Verification of aerosol classification methods through satellite and ground-based measurements over Harbin, Northeast China

Atmospheric Research 216 (2019) 167–175

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Verification of aerosol classification methods through satellite and groundbased measurements over Harbin, Northeast China

T

⁎

Qi-Xiang Chena, Wen-Xiang Shena, Yuan Yuana,b, , He-Ping Tana,b a

School of Energy Science and Engineering, Harbin Institute of Technology, 92 West Dazhi Street, Harbin 150001, PR China Key Laboratory of Aerospace Thermophysics, Ministry of Industry and Information Technology, Harbin Institute of Technology, 92 West Dazhi Street, Harbin 150001, PR China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Aerosol classification method Sun-photometer Satellite observation Chemical analysis Verification Northeast China

Classifying aerosol types using spectral aerosol optical properties plays a key role in promoting the accuracy of remote sensing observations and expanding our knowledge of the internal relations between aerosol and climate. However, studies on verifying the correctness for aerosol type classification methods are particularly scarce. In this study, data from three month optical and chemical measurements of atmospheric aerosols in Harbin, Northeast China, were used to verify the optical classification methods by comparing their results with the actual aerosol types. Aerosol optical properties like aerosol optical depth (AOD), Angstrom Exponent (AE), and single scattering albedo (SSA) were obtained through a CE318 sun-photometer, and aerosol type was then identified based on four previously published aerosol classification schemes. To acquire the actual aerosol states, satellite observations from MODIS and CALIPSO was combined and then the morphology, chemical composition, and molecular structure of sample particles were examined by energy-dispersive X-ray spectroscopy (SEM-DES), XRay fluorescence spectrophotometer (XRF), and Fourier transform infrared spectroscopy (FTIR) measurements. Results show that aerosol types over urban Harbin varied a lot during the studying period and mixed aerosol was the dominant type. The optical classification methods mentioned in this study could identify aerosol types correctly in most cases, but sometimes made improper estimations. Besides, they are able to descript the distinct variation of aerosol type on seasonal and daily scale, but at present the result is still too rough to discriminate the detailed changes of actual aerosol state accurately. The combination of different classification methods or chemical measurements would help reducing the misjudgment of aerosol types. Our findings demonstrate that a more comprehensive way to identify changes in aerosol state is urgently needed to enhance our understanding of the impacts of aerosols on climate and remote sensing.

1. Introduction Different types of atmospheric aerosol particles play a key role in affecting the earth-ocean-atmosphere system through not only the direct effect of absorbing and scattering short wave radiation of the sun and long wave radiation of earth but also the indirect processes such as acting as cloud condensation nuclei (Ealo et al., 2018). Previous studies showed that different aerosol types have different effects on climate because their diverse morphology, size distribution, and chemical component will lead to different aerosol optical properties (He et al., 2018b; Kumar et al., 2018a; Roman et al., 2017). For example, dust aerosols are often large particles and have a scattering tendency (Renard et al., 2018), whereas black carbon aerosols are usually small particles and have an absorbing nature (Tan et al., 2016). Also, determination of aerosol types helps identify the aerosol emission sources ⁎

and thus, provides fundamental information in implementing control policies to reduce adverse aerosol origins and improves our knowledge of aerosol long-range transport (He et al., 2015; Schmeisser et al., 2017). Additionally, priori knowledge of aerosol types and its constraining are important in promoting the performance of satellite retrieval algorithms (Kumar et al., 2018b; Zhang et al., 2016). Thus, classifying aerosol type counts a great deal in related fields of atmospheric, environmental and remote sensing science. To classify various aerosol types, previous researchers contributed their efforts to develop many aerosol classification frameworks by using different optical parameter. For example, Kalapureddy et al. (2009) employed aerosol optical depth (AOD) and Angstrom Exponent (AE) to classify four aerosol types of clean maritime, biomass burning/urban, desert dust and mixed type; Lee et al. (2010) use fine-mode fraction (FMF) and single scattering albedo (SSA) to determine dust, mixed type,

Corresponding author at: School of Energy Science and Engineering, Harbin Institute of Technology, 92 West Dazhi Street, Harbin 150001, PR China. E-mail address: [email protected] (Y. Yuan).

https://doi.org/10.1016/j.atmosres.2018.09.022 Received 28 May 2018; Received in revised form 1 September 2018; Accepted 30 September 2018 Available online 19 October 2018 0169-8095/ © 2018 Elsevier B.V. All rights reserved.

