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Meng W, Cheng Y, Shen G, Shen H, Su H, Tao S. The Long Hazy Tail: Analysis of the Impacts and Trends of Severe Outdoor and Indoor Air Pollution in North China. Environ Sci Technol 2024. [PMID: 38696616 DOI: 10.1021/acs.est.4c02778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2024]
Abstract
China, especially the densely populated North China region, experienced severe haze events in the past decade that concerned the public. Although the most extreme cases have been largely eliminated through recent mitigation measures, severe outdoor air pollution persists and its environmental impact needs to be understood. Severe indoor pollution draws less public attention due to the short visible distance indoors, but its public health impacts cannot be ignored. Herein, we assess the trends and impacts of severe outdoor and indoor air pollution in North China from 2014 to 2021. Our results demonstrate the uneven contribution of severe hazy days to ambient and exposure concentrations of particulate matter with an aerodynamic diameter <2.5 (PM2.5). Although severe indoor pollution contributes to indoor PM2.5 concentrations (23%) to a similar extent as severe haze contributes to ambient PM2.5 concentrations (21%), the former's contribution to premature deaths was significantly higher. Furthermore, residential emissions contributed more in the higher PM2.5 concentration range both indoors and outdoors. Notably, severe haze had greater health impacts on urban residents, while severe indoor pollution was more impactful in rural areas. Our findings suggest that, besides reducing severe haze, mitigating severe indoor pollution is an important aspect of combating air pollution, especially toward improving public health.
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Affiliation(s)
- Wenjun Meng
- Institute of Carbon Neutrality, Peking University, 100871 Beijing, China
- Laboratory for Earth Surface Processes and College of Urban and Environmental Sciences, Peking University, 100871 Beijing, China
- Minerva Research Group, Max Planck Institute for Chemistry, 55128 Mainz, Germany
| | - Yafang Cheng
- Minerva Research Group, Max Planck Institute for Chemistry, 55128 Mainz, Germany
| | - Guofeng Shen
- Institute of Carbon Neutrality, Peking University, 100871 Beijing, China
- Laboratory for Earth Surface Processes and College of Urban and Environmental Sciences, Peking University, 100871 Beijing, China
| | - Huizhong Shen
- School of Environmental Science and Engineering, Southern University of Science and Technology, 518055 Shenzhen, China
| | - Hang Su
- Institute for Atmospheric Physics, Chinese Academy of Science, 100029 Beijing, China
| | - Shu Tao
- Institute of Carbon Neutrality, Peking University, 100871 Beijing, China
- Laboratory for Earth Surface Processes and College of Urban and Environmental Sciences, Peking University, 100871 Beijing, China
- School of Environmental Science and Engineering, Southern University of Science and Technology, 518055 Shenzhen, China
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2
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Wu W, Fu TM, Arnold SR, Spracklen DV, Zhang A, Tao W, Wang X, Hou Y, Mo J, Chen J, Li Y, Feng X, Lin H, Huang Z, Zheng J, Shen H, Zhu L, Wang C, Ye J, Yang X. Temperature-Dependent Evaporative Anthropogenic VOC Emissions Significantly Exacerbate Regional Ozone Pollution. Environ Sci Technol 2024; 58:5430-5441. [PMID: 38471097 DOI: 10.1021/acs.est.3c09122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
Abstract
The evaporative emissions of anthropogenic volatile organic compounds (AVOCs) are sensitive to ambient temperature. This sensitivity forms an air pollution-meteorology connection that has not been assessed on a regional scale. We parametrized the temperature dependence of evaporative AVOC fluxes in a regional air quality model and evaluated the impacts on surface ozone in the Beijing-Tianjin-Hebei (BTH) area of China during the summer of 2017. The temperature dependency of AVOC emissions drove an enhanced simulated ozone-temperature sensitivity of 1.0 to 1.8 μg m-3 K-1, comparable to the simulated ozone-temperature sensitivity driven by the temperature dependency of biogenic VOC emissions (1.7 to 2.4 μg m-3 K-1). Ozone enhancements driven by temperature-induced AVOC increases were localized to their point of emission and were relatively more important in urban areas than in rural regions. The inclusion of the temperature-dependent AVOC emissions in our model improved the simulated ozone-temperature sensitivities on days of ozone exceedance. Our results demonstrated the importance of temperature-dependent AVOC emissions on surface ozone pollution and its heretofore unrepresented role in air pollution-meteorology interactions.
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Affiliation(s)
- Wenlu Wu
- 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, Guangdong 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, Guangdong 518055, China
- Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds LS2 9JT, U.K
| | - 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, Guangdong 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, Guangdong 518055, China
| | - Steve R Arnold
- Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds LS2 9JT, U.K
| | - Dominick V Spracklen
- Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds LS2 9JT, U.K
| | - 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, Guangdong 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, Guangdong 518055, China
| | - Wei 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, Guangdong 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, Guangdong 518055, China
| | - Xiaolin Wang
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Yue Hou
- 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, Guangdong 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, Guangdong 518055, China
| | - Jiajia Mo
- 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, Guangdong 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, Guangdong 518055, China
| | - Jiongkai Chen
- 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, Guangdong 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, Guangdong 518055, China
| | - Yumin Li
- 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, Guangdong 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, Guangdong 518055, China
| | - Xu Feng
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Haipeng Lin
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Zhijiong Huang
- Institute for Environmental and Climate Research, Jinan University, Guangzhou, Guangdong 511443, China
| | - Junyu Zheng
- Sustainable Energy and Environment Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong 511453, 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, Guangdong 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, Guangdong 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, Guangdong 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, Guangdong 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, Guangdong 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, Guangdong 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, Guangdong 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, Guangdong 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, Guangdong 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, Guangdong 518055, China
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3
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Xing R, Luo Z, Zhang W, Xiong R, Jiang K, Meng W, Meng J, Dai H, Xue B, Shen H, Shen G. Household fuel and direct carbon emission disparity in rural China. Environ Int 2024; 185:108549. [PMID: 38447453 DOI: 10.1016/j.envint.2024.108549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 02/27/2024] [Accepted: 02/29/2024] [Indexed: 03/08/2024]
Abstract
Universal access to clean fuels in household use is one explicit indicator of sustainable development while currently still billions of people rely on solid fuels for daily cooking. Despite of the recognized clean transition trend in general, disparities in household energy mix in different activities (e.g. cooking and heating) and historical trends remain to be elucidated. In this study, we revealed the historical changing trend of the disparity in household cooking and heating activities and associated carbon emissions in rural China. The study found that the poor had higher total direct energy consumption but used less modern energy, especially in cooking activities, in which the poor consumed 60 % more energy than the rich. The disparity in modern household energy use decreased over time, but conversely the disparity in total residential energy consumption increased due to the different energy elasticities as income increases. Though per-capita household CO2 and Black Carbon (BC) emissions were decreasing under switching to modern energies, the disparity in household CO2 and BC deepened over time, and the low-income groups emitted ∼ 10 kg CO2 more compared to the high-income population. Relying solely on spontaneous clean cooking transition had limited impacts in reducing disparities in household energy and carbon emissions, whereas improving access to modern energy had substantial potential to reduce energy consumption and carbon emissions and its disparity. Differentiated energy-related policies to promote high-efficiency modern heating energies affordable for the low-income population should be developed to reduce the disparity, and consequently benefit human health and climate change equally.
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Affiliation(s)
- Ran Xing
- College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Zhihan Luo
- College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Wenxiao Zhang
- College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Rui Xiong
- College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Ke Jiang
- College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Wenjun Meng
- College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Jing Meng
- The Bartlett School of Sustainable Construction, University College London, London WC1E 7HB, United Kingdom
| | - Hancheng Dai
- College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Bing Xue
- Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China
| | - Huizhong Shen
- College of Environmental Science and Technology, Southern University of Science and Technology, Shenzhen, China
| | - Guofeng Shen
- College of Urban and Environmental Sciences, Peking University, Beijing, China; Institute of Carbon Neutrality, Peking University, Beijing 100871, China.
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4
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Zheng L, Adalibieke W, Zhou F, He P, Chen Y, Guo P, He J, Zhang Y, Xu P, Wang C, Ye J, Zhu L, Shen G, Fu TM, Yang X, Zhao S, Hakami A, Russell AG, Tao S, Meng J, Shen H. Health burden from food systems is highly unequal across income groups. Nat Food 2024; 5:251-261. [PMID: 38486126 DOI: 10.1038/s43016-024-00946-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 02/21/2024] [Indexed: 03/27/2024]
Abstract
Food consumption contributes to the degradation of air quality in regions where food is produced, creating a contrast between the health burden caused by a specific population through its food consumption and that faced by this same population as a consequence of food production activities. Here we explore this inequality within China's food system by linking air-pollution-related health burden from production to consumption, at high levels of spatial and sectorial granularity. We find that low-income groups bear a 70% higher air-pollution-related health burden from food production than from food consumption, while high-income groups benefit from a 29% lower health burden relative to their food consumption. This discrepancy largely stems from a concentration of low-income residents in food production areas, exposed to higher emissions from agriculture. Comprehensive interventions targeting both production and consumption sides can effectively reduce health damages and concurrently mitigate associated inequalities, while singular interventions exhibit limited efficacy.
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Affiliation(s)
- 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, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China
| | - Wulahati Adalibieke
- Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Feng Zhou
- Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China.
- College of Geography and Remote Sensing, Hohai University, Nanjing, China.
| | - Pan He
- School of Earth and Environmental Sciences, Cardiff University, Cardiff, UK.
| | - Yilin Chen
- 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, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China
- School of Urban Planning and Design, Peking University, Shenzhen Graduate School, Shenzhen, China
| | - Peng Guo
- 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, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China
| | - Jinling He
- 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, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China
| | - Yuanzheng Zhang
- Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Peng Xu
- Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin, 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, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, 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, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, 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, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China
| | - Guofeng Shen
- Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, 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, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, 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, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China
| | - Shunliu Zhao
- Department of Civil and Environmental Engineering, Carleton University, Ottawa, Ontario, Canada
| | - Amir Hakami
- Department of Civil and Environmental Engineering, Carleton University, Ottawa, Ontario, Canada
| | - Armistead G Russell
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - 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, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China
- Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Jing Meng
- The Bartlett School of Sustainable Construction, University College London, London, UK.
| | - 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, China.
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China.
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5
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Wang Y, Xing C, Cai B, Qiu W, Zhai J, Zeng Y, Zhang A, Shi S, Zhang Y, Yang X, Fu TM, Shen H, Wang C, Zhu L, Ye J. Impact of antioxidants on PM 2.5 oxidative potential, radical level, and cytotoxicity. Sci Total Environ 2024; 912:169555. [PMID: 38157913 DOI: 10.1016/j.scitotenv.2023.169555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/17/2023] [Accepted: 12/18/2023] [Indexed: 01/03/2024]
Abstract
Antioxidants are typically seen as agents that mitigate environmental health risks due to their ability to scavenge free radicals. However, our research presents a paradox where these molecules, particularly those within lung fluid, act as prooxidants in the presence of airborne particulate matter (PM2.5), thus enhancing PM2.5 oxidative potential (OP). In our study, we examined a range of antioxidants found in the respiratory system (e.g., vitamin C, glutathione (GSH), and N-acetylcysteine (NAC)), in plasma (vitamin A, vitamin E, and β-carotene), and in food (tert-butylhydroquinone (TBHQ)). We aimed to explore antioxidants' prooxidant and antioxidant interactions with PM2.5 and the resulting OP and cytotoxicity. We employed OH generation assays and electron paramagnetic resonance assays to assess the pro-oxidative and anti-oxidative effects of antioxidants. Additionally, we assessed cytotoxicity interaction using a Chinese hamster ovary cell cytotoxicity assay. Our findings revealed that, in the presence of PM2.5, all antioxidants except vitamin E significantly increased the PM2.5 OP by generating more OH radicals (OH generation rate: 0.16-24.67 pmol·min-1·m-3). However, it's noteworthy that these generated OH radicals were at least partially neutralized by the antioxidants themselves. Among the pro-oxidative antioxidants, vitamin A, β-carotene, and TBHQ showed the least ability to quench these radicals, consistent with their observed impact in enhancing PM2.5 cytotoxicity (PM2.5 LC50 reduced to 91.2 %, 88.8 %, and 75.1 % of PM2.5's original level, respectively). Notably, vitamin A and TBHQ-enhanced PM2.5 OP were strongly associated with the presence of metals and organic compounds, particularly with copper (Cu) contributing significantly (35 %) to TBHQ's pro-oxidative effect. Our study underscores the potential health risks associated with the interaction between antioxidants and ambient pollutants.
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Affiliation(s)
- Yixiang 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, Shenzhen 518055, China
| | - Chunbo Xing
- 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, Shenzhen 518055, China
| | - Baohua Cai
- 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, Shenzhen 518055, China
| | - Wenhui Qiu
- 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
| | - Jinghao Zhai
- 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, Shenzhen 518055, China
| | - Yaling Zeng
- 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, Shenzhen 518055, China
| | - Antai 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, Shenzhen 518055, China
| | - Shao Shi
- 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, Shenzhen 518055, China
| | - Yujie 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, 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, 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, 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, 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, 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, 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, Shenzhen 518055, China
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6
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Zeng Y, Zhang A, Yang X, Xing C, Zhai J, Wang Y, Cai B, Shi S, Zhang Y, Shen Z, Fu TM, Zhu L, Shen H, Ye J, Wang C. Internal exposure potential of water-soluble organic molecules in urban PM 2.5 evaluated by non-covalent adductome of human serum albumin. Environ Int 2024; 184:108492. [PMID: 38350258 DOI: 10.1016/j.envint.2024.108492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 02/06/2024] [Accepted: 02/06/2024] [Indexed: 02/15/2024]
Abstract
Water-soluble organic molecules (WSOMs) in inhaled PM2.5 can readily translocate from the lungs into the blood circulation, facilitating their distribution to and health effects on distant organs and tissues in the human body. Human serum albumin (HSA), the most abundant protein carrier in the blood, readily binds exogenous substances to form non-covalent adducts and subsequently transports them throughout the circulatory system, thereby indicating their internal exposure. The direct internal exposure of WSOMs in PM2.5 needs to be understood. In this study, the non-covalent HSA-WSOM adductome was developed as a dosimeter to evaluate the internal exposure potential of WSOMs in urban PM2.5. The WSOM composition was acquired from non-target high-resolution mass spectrometry analysis coupled with multiple ionizations. The binding level of HSA-WSOM non-covalent adducts was obtained from surface plasma resonance. Machine learning combined WSOM composition and the binding level of HSA-WSOM non-covalent adducts to screen bindable (also internalizable) WSOMs. The concentration of WSOM ranged from 4 to 13 μg/m3 during our observation period. Of the 17,513 mass spectral features detected, 9,484 contributed to the non-covalent adductome and possessed the internal exposure potential. 102 major contributors accounted for 90.6 % of the HSA-WSOM binding level. The fraction of internalizable WSOMs in PM2.5 varied from 11.9 % to 61.3 %, averaging 26.2 %. WSOMs that have internal exposure potential were primarily lignin-like and lipid-like substances. The HSA-WSOMs non-covalent adductome represents direct internal exposure potential, which can provide crucial insights into the molecular diagnosis of PM2.5 exposure and precise assessments of PM2.5 health effects.
