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Kim Y, Yi SM, Heo J. Fifteen-year trends in carbon species and PM 2.5 in Seoul, South Korea (2003-2017). CHEMOSPHERE 2020; 261:127750. [PMID: 32712379 DOI: 10.1016/j.chemosphere.2020.127750] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 07/10/2020] [Accepted: 07/17/2020] [Indexed: 06/11/2023]
Abstract
This study focused on particulate matter (PM2.5) and carbon species in Seoul, South Korea, to quantitatively evaluate their long-term trends and assess the main correlating factors. Ambient PM2.5 samples were collected over a 24 h period every third or sixth day from March 2003 to December 2017. The mean concentrations of PM2.5, organic carbon (OC), elemental carbon (EC), primary and secondary OC (POC and SOC) in Seoul over 15 years were 32.2 μg/m3 and 7.28 μg/m3, 1.85 μg/m3, 4.29 μg/m3 and 3.54 μg/m3 respectively. The long-term concentration trends in PM2.5, OC, EC, POC, and SOC decreased significantly at rates of -2.09, -3.13, -6.31, -2.86, and -3.88 per year, respectively from 2003 to 2017 (p < 0.001), whereas the long-term trends in OC/EC significantly increased at a rate of 12.9/year (p < 0.001). These long-term decreases in PM2.5 and carbon species concentrations were most pronounced in 2008 but almost disappeared from 2013 onwards. Considering the decrease in wind speed and variations in the concentration of gaseous air pollutants (carbon monoxide, sulfur dioxide, nitrogen dioxide, and volatile organic compounds) without a tendency to increase or decrease since 2013, secondary aerosol formation by atmospheric stagnation alleviated long-term decreases in PM2.5 and carbon species concentrations. The long-term decreases in EC concentration were the most consistent and rapid, strongly suggesting that atmospheric policies related to mobile in South Korea were effective in reducing EC concentration. Future air quality management should focus on the secondary formation of air pollutants based on regional trends in air pollutant concentrations.
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Affiliation(s)
- Youngkwon Kim
- Department of Environmental Health, Graduate School of Public Health, Seoul National University, Gwanak-ro Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Seung-Muk Yi
- Department of Environmental Health, Graduate School of Public Health, Seoul National University, Gwanak-ro Gwanak-gu, Seoul, 08826, Republic of Korea; Institute of Health and Environment, Seoul National University, 1 Gwanak Gwanak-ro Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Jongbae Heo
- Busan Development Institute, 955 Jungangdae-ro, Busanjin-gu, Busan, 47210, Republic of Korea.
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Gao J, Wang K, Wang Y, Liu S, Zhu C, Hao J, Liu H, Hua S, Tian H. Temporal-spatial characteristics and source apportionment of PM 2.5 as well as its associated chemical species in the Beijing-Tianjin-Hebei region of China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2018; 233:714-724. [PMID: 29126093 DOI: 10.1016/j.envpol.2017.10.123] [Citation(s) in RCA: 150] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2016] [Revised: 10/29/2017] [Accepted: 10/30/2017] [Indexed: 05/02/2023]
Abstract
PM2.5 and its major chemical compositions were sampled and analyzed in January, April, July and October of 2014 at Beijing (BJ), Tianjin (TJ), Langfang (LF) and Baoding (BD) in order to probe the temporal and spatial characteristics as well as source apportionment of PM2.5 in the Beijing-Tianjin-Hebei (BTH) region. The results showed that PM2.5 pollution was severe in the BTH region. The average annual concentrations of PM2.5 at four sampling sites were in the range of 126-180 μg/m3, with more than 95% of sampling days exceeding 35 μg/m3, the limit ceiling of average annual concentration of PM2.5 regulated in the Chinese National Ambient Air Quality Standards (GB3095-2012). Additionally, concentrations of PM2.5 and its major chemical species were seasonally dependent and demonstrated spatially similar variation characteristics in the BTH region. Concentration of toxic heavy metals, such as As, Cd, Cr, Cu, Mn, Ni, Pb, Sb, Se, and Zn, were higher in winter and autumn. Secondary inorganic ions (SO42-, NO3-, and NH4+) were the three-major water-soluble inorganic ions (WSIIs) of PM2.5 and their mass ratios to PM2.5 were higher in summer and autumn. The organic carbon (OC) and elemental carbon (EC) concentrations were lower in spring and summer than in autumn and winter. Five factors were selected in Positive Matrix Factorization (PMF) model analysis, and the results showed that PM2.5 pollution was dominated by vehicle emissions in Beijing, combustion emissions including coal burning and biomass combustion in Langfang and Baoding, and soil and construction dust emissions in Tianjin, respectively. The air mass that were derived from the south and southeast local areas around BTH regions reflected the features of short-distant and small-scale air transport. Shandong, Henan, and Hebei were identified the major potential sources-areas of secondary aerosol emissions to PM2.5.
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Affiliation(s)
- Jiajia Gao
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Department of Air Pollution Control, Beijing Municipal Institute of Labour Protection, Beijing 100054, China
| | - Kun Wang
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Department of Air Pollution Control, Beijing Municipal Institute of Labour Protection, Beijing 100054, China
| | - Yong Wang
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Shuhan Liu
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Chuanyong Zhu
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; School of Environmental Science and Engineering, Qilu University of Technology, Jinan 250353, China
| | - Jiming Hao
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, School of Environment, Tsinghua University, Beijing 10084, China
| | - Huanjia Liu
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Shenbing Hua
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Hezhong Tian
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, School of Environment, Tsinghua University, Beijing 10084, China.
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