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2. Materials and methods

non-absorbing, and black carbon aerosols; Chen et al. (2016) use AOD and aerosol relative optical depth (AROD) to identify different aerosols including maritime, clean continental, desert dust, urban industry, biomass burning, and polluted continental (mixed) aerosols; Hamill et al. (2016) applied AE and SSA to discriminate maritime, dust, urban industrial, biomass burning, and mixed aerosols. A common approach to validate the classification results is to find connections between typical aerosol types and optical properties through the ground-based sun-photometer measurements at locations with a distinct dominant aerosol type, such as deserts, urban polluted regions, ocean islands, and remote continent (Balarabe et al., 2007; Bibi et al., 2017; Chen et al., 2016; Lee et al., 2010; Schmeisser et al., 2017). However, such a common approach was based on the hypothesis that aerosol optical properties collected in typical sites worldwide represent the aerosol types we expected. But the fact is that aerosol types in selected regions may also be influenced or vary due to long range transport of different aerosol types or changes of aerosol sources, which is likely to mislead the classification results (Cazorla et al., 2013). In addition, considering the volatile nature of aerosols, directly applying these classification methods to other regions may lead to an unpleasant situation, too. Thus, further efforts is needed to validate these classification methods. Satellite observation like MODIS and CALIPSO is an option for verifying the results and is widely used in related researches. For example, Bibi et al. (2017) verified the absorbing aerosol types by comparing with MODIS true color images and CALIPSO retrieved subtypes; Chen et al. (2016) applied MODIS true color images to prove the correctness of their aerosol classification method; Gharibzadeh et al. (2018) studied variation of aerosol types using AERONET data over Zanjan, Iran, and validated the results with CALIPSO-retrieved aerosol subtypes profiles. In addition to satellite observation, chemical analysis of atmospheric aerosols is a considerable verification means (Hu et al., 2018a; Siepka et al., 2018). There are many research studying the chemical properties of atmospheric aerosols and find their correlations with optical measurements, for example, Cazorla et al. (2013) used aerosol optical and chemical data collected from three aircraft field campaigns over California to assess the performance of an aerosol classification method; Zhang et al. (2017) conducted aerosol particle measurements in roadside and ambient environments in Hong Kong to enhance the knowledge of the impact of aging processes on the lightabsorbing properties; Ealo et al. (2018) carried out different chemical and optical measurements over the north-western Mediterranean area to find the impact of aerosol particle sources on optical properties; Vecchi et al. (2018) comprehensively characterized the chemical properties of PM1 samples collected at a European polluted urban area during wintertime, and estimated the impact of different emission sources on light extinction. Unfortunately, particularly scarce studies are available on validating aerosol classification schemes by comparing the results with the real aerosol states, especially in Asia. And this lack of studies hinders our further knowledge of the typical aerosol impacts on global climate and remote sensing. For this study, aerosol optical properties were measured using a CE318 sun-photometer over urban Harbin, Northeast China, from April 1 to June 30, 2017. Firstly, aerosol types were determined through four classification schemes and local aerosol condition was analyzed. MODIS true color images and CALIPSO retrieved subtypes were then applied for the mutual verification between the aforementioned schemes. Thirdly, chemical properties of sample particles were measured to obtain the real states of aerosol type and we validated these optical schemes by comparing the results with the real aerosol states on seasonal and on daily scales. At last, possible error occurred in the verifying process was briefly discussed. Fig. 1 illustrates the main research framework of the presented study.

2.1. Site description Harbin, capital of Heilongjiang province, is located in the northernmost of Northeast China Plain and has the continental monsoon climate in the middle temperate zone. It has a total area of 53,100 km2 and the urban area is 10,200 km2 with permanent population of about 10.7 million (Heilongjiang provincial bureau of statistics and team, 2017). It is representative of urban industry conditions with irregular transported dust during the whole studying period, while coal heating emission was a major aerosol source in early April (Chen et al., 2018). And such a changeable aerosol condition is suitable for the current study. Background information about meteorology and air quality is shown in Supplementary Material.

2.2. Instrument and inversion scheme Aerosol optical properties were continuously observed using a CE318 sun-photometer (Cimel Electronique, France) placed on the roof of Dongli Building (next to the particle sampler) in Harbin Institute of Technology at the central area of urban Harbin. It is a high-precision multiband photometer which measures the optical properties of atmosphere through the measurements of direct sun irradiance and diffuse sky radiance, and provides the quantification and physical-optical characteristics of the atmospheric aerosols (Chen et al., 2018). The standard measuring schedule of CE-318 is composed of direct sun triplets every 15 min and sky diffuse almucantar and principle plane scenarios every 1 h. According to Beer's law, monochromatic ground irradiance F after passing through the atmosphere can be expressed as:

F = F0 ∙ds ∙exp(−m∙τtotal )

(1)

where F0 is the solar irradiation at the top of the earth atmosphere; ds represents the correction factor of diurnal distance; m is the optical air mass of atmosphere; τtotal is the total aerosol optical depth generated from ozone absorption, Rayleigh scattering, and extinction by aerosols (Nakajima et al., 1996). Thus, AOD can be obtained by removing the influence caused by ozone absorption and Rayleigh scattering after cloud screening, seeing Eq. (2):

