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Yang H, Liu L, Shu Z, Zhang W, Huang C, Zhu Y, Li S, Wang W, Li G, Zhang Q, Liu Q, Jiang G. Magnetic iron oxide nanoparticles: An emerging threat for the environment and human health. J Environ Sci (China) 2025; 152:188-202. [PMID: 39617545 DOI: 10.1016/j.jes.2024.04.045] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 04/27/2024] [Accepted: 04/29/2024] [Indexed: 12/18/2024]
Abstract
Magnetic iron oxide nanoparticles (FexOy NPs, mainly Fe3O4 and γ-Fe2O3) are nanomaterials ubiquitously present in aquatic, terrestrial, and atmospheric environments, with a high prevalence and complex sources. Over the past decade, numerous reports have emerged on the presence of exogenous particles in human body, facilitated by the rapid development of separation and detection methods. The health risk associated with magnetic FexOy NP have garnered escalating attention due to their presence in human blood and brain tissues, especially for their potential association with neurodegenerative diseases like Alzheimer's disease. In this paper, we provide a comprehensive overview of sources, analysis methods, environmental impacts, and health risks of magnetic FexOy NP. Currently, most researches are primarily based on engineered FexOy NP, while reports about magnetic FexOy NP existing in real-world environments are still limited, especially for their occurrence levels in various environmental matrices, environmental transformation behavior, and biotoxic effects. Our study reviews this emerging pollutant, providing insights to address current research deficiencies and chart the course for future studies.
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Affiliation(s)
- Hang Yang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lin Liu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhao Shu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weican Zhang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Cha Huang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yanhuan Zhu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Si Li
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weichao Wang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310000, China
| | - Gang Li
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Qinghua Zhang
- College of Geography and Environmental Science, Henan University, Kaifeng 475004, China.
| | - Qian Liu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; Institute of Environment and Health, Jianghan University, Wuhan 430056, China.
| | - Guibin Jiang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
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Córdova-Udaeta M, Cheng B, Fuchida S, Takaya Y, Horiuchi J, Masuoka H, Oyama K, Tokoro C. Selective Manganese Precipitation via Neutralization and Ozone Oxidation under pH Conditions Similar to Steel Pickling Wastewater: Thermodynamic Assessment and Experimental XANES Evaluation. ACS OMEGA 2025; 10:18085-18097. [PMID: 40352491 PMCID: PMC12059921 DOI: 10.1021/acsomega.5c01588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2025] [Revised: 04/10/2025] [Accepted: 04/17/2025] [Indexed: 05/14/2025]
Abstract
Steel pickling wastewater contains valuable iron. Nonetheless, coexisting elements such as Mn need to be separated before Fe recovery. This work studies Mn precipitation phenomena under a pH resembling steel pickling wastewater and compares it to that of Fe under the same conditions. A neutralization-oxidation approach was studied, whereby either NaOH or NH3 were used as neutralizers and O3 was the oxidizer. A thermodynamic assessment indicated that NaOH is more effective than NH3 for precipitation because Mn can react freely with O3 after NaOH addition, whereas NH3 may react with O3 instead. Experimental data showed that neutralization followed by oxidation results in the formation of different Mn oxides, with NaOH confirmed as the most effective neutralizer. Moreover, XRD and XANES analyses showed that the Mn oxidation state in the solids depends on the neutralizer used. Conversely, Fe precipitation was thermodynamically and experimentally observed to depend entirely on pH, with NaOH being a better neutralizer than NH3, and pH = 1.5 being the maximum pH where Fe remains dissolved. These insights suggest that using a neutralization-oxidation method that increases the oxidation potential high enough for Mn oxidation while keeping the pH low enough for Fe to remain dissolved could be an effective approach for the selective precipitation of Mn from steel pickling wastewater.
