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Aerosol Science and Engineering

, Volume 1, Issue 2, pp 78–92 | Cite as

Comparison of Aerosol Optical Properties Between Two Nearby Urban Sites in Beijing, China

  • Jie Yu
  • Huizheng Che
  • Quanliang Chen
  • Hongbin Chen
  • Philippe Goloub
  • Ke Gui
  • Yu Zheng
  • Hong Wang
  • Yaqiang Wang
  • Linchang An
  • Tianze Sun
  • Xiaoye Zhang
  • Renjian Zhang
  • Mingkai Dai
Original Paper

Abstract

This study compares the aerosol optical properties measured by CE318 sunphotometers at the Institute of Atmospheric Physics and the Chinese Academy of Meteorological Sciences in Beijing between January 2013 and July 2015 to provide the framework to quantify the spatial and temporal variability of aerosol properties. Aerosol optical parameters included extinction (scattering plus absorption) aerosol optical depth (EAOD), extinction Ångström exponent (EAE), columnar water vapor (CWV), absorption aerosol optical depth (AAOD), absorption Ångström exponent (AAE), extinction aerosol optical depth of fine particles (EAODf), extinction aerosol optical depth of coarse particles (EAODc), real parts of the refractive index (REFR), imaginary parts of the refractive index (REFI), single scattering albedo (SSA), asymmetry factor (ASYM), size distribution, and sphericity fraction. Comparison of aerosol optical properties using the simultaneous observations between two sites showed that correlation coefficients were larger than or equal to 0.98 for EAOD, EAE, and CWV, but smaller than or equal to 0.90 for AAOD, REFR, REFI, and SSA; the percentage differences for most of the parameters were less than 2%, but for EAODf were relatively large, ranging from 4.35 to 6.45%; the mean size distributions simultaneously showed bi-modal patterns, with two peak volumes at the radii of 0.15 and 2.94 μm; two kinds of tri-peak model were apparent during the study period; a case of EAODs at 440 nm differing by more than 0.2 between the two sites reflected the effect of local aerosol pollution. Comparison of aerosol characterization inferred by absorption properties using all the inversion data showed that classification using EAE, AAE, and sphericity fraction indicated the main aerosol type was “U/I&BB” (urban/industrial and biomass-burning), accounting for 59.87 and 57.43%, respectively; the volume size distribution retrievals binned by AAE exhibited coarse mode particles became dominant as AAE increased to 2.0; the SSA retrievals binned by AAE demonstrated SSA transitioned from spectra representing dust to U/I&BB pollution; averaged SSA for all the retrievals and SSA data partitioned by the EAE and η 675 nm suggested there were more absorbing aerosols at IAP. The results of the study will be beneficial in validating satellite observations and model simulation results, providing more accurate input parameters for model simulations.

Keywords

Comparison Aerosol optical parameters Absorption Beijing 

1 Introduction

The impact of aerosol particles on climate is twofold (Aoki and Fujiyoshi 2003). On one hand, aerosols directly affect the earth–atmosphere radiative balance through scattering and absorbing shortwave and longwave radiations in the air (Charlson et al. 1992; IPCC 2013). On the other hand, indirectly, they serve as cloud condensation nuclei and ice-forming nuclei to affect the size distribution of cloud droplets (Twomey et al. 1984). Many studies have emphasized that the nature of aerosol optical properties is one of the largest sources of uncertainty in current assessments of climate forcing (Ramanathan et al. 2001). Precise and consistent measurements of aerosol optical properties, such as EAOD (extinction aerosol optical depth), SSA (single scattering albedo), ASYM (asymmetry factor), and phase function, key parameters of the aerosol direct effect, are required in a timely manner to reduce such large uncertainties (Xie et al. 2011). Myhre (2009) also emphasized that research on the influence of aerosols needs more information on aerosol characteristics, such as size distribution, chemical composition and optical properties, especially light absorption properties.

The power of aerosol optical properties in assessing and predicting global climate change needs further verification (Hansen et al. 2000). Detailed and accurate knowledge of aerosol optical properties remains insufficient (Aoki and Fujiyoshi 2003). EAOD and EAE can be used to study the size and growth of aerosol particles. The SSA defined as the ratio between the particle scattering coefficient and the total extinction coefficient is usually used to characterize the aerosol absorption properties and is a key variable in assessing the radiative forcing of aerosols (Bi et al. 2011). The SSA is mainly dependent on the chemical composition, size distribution and matter concentration of aerosol particles. The aerosol volume size distribution is one of the decisive factors in the transport and persistence of aerosols, as well as their optical properties. The ASYM represents an estimation of the asymmetry distribution of the scattering radiation.

