Abstract
AbstractThe key problems with Positive Matrix Factorization (PMF) model for PM2.5 source apportionment were inconsistent results with different species selections and a lack of evaluation criteria for results accuracy. Moreover, high proportions of secondary inorganic aerosols sources (SNA) were identified by PMF without corresponding primary sources. This study develops a new method that combines multi‐isotopes (34S, 15N, 18O and 14C) and PMF model to optimize source apportionment. Data sets A–F, constructed from PM2.5 components, were input into PMF model to obtain optimal results (3–9 factors), which changed with the selection of species. Specifically, the contributions of coal combustion (CC, 3%–36%), biomass burning (BB, 11%–38%), and vehicle sources (VS, 4%–15%) showed significant differences in data sets, indicating that conventional methods cannot obtain accurate results. Then, 15N, 34S, 18O were introduced to restrict and reallocate identified SNA sources to primary sources, overcoming the influence of species on results. Additionally, 14C was used to evaluate data sets results, which showed that the combination of PMF model with more markers (data set F, 9‐factor) and multi‐isotopes techniques obtained optimized results that aligned with 14C results. Compared with the initial results, the contributions of CC, VS, and BB in the allocated 9‐factor increased by 26.4%, 5%, and 19.5%, respectively, becoming main sources of PM2.5. This study represents the first time that combination of PMF model and multi‐isotopes achieves SNA sources reapportionment and results evaluation, improving source apportionment methods.
Published Version
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