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Takahara H, Morikawa A, Kitayama S, Matsuyama T, Tsuji K. Elemental analysis of hourly collected air filters with X-ray fluorescence under grazing incidence. ANAL SCI 2024; 40:519-529. [PMID: 38143248 DOI: 10.1007/s44211-023-00483-6] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 11/27/2023] [Indexed: 12/26/2023]
Abstract
The X-ray fluorescence under grazing incidence condition (XRF-UGI) was applied for the direct analysis of aerosol filters. Particulate matter less than 2.5 microns (PM2.5) was collected hourly on polytetrafluoroethylene filters using a continuous PM monitor with a virtual impactor method. Although the sampling mass is in trace amounts of 5-30 μg, the metallic contents, such as V, Cr, Mn, Fe, Zn, and Pb, can be measured at sub-ng m-3 detection limits. The effects of the non-uniformity and poor flatness of the PM filters were discussed with regard to the measurement repeatability. The relationship between the XRF-UGI intensities and the mass concentrations obtained via conventional X-ray fluorescence (XRF) analysis was confirmed using the fundamental parameter method. Finally, quantification was successfully demonstrated using the XRF-UGI results with the relative sensitivity factors.
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Affiliation(s)
- Hikari Takahara
- X-ray Product Div., Rigaku Corporation, 14-8 Akaoji-cho, Takatsuki, Osaka, 569-1146, Japan.
| | - Atsushi Morikawa
- X-ray Product Div., Rigaku Corporation, 14-8 Akaoji-cho, Takatsuki, Osaka, 569-1146, Japan
| | - Saori Kitayama
- Engineering Sect., Kimoto Electric Co., Ltd., 3-1, Funabashi-cho, Tennoji-ku, Osaka, 543-0024, Japan
| | - Tsugufumi Matsuyama
- Graduate School of Engineering, Osaka Metropolitan University, 3-3-138 Sugimoto-cho, Sumiyoshi-ku, Osaka, 558-8585, Japan
| | - Kouichi Tsuji
- Graduate School of Engineering, Osaka Metropolitan University, 3-3-138 Sugimoto-cho, Sumiyoshi-ku, Osaka, 558-8585, Japan
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2
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Shen J, Song Y, Cheng C, Duan F, Liu C, Chai Y, Wang S, Xiong Q, Wu J. Spectroscopic and compositional profiles of dissolved organic matters in urban snow from 2019 to 2021: Focusing on pollution features identification. Water Res 2023; 229:119408. [PMID: 36462254 DOI: 10.1016/j.watres.2022.119408] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [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: 08/29/2022] [Revised: 11/20/2022] [Accepted: 11/21/2022] [Indexed: 06/17/2023]
Abstract
Snow owns stronger adsorption capacity for organic pollutants compared with rain. Huge amounts of anthropogenic dissolved organic matters (DOMs) in the atmosphere may enter the water environment with urban snow and increase water pollution risk. Extracting stable pollution features of urban snow is conducive to identifying the urban snow pollution from the water environment. Herein, we systematically explored the spectroscopic and compositional profiles of urban snow in Beijing from three snow events by multiple analytical tools and extracted stable pollution features of urban snow for the first time. Results showed that conventional pollutants with high concentration were detected in urban snow. The fluorescence signals of humic-like and some protein-like materials, the molecular weight distributions of chromophoric DOM at 254 nm and humic-like materials, and 172 kinds of lignin-like molecular formulas were extracted as stable features for urban snow. These stable features of urban snow laid the foundation for the identification of urban snow pollution and the analysis of the impact mechanisms of atmospheric pollution sources on the water environment.
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Affiliation(s)
- Jian Shen
- Research Center of Environmental Technology in Water Pollution Source Identification and Precise Supervision, School of Environment, Tsinghua University, Beijing, 100084, China; Research and Development Center of Advanced Environmental Supervision Technology and Instrument, Research Institute for Environmental Innovation (Suzhou) Tsinghua, Suzhou, 215163, China; State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Yiming Song
- Research Center of Environmental Technology in Water Pollution Source Identification and Precise Supervision, School of Environment, Tsinghua University, Beijing, 100084, China; Research and Development Center of Advanced Environmental Supervision Technology and Instrument, Research Institute for Environmental Innovation (Suzhou) Tsinghua, Suzhou, 215163, China; State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Cheng Cheng
- Research Center of Environmental Technology in Water Pollution Source Identification and Precise Supervision, School of Environment, Tsinghua University, Beijing, 100084, China; Research and Development Center of Advanced Environmental Supervision Technology and Instrument, Research Institute for Environmental Innovation (Suzhou) Tsinghua, Suzhou, 215163, China; State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Fengkui Duan
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Chuanyang Liu
- Research Center of Environmental Technology in Water Pollution Source Identification and Precise Supervision, School of Environment, Tsinghua University, Beijing, 100084, China; Research and Development Center of Advanced Environmental Supervision Technology and Instrument, Research Institute for Environmental Innovation (Suzhou) Tsinghua, Suzhou, 215163, China; State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Yidi Chai
- Research Center of Environmental Technology in Water Pollution Source Identification and Precise Supervision, School of Environment, Tsinghua University, Beijing, 100084, China; Research and Development Center of Advanced Environmental Supervision Technology and Instrument, Research Institute for Environmental Innovation (Suzhou) Tsinghua, Suzhou, 215163, China
| | - Siting Wang
- Research and Development Center of Advanced Environmental Supervision Technology and Instrument, Research Institute for Environmental Innovation (Suzhou) Tsinghua, Suzhou, 215163, China
| | - Qiuran Xiong
- Research Center of Environmental Technology in Water Pollution Source Identification and Precise Supervision, School of Environment, Tsinghua University, Beijing, 100084, China; Research and Development Center of Advanced Environmental Supervision Technology and Instrument, Research Institute for Environmental Innovation (Suzhou) Tsinghua, Suzhou, 215163, China; State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Jing Wu
- Research Center of Environmental Technology in Water Pollution Source Identification and Precise Supervision, School of Environment, Tsinghua University, Beijing, 100084, China; Research and Development Center of Advanced Environmental Supervision Technology and Instrument, Research Institute for Environmental Innovation (Suzhou) Tsinghua, Suzhou, 215163, China; State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China.
