Source apportionment of PM2.5 at a regional background site in North China using PMF linked with radiocarbon analysis: insight into the contribution of biomass burning

被引:118
|
作者
Zong, Zheng [1 ,6 ]
Wang, Xiaoping [2 ]
Tian, Chongguo [1 ]
Chen, Yingjun [3 ]
Qu, Lin [4 ]
Ji, Ling [4 ]
Zhi, Guorui [5 ]
Li, Jun [2 ]
Zhang, Gan [2 ]
机构
[1] Chinese Acad Sci, Yantai Inst Coastal Zone Res, Key Lab Coastal Environm Proc & Ecol Remediat, Yantai 264003, Peoples R China
[2] Chinese Acad Sci, Guangzhou Inst Geochem, State Key Lab Organ Geochem, Guangzhou 510640, Guangdong, Peoples R China
[3] Tongji Univ, Key Lab Cities Mitigat & Adaptat Climate Change S, Coll Environm Sci & Engn, Shanghai 200092, Peoples R China
[4] SOA, Yantai Ocean Environm Monitoring Cent Stn, Yantai 264006, Peoples R China
[5] Chinese Res Inst Environm Sci, Beijing 100012, Peoples R China
[6] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
关键词
ATMOSPHERIC HEAVY-METALS; CARBONACEOUS AEROSOLS; EMISSION INVENTORY; CHEMICAL-COMPOSITIONS; VEHICLE EMISSIONS; HIGH-RESOLUTION; ORGANIC-CARBON; SHIPPING EMISSIONS; RENEWABLE ENERGY; TRACE-ELEMENTS;
D O I
10.5194/acp-16-11249-2016
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Source apportionment of fine particles (PM2.5) at a background site in North China in the winter of 2014 was done using statistical analysis, radiocarbon (C-14) measurement and positive matrix factorization (PMF) modeling. Results showed that the concentration of PM2.5 was 77.6 +/- 59.3 mu g m(-3), of which sulfate (SO42-) concentration was the highest, followed by nitrate (NO3-), organic carbon (OC), elemental carbon (EC) and ammonium (NH4+). As demonstrated by backward trajectory, more than half of the air masses during the sampling period were from the Beijing-Tianjin-Hebei (BTH) region, followed by Mongolia and the Shandong Peninsula. Cluster analysis of chemical species suggested an obvious signal of biomass burning in the PM2.5 from the Shandong Peninsula, while the PM2.5 from the BTH region showed a vehicle emission pattern. This finding was further confirmed by the C-14 measurement of OC and EC in two merged samples. The C-14 result indicated that biogenic and biomass burning emission contributed 59 +/- 4 and 52 +/- 2 % to OC and EC concentrations, respectively, when air masses originated from the Shandong Peninsula, while the contributions fell to 46 +/- 4 and 38 +/- 1 %, respectively, when the prevailing wind changed and came from the BTH region. The minimum deviation between source apportionment results from PMF and C-14 measurement was adopted as the optimal choice of the model exercises. Here, two minor overestimates with the same range (3 %) implied that the PMF result provided a reasonable source apportionment of the regional PM2.5 in this study. Based on the PMF modeling, eight sources were identified; of these, coal combustion, biomass burning and vehicle emission were the main contributors of PM2.5, accounting for 29.6, 19.3 and 15.9 %, respectively. Compared with overall source apportionment, the contributions of vehicle emission, mineral dust, coal combustion and biomass burning increased when air masses came from the BTH region, Mongolia and the Shandong Peninsula, respectively. Since coal combustion and vehicle emission have been considered as the leading emission sources to be controlled for improving air quality, biomass burning was highlighted in the present study.