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Affiliation(s)
- Yaling Zeng
- 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, Shenzhen 518055, China
| | - Antai 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, 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, Shenzhen 518055, China.
| | - Chunbo Xing
- 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, Shenzhen 518055, China
| | - Jinghao Zhai
- 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, Shenzhen 518055, China
| | - Yixiang 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, Shenzhen 518055, China
| | - Baohua Cai
- 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, Shenzhen 518055, China
| | - Shao Shi
- 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, Shenzhen 518055, China
| | - Yujie 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, Shenzhen 518055, China
| | - Zhenxing Shen
- Department of Environmental Science and Engineering, Xi'an Jiaotong University, Xi'an 710049, 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, 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, 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, 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, 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, Shenzhen 518055, China
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7
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Xu P, Li G, Zheng Y, Fung JCH, Chen A, Zeng Z, Shen H, Hu M, Mao J, Zheng Y, Cui X, Guo Z, Chen Y, Feng L, He S, Zhang X, Lau AKH, Tao S, Houlton BZ. Fertilizer management for global ammonia emission reduction. Nature 2024; 626:792-798. [PMID: 38297125 DOI: 10.1038/s41586-024-07020-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 01/03/2024] [Indexed: 02/02/2024]
Abstract
Crop production is a large source of atmospheric ammonia (NH3), which poses risks to air quality, human health and ecosystems1-5. However, estimating global NH3 emissions from croplands is subject to uncertainties because of data limitations, thereby limiting the accurate identification of mitigation options and efficacy4,5. Here we develop a machine learning model for generating crop-specific and spatially explicit NH3 emission factors globally (5-arcmin resolution) based on a compiled dataset of field observations. We show that global NH3 emissions from rice, wheat and maize fields in 2018 were 4.3 ± 1.0 Tg N yr-1, lower than previous estimates that did not fully consider fertilizer management practices6-9. Furthermore, spatially optimizing fertilizer management, as guided by the machine learning model, has the potential to reduce the NH3 emissions by about 38% (1.6 ± 0.4 Tg N yr-1) without altering total fertilizer nitrogen inputs. Specifically, we estimate potential NH3 emissions reductions of 47% (44-56%) for rice, 27% (24-28%) for maize and 26% (20-28%) for wheat cultivation, respectively. Under future climate change scenarios, we estimate that NH3 emissions could increase by 4.0 ± 2.7% under SSP1-2.6 and 5.5 ± 5.7% under SSP5-8.5 by 2030-2060. However, targeted fertilizer management has the potential to mitigate these increases.
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Affiliation(s)
- Peng Xu
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
- Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin, China
| | - Geng Li
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China
- Division of Emerging Interdisciplinary Areas, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Yi Zheng
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China.
- State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China.
- Shenzhen Municipal Engineering Lab of Environmental IoT Technologies, Southern University of Science and Technology, Shenzhen, China.
| | - Jimmy C H Fung
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China.
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China.
| | - Anping Chen
- Department of Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA
| | - Zhenzhong Zeng
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Huizhong Shen
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Min Hu
- State Joint Key Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Jiafu Mao
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Yan Zheng
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Xiaoqing Cui
- School of Grassland Science, Beijing Forestry University, Beijing, China
| | - Zhilin Guo
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Yilin Chen
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Lian Feng
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Shaokun He
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Xuguo Zhang
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Alexis K H Lau
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China
- Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Shu Tao
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Benjamin Z Houlton
- Department of Ecology and Evolutionary Biology and Department of Global Development, Cornell University, Ithaca, NY, USA
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8
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Chen K, Wang T, Tong X, Song Y, Hong J, Sun Y, Zhuang Y, Shen H, Yao XI. Osteoporosis is associated with depression among older adults: a nationwide population-based study in the USA from 2005 to 2020. Public Health 2024; 226:27-31. [PMID: 37988825 DOI: 10.1016/j.puhe.2023.10.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 09/27/2023] [Accepted: 10/11/2023] [Indexed: 11/23/2023]
Abstract
OBJECTIVES The global prevalence of osteoporosis is rising, yet it is unclear whether people with osteoporosis have a higher risk of depression than those without osteoporosis. STUDY DESIGN A cross-sectional study. METHODS We used nationally representative data from the US National Health and Nutrition Examination Survey (NHANES) in 2005-2006, 2007-2008, 2009-2010, 2013-2014, and 2017-2020. The diagnosis of osteoporosis was based on the bone mineral density of the femoral neck measured by dual-energy X-ray absorptiometry. Depression was assessed by the Patient Health Questionnaire-9 (PHQ-9), with a score ≥5 as depressive symptoms and a score ≥10 as probable depression. We used logistic regression models to evaluate the association between osteoporosis and depressive symptoms and probable depression. RESULTS We included 11,603 adults (aged 50 years and older, 52.3% male) and observed 5.2% of them had osteoporosis. 31.9% of these osteoporotic people had depressive symptoms, and 10.0% had probable depression. Compared to participants without osteoporosis, those with osteoporosis were 1.73 times more likely to experience depressive symptoms (odds ratio [OR] = 1.73, 95% confidence interval [CI] 1.20-2.50) and 1.91 times more likely to experience probable depression (OR = 1.91, 95% CI 1.02-3.59), after adjusting for sex, age, race/ethnicity, education, marital status, family income, body mass index, smoking, physical activity, and alcohol abuse. Moderate-to-vigorous activities mediated the associations between osteoporosis and depression and depressive symptoms. CONCLUSIONS Osteoporosis is an independent risk factor for depression. This study highlights the need to evaluate the mental well-being of patients with osteoporosis in clinical and primary health care.
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Affiliation(s)
- K Chen
- Department of Orthopaedics, The Eighth Affiliated Hospital, Sun Yat-sen University, 3025 Shennan Road, Shenzhen 518000, PR China.
| | - T Wang
- Department of Orthopaedics, The Eighth Affiliated Hospital, Sun Yat-sen University, 3025 Shennan Road, Shenzhen 518000, PR China.
| | - X Tong
- Department of Orthopaedics, The Eighth Affiliated Hospital, Sun Yat-sen University, 3025 Shennan Road, Shenzhen 518000, PR China.
| | - Y Song
- Department of Orthopaedics, The Eighth Affiliated Hospital, Sun Yat-sen University, 3025 Shennan Road, Shenzhen 518000, PR China.
| | - J Hong
- Department of Orthopaedics, The Eighth Affiliated Hospital, Sun Yat-sen University, 3025 Shennan Road, Shenzhen 518000, PR China.
| | - Y Sun
- Department of Preventive Medicine, The University of Tennessee Health Science Center, Memphis, TN 38163, USA.
| | - Y Zhuang
- Department of Orthopaedics, The Eighth Affiliated Hospital, Sun Yat-sen University, 3025 Shennan Road, Shenzhen 518000, PR China.
| | - H Shen
- Department of Orthopaedics, The Eighth Affiliated Hospital, Sun Yat-sen University, 3025 Shennan Road, Shenzhen 518000, PR China; Department of Clinical Research, The Eighth Affiliated Hospital, Sun Yat-sen University, 3025 Shennan Road, Shenzhen 518000, PR China.
| | - X I Yao
- Department of Orthopaedics, The Eighth Affiliated Hospital, Sun Yat-sen University, 3025 Shennan Road, Shenzhen 518000, PR China; Department of Clinical Research, The Eighth Affiliated Hospital, Sun Yat-sen University, 3025 Shennan Road, Shenzhen 518000, PR China.
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9
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He J, Shen H, Lei T, Chen Y, Meng J, Sun H, Li M, Wang C, Ye J, Zhu L, Zhou Z, Shen G, Guan D, Fu TM, Yang X, Tao S. Investigation of Plant-Level Volatile Organic Compound Emissions from Chemical Industry Highlights the Importance of Differentiated Control in China. Environ Sci Technol 2023; 57:21295-21305. [PMID: 38064660 DOI: 10.1021/acs.est.3c08570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
The chemical industry is a significant source of nonmethane volatile organic compounds (NMVOCs), pivotal precursors to ambient ozone (O3), and secondary organic aerosol (SOA). Despite their importance, precise estimation of these emissions remains challenging, impeding the implementation of NMVOC controls. Here, we present the first comprehensive plant-level assessment of NMVOC emissions from the chemical industry in China, encompassing 3461 plants, 127 products, and 50 NMVOC compounds from 2010 to 2019. Our findings revealed that the chemical industry in China emitted a total of 3105 (interquartile range: 1179-8113) Gg of NMVOCs in 2019, with a few specific products accounting for the majority of the emissions. Generally, plants engaged in chemical fibers production or situated in eastern China pose a greater risk to public health due to their higher formation potentials of O3 and SOA or their proximity to residential areas or both. We demonstrated that targeting these high-risk plants for emission reduction could enhance health benefits by 7-37% per unit of emission reduction on average compared to the current situation. Consequently, this study provides essential insights for developing effective plant-specific NMVOC control strategies within China's chemical industry.
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Affiliation(s)
- Jinling He
- 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
- 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
| | - Huizhong Shen
- 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
- 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
| | - Tianyang Lei
- Department of Earth System Sciences, Tsinghua University, Beijing 100080, China
| | - Yilin Chen
- 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
- 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
- School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
| | - Jing Meng
- The Bartlett School of Sustainable Construction, University College London, London WC1E 7HB, U.K
| | - Haitong Sun
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1 EW, U.K
- Centre for Sustainable Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117609, Republic of Singapore
| | - Mei Li
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for On-line Source Apportionment System of Air Pollution, Jinan University, Guangzhou 510632, China
| | - Chen Wang
- 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
- 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
| | - Jianhuai Ye
- 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
- 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
| | - Lei Zhu
- 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
- 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
| | - Zhihua Zhou
- Shenzhen Ecological and Environmental Monitoring Center of Guangdong Province, Shenzhen 518055, China
| | - Guofeng Shen
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Dabo Guan
- Department of Earth System Sciences, Tsinghua University, Beijing 100080, China
| | - Tzung-May Fu
- 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
- 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
| | - Xin Yang
- 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
- 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
| | - Shu Tao
- 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
- 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
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
- Institute of Carbon Neutrality, Peking University, Beijing 100871, China
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10
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Zhang Y, Wu P, Xu R, Wang X, Lei L, Schartup AT, Peng Y, Pang Q, Wang X, Mai L, Wang R, Liu H, Wang X, Luijendijk A, Chassignet E, Xu X, Shen H, Zheng S, Zeng EY. Author Correction: Plastic waste discharge to the global ocean constrained by seawater observations. Nat Commun 2023; 14:8409. [PMID: 38110420 PMCID: PMC10728108 DOI: 10.1038/s41467-023-43910-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2023] Open
Affiliation(s)
- Yanxu Zhang
- School of Atmospheric Sciences, Nanjing University, 210023, Nanjing, China.
- Frontiers Science Center for Critical Earth Material Cycling, Nanjing University, 210023, Nanjing, China.
| | - Peipei Wu
- School of Atmospheric Sciences, Nanjing University, 210023, Nanjing, China
| | - Ruochong Xu
- School of Atmospheric Sciences, Nanjing University, 210023, Nanjing, China
| | - Xuantong Wang
- School of Atmospheric Sciences, Nanjing University, 210023, Nanjing, China
| | - Lili Lei
- School of Atmospheric Sciences, Nanjing University, 210023, Nanjing, China.
| | - Amina T Schartup
- Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA
| | - Yiming Peng
- School of Atmospheric Sciences, Nanjing University, 210023, Nanjing, China
| | - Qiaotong Pang
- School of Atmospheric Sciences, Nanjing University, 210023, Nanjing, China
| | - Xinle Wang
- School of Atmospheric Sciences, Nanjing University, 210023, Nanjing, China
| | - Lei Mai
- Center for Environmental Microplastics Studies, Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University, 511443, Guangzhou, China
| | - Ruwei Wang
- Center for Environmental Microplastics Studies, Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University, 511443, Guangzhou, 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, China
| | - Xiaotong Wang
- 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, China
| | - Arjen Luijendijk
- Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, Netherlands
- Hydraulic Engineering, Deltares, Delft, Netherlands
| | - Eric Chassignet
- Center for Ocean-Atmospheric Prediction Studies (COAPS), Florida State University, Tallahassee, FL, USA
| | - Xiaobiao Xu
- Center for Ocean-Atmospheric Prediction Studies (COAPS), Florida State University, Tallahassee, FL, USA
| | - Huizhong Shen
- School of Environmental Science and Technology, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Shuxiu Zheng
- College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Eddy Y Zeng
- Center for Environmental Microplastics Studies, Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University, 511443, Guangzhou, China.
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11
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Liang X, Wang L, Du W, Chen Y, Yun X, Chen Y, Shen G, Shen H, Yang X, Tao S. Emission factors of oxygenated polycyclic aromatic hydrocarbons from ships in China. Environ Pollut 2023; 337:122483. [PMID: 37669698 DOI: 10.1016/j.envpol.2023.122483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 08/10/2023] [Accepted: 08/29/2023] [Indexed: 09/07/2023]
Abstract
The rapid growth of maritime traffic, transportation, and fishery activities has increased shipping emissions and degraded the air quality in coastal areas. As a result, controlling ocean-based pollution sources have become increasingly important. This study investigated the real-world emission characteristics of oxygenated polycyclic aromatic hydrocarbons (OPAHs, a group of highly toxic semi-volatile organic compounds) from five types of offshore ships using diesel oil: small and medium fishing ships, tug boats, ferry, and engineering ships, under various driving mode. Both gaseous and particle emission factors (EF) of four specific OPAHs were determined in our study. Among the OPAHs species emitted from ships, 9-fluorenone (9FO; 72%) and anthrathrace-9,10-quinone (ATQ; 25%) were the most abundant. The arithmetic mean of the sum of gaseous OPAHs EFs for all ships in this study was 2.5 ± 4.4 mg/kg fuel burned, and the mean particulate OPAHs EF was 4.7 ± 7.9 mg/kg. Small fishing ships had the highest total OPAHs EFs (31.0 ± 17.0 mg/kg). Apart from small fishing ships, there was no significant difference in the total EF of OPAHs for the other four types of ships. The emissions of the four OPAHs are predominantly in the particulate phase. There were no significant differences in the emissions of the four OPAHs under different driving mode. According to estimates, the annual OPAH emissions from the four types of ships in Hainan in 2017 were approximately 4.2 (range: 2.7-7.0) tons, dwarfing the OPAH emissions from diesel-powered on-road vehicles in China (23.5 kg).