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3
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Ma T, Furutani H, Duan F, Kimoto T, Ma Y, Zhu L, Huang T, Toyoda M, He K. Distinct diurnal chemical compositions and formation processes of individual organic-containing particles in Beijing winter. Environ Pollut 2023; 318:120846. [PMID: 36496065 DOI: 10.1016/j.envpol.2022.120846] [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: 06/15/2022] [Revised: 12/05/2022] [Accepted: 12/06/2022] [Indexed: 06/17/2023]
Abstract
Organic aerosols (OA) are major components of fine particulate matter, yet their formation mechanism remains unclear, especially in polluted environments. In this study, we investigated the diurnal chemical compositions and formation processes of OA in carbonaceous particles during winter in Beijing using aerosol time-of-flight mass spectrometry. We found that 84.5% of the measured carbonaceous particles underwent aging processes, characterized by larger diameter and more secondary species compared to fresh carbonaceous particles, and presented different chemical compositions of OA in the daytime and nighttime. During the day, under high O3 concentrations, organosulfates and oligomers existed in the aged carbonaceous particles, which were mixed with a higher signal of nitrate compared with sulfate. At night, under high relative humidity, distinct spectral signatures of hydroxymethanesulfonate and organic nitrogen compounds, and minor signals of other hydroxyalkylsulfonates and high molecular weight organic compounds were present in the aged carbonaceous particles, which were mixed with a higher signal of sulfate compared with nitrate. Our results indicated that photochemistry contributed to OA formation in the daytime, while aqueous chemistry played an important role in OA formation in the nighttime. The findings can help improve the performance of air quality and climate models on OA simulation.
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Affiliation(s)
- Tao Ma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Hiroshi Furutani
- Support Center for Scientific Instrument Renovation and Custom Fabrication, Osaka University, Osaka, 560-0043, Japan; Project Research Center for Fundamental Sciences, Graduate School of Science, Osaka University, Osaka, 560-0043, Japan
| | - Fengkui Duan
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China.
| | - Takashi Kimoto
- Kimoto Electric Co., Ltd., 3-1 Funahashi-cho Tennoji-ku, Osaka 543-0024, Japan
| | - Yongliang Ma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Lidan Zhu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Tao Huang
- Kimoto Electric Co., Ltd., 3-1 Funahashi-cho Tennoji-ku, Osaka 543-0024, Japan
| | - Michisato Toyoda
- Project Research Center for Fundamental Sciences, Graduate School of Science, Osaka University, Osaka, 560-0043, Japan
| | - Kebin He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
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Ma T, Duan F, Ma Y, Zhang Q, Xu Y, Li W, Zhu L, He K. Unbalanced emission reductions and adverse meteorological conditions facilitate the formation of secondary pollutants during the COVID-19 lockdown in Beijing. Sci Total Environ 2022; 838:155970. [PMID: 35588831 PMCID: PMC9109998 DOI: 10.1016/j.scitotenv.2022.155970] [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] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 04/23/2022] [Accepted: 05/11/2022] [Indexed: 06/02/2023]
Abstract
During the coronavirus disease 2019 (COVID-19) lockdown in 2020, severe haze pollution occurred in the North China Plain despite the significant reduction in anthropogenic emissions, providing a natural experiment to explore the response of haze pollution to the reduction of human activities. Here, we study the characteristics and causes of haze pollution during the COVID-19 outbreak based on comprehensive field measurements in Beijing during January and February 2020. After excluding the Spring Festival period affected by fireworks activities, we found the ozone concentrations and the proportion of sulfate and nitrate in PM2.5 increased during the COVID-19 lockdown compared with the period before the lockdown, and sulfate played a more important role. Heterogeneous chemistry and photochemistry dominate the formation of sulfate and nitrate during the whole campaign, respectively, and the heterogeneous formation of nitrate at night was enhanced during the lockdown. The coeffects of more reductions in NOx than VOCs, weakened titration of NO, and increased temperature during the lockdown led to the increase in ozone concentrations, thereby promoting atmospheric oxidation capacity and photochemistry. In addition, the increase in relative humidity during the lockdown facilitated heterogeneous chemistry. Our results indicate that unbalanced emission reductions and adverse meteorological conditions induce the formation of secondary pollutants during the COVID-19 lockdown haze, and the formulation of effective coordinated emission-reduction control measures for PM2.5 and ozone pollution is needed in the future, especially the balanced control of NOx and VOCs.
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Affiliation(s)
- Tao Ma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Fengkui Duan
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China.
