ABSTRACT
Owing to a lack of vertical observations, the impacts of black carbon (BC) on radiative forcing (RF) have typically been analyzed using ground observations and assumed profiles. In this study, a UAV platform was used to measure high-resolution in-situ vertical profiles of BC, fine particles (PM2.5), and relevant meteorological parameters in the boundary layer (BL). Further, a series of calculations using actual vertical profiles of BC were conducted to determine its impact on RF and heating rate (HR). The results show that the vertical distributions of BC were strongly affected by atmospheric thermodynamics and transport. Moreover. Three main types of profiles were revealed: Type I, Type II, Type III, which correspond to homogenous profiles (HO), negative gradient profiles (NG), and positive gradient profiles (PG), respectively. Types I and II were related to the diurnal evolution of the BL, and Type III was caused by surrounding emissions from high stacks and regional transport. There were no obvious differences in RF calculated for HO profiles and corresponding surface BC concentrations, unlike for NG and PG profiles. RF values calculated using surface BC concentrations led to an overestimate of 13.2 W m-2 (27.5%, surface) and 18.2 W m-2 (33.4%, atmosphere) compared to those calculated using actual NG profiles, and an underestimate of approximately 15.4 W m-2 (35.0%, surface) and 16.1 W m-2 (29.9%, atmosphere) compared to those calculated using actual PG profiles. In addition, the vertical distributions of BC HR exhibited clear sensitivity to BC profile types. Daytime PG profiles resulted in a positive vertical gradient of HR, which may strengthen temperature inversion at high altitudes. These findings indicate that calculations that use BC surface concentrations and ignore the vertical distribution of BC will lead to substantial uncertainties in the effects of BC on RF and HR.
ABSTRACT
Previous studies have developed a stable weather index (SWI) based on meteorological elements that adequately represent PM2.5 pollution over the North China Plain (NCP). However, the SWI performs poorly over the Yangtze River Delta (YRD) region because air pollution over this region is affected not only by stagnant weather (STAG) but also by transport (TRANS). For example, air pollutants can be transported from the NCP to the YRD by cold fronts. In this study, an obliquely rotated principal component analysis in the T-model is applied to classify the synoptic patterns of winter weather over the YRD region from 2013 to 2018. Among the four identified synoptic patterns, two of which cause TRANS, one pattern is most likely to cause STAG, and one pattern can lead to either STAG or TRANS depending on the location of high pressure around Shandong province. Due to the large contribution (63%) of TRANS to the total PM2.5 pollution events, a transport pollution index (TPI) is constructed to describe the transport features of PM2.5 pollution over the YRD region. Our results show that, when considering the SWI alone, the correlation coefficients between the SWI and ln(PM2.5) range from 0.50 to 0.57 in the main cities of the YRD. Excitingly, when considering both the TPI and SWI (TPI+SWI), the correlation coefficients increase significantly to 0.63-0.78, suggesting that TPI+SWI better reflects the wintertime PM2.5 pollution level over the YRD region. In addition, satisfactory performance in validation also suggests that TPI+SWI can increase the accuracy of evaluating and forecasting of PM2.5 pollution episodes over regions downstream of source emissions.