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Affiliation(s)
- Xuyang Liang
- 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; 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
| | - Lizhi Wang
- College of Ecology and Environment, Hainan University, Haikou, 570228, China; College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing, 100871, China; Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province, Hainan University, Haikou, 570228, China.
| | - Wei Du
- Faculty of Environmental Science and Engineering, Kunming University of Science & Technology, Kunming, 650500, China
| | - Yuanchen Chen
- College of Environment, Research Centre of Environmental Science, Zhejiang University of Technology, Hangzhou, 310032, China
| | - Xiao Yun
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing, 100871, China
| | - Yilin Chen
- 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; 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
| | - Guofeng Shen
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing, 100871, China
| | - Huizhong Shen
- 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; 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
| | - Xin Yang
- 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; 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
| | - Shu Tao
- 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; 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; College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing, 100871, China; Institute of Carbon Neutrality, Peking University, Beijing, 100871, China
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12
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Jiang K, Xing R, Luo Z, Li J, Men Y, Shen H, Shen G, Tao S. Trends in air pollutants emissions in the Qinghai-Tibet Plateau and its surrounding areas under different socioeconomic scenarios. Sci Total Environ 2023; 899:165745. [PMID: 37495127 DOI: 10.1016/j.scitotenv.2023.165745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/11/2023] [Accepted: 07/21/2023] [Indexed: 07/28/2023]
Abstract
The Qinghai-Tibetan Plateau (QTP) and its surrounding areas are undergoing rapid changes in socioeconomic conditions, activity sectors, and emission levels. These changes underscore the significance of conducting local environmental assessments in the future and generating air pollutant emission forecasts necessary for effective evaluation. Current pollutants emissions pathways exhibit regional limitation since their based historical inventory could not accurately reflect the emission characteristics in QTP. This study constructed a high spatial resolution (0.1° × 0.1°) atmospheric pollutant emissions dataset in the Qinghai-Tibet Plateau and its surrounding Areas (QTPA) based on updated emission inventory and various socioeconomic scenarios. We found that the pollutant emissions levels are distinct among different social development scenarios, with SSP3-7.0 demonstrating the highest magnitude of emissions. Regional and sectoral contributions exhibit substantial variations. Notably, solid fuel combustion originating from residential sectors in Northeast India and open fires in Myanmar are identified as high-density sources of PM2.5 emissions. Current pollutant emission patterns in the QTPA are more akin to SSP2-4.5, however, specific regions such as Qinghai and Tibet have exhibited more pronounced trends of emission reduction. The comparison with previous datasets reveals that the predicted pollutant emissions in this study are lower than Scenario Model Intercomparison Project (SMIP) dataset but higher than Asian-Pacific Integrated Model (AIM) dataset due to the revised inventory data and model variations, in which the latter might be the main obstacle to accurate emissions prediction.
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Affiliation(s)
- Ke Jiang
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Ran Xing
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Zhihan Luo
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Jin Li
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Yatai Men
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Huizhong Shen
- School of Environmental Sciences and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Guofeng Shen
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China; Institute of Carbon Neutrality, Peking University, Beijing 100871, China; School of Ecology and Environment, Zhengzhou University, Zhengzhou 45001, China.
| | - Shu Tao
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China; Institute of Carbon Neutrality, Peking University, Beijing 100871, China; School of Environmental Sciences and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
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13
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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: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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14
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Zhang J, Shen H, Chen Y, Meng J, Li J, He J, Guo P, Dai R, Zhang Y, Xu R, Wang J, Zheng S, Lei T, Shen G, Wang C, Ye J, Zhu L, Sun HZ, Fu TM, Yang X, Guan D, Tao S. Iron and Steel Industry Emissions: A Global Analysis of Trends and Drivers. Environ Sci Technol 2023; 57:16477-16488. [PMID: 37867432 PMCID: PMC10621597 DOI: 10.1021/acs.est.3c05474] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 10/06/2023] [Accepted: 10/10/2023] [Indexed: 10/24/2023]
Abstract
The iron and steel industry (ISI) is important for socio-economic progress but emits greenhouse gases and air pollutants detrimental to climate and human health. Understanding its historical emission trends and drivers is crucial for future warming and pollution interventions. Here, we offer an exhaustive analysis of global ISI emissions over the past 60 years, forecasting up to 2050. We evaluate emissions of carbon dioxide and conventional and unconventional air pollutants, including heavy metals and polychlorinated dibenzodioxins and dibenzofurans. Based on this newly established inventory, we dissect the determinants of past emission trends and future trajectories. Results show varied trends for different pollutants. Specifically, PM2.5 emissions decreased consistently during the period 1970 to 2000, attributed to adoption of advanced production technologies. Conversely, NOx and SO2 began declining recently due to stringent controls in major contributors such as China, a trend expected to persist. Currently, end-of-pipe abatement technologies are key to PM2.5 reduction, whereas process modifications are central to CO2 mitigation. Projections suggest that by 2050, developing nations (excluding China) will contribute 52-54% of global ISI PM2.5 emissions, a rise from 29% in 2019. Long-term emission curtailment will necessitate the innovation and widespread adoption of new production and abatement technologies in emerging economies worldwide.
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Affiliation(s)
- Jinjian Zhang
- 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
- 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
| | - Huizhong Shen
- 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
- 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
| | - Yilin Chen
- 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
- 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
- School
of Urban Planning and Design, Peking University, Shenzhen Graduate School, Shenzhen 518055, China
| | - Jing Meng
- The
Bartlett School of Sustainable Construction, University College London, London WC1E 7HB, U.K.
| | - Jin Li
- College
of Urban and Environmental Sciences, Peking
University, Beijing 100871, China
| | - Jinling He
- 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
- 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
| | - Peng Guo
- 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
- 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
| | - Rong Dai
- College
of Urban and Environmental Sciences, Peking
University, Beijing 100871, China
| | - Yuanzheng Zhang
- College
of Urban and Environmental Sciences, Peking
University, Beijing 100871, China
| | - Ruibin Xu
- 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
- 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
| | - Jinghang Wang
- College
of Urban and Environmental Sciences, Peking
University, Beijing 100871, China
| | - Shuxiu Zheng
- College
of Urban and Environmental Sciences, Peking
University, Beijing 100871, China
| | - Tianyang Lei
- Department
of Earth System Sciences, Tsinghua University, Beijing 100080, China
| | - Guofeng Shen
- College
of Urban and Environmental Sciences, Peking
University, Beijing 100871, China
| | - Chen Wang
- 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
- 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
| | - Jianhuai Ye
- 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
- 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
| | - Lei Zhu
- 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
- 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
| | - Haitong Zhe Sun
- Centre
for Atmospheric Science, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1 EW, U.K.
| | - Tzung-May Fu
- 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
- 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
| | - Xin Yang
- 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
- 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
| | - Dabo Guan
- Department
of Earth System Sciences, Tsinghua University, Beijing 100080, China
| | - Shu Tao
- 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
- 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
- College
of Urban and Environmental Sciences, Peking
University, Beijing 100871, China
- Institute
of Carbon Neutrality, Peking University, Beijing 100871, China
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15
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Cai J, Jiang H, Chen Y, Liu Z, Han Y, Shen H, Song J, Li J, Zhang Y, Wang R, Chen J, Zhang G. Char dominates black carbon aerosol emission and its historic reduction in China. Nat Commun 2023; 14:6444. [PMID: 37833278 PMCID: PMC10575950 DOI: 10.1038/s41467-023-42192-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 10/02/2023] [Indexed: 10/15/2023] Open
Abstract
Emission factors and inventories of black carbon (BC) aerosols are crucial for estimating their adverse atmospheric effect. However, it is imperative to separate BC emissions into char and soot subgroups due to their significantly different physicochemical properties and potential effects. Here, we present a substantial dataset of char and soot emission factors derived from field and laboratory measurements. Based on the latest results of the char-to-soot ratio, we further reconstructed the emission inventories of char and soot for the years 1960-2017 in China. Our findings indicate that char dominates annual BC emissions and its huge historical reduction, which can be attributable to the rapid changes in energy structure, combustion technology and emission standards in recent decades. Our results suggest that further BC emission reductions in both China and the world should focus on char, which mainly derives from lower-temperature combustion and is easier to decrease compared to soot.
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Affiliation(s)
- Junjie Cai
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai, 200438, China
| | - Hongxing Jiang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai, 200438, China
| | - Yingjun Chen
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai, 200438, China.
- Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, China.
| | - Zeyu Liu
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai, 200438, China
| | - Yong Han
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai, 200438, China
| | - Huizhong Shen
- 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
| | - Jianzhong Song
- State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Science, Guangzhou, 510640, China
| | - Jun Li
- State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Science, Guangzhou, 510640, China
| | - Yanlin Zhang
- Yale-NUIST Center on Atmospheric Environment, International Joint Laboratory on Climate and Environment Change (ILCEC), Nanjing University of Information Science and Technology, Nanjing, 210044, China
- Jiangsu Provincial Key Laboratory of Agricultural Meteorology, College of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Rong Wang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai, 200438, China
| | - Jianmin Chen
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai, 200438, China
| | - Gan Zhang
- State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, China.
- Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Science, Guangzhou, 510640, China.
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16
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Zhang G, Shen H, Long Y, Lin Y, Chen RC, Gao H. A New Treatment Planning Method for Efficient Proton ARC Therapy with Direct Minimization of Number of Energy Jumps. Int J Radiat Oncol Biol Phys 2023; 117:e716. [PMID: 37786092 DOI: 10.1016/j.ijrobp.2023.06.2220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) The optimization of energy layer distributions is crucial for efficient proton ARC therapy: on one hand, a sufficient number of energy layers is needed to ensure the plan quality; on the other hand, an excess number of energy jumps can substantially slow down the treatment delivery. This work will develop a new treatment plan optimization method with direct minimization of number of energy jumps (NEJ), which will be shown to outperform state-of-the-art methods in both plan quality and delivery efficiency. MATERIALS/METHODS The proposed method jointly optimizes the plan quality and minimizes the NEJ. To minimize NEJ, (1) the proton spots x is summed per energy layer to form the energy vector y; (2) y is binarized via sigmoid transform into y1; (3) y1 is multiplied with a predefined energy order vector via dot product into y2; (4) y2 is filtered through the finite-differencing kernel into y3 in order to identify NEJ; (5) only the NEJ of y3 is penalized, while x is optimized for plan quality. The solution algorithm to this new method is based on iterative convex relaxation. RESULTS The new method is validated in comparison with state-of-the-art methods called energy sequencing (ES) method and energy matrix (EM) method. In terms of delivery efficiency, the new method had fewer NEJ, less energy switching time, and generally less total delivery time. In terms of plan quality, the new method had smaller optimization objective values, lower normal tissue dose, and generally better target coverage. A head-and-neck case is provided in the table with the following dosimetric parameters: planning objective value F; conformity index CI; homogeneity index HI; mean dose of larynx DOAR; mean body dose Dbody; the unit of dose is Gy. CONCLUSION We have developed a new treatment plan optimization method with direct minimization of NEJ, and demonstrated that this new method outperformed state-of-the-art methods (ES and EM) in both plan quality and delivery efficiency.
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Affiliation(s)
- G Zhang
- University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, China
| | - H Shen
- University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, China; Department of Radiation Oncology, University of Kansas Medical Center, Kansas City, KS
| | - Y Long
- University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Y Lin
- Department of Radiation Oncology, University of Kansas Medical Center, Kansas City, KS
| | - R C Chen
- Department of Radiation Oncology, University of Kansas Medical Center, Kansas City, KS
| | - H Gao
- Department of Radiation Oncology, University of Kansas Medical Center, Kansas City, KS
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17
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Zhai J, Yu G, Zhang J, Shi S, Yuan Y, Jiang S, Xing C, Cai B, Zeng Y, Wang Y, Zhang A, Zhang Y, Fu TM, Zhu L, Shen H, Ye J, Wang C, Tao S, Li M, Zhang Y, Yang X. Impact of Ship Emissions on Air Quality in the Greater Bay Area in China under the Latest Global Marine Fuel Regulation. Environ Sci Technol 2023; 57:12341-12350. [PMID: 37552529 DOI: 10.1021/acs.est.3c03950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/09/2023]
Abstract
As the main anthropogenic source in open seas and coastal areas, ship emissions impact the climate, air quality, and human health. The latest marine fuel regulation with a sulfur content limit of 0.5% went into effect globally on January 1, 2020. Investigations of ship emissions after fuel switching are necessary. In this study, online field measurements at an urban coastal site and modeling simulations were conducted to detect the impact of ship emissions on air quality in the Greater Bay Area (GBA) in China under new fuel regulation. By utilizing a high mass-resolution single particle mass spectrometer, the vanadium(V) signal was critically identified and was taken as a robust indicator for ship-emitted particles (with relative peak area > 0.1). The considerable number fractions of high-V particles (up to 30-40% during ship plumes) indicated that heavy fuel oils via simple desulfurization or blending processes with low-sulfur fuels were extensively used in the GBA to meet the global 0.5% sulfur cap. Our results showed that ship-emitted particulate matter and NOx contributed up to 21.4% and 39.5% to the ambient, respectively, in the summertime, significantly affecting the air quality in the GBA. The sea-land breeze circulation also played an important role in the transport pattern of ship-emitted pollutants in the GBA.
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Affiliation(s)
- Jinghao Zhai
- 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-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China
| | - Guangyuan Yu
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
| | - Jingyi 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, Shenzhen 518055, China
| | - Shao Shi
- 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, Shenzhen 518055, China
| | - Yupeng Yuan
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
| | - Shenglan Jiang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
| | - Chunbo Xing
- 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, Shenzhen 518055, China
| | - Baohua Cai
- 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, Shenzhen 518055, China
| | - Yaling Zeng
- 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, Shenzhen 518055, China
| | - Yixiang 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, Shenzhen 518055, China
| | - Antai 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, Shenzhen 518055, China
| | - Yujie 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, 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, 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, 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, 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, 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, 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, Shenzhen 518055, China
| | - Mei Li
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for On-line Source Apportionment System of Air Pollution, Jinan University, Guangzhou 510632, China
| | - Yan Zhang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, 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, Shenzhen 518055, China
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18
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Liang JY, Jing Y, Shen H, Chen XJ, Luo WJ, Song Y, Wang Y, Hu JB, Yang SM, Wu FF, Li QF. [Distribution characteristics of plasma renin concentration in patients with aldosterone-producing adenoma]. Zhonghua Nei Ke Za Zhi 2023; 62:972-978. [PMID: 37528035 DOI: 10.3760/cma.j.cn112138-20230105-00007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/03/2023]
Abstract
Objective: To analyze the distribution characteristics of plasma renin concentration (PRC) in patients with aldosterone-producing adenoma (APA) and its impact on diagnosis. Methods: In this retrospective case series, clinical data from 200 patients with APA (80 men and 120 women; mean age 45.6 years) in the First Affiliated Hospital of Chongqing Medical University from November 2013 to January 2022 were evaluated. PRC was determined by automated chemiluminescence immunoassay. The distribution characteristics of PRC were analyzed, and 8.2 mU/L was used as the low renin cutoff to evaluate whether renin was suppressed. Results: The median PRC was 1.6 mU/L (range, 0.4-41.5 mU/L). There were 116 patients with APA with PRC of ≤2 mU/L, 41 patients with 2<PRC≤4 mU/L. PRC was not suppressed (PRC>8.2 mU/L) in 8.0% (16/200) of the patients with APA. And PRC was not suppressed in 2.5% (5/200) of the patients with APA, resulting in a primary aldosteronism negative screening outcome. Conclusions: Although most patients with APA have low PRC, there are a small number (8%) of patients whose PRC has not been fully suppressed, which can lead to missed diagnoses during primary aldosteronism screening. While primary aldosteronism is highly suspected, further investigations are required to determine the diagnosis, even if PRC is not fully suppressed at screening.