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Affiliation(s)
- Mauricio Córdova-Udaeta
- Waseda
Research Institute for Science and Engineering, Faculty of Science
and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku, Tokyo 169-8555, Japan
| | - Bowen Cheng
- School
of Creative Science and Engineering, Waseda
University, 3-4-1 Okubo, Shinjuku, Tokyo 169-8555, Japan
| | - Shigeshi Fuchida
- Department
of Marine Resources and Environment, Tokyo
University of Marine Science and Technology, Konan 4-5-7, Minato, Tokyo 108-8477, Japan
| | - Yutaro Takaya
- Department
of Systems Innovation, Graduate School of Engineering, The University of Tokyo, Hongo 7-3-1, Bunkyo, Tokyo 113-8656, Japan
| | - Jun Horiuchi
- Functional
Materials Research Department, JFE Steel
Corporation, Kawasaki 1, Chuo, Chiba 260-0835, Japan
| | - Hiroyuki Masuoka
- Functional
Materials Research Department, JFE Steel
Corporation, Kawasaki 1, Chuo, Chiba 260-0835, Japan
| | - Keishi Oyama
- Department
of Earth Resources Engineering, Graduate School of Engineering, Kyushu University, Motooka 744, Nishi-ku, Fukuoka 819-0395, Japan
| | - Chiharu Tokoro
- Department
of Systems Innovation, Graduate School of Engineering, The University of Tokyo, Hongo 7-3-1, Bunkyo, Tokyo 113-8656, Japan
- Faculty
of Science and Engineering, Waseda University, Okubo 3-4-1, Shinjuku, Tokyo 169-8555, Japan
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Zhang Y, Li J, Zheng S, Dai R, Wang J, Zhu Y, Zhang W, Xu H, Shen G, Shen H, Ma J, Wang X, Tao S. Trends in the sizes and carbonaceous fractions of primary emitted particulate matter in China from 1960 to 2019. Natl Sci Rev 2025; 12:nwaf003. [PMID: 39963350 PMCID: PMC11831801 DOI: 10.1093/nsr/nwaf003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Revised: 09/19/2024] [Accepted: 01/07/2025] [Indexed: 02/20/2025] Open
Abstract
The health impacts of particulate matter (PM) depend on its concentration, size and composition. Herein, we quantified the changes in the emissions of primary PM2.5, PM2.5-10 and PM>10 with aerodynamic diameters of <2.5 μm, 2.5-10 μm and >10 μm, respectively, black carbon (BC), and organic carbon (OC) to address the changes and driving factors. The temporal trends of PM emissions follow Kuznets curves, with 1995 as the peak year when the gross domestic product per capita was only US$1023, showing a late-mover advantage. The fractions of PM2.5 : PM2.5-10 : PM>10 and BC : OC : non-carbonaceous-PM2.5 from various sectors varied following different trajectories. The mass fractions of PM2.5 : PM2.5-10 : PM>10 from iron-steel production industries changed from 21% : 12% : 67% in 1960 to 50% : 13% : 37% in 2019, showing a decrease in PM size. The fractions of BC were linearly correlated with PM2.5, whereas the dependence of OC on PM2.5 differed before and after 1995, owing to changes in residential emissions. Various factors influencing the changes in size and carbonaceous fraction were explored. The major factors were the promotion of dust-removal capacity and the transition in residential energy from solid fuels to emission-free fuels, which increased the fractions of fine PM and carbonaceous fraction.
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Affiliation(s)
- Yuanzheng Zhang
- Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Jin Li
- Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Shuxiu Zheng
- Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Rong Dai
- Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Jinghang Wang
- Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Yaqi Zhu
- Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Wenxiao Zhang
- Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Haoran Xu
- Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Guofeng Shen
- Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Huizhong Shen
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Jianmin Ma
- Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Xuejun Wang
- Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Shu Tao
- Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
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Yang J, Tian H, Fu Z, Bai X, Wang K, Liu W, Lu Y, Zhou Y, Zhao H, Cui J, Du Q. Historical inventories and future scenarios of multiple hazardous air pollutants (HAPs) emissions from the iron and steel production industry in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2025; 958:178051. [PMID: 39708751 DOI: 10.1016/j.scitotenv.2024.178051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2024] [Revised: 12/08/2024] [Accepted: 12/09/2024] [Indexed: 12/23/2024]
Abstract
Iron and steel production (ISP) is one of significant atmospheric pollution emission sources in China. With the implementation of ultra-low emission (ULE) standards, a detailed and new updated emission inventory is urgently needed for better understanding of the temporal trends and spatial variation of emission characteristics. In this study, a unit-based comprehensive emission inventory of multiple hazardous air pollutants (HAPs) for the Chinese ISP spanned from 2012 to 2021, including the conventional pollutants, 13 kinds of Trace elements as well as 2 unconventional but toxic pollutants (PCDD/Fs, F), was dedicatedly developed by integrating dynamic localized emission factors with unit-based information of both the detailed activity level and abatement technology application. Scenario analyses were also conducted to forecast future emission trends up to 2050. The results showed that the lower emission factors owing to ULE retrofits had resulted in different spatial-temporal emission characteristics between outputs and HAPs emissions. The atmospheric emissions of SO2, NOX, PM2.5, PM10, TSP, F and PCDD/Fs from Chinese ISP in 2021 were estimated at about 414.4, 450.5, 1044.3, 2001.6, 3958.8, 9.42 kt and 9.35 kg-TEQ, respectively. And most HAPs emissions were lower than those in 2012. Regarding the spatial distribution, Hebei, Jiangsu, Shanxi, ranked as the top 3 emission provinces. Moreover, different manufacturing processes (sintering, blast furnace, steel making, etc.) contributed with different magnitude for specific species, and the future trends estimation showed a great reducing potential caused by the implementation of ULE abatement measures and structure adjustment in future.