Currently, studies on aerosol optical properties focus mainly on EAOD and EAE (Xiao et al. 2011; Dong et al. 2013; Wang et al. 2010a; Che et al. 2011; Che et al. 2015a; Zhao et al. 2013), with relatively less research taking place on other aerosol optical parameters. Dubovik et al. (2000) successfully retrieved aerosol characteristics (size distribution, complex refractive index, and SSA), providing the inversion included the combined data of spectral optical depth together with sky radiances in the full solar almucantar-used radiances measured by ground-based Sun–sky scanning radiometers of the Aerosol Robotic Network (AERONET). Bi et al. (2011) discussed the aerosol optical properties according to a CIMEL sunphotometer of the Aerosol Robotic Network at the Semi-Arid Climate and Environment Observatory of Lanzhou University. Che et al. (2007) analyzed the seasonal variation of aerosol optical properties including EAOD, EAE, SSA, volume size distribution used skyradiometer observation data in Beijing. Eck et al. (2005) investigated the column-integrated optical properties of aerosols in the central eastern region of Asia and the tropical mid-Pacific based on Sun–sky radiometer measurements made at AERONET sites in these regions. Toledano et al. (2011) analyzed the variations in the EAOD in the range of 340–1550 nm, the Ångström exponent, volume size distributions and single scattering albedo during the SAMUM-2 experiment in the Cape Verde islands from January to February 2008.

Because of the complexity of local emission sources in urban areas, model simulations and satellite inversions tend to be based on just one site, used as representative of the whole Beijing urban areas. This present study aims to use ground-based sunphotometer measurements from two neighboring urban AERONET sites in Beijing to investigate the influence of local pollution and the differences in aerosol optical properties in neighboring regions. Ground-based observation which is considered to be the most accurate aerosol research means is the basis of aerosol research; it can be obtained by various aerosol physical and radiative characteristics directly. The two nearby sites provided the framework to quantify the spatial and temporal variability of aerosol properties. Thus, more ground-based measurements of aerosol characteristics offered better representation to validate aerosol retrievals from satellites, improve satellite remote-sensing algorithms, and further improve the quality of satellite products. For the model simulation, the quantified spatial and temporal variability of aerosol properties will be useful for providing more accurate input parameters for the parametric scheme.

The remainder of the paper is structured as follows: The study sites, instruments, and data are described first. Next, the averages of different aerosol optical parameters for simultaneous observations at the two urban sites in Beijing are compared and discussed. Third, cases in which three peaks in the size distribution and measurements of EAOD differed by more than 0.2 for simultaneous observations between the two sites are then analyzed. Then, the aerosol characterizations inferred by absorption properties for all the inversion data are then analyzed. Finally, conclusions are drawn.

2 Sites, Instruments, Data and Method

2.1 Sites

Figure 1 shows the two nearby (6 km apart) AERONET sites in Beijing: the Institute of Atmospheric Physics [IAP; (39.98°N, 116.38°E); 92.0 m ASL], Chinese Academy of Sciences and the Chinese Academy of Meteorological Sciences [CAMS; (39.93°N, 116.32°E); 106.0 m ASL], China Meteorological Administration. IAP is located between the north 3rd and 4th ring road in Beijing. The sampling site is located on a terrace on the roof of the IAP building (30 m high) in Beijing, which is next to the traffic road. While CAMS’s site is located in the interior of China Meteorological Administration, and away from the roadside.
Fig. 1

Location of the IAP and CAMS sites in Beijing

2.2 Instruments

Two CIMEL CE318 sunphotometers (one at each site) were installed in March 2001 at IAP and August 2012 at CAMS, and since then have been running continuously. The instruments differ in terms of the wavelengths at which they operate, but only the measurements of common channels (440, 675, 870, 1020 and 940 nm) were used in this study. The main components of the CE318 sunphotometer are the robot, optical head, collimator, and control box. The instrument takes direct spectral solar irradiance and sky radiance measurements for the solar almucantar scenario or principal plane scenario, within a 1.2° full field-of-view every 15 min (Holben et al. 1998). Measurements of direct solar radiation were used to calculate the EAOD and the total amount of atmospheric columnar water vapor (CWV). Sky radiance measurements for the solar almucantar scenario or principal plane scenario can be used for the inversion of other aerosol optical parameters, such as SSA, size distribution, refractive index, ASYM, and phase function (Dubovik et al. 2000).

