Assessment of source contributions to organic carbon in ambient fine particle using receptor model with inorganic and or
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Assessment of source contributions to organic carbon in ambient fine particle using receptor model with inorganic and organic source tracers at an urban site of Beijing Qingyang Liu1 · Jiaxing Gong1 Received: 5 February 2020 / Accepted: 16 April 2020 / Published online: 24 April 2020 © Springer Nature Switzerland AG 2020
Abstract The collections of ambient fine particles were carried out in the period of January 16 to 31, 2013, in Beijing. The levels of carbonaceous aerosols (i.e., organic carbon and elemental carbon) in fine particles were determined. The chemical compositions of primary source tracers including alkanes, benzo(b)fluoranthene, benzo(k)fluoranthene, benzo(e)pyrene, benzo(ghi)perylene, picene, 17a(H)-22,29,30-trisnorhopane, levoglucosan, Al and Fe in fine particles were analyzed. Chemical mass balance (CMB) model coupled with inorganic and organic source tracers was utilized to estimate daily contributions of primary sources (e.g., coal combustion, biomass burning, dust source, mobile source) to organic carbon (OC). The sensitivity analysis of specific primary source was carried out in order to obtain the accurate contribution from primary sources. Our study indicated that CMB with inorganic and organic source tracer method was efficient for apportioning primary sources of OC in Beijing during high air pollution episodes. Keywords Air pollution · Source apportionment · Organic source tracer · CMB
1 Introduction Fine particulate matter (PM2.5) is proven to have negative effects on human health [1, 2]. High PM2.5 concentrations in the atmosphere could lead to form the haze days, which result in a diversity of respiratory illnesses [3–6]. Therefore, understanding emissions sources responsible for high PM2.5 concentration is important for air quality management to abate specific emission sources [6–9]. Source apportionment studies are able to provide useful information to policymakers on source estimations of ambient PM2.5 concentrations [1, 9]. Nowadays, receptor models are widely used for source apportionment studies worldwide [7, 9, 10]. Positive matrix factorization (PMF) is one of the receptor models
for apportioning P M2.5 sources. PMF is able to apportion emission sources without obtaining the local profiles of source [11]. Chemical mass balance (CMB) is another receptor model [12]. CMB model could apportion PM2.5 sources with the input of local profile of specific sources [3, 12]. However, it has no requirement on the minimum size of ambient PM2.5 sample [7, 13]. Because of this advantage, CMB is suitable for understanding source contributions of PM2.5 concentration in the areas with polluted air during short periods [12]. CMB coupled with inorganic and organic source tracer method is widely used in various source apportionment studies worldwide, which has led to the significant improvements in air quality in areas such as London and the Central Valley of California [14–16]. Beijing is experiencing worse air pollution in winter [17]. Datasets
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