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īelis CA, Pernigotti D, Karagulian F, Pirovano G, Larsen BR, Gerboles M, Hopke PK (2015) A new methodology to assess the performance and uncertainty of source apportionment models in intercomparison exercises. īaudic A, Gros V, Sauvage S, Locoge N, Sanchez O, Sarda-Estève R, Kalogridis C, Petit J-E, Bonnaire N, Baisnée D, Favez O, Albinet A, Sciare J, Bonsang B (2016) Seasonal variability and source apportionment of volatile organic compounds (VOCs) in the Paris megacity (France). The results indicated that the PMF model was most reasonable in PAH source apportionment research in this study.Īndersson M, Klug M, Eggen OA, Ottesen RT (2014) Polycyclic aromatic hydrocarbons (PAHs) in sediments from lake Lille Lungegårdsvannet in Bergen, western Norway appraising pollution sources from the urban history. Four aspects including intra-comparison, inter-comparison, source numbers and compositions, and source contributions were considered in comparison study. The PMF model segregated an additional source: domestic coal combustion (contributed 20.9% to the ∑ 16PAHs).
The source apportionment results derived from three receptor models were similar, with three common sources: mixed sources of biomass burning and coal combustion (31.0–41.4% on average), petroleum combustion (31.8–45.5%), and oil leakage (13.1–21.3%). Sixteen USEPA priority PAHs in 37 sliced sediment layers (1-cm interval) were measured, with the concentrations of ∑ 16PAH (sum of 16 PAHs) ranging from 93.0 to 431 ng g −1. Thus, in this paper, three receptor models including principle component analysis-multiple linear regression (PCA-MLR), positive matrix factorization (PMF), and Unmix were used to PAH source apportionment study in a sediment core from Honghu Lake, China. However, the temporal distribution of PAH source contributions is less studied.
The spatial distribution of polycyclic aromatic hydrocarbons (PAHs) and their source contributions employing receptor models has been widely reported.
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