# Extensive characterization of aerosol optical properties and chemical component concentrations: Application of the GRASP/Component approach to long-term AERONET measurements

Xindan Zhang, Lei Li, Cheng Chen, Yu Zheng, Oleg Dubovik, Yevgeny

Derimian, Anton Lopatin, Ke Gui, Yaqiang Wang, Hujia Zhao, et al.

► To cite this version:

Xindan Zhang, Lei Li, Cheng Chen, Yu Zheng, Oleg Dubovik, et al. Extensive characterization of aerosol optical properties and chemical component concentrations: Application of the GRASP/Component approach to long-term AERONET measurements. Science of the Total Environment, 2022, 812, pp.152553. 10.1016/j.scitotenv.2021.152553. hal-03830352

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<figure><img src="image_1.png" /></figure>
Extensive characterization of aerosol optical properties and chemical component concentrations: application of the GRASP/Component approach to long-term AERONET measurements

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6 Xindan Zhang¹, Lei Li¹*, Cheng Chen²,³, Yu Zheng¹, Oleg Dubovik², Yevgeny Derimian²,

7 Anton Lopatin³, Ke Gui¹, Yaqiang Wang¹, Hujia Zhao⁴, Yuanxin Liang¹, Brent Holben⁵,

8 Huizheng Che¹, Xiaoye Zhang¹

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¹State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing, 100081,

11 China

¹² Univ. Lille, CNRS, UMR 8518 – LOA – Laboratoire d'Optique Atmosphérique, 59000 Lille, France

³GRASP-SAS, Villeneuve d'Ascq, France

⁴Institute of Atmospheric Environment, Shenyang, China

¹⁵Biospheric Sciences Branch, Code 923, NASA/Goddard Space Flight Center, Greenbelt, MD, USA

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20 Correspondence to:

21 L. Li (lilei@cma.gov.cn)

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**Abstract:** A recently developed GRASP/Component approach (GRASP: Generalized Retrieval of Atmosphere and Surface Properties) was applied to AERONET (Aeronet Robotic Network) sun photometer measurements in this study. Unlike traditional aerosol component retrieval, this approach allows the inference of some information about aerosol composition directly from measured radiance, rather than indirectly through the inversion of optical parameters, and has been integrated into the GRASP algorithm. The newly developed GRASP/Component approach was applied to 13 AERONET sites for different aerosol types under the assumption of aerosol internal mixing rules to analyze the characteristics of aerosol components and their distribution patterns. The results indicate that the retrievals can characterize well the spatial and temporal variability of the component concentration for different aerosol types. A reasonable agreement between GRASP BC retrievals and MERRA-2 BC products is found for all different aerosol types. In addition, the relationships between aerosol component content and aerosol optical parameters such as aerosol optical depth (AOD), fine-mode fraction (FMF), absorption Ångström exponent (AAE), scattering Ångström exponent (SAE), and single scattering albedo (SSA) are also analyzed for indirect verifying the reliability of the component retrieval. It was demonstrated the GRASP/Component optical retrievals are in good agreement with AERONET standard products [e.g., correlation coefficient ($R$) of 0.93 – 1.0 for AOD, fine-mode AOD (AODF), coarse-mode AOD (AODC) and Ångström exponent (AE); $R$ = ~ 0.8 for absorption AOD (AAOD) and SSA; RMSE (root mean square error) < 0.03 for AOD, AODF, AODC, AAOD and SSA]. Thus, it is demonstrated the
45 GRASP/Component approach can provide aerosol optical products with comparable
46 accuracy as the AERONET standard products from the ground-based sun photometer
47 measurements as well as some additional important inside on aerosol composition.

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49 **Key words:** aerosol component retrieval, aerosol optical property,
50 GRASP/Component approach, AERONET sun photometer measurements, component
51 mixing rule
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53 1. Introduction

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The composition content of atmospheric aerosols has important influences on
their optical properties and radiative effects (Hu et al., 2005), which is of great
significance for monitoring and understanding various aspects of the climate and
environment (Jin et al., 2019a, 2019b; Ma et al., 2019). Aerosol radiative forcing has
been one of the most uncertain factors in assessments of climate change (IPCC, 2013,
2021). For example, there is a large uncertainty in the assessment of the mass
concentration and radiative forcing of black carbon (BC) (Perlwitz and Seland, 2009;
Schuster et al., 2016; Sato et al., 2003). Therefore, to reduce these uncertainties, there
is a need to better characterize the composition of aerosols using observations (Van
Beelen et al., 2014). Although the in-situ measurements can provide very detailed
information on aerosol composition, using sampling techniques or laboratory analysis
can modify aerosol properties. In addition, it is practically impossible to provide
information about aerosol properties in the entire atmospheric column and with large
spatial coverage using the in-situ measurements only (Bond et al., 1998; Streets et al., 2001). In contrast, the retrieval of aerosol components from remote sensing data carries the advantage of non-contact, instantaneous state detection, and the ability to maintain the natural state of aerosols, thus avoiding the loss of any information during the measurement process.

A variety of aerosol properties parameters can be provided by ground-based observations such as high-precision observations of direct solar light and sky-scattered light. For example, a dataset of aerosol parameters can then be retrieved via the statistical method developed by Dubovik and King (2000), which simultaneously employs all available multi-spectral atmospheric radiance measurements. Global and regional aerosol monitoring networks such as AERONET (Aerosol Robotic Network) (Holben et al., 1998), PHOTONS (Photométrie pour le Traitement Opérationnel de Normalisation Satellitaire) (Goloub et al., 2007), CARSNET (China Aerosol Remote Sensing Network) (Che et al., 2009) and SKYNET (Takamura and Nakajima, 2004) have made significant contributions to the development of ground-based aerosol remote sensing. In recent years, the AERONET algorithm has evolved through three versions (V1, V2 and V3) and V2 has been updated to V3 with improvements to data input and calibration, fully automated cloud screening and instrument anomaly quality control (Giles et al., 2019). The main products of aerosol optical parameters, including aerosol optical depth (AOD), Ångström exponent (AE), single scattering albedo (SSA), size distribution, and complex refractive index, can be provided by AERONET with a high degree of
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accuracy (Giles et al., 2019). However, AERONET algorithm does not provide
aerosol components product.

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On the other hand, estimation of aerosol components can be achieved by fitting
aerosol optical parameters such as aerosol complex refractive index and SSA. For
example, a series of component algorithms were proposed and developed using the
AERONET extended aerosol optical parameters. Specifically, Schuster et al. (2005)
developed a three-component model for aerosols, including BC, sulphate and water,
which uses the differences in real and imaginary parts of the refractive index between
BC and aerosol water content (AWC) to estimate their concentrations. Similarly, Dey
et al. (2006) and Arola et al. (2011) took advantage of the significant variation in the
light absorption of brown carbon (BrC) with wavelength to achieve the retrieval of
BrC as well. Subsequently, Wang et al. (2012) added to the Schuster three-component
model (Schuster et al., 2005) a dust component that also has strong absorption at short
wavelengths. The spectral dependence of the imaginary part of the complex refractive
index is similar for dust and BrC, making it difficult to distinguish between these two
absorption components. To address this challenge, Wang et al. (2013) subsequently
used the spectral variability of SSA to distinguish between the two components,
developed a five-component model including three absorbing components (BC, dust
and BrC) simultaneously, and assessed the inversion uncertainty. Then, a more
complete model of aerosol components was developed by Van Beelen et al. (2014),
and various optical parameters such as complex refractive index, AOD, SSA and
sphericity were used jointly to infer the aerosol components. In fact, since SSA is a

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metric that contains both optical absorption and particle size, these studies therefore implicitly demonstrate the importance of particle size in the estimation of aerosol components. Taking into account the effect of particle size, Schuster et al. (2016) developed a more complex composition model with different components for coarse and fine modes, and further considered the effect of light absorption by dust containing iron oxides on the retrieval of BC, thus minimized the influence on the BC retrieval. Since the differences in the real and imaginary parts of the complex refractive index among various scattering components are quite small, additional information provided by other parameters is needed to identify the scattering component species. The size distribution of particles and spherical particle ratio are helpful to distinguish sea salt from ammonium sulfate and mineral dust, respectively (Van Beelen et al., 2014). It is noted that all the aerosol component algorithms mentioned above estimate the aerosol components in an intermediate step in which they are retrieved from AERONET products (such as the real and imaginary parts of the complex refractive index, SSA, size distribution, etc.), i.e. not directly from the measured radiance. It has been demonstrated that the uncertainties of component retrieval in these intermediate methods are mainly driven by uncertainties in the aerosol optical and microphysical parameters used as input (Schuster et al., 2016). Therefore, uncertainty in the aerosol optical parameters could result in the amplification of uncertainty in the aerosol components estimated by indirect retrieval methods, which could be improved by directly fitting the solar radiation observations for the retrievals of aerosol components and aerosol optical properties together. Based
on this idea, a new aerosol component module for the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm (Dubovik et al., 2011, 2014, 2021) was recently developed for the retrieval of aerosol components directly from remote-sensing data (Li et al., 2019, 2020a). The retrieval uncertainties associated with the employed refractive index for each component were estimated to be 30% for BC, 50% for BrC, 100%-150% for dust or organic carbon (OC), and 70% for iron oxides, based on POLDER/PARASOL observations (Li et al., 2019). It has been demonstrated that the GRASP/Component approach not only provides commonly comparable aerosol optical parameters, but also enables remote monitoring of aerosol components over large spatial and temporal scales (Li et al., 2019, 2020a, 2020b).

Feasibility of the GRASP/Component approach application to AERONET sun photometer measurements was illustrated in (Li et al., 2019), in this study we now apply the GRASP/Component algorithm for a direct estimation of aerosol components from AERONET sun/sky radiance measurements for long-time series and for different sites. We also illustrate that the derived optical characteristics are consistent with the standard AERONET optical product.

The paper is organized as follows: section 2 presents the data and methods used in this study; section 3 analyzes the characteristics of different aerosol component in different aerosol types and the relationship between the aerosol component content and aerosol optical properties [AOD, fine mode fraction (FMF), absorption AE (AAE), scattering AE (SAE) and SSA], which is also an indirect validation and evaluation of component retrievals, followed by a comprehensive intercomparison of the aerosol
optical parameters [AOD, fine-mode AOD (AODF), coarse-mode AOD (AODC), absorption AOD (AAOD), SSA, AE] between GRASP/Component approach and the corresponding AERONET standard products during the period 2014–2019; and finally, in section 4, the main conclusions are presented.