τa = τtotal − τr − τOZ

(2)

where τr and τOZ represent the optical depth caused by Rayleigh scattering and ozone absorbing respectively. Parameters like SSA, refractive index (RI), and volume size distribution (VSD) were retrieved through skyrad.pack 4.2 software developed by (Nakajima et al., 1996). In the inversion algorithm, relative intensity R(Θ) is defined as the ratio of the direct sun irradiance F (seeing Eq. (1)) and the sky diffuse radiance E(Θ),

R (Θ) =

E (Θ) = ω∙τ∙P (Θ) + q (Θ) = β (Θ) + q (Θ) F∙m 0 ∙∆Ω

(3)

where ΔΩ is the solid view angle of the sun-photometer; ω is the single scattering albedo (same as SSA); P(Θ) is the phase function at scattering angle Θ; β(Θ) represents the single scattering term; q(Θ) represents the multiple scattering contribution term. To retrieve the aerosol properties we need, Eq. (3) needs to be iteratively inverted because this iterative calculations eliminate the multiple scattering term q(Θ) from the relative intensity R(Θ) and recover the single scattering term β(Θ) (Estelles et al., 2012b). In each iteration step, R(Θ) is simulated through the radiative transfer code and then compared with the experimental R (Θ) to calculate the root mean square difference. And the iteration repeats until the difference is lower than the given threshold, or the solution is rejected (Estelles et al., 2012a). 168

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Fig. 1. General research framework for validating aerosol optical classification methods.

2.3. Optical properties for aerosol type classification

et al., 2014).

There are several key aerosol types observed in world locations, such as urban-industry, biomass burning, desert dust, and oceanic aerosols (Chen et al., 2016; Dubovik et al., 2002). Based on long-term field observation across the world, typical aerosols have their specific physical and optical properties, which can be used to discriminate different aerosols. Particle size information like AE or FMF and optical information like SSA or AROD were common factors to achieve classification according to previous works. AE is defined by a linear fit of AOD against wavelength (λ) on logarithmic scale (Gharibzadeh et al., 2018).

AROD =

AE = −

d ln (AODλ ) d ln (λ )

(4)

2.4. Satellite observation MODIS, the MODerate resolution Imaging Spectroradiometer, is a key instrument aboard the Terra and Aqua satellites launched by NASA, and it acquires data in 36 spectral bands and views the earth twice in the morning and afternoon (He et al., 2018a; Tian et al., 2018). The true color images used to identify aerosols was retrieved from MODIS Terra and Aqua satellite. Related images can be downloaded from the following website: https://worldview.earthdata.nasa.gov/. CALIPSO, the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation, is a joint mission between NASA and the French space agency, CNES. It is designed to fine the role that clouds and atmospheric aerosols (airborne particles) play in regulating Earth's weather, climate, and air quality (Josset et al., 2018; Tackett et al., 2018). It is now the only satellite on service providing vertical profiles of aerosols at 532 and 1064 nm over the globe. In this study, aerosol subtype information from CALIPSO Level 2.0 (Version 4.10) was utilized and it can be found from NASA site: https://www-calipso.larc.nasa.gov/.

AODfine AODtotal

(5)

It ranges from 0 of single coarse mode particles to 1 of single finemode particles (Bibi et al., 2017). Similar to AE, FMF provides information on the size of aerosol particles too, but their physical meaning are slightly different. AE focuses on the radius of the dominant particle, while FMF focuses on the contribution of the total number of fine-mode particles to AOD. Basically, the smaller FMF is, the more finemode particles are. Here, FMF at 500 nm is used for aerosol type recognition. SSA is defined as the ration of scattering coefficient (κsca) to extinction coefficient (κext) of the column aerosols (Eq. (7))

κ SSA = sca κ ext

(7)

AROD directly describes the spectral difference of aerosol attenuations. Based on the results of theoretical calculation and experimental measurement, spectral attenuation characteristics of different types of aerosol particles are significantly different. For example, the attenuation caused by dust particles in the whole band of 440–1020 nm is very strong, while the attenuation caused by urban industry and biomass burning aerosol decreases significantly with the increase of wavelength. AROD1020/440 larger than 0.8 indicates the dominant presence of dustlike aerosol; AROD1020/440 lower than 0.25 reveals the presence of biomass burning aerosols; AROD1020/440 between 0.25 and 0.4 means the urban industry aerosols (Chen et al., 2016). Thus, such a spectral difference can be used to distinguish aerosol types effectively. Here, λ1 at 1020 nm and λ2 at 440 nm are used in this work.

The physical meaning of AE for aerosol type classification is closely related to the radius of aerosol particles. Usually, aerosol from natural sources are large particles like dust, while polluted particles originated from human activities are ultrafine particles such as auto exhaust and coal burning emissions (Dubovik et al., 2002). Thus, we can preliminarily divide dust aerosols from urban-industry or biomass burning aerosols based on the absolute value of AE in general. In the presented study, AE between 440 nm and 870 nm is used. FMF is defined as the ratio of AOD originated from fine-mode particles to the total AOD (Ou et al., 2017).