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Affiliation(s)
- J Y Liang
- Department of Endocrinology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Y Jing
- Department of Endocrinology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - H Shen
- Department of Endocrinology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - X J Chen
- Department of Endocrinology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - W J Luo
- Department of Endocrinology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Y Song
- Department of Endocrinology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Y Wang
- Department of Endocrinology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - J B Hu
- Department of Endocrinology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - S M Yang
- Department of Endocrinology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - F F Wu
- Department of Endocrinology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi 046099, China
| | - Q F Li
- Department of Endocrinology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
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19
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Fei J, Shen H, Yang SM, Du ZP, Hu JB, Wang HB, Qin GJ, Ji HF, Li QF, Song Y. [Establishment and validation of a nomogram-based predictive model for idiopathic aldosteronism]. Zhonghua Nei Ke Za Zhi 2023; 62:693-699. [PMID: 37263953 DOI: 10.3760/cma.j.cn112138-20221108-00836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Objective: To establish and validate a nomogram-based predictive model for idiopathic hyperaldosteronism (IHA). Methods: This cross-sectional study was conducted with the collected clinical and biochemical data of patients with primary aldosteronism (PA) including 249 patients with unilateral primary aldosteronism (UPA) and 107 patients with IHA, who were treated at the Department of Endocrinology of the First Affiliated Hospital of Chongqing Medical University from November 2013 to November 2022. Plasma aldosterone concentration (PAC) and plasma renin concentration (PRC) were measured by chemiluminescence. Stepwise regression analysis was applied to select the key predictors of IHA, and a nomogram-based scoring model was developed. The model was validated in another external independent cohort of patients with PA including 62 patients with UPA and 43 patients with IHA, who were diagnosed at the Department of Endocrinology, First Affiliated Hospital of Zhengzhou University. An independent-sample t test, Mann-Whitney U test, and χ2 test were used for statistical analysis. Results: In the training cohort, in comparison with the UPA group, the IHA group showed a higher serum potassium level [M(Q1, Q3), 3.4 (3.1, 3.8) mmol/L vs. 2.7 (2.1, 3.1) mmol/L] and higher PRC [4.0 (2.1, 8.2) mU/L vs. 1.5 (0.6, 3.4) mU/L] and a lower PAC post-saline infusion test (SIT) [305 (222, 416) pmol/L vs. 720 (443, 1 136) pmol/L] and a lower rate of unilateral adrenal nodules [33.6% (36/107) vs. 81.1% (202/249)]; the intergroup differences in these measurements were statistically significant (all P<0.001). Serum potassium level, PRC, PAC post-SIT, and the rate of unilateral adrenal nodules showed similar performance in the IHA group in the validation cohort. After stepwise regression analysis for all significant variables in the training cohort, a scoring model based on a nomogram was constructed, and the predictive parameters included the rate of unilateral adrenal nodules, serum potassium concentration, PAC post-SIT, and PRC in the standing position. When the total score was ≥14, the model showed a sensitivity of 0.65 and specificity of 0.90 in the training cohort and a sensitivity of 0.56 and specificity of 1.00 in the validation cohort. Conclusion: The nomogram was used to successfully develop a model for prediction of IHA that could facilitate selection of patients with IHA who required medication directly.
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Affiliation(s)
- J Fei
- Department of Endocrinology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - H Shen
- Department of Endocrinology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - S M Yang
- Department of Endocrinology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Z P Du
- Department of Endocrinology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - J B Hu
- Department of Endocrinology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - H B Wang
- Department of Endocrinology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - G J Qin
- Department of Endocrinology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - H F Ji
- Department of Endocrinology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Q F Li
- Department of Endocrinology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Y Song
- Department of Endocrinology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
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20
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Meng W, Zhu L, Liang Z, Xu H, Zhang W, Li J, Zhang Y, Luo Z, Shen G, Shen H, Chen Y, Cheng H, Ma J, Tao S. Significant but Inequitable Cost-Effective Benefits of a Clean Heating Campaign in Northern China. Environ Sci Technol 2023. [PMID: 37256786 DOI: 10.1021/acs.est.2c07492] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Residential emissions significantly contribute to air pollution. To address this issue, a clean heating campaign was implemented to replace coal with electricity or natural gas among 13.9 million rural households in northern China. Despite great success, the cost-benefits and environmental equity of this campaign have never been fully investigated. Here, we modeled the environmental and health benefits, as well as the total costs of the campaign, and analyzed the inequality and inequity. We found that even though the campaign decreased only 1.1% of the total energy consumption, PM2.5 emissions and PM2.5 exposure experienced 20% and 36% reduction, respectively, revealing the amplification effects along the causal pathway. Furthermore, the number of premature deaths attributable to residential emissions reduced by 32%, suggesting that the campaign was highly beneficial. Governments and residents shared the cost of 2,520 RMB/household. However, the benefits and the costs were unevenly distributed, as the residents in mountainous areas were not only less benefited from the campaign but also paid more because of the higher costs, resulting in a notably lower cost-effectiveness. Moreover, villages in less developed areas tended to choose natural gas with a lower initial investment but a higher total cost (2,720 RMB/household) over electricity (2,190 RMB/household). With targeted investment and subsidies in less developed areas and the promotion of electricity and other less expensive alternatives, the multidevelopment goals of improved air quality, reduced health impacts, and reduced inequity in future clean heating interventions could be achieved.
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Affiliation(s)
- Wenjun Meng
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Lei Zhu
- School of Economics and Management, Beihang University, Beijing 100191, P. R. China
| | - Zhuang Liang
- School of Economics and Management, Beihang University, Beijing 100191, P. R. China
| | - Haoran Xu
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Wenxiao Zhang
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Jin Li
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Yuanzheng Zhang
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Zhihan Luo
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Guofeng Shen
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Huizhong Shen
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, P. R. China
| | - Yilin Chen
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, P. R. China
| | - Hefa Cheng
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Jianmin Ma
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Shu Tao
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, P. R. China
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Ko CC, Merodio MM, Spronk E, Lehman JR, Shen H, Li G, Derscheid RJ, Piñeyro PE. Diagnostic investigation of Mycoplasma hyorhinis as a potential pathogen associated with neurological clinical signs and central nervous system lesions in pigs. Microb Pathog 2023; 180:106172. [PMID: 37230257 DOI: 10.1016/j.micpath.2023.106172] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 05/23/2023] [Accepted: 05/23/2023] [Indexed: 05/27/2023]
Abstract
Mycoplasma hyorhinis (M. hyorhinis) is a commensal of the upper respiratory tract in swine with the typical clinical presentations of arthritis and polyserositis in postweaning pigs. However, it has also been associated with conjunctivitis and otitis media, and recently has been isolated from meningeal swabs and/or cerebrospinal fluid of piglets with neurological signs. The objective of this study is to evaluate the role of M. hyorhinis as a potential pathogen associated with neurological clinical signs and central nervous system lesions in pigs. The presence of M. hyorhinis was evaluated in a clinical outbreak and a six-year retrospective study by qPCR detection, bacteriological culture, in situ hybridization (RNAscope®), and phylogenetic analysis and with immunohistochemistry characterization of the inflammatory response associated with its infection. M. hyorhinis was confirmed by bacteriological culture and within central nervous system lesions by in situ hybridization on animals with neurological signs during the clinical outbreak. The isolates from the brain had close genetic similarities from those previously reported and isolated from eye, lung, or fibrin. Nevertheless, the retrospective study confirmed by qPCR the presence of M. hyorhinis in 9.9% of cases reported with neurological clinical signs and histological lesions of encephalitis or meningoencephalitis of unknown etiology. M. hyorhinis mRNA was confirmed within cerebrum, cerebellum, and choroid plexus lesions by in situ hybridization (RNAscope®) with a positive rate of 72.7%. Here we present strong evidence that M. hyorhinis should be included as a differential etiology in pigs with neurological signs and central nervous system inflammatory lesions.
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Affiliation(s)
- Calvin C Ko
- Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA, USA
| | - Maria M Merodio
- Department of Biomedical Sciences, College of Veterinary Medicine, Iowa State University, Ames, IA, USA
| | - E Spronk
- Swine Vet Center P.A., 1608 South Minnesota Avenue, St. Peter, Minnesota, USA
| | - J R Lehman
- Swine Technical Services, Merck Animal Health, Lenexa, KS, USA
| | - H Shen
- Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA, USA
| | - G Li
- Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA, USA
| | - Rachel J Derscheid
- Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA, USA
| | - Pablo E Piñeyro
- Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA, USA.
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Zhang C, Huang L, Tang Y, Wang P, Chen Y, Zhang L, Shen H, Yu Y, Tian X, Wang Y. [Identification and verification of α-11 giardin-interacting protein]. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi 2023; 35:155-162. [PMID: 37253564 DOI: 10.16250/j.32.1374.2022288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
OBJECTIVE To identify and verify the interacting protein of α-11 giardin, so as provide the experimental evidence for studies on the α-11 giardin function. METHODS The yeast two-hybrid cDNA library of the Giardia lambia C2 strain and the bait plasmid of α-11 giardin were constructed. All proteins interacting with α-11 giardin were screened using the yeast two-hybrid system. α-11 giardin and all screened potential interacting protein genes were constructed into pBiFc-Vc-155 and pBiFc-Vn-173 plasmids, and co-transfected into the breast cancer cell line MDA-MB-231. The interactions between α-11 giardin and interacting proteins were verified using bimolecular fluorescence complementation (BiFC). RESULTS The yeast two-hybrid G. lambia cDNA library which was quantified at 2.715 × 107 colony-forming units (CFU) and the bait plasmid containing α-11 giardin gene without an autoactivation activity were constructed. Following two-round positive screening with the yeast two-hybrid system, two potential proteins interacting with α-11 giardin were screened, including eukaryotic translation initiation factor 5A (EIF5A), calmodulin-dependent protein kinase (CAMKL) and nicotinamide adenine dinucleotide phosphate-specific glutamate dehydrogenase (NADP-GDH), hypothetical protein 1 (GL50803_95880), hypothetical protein 2 (GL50803_87261) and a protein from Giardia canis virus. The α-11 giardin and EIF5A genes were transfected into the pBiFc-Vc-155 and pBiFc-Vn-173 plasmids using BiFC, and the recombinant plasmids pBiFc-Vc-155-α-11 and pBiFc-Vn-173-EIF5A were co-tranfected into MDA-MB-231 cells, which displayed green fluorescence under a microscope, indicating the interaction between α-11 giardin and EIF5A protein in cells. CONCLUSIONS The yeast two-hybrid cDNA library of the G. lambia C2 strain has been successfully constructed, and six potential protein interacting with α-11 giardin have been identified, including EIF5A that interacts with α-11 giardin in cells.
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Affiliation(s)
- C Zhang
- College of Life Sciences, North China University of Technology, Tangshan, Hebei 063000, China
| | - L Huang
- Hongci Hospital of Tangshan City, Hebei Province, China
| | - Y Tang
- College of Life Sciences, North China University of Technology, Tangshan, Hebei 063000, China
| | - P Wang
- College of Life Sciences, North China University of Technology, Tangshan, Hebei 063000, China
| | - Y Chen
- College of Life Sciences, North China University of Technology, Tangshan, Hebei 063000, China
| | - L Zhang
- College of Life Sciences, North China University of Technology, Tangshan, Hebei 063000, China
| | - H Shen
- College of Life Sciences, North China University of Technology, Tangshan, Hebei 063000, China
| | - Y Yu
- College of Life Sciences, North China University of Technology, Tangshan, Hebei 063000, China
| | - X Tian
- College of Life Sciences, North China University of Technology, Tangshan, Hebei 063000, China
| | - Y Wang
- College of Life Sciences, North China University of Technology, Tangshan, Hebei 063000, China
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Cattley RC, Kromhout H, Sun M, Tokar EJ, Abdallah MAE, Bauer AK, Broadwater KR, Campo L, Corsini E, Houck KA, Ichihara G, Matsumoto M, Morais S, Mráz J, Nomiyama T, Ryan K, Shen H, Toyoda T, Vähäkangas K, Yakubovskaya MG, Yu IJ, DeBono NL, de Conti A, El Ghissassi F, Madia F, Mattock H, Pasqual E, Suonio E, Wedekind R, Benbrahim-Tallaa L, Schubauer-Berigan MK. Carcinogenicity of anthracene, 2-bromopropane, butyl methacrylate, and dimethyl hydrogen phosphite. Lancet Oncol 2023; 24:431-432. [PMID: 36966774 DOI: 10.1016/s1470-2045(23)00141-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2023]
Affiliation(s)
| | - Hans Kromhout
- International Agency for Research on Cancer, Lyon, France
| | - Meng Sun
- International Agency for Research on Cancer, Lyon, France
| | - Erik J Tokar
- International Agency for Research on Cancer, Lyon, France
| | | | - Alison K Bauer
- International Agency for Research on Cancer, Lyon, France
| | | | - Laura Campo
- International Agency for Research on Cancer, Lyon, France
| | | | - Keith A Houck
- International Agency for Research on Cancer, Lyon, France
| | - Gaku Ichihara
- International Agency for Research on Cancer, Lyon, France
| | | | - Simone Morais
- International Agency for Research on Cancer, Lyon, France
| | - Jaroslav Mráz
- International Agency for Research on Cancer, Lyon, France
| | | | - Kristen Ryan
- International Agency for Research on Cancer, Lyon, France
| | - Huizhong Shen
- International Agency for Research on Cancer, Lyon, France
| | - Takeshi Toyoda
- International Agency for Research on Cancer, Lyon, France
| | | | | | - Il Je Yu
- International Agency for Research on Cancer, Lyon, France
| | | | - Aline de Conti
- International Agency for Research on Cancer, Lyon, France
| | | | - Federica Madia
- International Agency for Research on Cancer, Lyon, France
| | - Heidi Mattock
- International Agency for Research on Cancer, Lyon, France
| | - Elisa Pasqual
- International Agency for Research on Cancer, Lyon, France
| | - Eero Suonio
- International Agency for Research on Cancer, Lyon, France
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Jiang C, Pei C, Cheng C, Shen H, Zhang Q, Lian X, Xiong X, Gao W, Liu M, Wang Z, Huang B, Tang M, Yang F, Zhou Z, Li M. Emission factors and source profiles of volatile organic compounds from typical industrial sources in Guangzhou, China. Sci Total Environ 2023; 869:161758. [PMID: 36702262 DOI: 10.1016/j.scitotenv.2023.161758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 01/16/2023] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
Volatile organic compounds (VOCs) are important precursors of ozone (O3) and fine particulate matter (PM2.5). An accurate depiction of the emission characteristics of VOCs is the key to formulating VOC control strategies. In this study, the VOC emission factors and source profiles in five industrial sectors were developed using large-scale field measurements conducted in Guangzhou, China (100 samples for the emission factors and 434 samples for the source profile measurements). The emission factors based on the actual measurement method and the material balance method were 1.6-152.4 kg of VOCs per ton of raw materials (kg/t) and 3.1-242.2 kg/t, respectively. The similarities between the emission factors obtained using these two methods were examined, which showed a coefficient of divergence (CD) of 0.34-0.72. Among the 33 subdivided VOC source profiles developed in this study, sources including light guide plate (LGP), photoresist mask, and plastic products were the first time developed in China. Due to regional diversities in terms of production technologies, materials, and products, the emission characteristics of the VOCs varied, even in the same sector, thereby demonstrating the importance of developing localized source profiles of VOCs. The ozone formation potential (OFP) of the shipbuilding and repair sector from fugitive emissions was the highest value among all the industrial sectors. Controlling the emissions of aromatics and OVOCs was critical to reducing the O3 growth momentum in industrial sectors. In addition, 1,2-dibromoethane showed high carcinogenic risk potentials (CRPs) during most of the industrial sectors and should be prioritized for controlling.