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## 2. Data and methods

### 2.1. AERONET measurement

AERONET is a global network of ground-based multi-spectral sun/sky photometers (Holben et al., 1998, 2001). The radiometers automatically provide direct measurements of solar radiation (nominally centered at 340, 380, 440, 500, 670, 870, 940, 1020, and 1640 nm), which can be processed to obtain AOD. In addition, sky radiance is measured at four wavelengths (440, 675, 870 and 1020 nm) by combination with almucantar and principal plan scans (in some locations sky-radiance measurements are also available at other wavelengths). Many aerosol optical and microphysical parameters can be derived from sky radiance measurements (Dubovik and King, 2000; Dubovik et al., 2000). The AERONET aerosol dataset consists of three quality levels: Level 1.0 for non-cloud-screened data, Level 1.5 for cloud-screened data, and Level 2.0 for cloud-screened and quality-assured data. Based on the measured solar radiance, the AERONET algorithm can provide the aerosol properties in real time, such as multi-wavelength AOD products with high temporal resolution and highly reliable accuracy (~ ±0.01 to ±0.02) (Eck et al., 1999). Strict calibration and maintenance protocols are conducted to ensure consistency between
instruments, which is helpful for providing high-quality aerosol products that contribute to the establishment and validation of satellite retrieval algorithms (Levy et al., 2007; Lyapustin et al., 2018; Bréon et al., 2011; Mao et al., 2019; Mélin et al., 2010; Remer et al., 2005; Kahn et al., 2010; Ahn et al., 2014; Kumar et al., 2015).

The AERONET sites used in this study are grouped into five categories based on the classification of aerosol types in the AERONET climatology (Dubovik et al., 2002; Giles et al., 2012) and some existing literature (see Figure 1), as follows: (a) Biomass Burning (BB) (Holben et al., 2001; Dubovik et al., 2002), typical sites such as the CUIABA-MIRANDA site in central South America, which is affected by a variety of biomass burning aerosols from cerrado vegetation, agricultural land, tropical forests, etc. during the annual burning season (generally August-September); (b) Dust (DU) (Holben et al., 2001), a typical dust site, Banizoumbou, is located in Niger, north of the Sahara, and is affected by the seasonal wind Harmattan, which brings dust from the Sahara desert region to the area; (c) Marine (MA) (Mallet et al., 2018), considering that sea-air interactions play a key role in the atmospheric system and that the ocean is a reservoir of sea spray aerosols, a typical marine site REUNION_ST DENIS in the Southern Indian Ocean was chosen for the study of the optical properties and chemical composition of aerosols; (d) Dust Pollution (DP), representing desert dust predominance with urban/industrial pollution transports (Derimian et al., 2006). Here we chosen the Sede Boker site in the Negev desert in Israel, which is heavily affected by dust from April to May and mainly by urban/industrial pollution from July to August; and (e) Multi-source Urban (MU),
representing a variety of different aerosol sources in an urban background (Dubovik et al., 2002; Bergamo et al., 2008; Mitchell et al., 2013; Kabashnikov et al., 2014; Chubarova et al., 2011; Eck et al., 2005; Giannakaki et al., 2010). Level 2 total optical depth data and Level 2 almucantar data are downloaded from the AERONET website to prepare the input data for the GRASP/Component approach. The measured data at these AERONET sites are processed by the GRASP/Component approach for the aerosol component and optical properties retrievals. Meanwhile, the AERONET V3 Level 2.0 quality-assured products (http://www.aeronet.gsfc.nasa.gov; last accessed 7 April 2021) (Giles et al., 2019) were used to validate the GRASP/Component aerosol optical retrievals from sun photometer measurements.

<figure><img src="image_2.png" /></figure>

**Figure 1.** Geographical distribution of the AERONET sites used in this study. The colors of sites are associated with the main aerosol types as: (a) Biomass Burning (BB); (b) Dust (DU); (c) Marine (MA); (d) Dust_Pollution (DP), representing dust and pollution events occurring alternately; and (e) Multi-source Urban (MU), representing a variety of different aerosol sources in an urban background.
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## 2.2 MERRA-2 data

217 The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) is a re-analysis dataset with global coverage of long time series that begins in 1980, with a spatial resolution of 0.5° × 0.625° and a temporal resolution of 1 hour. MERRA-2 uses an upgraded version of the Goddard Earth Observing System model, Version 5 (GEOS-5) data assimilation system to replace the original MERRA reanalysis. Compared to the MERRA-1, MERRA-2 offers a number of improvements that reflected throughout the period of aerosol assimilation (Molod et al. 2015, Rienecker et al., 2008, 2011). The aerosols in MERRA-2 were simulated by coupling the GEOS-5 model with the Goddard Chemistry, Aerosol, Radiation, and Transport (GOCART) (Chin et al. 2002; Colarco et al. 2010) aerosol module to take into account the estimation of the source, sink and chemical components of the aerosols. Here we employ MERRA-2 monthly mean BC concentration (2015-2019) to validate our BC products derived by the component approach.

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## 2.3 GRASP/Component approach

232 The GRASP algorithm is a highly rigorous and versatile algorithm for the enhanced characterization of aerosol properties from a diverse range of remote sensing data such as sun photometers, lidar, satellite observations, etc. (Dubovik et al., 2014, 2021; Torres et al., 2017; Titos et al., 2019; Hu et al., 2019; Tsekeri et al., 2017), and is available at https://www.grasp-open.com. The multi-term least-squares method

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237 (LSM) was employed in the GRASP algorithm to achieve a statistically optimal fit to
238 measurements (Dubovik et al., 2021). A detailed description of the GRASP algorithm
239 can be found in Dubovik et al. (2011, 2014, 2021). As described in the study of
240 Dubovik et al. (2021), the GRASP package can be considered as a platform for
241 developing, testing and refining novel retrieval concepts and their utilization in
242 operational processing environments. The aerosol component module was developed
243 and incorporated into the GRASP algorithm with detailed descriptions in Li et al.
244 (2019, 2020a, 2020b). The GRASP/Component approach employs a specific
245 component module that aerosol mixture particles are made up of multiple components
246 internal mixing with known complex refractive index of each component and values
247 of the aerosol mixture complex refractive index are estimated by the fraction of each
248 component together with its refractive index under a certain effective medium
249 approximation mixing rules on aerosol components. Thus, the GRASP/Component
250 approach can directly invert the content of different aerosol components instead of the
251 spectral dependence of the complex refractive index, and directly infer the aerosol
252 component from the measured radiation (such as sun photometer measurements),
253 avoiding the uncertainties associated with intermediate steps for the component
254 estimations (Li et al., 2019), which has been successfully applied to
255 POLDER-3/PARASOL observations. Here, we provide a general description of the
256 GRASP/Component approach for the ground-based AERONET sun photometer
257 measurements.
The description of GRASP/Component products derived from AERONET sun photometer measurements together with AERONET standard products are listed in Table 1. In the GRASP/Component approach, differentiating aerosol components by coarse and fine modes allows aerosol components with similar spectral absorption properties to be distinguished (Schuster et al., 2016; Li et al., 2019), e.g., BrC and iron oxides (hematite and goethite) have similar spectral absorption properties (Chen and Cahan, 1981; Chen and Bond, 2010; Schuster et al., 2016). Considering that aerosol particles containing carbon dominate the fine mode, while dust containing iron are mainly distributed in the coarse mode, the GRASP/Component approach therefore classifies BC and BrC as absorbing insoluble components in the fine-mode state, while iron oxides are classified as absorbing insoluble components in the coarse-mode state. In addition, the fine-mode non-absorbing insoluble species (FNAI) are included under the fine mode to represent mainly fine dust or non-absorbing OC, and fine-mode non-absorbing soluble species (FNAS) to represent inorganic salts and fine-mode AWC (FAWC). In the coarse mode, the absorbing insoluble species (CAI) is the only absorptive component and represents mainly iron oxides, in addition to a number of other absorptive elements. The non-absorbing insoluble species (CNAI), mainly representing dust, but also non-absorbing insoluble OC particles. Coarse-mode non-absorbing soluble species (CNAS) represent inorganic salts. In addition to this, the coarse-mode also contains AWC (CAWC).

<caption>**Table 1.** Intercomparison of aerosol products derived by the GRASP/Component from</caption>
AERONET sun photometer measurements and AERONET standard products. *f* and *c* indicate fine- and coarse-mode aerosol, respectively. The main differences are in bold.

<table><thead><tr><th rowspan="2">Retrieval<br>Data<br>Products</th><th colspan="2">GRASP/Component Retrievals</th><th colspan="2">AERONET Retrievals</th></tr><tr><th>Total<br>Only</th><th>Total/<br>Fine/<br>Coarse</th><th>Total<br>Only</th><th>Total/<br>Fine/<br>Coarse</th></tr></thead><tbody><tr><td>AOD</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>AE</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>SSA</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>AAOD</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>AAE</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Refractive Index</td><td>No</td><td>Yes (f, c)</td><td>Yes</td><td>No</td></tr><tr><td>Asymmetry Factor</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Sphere Fraction</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>Size Distribution</td><td>No</td><td>f: 15 bins; c: 10 bins</td><td>Yes (22 bins)</td><td>No</td></tr><tr><td>BC</td><td>No</td><td>Yes (f)</td><td>No</td><td>No</td></tr><tr><td>BrC</td><td>No</td><td>Yes (f)</td><td>No</td><td>No</td></tr><tr><td>NAI</td><td>No</td><td>Yes (f, c)</td><td>No</td><td>No</td></tr><tr><td>CAI</td><td>No</td><td>Yes (c)</td><td>No</td><td>No</td></tr><tr><td>NAS</td><td>No</td><td>Yes (f, c)</td><td>No</td><td>No</td></tr><tr><td>AWC</td><td>No</td><td>Yes (f, c)</td><td>No</td><td>No</td></tr></tbody></table>

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## 2.4 Statistical metrics for intercomparison

Standard statistical parameters including Pearson's linear correlation coefficient (*R*), root-mean-square error (RMSE), mean absolute error (MAE), mean relative error (MRE), slope and offset of linear regression are used to evaluate the intercomparison:

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284 R

$$285 = \frac{\sum_{i=1}^{N} (X_{i,AERONET} - \bar{X}_{i,AERONET})(X_{i,GRASP} - \bar{X}_{i,GRASP})}{\sqrt{\sum_{i=1}^{N} (X_{i,AERONET} - \bar{X}_{i,AERONET})^2 \sum_{i=1}^{N} (X_{i,GRASP} - \bar{X}_{i,GRASP})^2}}, \quad (1)$$
286 RMSE

$$287 = \sqrt{\frac{\sum_{i=1}^{N} (X_{i,GRASP} - X_{i,AERONET})^2}{N}},$$

288 MAE

$$289 = \frac{1}{N} \sum_{i=1}^{N} |X_{i,AERONET/GRASP}|$$

$$290 - X_{i,MERRA} \qquad (3)$$

$$291 MRE = \frac{1}{N} \sum_{i=1}^{N} \left| \frac{X_{i,AERONET/GRASP} - X_{i,MERRA}}{X_{i,AERONET/GRASP}} \right|$$

$$292 \times 100\% \qquad (4)$$

293 where N is the number of matched data points, $X_{GRASP}$ represents the retrieval of the 294 GRASP/Component approach, $X_{AERONET}$ represents the standard AERONET, and 295 $\bar{X}_{GRASP}$ and $\bar{X}_{AERONET}$ represent the average of the GRASP/Component retrievals 296 and AERONET products, respectively. $X_{MERRA}$ and $X_{AERONET/GRASP}$ represents the 297 MERRA-2 BC products and GRASP BC retrievals inverted from AERONET, 298 respectively.