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Affiliation(s)
- Junqi Yang
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Hezhong Tian
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China.
| | - Zhiqiang Fu
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Xiaoxuan Bai
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China; Jibei Electric Power Research Institute, State Grid Jibei Electric Power Co., Ltd., (North China Electric Power Research Institute Company Limited), Beijing 100045, China
| | - Kun Wang
- Institute of Urban Safety and Environmental Science, Beijing Academy of Science and Technology, Beijing 100054, China
| | - Wenjun Liu
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Yiping Lu
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Yu Zhou
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Hongyan Zhao
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Jiangyu Cui
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Qinwei Du
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
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5
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Bai Y, Huang Y, Jiang M, Zhao P, Qi Q, Wang Q. Spillover effects of structure-adjustment pollution reduction measures in China's iron and steel industry. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 368:122133. [PMID: 39163675 DOI: 10.1016/j.jenvman.2024.122133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 07/31/2024] [Accepted: 08/06/2024] [Indexed: 08/22/2024]
Abstract
The iron and steel industry (ISI) is a significant source of sulfur dioxide and particulate matter pollution in China. Existing research on regional environmental regulation or ISI emission reduction strategies tends to overlook spillover effects and the enterprise perspective. During the heating season, production limitations in ISI are potential policy measures for achieving structural emission reductions in heavily polluted cities in China's Jing-Jin-Ji and surrounding regions. We adopt a bottom-up modeling approach, incorporating effective production time to describe enterprise behavior and establishing a quantitative trade model based on trade theory. By modeling three types of production restriction policies outlined in policy documents, we evaluate the emission reduction effects of structure-adjustment measures using the example of reduced effective production time for steel-producing enterprises in the air pollution transmission channel in the Beijing-Tianjin-Hebei area. The results indicate the following: (1) Reducing the effective production time of ISI enterprises can help decrease domestic production value and total factor productivity in pollution-intensive industries, including but not limited to ISI. It also leads to reduced emissions of various pollutants in the implementation regions. (2) Due to interprovincial trade and input-output linkages, structural reduction measures in certain regions have implications for almost all other provinces' industrial structures. Differences in initial industrial structures, factor endowments, and geographical locations contribute to varying directions and magnitudes of industrial structural changes. Pollution-intensive industries' share tends to increase higher in less developed regions. (3) Our estimated pollution reduction is smaller compared to the literature evaluating clean air policies in similar regions using top-down strategies. This discrepancy arises because we analyze a single policy tool rather than modeling industry-wide emission fluctuations from the top down. Additionally, our modeling approach allows us to examine dynamic changes in comparative advantages. The increase in production scale for certain industries in policy-affected regions partially offsets the decline in pollution emissions. These findings enhance our understanding of structure-adjustment reduction measures' role and highlight their potential advantages and limitations.
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Affiliation(s)
- Yang Bai
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China.
| | - Yumeng Huang
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China.
| | - Mingdong Jiang
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China.
| | - Pujie Zhao
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China.
| | - Qiuyue Qi
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China.
| | - Qi Wang
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China.
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6
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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. ENVIRONMENTAL SCIENCE & TECHNOLOGY 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] [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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