| | - Yongliang Ma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Qinqin Zhang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Yunzhi Xu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Wenguang Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Lidan Zhu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Kebin He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
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Li H, Ma Y, Duan F, Huang T, Kimoto T, Hu Y, Huo M, Li S, Ge X, Gong W, He K. Characterization of haze pollution in Zibo, China: Temporal series, secondary species formation, and PM x distribution. Chemosphere 2022; 286:131807. [PMID: 34371362 DOI: 10.1016/j.chemosphere.2021.131807] [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: 02/26/2021] [Revised: 07/13/2021] [Accepted: 08/03/2021] [Indexed: 06/13/2023]
Abstract
An online field observation was conducted in Zibo, China from September 1, 2018 to February 28, 2019, covering autumn and winter. Within the investigation period, the mean mass concentrations of PM1, PM2.5, and PM10 were 49.3, 86.1, and 136.5 μg m-3, respectively. OA (organic aerosol) was the most dominant species in PM2.5 (39.7 %), followed by NO3- (26.3 %) and SO42- (17.0 %), indicating the importance of secondary species on PM2.5. Increase of particles were always accompanied increasing relative humidity (RH), slow wind, and increasing precursors, contributing the secondary transition. SO42- was more susceptible to RH, indicating the dominant role of heterogeneous processes in its secondary formation. As RH increased, its strengthening effect on SO42- increased as well. Photochemistry was the main contributor to the secondary formation of NO3-. The morning and evening rush hours determined the peak of absolute NO3- throughout the day. By classifying particles into three bins, we found that smaller particles were the biggest contributors (larger PM1/PM2.5) of slight pollution (35 < PM2.5<115 μg m-3). When severe haze occurred, PM2.5 contributed more than particles of other sizes (PM1 or PM10). Secondary species contributed more to particles within 2.5 μm but less to larger particles. PM1/PM2.5 was high from 9:00 to 15:00, indicating the strong effect of photochemistry on smaller particles. In comparison, larger particles favored more humid conditions. NO3- preferentially existed in larger particles because the hygroscopicity of preexisting species (SO42- and NO3-) promoted partitioning. SO42- appeared a stable diurnal variation, replying its stable contribution to particles of different sizes.
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Affiliation(s)
- Hui Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China
| | - Yongliang Ma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China
| | - Fengkui Duan
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China.
| | - Tao Huang
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku, Osaka, 543-0024, Japan
| | - Takashi Kimoto
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku, Osaka, 543-0024, Japan
| | - Yunxing Hu
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku, Osaka, 543-0024, Japan
| | - Mingyu Huo
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku, Osaka, 543-0024, Japan
| | - Shihong Li
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku, Osaka, 543-0024, Japan
| | - Xiang Ge
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku, Osaka, 543-0024, Japan
| | - Wanru Gong
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku, Osaka, 543-0024, Japan
| | - Kebin He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China
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Li H, Ma Y, Duan F, Zhu L, Ma T, Yang S, Xu Y, Li F, Huang T, Kimoto T, Zhang Q, Tong D, Wu N, Hu Y, Huo M, Zhang Q, Ge X, Gong W, He K. Stronger secondary pollution processes despite decrease in gaseous precursors: A comparative analysis of summer 2020 and 2019 in Beijing. Environ Pollut 2021; 279:116923. [PMID: 33751950 DOI: 10.1016/j.envpol.2021.116923] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [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: 11/25/2020] [Revised: 03/04/2021] [Accepted: 03/05/2021] [Indexed: 06/12/2023]
Abstract
To control the spread of COVID-19, China implemented a series of lockdowns, limiting various offline interactions. This provided an opportunity to study the response of air quality to emissions control. By comparing the characteristics of pollution in the summers of 2019 and 2020, we found a significant decrease in gaseous pollutants in 2020. However, particle pollution in the summer of 2020 was more severe; PM2.5 levels increased from 35.8 to 44.7 μg m-3, and PM10 increased from 51.4 to 69.0 μg m-3 from 2019 to 2020. The higher PM10 was caused by two sandstorm events on May 11 and June 3, 2020, while the higher PM2.5 was the result of enhanced secondary formation processes indicated by the higher sulfate oxidation rate (SOR) and nitrate oxidation rate (NOR) in 2020. Higher SOR and NOR were attributed mainly to higher relative humidity and stronger oxidizing capacity. Analysis of PMx distribution showed that severe haze occurred when particles within Bin2 (size ranging 1-2.5 μm) dominated. SO42-(1/2.5) and SO42-(2.5/10) remained stable under different periods at 0.5 and 0.8, respectively, indicating that SO42- existed mainly in smaller particles. Decreases in NO3-(1/2.5) and increases in NO3-(2.5/10) from clean to polluted conditions, similar to the variations in PMx distribution, suggest that NO3- played a role in the worsening of pollution. O3 concentrations were higher in 2020 (108.6 μg m-3) than in 2019 (96.8 μg m-3). Marked decreases in fresh NO alleviated the titration of O3. Furthermore, the oxidation reaction of NO2 that produces NO3- was dominant over the photochemical reaction of NO2 that produces O3, making NO2 less important for O3 pollution. In comparison, a lower VOC/NOx ratio (less than 10) meant that Beijing is a VOC-limited area; this indicates that in order to alleviate O3 pollution in Beijing, emissions of VOCs should be controlled.
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Affiliation(s)
- Hui Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China
| | - Yongliang Ma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China
| | - Fengkui Duan
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China.
