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Lü JL, Han Q, Wang Z, Tao M, Hu X, Cao M, Huang Q, Shi M, He Z, Zhao X. Characteristics, sources, and health risks of fine particulate matter in Wuhan subway, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2025; 967:178842. [PMID: 39955941 DOI: 10.1016/j.scitotenv.2025.178842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2024] [Revised: 02/10/2025] [Accepted: 02/11/2025] [Indexed: 02/18/2025]
Abstract
The concentrations and characteristics of PM2.5-bound metals in subway station atmosphere change in time and location within subway stations, which can intensely influence the health of subway workers and passengers. Therefore, this study aimed to reveal the characteristics and distribution of PM2.5-bound metals in subway stations and across subway stations, further identify the possible sources and perform health risk assessment by integrating source apportionment with element-specific health risk analysis. The PM2.5 samples were collected from entrances and platforms in seventeen subway stations in Wuhan, China. Fifteen metals in PM2.5 from subway stations were detected. Enrichment factors (EFs) and the positive matrix factorization (PMF) model were applied to identify potential sources. Non-carcinogenic and carcinogenic health risks to subway workers and passengers were conducted. The most abundant metallic element in PM2.5 at subway stations was Fe. The annual mean concentrations of Fe at subway station entrances and platforms were respectively 1898.29 ± 1554.66 ng/m3 and 6615.43 ± 6515.85 ng/m3, which is significantly higher than the other metals. Six sources of metallic elements in PM2.5 were identified at entrances and platforms in four seasons. Although there were seasonal and spatial differences of the six sources, anthropogenic sources consistently dominated PM2.5-bound metals in subway stations. Rails and wheels were the most predominant source in subway stations. Cr(VI) and As had carcinogenic risks to subway workers. In conjunction with source apportionment with element-specific health risk analysis, rails and wheels mainly contributed carcinogenic risks to subway workers, followed by coal burning. This study provides basic data for source control and mitigation measures to protect subway workers' health.
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Affiliation(s)
- Ji-Liang Lü
- Wuhan Center for Disease Prevention and Control, Wuhan 430022, China; School of Environmental Science and Engineering / Hubei Key Laboratory of Mine Environmental Pollution Control & Remediation, Hubei Polytechnic University, Huangshi 435003, China
| | - Qing Han
- Wuhan Center for Disease Prevention and Control, Wuhan 430022, China
| | - Zhen Wang
- School of Environmental Science and Engineering / Hubei Key Laboratory of Mine Environmental Pollution Control & Remediation, Hubei Polytechnic University, Huangshi 435003, China
| | - Min Tao
- School of Environmental Science and Engineering / Hubei Key Laboratory of Mine Environmental Pollution Control & Remediation, Hubei Polytechnic University, Huangshi 435003, China
| | - Xun Hu
- Wuhan Center for Disease Prevention and Control, Wuhan 430022, China
| | - Meiling Cao
- Wuhan Center for Disease Prevention and Control, Wuhan 430022, China
| | - Qingzhu Huang
- Wuhan Center for Disease Prevention and Control, Wuhan 430022, China
| | - Mengdie Shi
- Wuhan Center for Disease Prevention and Control, Wuhan 430022, China
| | - Zhenyu He
- Wuhan Center for Disease Prevention and Control, Wuhan 430022, China.
| | - Xiaohu Zhao
- College of Resources and Environment, Huazhong Agricultural University / Research Center of Trace Elements, Wuhan 430070, China.