FMF =

AODλ1 AODλ2

2.5. Particle sampling and chemical analysis Aerosol particles were collected using a multi-stage particle sampler under an airflow capacity of 16 Lmin-1 (TE-10-800, Tisch Environmental, America). The sampler was placed on the roof of the Dongli Building (next to the sun-photometer) in the campus of Harbin Institute of Technology (approximately 20 m above ground) near the main streets, residential areas, and commercial areas, with no major industrial pollution sources nearby. Before sampling, metal plates and cutters of the sampler were cleaned in an ultrasonic cleaner, and then samples were sealed in the centrifuge tube for further analysis. A total number of 23 samples were collected with a 48 h sampling period. Chemical analysis of sample particles was conducted using scanning electron microscope coupled with energy dispersed spectrometer (SEMEDS), Fourier transform infrared spectrometer (FTIR), and X-ray fluorescence spectrometry (XRF) measurements. SEM-EDS (SU8010, Hitachi, Japan) was used to obtain the morphology and surface element

(6)

The physical meaning of SSA lies in the scattering nature of aerosols (Patel et al., 2017). SSA is able to distinguish between biomass burning and urban aerosols based on their scattering properties. Biomass burning aerosols contain more black carbon particles and have stronger absorption characteristics, while urban industrial aerosols, in addition to carbon black particles, also have a large number of sulfate nitrates, resulting in stronger scattering characteristics. And with the help of AE or FMF, it can also identify dust aerosols, for the metal compounds contained in dust particles enhance the absorption ability in the short wave. In this study, SSA at 440 nm is used. AROD is defined as the ratio of AOD in different wavelengths (Eq. (5)) and can be used as a practical method for identifying aerosol particle species including the situation that AE cannot work well (Yuan 169

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Fig. 2c and d. Absorbing aerosols, such as black carbon (BC), are mainly fine mode aerosols produced by incomplete combustions of fossil fuel, biomass burning, and wild fires, which strongly absorb incoming solar radiation and outgoing terrestrial radiation (Rajesh and Ramachandran, 2018). Non-absorbing aerosols, like sulfate, are mainly produced by the oxidation process of sulfur dioxide from volcanic eruptions and anthropogenic emissions, which mainly scatter the incoming and outgoing radiations (Pattantyus et al., 2018). Taking in to account the Aerosol Reference Clusters used by Hamill et al. (2016), SSA440 > 0.92 is mostly UI with a moderate absorbing or non-absorbing nature, while SSA440 < 0.92 is mostly BB characterized by absorbing nature. In Fig. 2c, we chose to replace the original cluster of Mostly BC by UI and BB clusters with a demarcation line of SSA440 = 0.92, and methods shown in Fig. 2c and d are able to classify UI and BB aerosols. For the threshold of FMF500, FMF500 < 0.3 and FMF500 > 0.7 are characterized by DD and UI/BB, respectively. (Giles et al., 2011). Table 1 summarizes the optical classification results. From Table 1, familiar aerosol type identification results are reached by different classification schemes. MIX and CC aerosols are two dominant aerosol types over Harbin during the studying period, while DD and UI/BB aerosols are minor aerosol types. In general, around half of the experimental days was in a mixing state during the observation period. For example, in Fig. 2b, data fallen into the area with AOD > 0.2, AOD < 0.5, and AROD < 0.4 are mostly the mix of CC and UI/BB aerosols, while data fallen into the area with AOD > 0.5 and AROD between 0.4 and 0.8 are mostly the mix of DD and UI aerosols. Relatively large DD percentage in spring agrees well with the previously published reports, which states that East Asia is seriously affected by long range transported dust aerosols from northwestern arid and desert regions (Zhang et al., 2018). Compared with Beijing, mixed type is dominant in spring too, but the percentage of UI is much higher than that in Harbin (Ou et al., 2017). CC is the second major aerosol type with a large percentage of 36.4% indicating a good air condition in the studying site.