| | - Lidan Zhu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China
| | - Tao Ma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China
| | - Shuo Yang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China
| | - Yunzhi Xu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China
| | - Fan Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China
| | - Tao Huang
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku Osaka, 543-0024, Japan
| | - Takashi Kimoto
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku Osaka, 543-0024, Japan
| | - Qinqin Zhang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China
| | - Dan Tong
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Nana Wu
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Yunxing Hu
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku Osaka, 543-0024, Japan
| | - Mingyu Huo
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku Osaka, 543-0024, Japan
| | - Qiang Zhang
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Xiang Ge
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku Osaka, 543-0024, Japan
| | - Wanru Gong
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku Osaka, 543-0024, Japan
| | - Kebin He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China
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Ikemori F, Uranishi K, Sato T, Fujihara M, Hasegawa H, Sugata S. Time-resolved characterization of organic compounds in PM 2.5 collected at Oki Island, Japan, affected by transboundary pollution of biomass and non-biomass burning from Northeast China. Sci Total Environ 2021; 750:142183. [PMID: 33182173 DOI: 10.1016/j.scitotenv.2020.142183] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [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: 05/03/2020] [Revised: 08/21/2020] [Accepted: 09/02/2020] [Indexed: 06/11/2023]
Abstract
To evaluate the transboundary pollution of organic aerosols from Northeast Asia, a highly time-resolved measurement of organic compounds was performed in March 2019 at Oki Island located in Japan, which is a remote site and less affected by local anthropogenic sources. PM2.5, water-soluble organic carbon (WSOC) concentrations, and WSOC fraction in PM2.5 showed high values on March 22-23 (high-WSOC period (HWSOC)) when the air mass passed through the area where many fire spots were detected in Northeast China. Biomass burning tracers showed higher concentration, especially levoglucosan exceeded 1 μg/m3 during the HWSOC than the low-WSOC period (LWSOC). Notably, high time-resolved measurements of biomass burning tracers and back trajectory analysis during HWSOC revealed a difference in the variation of lignin pyrolyzed compounds and anhydrous sugars on 22 and 23 March. The air mass passed to different areas in Northeast China in which fire spots were detected, such as the eastern area on the 22nd and the western area on the 23rd. Almost-organic compounds also showed high concentration and strong correlations with levoglucosan and sulfate during HWSOC. Moreover, low-carbon dicarboxylic acids (e.g., adipic acid) and secondary products from anthropogenic volatile organic compounds (e.g., 2,3-dihydroxy-4-oxopentanoic, phthalic, 5-nitrosalicylic acids), also showed a strong correlation with sulfate ions during the HWSOC and LWSOC, respectively. These higher concentrations and strong correlations with levoglucosan and sulfate during the HWSOC propose that their generation could be enhanced by biomass burning. The ratios of organics (e.g., levoglucosan/mannnosan, pinic/3-methylbutane-1,2,3-tricarboxylic acids) suggest that the high concentrations of PM2.5 and WSOC observed during the HWSOC were caused by aged organic aerosols that originated from the combustion of herbaceous plants transported from Northeast China. Our findings indicate that biomass combustion in Northeast China could significantly affect the chemical compositions and the characterization of organic aerosols in downwind regions of Northeast China.
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Affiliation(s)
- Fumikazu Ikemori
- Nagoya City Institute for Environmental Sciences, 5-16-8, Toyoda, Minami-ku, Nagoya 457-0841, Japan; Institute of Science and Engineering, Kanazawa University, Kakuma-machi, Kanazawa, Ishikawa 920-1192, Japan.
| | - Katsushige Uranishi
- Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Takahiro Sato
- Shimane Prefectural Institute of Public Health and Environmental Science, 582-1 Nishihamasada, Matsue, Shimane 690-0122, Japan
| | - Makoto Fujihara
- Shimane Prefectural Institute of Public Health and Environmental Science, 582-1 Nishihamasada, Matsue, Shimane 690-0122, Japan
| | - Hitomi Hasegawa
- Nagoya City Institute for Environmental Sciences, 5-16-8, Toyoda, Minami-ku, Nagoya 457-0841, Japan
| | - Seiji Sugata
- National Institute for Environmental studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan
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Yang S, Duan F, Ma Y, Li H, Wang J, Du Z, Xu Y, Zhang T, Zhu L, Huang T, Kimoto T, Zhang L, He K. Characteristics and seasonal variations of high-molecular-weight oligomers in urban haze aerosols. Sci Total Environ 2020; 746:141209. [PMID: 32763608 DOI: 10.1016/j.scitotenv.2020.141209] [Citation(s) in RCA: 2] [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: 06/04/2020] [Revised: 07/19/2020] [Accepted: 07/22/2020] [Indexed: 06/11/2023]
Abstract
Organic aerosols (OA) undergo sophisticated physiochemical processes in the atmosphere, playing a crucial role in extreme haze formations over the Northern China Plain. However, current understandings of the detailed composition and formation pathways are limited. In this study, high-molecular weight (HMW) species were observed in samples collected year-round in urban Beijing, especially in autumn and winter, during 2016-2017. The positive-ion-mode mass spectra of matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) showed that higher signal intensities were obtained in the mass-to-charge (m/z) ranges of 200-500 and 800-900, with repetitive mass difference patterns of m/z 12, 14, 16, and 18. This provided sound evidence that high-molecular-weight oligomers were generated as haze episodes became exacerbated. These oligomer signal intensities were enhanced in the presence of high relative humidity, aerosol water content, and PM2.5 (particles with an aerodynamic diameter ≤ 2.5 μm) mass, proving that the multiphase reaction processes play a fundamental role in haze formation in Beijing. Our study can form a basis for improved air pollution mitigation measures aimed at OA to improve health outcomes.
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Affiliation(s)
- Shuo Yang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Fengkui Duan
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China.
| | - Yongliang Ma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Hui Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Jiali Wang
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku, Osaka 543-0024, Japan
| | - Zhenyu Du
- National Research Center for Environmental Analysis and Measurement, Beijing 100029, China
| | - Yunzhi Xu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Ting Zhang
- National Research Center for Environmental Analysis and Measurement, Beijing 100029, China
| | - Lidan Zhu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Tao Huang
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku, Osaka 543-0024, Japan
| | - Takashi Kimoto
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku, Osaka 543-0024, Japan
| | - Lifei Zhang
- National Research Center for Environmental Analysis and Measurement, Beijing 100029, China
| | - Kebin He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China.
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9
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Yang S, Duan F, Ma Y, Li H, Ma T, Zhu L, Huang T, Kimoto T, He K. Mixed and intensive haze pollution during the transition period between autumn and winter in Beijing, China. Sci Total Environ 2020; 711:134745. [PMID: 31822400 DOI: 10.1016/j.scitotenv.2019.134745] [Citation(s) in RCA: 2] [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: 07/18/2019] [Revised: 09/24/2019] [Accepted: 09/29/2019] [Indexed: 05/13/2023]
Abstract
In the Northern China Plain (NCP), extreme haze events with high concentrations of fine particles occur frequently during the winter but rarely occur in autumn. In this study, we present a synthetic analysis of particulate constituents during the historically polluted transition period of autumn-winter in 2018, revealing that mixed-type haze episodes are the result of regional transport, homogeneous/heterogeneous conversion, and sandstorm influences. The hydrolysis process of N2O5 at higher relative humidity levels (>70%), which feature an enhanced nitrate oxidation ratio (0.30-0.70) and NO3- concentration (>60 μg m-3), was the driving factor for high PM2.5 mass concentrations during the observation periods. The long-distance transport of sandstorms, characterized by decreasing PM2.5/PM10 ratios (<30%) from the north/northwest, is the most important factor for the explosive growth of PM10 concentration. These results can help us gain a comprehensive understanding of haze formation and highlight the importance of nitrate chemistry in the aqueous phase. The results suggest that persistent NOx emission reduction measures must be made to better achieve air quality standards in Beijing and the NCP region.