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## 3. Results and discussion

### 3.1. Aerosol component retrievals

302 The approach provides both the information about absolute concentration of 303 aerosol and the distribution of the aerosol volume among different components. The 304 presence of different component is assumed different in fine and coarse aerosol modes 305 as described in Li et al. (2019).

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307 **3.1.1. Volume concentrations of aerosol components**

308 We present the distribution characteristics of aerosol component concentrations
309 for different aerosol types in 2017 (Figure 2) because we consider that the
310 measurements of each aerosol type site should have a good temporal coverage during
311 the whole year, in particular for the Biomass Burning site where there are usually not
312 enough observations during the biomass burning season. It can be seen that in Figure
313 2 the highest volume concentration of BC (1.83 mm³/m²) is found at Biomass Burning
314 site, followed by relatively high volume concentrations of BC at Multi-source Urban
315 (1.54 mm³/m²) and Dust_Pollution (1.21 mm³/m²) sites, which is consistent with the
316 fact that BC is mainly emitted from biomass burning and fossil fuel combustion. The
317 volume concentrations of CNAI are much higher than other aerosol components for
318 all different aerosol types, in particular for pure Dust sites. Specifically, the maximum
319 concentration of retrieved CNAI was found at Dust site (646.51 mm³/m²), which was
320 twice as high as the Dust_Pollution site and 6 ~ 12 times higher than the rest of other
321 sites, indicating a significant contribution of scattering dust particles to the aerosol
322 loading at DU site. Similarly, CAI concentration at the Dust site (6.56 mm³/m²) is
323 several times higher than that in other aerosol types (Biomass Burning: 1.08 mm³/m²,
324 Dust_Pollution: 0.65 mm³/m², Marine: 0.07 mm³/m², Multi-source Urban: 0.38
325 mm³/m²), thus revealing the contribution of absorbing dust particles (mainly iron
326 oxides) to aerosol loading at the Dust site. The relatively high concentrations of BC
327 and CNAI are observed at the Dust_Pollution site because dust and urban/industrial
328 pollution aerosols dominate at this site. The highest concentration of FNAI is
observed at Biomass Burning site, followed by Multi-source Urban and Dust_Pollution sites. Based on the assumption and definition of FNAI in this aerosol component approach, high FNAI concentration at the Biomass Burning site can be interpreted as fine-mode scattering organic carbon (i.e., OC) generated from biomass burning, while high FNAI concentration at the Dust site can be interpreted as fine-mode scattering dust particles, which could be post-classified by the AE parameter as well (Li et al., 2019). Similarly, the retrieved FNAI at the Multi-source Urban and Dust_Pollution sites can be considered as a mixture of scattering organic carbon and scattering dust particles. Except AWC component (147.64 mm³/m²), low component concentrations (BC: 0.77 mm³/m², BrC: 0.11 mm³/m², CNAI: 55.64 mm³/m², CAI: 0.07 mm³/m², FNAI: 1.71 mm³/m², NAS: 16.81 mm³/m²) obtained at the Marine site are attributed to the very low aerosol loading and high relative humidity over ocean. On the contrary, high AWC concentration is also observed at the Dust site because many large particles could provide more opportunities and space to be surrounded by water even for the low aerosol water fraction (in Figure 3). For NAS results, it is also a similar situation. In addition, soluble organic carbon emitted from biomass burning and soluble mineral salts contained in dust particles are regarded as NAS component as well. Same as BC component, high BrC concentration is observed at Biomass Burning site. However, it is noted that high BrC is also obtained at the Dust site, which is mainly attributed to the contributions of iron oxides (hematite and goethite) absorption in the fine-mode dust particles. Due to the similar spectral absorption of BrC and iron oxides (hematite and goethite), the fine-mode absorbing
351 dust at the Dust site could be misjudged as BrC in the current component approach

352 (Li et al., 2019, 2020a). Such limitations of this component retrieval method could be

353 addressed in future by including some measured information at more wavelengths

354 (such as UV or IR bands).
<figure><img src="image_3.png" /><figcaption>**Figure 2.** Yearly average volume concentration of aerosol components for five different aerosol types: (a) Biomass Burning (BB); (b) Dust (DU); (c) Marine (MA);</figcaption></figure>
358 (d) Dust_Pollution (DP); and (e) Multi-source Urban (MU).

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<figure><img src="image_4.png" /><figcaption>360 **Figure 3.** Examples of monthly average aerosol component volume fractions inversion using the GRASP/Component approach: (a, e, i) present component fractions at Banizoumbou site for dust type in June; (b, f, j) present component fractions at CUIABA-MIRANDA site for biomass burning type in October; (c, g, k) present component fractions at SEDE_BOKER site for dust and pollution mixing type in April; (d, h, l) presents component fractions at REUNION_ST DENIS site for marine aerosol type in March. The values of AODF and AODC at 675 nm are also provided in the figure.</figcaption></figure>

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370 **3.1.2 Volume fractions of aerosol components**

371 Figure 3 shows the characteristics of aerosol component fractions in the fine- and
372 coarse-mode as well as fractions in total aerosol. Considering the quite different
values of aerosol loading in fine- and coarse-mode aerosol particles for different aerosol types, the component retrievals in very low aerosol loading cases could have large uncertainties such as coarse-mode component retrievals in biomass burning sites (AODC is very low). We chose retrievals in June at Banizoumbou site to represent dust aerosol, in October at CUIABA-MIRANDA site to represent biomass burning aerosol, in April at SEDE-BOKER site to represent a mixture of dust and urban/industrial pollution aerosol, and in March at REUNION_ST暑DENIS site to represent marine aerosol. It can be seen that the AODC at 675 nm at Banizoumbou site (0.504) is much higher than other aerosol type sites (CUIABA-MIRANDA: 0.034, SEDE_BOKER: 0.122, REUNION_ST暑DENIS: 0.029) due to the predominance of dust. Corresponding to high AODC, the dust aerosol at Banizoumbou site has the highest CNAI volume fraction (54.33%) compared to other aerosol types (CUIABA-MIRANDA: 42.18%, SEDE_BOKER: 48.1%, REUNION_ST暑DENIS: 23.82%) in the total aerosol fraction, and the CNAI volume fraction can be up to 70.68% in the coarse-mode aerosol fraction, indicating a significant contribution of scattering dust particles to the aerosol loading for dust aerosol at Banizoumbou site. Similarly, CAI fraction at the Banizoumbou site (0.62%) is several times higher than that at other aerosol type sites (CUIABA-MIRANDA: 0.45%, SEDE_BOKER: 0.09%, REUNION_ST暑DENIS: 0.03%), revealing the contribution of absorbing dust particles in dust aerosol at the Banizoumbou site. Unlike other aerosol type sites where AODC is larger than AODF, the AODF at CUIABA-MIRANDA site (0.118 at 675 nm) is much higher than AODC (0.034 at 675 nm), indicating that
CUIABA-MIRANDA site is dominated by fine-mode aerosols. The highest volume fractions of BC and BrC (0.77%, 1.67% in total aerosol fraction, respectively) are found in the biomass burning aerosol at CUIABA-MIRANDA site, which is much higher than that in other aerosol type cases (Banizoumbou: 0.03%, 0.27% in total aerosol fraction; SEDE_BOKER: 0.38%, 0.11% in total aerosol fraction; REUNION_ST暑DENIS: 0.32%, 0.04% in total aerosol fraction). High fractions of BC and BrC observed at CUIABA-MIRANDA site present a significant contribution of light-absorbing carbon (BC and BrC) in the biomass burning aerosol. In addition, the SEDE_BOKER site representing a mixing type of dust and urban/industrial pollution has large fraction of BC (2.34%) in fine-mode aerosol and large fraction of CNAI (76.31%) in coarse-mode aerosol. It is noted that fine-mode absorbing dust at Banizoumbou site could be misinterpreted as a contribution of BrC fraction in fine-mode aerosol. The highest volume fraction of AWC was retrieved at REUNION_ST暑DENIS site, a typical marine site, that the AWC accounted for 70.27% in total aerosol, 86.48% in fine-mode aerosol, and 54.25% in coarse-mode aerosol. The component retrievals at REUNION_ST暑DENIS site may have large uncertainties because of very low aerosol loading (AODF: 0.019 and AODC: 0.029).
<figure><img src="image_5.png" /><figcaption>**Figure 4.** Yearly average volume concentration of aerosol components and AOD for all different aerosol types and in total: (a) Total; (b) Biomass Burning (BB); (c) Dust (DU); (d) Marine (MA); (e) Dust\_Pollution (DP); and (f) Multi-source Urban (MU).</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Figure 5.** Monthly average volume concentration of aerosol components and fine-mode fraction (FMF) at Banizoumbou for the period 2014–2019.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Figure 6.** Monthly average volume concentration of aerosol components and fine-mode fraction (FMF) at CUIABA-MIRANDA for the period 2014–2019.</figcaption></figure>

### 3.1.3. Relationship between aerosol optical properties and aerosol components

In order to validate the applicability of the GRASP/Component approach for aerosol component retrieval, the relationship between aerosol component retrievals and aerosol optical retrievals are discussed in this section. We can see that the trend of annual average aerosol component concentration is the same as the trend of AOD for all sites as well as for each aerosol type (in Figure 4). Figures 5 and 6 show the trends
of monthly average component concentrations (BC, BrC, FNAI, CANI, and CAI) and
FMF at a typical Dust site (Banizoumbou) and Biomass Burning site
(CUIABA-MIRANDA), respectively. At the Dust site (Figure 5), the FMF is usually
less than 60% and in March–July the FMF is about 40% or less. At the same time,
high volume concentration of CNAI and CAI are apparent in March to June and reach
a maximum (1113.79 mm³/m² for CNAI and 14.78 mm³/m² for CAI) in June at
Banizoumbou, suggesting that spring and early summer are periods of high dust
outbreaks. The monthly average concentration of BC and BrC follow the same trend
as FMF, while FNAI sometimes shows an opposite trend. Therefore, the total of BC,
BrC and FNAI volume concentration (Figure 5d) show an opposite trend to FMF,
suggesting that the FNAI at Banizoumbou links strongly to CNAI and it could be
mainly fine-mode non-absorbing dust. Finally, when the FNAI concentration is high,
the CNAI concentration is also very high and the change of FMF is uncertain. At the
Biomass Burning site (Figure 6), the FMF is above 80% in all months except March
and April (75% for March and 79% for April), indicating that the aerosol at
CUIABA-MIRANDA is dominated by fine-mode particles. The trend of BC
concentration follows much similar pattern as FMF with maximum BC concentration
(2.92 mm³/m²) and the FMF value (larger than 90%) in October, which is also in a
similar case for BrC retrieval. It is noteworthy that the relationships of FMF to FNAI
(Figure 6c) and CNAI (Figure 6e) are indefinite, which is probably attributed to the
fact that FNAI mainly represents fine-mode non-absorbing OC particles, CNAI
mainly represent coarse-mode non-absorbing dust particles and their changes have no
451 associated relationship.

