To investigate the contributions of atmospheric particulate matter less than 2.5 μm in aerodynamic diameter (PM_2.5) 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 PM_2.5 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 PM_2.5 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 PM_2.5 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 wintertime heavy air pollution 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.

结合小波分析和低原子序数（Z）粒子，调查空气动力学直径小于2.5μm的大气颗粒物（PM _2.5）对严重的空气污染事件的贡献并确定其来源电子探针X射线微分析（EPMA）用于典型的霾天气，持续10天（2016年11月11日至20日），发生在山西省省会太原市附近的内陆城市晋中。使用daubechies小波函数（db6）进行了三层小波分解过滤，并绘制了240小时内各种污染物的实际小波变换系数的分布和小波序列的方差。PM _2.5的五个主要时期在小波方差图中从高到低依次观察到质量浓度的峰值，分别在101h，144h，50-51h，27-28h和18h达到峰值。它们与PM ₁₀，SO ₂和CO的那些相似，但与NO _2的部分重叠，但与O _3的那些不同，这意味着PM _2.5与PM ₁₀，SO ₂和CO的来源相同。与O ₃和NO _2的复杂关系。进一步发现PM _2.5的化学成分与非主导时期相比，主导时期的变化很大。第一和第二阶段中元素碳（EC）和富含CNOS的液滴（可能是碳质物质与二次气溶胶（例如NH ₄ NO ₃和（NH ₄）₂ SO ₄ / NH ₄ HSO ₄）的混合物）的相对数量丰度优势期显着高于其他时期。结论是coal渭平原煤和生物质燃烧及二次颗粒仍是冬季重度空气污染的主要因素，这表明小波分析与低Z值相结合。 粒子EPMA可用于检查大气颗粒物的起源，演变以及在霾事件中扮演的角色。