composition of single particles (Hu et al., 2018b). Samples were dropped onto the separate monocrystalline silicon plates and was sprayed with a thin layer of gold before sending into the electron microscope sample room. FTIR (Spectrum 100, PerkinElmer, America) was used to analysis the organic and inorganic molecular structures (Tang et al., 2016). The spectra were recorded in the range of 4000–400 cm−1. And samples were mixed with dried potassium bromide (KBr) in an agate mortar before analyzing. XRF (PW4400, Malvern Panalytical, Netherlands) was used to acquire the elemental composition (Reyes-Herrera et al., 2015). Original particles were placed in a drying box and then were ground in an agate mortar before being made into tablets for analyzing. 3. Results and discussion In this section, we firstly applied four aerosol classification schemes to estimate local aerosol types over urban Harbin. Then, mutual verification between these four classification schemes was performed by using MODIS and CALIPSO measurements. Thirdly, we verified the correctness of classification methods on seasonal scale by comparing the results with chemical analysis from SEM-DES and FTIR, as well as on daily scale by comparing the theoretical estimation with real aerosol states from XRF. At last, the possible error was discussed in detail. 3.1. Classification of aerosol types Four classification methods using different aerosol optical parameters such as AOD440-AE440–870 (Kumar et al., 2018a), AOD440AROD1020/440 (Chen et al., 2017; Chen et al., 2016), SSA440-FMF500 (Lee et al., 2010; Ou et al., 2017), and AE440–870-SSA440 (Hamill et al., 2016) were implemented to obtain the best estimation of aerosol types. Fig. 5 illustrates the results of the four aerosol classification methods applied in the studying site. Due to the geographical location of Harbin, MT is extracted in the below graphical schemes for MT aerosol is almost impossible to be transported to such a place far away from the coast. Common parameters used in different aerosol classification schemes were set to same threshold values. For example, data with AOD440 < 0.2 is regarded as CC aerosol in all the classification schemes, while AOD440 > 0.2 is related to abnormal natural sources or anthropogenic emissions. And when AOD440 > 0.2, as mentioned in section 2.3, data with AE440–870 < 0.5 is considered to be DD aerosol with abundant coarse mode particles, while AE440–870 > 1.5 is mostly emitted from human activities with large amount of fine-mode particles. In Fig. 2a and b, Mixed and CC are both classified as the major aerosol type with same ratios, but the minor DD and UI/BB are identified with different ratios. As AOD440 is a common parameter, such difference comes from the use of AE and AROD. It is observed that the four points located in the DD area in Fig. 2a are closely separated near the line of AROD = 0.8 in Fig. 2b. Thus, such a difference mostly comes from the changeable choice of threshold values. What's more, there are seven data points with AOD440 > 0.5, and only two data points fall into the UI/BB cluster in Fig. 2a, while in Fig. 2b, one data point is located in the BB cluster and three data points are located in the UI cluster. AE440–870 is often used as an effective indicator of the size of dominant aerosol particles, but it is less effective in identifying the difference of aerosols dominant by fine mode particles like UI and BB, because AE440–870 observed in typical UI and BB sites shows no difference (Dubovik et al., 2002). Also, the mixture of fine- and coarse-mode particles makes the aerosol type ambiguous in some cases (Bibi et al., 2017; Wu et al., 2015). Compared with AE440–870, AROD directly relates the spectral difference of AOD to aerosol types and is proved to be a proper tool to distinguish UI and BB aerosols based on theoretical calculations and measurements in typical sites (Chen et al., 2016). According to Giles et al. (2011), SSA440 was applied to discriminate absorbing, moderate absorbing, and non-absorbing aerosol types in

3.2. Verification based on satellite measurements Although the overall statistics are close from Table 1, it is needed to clarify their identification differences during the whole studying period. Fig. 3 shows the detailed discrimination results during the studying period. In general, the classification results are relatively consistent, but there are also some inconsistencies. The classification results of CC are identical because the criterion for classifying CC are the same, while the recognition results of DD, UI/BB and MIX aerosols are quite different. In order to clarify the differences, actual aerosol state of the observations with recognition difference was inferred according to MODIS true color images and CALIPSO aerosol products. Fig. 4 shows the MODIS true color images and CALIPSO retrieved subtypes on selected days when there are differences. In Fig. 4a–f, the yellow marks indicate the location of our studying point, while the red dots are closely related to biomass burning activities; clouds are white and polluted aerosols are gray in these MODIS images. In Fig. 4g, the selected part in the black rectangle represents the vertical aerosol profiles over or near Harbin. In D1, Harbin was surrounded by a mass of fire anomalies in Fig. 4a and the ‘gray’ aerosol layer was probably biomass burning emissions (Bibi et al., 2017). According to Fig. 4g, polluted continental/smoke dominated the atmosphere over Harbin although there was a small amount of suspended dust. Combining these two measurements, D1 was mostly dominated by BB. It can be found in Fig. 3 that D1 was identified as UI/BB by SSA-FMF and AE-SSA while it was classified as MIX by AOD-AE and AOD-AROD. Seeing that AOD in D1 was 0.36, D1 had to fall into MIX region due to the constraint of AOD by AOD-AE and AODAROD. If the threshold of AOD in AOD-AE and AOD-AROD is adjusted to be larger than 0.2 or 0.3 rather than 0.5, D1 would fall into UI/BB region too. Familiar with D1, D23 was identified as MIX by SSA-FMF and AOD-AROD but as DD by AOD-AE and AE-SSA. According to Fig. 4e 170