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Affiliation(s)
- Chunyan Jiang
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for On-line Source Apportionment System of Air Pollution, Jinan University, Guangzhou, PR China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou, PR China
| | - Chenglei Pei
- Guangzhou Sub-branch of Guangdong Ecological and Environmental Monitoring Center, Guangzhou 510060, PR China
| | - Chunlei Cheng
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for On-line Source Apportionment System of Air Pollution, Jinan University, Guangzhou, PR China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou, PR China
| | - Huizhong Shen
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, PR China; Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen 518055, PR China
| | - Qianhua Zhang
- Guangzhou Sub-branch of Guangdong Ecological and Environmental Monitoring Center, Guangzhou 510060, PR China
| | - Xiufeng Lian
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for On-line Source Apportionment System of Air Pollution, Jinan University, Guangzhou, PR China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou, PR China
| | - Xin Xiong
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for On-line Source Apportionment System of Air Pollution, Jinan University, Guangzhou, PR China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou, PR China
| | - Wei Gao
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for On-line Source Apportionment System of Air Pollution, Jinan University, Guangzhou, PR China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou, PR China
| | - Ming Liu
- Guangzhou Hexin Instrument Co., Ltd, Guangzhou, PR China
| | - Zixin Wang
- Guangzhou Hexin Instrument Co., Ltd, Guangzhou, PR China
| | - Bo Huang
- Guangzhou Hexin Instrument Co., Ltd, Guangzhou, PR China
| | - Mei Tang
- Guangdong MS Institute of Scientific Instrument Innovation, Guangzhou, PR China
| | - Fan Yang
- Environmental Monitoring Station of Pudong New District, Shanghai, PR China
| | - Zhen Zhou
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for On-line Source Apportionment System of Air Pollution, Jinan University, Guangzhou, PR China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou, PR China
| | - Mei Li
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for On-line Source Apportionment System of Air Pollution, Jinan University, Guangzhou, PR China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou, PR China.
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Hu J, Tang X, Guo R, Wang Y, Shen H, Wang H, Yao Y, Cai X, Yu Z, Dong G, Liang F, Cao J, Zeng L, Su M, Kong W, Liu L, Huang W, Cai C, Xie Y, Mao W. 37P Pralsetinib in acquired RET fusion-positive advanced non-small cell lung cancer patients after resistance to EGFR/ALK-TKI: A China multi-center, real-world data (RWD) analysis. J Thorac Oncol 2023. [DOI: 10.1016/s1556-0864(23)00291-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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Chen X, He J, Shen H, Xi Y, Chen B, He X, Gao J, Yu H, Shen W. 97P Aumolertinib as adjuvant therapy in postoperative EGFR-mutated stage I–III non-small cell lung cancer with high-risk pathological factors. J Thorac Oncol 2023. [DOI: 10.1016/s1556-0864(23)00352-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2023]
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Song S, Chen B, Huang T, Ma S, Liu L, Luo J, Shen H, Wang J, Guo L, Wu M, Mao X, Zhao Y, Gao H, Ma J. Assessing the contribution of global wildfire biomass burning to BaP contamination in the Arctic. Environ Sci Ecotechnol 2023; 14:100232. [PMID: 36685748 PMCID: PMC9852607 DOI: 10.1016/j.ese.2022.100232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 12/15/2022] [Accepted: 12/16/2022] [Indexed: 06/17/2023]
Abstract
Polycyclic aromatic hydrocarbons (PAHs) have become cause for growing concern in the Arctic ecosystems, partly due to their stable levels despite global emission reduction. Wildfire is considered one of the primary sources that influence PAH levels and trends in the Arctic, but quantitative investigations of this influence are still lacking. This study estimates the global emissions of benzo[a]pyrene (BaP), a congener of PAHs with high carcinogenicity, from forest and grassland fires from 2001 to 2020 and simulates the contributions of wildfire-induced BaP emissions from different source regions to BaP contamination in the Arctic. We find that global wildfires contributed 29.3% to annual averaging BaP concentrations in the Arctic from 2001 to 2020. Additionally, we show that wildfires contributed significantly to BaP concentrations in the Arctic after 2011, enhancing it from 10.1% in 2011 to 83.9% in 2020. Our results reveal that wildfires accounted for 94.2% and 50.8% of BaP levels in the Asian Arctic during boreal summer and autumn, respectively, and 74.2% and 14.5% in the North American Arctic for the same seasons. The source-tagging approach identified that local wildfire biomass emissions were the largest source of BaP in the Arctic, accounting for 65.7% of its concentration, followed by those of Northern Asia (17.8%) and Northern North America (13.7%). Our findings anticipate wildfires to play a larger role in Arctic PAH contaminations alongside continually decreasing anthropogenic emissions and climate warming in the future.
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Affiliation(s)
- Shijie Song
- Key Laboratory for Environmental Pollution Prediction and Control, Gansu Province, Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, PR China
| | - Boqi Chen
- Key Laboratory for Environmental Pollution Prediction and Control, Gansu Province, Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, PR China
| | - Tao Huang
- Key Laboratory for Environmental Pollution Prediction and Control, Gansu Province, Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, PR China
| | - Shuxin Ma
- Key Laboratory for Environmental Pollution Prediction and Control, Gansu Province, Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, PR China
| | - Luqian Liu
- Key Laboratory for Environmental Pollution Prediction and Control, Gansu Province, Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, PR China
| | - Jinmu Luo
- Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY, 14853, USA
- Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, 14853, USA
| | - Huizhong Shen
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, 5180551, PR China
| | - Jiaxin Wang
- Key Laboratory for Environmental Pollution Prediction and Control, Gansu Province, Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, PR China
| | - Liang Guo
- Key Laboratory for Environmental Pollution Prediction and Control, Gansu Province, Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, PR China
| | - Min Wu
- Key Laboratory for Environmental Pollution Prediction and Control, Gansu Province, Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, PR China
| | - Xiaoxuan Mao
- Key Laboratory for Environmental Pollution Prediction and Control, Gansu Province, Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, PR China
| | - Yuan Zhao
- Key Laboratory for Environmental Pollution Prediction and Control, Gansu Province, Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, PR China
| | - Hong Gao
- Key Laboratory for Environmental Pollution Prediction and Control, Gansu Province, Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, PR China
| | - Jianmin Ma
- Key Laboratory for Environmental Pollution Prediction and Control, Gansu Province, Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, PR China
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, PR China
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Zhang Y, Wu P, Xu R, Wang X, Lei L, Schartup AT, Peng Y, Pang Q, Wang X, Mai L, Wang R, Liu H, Wang X, Luijendijk A, Chassignet E, Xu X, Shen H, Zheng S, Zeng EY. Plastic waste discharge to the global ocean constrained by seawater observations. Nat Commun 2023; 14:1372. [PMID: 36914656 PMCID: PMC10011382 DOI: 10.1038/s41467-023-37108-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 02/28/2023] [Indexed: 03/16/2023] Open
Abstract
Marine plastic pollution poses a potential threat to the ecosystem, but the sources and their magnitudes remain largely unclear. Existing bottom-up emission inventories vary among studies for two to three orders of magnitudes (OMs). Here, we adopt a top-down approach that uses observed dataset of sea surface plastic concentrations and an ensemble of ocean transport models to reduce the uncertainty of global plastic discharge. The optimal estimation of plastic emissions in this study varies about 1.5 OMs: 0.70 (0.13-3.8 as a 95% confidence interval) million metric tons yr-1 at the present day. We find that the variability of surface plastic abundance caused by different emission inventories is higher than that caused by model parameters. We suggest that more accurate emission inventories, more data for the abundance in the seawater and other compartments, and more accurate model parameters are required to further reduce the uncertainty of our estimate.
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Affiliation(s)
- Yanxu Zhang
- School of Atmospheric Sciences, Nanjing University, 210023, Nanjing, China.
- Frontiers Science Center for Critical Earth Material Cycling, Nanjing University, 210023, Nanjing, China.
| | - Peipei Wu
- School of Atmospheric Sciences, Nanjing University, 210023, Nanjing, China
| | - Ruochong Xu
- School of Atmospheric Sciences, Nanjing University, 210023, Nanjing, China
| | - Xuantong Wang
- School of Atmospheric Sciences, Nanjing University, 210023, Nanjing, China
| | - Lili Lei
- School of Atmospheric Sciences, Nanjing University, 210023, Nanjing, China.
| | - Amina T Schartup
- Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA
| | - Yiming Peng
- School of Atmospheric Sciences, Nanjing University, 210023, Nanjing, China
| | - Qiaotong Pang
- School of Atmospheric Sciences, Nanjing University, 210023, Nanjing, China
| | - Xinle Wang
- School of Atmospheric Sciences, Nanjing University, 210023, Nanjing, China
| | - Lei Mai
- Center for Environmental Microplastics Studies, Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University, 511443, Guangzhou, China
| | - Ruwei Wang
- Center for Environmental Microplastics Studies, Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University, 511443, Guangzhou, 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, China
| | - Xiaotong Wang
- 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, China
| | - Arjen Luijendijk
- Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, Netherlands
- Hydraulic Engineering, Deltares, Delft, Netherlands
| | - Eric Chassignet
- Center for Ocean-Atmospheric Prediction Studies (COAPS), Florida State University, Tallahassee, FL, USA
| | - Xiaobiao Xu
- Center for Ocean-Atmospheric Prediction Studies (COAPS), Florida State University, Tallahassee, FL, USA
| | - Huizhong Shen
- School of Environmental Science and Technology, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Shuxiu Zheng
- College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Eddy Y Zeng
- Center for Environmental Microplastics Studies, Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University, 511443, Guangzhou, China.
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Liu X, Li Y, Luo Z, Xing R, Men Y, Huang W, Jiang K, Zhang L, Sun C, Xie L, Cheng H, Shen H, Chen Y, Du W, Shen G, Tao S. Identification of Factors Determining Household PM 2.5 Variations at Regional Scale and Their Implications for Pollution Mitigation. Environ Sci Technol 2023; 57:3722-3732. [PMID: 36826460 DOI: 10.1021/acs.est.2c05750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Indoor PM2.5, particulate matter no more than 2.5 μm in aerodynamic equivalent diameter, has very high spatiotemporal variabilities; and exploring the key factors influencing the variabilities is critical for purifying air and protecting human health. Here, we conducted a longer-term field monitoring campaign using low-cost sensors and evaluated inter- and intra-household PM2.5 variations in rural areas where energy or stove stacking is common. Household PM2.5 varied largely across different homes but also within households. Using generalized linear models and dominance analysis, we estimated that outdoor PM2.5 explained 19% of the intrahousehold variation in indoor daily PM2.5, whereas factors like the outdoor temperature and indoor-outdoor temperature difference that was associated with energy use directly or indirectly, explained 26% of the temporal variation. Inter-household variation was lower than intrahousehold variation. The inter-household variation was strongly associated with distinct internal sources, with energy-use-associated factors explaining 35% of the variation. The statistical source apportionment model estimated that solid fuel burning for heating contributed an average of 31%-55% of PM2.5 annually, whereas the contribution of sources originating from the outdoors was ≤10%. By replacing raw biomass or coal with biomass pellets in gasifier burners for heating, indoor PM2.5 could be significantly reduced and indoor temperature substantially increased, providing thermal comforts in addition to improved air quality.