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Affiliation(s)
- Shuo Yang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Fengkui Duan
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China.
| | - Yongliang Ma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Hui Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Tao Ma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Lidan Zhu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Tao Huang
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku, Osaka 543-0024, Japan
| | - Takashi Kimoto
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku, Osaka 543-0024, Japan
| | - Kebin He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
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10
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Ye S, Ma T, Duan F, Li H, He K, Xia J, Yang S, Zhu L, Ma Y, Huang T, Kimoto T. Characteristics and formation mechanisms of winter haze in Changzhou, a highly polluted industrial city in the Yangtze River Delta, China. Environ Pollut 2019; 253:377-383. [PMID: 31325882 DOI: 10.1016/j.envpol.2019.07.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [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: 01/03/2019] [Revised: 07/03/2019] [Accepted: 07/03/2019] [Indexed: 06/10/2023]
Abstract
Changzhou, an industrial city in the Yangtze River Delta, has been experiencing serious haze pollution, particularly in winter. However, studies pertaining to the haze in Changzhou are very limited, which makes it difficult to understand the characteristics and formation of winter haze in this area, and develop effective control measures. In this study, we carried out continuous online observation of particulate matter, chemical components, and meteorology in Changzhou in February 2017. Our results showed that haze pollution occurred frequently in Changzhou winter and exhibited two patterns: dry haze with low relative humidity (RH) and wet haze with high RH. Water-soluble inorganic ions (SO42-, NO3-, and NH4+) accounted for ∼52.2% of the PM2.5 mass, of which sulfate was dominant in wet haze periods while nitrate was dominant in other periods. With the deterioration of haze pollution, the proportion of nitrate in PM2.5 increased, while sulfate proportion increased under wet haze and decreased under dry haze. Dry haze and wet haze appeared under slow north wind and south wind, respectively, and strong north wind or sea breeze scavenged pollution. We found that formation of nitrate occurred rapidly in daytime with high concentrations of odd oxygen (Ox = O3 + NO2), whereas formation of sulfate occurred rapidly during nighttime with high RH, indicating that photochemistry and heterogeneous reaction were the major formation mechanisms for nitrate and sulfate, respectively. Through the cluster analysis of 36-h backward trajectories, five sources of air masses from three directions were identified. High PM2.5 concentrations (84.1 μg m-3 on average) usually occurred under the influence of two clusters (46%) from the northwest, indicating that regional transport from northern China aggravated the winter haze pollution in Changzhou. Emission reduction, particularly the mobile sources, and regional joint prevention and control can help to mitigate the winter haze in Changzhou.
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Affiliation(s)
- Siqi Ye
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Tao Ma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Fengkui Duan
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China.
| | - Hui Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Kebin He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Jing Xia
- Changzhou Environmental Monitoring Center, Changzhou 213001, China
| | - Shuo Yang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Lidan Zhu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Yongliang Ma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Tao Huang
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku, Osaka 543-0024, Japan
| | - Takashi Kimoto
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku, Osaka 543-0024, Japan
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11
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Yang L, Duan F, Tian H, He K, Ma Y, Ma T, Li H, Yang S, Zhu L. Biotoxicity of water-soluble species in PM 2.5 using Chlorella. Environ Pollut 2019; 250:914-921. [PMID: 31085478 DOI: 10.1016/j.envpol.2019.04.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [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: 01/04/2019] [Revised: 04/04/2019] [Accepted: 04/04/2019] [Indexed: 06/09/2023]
Abstract
China has been faced with severe haze pollution, which is hazardous to human health. Among the air pollutants, PM2.5 (particles with an aerodynamic diameter ≤ 2.5 μm) is the most dangerous because of its toxicity and impact on human health and ecosystems. However, there has been limited research on PM2.5 particle toxicity. In the present study, we collected daily PM2.5 samples from January 1 to March 31, 2018 and selected samples to extract water-soluble species, including SO42-, NO3-, WSOC, and NH4+. These samples represented clean, good, slight, moderate, and heavy pollution days. After extraction using an ultrasonic method, PM2.5 solutions were obtained. We used Chlorella as the test algae and studied the content of chlorophyll a, as well as the variation in fluorescence when they were placed into the PM2.5 extraction solution, and their submicroscopic structure was analyzed using transmission electron microscopy (TEM). The results showed that when the air quality was relatively clean and good (PM2.5 concentration ≤ 75 μg m-3), the PM2.5 extraction solutions had no inhibiting effects on Chlorella, whereas when the air quality was polluted (PM2.5 concentration > 75 μg m-3) and heavily polluted (PM2.5 concentration > 150 μg m-3), with increasing PM2.5 concentrations and exposure time, the chlorophyll a content in Chlorella decreased. Moreover, the maximum photochemical quantum yield (Fv/Fm) of Chlorella obviously decreased, indicating chlorophyll inhibition during polluted days with increasing PM2.5 concentrations. The effects on the chlorophyll fluorescence parameters were also obvious, leading to an increase of energy dissipated per unit reaction center (DIo/RC), suggesting that Chlorella could survive when exposed to PM2.5 solutions, whereas the physiological activities were significantly inhibited. The TEM analysis showed that there were few effects on Chlorella cell microstructure during clean days, whereas plasmolysis occurred during light- and medium-polluted days. With increasing pollution levels, plasmolysis became more and more apparent, until the organelles inside the cells were thoroughly destroyed and most of the parts could not be recognized.