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Li Y, Wang X, Xu P, Gui J, Guo X, Yan G, Fei X, Yang A. Chemical characterization and source identification of PM 2.5 in the Huaxi urban area of Guiyang. Sci Rep 2024; 14:30451. [PMID: 39668154 PMCID: PMC11638253 DOI: 10.1038/s41598-024-81048-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2024] [Accepted: 11/25/2024] [Indexed: 12/14/2024] Open
Abstract
In 2020, 123 PM2.5 samples were collected across different seasons in Huaxi District, Guiyang. The primary chemical components of PM2.5, including water-soluble ions (WSIIs), metallic elements, organic carbon (OC), and elemental carbon (EC), were analyzed. During the sampling period, the average PM2.5 concentration was 39.7 ± 22.3 µg/m2. Chemical mass closure (CMC) was used to reconstruct PM2.5 mass, yielding a reconstructed concentration of 29.1 ± 16.5 µg/m2. The major components were organic matter (OM), sulfate + nitrate + ammonium (SNA), and mineral dust (MD), with mean concentrations of 12.2 ± 6.3 µg/m2, 8.2 ± 4.0 µg/m2, and 6.3 ± 4.6 µg/m2, respectively. From clean days (CD) to lightly-moderately polluted days (LMPD), nitrate oxidation ratio (NOR) increased from 0.09 to 0.16, while sulfate oxidation ratio (SOR) and OC/EC ratio rose by 21.7% and 13.5%, indicating stronger secondary reactions on polluted days. The study also examined changes in chemical components under different atmospheric oxidizing and humidity conditions, revealing that sulfate and nitrate concentrations increased with relative humidity (RH) between 60 and 80%, while other components, especially MD, showed a declining trend due to hygroscopic growth and subsequent gravitational settling and precipitation. The average NO3-/SO42- ratio was 0.67, indicating that fixed sources such as industrial and coal emissions were the main contributors to PM2.5. This study provides insights into the chemical composition, pollution processes, and formation mechanisms of PM2.5, which are crucial for developing effective air pollution control strategies. Furthermore, source apportionment was conducted with the positive matrix factorization (PMF) model. The Coal combustion, secondary, traffic, Industrial and dust source contributions to the PM2.5 mass were approximately 30.5%, 20.0%, 18.3%,16.7% and 14.5%, respectively.
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Affiliation(s)
- Yunwu Li
- College of Resources and Environmental Engineering, Guizhou Karst Environmental Ecosystems Observation and Research Station, Key Laboratory of Karst Georesources and Environment, Ministry of Education, Guizhou University, Guiyang, 550025, China
| | - Xianqin Wang
- College of Resources and Environmental Engineering, Guizhou Karst Environmental Ecosystems Observation and Research Station, Key Laboratory of Karst Georesources and Environment, Ministry of Education, Guizhou University, Guiyang, 550025, China
| | - Peng Xu
- College of Resources and Environmental Engineering, Guizhou Karst Environmental Ecosystems Observation and Research Station, Key Laboratory of Karst Georesources and Environment, Ministry of Education, Guizhou University, Guiyang, 550025, China.
| | - Jiaqun Gui
- College of Resources and Environmental Engineering, Guizhou Karst Environmental Ecosystems Observation and Research Station, Key Laboratory of Karst Georesources and Environment, Ministry of Education, Guizhou University, Guiyang, 550025, China
| | - Xingqiang Guo
- College of Resources and Environmental Engineering, Guizhou Karst Environmental Ecosystems Observation and Research Station, Key Laboratory of Karst Georesources and Environment, Ministry of Education, Guizhou University, Guiyang, 550025, China
| | - Guangxuan Yan
- School of Environment, Key Laboratory for Yellow River and Huai River Water Environment and Pollution Control, Ministry of Education, Henan Normal University, Xinxiang, 453007, China
| | - Xuehai Fei
- College of Resources and Environmental Engineering, Guizhou Karst Environmental Ecosystems Observation and Research Station, Key Laboratory of Karst Georesources and Environment, Ministry of Education, Guizhou University, Guiyang, 550025, China
| | - Aijiang Yang
- College of Resources and Environmental Engineering, Guizhou Karst Environmental Ecosystems Observation and Research Station, Key Laboratory of Karst Georesources and Environment, Ministry of Education, Guizhou University, Guiyang, 550025, China
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Chen Y, Ye X, Yao Y, Lv Z, Fu Z, Huang C, Wang R, Chen J. Characteristics and sources of PM 2.5-bound elements in Shanghai during autumn and winter of 2019: Insight into the development of pollution episodes. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 881:163432. [PMID: 37059141 DOI: 10.1016/j.scitotenv.2023.163432] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/24/2023] [Accepted: 04/07/2023] [Indexed: 06/01/2023]
Abstract
Elemental composition of PM2.5 dispersed in the atmosphere has received increasing attention due to its health effect and catalytic activities. In this study, the characteristics and source apportionment of PM2.5-bound elements were investigated using hourly measurements. K is the most abundant metal element, followed by Fe > Ca > Zn > Mn > Ba > Pb > Cu > Cd. With an average of 8.8 ± 4.1 ng m-3, Cd was the only element whose pollution level exceeded the limits of Chinese standards and WHO guidelines. The concentrations of As, Se, and Pb doubled in December compared to November, indicating a large increase in coal consumption in winter. The enrichment factors of As, Se, Hg, Zn, Cu, Cd, and Ag were larger than 100, indicating that anthropogenic activities greatly affected them. Ship emissions, coal combustion, soil dust, vehicle emissions, and industrial emissions were identified as major sources of trace elements. In November, the pollution from coal burning and industrial activities was significantly reduced, demonstrating the remarkable achievement of coordinated control measures. For the first time, hourly measurements of PM2.5-bound elements and secondary sulfate and nitrate were used to investigate the development of dust and PM2.5 events. During a dust storm event, secondary inorganic salts, potentially toxic elements, and crustal elements sequentially reached peak concentrations, indicating different source origins and formation mechanisms. During the winter PM2.5 event, the sustained increase of trace elements was attributed to the accumulation of local emissions, while regional transport was responsible for the explosive growth before the end of the event. This study highlights the important role of hourly measurement data in distinguishing local accumulation from regional and long-range transport.
