Aerosols over East and South Asia: Type Identification, Optical Properties, and Implications for Radiative Forcing

Abstract

Identification of aerosol types has long been a difficult problem over East and South Asia due to various limitations. In this study, we use 2-dimensional (2-D) and multi-dimensional Mahalanobis distance (MD) clustering algorithms to identify aerosol characteristics based on the data from the Aerosol Robotic Network from March 1998 to February 2018 over the South and East Asian region (10°N~50°N, 70°E~135°E). The single scattering albedo (SSA), absorption Angstrom exponent (AAE), extinction Angstrom exponent (EAE), real index of refraction (RRI), and imaginary index of refraction (IRI) are utilized for classification of aerosols. Sub-regions with similar background conditions over East and South Asia are identified by hierarchical clustering algorithm to illustrate distinctive meteorological states in different areas. The East and South Asian aerosols are found to have distinct regional and seasonal features relating to the meteorological conditions, land cover, and industrial infrastructure. It is found that the proportions of dust aerosol are the highest in spring at the SACOL site and in summer at the sites near the Northern Indo-Gangetic Plain area. In spring, biomass-burning aerosols are dominant over the central Indo-China Peninsula area. The aerosol characteristics at coastal sites are also analyzed and compared with previous results. The 2-D clustering method is useful when limited aerosol parameters are available, but the results are highly dependent on the sets of parameters used for identification. Comparatively, the MD method, which considers multiple aerosol parameters, could provide more comprehensive classification of aerosol types. It is estimated that only about 50% of the data samples that are identifiable by the MD method could be classified by the 2-D methods, and a lot of undetermined data samples could be mis-classified by the 2-D methods. The aerosol radiative forcing (ARF) and the aerosol radiative forcing efficiency (ARFE) of various aerosol types at the top and the bottom of the atmosphere (TOA and BOA) are determined based on the MD aerosol classification. The dust aerosols are found to have the largest ARF at the TOA (−36 W/m2), followed by the urban/industrial aerosols and biomass-burning aerosols. The ARFE of biomass-burning aerosols at the BOA (−165 W/m2/AOD550nm) is the strongest among those of the other aerosol types. The comparison of the results by MD and 2-D methods shows that the differences in ARF and ARFE are generally within 10%. Our results indicate the importance of aerosol type classification in accurately attributing the radiative contributions of different aerosol components.

Analysis of Long-Term Aerosol Optical Properties Combining AERONET Sunphotometer and Satellite-Based Observations in Hong Kong

“…Using ground-based AERONET measurements taken over 16 years and combined with satellite-based observations, our study explores the dominant aerosol types, long-term varying trends and the interactions with meteorological factors. The main contributions of our study can be summarized as follows: First, in addition to classifying the aerosol types based on AOD and AE values in previous studies [63][64][65][66], the aerosol-absorbing properties in Hong Kong were also categorized using AE, SSA and FMF. Moreover, the multi-seasonal AOD, AE and SSA distribution patterns in Hong Kong were discussed, which provides a basis for understanding the seasonal aerosol optical characteristics in subtropical urban areas such as Hong Kong.…”

“…Moreover, the multi-seasonal AOD, AE and SSA distribution patterns in Hong Kong were discussed, which provides a basis for understanding the seasonal aerosol optical characteristics in subtropical urban areas such as Hong Kong. Notably, the thresholds of the aerosol optical parameters are generally used for dominant aerosol type classification, while the proportions of specific aerosol types depend on the selected aerosol parameters and the thresholds used for the classification [63]. Additionally, using aerosol optical parameters for aerosol type identification is suitable for areas with a stable aerosol composition, but may not be well-suited for locations with complex topography, Arctic regions and remote marine areas, as aerosols in these regions are difficult to represent using median optical parameters [67].…”

Aerosol Optical Properties and Type Retrieval via Machine Learning and an All-Sky Imager

“…The performance of ML-ASI-predicted aerosol retrievals for the aerosol type classification is achieved in terms of total accuracy and precision: Total Accuracy = True cases Total cases (6) Precision = Predicted cases per aerosol type Observed cases per aerosol type (7) All the above statistical metrics are calculated solely using the testing dataset as described in Section 3.1. The testing dataset is also used to interpret the results of the proposed methodology (Section 4).…”

“…Under cloudless conditions, the aerosol radiative impact on the climate is described by direct aerosol radiative forcing (DARF), which is often characterized by significant uncertainties due to the high spatiotemporal variabilities of the aerosol's physical and optical properties [2]. Those properties depend on the emission sources; biogenic (e.g., sea salt and dust) and anthropogenic (e.g., biomass burning and fossil fuel combustion), and aerosol composition and size [3][4][5][6]. The contiguous and accurate monitoring of aerosol properties at the finest spatiotemporal resolution is desirable in order to (a) better understand the aerosol effect on climate and (b) model the solar irradiance.…”

Comparative Study of Aerosol Optical Properties along a Megacity

“…Many studies have been devoted to classifying aerosols at a given location using a network of surface stations around the world called Aerosol Robotic Network (AERONET) [12] or satellite data. Recently, great importance has been given to inversion products of optical properties to extract information [8,[13][14]. Inversion products are aerosol characteristics such as size distribution, single scattering albedo, phase functions, and the complex index of refraction, and are derived from Sun photometer sky radiance measurements [12].…”