Abstract
PM0.1 has been believed to have adverse short- and long-term effects on human health. However, the information of PM0.1 that is needed to fully evaluate its influence on human health and environment is still scarce in many developing countries. This is a comprehensive study on the levels, chemical compositions, and source apportionment of PM0.1 conducted in Hanoi, Vietnam. Twenty-four-hour samples of PM0.1 were collected during the dry season (November to December 2015) at a mixed site to get the information on mass concentrations and chemical compositions. Multiple linear regression analysis was utilized to investigate the simultaneous influence of meteorological factors on fluctuations in the daily levels of PM0.1. Multiple linear regression models could explain about 50% of the variations of PM0.1 concentrations, in which wind speed is the most important variable. The average concentrations of organic carbon (OC), elemental carbon (EC), water-soluble ions (Ca2+, K+, Mg2+, Na+, NH4+, Cl−, NO3−, SO42−, C2O42−), and elements (Be, Al, V, Cr, Mn, Co, Ni, Cu, Zn, As, Se, Mo, Cd, Sb, Ba, Tl, Pb, Na, Fe, Mg, K, and Ca) were 2.77 ± 0.90 μg m−3, 0.63 ± 0.28 μg m−3, 0.88 ± 0.39 μg m−3, and 0.05 ± 0.02 μg m−3, accounting for 51.23 ± 9.32%, 11.22 ± 2.10%, 16.28 ± 2.67%, and 1.11 ± 0.94%, respectively. A positive matrix factorization model revealed the contributions of five major sources to the PM0.1 mass including traffic (gasoline and diesel emissions, 46.28%), secondary emissions (31.18%), resident/commerce (12.23%), industry (6.05%), and road/construction (2.92%).
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References
Cao JJ, Lee SC, Ho KF, Fung K, Chow JC, Watson JG (2006) Characterization of roadside fine particulate carbon and its eight fractions in Hong Kong. Aerosol Air Qual Res 6(2):106–122. https://doi.org/10.4209/aaqr.2006.06.0001
Chen SC, Tsai CJ, Huang CY, Chen HD, Chen SJ, Lin CC, Tsai JH, Chou CCK, Lung SCC, Huang WR, Roam GD, Wu WY, Smolik J, Dzumbova L (2010) Chemical mass closure and chemical characteristics of ambient ultrafine particles and other PM fractions. Aerosol Sci Technol 44(9):713–723. https://doi.org/10.1080/02786826.2010.486385
Chen R, Hu B, Liu Y, Xu J, Yang G, Xu D, Chen C (2016) Beyond PM2.5: the role of ultrafine particles on adverse health effects of air pollution. Biochim Biophys Acta Gen Subj 1860(12):2844–2855. https://doi.org/10.1016/j.bbagen.2016.03.019
Chow JC, Watson JG (2007) Review of measurement methods and compositions for ultrafine particles. Aerosol Air Qual Res 7(2):121–173. https://doi.org/10.4209/aaqr.2007.05.0029
Chow JC, Watson JG, Crow D, Lowenthal DH, Merrifield T (2001) Comparison of IMPROVE and NIOSH carbon measurements. Aerosol Sci Technol 34:23–34
Cohen DD, Crawford J, Stelcer E, Bac VT (2010a) Characterisation and source apportionment of fine particulate sources at Hanoi from 2001 to 2008. Atmos Environ 44(3):320–328. https://doi.org/10.1016/j.atmosenv.2009.10.037
Cohen DD, Crawford J, Stelcer E, Bac VT (2010b) Long range transport of fine particle windblown soils and coal fired power station emission into Hanoi between 2001 to 2008. Atmos Environ 44(31):3761–3769. https://doi.org/10.1016/j.atmosenv.2010.06.047
Comero S, Capitani L, Gawlik BM (2009) Positive matrix factorisation (PMF) - an introduction to the chemometric evaluation of environmental monitoring data using PMF. JRC Scientific and Technical Reports, Institute for Environment and Sustainability, Joint Research Centre, European Commission DOI: https://doi.org/10.2788/2497
Crilley LR, Lucarelli F, Bloss WJ, Roy M, Harrison RM, Beddows DC, Calzolai G, Nava S, Valli G, Bernardoni V, Vecchi R (2017) Source apportionment of fine and coarse particles at a roadside and urban background site in London during the 2012 summer Clearflo campaign. Environ Pollut 220(Part B):766–778. https://doi.org/10.1016/j.envpol.2016.06.002
