Transport Patterns, Size Distributions, and Depolarization Characteristics of Dust Particles in East Asia in Spring 2018

Abstract

The long‐range transport of Asian dust and its complex interaction with anthropogenic pollutants have a significant yet poorly quantified effect on the global climate. In this study, dust events were observed based on an optical particle counter with a polarization detection module at an urban site in North China. The temporal variations in the size distribution and morphological changes of dust particles were analyzed. The results demonstrate that the dust events were induced by a deep Siberian low‐pressure anomaly and/or Mongolian cyclone in the low troposphere. The morphology of dust particles, as indicated by the depolarization ratio (δ = s/(s + p), the ratio of the parallel signal to the total backscattering signal), varied significantly at different transport heights. A decreasing tendency was found when dust was trapped within the boundary layer over polluted areas, particularly when the atmospheric loading of pollutants and the relative humidity were high. During a typical polluted dust case, the δ value of mineral dust (Dp = 5 μm) decreased by 39%, as the conditions were stagnant in East China, consistent with the Cloud‐Aerosol LIDAR with Orthogonal Polarization results. The morphological variation was evaluated according to the aspect ratio, presuming that the dust particles were ellipsoidal. The interaction between dust particles and water vapor could lead to an increase of at least 6% in volume when the relative humidity increased up to 90%. An aerosol classification based on δ values and size information could be performed to estimate the contributions of different aerosol types to pollution days. The results should be helpful for evaluating satellite‐borne and ground‐based light detection and ranging data analyses.

Emerging investigator series: the red sky: investigating the hurricane Ophelia Saharan dust and biomass burning aerosol event

“…However, after 12:00 UTC the boundary layer and the dust/smoke layer became decoupled, and the boundary layer (labelled as (vii) in Figure 8) went on to be dominated by spherical aerosol, as indicated by the low volume depolarisation ratios seen in Figure 8b, which are typical of locally emitted pollution. 67,73,74 This decoupling was due to the change in wind direction with height;…”

Modeling and Evaluation of the Systematic Errors for the Polarization-Sensitive Imaging Lidar Technique

“…p is the DoLP of the laser beam that is transmitted into the atmosphere, 0  is the true value of the LVDR, which is only dependent on the optical properties of the scatters (see Equation ( 12)). Figure 6 illustrates the relationship between the relative error and the DoLP of the transmitted laser beam at typical atmospheric LVDRs for molecules (0.004), anthropogenic aerosol (0.05), and dust (0.3) [35,73,74]. As can be seen, the relative error decreases with the increasing of the DoLP, and increases when the LVDR becomes smaller.…”

“…The relative errors of the LVDR due to the uncertainties of the PERs were calculated with a LVDR value varying from 0.004 to 0.3, as shown in Figure 7. For urban aerosols or dust, the value of LVDR is often larger than 0.05 and can reach up to 0.3 [35,74]. Under these circumstances, the relative error can be limited to 1.5% at 450 and 520 nm even if the uncertainty of the PER reaches up ±20%, while the relative error is less than 7% at 808 nm.…”

Detecting and Evaluating Dust‐Events in North China With Ground Air Quality Data

“…The China Meteorological Administration (CMA) has classified dust events into four categories as floating dust, blowing dust, dust storm and severe dust storm (CMA, 1979; Yang et al., 2008), which correspond to horizontal visibility of fewer than 10 km, 1–10 km with blowing wind, 500 m–1 km and fewer than 500 m, respectively. Remote sensing techniques have been the most popular method to determine dust related Aerosol Optical Depth (AOD) and near surface particulate matter (PM) concentrations based on satellite images (Sowden et al., 2018; Tian et al., 2020; van Donkelaar et al., 2015; You et al., 2016). Radiation and the spectra absorption differences between dust and other substances are the main basis for distinguishing dust events and other processes.…”