4.7 Article

Uncertainty in positive matrix factorization solutions for PAHs in surface sediments of the Yangtze River Estuary in different seasons

期刊

CHEMOSPHERE
卷 191, 期 -, 页码 922-936

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.chemosphere.2017.10.070

关键词

Polycyclic aromatic hydrocarbons; Uncertainty; Positive matrix factorization; Bootstrap; Sediments; Yangtze River Estuary

资金

  1. National Natural Science Foundation of China [41571486]
  2. Ministry of Education and Social Science [14YJAZH048]
  3. National Basic Research Program of China (973 Project) [2010CB429003]

向作者/读者索取更多资源

To examine the variabilities of source contributions in the Yangtze River Estuary (YRE), the uncertainty based on the positive matrix factorization (PMF) was applied to the source apportionment of the 16 priority PAHs in 120 surface sediment samples from four seasons. Based on the signal-to-noise ratios, the PAHs categorized as Bad might drop out of the estimation of bootstrap. Next, the spatial variability of residuals was applied to determine which species with non-normal curves should be excluded. The median values from the bootstrapped solutions were chosen as the best estimate of the true factor contributions, and the intervals from 5th to 95th percentile represent the variability in each sample factor contribution. Based on the results, the median factor contributions of wood grass combustion and coke plant emissions were highly correlated with the variability (R-2 = 0.6797-0.9937) in every season. Meanwhile, the factor of coal and gasoline combustion had large variability with lower R-2 values in every season, especially in summer (0.4784) and winter (0.2785). The coefficient of variation (CV) values based on the Bootstrap (BS) simulations were applied to indicate the uncertainties of PAHs in every factor of each season. Acy, NaP and BgP always showed higher CV values, which suggested higher uncertainties in the BS simulations, and the PAH with the lowest concentration among all PAH5 usually became the species with higher uncertainties. (C) 2017 Elsevier Ltd. All rights reserved.

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