DRI (Desert Research Institute) (2005) DRI SOP #2-216.1. Division of Atmospheric Sciences, DRI STANDARD OPERATING PROCEDURE, DRI Model 2001 Thermal/Optical Carbon Analysis (TOR/TOT) of Aerosol Filter Samples
GSO (General Statistics Office) (2018) Statistical yearbook of Vietnam. Statistical Publisher, Hanoi (in Vienamese)
Gugamsetty B, Wei H, Liu CN, Awasthi A, Hsu SC, Tsai CJ, Roam GD, Wu YC, Chen CF (2012) Source characterization and apportionment of PM10, PM2.5 and PM0.1 by using positive matrix factorization. Aerosol Air Qual Res 12(4):476–491. https://doi.org/10.4209/aaqr.2012.04.0084
Guo T, Li K, Zhu Y, Gao H, Yao X (2016) Concentration and size distribution of particulate oxalate in marine and coastal atmospheres: implication for the increased importance of oxalate in nanometer atmospheric particles. Atmos Environ 142:19–31. https://doi.org/10.1016/j.atmosenv.2016.07.026
Hai CD, Oanh NTK (2013) Effects of local, regional meteorology and emission sources on mass and compositions of particulate matter in Hanoi. Atmos Environ 78:105–112. https://doi.org/10.1016/j.atmosenv.2012.05.006
Hien PD, Bac VT, Tham HC, Nhan DD, Vinh LD (2002) Influence of meteorological conditions on PM2.5 and PM2.5-10 concentrations during the monsoon season in Hanoi, Vietnam. Atmos Environ 36(21):3473–3484. https://doi.org/10.1016/S1352-2310(02)00295-9
Hien PD, Bac VT, Thinh NTH (2004) PMF receptor modelling of fine and coarse PM10 in air masses governing monsoon conditions in Hanoi, northern Vietnam. Atmos Environ 38(2):189–201. https://doi.org/10.1016/j.atmosenv.2003.09.064
Hien PD, Loc PD, Dao NV (2011) Air pollution episodes associated with East Asian winter monsoons. Sci Total Environ 409:5063–5068. https://doi.org/10.1016/j.scitotenv.2011.08.049
Jaiprakash AS, Singhai A, Habib G, Raman RS, Gupta T (2017) Chemical characterization of PM0.1 aerosol in Delhi and source apportionment using positive matrix factorization. Environ Sci Pollut Res 24(1):445–462. https://doi.org/10.1007/s11356-016-7708-8
Jeričević A, Gašparac G, Mikulec MM, Kumar P, Prtenjak MT (2019) Identification of diverse air pollution sources in a complex urban area of Croatia. J Environ Manag 243:67–77. https://doi.org/10.1016/j.jenvman.2019.04.024
Kim KH, Sekiguchi K, Kudo S, Sakamoto K (2011) Characteristics of atmospheric elemental carbon (char and soot) in ultrafine and fine particles in a roadside environment, Japan. Aerosol Air Qual Res 11(1):1–12. https://doi.org/10.4209/aaqr.2010.07.0061
Kim S, Kim TY, Yi SM, Heo J (2018) Source apportionment of PM2.5 using positive matrix factorization (PMF) at a rural site in Korea. J Environ Manag 214:325–334. https://doi.org/10.1016/j.jenvman.2018.03.027
Kovač-Andrić E, Brana J, Gvozdić V (2009) Impact of meteorological factors on ozone concentrations modelled by time series analysis and multivariate statistical methods. Ecol Inform 4:117–122. https://doi.org/10.1016/j.ecoinf.2009.01.002
Kudo S, Sekiguchi K, Kim KH, Kinoshita M, Wang Q, Yoshikado H, Sakamoto K, Möller D (2012) Differences of chemical species and their ratios between fine and ultrafine particles in the roadside environment. Atmos Environ 62:172–179. https://doi.org/10.1016/j.atmosenv.2012.08.039
Kumar P, Wiedensohler A, Birmili W, Quincey P, Hallquist M (2016) Chapter 15 - ultrafine particles pollution and measurements. Compr Anal Chem 73:369–390. https://doi.org/10.1016/bs.coac.2016.04.004
Kuwayama T, Ruehl CR, Kleeman MJ (2013) Daily trends and source apportionment of ultrafine particulate mass (PM0.1) over an annual cycle in a typical California city. Environ Sci Technol 47(24):13957–13966. https://doi.org/10.1021/es403235c