452 As shown in Figure 7, we also analyzed the relationship between the absorbing
453 aerosol components (BC, BrC and CAI) and AAE (440/870), and the scattering
454 aerosol components (CNAI, NAS and FNAI) with SAE (440/870) and SSA, for
455 different aerosol types, respectively. The AAE is usually considered to be related to
456 absorbing aerosol components, e.g., pure BC with strong absorption in all
457 wavelengths has a theoretical AAE value of 1. We can see that the AAE is larger than
458 2.0 at the Dust site and the AAE values for Biomass Burning, Dust_Pollution and
459 Multi-source Urban sites are all above 1.2 (in Figure 7a), which is consistent with
460 previous studies that for the absorbing components from biomass burning and/or
461 fossil fuel combustion, the AAE is considered to be greater than 1.1, with dust regions
462 having AAEs ranging from 2.0 to 3.0 (Giles et al., 2012; Wang et al., 2016; Bahadur
463 et al., 2012; Lack and Cappa, 2010; Russell et al., 2010). The lowest AAE value is
464 close to 1.0 observed at the Marine site, which is related to the fact that the absorbing
465 component at the Marine site is mainly BC. SAE (usually ranging from 0 to 4) is an
466 important parameter for identifying the size of aerosol particles, that is, small values
467 indicates a dominance of coarse particles and large values indicates a dominance of
468 fine particles (Bergstrom et al., 2007; Russell et al., 2010; Cazorla et al., 2013). As
469 shown in Figure 7b, the lowest SAE values (smaller than 0.4) among all types of
470 aerosol sites are found at the Dust site with high CNAI concentration (more than 600
471 mm3/m2), followed by the Dust_Pollution site, indicating that aerosol particles at
472 Dust site are dominated by the coarse mode. Meanwhile, the Biomass Burning and
Multi-source Urban sites have the highest SAE values (larger than 1.4), indicating that they are dominated by fine-mode particles. SSA is not only related to the relative content of absorbing components, but also influenced by the aerosol particle size (Jacobson, 2001). Figure 7c shows the relationship between the volume concentration of the scattering aerosol components and SSA. The trend for the concentration of the scattering components is basically the same as that of SSA, e.g., the highest volume concentration of the scattering component and the highest SSA value are obtained at the Dust site, and medium SSA values at the Marine site are linked to the concentration of the scattering components, which is related to the predominantly coarse mode of the aerosol particles in marine areas (Chen et al., 2020). The smallest SSA values are observed at Biomass Burning and Multi-source Urban sites, where there are many absorbing matters/components emitted from biomass burning or fossil fuel combustion.

Figure 8 shows the distribution of different aerosol types as a function of AAE and SAE together with aerosol component retrievals derived by the GRASP/Component approach from sun photometer measurements. The color coding of the data points was determined by the BC and CNAI volume concentration, BC/(BC + BrC) ratio, and the ratio of the absorbing component concentration to the total component volume concentration, respectively. As the AAE value increases and the SAE value decreases, the BC content decreases (Figure 8a) and the CNAI content increases (Figure 8b) at the Dust site, which is consistent with the findings of Yang et al. (2009) that dust-type aerosols have an SAE close to 0 owing to their large particle
sizes and variable AAE values (~ 1.2 to 3) (Dubovik et al., 2002). At the Dust_Pollution site, the range of SAE values is large (~ 0.2 to 1.4) owing to the wide range of particle sizes across the various shapes, and AAE values range from 1 to 2 with high BC concentration (Figure 8a) and the value of BC/(BC + BrC) close to 1 (Figure 8c) when the AAE is close to 1. As shown in Figure 8d, the Biomass Burning site has the largest proportion of absorbing components with a high BC proportion in sum of BC and BrC (see Fig 8c). At Multi-source Urban sites, the range of variation in AAE and SAE is small with data points concentrated between 1.1 and 1.2 for AAE and between 1.3 and 1.8 for SAE, indicating a predominance of fine particles. The closer the AAE value is to 1, the higher the BC/(BrC + BC) value (see Fig 8c). At the Marine site, the SAE values have a large span and the AAE values are close to 1, which is related to the high proportion of BC in sum of BC and BrC at the Marine site. In parallel with the results of Russel et al. (2010), AAE values at Multi-source Urban sites are close to 1, larger AAE values for Biomass Burning site, and the largest AAE values for Dust site. Overall, the aerosol component results derived by the GRASP/Component approach from sun photometer measurements have been demonstrated and validated using the aerosol optical property retrievals (AOD, FMF, AAE, SAE and SSA).

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>
**Figure 7.** The relationship between absorbing components (BC, BrC, and CAI) volume concentration and AAE (a), scattering components (CNAI, NAS, and FNAI) volume concentration and SAE (b), and SSA (c) in five different aerosol types: Biomass Burning (BB), Dust (DU), Marine (MA), Dust_Pollution (DP), and Multi-source Urban (MU) for the period 2014–2019.

<figure><img src="image_11.png" /></figure>

**Figure 8.** The distribution of different aerosol types as a function of AAE and SAE together with aerosol component retrievals derived by the GRASP/Component approach from sun photometer measurements. The colors of monthly average retrievals are linked to the color-bar values. *T*<sub>absorption</sub> indicates the volume concentration of the absorbing component; *T*<sub>component</sub> indicates the volume concentration of all components.
## 3.2 Validation

### 3.2.1 Comparison of GRASP/Component BC retrieval and MERRA-2 BC products

To validate the reliability of the GRASP/Component retrieval results, the MERRA-2 BC data were selected to compare with the BC column mass concentration inverted by the GRASP/Component approach from AERONET sun photometer measurements in this study. For the spatial matching of MERRA-2 to AERONET sites, a 0.5° × 0.625° single-pixel of the MERRA-2, which is the nearest to the AERONET site, is used to represent the MERRA-2 BC product. Figure 9 shows the intercomparison between the BC concentration retrieved by the GRASP/Component approach and MERRA-2 BC concentration for the monthly means. It can be seen that for all sites (see Fig 9a) there is no significant variability of MERRA-2 BC concentration concentrated around 1.0 mg/m², while the BC concentration retrieved by GRASP/Component approach from AERONET shows a significant monthly variation with a mean absolute error (MAE) of 1.79 mg/m² and a mean relative error (MRE) of 63.44% that are higher than the MERRA-2 BC concentration. Specifically, the trends of BC concentration revealed by the GRASP/Component and the MERRA-2 data for the Biomass Burning (Fig. 9b) and Marine (Fig. 9e) sites are similar although there exist a few BC differences. For example, both GRASP/Component BC and MERRA-2 BC present high concentration during the biomass burning period (September-October). Noted that high MRE (811.28%)
shown in Fig. 9b at biomass burning site is mainly attributed to very low BC concentration (close to 0) retrieved by GRASP/Component in June. BC concentrations shown in Fig. 9e for Marine aerosols are low but their trends have good consistency. The minimum MAE (0.78 mg/m²) and MRE (53.19%) are found at Dust sites (Fig. 9c), which is associated with the almost same BC concentrations from March to October. The largest differences are obtained at Dust_Polution and Multi-source Urban sites (in Fig. 9d and 9f), which is probably because GRASP/Component BC retrievals could show the monthly variability and MERRA-2 BC products do not characterize this variability well when there are different several aerosol emission sources. Therefore, the GRASP/Component BC products show a reasonable agreement with MERRA-2 BC data.

<figure><img src="image_12.png" /><figcaption>**Figure 9.** Intercomparison between the monthly average BC concentration derived by</figcaption></figure>
the MERRA-2 and GRASP/Component approach for all different aerosol types and in total: (a) Total; (b) Biomass Burning (BB); (c) Dust (DU); (d) Marine (MA); (e) Dust_Pollution (DP); and (f) Multi-source Urban (MU).

565

## 3.2.2 Intercomparison of GRASP/Component optical retrievals and AERONET products

In order to characterize the quality of the aerosol optical properties retrieved by the GRASP/Component approach from AERONET measurements, the GRASP/Component basic aerosol optical properties such as AOD, AODF, AODC, AAOD, SSA and AE are compared and evaluated by the AERONET standard products at 13 sites for different aerosol types during the period 2014–2019. Figure 10 shows the validation of GRASP/Component aerosol optical retrievals for all sites using the corresponding AERONET standard products. Figures A1–A5 in the appendix show the intercomparison of aerosol optical properties (AOD, AODF, AODC, AAOD and SSA) for different aerosol types. For the comparison of AOD (see Figure 10a), we can see that the correlation coefficient *R* is close to 1 with a good linear regression expression (slope ~ 1, offset ~ 0) for all sites, and the same conclusion for each aerosol type. In addition, the RMSE is smaller than 0.02 for all sites, with the smallest RMSE of 0.003 found for the marine aerosol and the largest RMSE of 0.033 for the dust aerosol (see Figure A1 in the appendix). The metrics for spectral AOD validation of different aerosol types are shown in Table 2. The results indicate that all wavelengths of AOD inversions show good agreement with the
AERONET standard AOD products. Therefore, the GRASP/Component approach can provide spectral AOD products with the same accuracy as the AERONET standard products. As shown in Figure 10b, the GRASP/Component AODF retrievals are in line with the AERONET products for all aerosol types. AODC products derived by the GRASP/Component approach are also consistent with the AERONET AODC for all sites (see Figure 10c), with a correlation coefficient of R = 0.986. AAOD indicates the absorption of atmospheric particles, which is generally related to the atmospheric loading of absorbing substances such as BC, BrC and iron oxides contained in dust (Charlson et al., 1992; Dehkhoda et al., 2020; Sun et al., 2019). It can be seen that the AAOD retrievals of the GRASP/Component approach show good agreement with the AERONET standard products, with R = 0.796, slope = 0.953 and RMSE < 0.01 (see Figure 10d). The SSA, determined by the proportion of scattering AOD in the total AOD, is a key parameter for characterizing the absorption properties of aerosols and for assessing the effects of aerosols on the earth's radiation balance (Charlson et al., 1992). The results indicate that the GRASP/Component SSA retrievals have a high (taking into account rather small dynamic range of SSA) correlation (R > 0.8) and small RMSE (RMSE < 0.03) when comparing to the AERONET SSA products for all sites (see Figure 10e). The AE was determined from the fitted AOD at two different wavelengths as $AE = \frac{\ln[\tau(\lambda_1)/\tau(\lambda_2)]}{\ln(\lambda_2/\lambda_1)}$. The validation of AE (440/870) derived by the GRASP/Component approach with the corresponding AERONET AE (440/870) is shown in Figure 10f. The GRASP/Component retrieval shows high agreement with the AERONET measurements, with close statistical correlation in terms the R, slope
and offset, e.g., R = 0.971, slope = 0.978, and offset = 0.04. We can see that the AE retrievals of the GRASP/Component approach are in line with the corresponding AERONET AE products.

<figure><img src="image_13.png" /></figure>

**Figure 10.** Comparison of aerosol optical products derived by the GRASP/Component approach from sun photometer measurements with the corresponding AERONET standard products at all sites. The aerosol optical products are (a) AOD, (b) AODF, (c) AODC, (d) AAOD, (e) SSA, and (f) AE. The 1:1 reference line and the linear regression line are denoted by the black and red solid lines, respectively.