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Affiliation(s)
- Yanan Chen
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP(3)), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
| | - Xingnan Ye
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP(3)), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China; Institute of Eco-Chongming (IEC), Chongming District, Shanghai 202162, China.
| | - Yinghui Yao
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP(3)), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
| | - Zhixiao Lv
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP(3)), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
| | - Zhenghang Fu
- Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China
| | - Cheng Huang
- Shanghai Academy of Environmental Sciences, Shanghai 200233, China
| | - Ruoyan Wang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP(3)), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
| | - Jianmin Chen
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP(3)), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China; Institute of Eco-Chongming (IEC), Chongming District, Shanghai 202162, China; Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China
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Zhou X, Xie M, Zhao M, Wang Y, Luo J, Lu S, Li J, Liu Q. Pollution characteristics and human health risks of PM 2.5-bound heavy metals: a 3-year observation in Suzhou, China. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2023:10.1007/s10653-023-01568-x. [PMID: 37072576 PMCID: PMC10113128 DOI: 10.1007/s10653-023-01568-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 04/05/2023] [Indexed: 05/03/2023]
Abstract
This study aimed to analyze the temporal trends, pollution levels, and health risks associated with eleven PM2.5-bound heavy metals (Sb, Al, As, Hg, Cd, Cr, Mn, Ni, Pb, Se and Tl). A total of 504 PM2.5 samples were collected in Suzhou from January 2019 to December 2021. The pollution levels were estimated based on enrichment factors (EFs) which can be used to calculate the enrichment of heavy metals in PM2.5 and determine whether the concentrations of PM2.5-bound heavy metals are influenced by the crustal or anthropogenic sources, and the health risk of PM2.5-bound heavy metals via inhalation was assessed following US EPA's Risk Assessment Guidance for Superfund (RAGS). The annual average concentration of PM2.5 was 46.76 μg m-3, which was higher than the WHO recommended limit of 5 μg m-3. The average of the sum of eleven PM2.5-bound heavy metals was 180.61 ng m-3, dominated by Al, Mn, and Pb. The concentration of PM2.5 in 2020 was significantly lower than that in 2019 and 2021. The PM2.5 and PM2.5-bound heavy metal concentrations in winter and spring were significantly higher than those in autumn and summer. The EF of As, Cr, Cd, Hg, Ni, Pb, Sb, Mn, Se, and Tl was higher than 10, indicating they were mainly from anthropogenic sources. Exposure to a single non-carcinogenic heavy metal via inhalation was unlikely to cause non-carcinogenic effects (HQ < 1), but the integrated non-carcinogenic risks should be taken seriously (HI > 1). The cumulative carcinogenic risks from the carcinogenic elements were exceeding the lower limit (1 × 10-6) of the acceptable risk range. The carcinogenic risks of As and Cr(VI) contributed 60.98% and 26.77%, respectively, which were regarded as two key carcinogenic risk factors. Overall, the government policies and countermeasures for the PM2.5 pollution control should be performed not only based on the PM2.5 concentration but also based on the PM2.5-bound heavy metals and their health risks for the local residents.
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Affiliation(s)
- Xiaolong Zhou
- Department of Environmental Hygiene, Suzhou Center for Disease Control and Prevention, Suzhou, China
| | - Mengmeng Xie
- Department of Clinical Nutrition, Suzhou Ninth People's Hospital, Suzhou, China
| | - Minxian Zhao
- Department of Environmental Hygiene, Suzhou Center for Disease Control and Prevention, Suzhou, China
| | - Ying Wang
- Department of Environmental Hygiene, Suzhou Center for Disease Control and Prevention, Suzhou, China
| | - Jia Luo
- Physical and Chemical Laboratory, Suzhou Center for Disease Control and Prevention, Suzhou, China
| | - Songwen Lu
- Department of Environmental Hygiene, Suzhou Center for Disease Control and Prevention, Suzhou, China
| | - Jie Li
- Department of Environmental Hygiene, Suzhou Center for Disease Control and Prevention, Suzhou, China
| | - Qiang Liu
- Department of Environmental Hygiene, Suzhou Center for Disease Control and Prevention, Suzhou, China.