Lim S, Lee M, Lee G, Kim S, Yoon S, Kang K (2012) Ionic and carbonaceous compositions of PM10, PM2.5 and PM1.0 at Gosan ABC superstation and their ratios as source signature. Atmos Chem Phys 12:2007–2012. https://doi.org/10.5194/acp-12-2007-2012
Lin B, Zhu J (2018) Changes in urban air quality during urbanization in China. J Clean Prod 188:312–321. https://doi.org/10.1016/j.jclepro.2018.03.293
Ly LMT, Dung P, Peter SD, Lidia M, Phong TK (2017) The association between particulate air pollution and respiratory admissions among young children in Hanoi, Vietnam. Sci Total Environ 578:249–255. https://doi.org/10.1016/j.scitotenv.2016.08.012
Mason B (1966) Principles of geochemistry. Wiley, New York
Mbengue S, Alleman LY, Flament P (2014) Size-distributed metallic elements in submicronic and ultrafine atmospheric particles from urban and industrial areas in northern France. Atmos Res 135-136:35–47. https://doi.org/10.1016/j.atmosres.2013.08.010
Oanh NTK, Upadhyay N, Zhuang YH, Hao ZP, Murthy DVS, Lestari P, Villarin JT, Chengchua K, Co HX, Dung NT, Lindgren ES (2006) Particulate air pollution in six Asian cities: spatial and temporal distributions, and associated sources. Atmos Environ 40(18):3367–3380. https://doi.org/10.1016/j.atmosenv.2006.01.050
Oanh NTK, Pongkiatkul P, Upadhyay N, Hopke PP (2009) Designing ambient particulate matter monitoring program for source apportionment study by receptor modeling. Atmos Environ 43(21):3334–3344. https://doi.org/10.1016/j.atmosenv.2009.04.016
Paatero P, Hopke PK (2003) Discarding or downweighting high-noise variables in factor analytic models. Anal Chim Acta 490:277–289. https://doi.org/10.1016/S0003-2670(02)01643-4
Pakkanen TA, Kerminen VM, Korhonen CH, Hillamo RE, Aarnio P, Koskentalo T, Maenhaut W (2001) Urban and rural ultrafine (PM0.1) particles in the Helsinki area. Atmos Environ 35(27):4593–4607. https://doi.org/10.1016/S1352-2310(01)00167-4
Panicker AS, Ali K, Beig Yadav S (2015) Characterization of particulate matter and carbonaceous aerosol over two urban environments in northern India. Aerosol Air Qual Res 15(7):2584–2595. https://doi.org/10.4209/aaqr.2015.04.0253
Park MB, Lee TJ, Lee ES, Kim DS (2019) Enhancing source identifcation of hourly PM2.5 data in Seoul based on a dataset segmentation scheme by positive matrix factorization (PMF). Atmos Pollut Res 10(4):1042–1059. https://doi.org/10.1016/j.apr.2019.01.013
Polissar AV, Hopke PK, Paatero P, Malm WC, Sisler JF (1998) Atmospheric aerosol over Alaska: 2. Elemental composition and sources. J Geophys Res Atmos Chem Phys 103(D15):19045–19057. https://doi.org/10.1029/98JD01212
Pongkiatkul P, Oanh NTK (2013) Receptor modeling for air pollution source apportionment study. In: Oanh NTK (ed) Intergrated air quality management - Asian case study. CRC Press, Taylor & Francis Group, pp 63–95 http://www.crcpress.com/product/isbn/9781439862254
Quang TN, Hue NT, Thai P, Mazaheri M, Morawska L (2017) Exploratory assessment of indoor and outdoor particle number concentrations in Hanoi households. Sci Total Environ 599-600:284–290. https://doi.org/10.1016/j.scitotenv.2017.04.154
Sanderson P, Delgado-Saborit JM, Harrison RM (2014) A review of chemical and physical characterisation of atmospheric metallic nanoparticles. Atmos Environ 94:353–365. https://doi.org/10.1016/j.atmosenv.2014.05.023
Sharma SK, Mandal TK, Jain S, Saraswati SA, Saxena M (2016) Source apportionment of PM2.5 in Delhi, India using PMF model. Bull Environ Contam Toxicol 97(2):286–293. https://doi.org/10.1007/s00128-016-1836-1
Sofowote UM, Su Y, Dabek-Zlotorzynska E, Rastogi AK, Brook J, Hopke PP (2015) Sources and temporal variations of constrained PMF factors obtained from multiple-year receptor modeling of ambient PM2.5 data from five speciation sites in Ontario, Canada. Atmos Environ 108:140–150. https://doi.org/10.1016/j.atmosenv.2015.02.055