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2.5

1.0 CC: 36.4%

DD: 4.5% UI/BB: 4.5%

0.8

AROD1020/440

AE440-870

2.0 1.5 1.0

Mixed: 50.0%

0.6

Mixed: 50.0%

0.4 UI: 6.8%

0.5

0.2 DD: 9.1%

0.0 0.0

0.2

0.4

0.6

0.8

CC: 36.4%

1.0

BB: 2.3%

0.0 0.0

1.2

0.2

0.4

AOD440

(a) UI: 0%

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1

Mixed: 45.4%

0.80

0.85

0.90

0.95

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1

0.90 0.85 BB: 4.5%

DD: 9.1% 0.80

AOD < 0.2 CC: 36.4%

DD: 9.1%

AOD440 UI: 4.5%

Mixed: 53.6%

0.95

SSA440

FMF500

0.6

0.75

1.2

(b)

0.8

0.0 0.70

1.0

1.00

AOD440

BB: 9.1%

0.2

0.8

AOD440

1.0

0.4

0.6

AOD < 0.2 CC: 36.4%

0.75 0.70 -0.5

1.00

0.0

0.5

1.0

1.5

2.0

2.5

AE440-870

SSA440

(d)

(c)

Fig. 2. Results of different aerosol classification methods over urban Harbin from April 1 to June 30, 2017, (a)AOD440-AE440–870, (b) AOD440-AROD1020/440, (c) SSA440-FMF500, and (d) AE440–870-SSA440. Table 1 Summary of the results from different aerosol classification schemes over urban Harbin. (%)

AOD-AE

AOD-AROD

SSA-FMF

AE-SSA

CC DD UI/BB UI BB Mixed

36.4 9.1 4.5 – – 50.0

36.4 4.5 9.1 6.8 2.3 50.0

36.4 9.1 9.1 0 9.1 45.4

36.4 9.1 15.9 4.5 11.4 38.6

AOD-AE

AOD-AROD

SSA-FMF

AE-SSA

UI/BB

MIX

DD

and g, dust aerosol layer dominated the studying site with no fire anomalies surrounded, and AOD-AE and AE-SSA accurately identified it. It is observed that D23 was located near the“border” between DD and MIX in Fig. 2b and d, which means SSA-FMF and AOD-AROD were able to reflect the aerosol change but they did not hit the threshold value and thus not respond to the change. In D3, Harbin was covered by heavy polluted BB aerosol or polluted continental/smoke from MODIS true color image and CALIPSO subtype, respectively. D3 was identified as UI/BB by AOD-AE, AOD-AROD, and AE-SSA but as MIX by SSA-FMF. Although there was some polluted dust layer, the identification of UI/ BB was more precise to the actual situation. Similar situations can be found in D11 and D40. The biggest difference between the classifications occurred in D4 and D5 when they were identified as UI/BB by AOD-AROD, DD by SSA-FMF, and MIX by AOD-AE and AE-SSA. Due to the lack of CALIPSO data in D5, we discussed D4 only. Although Harbin was shrouded in thick smog, D4 was different from D3 for aerosol subtype from CALIPSO indicated the abundant presence of polluted dust in D4 rather than polluted continental/smoke in D3. So MIX identified by AOD-AE and AE-SSA was preferable to describe the aerosol state.

CC

D1

D5

D9 D13 D17 D21 D25 D29 D33 D37 D41 D45

DATE Fig. 3. Discrimination result of different classification methods during the studying period. The rectangles in green, yellow, buff, and purple represent clean continental (CC), desert dust (DD), mixed (MIX), and urban industry/ biomass burning (UI/BB) aerosol clusters respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Based on the analysis above, we find that the results of aerosol type classification are completely controlled by the choice of threshold value, and the threshold value of these standard classification schemes mainly comes from measurements at typical sites worldwide (Chen et al., 2016; Hamill et al., 2016; Schmeisser et al., 2017). When we try to apply these classification schemes at a particular site, the threshold 171

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Fig. 4. Satellite observation on selected days. (a) MODIS true color images with fire anomalies, the yellow mark and red dot represent the studying site and biomass burning activities respectively, (b) CALIPSO aerosol subtypes products, version 4.10, the black box shows the vertical aerosol profiles over the studying point. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

value should be more or less modified to fit local aerosol state so that we can obtain a more accurate aerosol type information. Usually aerosols are not a single type and show fuzzy properties (Schmeisser et al., 2017). In this case, different classification schemes may diverge and data may fall at or near the boundary of two types. Faced with this situation, the combination of different schemes would be a preferable

option, in other words, the more useful optical parameters involved in describing aerosol state would help reduce the misjudgment of actual aerosol types.