**Table 2.** The statistics for the intercomparison of AOD derived by the GRASP/Component approach using MG effective medium approximation
rules from sun photometer measurements to the corresponding AERONET standard products. The number in parentheses indicates the number of validation points.

<table><thead><tr><th>Aerosol type</th><th>Band (nm)</th><th>R</th><th>Slope</th><th>Offset</th><th>RMSE</th></tr></thead><tbody><tr><td rowspan="4">All sites</td><td>440 (18923)</td><td>0.997</td><td>0.998</td><td>0.002</td><td>0.013</td></tr><tr><td>675 (18923)</td><td>0.997</td><td>1.000</td><td>0.000</td><td>0.011</td></tr><tr><td>870 (18923)</td><td>0.997</td><td>1.005</td><td>0.000</td><td>0.011</td></tr><tr><td>1020 (18923)</td><td>0.997</td><td>1.005</td><td>0.000</td><td>0.010</td></tr><tr><td rowspan="4">BB</td><td>440 (111)</td><td>1.000</td><td>0.995</td><td>0.002</td><td>0.006</td></tr><tr><td>675 (111)</td><td>0.999</td><td>0.990</td><td>0.001</td><td>0.004</td></tr><tr><td>870 (111)</td><td>0.996</td><td>0.988</td><td>-0.000</td><td>0.005</td></tr><tr><td>1020 (111)</td><td>0.985</td><td>0.955</td><td>0.002</td><td>0.007</td></tr><tr><td rowspan="4">DU</td><td>440 (1751)</td><td>0.994</td><td>0.999</td><td>0.004</td><td>0.034</td></tr><tr><td>675 (1751)</td><td>0.994</td><td>1.001</td><td>0.001</td><td>0.033</td></tr><tr><td>870 (1751)</td><td>0.994</td><td>1.001</td><td>0.003</td><td>0.032</td></tr><tr><td>1020 (1751)</td><td>0.994</td><td>0.999</td><td>0.004</td><td>0.031</td></tr><tr><td rowspan="4">DP</td><td>440 (4402)</td><td>0.999</td><td>0.994</td><td>0.001</td><td>0.005</td></tr><tr><td>675 (4402)</td><td>0.999</td><td>0.998</td><td>0.000</td><td>0.005</td></tr><tr><td>870 (4402)</td><td>0.999</td><td>1.002</td><td>0.001</td><td>0.005</td></tr><tr><td>1020 (4402)</td><td>0.999</td><td>1.000</td><td>0.001</td><td>0.005</td></tr><tr><td rowspan="4">MA</td><td>440 (962)</td><td>0.999</td><td>0.986</td><td>0.003</td><td>0.004</td></tr><tr><td>675 (962)</td><td>0.997</td><td>0.994</td><td>0.003</td><td>0.003</td></tr><tr><td>870 (962)</td><td>0.994</td><td>1.045</td><td>-0.002</td><td>0.003</td></tr><tr><td>1020 (962)</td><td>0.995</td><td>1.032</td><td>-0.002</td><td>0.002</td></tr><tr><td rowspan="4">MU</td><td>440 (11697)</td><td>0.998</td><td>0.997</td><td>0.002</td><td>0.010</td></tr><tr><td>675 (11697)</td><td>0.997</td><td>0.998</td><td>0.000</td><td>0.006</td></tr><tr><td>870 (11697)</td><td>0.997</td><td>1.007</td><td>-0.000</td><td>0.005</td></tr><tr><td>1020 (11697)</td><td>0.997</td><td>1.004</td><td>-0.000</td><td>0.004</td></tr></tbody></table>
### 3.2.3 Influences of component effective medium approximation on aerosol optical property retrievals

The retrieval of aerosol components requires to choose/assume the aerosol species mixing rule and the GRASP/Component approach employed a simple and extensively tested Maxwell-Garnett effective medium approximation (MG) for mixture of complex refractive indices of different aerosol species. The MG mixing rule has been widely used in many studies to retrieve aerosol components from ground-based remote sensing measurements (Schuster et al., 2005, 2009, 2016; Li et al., 2019). It is applicable to the case of insoluble small particles discretely suspended in solution (Bohren and Hoffman, 1983). The MG approximation assumes that non-homogeneous particles consist of a variety of insoluble inclusions and a homogeneous matrix. Generally, the matrix can consist of water or soluble inorganic salt particles (e.g., ammonium sulfate, ammonium nitrate and sea salt) and the refractive index of the matrix is calculated using the hygroscopic properties and the ratio of soluble inorganic substances to water (Schuster et al., 2005; Li et al., 2019).
As described in Li et al. (2019), two different mixing rules [MG and volume-weighted (VW) effective medium approximation] for the estimation of aerosol components can be chosen in the GRASP/Component approach. In order to quantitatively evaluate the effect of different aerosol component mixing rules on aerosol optical property inversion from ground-based sun photometer measurements, intercomparison of the monthly average aerosol optical properties derived by the MG and VW effective
medium approximation rules in the GRASP/Component approach are shown in Figure 640
11, and their validation metrics using AERONET products are listed in Table 3. The 641
results demonstrate that both MG and VW can provide highly similar aerosol optical 642
retrievals. Specifically, the differences in optical retrievals between the MG and VW 643
mixing rules are -1.13% to 0.52% for AOD, 0.34% to 4.66% for AODF, -3.32% to 644
-0.64% for AODC, -3.52% to 4.66% for AAOD, -0.40% to 0.07% for SSA, and 645
-0.36% to 1.28% for AE. The statistics shown in Table 3 indicate that both the MG 646
and VW retrievals agree well with corresponding AERONET products, with *R* almost 647
equal to 1 for AOD, AODC and AE, and being larger than 0.9 for AODF, larger than 648
0.8 for SSA, and about 0.7 for AAOD. The *R* of the AAOD retrievals are slightly low 649
for both mixing rules listed in Table 3, which is in fact due to the presence of several 650
outliers at the Banizoumbou site, located in the dust source area (see Figure A4 in the 651
appendix for MG retrieval). 652

653
<table><caption>**Table 3.** The statistics for the intercomparison of aerosol optical properties derived by the GRASP/Component approach with MG and VW effective medium approximation rules from sun photometer measurements to the corresponding AERONET products. The number in parentheses indicates the number of validation points.</caption><thead><tr><th>Optical property</th><th>Mixing rule</th><th>R</th><th>Slope</th><th>Offset</th><th>RMSE</th></tr></thead><tbody><tr><td rowspan="2">AOD (675 nm)</td><td>MG (18923)</td><td>0.997</td><td>1.000</td><td>0.000</td><td>0.011</td></tr><tr><td>VW (18930)</td><td>0.996</td><td>0.998</td><td>0.000</td><td>0.014</td></tr><tr><td rowspan="2">AODF (675 nm)</td><td>MG (18923)</td><td>0.923</td><td>0.993</td><td>0.005</td><td>0.025</td></tr><tr><td>VW (18930)</td><td>0.911</td><td>0.903</td><td>0.008</td><td>0.025</td></tr><tr><td rowspan="2">AODC (675 nm)</td><td>MG (18923)</td><td>0.986</td><td>0.863</td><td>0.005</td><td>0.026</td></tr><tr><td>VW (18930)</td><td>0.979</td><td>0.969</td><td>0.000</td><td>0.026</td></tr><tr><td rowspan="2">AAOD (675 nm)</td><td>MG (2127)</td><td>0.796</td><td>0.953</td><td>-0.002</td><td>0.008</td></tr><tr><td>VW (2120)</td><td>0.669</td><td>0.974</td><td>-0.002</td><td>0.012</td></tr><tr><td rowspan="2">SSA (675 nm)</td><td>MG (2127)</td><td>0.828</td><td>1.070</td><td>-0.060</td><td>0.028</td></tr><tr><td>VW (2120)</td><td>0.845</td><td>1.034</td><td>-0.026</td><td>0.025</td></tr><tr><td rowspan="2">AE (440/870)</td><td>MG (18923)</td><td>0.971</td><td>0.978</td><td>0.040</td><td>0.126</td></tr><tr><td>VW (18930)</td><td>0.973</td><td>0.976</td><td>0.041</td><td>0.122</td></tr></tbody></table>

654

655
<figure><img src="image_14.png" /><figcaption>**Figure 11.** Intercomparison between the monthly average aerosol optical properties derived by the MG and VW effective medium approximation rules in the GRASP/Component approach from AERONET sun photometer measurements.</figcaption></figure>

## 4. Conclusions

This paper presents the application of GRASP/Component approach (Li et al., 2019; Dubovik et al., 2014, 2021) to a series of AERONET sun/sky photometer measurements in different locations and representing different aerosol types for the extended retrievals of aerosol components, as well as of aerosol optical properties.
The intercomparison of aerosol optical properties provided by GRASP/Component algorithm and by operational AERONET algorithm are also discussed. The main conclusions are present as follows.

The GRASP/Component approach allows the separate characterization of fine- and coarse-mode aerosol components (concentration and fraction) from ground-based sun photometer measurements. The aerosol component retrieval results able to represent well the different aerosol types including Biomass Burning (BB), Dust (DU), Marine (MA), Dust_Pollution (DP), and Multi-source Urban (MU) cases. GRASP/Component BC retrievals derived from AERONET have a reasonable agreement with MERRA-2 BC products. In addition, the results showed that the temporal variation and spatial distribution of aerosol components in the different aerosol-type regions are in general agreement with the physical expectations described by the optical parameters (AOD, FMF, AAE, SAE and SSA).

The accuracy of aerosol optical products inverted from sun photometer measurements using the GRASP/Component approach are assessed by AERONET standard products. The evaluations indicated that the optical products retrieved by the GRASP/Component approach were consistent with AERONET standard products for all considered aerosol types, e.g., the correlation coefficient for AOD, AODF, AODC, AE, SSA and AAOD is 0.997, 0.923, 0.986, 0.971, 0.828, and 0.796, respectively, and the RMSE for all spectral AODs are less than 0.02. In addition, we verified the effect of different aerosol species mixing rule assumption on retrievals of aerosol optical property. The differences between MG and VW effective medium approximation
approaches were found to be within 2.3%, which demonstrates both MG and VW approaches can produce reliable aerosol optical products with same accuracy as the standard AERONET product. Therefore, the GRASP/Component approach can be applied to AERONET sun photometer measurements not only for aerosol optical properties inversion with similar accuracy as the AERONET standard products (such as AOD, AODF, AODC, AAOD, SSA and AE etc.), but also for the direct aerosol component retrievals from radiance measurements.