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Affiliation(s)
- Liu Yang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China; College of Geology and Environment, Xi'an University of Science and Technology, Xi'an, 710054, China
| | - Fengkui Duan
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China.
| | - Hua Tian
- College of Geology and Environment, Xi'an University of Science and Technology, Xi'an, 710054, China
| | - Kebin He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China
| | - Yongliang Ma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China
| | - Tao Ma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China
| | - Hui Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China
| | - Shuo Yang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China
| | - Lidan Zhu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China
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12
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Yamazaki S, Shima M, Yoda Y, Kurosaka F, Isokawa T, Shimizu S, Ogawa T, Kamiyoshi N, Terada K, Nishikawa J, Hanaoka K, Yamada T, Matsuura S, Hongo A, Yamamoto I. Association between chemical components of PM 2.5 and children's primary care night-time visits due to asthma attacks: A case-crossover study. Allergol Int 2019; 68:329-334. [PMID: 30744923 DOI: 10.1016/j.alit.2019.01.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [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: 10/03/2018] [Revised: 12/24/2018] [Accepted: 01/08/2019] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND Few papers have examined the association between the chemical components of PM2.5 and health effects. The existence of an association is now under discussion. METHODS This case-crossover study aimed to examine the association between the chemical components of PM2.5 and night-time primary care visits (PCVs) due to asthma attacks. The subjects were 1251 children aged 0-14 years who received medical care for asthma at a municipal emergency clinic. We measured daily average concentrations of hydrogen ion, sulfate ion, nitrate ion and water-soluble organic compounds (WSOCs), which are components of PM2.5. We estimated the odds ratios (ORs) of PCVs per unit increment (inter quartile ranges) in each chemical component of PM2.5 for the subgroups of warmer months and colder months separately. RESULTS No association was seen between PCVs and PM2.5 mass concentrations the day before the PCVs in either warmer or colder months. In the warmer months, an association was seen with the concentrations of WSOCs and hydrogen ion the day before the PCVs (OR = 1.33; 95% CI: 1.00-1.76, OR = 1.18; 95% CI: 1.02-1.36, respectively). Furthermore, a negative association was seen between sulfate ion and PCVs (OR = 0.85; 95%CI: 0.74-0.98). No associations were observed in the colder months. CONCLUSIONS We observed a positive association between PCVs and certain concentrations of WSOCs and hydrogen ions in warmer months. In contrast, sulfate ion showed a negative association.
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Affiliation(s)
- Shin Yamazaki
- Environmental Epidemiology Section, National Institute for Environmental Studies, Tsukuba, Japan
| | - Masayuki Shima
- Department of Public Health, Hyogo College of Medicine, Nishinomiya, Japan.
| | - Yoshiko Yoda
- Department of Public Health, Hyogo College of Medicine, Nishinomiya, Japan
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Zhang S, Xing J, Sarwar G, Ge Y, He H, Duan F, Zhao Y, He K, Zhu L, Chu B. Parameterization of heterogeneous reaction of SO 2 to sulfate on dust with coexistence of NH 3 and NO 2 under different humidity conditions. Atmos Environ (1994) 2019; 208:133-140. [PMID: 31186616 PMCID: PMC6559380 DOI: 10.1016/j.atmosenv.2019.04.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Sulfate plays an important role in atmospheric haze in China, which has received considerable attention in recent years. Various types of parameterization methods and heterogeneous oxidation rates of SO2 have been used in previous studies. However, properly representing heterogeneous sulfate formation in air quality models remains a big challenge. In this study, we quantified the heterogeneous oxidation reaction using experimental results that approximate the haze conditions in China. Firstly, a series of experiments were conducted to investigate the heterogeneous uptake of SO2 with different relative humidity (RH) levels and the presence of NH3 and NO2 on natural dust surfaces. Then the uptake coefficients for heterogeneous oxidation of SO2 to sulfate at different RH under NH3 and NO2coexistence were parameterized based on the experimental results and implemented in the Community Multiscale Air Quality modeling system (CMAQ). Simulation results suggested that this new parameterization improved model performance by 6.6% in the simulation of wintertime sulfate concentrations for Beijing. The simulated maximum growth rate of SO4 2- during a heavy pollution period increased from 0.97 μg m-3 h-1 to 10.11 μg m-3 h-1. The heterogeneous oxidation of SO2 in the presence of NH3 contributed up to 23% of the sulfate concentration during heavy pollution periods.
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Affiliation(s)
- Shuping Zhang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jia Xing
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Golam Sarwar
- National Exposure Research Laboratory, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA
| | - Yanli Ge
- State Key Joint Laboratory of Environment Simulation and Pollution Control, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Hong He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China
| | - Fengkui Duan
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Yan Zhao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Kebin He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Lidan Zhu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Biwu Chu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China
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14
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Yang S, Duan F, Ma Y, He K, Zhu L, Ma T, Ye S, Li H, Huang T, Kimoto T. Haze formation indicator based on observation of critical carbonaceous species in the atmosphere. Environ Pollut 2019; 244:84-92. [PMID: 30326389 DOI: 10.1016/j.envpol.2018.10.006] [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: 04/29/2018] [Revised: 10/01/2018] [Accepted: 10/01/2018] [Indexed: 06/08/2023]
Abstract
Organic aerosol (OA) are always the most abundant species in terms of relative proportion to PM2.5 concentration in Beijing, while in previous studies, poor link between carbonaceous particles and their gaseous precursors were established based on field observation results. Through this study, we provided a comprehensive analysis of critical carbonaceous species in the atmosphere. The concentrations, diurnal variations, conversions, and gas-particle partitioning (F-factor) of 8 carbonaceous species, carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), volatile organic compounds (VOCs), non-methane hydrocarbon (NMHC), organic carbon (OC), elemental carbon (EC), and water soluble organic compounds (WSOCs), in Beijing were analyzed synthetically. Carbonaceous gases (CO, CO2, VOCs, and CH4) and OC/EC ratios exhibited double-peak diurnal patterns with a pronounced midnight peak, especially in winter. High correlation between VOCs and OC during winter nighttime indicated that OC was formed from VOCs precursors via an unknown mechanism at relative humidity greater than 50% and 80%, thereby promoting WSOC formation in PM1 and PM2.5 respectively. The established F-factor method was effective to describe gas-to-particle transformation of carbonaceous species and was a good indicator for haze events since high F-factors corresponded with enhanced PM2.5 level. Moreover, higher F-factors in winter indicated carbonaceous species were more likely to exist as particles in Beijing. These results can help gain a comprehensive understanding of carbon cycle and formation of secondary organic aerosols from gaseous precursors in the atmosphere.