172

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CH2 CH3 NO-3

SO24

C=H SiO2

C=C

40 0 38 0 00 36 0 34 0 0 32 0 0 30 0 0 28 0 0 26 0 0 24 0 0 22 0 0 20 0 0 18 0 00 16 0 14 0 00 12 0 10 0 00 80 0 60 0 40 0

H=O

Wave number (cm-1)

(d) Fig. 5. Results of (a, b, c) morphology and surface elements by SEM-EDS and (d) molecular structure observed by FTIR.

condition in E5 is supposed to be linked to no strong contribution from both natural and anthropogenic emissions. Compared with E5, E1 and E2 are identified as UI/BB. Relatively high percentage of Cr indicates the dominance of vehicle exhaust in the local atmosphere in E1, while the sharp increase of S and Si and quick decline of S observed in E2 mean the major emission changes from traffic emission to coal and oil combustion and dust. From the comparison of E1 and E2, although they are identified as the same aerosol type via optical methods, the actual aerosol states differ a lot from each other and optical means failed to notice the difference. E3 and E4 are characterized by MIX because of their high aerosol loads and nonspecific aerosol optical properties, while E5 is identified as CC due to its background-level AOD value. The content of Si, S, and Cr in E3 and E4 are familiar with those in E5, and such a mixed state means dust, industry and traffic emissions all contribute to the high AOD value. For E6, a significant increase in the content of S and Cr is observed indicating the dominance of dust particles and traffic exhausts in the local air. The optical classification method catches the changes of dust component accurately, but ignores the increase of traffic exhaust. Based on the chemical analysis results, aerosol over urban Harbin was mainly composed of particles originated from natural source of dust and anthropogenic source of vehicle exhaust and coal emission, and obviously it was in a mixed state during the studying period. Optical classification methods provide rough judgments about aerosol type on seasonal and daily scale. Although the distinct variation of chemical components can be generally identified from optical properties, the change of actual aerosol state cannot be fully discriminated through current optical methods. The reason for the difference lies in the amount of information they provide. For chemical analysis, large amount of physical and chemical information can be obtained to directly analyze the actual state of particles, while for optical measurement, much fewer information can be used. Also, the original intention of developing optical classification methods is to preliminarily divide typical aerosol clusters to simplify its calculation in climate and remote sensing. So the current aerosol classification methods generally tried to use less parameters to identified more different aerosol types. These studies means a lot in figuring out the potential of specific parameters in dividing aerosol types. But with the deepening of aerosol researches, more accurate classification methods are urgently needed to enhance our knowledge of their impact on regional environment and global climate, as well as remote sensing. The attempt of combining the optical and chemical measurements may be a considerable choice to acquire

3.3. Verification based on chemical analysis Seasonal aerosol state was characterized by analyzing the morphology, surface element, and molecular structure of atmospheric aerosol particles on seasonal scale, which provide detailed information about physical and chemical features pertaining to their origin (Zong et al., 2018). Fig. 5a–c show the morphologies together with their surface elements of three typical aerosol particles collected during the studying period. These particles exhibit quite different patterns ranging from spherical to irregular shapes. The spherical particles are rich in Si, O, Fe, Al, K, and C, which mainly come from the combustion process of coal-fired power plants and industrial boilers (Zong et al., 2018). Platelike porous particles are mainly composed of O, Si, C, Al, Ti, Fe, and K, and are mainly from the process of dust and rock weathering (CamposRamos et al., 2009). The agglomerated particles are rich in O, C, Si, Al, S, Fe, K, and Mg, which are mainly the result of accumulation process of incomplete combustion products of fossil and biomass fuel (Gonzalez et al., 2017). The modified FTIR spectrogram after subtracting the baseline is shown in Fig. 5d, and the molecular structures in red are observed in most samples. The main molecular structures are aliphatic CH (900–700 cm−1), aromatic C]C (1600 cm−1), aliphatic CH2 (2925 cm−1), aliphatic CH3 (2960 cm−1), and hydroxyl OH (3410–3460 cm−1) indicating the presence of complicated organic matters from incomplete combustion of fuels. NO3− (1385 cm−1), SO42− (1100 cm−1), and SiO2 (1035 cm−1) were also generally observed in sample particles revealing the influence of coal fire, vehicle exhaust, and transported dust on local aerosols (Zong et al., 2018). Daily aerosol changes were depicted by element composition of aerosol samples in 6 sampling days. According to Fig. 6a, sample particles contain various kind of elements including metallic elements like Na, Mg, K, Ti, Cr, Fe, Mn, and Zn, and nonmetallic elements like Si, P, S, and Cl. The elements Si, Mg, Al, and Ti represent the contribution from dust, the elements S, P, Fe, Cu, Ca, and Mn are related to coal and oil combustion, the elements Cr and Ni are associated with traffic emissions (Campos-Ramos et al., 2009; Reyes-Herrera et al., 2015). Here we chose to use Si, S, and Cr to characterize the variation of actual aerosol states. Fig. 6b show the variation of AOD440 and the changes of relative content of Si, S, and Cr by setting the maximum value to unit, and the boxes with different colors represent the optical classification result of aerosol types. E5 is classified as CC with low AOD440 of 0.087, and the content of Si, S, and Cr are in low level. Thus the background-like aerosol 173

