High-time resolution PM2.5 source apportionment assisted by spectrum-based characteristics analysis

Characteristics extraction and anomaly analysis based on frequency spectrum can provide crucial support for source apportionment of PM2.5 pollution. In this study, an effective source apportionment framework combining the Fast Fourier Transform (FFT)-and Continuous Wavelet Transform (CWT)-based spectral analyses and Positive Matrix Factorization (PMF) receptor model is developed for spectrum characteristics extraction and source contribution assessment. The developed framework is applied to Beijing during the winter heating period with 1h time resolution. The spectrum characteristics of anomaly frequency, location, duration and intensity of PM2.5 pollution can be captured to gain an in-depth understanding of source-oriented information and provide necessary indicators for reliable PMF source apportionment. The combined analysis demonstrates that the secondary inorganic aerosols make relatively high contributions (50.59 %) to PM2.5 pollution during the winter heating period in Beijing, followed by biomass burning, vehicle emission, coal combustion, road dust, industrial process and firework emission sources accounting for 15.01 %, 11.00 %, 10.70 %, 5.31 %, 3.88 %, and 3.51 %, respectively. The source apportionment result suggests that combining frequency spectrum characteristics with source apportionment can provide consistent rationales for understanding the temporal evolution of PM2.5 pollution, identifying the potential source types and quantifying the related contributions.