引用
收藏
页码:11249 / 11265
页数:17
相关论文
共 50 条
  • [21] Chemical characteristics and source apportionment of PM2.5 using PCA/APCS, UNMIX, and PMF at an urban site of Delhi, India
    Jain, Srishti
    Sharma, Sudhir Kumar
    Choudhary, Nikki
    Masiwal, Renu
    Saxena, Mohit
    Sharma, Ashima
    Mandal, Tuhin Kumar
    Gupta, Anshu
    Gupta, Naresh Chandra
    Sharma, Chhemendra
    ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2017, 24 (17) : 14637 - 14656
  • [22] Chemical characteristics and source apportionment of PM2.5 using PCA/APCS, UNMIX, and PMF at an urban site of Delhi, India
    Srishti Jain
    Sudhir Kumar Sharma
    Nikki Choudhary
    Renu Masiwal
    Mohit Saxena
    Ashima Sharma
    Tuhin Kumar Mandal
    Anshu Gupta
    Naresh Chandra Gupta
    Chhemendra Sharma
    Environmental Science and Pollution Research, 2017, 24 : 14637 - 14656
  • [23] Source apportionment of PM2.5 using DN-PMF in three megacities in South Korea
    Cheong, Yeonseung
    Kim, Taeyeon
    Ryu, Jiwon
    Ryoo, Ilhan
    Park, Jieun
    Jeon, Kwon-ho
    Yi, Seung-Muk
    Hopke, Philip K.
    AIR QUALITY ATMOSPHERE AND HEALTH, 2024, 17 (11): : 2579 - 2599
  • [24] Chemical characterization and source apportionment of PM1 and PM2.5 in Tianjin, China: Impacts of biomass burning and primary biogenic sources
    Khan, Jahan Zeb
    Sun, Long
    Tian, Yingze
    Shi, Guoliang
    Feng, Yinchang
    JOURNAL OF ENVIRONMENTAL SCIENCES, 2021, 99 : 196 - 209
  • [25] Chemical characterization and source apportionment of PM1 and PM2.5 in Tianjin, China: Impacts of biomass burning and primary biogenic sources
    Jahan Zeb Khan
    Long Sun
    Yingze Tian
    Guoliang Shi
    Yinchang Feng
    Journal of Environmental Sciences, 2021, 99 (01) : 196 - 209
  • [26] The Analysis of PM2.5 Source Apportionment Technique's Competitiveness in China
    Qian, K.
    Deng, L.
    An, Y. B.
    Liu, S. Y.
    Hao, H. Z.
    PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON MECHANICAL ENGINEERING AND CONTROL SYSTEMS (MECS2015), 2016, : 509 - 512
  • [27] Estimation of Source Apportionment for PM2.5 Data of Air Pollution Monitoring Site in Pohang Using the EPA-PMF Model
    Hwang, InJo
    JOURNAL OF KOREAN SOCIETY FOR ATMOSPHERIC ENVIRONMENT, 2022, 38 (03) : 354 - 374
  • [28] PM2.5 Source Apportionment and Implications for Particle Hygroscopicity at an Urban Background Site in Athens, Greece
    Diapouli, Evangelia
    Fetfatzis, Prodromos
    Panteliadis, Pavlos
    Spitieri, Christina
    Gini, Maria, I
    Papagiannis, Stefanos
    Vasilatou, Vasiliki
    Eleftheriadis, Konstantinos
    ATMOSPHERE, 2022, 13 (10)
  • [29] Source contribution analysis of PM2.5 using Response Surface Model and Particulate Source Apportionment Technology over the PRD region, China
    Li, Zhifang
    Zhu, Yun
    Wang, Shuxiao
    Xing, Jia
    Zhao, Bin
    Long, Shicheng
    Li, Minhui
    Yang, Wenwei
    Huang, Ruolin
    Chen, Ying
    SCIENCE OF THE TOTAL ENVIRONMENT, 2022, 818
  • [30] Source apportionment of PM2.5 using dispersion normalized positive matrix factorization (DN-PMF) in Beijing and Baoding, China
    Ryoo, Ilhan
    Kim, Taeyeon
    Ryu, Jiwon
    Cheong, Yeonseung
    Moon, Kwang-joo
    Jeon, Kwon-ho
    Hopke, Philip K.
    Yi, Seung-Muk
    Park, Jieun
    JOURNAL OF ENVIRONMENTAL SCIENCES, 2025, 155 : 395 - 408