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Affiliation(s)
- Xinlei Liu
- Laboratory for Earth Surface Process, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
- Key Laboratory of Agricultural Renewable Resource Utilization Technology, Northeast Agricultural University, Harbin 150006, China
| | - Yaojie Li
- Laboratory for Earth Surface Process, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Zhihan Luo
- Laboratory for Earth Surface Process, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Ran Xing
- Laboratory for Earth Surface Process, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Yatai Men
- Laboratory for Earth Surface Process, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Wenxuan Huang
- Laboratory for Earth Surface Process, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Ke Jiang
- Laboratory for Earth Surface Process, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Lu Zhang
- Laboratory for Earth Surface Process, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Chao Sun
- Shandong Warm Valley New Energy and Environmental Protection, Yantai 264001, China
| | - Longjiao Xie
- Health Science Center, Peking University, Beijing 100871, China
| | - Hefa Cheng
- Laboratory for Earth Surface Process, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Huizhong Shen
- College of Environmental Science and Technology, Southern University of Science and Technology, Shenzhen 518055, China
| | - Yuanchen Chen
- Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Research Center of Environmental Science, Zhejiang University of Technology, Hangzhou 310032, China
| | - Wei Du
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Guofeng Shen
- Laboratory for Earth Surface Process, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Shu Tao
- Laboratory for Earth Surface Process, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
- College of Environmental Science and Technology, Southern University of Science and Technology, Shenzhen 518055, China
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Rosentreter R, Cheng E, Shen H, Ma C, Bhayana D, Panaccione R, Raman M, Medellin A, Lu C. A107 VISCERAL ADIPOSE TISSUE VOLUME DIFFERENTIATES BETWEEN FIBROSTENOTIC AND INFLAMMATORY CROHN’S DISEASE. J Can Assoc Gastroenterol 2023. [PMCID: PMC9991293 DOI: 10.1093/jcag/gwac036.107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/09/2023] Open
Abstract
Background Creeping fat, a form of visceral adipose tissue (VAT) that wraps the intestinal wall, influences the formation of Crohn’s disease (CD) strictures. The degree of fat wrapping from intestinal stricture resections is correlated with the extent of chronic inflammation, fibrosis, stricture formation, and response to biologic therapy. VAT and subcutaneous adipose tissue (SAT) ratios from CTE (computed tomography) scans are elevated in CD strictures. However, the definition of strictures in these studies has been poorly defined and not included current well-recognized criteria: 1) bowel wall thickness (BWT), 2) narrowed luminal diameter, and 3) pre-stenotic dilation. (PSD). Purpose The objective of this pilot study was to assess the relationship of 2D and 3D VAT:SAT ratios with CT stricture parameters in patients with terminal ileal (TI) CD strictures. Method 2D VAT:SAT ratios from CT’s of CD patients with TI strictures defined as increased BWT, narrowed luminal diameter (< 50% relative to normal adjacent distended loop), and PSD greater than the stricture diameter were retrospectively obtained from a database and chart review. CT’s from fibrostenotic CD patients were sex and BMI matched to patients with only TI inflammatory behaviour. Patient demographics, medication, smoking, and surgical history were also obtained. Analyses were adjusted for age, sex, and BMI covariates. Unpaired t-tests and multi-variable logistic regression analyses were conducted. Result(s) Twenty-eight patients with stricturing CD had a significantly greater mean VAT:SAT volume ratio than 29 non-stricturing CD (41.5 cm3 vs 34.2 cm3, p=0.03). Thirty-six percent (10/28) of CD stricture patients had prior ileocolic resection with a mean disease duration of 13.5 years (range 0-48). The median ileal BWT (7.0 mm, range 4.0-13.0 mm) for the stricturing group was significantly greater than those with inflammatory behaviour (BWT 2.0 mm, p<0.0001). The median luminal diameter and PSD for the stricture group was 2.0 mm (range 0 - 14.0 mm), and 3.0 cm (range 1.0 - 7.3 cm), respectively. Image ![]()
Conclusion(s) Fibrostenotic TI CD patients have increased VAT:SAT ratios in comparison to those with only inflammatory behaviour. These pilot VAT:SAT results provide an initial foundation for further studies to assess its predictive role in responsiveness of medical or surgical therapies in stricturing CD. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest None Declared
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Affiliation(s)
| | | | - H Shen
- Department of Mathematics and Statistics
| | - C Ma
- Department of Medicine,Department of Community Health Sciences, University of Calgary, Calgary, Canada
| | | | | | - M Raman
- Department of Medicine,Department of Community Health Sciences, University of Calgary, Calgary, Canada
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Wang W, Ni B, Shen H, Lu H. Meta-analysis of InterTan, PFNA and PFNA-II internal fixation for the treatment of unstable intertrochanteric fractures in elderly individuals. Acta Orthop Belg 2023; 89:51-58. [PMID: 37294985 DOI: 10.52628/89.1.9923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Elderly individuals are often affected by osteoporosis and have poor stability after fracture reduction. Moreover, there is still controversy regarding the clinical effects of the treatment for unstable intertrochanteric fractures in the elderly. The Cochrane, Embase, PubMed, and other databases were searched, and a meta-analysis of the literature on the treatment of unstable intertrochanteric fractures of the elderly with InterTan, PFNA, and PFNA-II was conducted. Seven studies were screened, with a total of 1236 patients. Our meta-analysis results show that InterTan is not significantly different from PFNA in terms of operation and fluoroscopy times, but it takes longer than PFNA-II. In terms of postoperative screw cut, pain, femoral shaft fracture, and secondary operations, InterTan is superior to PFNA and PFNA-II. Conversely, in terms of intraoperative blood loss, hospital stay, and postoperative Harris score, there is no significant difference between InterTan and PFNA and PFNA-II. Compared to PFNA and PFNA-II, InterTan internal fixation has advantages in the treatment of unstable intertrochanteric fractures in elderly individuals in terms of screw cutting, femoral shaft fractures, and secondary operations. However, InterTan operation and fluoroscopy times take longer than PFNA and PFNA-II.
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Wang B, Deng Y, Xu Q, Gao J, Shen H, He X, Ding Q, Wang F, Guo H. Exploration of 68Ga-labelled prostate-specific membrane antigen-11 PET/CT parameters for identifying PBRM1 status in primary clear cell renal cell carcinoma. Clin Radiol 2023; 78:e417-e424. [PMID: 36805287 DOI: 10.1016/j.crad.2023.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 12/26/2022] [Accepted: 01/10/2023] [Indexed: 02/04/2023]
Abstract
AIM To investigate the predictive value of 68Ga-labelled prostate-specific membrane antigen-11 (68Ga-PSMA-11) integrated positron-emission tomography (PET)/computed tomography (CT) in PBRM1-deficient clear cell renal cell carcinoma (ccRCC). MATERIALS AND METHODS A total of 41 patients with ccRCC, were enrolled retrospectively and underwent 68Ga-PSMA-11 PET/CT preoperatively. Radiological parameters, including CT attenuation value and maximum standard uptake value (SUVmax), were derived. Immunohistochemical and multiple immunofluorescences staining were performed to evaluate the PBRM1 status and immune response. The predictive value of imaging factors was analysed using a receiver operator characteristic curve analysis. Univariate and multivariate logistic regression analyses were used to investigate the relationship between clinical and radiological variables and PBRM1 status. RESULTS A total of 41 patients were included in this study, with 14 patients having PBRM1-deficient status. The tumour diameter on imaging and SUVmax differed significantly in patients with different PBRM1 expression statuses and no difference in CT attenuation was identified. Univariate and multivariate logistic regression analyses showed SUVmax was an obvious predictor for identification of PBRM1-deficient tumours. In addition, PBRM1-deficient tumours tended to be accompanied by greater cytotoxic T-cell infiltration, although most of them were in an exhausted state. CONCLUSIONS 68Ga-PSMA-11 PET/CT could be used to discriminate invasive PBRM1-deficient ccRCC.
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Affiliation(s)
- B Wang
- Department of Urology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Y Deng
- Department of Urology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Q Xu
- Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - J Gao
- Department of Urology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - H Shen
- Department of Urology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - X He
- Department of Urology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Q Ding
- Department of Urology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Xuzhou Medical University, Nanjing, Jiangsu, China
| | - F Wang
- Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - H Guo
- Department of Urology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China; Department of Urology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
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Guan SW, Wen F, Shen H, Zhao EM, Qin Y, Xiao SF. [Comparison between transoral radiofrequency coblation surgery and open partial laryngectomy for the treatment of supraglottic laryngeal carcinoma]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2022; 57:1457-1462. [PMID: 36707950 DOI: 10.3760/cma.j.cn115330-20220321-00123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Objective: To explore the feasibility and efficacy of radiofrequency coblation assisted transoral surgery for the treatment of supraglottic laryngeal carcinoma by comparing with concurrent patients treated with conventional transcervical approach. To clarify the advantages of different surgical methods and to summarize the experience of supraglottic carcinoma radiofrequency ablation. Methods: Forty-six patients with supraglottic laryngeal carcinoma treated in department of otorhinolaryngology head and neck surgery, Peking University First Hospital from March 2014 to January 2021 were analyzed retrospectively. Among them(43 males, 3 females, aged from 45 to 79 years old), 23 patients were treated with radiofrequency coblation and 23 patients with partial laryngectomy with conventional transcervical approach. The operation time, intra-operative blood loss volume, recovery time, inpatient total medical cost and follow-up information of the two groups were analyzed. SPSS 26.0 software was used for statistical analysis. Results: There were no significant differences in age, gender, TNM staging,tumor staging and postoperative radiotherapy between the two groups (all P>0.05).The operation time, intra-operative blood loss volume, recovery time, inpatient total medical cost of the RFC-TOS group were110.0(60.0,150.0)min,5.0(5.0,30.0)ml,3.0(2.0,5.0)days,6.0(4.0,14.0)days and 26 100.7(16 145.5,47 044.4)yuan. The data of conventional transcervical approach group were 205.0(156.5,272.3)min, 150.0(50,200) ml, 18.0(16.3,22.8)days and 56520.1(440 992.5,67 109.9)yuan, (Z=-4.03, -4.94, -4.97, -4.98 and -4.13;all P<0.001).The 5-year local control rate, disease-specific survival rate and overall survival rate of the two groups were 86.96%,95.65%,91.30% and 86.96%,91.30%,73.90% renspectively, which had no significant difference between the two groups(all P>0.05). Conclusions: Compared with conventional transcervical surgeries, RFC-TOS could be a reliable new surgical option for organ-function preservation strategy in the treatment of supraglottic laryngeal carcinoma.The RFC is a suitable new technique and deserving more multi-center clinical trials for its clinical promotion.
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Affiliation(s)
- S W Guan
- Department of Otorhinolaryngology Head and Neck Surgery, Peking University First Hospital, Beijing 100034, China
| | - F Wen
- Department of Otorhinolaryngology Head and Neck Surgery, Peking University First Hospital, Beijing 100034, China
| | - H Shen
- Department of Otorhinolaryngology Head and Neck Surgery, Peking University First Hospital, Beijing 100034, China
| | - E M Zhao
- Department of Otorhinolaryngology Head and Neck Surgery, Peking University First Hospital, Beijing 100034, China
| | - Y Qin
- Department of Otorhinolaryngology Head and Neck Surgery, Peking University First Hospital, Beijing 100034, China
| | - S F Xiao
- Department of Otorhinolaryngology Head and Neck Surgery, Peking University First Hospital, Beijing 100034, China
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Shu Y, Ma P, Shen H, Gao W, Chen X, Sun J, Xu L. 145P Preliminary results of a phase Ⅱ study of fruquintinib combined with sintilimab and chemotherapy as the first-line treatment in advanced naive EGFR- and ALK-negative non-squamous non-small cell lung cancer (nsq-NSCLC). Immuno-Oncology and Technology 2022. [DOI: 10.1016/j.iotech.2022.100257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Luo Z, Shen G, Men Y, Zhang W, Meng W, Zhu W, Meng J, Liu X, Cheng Q, Jiang K, Yun X, Cheng H, Xue T, Shen H, Tao S. Reduced inequality in ambient and household PM 2.5 exposure in China. Environ Int 2022; 170:107599. [PMID: 36323065 DOI: 10.1016/j.envint.2022.107599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 10/18/2022] [Accepted: 10/21/2022] [Indexed: 06/16/2023]
Abstract
The society has high concerns on the inequality that people are disproportionately exposed to ambient air pollution, but with more time spent indoors, the disparity in the total exposure considering both indoor and outdoor exposure has not been explored; and with the socioeconomical development and efforts in fighting against air pollution, it is unknown how the exposure inequality changed over time. Based on the city-level panel data, this study revealed the Concentration Index (C) in ambient PM2.5 exposure inequality was positive, indicating the low-income group exposed to lower ambient PM2.5; however, the total PM2.5 exposure was negatively correlated with the income, showing a negative C value. The low-income population exposed to high PM2.5 associated with larger contributions of indoor exposure from the residential emissions. The total PM2.5 exposure caused 1.13 (0.63-1.73) million premature deaths in 2019, with only 14 % were high-income population. The toughest-ever air pollution countermeasures have reduced ambient PM2.5 exposures effectively that, however, benefited the rich population more than the others. The transition to clean household energy sources significantly affected on indoor air quality improvements, as well as alleviation of ambient air pollution, resulting in notable reductions of the total PM2.5 exposure and especially benefiting the low-income groups. The negative C values decreased from 2000 to 2019, indicating a significantly reducing trend in the total PM2.5 exposure inequality over time.
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Affiliation(s)
- Zhihan Luo
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Guofeng Shen
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China.
| | - Yatai Men
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Wenxiao Zhang
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Wenjun Meng
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Wenyuan Zhu
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Jing Meng
- The Bartlett School of Sustainable Construction, University College London, London WC1E 7HB, United Kingdom
| | - Xinlei Liu
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Qin Cheng
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Ke Jiang
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Xiao Yun
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Hefa Cheng
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Tao Xue
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Huizhong Shen
- College of Environmental Science and Technology, Southern University of Science and Technology, Shenzhen 518055, China
| | - Shu Tao
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China; College of Environmental Science and Technology, Southern University of Science and Technology, Shenzhen 518055, China
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Gee H, Szymd R, Casolin S, French L, Shen H, Chang C, Hau E, Cesare A. Ablative Dose Radiation Induces Distinct Waves of Cell Death Dependent on Cell Cycle Phase via DNA Repair Pathway Choice. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2022.07.1799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Yu Y, Dong W, Shi Y, Wu R, Yu Q, Ye F, Zhou C, Dong X, Li X, Li Y, Li Z, Pan Y, Shen H, Wu D, Xu Z, Wu J, Xu N, Qin Y, Li J, Lu S. 313P A pool analysis of MET TKI SCC244 in NSCLC patients with MET overexpression. Ann Oncol 2022. [DOI: 10.1016/j.annonc.2022.10.342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022] Open
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Liu YT, Hu YY, Shen H, Liu S. [Research progress on screen exposure and negative emotions in adolescents]. Zhonghua Er Ke Za Zhi 2022; 60:1089-1092. [PMID: 36207863 DOI: 10.3760/cma.j.cn112140-20220328-00250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Affiliation(s)
- Y T Liu
- School of Public Health, Hangzhou Medical College, Hangzhou 310000, China
| | - Y Y Hu
- School of Public Health, Hangzhou Medical College, Hangzhou 310000, China
| | - H Shen
- School of Public Health, Hangzhou Medical College, Hangzhou 310000, China
| | - S Liu
- School of Public Health, Hangzhou Medical College, Hangzhou 310000, China
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Xiong R, Li J, Zhang Y, Zhang L, Jiang K, Zheng H, Kong S, Shen H, Cheng H, Shen G, Tao S. Global brown carbon emissions from combustion sources. Environ Sci Ecotechnol 2022; 12:100201. [PMID: 36157345 PMCID: PMC9500369 DOI: 10.1016/j.ese.2022.100201] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 07/10/2022] [Accepted: 07/11/2022] [Indexed: 05/06/2023]
Abstract
Light-absorbing organic carbon (OC), sometimes known as Brown Carbon (BrC), has been recognized as an important fraction of carbonaceous aerosols substantially affecting radiative forcing. This study firstly developed a bottom-up estimate of global primary BrC, and discussed its spatiotemporal distribution and source contributions from 1960 to 2010. The global total primary BrC emission from both natural and anthropogenic sources in 2010 was 7.26 (5.98-8.93 as an interquartile range) Tg, with 43.5% from anthropogenic sources. High primary BrC emissions were in regions such as Africa, South America, South and East Asia with natural sources (wild fires and deforestation) contributing over 70% in the former two regions, while in East Asia, anthropogenic sources, especially residential solid fuel combustion, accounted for over 80% of the regional total BrC emissions. Globally, the historical trend was mainly driven by anthropogenic sources, which increased from 1960 to 1990 and then started to decline. Residential emissions significantly impacted on emissions and temporal trends that varied by region. In South and Southeast Asia, the emissions increased obviously due to population growth and a slow transition from solid fuels to clean modern energies in the residential sector. It is estimated that in primary OC, the global average was about 20% BrC, but this ratio varied from 13% to 47%, depending on sector and region. In areas with high residential solid fuel combustion emissions, the ratio was generally twice the value in other areas. Uncertainties in the work are associated with the concept of BrC and measurement technologies, pointing to the need for more studies on BrC analysis and quantification in both emissions and the air.