The CIMEL sunphotometers of both IAP and CAMS are calibrated using the PHOTONS (http://www-loa.univ-lille1.fr/photons/) calibration facility in Lille, France, and at the Izaña Observatory, Spain, following the calibration protocol used by NASA, periodically. And the sunphotometer calibrations were conducted in August of each year at IAP and CAMS during the measurement period. The calibration procedure has been described by Che et al. (2009). The uncertainty in EAOD under a cloud-free condition is <±1% for wavelengths >440 nm and <±2% for shorter wavelengths (Eck et al. 1999). The uncertainty in retrieval of CWV is typically <12% (Holben et al. 1998). The SSA with an expected accuracy to ±3% can be retrieved only for high aerosol loading (EAOD440 nm ≥0.40) for solar zenith angles >50° (Dubovik et al. 2002). For the intermediate particle size range (0.1 ≤ r ≤ 0.7 μm), the retrieval errors of particle volume size distribution do not exceed 10% in the maxima of the size distribution and may increase up to 35% for the points corresponding to the minimum values of dV(r)/dln r within this size range; for the particles less than 0.1 μm and larger than 0.7 μm, the accuracy decreases significantly because of the low sensitivity of the aerosol scattering at 440, 675, 870 and 1020 nm to particles in these size ranges (Dubovik et al. 2002; Dubovik and King 2000; Dubovik et al. 2000). The errors are estimated to be ±4% in the real parts and 30–50% in the imaginary parts of the refractive index (Dubovik et al. 2000). An accuracy assessment of the AERONET retrievals can be found in the work of Dubovik et al. (2000, 2006). We employ AERONET Level 1.5 data in this study which are quality assured and cloud screened by the method of Smirnov et al. (2000).

2.3 Data and Method

AERONET data from January 2013 to July 2015 are used in this paper (http://aeronet.gsfc.nasa.gov/). For details regarding the version 2 algorithm, readers are referred to Dubovik et al. (2006) and Eck et al. (2008).

The aerosol optical parameters in the AERONET database include EAOD, EAE, CWV, absorption AOD (AAOD), extinction AOD of fine particles (EAODf), extinction AOD of coarse particles (EAODc), real parts of the refractive index (REFR), imaginary parts of the refractive index (REFI), SSA, ASYM, volume size distribution, and sphericity fraction (fraction of spherical to spheroidal plus spherical particles). These parameters were used to compare the aerosol optical properties between IAP and CAMS.

The aerosol inversions between the two sites in Beijing were compared using the data measured less than 1 min apart (can be seen as simultaneous observations). Comparisons of EAOD, EAE440–870 nm and CWV between IAP and CAMS were based on 5970 pairs of measurements taken within 1 min from each other for the 477 days of the study period. Only results with EAOD440 nm >0.40 were used to retrieve the SSA, because of the greater level of uncertainty inherent in lower EAOD values (Che et al. 2015b). Therefore, comparisons of other aerosol optical parameters between IAP and CAMS were based on 126 pairs of measurements taken within 1 min from each other on 75 days. Comparisons of aerosol characterization inferred by absorption properties for all the retrievals were based on 942 measurements at IAP and 707 measurements at CAMS.

3 Results

3.1 Comparison of Aerosol Optical Properties for Simultaneous Observations

Figure 2 shows the results that enable us to compare the aerosol optical parameters between IAP and CAMS. The light-blue dashed line in each panel of Fig. 2 is the 1:1 ideal line (i.e., y = x). Additionally, mean values and differences in aerosol optical parameters between IAP and CAMS are shown in Table 1.
Fig. 2

Scattergrams of aerosol optical parameters between IAP and CAMS: a EAOD; b EAE; c CWV; d AAOD; e EAODf; f EAODc; g REFR; h REFI; i SSA; j ASYM

Table 1

Mean values and standard deviations of aerosol optical parameters at IAP and CAMS, and the differences in aerosol optical parameters between the two sites

Parameter

IAP

CAMS

Difference

Parameter

IAP

CAMS

Difference

EAOD440

0.53 ± 0.56

0.52 ± 0.56

1.92%

EAODc1020

0.18 ± 0.13

0.18 ± 0.13

0

EAOD675

0.32 ± 0.35

0.32 ± 0.34

0

REFR440

1.47 ± 0.06

1.47 ± 0.06

0

EAOD870

0.24 ± 0.25

0.23 ± 0.25

4.35%

REFR675

1.49 ± 0.05

1.49 ± 0.05

0

EAOD1020

0.20 ± 0.20

0.20 ± 0.20

0

REFR870

1.49 ± 0.05

1.49 ± 0.05

0

EAE 440–870

1.12 ± 0.34

1.13 ± 0.35

0.88%

REFR1020

1.49 ± 0.05

1.50 ± 0.05

0.67%

CWV (cm)