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Affiliation(s)
- Shuo Yang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China
| | - Fengkui Duan
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China.
| | - Yongliang Ma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China
| | - Kebin He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China
| | - Lidan Zhu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China
| | - Tao Ma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China
| | - Siqi Ye
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China
| | - Hui Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, 100084, China
| | - Tao Huang
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku, Osaka, 543-0024, Japan
| | - Takashi Kimoto
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku, Osaka, 543-0024, Japan
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15
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Li H, Duan F, Ma Y, He K, Zhu L, Ma T, Ye S, Yang S, Huang T, Kimoto T. Case study of spring haze in Beijing: Characteristics, formation processes, secondary transition, and regional transportation. Environ Pollut 2018; 242:544-554. [PMID: 30007265 DOI: 10.1016/j.envpol.2018.07.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [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: 03/16/2018] [Revised: 04/28/2018] [Accepted: 07/01/2018] [Indexed: 05/13/2023]
Abstract
Continuous haze monitoring was conducted from 12:00 3 April to 12:00 8 April 2016 in Beijing, China to develop a more detailed understanding of spring haze characteristics. The PM2.5 concentration ranged from 6.30 to 165 μg m-3 with an average of 63.8 μg m-3. Nitrate was the most abundant species, accounting for 36.4% of PM2.5, followed by organic carbon (21.5%), NH4+ (19.3%), SO42- (18.8%), and elemental carbon (4.10%), indicating the key role of nitrate in this haze event. Species contribution varied based on the phase of the haze event. For example, sulfate concentration was high during the haze formation phase, nitrate was high during the haze, and secondary organic carbon (SOC) had the highest contribution during the scavenging phase. The secondary transition of sulfate was influenced by SO2, followed by relative humidity (RH) and Ox (O3+NO2). Nitrate formation occurred in two stages: through NO2 oxidation, which was vulnerable to Ox; and by the partitioning of N (+5) which was susceptible to RH and temperature. SOC tended to form when Ox and RH were balanced. According to hourly species behavior, sulfate and nitrate were enriched during haze formation when the mixed layer height decreased. However, SOC accumulated prior to the haze event and during formation, which demonstrated the strong contribution of secondary inorganic aerosols, and the limiting contribution of SOC to this haze case. Investigating backward trajectories showed that high speed northwestern air masses following a straight path corresponded to the clear periods, while southwesterly air masses which traversed heavily polluted regions brought abundant pollutants to Beijing and stimulated the occurrence of haze pollution. Results indicate that the control of NO2 needs to be addressed to reduce spring haze. Finally, the correlation between air mass trajectories and pollution conditions in Beijing reinforce the necessity of inter-regional cooperation and control.
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Affiliation(s)
- Hui Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Fengkui Duan
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China.
| | - Yongliang Ma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Kebin He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China.
| | - Lidan Zhu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Tao Ma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Siqi Ye
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Shuo Yang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Tao Huang
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku Osaka 543-0024, Japan
| | - Takashi Kimoto
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku Osaka 543-0024, Japan
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Ishizuka S, Fujii T, Matsugi A, Sakamoto Y, Hama T, Enami S. Controlling factors of oligomerization at the water surface: why is isoprene such a unique VOC? Phys Chem Chem Phys 2018; 20:15400-15410. [DOI: 10.1039/c8cp01551a] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
The interfacial oligomerization of isoprene is facilitated by the resonance stabilization through the formation of a tertiary carbocation with a conjugated CC bond pair, and electron enrichment induced by the neighboring methyl group.
