Establishment of Conceptual Schemas of Surface Synoptic Meteorological Situations Affecting Fine Particulate Pollution Across Eastern China in the Winter

Abstract

In the present study, the characteristics of weather conditions and local meteorological variables over the Beijing‐Tianjin‐Hebei (BTH) and the Yangtze River Delta (YRD) regions in the winter are analyzed using the principal component analysis (PCA) method and daily PM2.5 accumulation rate. Typical synoptic weather patterns over China in the winter can be classified into four types. During the Type 1 synoptic weather pattern, China is under the influence of the Siberian High, and northerly winds prevail; this situation is beneficial to the transport of pollutants from north to south. However, when the Siberian High is weak, southerly winds prevail which may result in the transport of pollutants from south to north. The Type 2 weather pattern refers to a weak high pressure located in the BTH resulting in the accumulation of pollutants. During the Type 3 weather pattern, an intense cold Siberian High moves to the south and affects the northern areas of China. The associated front brings heavy precipitation in the YRD resulting in the wet deposition of pollution. During the Type 4 weather condition, the weak Siberian High is blocked by the northeast cold vortex and moves towards the south, causing the accumulation of pollution in the YRD. The PCA model shows that there are two transport pathways for pollutants to the BTH (YRD) area: one from the YRD (BTH) and Shandong during Types 1 and 2 (Types 1 and 3) situations and the other one from the central provinces during Type 4 (Types 1 and 4).

Air Quality over China

“…Meteorological effects are different in summer and winter [93]. Different synoptic situations in the winter result in different transport pathways and pollution episodes in the BTH and YRD [12].…”

“…The AOD over China shows strong spatial variability due to a variety of influences, as discussed in the Introduction section. Several studies were made to explain these variations in terms of transport pathways using meteorological information [12] and air mass trajectory analysis [44]. Wang et al [44] used sun photometer data together with ground-based data from Nanjing during episodes with heavy pollution, in the summer and in the winter.…”

“…During the winter episode, the aerosol concentrations were strongly influenced by long range transport of coarse particles (likely desert dust) from the arid areas in the west and north of China. Hou et al [12] characterized large scale weather patterns influencing the aerosol concentrations in the BTH and YRD in the winter, using the principal component analysis (PCA method). These authors divided the synoptic weather patterns in 4 types, according to the influence of the Siberian High and the northeast cold vortex, with different effects on the accumulation of air pollution and long-range transport.…”

“…Studies to provide PM 2.5 from AOD data have been undertaken using both sun photometer and satellite data, together with physical models [2,42] or a neural network [43]. In addition, studies were made on the effects of long-range transport of aerosol on PM 2.5 versus the effect of local production, and on the effects of large-scale meteorology on transport pathways [12,44].…”

Trans‐Regional Transport of Haze Particles From the North China Plain to Yangtze River Delta During Winter

“…Therefore, it is reasonable to observe the TRT haze events from the NCP to YRD in winter because of cold fronts (He et al., 2018; Kang et al., 2019). Although a few studies have observed some TRT haze events from the NCP to YRD during winter, they only analyzed the impact of weather system on TRT by weather patterns (He et al., 2018; Hou et al., 2020; J. Li et al., 2019; Shu et al., 2017) or variations of components and geographical contribution of PM 2.5 in the YRD during TRT by bulk analysis (Li, Wang, et al., 2019; Ming et al., 2017), radiosonde observation (Zhong et al., 2019), lidar measurement (T. Sun et al., 2018), and model simulation (X. Huang et al., 2020; Kang et al., 2019; Li, Huang, et al., 2012; Ming et al., 2017). For example, He et al.…”