Wavelet periodic and compositional characteristics of atmospheric PM2.5 in a typical air pollution event at Jinzhong city, China

Abstract

To investigate the contributions of atmospheric particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5) to heavy air pollution events in Fen-Wei Plain, China and identify their sources, a combination of wavelet analysis and low-atomic number (Z) particle electron probe X-ray microanalysis (EPMA) was utilized in a typical haze episode lasting 10 days (from Nov. 11 to 20, 2016) at Jinzhong, an inland city adjacent to Taiyuan, the capital of Shanxi Province. A 3-layer wavelet decomposition filtration was performed using the daubechies wavelet functions (db6) and the distribution of the real wavelet transformation coefficients and variance of wavelet series for various pollutants within 240 h was plotted. Five dominant periods of PM2.5 in mass concentration were observed, peaking at 101 h, 144 h, 50–51 h, 27–28 h, and 18 h in sequence from high to low in the wavelet variance chart. They were similar to those of PM10, SO2, and CO and partially overlapped with those of NO2, but different from those of O3, implying that PM2.5 have the same sources as PM10, SO2, and CO and have complicated relations with O3 and NO2. It was further found that the chemical compositions of PM2.5 in the dominant periods made great changes compared to the non-dominant periods. The relative number abundance of elemental carbon (EC) and droplets rich in CNOS (likely the mixture of carbonaceous matter with secondary aerosols such as NH4NO3 and (NH4)2SO4/NH4HSO4) in the first and second dominant period were significantly higher than that in other periods. It is concluded that coal and biomass combustion and secondary particles are still the main contributors to the wintertime heavy air pollution events in Fen-Wei Plain, suggesting that the wavelet analysis combined with low-Z particle EPMA is useful in examination of atmospheric particulate matter's origin, evolution, and roles played in haze events.