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Affiliation(s)
- Shinnosuke Ishizuka
- Institute of Low Temperature Science
- Hokkaido University
- Sapporo 060-0819
- Japan
- National Institute for Environmental Studies
| | - Tomihide Fujii
- Graduate School of Global Environmental Studies
- Kyoto University
- Kyoto 606-8501
- Japan
| | - Akira Matsugi
- Research Institute of Science for Safety and Sustainability
- National Institute of Advanced Industrial Science and Technology
- Tsukuba 305-8569
- Japan
| | - Yosuke Sakamoto
- Graduate School of Global Environmental Studies
- Kyoto University
- Kyoto 606-8501
- Japan
- Graduate School of Human and Environmental Studies
| | - Tetsuya Hama
- Institute of Low Temperature Science
- Hokkaido University
- Sapporo 060-0819
- Japan
| | - Shinichi Enami
- National Institute for Environmental Studies
- Tsukuba 305-8506
- Japan
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17
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Li H, Ma Y, Duan F, He K, Zhu L, Huang T, Kimoto T, Ma X, Ma T, Xu L, Xu B, Yang S, Ye S, Sun Z, An J, Zhang Z. Typical winter haze pollution in Zibo, an industrial city in China: Characteristics, secondary formation, and regional contribution. Environ Pollut 2017; 229:339-349. [PMID: 28609735 DOI: 10.1016/j.envpol.2017.05.081] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [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: 02/22/2017] [Revised: 05/09/2017] [Accepted: 05/29/2017] [Indexed: 06/07/2023]
Abstract
Heavy haze pollution occurs frequently in northern China, most critically in the Beijing-Tianjin-Hebei area (BTH). Zibo, an industrial city located in Shandong province, is often listed as one of the top ten most polluted cities in China, particularly in winter. However, no studies of haze in Zibo have been conducted, which limits the understanding of the source and formation of haze pollution in this area, as well as mutual effects with the BTH area. We carried out online and continuous integrated field observation of particulate matter in winter, from 11 to 25 January 2015. SO42-, NO3-, and NH4+ (SIA) and organics were the main constituents of PM2.5, contributing 59.4% and 33.6%, respectively. With the increasing severity of pollution, the contribution of SIA increased while that of organics decreased. Meteorological conditions play an important role in haze formation; high relative humidity (RH) and low wind speed increased both the accumulation of pollutants and the secondary transition from gas precursors (gas-particle phase partitioning). Since RH and the presence of O3 can indicate heterogeneous and photochemistry processes, respectively, we carried out correlation analysis and linear regression to identify their relative importance to the three main secondary species (sulfate, nitrate, and secondary organic carbon (SOC)). We found that the impact of RH is in the order of SO42- > NO3- > SOC, while the impact of O3 is reversed, in the order of SOC > NO3- > SO42-, indicating different effect of these factors on the secondary formation of main species in winter. Cluster analysis of backward trajectories showed that, during the observation period, six directional sources of air masses were identified, and more than 90% came from highly industrialized areas, indicating that regional transport from industrialized areas aggravates the haze pollution in Zibo. Inter-regional joint prevention and control is necessary to prevent further deterioration of the air quality.
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Affiliation(s)
- Hui Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Yongliang Ma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Fengkui Duan
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China.
| | - Kebin He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China.
| | - Lidan Zhu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Tao Huang
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku, Osaka 543-0024, Japan
| | - Takashi Kimoto
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku, Osaka 543-0024, Japan
| | - Xiaoxuan Ma
- School of Resources and Environmental Engineering, Shandong University of Technology, Zibo 255049, Shandong Province, China
| | - Tao Ma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Lili Xu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Beiyao Xu
- College of Resources and Environmental Sciences, China Agricultural University, Beijing 100094, China
| | - Shuo Yang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Siqi Ye
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Zhenli Sun
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Jiutao An
- School of Resources and Environmental Engineering, Shandong University of Technology, Zibo 255049, Shandong Province, China
| | - Zhaolu Zhang
- School of Resources and Environmental Engineering, Shandong University of Technology, Zibo 255049, Shandong Province, China
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Xu L, Duan F, He K, Ma Y, Zhu L, Zheng Y, Huang T, Kimoto T, Ma T, Li H, Ye S, Yang S, Sun Z, Xu B. Characteristics of the secondary water-soluble ions in a typical autumn haze in Beijing. Environ Pollut 2017; 227:296-305. [PMID: 28477554 DOI: 10.1016/j.envpol.2017.04.076] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [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: 10/25/2016] [Revised: 04/25/2017] [Accepted: 04/26/2017] [Indexed: 06/07/2023]
Abstract
Four haze episodes (EPs) were observed in October 2014 in Beijing, China. For better understanding of the characteristics and the formation mechanisms of PM2.5 (particulate matter with an aerodynamic diameter ≤ 2.5 μm), especially secondary water-soluble inorganic species in these haze events, hourly concentrations of PM2.5, sulfate, nitrate, and ammonium (SNA) were measured in this study. Concentrations of gaseous pollutants and meteorological parameters were also measured. The average concentration of PM2.5 was 106.6 ± 83.5 μg m-3, which accounted for around 53% of PM10 (particulate matter with an aerodynamic diameter ≤ 10 μm) mass. Nitrogen dioxide (NO2) concentration was much higher than that of sulfur dioxide (SO2) since October is a non-heating month. SNA is the most abundant secondary water-soluble inorganic species and contributed to 33% of PM2.5 mass concentration. Sulfur oxidation ratio (SOR) was much higher than nitrogen oxidation ratio (NOR). NOR and SOR increased with elevated PM2.5 levels and heterogeneous processes seemed to be the most plausible explanation of this increase. Relative humidity (RH), which is of great influence on aerosol liquid water content (ALWC), played a considerable role in the formation of secondary inorganic aerosols, accelerated the secondary transformation of gaseous precursors, and further aggravated haze pollution. The positive feedback loop associated with high aerosol levels and low planetary boundary layer (PBL) height led to the evolution and exacerbation of heavy haze pollution. Fire maps and 48-h air mass backward trajectories supported the significant impact of biomass burning activities and regional transport on haze formation over Beijing in October 2014.
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Affiliation(s)
- Lili Xu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Tsinghua University, Beijing 100084, China
| | - Fengkui Duan
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Tsinghua University, Beijing 100084, China.
| | - Kebin He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Tsinghua University, Beijing 100084, China.
| | - Yongliang Ma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Tsinghua University, Beijing 100084, China
| | - Lidan Zhu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Tsinghua University, Beijing 100084, China
| | - Yixuan Zheng
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Tsinghua University, Beijing 100084, China
| | - Tao Huang
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku, Osaka 543-0024, Japan
| | - Takashi Kimoto
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku, Osaka 543-0024, Japan
| | - Tao Ma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Tsinghua University, Beijing 100084, China
| | - Hui Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Tsinghua University, Beijing 100084, China
| | - Siqi Ye
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Tsinghua University, Beijing 100084, China
| | - Shuo Yang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Tsinghua University, Beijing 100084, China
| | - Zhenli Sun
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Tsinghua University, Beijing 100084, China
| | - Beiyao Xu
- College of Resources and Environmental Sciences, China Agricultural University, Beijing 100094, China
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