695

696 Acknowledgements

This research was supported by the National Key Research and Development Program (Grant No. 2019YFC0214603), the National Natural Science Foundation of China (Grant No. 41905117), the National Science Fund for Distinguished Young Scholars (Grant No. 41825011), and the Foundation of the Chinese Academy of Meteorological Sciences (2021Y002). The development of the GRASP/Component approach was supported by the CaPPA project (Chemical and Physical Properties of the Atmosphere), funded by the French National Research Agency through the Programme d'Investissement d'Avenir under contract "ANR-11-LABX-0005-01" and by the Regional Council Nord Pas de Calais-Picardie and the European Funds for Regional Economic Development. We would like to thank the AERONET team for their efforts in maintaining the instruments and making available their data (http://www.aeronet.gsfc.nasa.gov). We also acknowledge the entire GRASP team for their work developing the algorithm (https://www.grasp-open.com/).
<table><caption>**Table A1.** List of acronyms</caption><thead><tr><td>Abbreviation</td><td>Full name</td></tr></thead><tbody><tr><td>AAE</td><td>Absorption Ångström exponent</td></tr><tr><td>AAOD</td><td>Absorption aerosol optical depth</td></tr><tr><td>AE</td><td>Ångström exponent</td></tr><tr><td>AERONET</td><td>Aeronet Robotic Network</td></tr><tr><td>AOD</td><td>Aerosol optical depth</td></tr><tr><td>AODC</td><td>Coarse-mode aerosol optical depth</td></tr><tr><td>AODF</td><td>Fine-mode aerosol optical depth</td></tr><tr><td>BB</td><td>Biomass Burning aerosol</td></tr><tr><td>BC</td><td>Black carbon</td></tr><tr><td>BrC</td><td>Brown carbon</td></tr><tr><td>CAI</td><td>Coarse-mode absorbing insoluble component</td></tr><tr><td>CARSNET</td><td>China Aerosol Remote Sensing Network</td></tr><tr><td>CAWC</td><td>Coarse-mode aerosol water content</td></tr><tr><td>CNAI</td><td>Coarse-mode non-absorbing insoluble component</td></tr><tr><td>CNAS</td><td>Coarse-mode non-absorbing soluble component</td></tr><tr><td>DP</td><td>Dust_Pollution mixing aerosol</td></tr><tr><td>DU</td><td>Dust aerosol</td></tr><tr><td>FAWC</td><td>fine-mode aerosol water content</td></tr><tr><td>FMF</td><td>Fine-mode fraction</td></tr><tr><td>FNAI</td><td>fine-mode non-absorbing insoluble component</td></tr><tr><td>FNAS</td><td>fine-mode non-absorbing soluble component</td></tr><tr><td>GEOS-5</td><td>Goddard Earth Observing System model, Version 5</td></tr><tr><td>GRASP</td><td>Generalized Retrieval of Atmosphere and Surface Properties</td></tr><tr><td>LSM</td><td>Least-squares method</td></tr><tr><td>MA</td><td>Marine aerosol</td></tr><tr><td>MAE</td><td>Mean absolute error</td></tr><tr><td>MERRA-2</td><td>Modern-Era Retrospective Analysis for Research and Applications, Version 2</td></tr><tr><td>MRE</td><td>Mean relative error</td></tr><tr><td>MU</td><td>Multi-source Urban aerosol</td></tr><tr><td>OC</td><td>Organic carbon</td></tr><tr><td>PHOTONS</td><td>Photométrie pour le Traitement Opérationnel de Normalisation Satellitaire</td></tr><tr><td>SAE</td><td>Scattering Ångström exponent</td></tr><tr><td>SSA</td><td>Single scattering albedo</td></tr></tbody></table>
<figure><img src="image_15.png" /><figcaption>**Figure A1.** Comparison of AOD at 675 nm derived by the GRASP/Component approach along with the corresponding AERONET standard products. The 1:1 reference line and the linear regression line are denoted by the black and red solid lines, respectively.</figcaption></figure>

<figure><img src="image_16.png" /></figure>
**Figure A2.** Comparison of AODF at 675 nm derived by the GRASP/Component approach along with the corresponding AERONET standard products. The 1:1 reference line and the linear regression line are denoted by the black and red solid lines, respectively.

725

<figure><img src="image_17.png" /></figure>

726

**Figure A3.** Comparison of AODC at 675 nm derived by the GRASP/Component approach along with the corresponding AERONET standard products. The 1:1 reference line and the linear regression line are denoted by the black and red solid lines, respectively.

728
729
730

731
<figure><img src="image_18.png" /><figcaption>Figure A4. Comparison of AAOD at 675 nm derived by the GRASP/Component approach along with the corresponding AERONET standard products. The 1:1 reference line and the linear regression line are denoted by the black and red solid lines, respectively.</figcaption></figure>

<figure><img src="image_19.png" /></figure>
739 **Figure A5.** Comparison of SSA at 675 nm derived by the GRASP/Component
740 approach along with the corresponding AERONET standard products. The 1:1
741 reference line and the linear regression line are denoted by the black and red solid
742 lines, respectively.

743

744 **References**

745 Ahn, C., Torres, O., Jethva, H., 2014. Assessment of OMI near-UV aerosol optical depth over
746 land. Assessment of OMI near-UV aerosol optical depth over land 19, 456–476.

747 https://doi.org/10.1002/2014JD021891.Received

748 Arola, A., Schuster, G., Myhre, G., Kazadzis, S., Dey, S., Tripathi, S.N., 2011. Inferring
749 absorbing organic carbon content from AERONET data. Atmospheric Chemistry and
750 Physics 11, 215–225. https://doi.org/10.5194/acp-11-215-2011

751 Bahadur, R., Praveen, P.S., Xu, Y., Ramanathan, V., 2012. Solar absorption by elemental and
752 brown carbon determined from spectral observations. Proceedings of the National
753 Academy of Sciences of the United States of America 109, 17366–17371.

754 https://doi.org/10.1073/pnas.1205910109

755 Bergamo, A., Tafuro, A.M., Kinne, S., De Tomasi, F., Perrone, M.R., 2008.

756 Monthly-averaged anthropogenic aerosol direct radiative forcing over the Mediterranean
757 based on AERONET aerosol properties. Atmospheric Chemistry and Physics 8, 6995–
758 7014. https://doi.org/10.5194/acp-8-6995-2008

759 Bergstrom, R.W., Pilewskie, P., Russell, P.B., Redemann, J., Bond, T.C., Quinn, P.K., Sierau,
760 B., 2007. Spectral absorption properties of atmospheric aerosols. Atmospheric
761 Chemistry and Physics 7, 5937-5943. https://doi.org/10.5194/acp-7-5937-2007
762 Bohren, C., Huffman, D., 1983. Absorption and scattering of light by small particles. Wiley.
763 https://doi.org/10.1088/0031-9112/35/3/025
764 Bond, T.C., Charlson, R.J., Heintzenberg, L., 1998. Quantifying the emission of
765 light-absorbing particles: Measurements tailored to climate studies. Geophysical
766 Research Letters 25, 337-340. https://doi.org/10.1029/98GL00039
767 Bréon, F.M., Vermeulen, A., Descloitres, J., 2011. An evaluation of satellite aerosol products
768 against sunphotometer measurements. Remote Sensing of Environment 115, 3102-3111.
769 https://doi.org/10.1016/j.rse.2011.06.017
770 Cazorla, A., Bahadur, R., Suski, K.J., Cahill, J.F., Chand, D., Schmid, B., Ramanathan, V.,
771 Prather, K.A., 2013. Relating aerosol absorption due to soot, organic carbon, and dust to
772 emission sources determined from in-situ chemical measurements. Atmospheric
773 Chemistry and Physics 13, 9337-9350. https://doi.org/10.5194/acp-13-9337-2013
774 Charlson, R.J., Schwartz, S.E., Hales, J.M., Cess, R.D., Coakley, J.A., Hansen, J.E., Hofmann,
775 D.J., 1992. Climate forcing by anthropogenic aerosols. Science 255, 423-
776 430. https://doi.org/10.1126/science.255.5043.423
777 Che, H., Zhang, Xiaoye, Chen, H., Damiri, B., Goloub, P., Li, Z., Zhang, Xiaochun, Wei, Y.,
778 Zhou, H., Dong, F., Li, D., Zhou, T., 2009. Instrument calibration and aerosol optical
779 depth validation of the China aerosol remote sensing network. Journal of Geophysical
780 Research Atmospheres 114. https://doi.org/10.1029/2008JD011030
781 Chen, C., Dubovik, O., Fuertes, D., Litvinov, P., Lapyonok, T., Lopatin, A., Ducos, F.,
782 Derimian, Y., Herman, M., Tanré, D., Remer, L.A., Lyapustin, A., Sayer, A.M., Levy,
783 R.C., Christina Hsu, N., Descloitres, J., Li, L., Torres, B., Karol, Y., Herrera, M.,
784 Herreras, M., Aspetsberger, M., Wanzenboeck, M., Bindreiter, L., Marth, D., Hangler,
785 A., Federspiel, C., 2020. Validation of GRASP algorithm product from
786 POLDER/PARASOL data and assessment of multi-angular polarimetry potential for
787 aerosol monitoring. Earth System Science Data 12, 3573–3620.
788 https://doi.org/10.5194/essd-12-3573-2020
789 Chen, C.T., Cahan, B.D., 1981. Visible and ultraviolet optical properties of single-crystal and
790 polycrystalline hematite measured by spectroscopic ellipsometry. Optical Society of
791 America 71, 932–934.
792 Chen, Y., Bond, T.C., 2010. Light absorption by organic carbon from wood combustion.
793 Atmospheric Chemistry and Physics 10, 1773–1787.
794 https://doi.org/10.5194/acp-10-1773-2010
795 Chin, M., Ginoux, P., Kinne, S., Torres, O., Holben, B.N., Duncan, B.N., Martin, R. V.,
796 Logan, J.A., Higurashi, A., Nakajima, T., 2002. Tropospheric aerosol optical thickness
797 from the GOCART model and comparisons with satellite and sun photometer
798 measurements. Journal of the Atmospheric Sciences 59, 461 – 483.
799 https://doi.org/10.1175/1520-0469(2002)059<0461:taotft>2.0.co;2
800 Colarco, P., Da Silva, A., Chin, M., Diehl, T., 2010. Online simulations of global aerosol
801 distributions in the NASA GEOS-4 model and comparisons to satellite and
802 ground-based aerosol optical depth. Journal of Geophysical Research Atmospheres 115.
803 https://doi.org/10.1029/2009JD012820
804 Chubarova, N.Y., Sviridenkov, M.A., Smirnov, A., Holben, B.N., 2011. Assessments of
805 urban aerosol pollution in Moscow and its radiative effects. Atmospheric Measurement

806 Techniques 4, 367–378. https://doi.org/10.5194/amt-4-367-2011

807 Dehkhoda, N., Noh, Y., Joo, S., 2020. Long-term variation of black carbon absorption aerosol

808 optical depth from aeronet data over east Asia. Remote Sensing 12, 1–17.

809 https://doi.org/10.3390/rs12213551

810 Derimian, Y., Karnieli, A., Kaufman, Y.J., Andreae, M.O., Andreae, T.W., Dubovik, O.,

811 Maenhaut, W., Koren, I., Holben, B.N., 2006. Dust and pollution aerosols over the

812 Negev desert, Israel: Properties, transport, and radiative effect. Journal of Geophysical

813 Research Atmospheres 111, 1–14. https://doi.org/10.1029/2005JD006549

814 Dey, S., Tripathi, S.N., Singh, R.P., Holben, B.N., 2006. Retrieval of black carbon and

815 specific absorption over Kanpur city, northern India during 2001-2003 using

816 AERONET data. Atmospheric Environment 40, 445–456.

817 https://doi.org/10.1016/j.atmosenv.2005.09.053

818 Dubovik, O., Herman, M., Holdak, A., Lapyonok, T., Tanré, D., Deuzé, J.L., Ducos, F.,

819 Sinyuk, A., Lopatin, A., 2011. Statistically optimized inversion algorithm for enhanced

820 retrieval of aerosol properties from spectral multi-angle polarimetric satellite

821 observations. Atmospheric Measurement Techniques 4, 975–1018.