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Affiliation(s)
- Rui Xiong
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Jin Li
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Yuanzheng Zhang
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Lu Zhang
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Ke Jiang
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Huang Zheng
- Department of Environmental Science and Technology, School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China
| | - Shaofei Kong
- Department of Environmental Science and Technology, School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China
| | - Huizhong Shen
- School of Environmental Sciences and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Hefa Cheng
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Guofeng Shen
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
- Corresponding author. Peking University, China.
| | - Shu Tao
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
- School of Environmental Sciences and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
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Zheng S, Shen H, Shen G, Chen Y, Ma J, Cheng H, Tao S. Vertically-resolved indoor measurements of air pollution during Chinese cooking. Environ Sci Ecotechnol 2022; 12:100200. [PMID: 36157347 PMCID: PMC9500372 DOI: 10.1016/j.ese.2022.100200] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 06/24/2022] [Accepted: 06/28/2022] [Indexed: 05/07/2023]
Abstract
Chinese cooking features several unique processes, e.g., stir-frying and pan-frying, which represent important sources of household air pollution. However, factors affecting household air pollution and the vertical variations of indoor pollutants during Chinese cooking are less clear. Here, using low-cost sensors with high time resolutions, we measured concentrations of five gas species and particulate matter (PM) in three different sizes at multiple heights in a kitchen during eighteen different Chinese cooking events. We found indoor gas species were elevated by 21%-106% during cooking, compared to the background, and PMs were elevated by 44%-159%. Vertically, the pollutants concentrations were highly variable during cooking periods. Gas species generally showed a monotonic increase with height, while PMs changed more diversely depending on the cooking activity's intensity. Intense cooking, e.g., stir-frying, pan-frying, or cooking on high heat, tended to shoot PMs to the upper layers, while moderate ones left PMs within the breathing zone. Individuals with different heights would be subject to different levels of household air pollution exposure during cooking. The high vertical variability challenges the current indoor standard that presumes a uniform pollution level within the breathing zone and thus has important implications for public health and policy making.
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Affiliation(s)
- Shuxiu Zheng
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing, 100871, China
| | - Huizhong Shen
- 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, Southern University of Science and Technology, Shenzhen, 518055, China
- Corresponding author. School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
| | - Guofeng Shen
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing, 100871, China
| | - Yilin Chen
- 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, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Jianmin Ma
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing, 100871, China
| | - Hefa Cheng
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing, 100871, China
| | - Shu Tao
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing, 100871, China
- 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, Southern University of Science and Technology, Shenzhen, 518055, China
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Ainiwaer S, Chen Y, Shen G, Shen H, Ma J, Cheng H, Tao S. Characterization of the vertical variation in indoor PM 2.5 in an urban apartment in China. Environ Pollut 2022; 308:119652. [PMID: 35760202 DOI: 10.1016/j.envpol.2022.119652] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 05/29/2022] [Accepted: 06/15/2022] [Indexed: 06/15/2023]
Abstract
Indoor air pollution has aroused increasing concerns due to its significant adverse health impacts. Indoor PM2.5 exposure assessments often rely on PM2.5 concentration measured at a single height, which overlooks the vertical variation of PM2.5 concentrations accompanied by various indoor activities. In this study, we characterize the vertical profile of PM2.5 concentration by monitoring PM2.5 concentration at eight different heights in the kitchen and the bedroom, respectively, using low-cost sensors with high temporal resolution. The localized enhancement of PM2.5 concentration in elevated heights in the kitchen during cooking was observed on clean and polluted days, showing dominating contribution from cooking activities. The source contribution from cooking and outdoor penetration was semi-quantified using regression models. Stratified source contribution from cooking activities was evident in the kitchen during the cooking period. The contribution in elevated heights (above 170 cm) almost tripled the contrition in bottom layers (below 140 cm). In contrast, little vertical variation was observed during other times of the day in the kitchen or the bedroom. The exposure level calculated using the multi-height measurement in this study is consistently higher than the exposure level estimated from the single-height (at 110 cm) measurement. A more significant discrepancy existed for the cookers (17.8%) than the non-cookers (13.5%). By profiling the vertical gradient of PM2.5 concentration, we show the necessity to conduct multi-height measurements or proper breathing-height measurements to obtain unbiased concentration information for source apportionment and exposure assessment. In particular, the multi-height measuring scheme will be crucial to inform household cooking emission regulations.
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Affiliation(s)
- Subinuer Ainiwaer
- College of Urban Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System, Peking University, Beijing, 100871, China
| | - Yilin Chen
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
| | - Guofeng Shen
- College of Urban Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System, Peking University, Beijing, 100871, China
| | - Huizhong Shen
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Jianmin Ma
- College of Urban Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System, Peking University, Beijing, 100871, China
| | - Hefa Cheng
- College of Urban Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System, Peking University, Beijing, 100871, China
| | - Shu Tao
- College of Urban Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System, Peking University, Beijing, 100871, China; School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
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Xu T, Shen H, Lu B, Wei C, Wang Z. EP08.02-153 The Efficacy and Safety of EGFR-TKIs plus Anlotinib in Maintenance Therapy for Oligoprogressive Advanced or Metastatic EGFR Mutant NSCLC. J Thorac Oncol 2022. [DOI: 10.1016/j.jtho.2022.07.836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Jiang K, Fu B, Luo Z, Xiong R, Men Y, Shen H, Li B, Shen G, Tao S. Attributed radiative forcing of air pollutants from biomass and fossil burning emissions. Environ Pollut 2022; 306:119378. [PMID: 35500713 DOI: 10.1016/j.envpol.2022.119378] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/22/2022] [Accepted: 04/26/2022] [Indexed: 06/14/2023]
Abstract
Energy is vital to human society but significantly contributes to the deterioration of environmental quality and the global issue of climate change. Biomass and fossil fuels are important energy sources but have distinct pollutant emission characteristics during the burning process. This study aimed at attributing radiative forcing of climate forcers, including greenhouse gases but also short-lived climate pollutants, from the burning of fossil and biomass fuels, and the spatiotemporal characteristics. We found that air pollutant emissions from the burning process of biofuel and fossil fuels induced RFs of 68.2 ± 36.8 mW m-2 and 840 ± 225 mW m-2, respectively. The relatively contribution of biomass burning emissions was 7.6% of that from both fossil and biofuel combustion processes, while its contribution in energy supply was 11%. These relative contributions varied obviously across different regions. The per unit energy consumption of biomass fuel in the developed regions, such as North America (0.57 ± 0.33 mW m-2/107TJ) and Western Europe (0.98 ± 0.79 mW m-2/107TJ), had higher impacts of combustion emission related RFs compared to that of developing regions, like China (0.40 ± 0.26 mW m-2/107TJ), and South and South-East Asia (0.31 ± 0.71 mW m-2/107TJ) where low efficiency biomass burning in residential sector produced significant amounts of organic matter that had a cooling effect. Note that the study only evaluated fuel combustion emission related RFs, and those associated with the production of fuels and land use change should be studied later in promoting a comprehensive understanding on the climate impacts of biomass utilization.
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Affiliation(s)
- Ke Jiang
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Bo Fu
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Zhihan Luo
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Rui Xiong
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Yatai Men
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Huizhong Shen
- School of Environmental Sciences and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Bengang Li
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Guofeng Shen
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China.
| | - Shu Tao
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China; School of Environmental Sciences and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
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Yang X, Shen H, Li Q, Dai Z, Yang R, Huang G, Chen R, Wang F, Song J, Hua H. [Interference of P2X4 receptor expression in tumor-associated macrophages suppresses migration and invasion of glioma cells]. Nan Fang Yi Ke Da Xue Xue Bao 2022; 42:658-664. [PMID: 35673908 DOI: 10.12122/j.issn.1673-4254.2022.05.05] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
OBJECTIVE To investigate the effect of interference of P2X4 receptor expression in tumor-associated macrophages (TAMs) on invasion and migration of glioma cells. METHODS C57BL/6 mouse models bearing gliomas in the caudate nucleus were examined for glioma pathology with HE staining and expressions of Iba-1 and P2X4 receptor with immunofluorescence assay. RAW264.7 cells were induced into TAMs using conditioned medium from GL261 cells, and the changes in mRNA expressions of macrophage polarization-related markers and the mRNA and protein expressions of P2X4 receptor were detected with RT-qPCR and Western blotting. The effect of siRNA-mediated P2X4 interference on IL-1β and IL-18 mRNA and protein expressions in the TAMs was detected with RT-qPCR and Western blotting. GL261 cells were cultured in the conditioned medium from the transfected TAMs, and the invasion and migration abilities of the cells were assessed with Transwell invasion and migration experiment. RESULTS The glioma tissues from the tumor-bearing mice showed a significantly greater number of Iba-1-positive cells, where an obviously increased P2X4 receptor expression was detected (P=0.001), than the brain tissues of the control mice (P < 0.001). The M2 macrophage markers (Arg-1 and IL-10) and M1 macrophage markers (iNOS and TNF-α) were both significantly up-regulated in the TAMs derived from RAW264.7 cells (all P < 0.01), but the up-regulation of the M2 macrophage markers was more prominent; the expression levels of P2X4 receptor protein and mRNA were both increased in the TAMs (P < 0.05). Interference of P2X4 receptor expression significantly lowered the mRNA(P < 0.01)and protein (P < 0.01, P < 0.05)expression levels of IL-1β and IL-18 in the TAMs and obviously inhibited the ability of the TAMs to promote invasion and migration of the glioma cells (P < 0.05). CONCLUSION Interference of P2X4 receptor in the TAMs suppresses the migration and invasion of glioma cells possibly by lowering the expressions of IL-1β and IL-18.
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Affiliation(s)
- X Yang
- Department of Pathology and Pathophysiology, Kunming Medical University, Kunming 650500, China
| | - H Shen
- Department of Pathology, Zhaotong First People's Hospital, Zhaotong 657099, China
| | - Q Li
- Clinic Skill Center, Kunming Medical University, Kunming 650500, China
| | - Z Dai
- Institute of Stomatology, Affiliated Stomatology Hospital, Kunming Medical University, Kunming 650500, China
| | - R Yang
- Institute of Stomatology, Affiliated Stomatology Hospital, Kunming Medical University, Kunming 650500, China
| | - G Huang
- Institute of Stomatology, Affiliated Stomatology Hospital, Kunming Medical University, Kunming 650500, China
| | - R Chen
- Institute of Stomatology, Affiliated Stomatology Hospital, Kunming Medical University, Kunming 650500, China
| | - F Wang
- Department of Pathology and Pathophysiology, Kunming Medical University, Kunming 650500, China
| | - J Song
- Electron Microscope, Kunming Medical University, Kunming 650500, China
| | - H Hua
- Department of Pathology and Pathophysiology, Kunming Medical University, Kunming 650500, China
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Chen J, Yuan Y, Peng W, Tang Y, Chen X, Wang Y, Shen H, Li R. [Application of three-dimensional visualization technique in laparoscopic D3 radical resection of right colon cancer]. Nan Fang Yi Ke Da Xue Xue Bao 2022; 42:760-765. [PMID: 35673922 DOI: 10.12122/j.issn.1673-4254.2022.05.19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
OBJECTIVE To explore the clinical value of three-dimensional (3D) visualization technique in laparoscopic D3 radical resection of right colon cancer. METHODS We retrospectively analyzed the clinical data of 73 patients with right colon cancer undergoing laparoscopic D3 radical operation in our hospital between May, 2019 and March, 2021. Among these patients, 41 underwent enhanced CT examination with 3D visualization reconstruction to guide the actual operation, and 32 underwent enhanced CT examination only before the operation (control group). In 3D visualization group, we examined the coincidence rate between the 3D visualization model and the findings in surgical exploration of the anatomy and variations of the main blood vessels, supplying vessels of the tumor, and the tumor location, and the coincidence rate between the actual surgical plan for D3 radical resection of right colon cancer and the plan formulated based on the 3D model. The operative time, estimated blood loss, unexpected injury of blood vessels, number of harvested lymph nodes, mean time of the first flatus, complications, postoperative hospital stay and postoperative drainage volume were compared between the two groups. RESULTS The operative time was significantly shorter in 3D visualization group than in the control group (P < 0.05). The volume of blood loss, proportion of unexpected injury of blood vessel, the number of harvested lymph nodes, time of the first flatus, proportion of complications, postoperative hospital stay and postoperative drainage volume did not differ significantly between the two groups (P > 0.05). In the 3D visualization group, the 3D visualization model clearly displayed the shape and direction of the colon, the location of the tumor, the anatomy and variation of the main blood vessels and the blood vessels supplying the cancer, and showed a coincidence rate of 100% with the findings by surgical exploration. The surgical plan for D3 radical resection of right colon cancer was formulated based on the 3D model also showed a coincidence rate of 100% with the actual surgical plan. CONCLUSION The 3D visualization reconstruction technique allows clear visualization the supplying arteries of the tumor and their variations to improve the efficiency, safety and accuracy of laparoscopic D3 radical resection of right colon cancer.