Squizzato S, Masiol M, Rich DQ, Hopke PK (2018) A long-term source apportionment of PM2.5 in New York state during 2005-2016. Atmos Environ 192:35–47. https://doi.org/10.1016/j.atmosenv.2018.08.044
Stacey B (2019) Measurement of ultrafine particles at airports: a review. Atmos Environ 198:463–477. https://doi.org/10.1016/j.atmosenv.2018.10.041
Stölzel M, Breitner S, Cyrys J, Pitz M, Wölke G, Kreyling W, Heinrich J, Wichmann HE, Peters A (2007) Daily mortality and particulate matter in different size classes in Erfurt, Germany. J Expo Sci Environ Epidemiol 17:458–467. https://doi.org/10.1038/sj.jes.7500538
Tai APK, Mickley LJ, Jacob DJ (2010) Correlations between fine particulate matter (PM2.5) and meteorological variables in the United States: implications for the sensitivity of PM2.5 to climate change. Atmos Environ 44:3976–3984. https://doi.org/10.1016/j.atmosenv.2010.06.060
TDSI (Transport Development and Strategy Institute) (2017) A study to develop a set of indicators for sustainable development of urban road transport - application of calculations for the cities of Hanoi, Ho chi minh, Hai Phong, Da Nang and Can Tho. Ministry-level project, Hanoi (in Vienamese)
Thuy NTT, Dung NT, Sekiguchi K, Yamaguchi R, Thuy PC, Bang HQ (2017) Characteristics of elemental carbon and organic carbon in atmospheric nanoparticles at different sampling locations in Vietnam. Vietnam J Sci Technol 55(3):305–315. https://doi.org/10.15625/2525-2518/55/3/8820
Thuy NTT, Dung NT, Sekiguchi K, Thuy LB, Hien NTT, Yamaguchi R (2018) Mass concentrations and carbonaceous compositions of PM0.1, PM2.5, and PM10 at urban locations in Hanoi, Vietnam. Aerosol Air Qual Res 18(7):1591–1605. https://doi.org/10.4209/aaqr.2017.11.0502
Wu Z, Hu M, Lin P, Liu S, Wehner B, Wiedensohler A (2008) Particle number size distribution in the urban atmosphere of Beijing, China. Atmos Environ 42(34):7967–7980. https://doi.org/10.1016/j.atmosenv.2008.06.022
Xu L, Jiao L, Hong Z, Zhang Y, Dua W, Wu X, Chen Y, Deng J, Hong Y, Chen J (2018) Source identification of PM2.5 at a port and an adjacent urban site in a coastal city of China: impact of ship emissions and port activities. Sci Total Environ 634:1205–1213. https://doi.org/10.1016/j.scitotenv.2018.04.087
Zhu CS, Chen CC, Cao JJ, Tsai CJ, Chou CCK, Liu SC, Roam GD (2010) Characterization of carbon fractions for atmospheric fine particles and nanoparticles in a highway tunnel. Atmos Environ 44(23):2668–2673. https://doi.org/10.1016/j.atmosenv.2010.04.042
Acknowledgments
This research is funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under grant number B2019-24-01/HĐ-KHCN. Kanazawa University, Japan, is acknowledged for providing the sampler (KU-TSC 26A57C1) for this study. The authors would like to thank Dr. Kathryn Zimmermann at Georgia Gwinnett College and Dr. Chris Reinhard at the Georgia Institute of Technology for the generous support in elemental analysis. The authors also would like to thank students of HUST for their help in the sampling.
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Highlights
• PM0.1 was collected in 2 months in Hanoi for speciation and source apportionment.
• The chemical compositions could be explained 83% mass concentration of PM0.1.
• Main sources are traffic, secondary, resident/commerce, industry, and road/construction.
• Traffic contributes 46.28% PM0.1 mass; secondary is the next important source.
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Nghiem, TD., Nguyen, T.T.T., Nguyen, T.T.H. et al. Chemical characterization and source apportionment of ambient nanoparticles: a case study in Hanoi, Vietnam. Environ Sci Pollut Res 27, 30661–30672 (2020). https://doi.org/10.1007/s11356-020-09417-5
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DOI: https://doi.org/10.1007/s11356-020-09417-5