822 https://doi.org/10.5194/amt-4-975-2011

823 Dubovik, O., Holben, B., Eck, T.F., Smirnov, A., Kaufman, Y.J., King, M.D., Tanré, D.,

824 Slutsker, I., 2002. Variability of absorption and optical properties of key aerosol types

825 observed in worldwide locations. Journal of the Atmospheric Sciences 59, 590–608.

826 https://doi.org/10.1175/1520-0469(2002)059<0590:voaaop>2.0.co;2
827 Dubovik, O., King, M.D., 2000. A flexible inversion algorithm for retrieval of aerosol optical properties from Sun and sky radiance measurements. Journal of Geophysical Research Atmospheres 105, 20673-20696. https://doi.org/10.1029/2000JD900282

828

829

830 Dubovik, O., Lapyonok, T., Litvinov, P., Herman, M., Fuertes, D., Ducos, F., Torres, B., Derimian, Y., Huang, X., Lopatin, A., Chaikovsky, A., Aspetsberger, M., Federspiel, C., 2014. GRASP: a versatile algorithm for characterizing the atmosphere. SPIE Newsroom. https://doi.org/10.1117/2.1201408.005558

831

832

833

834 Dubovik, O., Schuster, G.L., Xu, F., Hu, Y., Bösch, H., Landgraf, J., Li, Z., 2021. Grand Challenges in Satellite Remote Sensing. Frontiers in Remote Sensing 2. https://doi.org/10.3389/frsen.2021.619818

835

836

837 Dubovik, O., Smirnov, A., Holben, B.N., King, M.D., Kaufman, Y.J., Eck, T.F., Slutsker, I., 2000. Accuracy assessments of aerosol optical properties retrieved from Aerosol Robotic Network (AERONET) Sun and sky radiance measurements. Journal of Geophysical Research Atmospheres 105, 9791-9806. https://doi.org/10.1029/2000JD900040

838

839

840

841

842 Eck, T.F., Holben, B.N., Dubovik, O., Smirnov, A., Goloub, P., Chen, H.B., Chatenet, B., Gomes, L., Zhang, X.Y., Tsay, S.C., Ji, Q., Giles, D., Slutsker, I., 2005. Columnar aerosol optical properties at AERONET sites in central eastern Asia and aerosol transport to the tropical mid-Pacific. Journal of Geophysical Research D: Atmospheres 110, 1-18. https://doi.org/10.1029/2004JD005274

843

844

845

846

847 Eck, T.F., Holben, B.N., Reid, J.S., Dubovik, O., Smirnov, A., O'Neill, N.T., Slutsker, I., Kinne, S., 1999. Wavelength dependence of the optical depth of biomass burning, urban, https://doi.org/10.1029/2000JD900282
849 and desert dust aerosols. Journal of Geophysical Research Atmospheres 104, 31333–

850 31349. https://doi.org/10.1029/1999JD900923

851 Giannakaki, E., Balis, D.S., Amiridis, V., Zerefos, C., 2010. Optical properties of different

852 aerosol types: Seven years of combined Raman-elastic backscatter lidar measurements

853 in Thessaloniki, Greece. Atmospheric Measurement Techniques 3, 569–578.

854 https://doi.org/10.5194/amt-3-569-2010

855 Giles, D.M., Holben, B.N., Eck, T.F., Sinyuk, A., Smirnov, A., Slutsker, I., Dickerson, R.R.,

856 Thompson, A.M., Schafer, J.S., 2012. An analysis of AERONET aerosol absorption

857 properties and classifications representative of aerosol source regions. Journal of

858 Geophysical Research Atmospheres 117. https://doi.org/10.1029/2012JD018127

859 Giles, D.M., Sinyuk, A., Sorokin, M.G., Schafer, J.S., Smirnov, A., Slutsker, I., Eck, T.F.,

860 Holben, B.N., Lewis, J.R., Campbell, J.R., Welton, E.J., Korkin, S. V., Lyapustin, A.I.,

861 2019. Advancements in the Aerosol Robotic Network (AERONET) Version 3 database -

862 Automated near-real-time quality control algorithm with improved cloud screening for

863 Sun photometer aerosol optical depth (AOD) measurements. Atmospheric Measurement

864 Techniques 12, 169–209. https://doi.org/10.5194/amt-12-169-2019

865 Goloub, P., Li, Z., Dubovik, O., Blarel, L., Podvin, T., Jankowiak, I., Lecoq, R., Deroo, C.,

866 Chatenet, B., Morel, J.P., Cuevas, E., Ramos, R., 2007. PHOTONS/AERONET

867 sunphotometer network overview: description, activities, results. Proceedings of SPIE

868 69360V-69360V–15. https://doi.org/10.1117/12.783171

869 Holben, B.N., Eck, T.F., Slutsker, I., Tanré, D., Buis, J.P., Setzer, A., Vermote, E., Reagan,

870 J.A., Kaufman, Y.J., Nakajima, T., Lavenu, F., Jankowiak, I., Smirnov, A., 1998.
871 AERONET - A federated instrument network and data archive for aerosol characterization. Remote Sensing of Environment 66, 1-16.
872 https://doi.org/10.1016/S0034-4257(98)00031-5
873 Holben, B.N., Tanré, D., Smirnov, A., Eck, T.F., Slutsker, I., Abuhassan, N., Newcomb, W.W., Schafer, J.S., Chatenet, B., Lavenu, F., Kaufman, Y.J., Vande Castle, J., Setzer, A., Markham, B., Clark, D., Frouin, R., Halthore, R., Karneli, A., O'Neill, N.T., Pietras, C., Pinker, R.T., Voss, K., Zibordi, G., 2001. An emerging ground-based aerosol climatology: Aerosol optical depth from AERONET. Journal of Geophysical Research Atmospheres 106, 12067-12097. https://doi.org/10.1029/2001JD900014
874 Hu, Q., Goloub, P., Veselovskii, I., Bravo-Aranda, J.A., Elisabeta Popovici, I., Podvin, T., Haeffelin, M., Lopatin, A., Dubovik, O., Pietras, C., Huang, X., Torres, B., Chen, C., 2019. Long-range-transported Canadian smoke plumes in the lower stratosphere over northern France. Atmospheric Chemistry and Physics 19, 1173-1193.
875
876
877
878
879
880
881
882
883
884 https://doi.org/10.5194/acp-19-1173-2019
885 Hu, R.M., Blanchet, J.P., Girard, E., 2005. Evaluation of the direct and indirect radiative and climate effects of aerosols over the western Arctic. Journal of Geophysical Research D: Atmospheres 110, 1-10. https://doi.org/10.1029/2004JD005043
886
887
888 IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp.
889
890
891
892
893 IPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science
894 Basis. Contribution of Working Group I to the Sixth Assessment Report of the
895 Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani,
896 S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M.
897 Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T. K. Maycock, T. Waterfield, O.
898 Yelekçi, R. Yu and B. Zhou (eds.)]. Cambridge University Press. In Press.
899 Jacobson, M.Z., 2001. Strong radiative heating due to the mixing state of black carbon in
900 atmospheric aerosols. Nature 409, 695-697. https://doi.org/10.1038/35055518
901 Jin, S., Ma, Y., Zhang, M., Gong, W., Dubovik, O., Liu, B., Shi, Y., Yang, C., 2019a. Retrieval
902 al of 500 m Aerosol Optical Depths from MODIS Measurements over Urban Surfaces under
903 Heavy Aerosol Loading Conditions in Winter. Remote Sensing 11. https://doi.org/10.1007/s11192-019-03390-8
904 0.3390/rs11192218
905 Jin, S., Ma, Y., Zhang, M., Gong, W., Lei, L., Ma, X., 2019b. Comparison of aerosol optical
906 properties and associated radiative effects of air pollution events between summer and winter.
907 inter: A case study in January and July 2014 over Wuhan, Central China. Atmospheric Environment 218, 117004. https://doi.org/10.1016/j.atmosenv.2019.117004
908
909 Kabashnikov, V., Milinevsky, G., Chaikovsky, A., Miatselskaya, N., Danylevsky, V.,
910 Aculinin, A., Kalinskaya, D., Korchemkina, E., Bovchaliuk, A., Pietruczuk, A.,
911 Sobolewsky, P., Bovchaliuk, V., 2014. Localization of aerosol sources in East-European
912 region by back-trajectory statistics. International Journal of Remote Sensing 35, 6993-7006. https://doi.org/10.1080/01431161.2014.960621
913
914 Kahn, R.A., Gaitley, B.J., Garay, M.J., Diner, D.J., Eck, T.F., Smirnov, A., Holben, B.N.,
915 2010. Multiangle Imaging SpectroRadiometer global aerosol product assessment by
916 comparison with the Aerosol Robotic Network. Journal of Geophysical Research
917 Atmospheres 115. https://doi.org/10.1029/2010JD014601

918 Kumar, K.R., Yin, Y., Sivakumar, V., Kang, N., Yu, X., Diao, Y., Adesina, A.J., Reddy, R.R.,
919 2015. Aerosol climatology and discrimination of aerosol types retrieved from MODIS,
920 MISR and OMI over Durban (29.88°S, 31.02°E), South Africa. Atmospheric
921 Environment 117, 9-18. https://doi.org/10.1016/j.atmosenv.2015.06.058

922 Lack, D.A., Cappa, C.D., 2010. Impact of brown and clear carbon on light absorption
923 enhancement, single scatter albedo and absorption wavelength dependence of black
924 carbon. Atmospheric Chemistry and Physics 10, 4207-4220.
925 https://doi.org/10.5194/acp-10-4207-2010

926 Levy, R.C., Remer, L.A., Mattoo, S., Vermote, E.F., Kaufman, Y.J., 2007. Second-generation
927 operational algorithm: Retrieval of aerosol properties over land from inversion of
928 Moderate Resolution Imaging Spectroradiometer spectral reflectance. Journal of
929 Geophysical Research Atmospheres 112, 1-21. https://doi.org/10.1029/2006JD007811

930 Li, L., Che, H., Derimian, Y., Dubovik, O., Schuster, G.L., Chen, C., Li, Q., Wang, Y., Guo,
931 B., Zhang, X., 2020a. Retrievals of fine mode light-absorbing carbonaceous aerosols
932 from POLDER/PARASOL observations over East and South Asia. Remote Sensing of
933 Environment 247, 111913. https://doi.org/10.1016/j.rse.2020.111913

934 Li, L., Che, H., Derimian, Y., Dubovik, O., Luan, Q., Li, Q., Huang, X., Zhao, H., Gui, K.,
935 Zheng, Y., An, L., Sun, T., Liang, Y., 2020b. Climatology of Fine and Coarse Mode
936 Aerosol Optical Thickness Over East and South Asia Derived From
937 POLDER/PARASOL Satellite. Journal of Geophysical Research: Atmospheres 125, 0-3.

