of Exercise and Health Sciences, Xi'an Physical Education University, Xi'an, Shaanxi 710068, China; School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710043, China.
| | - Xiaowei Zhu
- Department of Mechanical and Materials Engineering, Portland State University, Portland, OR 97207, USA.
| | - Dingming Zhang
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China
| | - Yan Wang
- School of Geography and Tourism, Shaanxi Normal University, Xi'an, Shaanxi 710119, China
| | - Pingping Luo
- School of Water and Environment, Chang'an University, Xi'an, Shaanxi 710054, China.
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19
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Surface Ozone Pollution: Trends, Meteorological Influences, and Chemical Precursors in Portugal. SUSTAINABILITY 2022. [DOI: 10.3390/su14042383] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Surface ozone (O3) is a secondary air pollutant, harmful to human health and vegetation. To provide a long-term study of O3 concentrations in Portugal (study period: 2009–2019), a statistical analysis of ozone trends in rural stations (where the highest concentrations can be found) was first performed. Additionally, the effect of nitrogen oxides (NOx) and meteorological variables on O3 concentrations were evaluated in different environments in northern Portugal. A decreasing trend of O3 concentrations was observed in almost all monitoring stations. However, several exceedances to the standard values legislated for human health and vegetation protection were recorded. Daily and seasonal O3 profiles showed high concentrations in the afternoon and summer (for all inland rural stations) or spring (for Portuguese islands). The high number of groups obtained from the cluster analysis showed the difference of ozone behaviour amongst the existent rural stations, highlighting the effectiveness of the current geographical distribution of monitoring stations. Stronger correlations between O3, NO, and NO2 were detected at the urban site, indicating that the O3 concentration was more NOx-sensitive in urban environments. Solar radiation showed a higher correlation with O3 concentration regarding the meteorological influence. The wind and pollutants transport must also be considered in air quality studies. The presented results enable the definition of air quality policies to prevent and/or mitigate unfavourable outcomes from O3 pollution.
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