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Yu P, Han Y, Wang M, Zhu Z, Tong Z, Shao X, Peng J, Hamid Y, Yang X, Deng Y, Huang Y. Heavy metal content and health risk assessment of atmospheric particles in China: A meta-analysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 867:161556. [PMID: 36640888 DOI: 10.1016/j.scitotenv.2023.161556] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 12/10/2022] [Accepted: 01/08/2023] [Indexed: 06/17/2023]
Abstract
In recent decades, China has devoted significant attention to the heavy metals pollution in particulate matter. However, the majority of studies have only focused on the field monitoring in relatively remote areas, which may not be representative of air quality across the country. This study reevaluated the characteristics, temporal and spatial changes, and health concerns associated with heavy metal pollution in atmospheric particulates on a national scale by coupling Meta-analysis and Monte Carlo simulation analysis. In terms of spatial distribution, the heavy metals pollution levels in the northern coast and northeastern regions are relatively high, whereas it is low along the middle Yellow River, middle Yangtze River, as well as Southwest. With the exception of Cu, the distribution of all elements in PM2.5 steadily decreased over time Moreover, PM10 and PM2.5 performed similar where Cd and Ni both first increased followed by a decline while, Cr displayed a decrease before it showed an increment. And since the implementation of prevention and control policies about the atmospheric release, the focus of industrial emission has gradually shifted from energy production and processing to living products manufacturing. Moreover, the carcinogenic risk was shown to be Cr > As, Pb > Ni, Cd, while the non-carcinogenic risk was as follows: As, Ni > Cr, Cd. Among all contaminants, Cd, As, and Cr in PM2.5 and PM10 exceeded the WHO standard in the cities with worst air quality. It was observed that As posed the largest non-carcinogenic risk to adults while, Cr caused the most carcinogenic risk to adults and children, where the carcinogenic risk of children remains higher than that of adults. Therefore, the findings of this study may offer data support to the China's heavy metal pollution standards in airborne particles and offer theoretical data support for pollution management.
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Affiliation(s)
- Pengyue Yu
- National Engineering Laboratory of High Efficient Use on Soil and Fertilizer, College of Resources and Environment, Hunan Agricultural University, Changsha 410128, China
| | - Yongliang Han
- National Engineering Laboratory of High Efficient Use on Soil and Fertilizer, College of Resources and Environment, Hunan Agricultural University, Changsha 410128, China
| | - Maodi Wang
- National Engineering Laboratory of High Efficient Use on Soil and Fertilizer, College of Resources and Environment, Hunan Agricultural University, Changsha 410128, China
| | - Zhen Zhu
- National Engineering Laboratory of High Efficient Use on Soil and Fertilizer, College of Resources and Environment, Hunan Agricultural University, Changsha 410128, China
| | - Zhenglong Tong
- National Engineering Laboratory of High Efficient Use on Soil and Fertilizer, College of Resources and Environment, Hunan Agricultural University, Changsha 410128, China
| | - XingYuan Shao
- National Engineering Laboratory of High Efficient Use on Soil and Fertilizer, College of Resources and Environment, Hunan Agricultural University, Changsha 410128, China
| | - Jianwei Peng
- National Engineering Laboratory of High Efficient Use on Soil and Fertilizer, College of Resources and Environment, Hunan Agricultural University, Changsha 410128, China
| | - Yasir Hamid
- Ministry of Education (MOE) Key Lab of Environ. Remediation and Ecol. Health, College of Environmental and Resources Science, Zhejiang University, Hangzhou 310058, China
| | - Xiaoe Yang
- Ministry of Education (MOE) Key Lab of Environ. Remediation and Ecol. Health, College of Environmental and Resources Science, Zhejiang University, Hangzhou 310058, China
| | - Yaocheng Deng
- National Engineering Laboratory of High Efficient Use on Soil and Fertilizer, College of Resources and Environment, Hunan Agricultural University, Changsha 410128, China.
| | - Ying Huang
- National Engineering Laboratory of High Efficient Use on Soil and Fertilizer, College of Resources and Environment, Hunan Agricultural University, Changsha 410128, China.
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