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Fig. 6. Variation of (a) elemental composition by XRF and (b) AOD440 together with typical elements, the boxes in purple, gray, green, and yellow represent UI/Bb, MIX, CC, and DD, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

studied carefully yet. In chemical analysis, aerosol particles were collected near surface and cannot represent the total column aerosol state. For example the evaluated dust layer detected by CALIPSO in Fig. 4g was likely to be different from near-surface contaminated aerosols. Another error comes from the sample size. There are about forty samples used for classification. Considering the topic of the current study, we focused on verifying the performance of different classification methods and clarifying the deficiencies of these methods. Although the sample size is not large enough, the quality of samples is high. The types of aerosol contained here are abundant and the changes are distinct. So the samples used here are believed to be acceptable to support our conclusion.

detailed aerosol states. 3.4. Error analysis It scarcely happens that atmospheric aerosols are composed of a single aerosol component unless it is close enough to the strong emission sources and without the influence of long range transported aerosols, but in most cases atmospheric aerosols are mixed (Cheng et al., 2015; Dubovik et al., 2002; Schmeisser et al., 2017). From the view of current aerosol classification framework, aerosols are divided in distinct clusters like CC, DD, and UI/BB, which means a dominant state of an aerosol type (Hamill et al., 2016; Kumar et al., 2018a). In other words, for example, the UI/BB dominates the local atmosphere leading to the increase of AOD and FMF and decrease of AE and AROD, but there are still minor dust aerosols suspended in the air showing weak or almost no influence on the optical properties of the column aerosol. Such an absolute classification mechanism is a basic factor that lead to a rough judgment on aerosol types when applying the current aerosol classification methods. The optical classification method works when a specific aerosol type dominates local atmosphere, however, when the few optical properties show no strong relations with any distinct aerosol type, it fails to work and generally classify it as MIX. Although clearly delineating aerosol types is very difficult, establishing a reasonable classification standard is the basis of further researches on aerosol and related subjects. At least, we need to refine the type of MIX to several sub-clusters, because the optical properties of MIX type aerosols vary in a wide rage, and such a simple classification of MIX is a key uncertainty in climate and remote sensing studies (Ealo et al., 2018; Gautam and Nainwal, 2017; Wang et al., 2016). As for optical identification of aerosol types, a basic opinion is held that although aerosols are complex and changeable in space and time, they can be preliminarily identified by a few parameters (Chen et al., 2016; Dubovik et al., 2002; Gharibzadeh et al., 2018). The classification schemes involved in the current study use two or three factors to divide aerosol types, thus the selection of threshold value is the primary error source when acquiring reasonable result. The threshold value used here was verified with data from typical sites in previous work and was believed to be widely applicable. Without modification, their behavior was not that good when applied to the studying point. For better classification, the threshold value should be adjusted according to local aerosol conditions. What's more, the combination of different classification methods is a practical way to reduce the misjudgment. Parameters used to identify aerosol types like SSA, FMF were retrieved from sky radiance measurements, and most of the time the sky is not cloudless and diffuse sky radiance may be influenced inestimably (Torres et al., 2017). This is also a potential error source and hasn't been

4. Conclusion Long-term estimation of aerosol types using optical properties provides an opportunity for us to assess the influence of aerosols on climate change and remote sensing observation. In this study, we applied four methods for the classification of regional aerosol types from spectral optical measurement in spring over urban Harbin, Northeast China. Satellite observation and chemical measurement of sample particles were analyzed to obtain actual aerosol states. Through the comparison between optical classification and actual aerosol states, we verified the correctness of these classification schemes and explored their limitations. Here are the main conclusions as follows: 1) Seasonal average AOD440, SSA440, and ASY440 were 0.37 ± 0.40, 0.91 ± 0.06, and 0.67 ± 0.06, respectively from April 1 to June 30. Drastic variation of daily AE440–870 and AROD1020/440 as well as spectral SSA and ASY indicated a significant change in local aerosol type. And MIX and CC were found to be the dominant aerosol types over urban Harbin. 2) The classification results of AOD-AE, AOD-AROD, SSA-FMF, and AESSA are generally consistent despite of occasionally inconsistencies during the studying period. From the synergetic analysis of MODIS and CALIPSO, the combined use of different classification schemes is preferable in complex aerosol conditions. 3) Through chemical analysis, natural source of dust and anthropogenic source of coal and oil combustion played a key role in influencing the local atmosphere. Chemical analysis was able to describe detailed changes of aerosol type compared with optical classification methods using few optical information. 4) Current classification methods provide rough judgments about the real aerosol type on seasonal and daily scales. When the aerosol shows ambiguous optical properties, the optical schemes fail to find the major types and leave it to MIX type. When a specific aerosol 174

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type dominates local atmosphere, they generally works but still cannot fully discriminate the changes of actual aerosol state. 5) The lack of sound aerosol classification standards and the selection of threshold values are the two major sources in misrecognizing aerosol types.

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