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Affiliation(s)
- J Chen
- Department of Gastrointestinal Surgery, Dongguan People's Hospital Affiliated to Southern Medical University, Dongguan 523059, China
| | - Y Yuan
- Department of Gastroenterology, Dongguan People's Hospital Affiliated to Southern Medical University, Dongguan 523059, China
| | - W Peng
- Department of Gastrointestinal Surgery, Dongguan People's Hospital Affiliated to Southern Medical University, Dongguan 523059, China
| | - Y Tang
- Department of Gastrointestinal Surgery, Dongguan People's Hospital Affiliated to Southern Medical University, Dongguan 523059, China
| | - X Chen
- Department of Gastrointestinal Surgery, Dongguan People's Hospital Affiliated to Southern Medical University, Dongguan 523059, China
| | - Y Wang
- Department of Gastrointestinal Surgery, Dongguan People's Hospital Affiliated to Southern Medical University, Dongguan 523059, China
| | - H Shen
- Department of Radiology, Dongguan People's Hospital Affiliated to Southern Medical University, Dongguan 523059, China
| | - R Li
- Department of Gastrointestinal Surgery, Dongguan People's Hospital Affiliated to Southern Medical University, Dongguan 523059, China
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Long X, Fu TM, Yang X, Tang Y, Zheng Y, Zhu L, Shen H, Ye J, Wang C, Wang T, Li B. Efficient Atmospheric Transport of Microplastics over Asia and Adjacent Oceans. Environ Sci Technol 2022; 56:6243-6252. [PMID: 35482889 PMCID: PMC9118543 DOI: 10.1021/acs.est.1c07825] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 04/15/2022] [Accepted: 04/18/2022] [Indexed: 05/30/2023]
Abstract
We developed a regional atmospheric transport model for microplastics (MPs, 10 μm to 5 mm in size) over Asia and the adjacent Pacific and Indian oceans, accounting for MPs' size- and shape-dependent aerodynamics. The model was driven by tuned atmospheric emissions of MPs from the land and the ocean, and the simulations were evaluated against coastal (n = 19) and marine (n = 56) observations. Our tuned atmospheric emissions of MPs from Asia and the adjacent oceans were 310 Gg y-1 (1 Gg = 1 kton) and 60 Gg y-1, respectively. MP lines and fragments may be transported in the atmosphere >1000 km; MP pellets in our model mostly deposited near-source. We estimated that 1.4% of the MP mass emitted into the Asian atmosphere deposited into the oceans via atmospheric transport; the rest deposited over land. The resulting net atmospheric transported MP flux from Asia into the oceans was 3.9 Gg y-1, twice as large as a previous estimate for the riverine-transported MP flux from Asia into the oceans. The uncertainty of our simulated atmospheric MP budget was between factors of 3 and 7. Our work highlighted the impacts of the size and morphology on the aerodynamics of MPs and the importance of atmospheric transport in the source-to-sink relationship of global MP pollution.
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Affiliation(s)
- Xin Long
- State
Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater
Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
- Guangdong
Provincial Observation and Research Station for Coastal Atmosphere
and Climate of the Greater Bay Area, Shenzhen, Guangdong 518055, China
| | - Tzung-May Fu
- State
Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater
Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
- Guangdong
Provincial Observation and Research Station for Coastal Atmosphere
and Climate of the Greater Bay Area, Shenzhen, Guangdong 518055, China
| | - Xin Yang
- State
Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater
Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
- Guangdong
Provincial Observation and Research Station for Coastal Atmosphere
and Climate of the Greater Bay Area, Shenzhen, Guangdong 518055, China
| | - Yuanyuan Tang
- State
Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater
Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Yan Zheng
- State
Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater
Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Lei Zhu
- State
Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater
Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
- Guangdong
Provincial Observation and Research Station for Coastal Atmosphere
and Climate of the Greater Bay Area, Shenzhen, Guangdong 518055, China
| | - Huizhong Shen
- State
Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater
Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
- Guangdong
Provincial Observation and Research Station for Coastal Atmosphere
and Climate of the Greater Bay Area, Shenzhen, Guangdong 518055, China
| | - Jianhuai Ye
- State
Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater
Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
- Guangdong
Provincial Observation and Research Station for Coastal Atmosphere
and Climate of the Greater Bay Area, Shenzhen, Guangdong 518055, China
| | - Chen Wang
- State
Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater
Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
- Guangdong
Provincial Observation and Research Station for Coastal Atmosphere
and Climate of the Greater Bay Area, Shenzhen, Guangdong 518055, China
| | - Teng Wang
- College
of Oceanography, Hohai University, Nanjing, Jiangsu 210098, China
| | - Baojie Li
- School
of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China
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47
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Sun HZ, Yu P, Lan C, Wan MW, Hickman S, Murulitharan J, Shen H, Yuan L, Guo Y, Archibald AT. Cohort-based long-term ozone exposure-associated mortality risks with adjusted metrics: A systematic review and meta-analysis. Innovation (N Y) 2022; 3:100246. [PMID: 35519514 PMCID: PMC9065904 DOI: 10.1016/j.xinn.2022.100246] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 04/16/2022] [Indexed: 11/30/2022] Open
Abstract
Long-term ozone (O3) exposure may lead to non-communicable diseases and increase mortality risk. However, cohort-based studies are relatively rare, and inconsistent exposure metrics impair the credibility of epidemiological evidence synthetization. To provide more accurate meta-estimations, this study updates existing systematic reviews by including recent studies and summarizing the quantitative associations between O3 exposure and cause-specific mortality risks, based on unified exposure metrics. Cross-metric conversion factors were estimated linearly by decadal observations during 1990-2019. The Hunter-Schmidt random-effects estimator was applied to pool the relative risks. A total of 25 studies involving 226,453,067 participants (14 unique cohorts covering 99,855,611 participants) were included in the systematic review. After linearly unifying the inconsistent O3 exposure metrics , the pooled relative risks associated with every 10 nmol mol-1 (ppbV) incremental O3 exposure, by mean of the warm-season daily maximum 8-h average metric, were as follows: 1.014 with 95% confidence interval (CI) ranging 1.009-1.019 for all-cause mortality; 1.025 (95% CI: 1.010-1.040) for respiratory mortality; 1.056 (95% CI: 1.029-1.084) for COPD mortality; 1.019 (95% CI: 1.004-1.035) for cardiovascular mortality; and 1.074 (95% CI: 1.054-1.093) for congestive heart failure mortality. Insignificant mortality risk associations were found for ischemic heart disease, cerebrovascular diseases, and lung cancer. Adjustment for exposure metrics laid a solid foundation for multi-study meta-analysis, and widening coverage of surface O3 observations is expected to strengthen the cross-metric conversion in the future. Ever-growing numbers of epidemiological studies supported the evidence for considerable cardiopulmonary hazards and all-cause mortality risks from long-term O3 exposure. However, evidence of long-term O3 exposure-associated health effects was still scarce, so more relevant studies are needed to cover more populations with regional diversity.
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Affiliation(s)
- Haitong Zhe Sun
- Centre for Atmospheric Science, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
- Department of Earth Sciences, University of Cambridge, Cambridge CB2 3EQ, UK
| | - Pei Yu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Changxin Lan
- Institute of Reproductive and Child Health, Key Laboratory of Reproductive Health, National Health Commission of the People’s Republic of China, Beijing 100191, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Michelle W.L. Wan
- Centre for Atmospheric Science, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Sebastian Hickman
- Centre for Atmospheric Science, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Jayaprakash Murulitharan
- Centre for Atmospheric Science, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Huizhong Shen
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Le Yuan
- Centre for Atmospheric Science, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Yuming Guo
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Alexander T. Archibald
- Centre for Atmospheric Science, 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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48
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Shih JS, Driscoll CT, Burtraw D, Shen H, Smith RA, Keyes A, Lambert KF, Chen Y, Russell AG. Energy policy and coastal water quality: An integrated energy, air and water quality modeling approach. Sci Total Environ 2022; 816:151593. [PMID: 34808177 DOI: 10.1016/j.scitotenv.2021.151593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 11/05/2021] [Accepted: 11/06/2021] [Indexed: 06/13/2023]
Abstract
Federal policy changes in the management of carbon emissions from power plants offer a potent real-world example for examining air-land-water interactions and their implications for coastal water quality. We integrate models of energy (Integrated Planning Model (IPM)), air quality (Community Multiscale Air Quality (CMAQ) and water quality (SPAtially Referenced Regression On Watershed attributes (SPARROW)) to investigate the potential water quality impacts of policy-driven changes in total nitrogen deposition in watersheds draining to US coastal areas. We estimate the combined effects of three recently proposed energy policy scenarios, population growth, and climate change. We decompose the combined effects into the roles of the individual components on the supply of riverine nitrogen for the entire US and eight coastal regions. We find that population growth is the most important driver of changes in coastal nitrogen flux. Energy policies play a minor role in offsetting the negative effects of population growth, although the effect varies by energy policy and region. The greatest population and policy effects are projected for the Gulf of Mexico. Given limited reductions in nitrogen emissions and deposition associated with energy policies, the net effect of policy and population changes is an increase in total nitrogen flux to all estuaries relative to the 2010 baseline. While population growth increases flux, and energy policies decrease flux in all regions, climate change can either increase or decrease flux depending on the region. That is because the relatively large individual effects of temperature and precipitation on watershed nitrogen processes work in opposing directions. The net result of the offsetting nature of individual climate processes varies in both magnitude and direction by coastal region. Further research is needed to sort out individual temperature and precipitation effects in different regions.
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Affiliation(s)
- Jhih-Shyang Shih
- Resources for the Future, 1616 P Street NW, Washington, DC 20036, United States of America.
| | - Charles T Driscoll
- Department of Civil and Environmental Engineering, Syracuse University, Syracuse, NY 13244, United States of America.
| | - Dallas Burtraw
- Resources for the Future, 1616 P Street NW, Washington, DC 20036, United States of America.
| | - Huizhong Shen
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States of America.
| | | | - Amelia Keyes
- Resources for the Future, 1616 P Street NW, Washington, DC 20036, United States of America.
| | - Kathy Fallon Lambert
- Center for Climate, Health and the Global Environment, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA 02215, United States of America.
| | - Yilin Chen
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States of America.
| | - Armstead G Russell
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States of America.
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49
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Zhang B, Shen H, Yun X, Zhong Q, Henderson BH, Wang X, Shi L, Gunthe SS, Huey LG, Tao S, Russell AG, Liu P. Global Emissions of Hydrogen Chloride and Particulate Chloride from Continental Sources. Environ Sci Technol 2022; 56:3894-3904. [PMID: 35319880 PMCID: PMC10558010 DOI: 10.1021/acs.est.1c05634] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Gaseous and particulate chlorine species play an important role in modulating tropospheric oxidation capacity, aerosol water uptake, visibility degradation, and human health. The lack of recent global continental chlorine emissions has hindered modeling studies of the role of chlorine in the atmosphere. Here, we develop a comprehensive global emission inventory of gaseous HCl and particulate Cl- (pCl), including 35 sources categorized in six source sectors based on published up-to-date activity data and emission factors. These emissions are gridded at a spatial resolution of 0.1° × 0.1° for the years 1960 to 2014. The estimated emissions of HCl and pCl in 2014 are 2354 (1661-3201) and 2321 (930-3264) Gg Cl a-1, respectively. Emissions of HCl are mostly from open waste burning (38%), open biomass burning (19%), energy (19%), and residential (13%) sectors, and the major sources classified by fuel type are combustion of waste (43%), biomass (32%), and coal (25%). Emissions of pCl are mostly from biofuel (29%) and open biomass burning processes (44%). The sectoral and spatial distributions of HCl and pCl emissions are very heterogeneous along the study period, and the temporal trends are mainly driven by the changes in emission factors, energy intensity, economy, and population.
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Affiliation(s)
- Bingqing Zhang
- School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
| | - Huizhong Shen
- School of Environmental science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Xiao Yun
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, China
| | - Qirui Zhong
- Department of Earth Science, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands
| | - Barron H. Henderson
- United States Environmental Protection Agency, Research Triangle Park, Durham, North Carolina 27709, USA
| | - Xuan Wang
- School of Energy and Environment, City University of Hong Kong, Hong Kong SAR, China
| | - Liuhua Shi
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, USA
| | - Sachin S. Gunthe
- EWRE Division, Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
- Laboratory for Atmospheric and Climate Sciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - Lewis Gregory Huey
- School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
| | - Shu Tao
- School of Environmental science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, China
| | - Armistead G. Russell
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
| | - Pengfei Liu
- School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
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50
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Wang L, Du W, Yun X, Chen Y, Zhu X, Shen H, Shen G, Liu J, Wang X, Tao S. On-site measured emission factors of polycyclic aromatic hydrocarbons for different types of marine vessels. Environ Pollut 2022; 297:118782. [PMID: 34979173 DOI: 10.1016/j.envpol.2021.118782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 12/28/2021] [Accepted: 12/29/2021] [Indexed: 06/14/2023]
Abstract
A portable emission sampling system was used to perform on-site measurements of the emission factors (EFs; quantities of pollutants emitted per unit of energy consumed) of 29 polycyclic aromatic hydrocarbons (PAHs) for five types of marine vessels using light diesel in Hainan Province, China. Both gaseous- and particulate-phase PAHs from vessel emissions were sampled and measured using gas chromatography coupled with mass spectrometry (GC-MS), and the PAH EFs were calculated based on the carbon mass balance method. The average EFs of gaseous- and particulate-phase PAHs were 6.2 ± 7.8 and 17 ± 26 mg/kg, with naphthalene (NAP) and phenanthrene (PHE) dominating the gaseous- and particulate-phase PAH emissions, respectively. Among the five types of vessels, the EFs for small fishing boats were significantly higher than those for other types of vessels, and the lowest EFs were found for tug boats. Composition profiles and typical isomer ratios of PAHs were calculated for five types of vessels. Particulate-phase PAHs accounted for 63 ± 16% of the total emissions of 29 PAH species, and the particulate/gaseous-phase partitioning of PAHs was dominated by organic carbon (OC) absorption rather than black carbon (BC) adsorption. Emission factors of PAHs under different activity conditions were measured and calculated, and relatively higher EFs were found in the maneuvering mode for medium fishing boats and in the operating mode for engineering vessels. No significant differences were found among the PAH composition profiles under different activity conditions.
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Affiliation(s)
- Lizhi Wang
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing, 100871, China; Institute of Ocean Research, Peking University, Beijing, 100871, China; College of Ecology and Environment, Hainan University, Haikou, 570228, China; Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province, Hainan University, Haikou, 570228, China
| | - Wei Du
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing, 100871, China; Key Laboratory of Geographic Information Science of the Ministry of Education, School of Geographic Sciences, East China Normal University, Shanghai, 200241, China
| | - Xiao Yun
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing, 100871, China.
| | - Yuanchen Chen
- College of Environment, Research Centre of Environmental Science, Zhejiang University of Technology, Hangzhou, 310032, China
| | - Xi Zhu
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing, 100871, China
| | - Huizhong Shen
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Guofeng Shen
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing, 100871, China
| | - Junfeng Liu
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing, 100871, China
| | - Xuejun Wang
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing, 100871, China
| | - Shu Tao
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing, 100871, China; School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
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