938 https://doi.org/10.1029/2020JD032665

939 Li, L., Dubovik, O., Derimian, Y., Schuster, G.L., Lapyonok, T., Litvinov, P., Ducos, F.,
940 Fuertes, D., Chen, C., Li, Z., Lopatin, A., Torres, B., Che, H., 2019. Retrieval of aerosol

941 components directly from satellite and ground-based measurements, Atmospheric
942 Chemistry and Physics. https://doi.org/10.5194/acp-19-13409-2019

943 Lyapustin, A., Wang, Y., Korkin, S., Huang, D., 2018. MODIS Collection 6 MAIAC
944 Algorithm. Atmospheric Measurement Techniques Discussions 1-50.

945 https://doi.org/10.5194/amt-2018-141

946 Ma, Y., Zhang, M., Jin, S., Gong, W., Chen, N., Chen, Z., Jin, Y., Shi, Y., 2019. Long-Term I
947 nvestigation of Aerosol Optical and Radiative Characteristics in a Typical Megacity of

948 Central China During Winter Haze Periods. Journal of Geophysical Research: Atmosph
949 eres 124, 12093-12106. https://doi.org/10.1029/2019JD030840

950 Mallet, P.É., Pujol, O., Brioude, J., Evan, S., Jensen, A., 2018. Marine aerosol distribution
951 and variability over the pristine Southern Indian Ocean. Atmospheric Environment 182,
952 17-30. https://doi.org/10.1016/j.atmosenv.2018.03.016

953 Mao, Q., Zhang, H., Chen, Q., Huang, C., Yuan, Y., 2019. Satellite-based assessment of
954 direct aerosol radiative forcing using a look-up table established through AERONET
955 observations. Infrared Physics and Technology 102, 103017.

956 https://doi.org/10.1016/j.infrared.2019.103017

957 Mélin, F., Clerici, M., Zibordi, G., Holben, B.N., Smirnov, A., 2010. Validation of SeaWiFS
958 and MODIS aerosol products with globally distributed AERONET data. Remote
959 Sensing of Environment 114, 230-250. https://doi.org/10.1016/j.rse.2009.09.003
960 Mitchell, R.M., Forgan, B.W., Campbell, S.K., Qin, Y., 2013. The climatology of Australian
961 tropical aerosol: Evidence for regional correlation. Geophysical Research Letters 40,
962 2384-2389. https://doi.org/10.1002/grl.50403
963 Molod, A., Takacs, L., Suarez, M., Bacmeister, J., 2015. Development of the GEOS-5
964 atmospheric general circulation model: Evolution from MERRA to MERRA2.
965 Geoscientific Model Development 8, 1339-1356.
966 https://doi.org/10.5194/gmd-8-1339-2015
967 Perlwitz, J., Seland, Ø., 2009. Evaluation of black carbon estimations in global aerosol
968 models. Atmospheric Chemistry and Physics Discussions 9, 15769-15825.
969 https://doi.org/10.5194/acpd-9-15769-2009
970 Remer, L.A., Kaufman, Y.J., Tanré, D., Mattoo, S., Chu, D.A., Martins, J. V., Li, R.R.,
971 Ichoku, C., Levy, R.C., Kleidman, R.G., Eck, T.F., Vermote, E., Holben, B.N., 2005.
972 The MODIS aerosol algorithm, products, and validation. Journal of the Atmospheric
973 Sciences 62, 947-973. https://doi.org/10.1175/JAS3385.1
974 Rienecker, M.M., Suarez, M.J., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., Bosilovich,
975 M.G., Schubert, S.D., Takacs, L., Kim, G.K., Bloom, S., Chen, J., Collins, D., Conaty,
976 A., Da Silva, A., Gu, W., Joiner, J., Koster, R.D., Lucchesi, R., Molod, A., Owens, T.,
977 Pawson, S., Pegion, P., Redder, C.R., Reichle, R., Robertson, F.R., Ruddick, A.G.,
978 Sienkiewicz, M., Woollen, J., 2011. MERRA: NASA's modern-era retrospective
979 analysis for research and applications. Journal of Climate 24, 3624-3648.
980 https://doi.org/10.1175/JCLI-D-11-00015.1
981 Rienecker, M.M., Suarez, M.J., Todling, R., Bacmeister, J., Takacs, L., Liu, H.-C., Gu, W.,

982 Sienkiewicz, M., Koster, R.D., Gelaro, R., Nielsen, J.E., 2008. The GEOS-5 Data

983 Assimilation System-Documentation of Versions 5.0. 1, 5.1. 0, and 5.2. 0. NASA

984 Technical Report 27, 118 pp.

985 Russell, P.B., Bergstrom, R.W., Shinozuka, Y., Clarke, A.D., Decarlo, P.F., Jimenez, J.L.,

986 Livingston, J.M., Redemann, J., Dubovik, O., Strawa, A., 2010. Absorption Angstrom

987 Exponent in AERONET and related data as an indicator of aerosol composition.

988 Atmospheric Chemistry and Physics 10, 1155–1169.

989 https://doi.org/10.5194/acp-10-1155-2010

990 Sato, M., Hansen, J., Koch, D., Lacis, A., Ruedy, R., Dubovik, O., Holben, B., Chin, M.,

991 Novakov, T., 2003. Global atmospheric black carbon inferred from AERONET.

992 Proceedings of the National Academy of Sciences of the United States of America 100,

993 6319–6324. https://doi.org/10.1073/pnas.0731897100

994 Schuster, G.L., Dubovik, O., Arola, A., 2016. Remote sensing of soot carbon - Part 1:

995 Distinguishing different absorbing aerosol species. Atmospheric Chemistry and Physics

996 16, 1565–1585. https://doi.org/10.5194/acp-16-1565-2016

997 Schuster, G.L., Dubovik, O., Holben, B.N., Clothiaux, E.E., 2005. Inferring black carbon

998 content and specific absorption from Aerosol Robotic Network (AERONET) aerosol

999 retrievals. Journal of Geophysical Research D: Atmospheres 110, 1–19.

1000 https://doi.org/10.1029/2004JD004548

1001 Schuster, G.L., Lin, B., Dubovik, O., 2009. Remote sensing of aerosol water uptake.

1002 Geophysical Research Letters 36, 1–5. https://doi.org/10.1029/2008GL036576
1003 Streets, D.G., Gupta, S., Waldhoff, S.T., Wang, M.Q., Bond, T.C., Yiyun, B., 2001. Black

1004 carbon emissions in China. Atmospheric Environment 35, 4281–4296.

1005 https://doi.org/10.1016/S1352-2310(01)00179-0

1006 Sun, E., Xu, X., Che, H., Tang, Z., Gui, K., An, L., Lu, C., Shi, G., 2019. Variation in

1007 MERRA-2 aerosol optical depth and absorption aerosol optical depth over China from

1008 1980 to 2017. Journal of Atmospheric and Solar-Terrestrial Physics 186, 8–19.

1009 https://doi.org/10.1016/j.jastp.2019.01.019

1010 Takamura, T., Nakajima, T., 2004. Overview of SKYNET and its activities. Opt. Pura Apl 37,

1011 3303–3308.

1012 Titos, G., Ealo, M., Román, R., Cazorla, A., Sola, Y., Dubovik, O., 2019. Retrieval of aerosol

1013 properties from ceilometer and photometer measurements : long-term evaluation with in

1014 situ data and statistical analysis at Montsec ( southern Pyrenees ) 3255–3267.

1015 Torres, B., Dubovik, O., Fuertes, D., Schuster, G., Eugenia Cachorro, V., Lapyonok, T.,

1016 Goloub, P., Blarel, L., Barreto, A., Mallet, M., Toledano, C., Tanré, D., 2017. Advanced

1017 characterisation of aerosol size properties from measurements of spectral optical depth

1018 using the GRASP algorithm. Atmospheric Measurement Techniques 10, 3743–3781.

1019 https://doi.org/10.5194/amt-10-3743-2017

1020 Tsekeri, A., Lopatin, A., Amiridis, V., Marinou, E., Igloffstein, J., Siomos, N., Solomos, S.,

1021 Kokkalis, P., Engelmann, R., Baars, H., Gratsea, M., Raptis, P.I., Binietoglou, I.,

1022 Mihalopoulos, N., Kalivitis, N., Kouvarakis, G., Bartsotas, N., Kallos, G., Basart, S.,

1023 Schuettemeyer, D., Wandinger, U., Ansmann, A., Chaikovsky, A.P., Dubovik, O., 2017.

1024 GARRLiC and LIRIC: Strengths and limitations for the characterization of dust and
1025 marine particles along with their mixtures. Atmospheric Measurement Techniques 10,

1026 4995–5016. https://doi.org/10.5194/amt-10-4995-2017

1027 Van Beelen, A.J., Roelofs, G.J.H., Hasekamp, O.P., Henzing, J.S., Röckmann, T., 2014.

1028 Estimation of aerosol water and chemical composition from AERONET Sun-sky

1029 radiometer measurements at Cabauw, the Netherlands. Atmospheric Chemistry and

1030 Physics 14, 5969–5987. https://doi.org/10.5194/acp-14-5969-2014

1031 Wang, L., Li, Z., Tian, Q., Ma, Y., Zhang, F., Zhang, Y., Li, D., Li, K., Li, L., 2013. Estimate

1032 of aerosol absorbing components of black carbon, brown carbon, and dust from

1033 ground-based remote sensing data of sun-sky radiometers. Journal of Geophysical

1034 Research Atmospheres 118, 6534–6543. https://doi.org/10.1002/jgrd.50356

1035 Wang, L., Li, Z.Q., Li, D.H., Li, K.T., Tian, Q.J., Li, L., Zhang, Y., Lü, Y., Gu, X.F., 2012.

1036 Retrieval of dust fraction of atmospheric aerosols based on spectra characteristics of

1037 refractive indices obtained from remote sensing measurements. Spectroscopy and

1038 Spectral Analysis 32, 1644–1649.

1039 https://doi.org/10.3964/j.issn.1000-0593(2012)06-1644-06

1040 Wang, X., Sedlacek, A.J., DeSá, S.S., Martin, S.T., Alexander, M.L., Alexander, M.L.,

1041 Watson, T.B., Aiken, A.C., Springston, S.R., Artaxo, P., 2016. Deriving brown carbon

1042 from multiwavelength absorption measurements: Method and application to AERONET

1043 and Aethalometer observations. Atmospheric Chemistry and Physics 16, 12733–12752.

1044 https://doi.org/10.5194/acp-16-12733-2016

1045 Yang, M., Howell, S.G., Zhuang, J., Huebert, B.J., 2009. Attribution of aerosol light

1046 absorption to black carbon, brown carbon, and dust in China - Interpretations of
1047 atmospheric measurements during EAST-AIRE. Atmospheric Chemistry and Physics 9,

1048 2035–2050. https://doi.org/10.5194/acp-9-2035-2009

1049