1.09 ± 0.88

1.11 ± 0.95

1.80%

REFI440

0.01 ± 0.00

0.01 ± 0.01

0

AAOD440

0.09 ± 0.04

0.09 ± 0.04

0

REFI675

0.01 ± 0.00

0.01 ± 0.00

0

AAOD675

0.04 ± 0.02

0.04 ± 0.02

0

REFI870

0.01 ± 0.00

0.01 ± 0.00

0

AAOD870

0.03 ± 0.01

0.03 ± 0.01

0

REFI1020

0.01 ± 0.00

0.01 ± 0.00

0

AAOD1020

0.03 ± 0.01

0.03 ± 0.01

0

SSA440

0.92 ± 0.04

0.92 ± 0.04

0

EAODf440

1.00 ± 0.53

0.95 ± 0.47

5.26%

SSA675

0.94 ± 0.03

0.93 ± 0.03

1.08%

EAODf675

0.53 ± 0.33

0.50 ± 0.30

6.00%

SSA870

0.93 ± 0.03

0.93 ± 0.03

0

EAODf870

0.33 ± 0.22

0.31 ± 0.20

6.45%

SSA10200

0.93 ± 0.04

0.93 ± 0.04

0

EAODf1020

0.24 ± 0.17

0.23 ± 0.15

4.35%

ASYM440

0.71 ± 0.03

0.71 ± 0.03

0

EAODc440

0.15 ± 0.11

0.15 ± 0.11

0

ASYM675

0.66 ± 0.64

0.65 ± 0.03

1.54%

EAODc675

0.16 ± 0.12

0.16 ± 0.12

0

ASYM870

0.64 ± 0.03

0.63 ± 0.04

1.59%

EAODc870

0.17 ± 0.13

0.17 ± 0.12

0

ASYM1020

0.63 ± 0.03

0.63 ± 0.04

0

Figure 2a shows the EAOD values and linear fitting curve at each wavelength between the two sites. High correlation was found, with a correlation coefficient of 0.99 at each wavelength. By and large, EAOD values at IAP were slightly higher than those at CAMS, the difference that could be associated with the fact that IAP has a roadside location. The mean EAODs decreased with increasing wavelength. The mean EAOD440 nm at IAP and CAMS was 0.53 ± 0.56 (the number behind the symbol “±” means the standard deviation) and 0.52 ± 0.56, respectively. The percentage differences in EAOD between the two sites at 440, 675, 870 and 1020 nm, calculated using the formula
$$ \frac{{\left| {\overline{\text{EAOD}}_{{_{{\lambda {\text{IAP}}}} }} - \overline{\text{EAOD}}_{{\lambda {\text{CAMS}}}} } \right|}}{{\overline{\text{EAOD}}_{{\lambda {\text{CAMS}}}} }} \times 100\% , $$
(1)
where 1.92, 0, 4.35 and 0%, respectively. This confirms the high consistency in EAOD for the two sets of CIMEL sunphotometer measurements at IAP and CAMS. The uncertainty in EAODs is less than ±1% for wavelengths >440 nm (Eck et al. 1999). Therefore, the large percentage differences of EAOD at 440 and 870 nm may be caused by the different aerosol properties at two sites.

There was notable linear correlation between the EAEs of the two sites computed from the instantaneous measurements (Fig. 2b). The correlation coefficient between the EAEs at 440–870 nm was 0.98. The linear fitting curve of the EAEs was slightly higher than the y = x line. The mean EAE at IAP and CAMS was 1.12 ± 0.34 and 1.13 ± 0.35, respectively. The total average EAE based on 5970 pairs of data between the two sites differed by 0.88%.

On the whole, the amount of water vapor at CAMS was higher than that at IAP (Fig. 2c), which could be due to the presence of the air conditioner outdoor unit beside the CAMS’s site and the presence of the lake around the CAMS’s site. Nevertheless, a highly significant linear relationship was found between the two sites, with a correlation coefficient of 0.99. The total average CWV based on 5970 pairs of data between the two sites differed by 1.80%.

The correlation coefficients between the AAODs ranged from 0.75 to 0.90 (Fig. 2d). Relatively poor correlation may be due to the different kinds of aerosols caused by local emission source, especially absorption aerosol particles, such as the black carbon and brown carbon (Myhre 2009; Che et al. 2014). Song et al. (2013) pointed out the average black carbon concentrations at the roadside site in Beijing were 12.3 and 17.9 μg m−3 in summer and winter, respectively. The mean AAODs between two sites are the same at all four wavelengths, ranging from 0.03 ± 0.01 to 0.09 ± 0.04.

When EAODf values were larger than 0.5, the EAODfs at IAP were larger than those at CAMS (Fig. 2e). The correlation coefficients for the EAODf at 440, 675, 870 and 1020 nm were 0.98, 0.98, 0.97, and 0.97, with slopes of 0.91, 0.89, 0.87 and 0.87, respectively. The mean EAODf decreased with increasing wavelength, ranging from 0.24 ± 0.17 (1020 nm) to 1.00 ± 0.53 (440 nm) at IAP and from 0.23 ± 0.15 (1020 nm) to 0.95 ± 0.49 (440 nm) at CAMS. The differences in EAODf ranged from 4.35% (1020 nm) to 6.45% (870 nm), which were the largest of the differences among all the measured parameters. The difference in EAODf was likely due to differences in the chemical components of aerosol (Zhang et al. 2013a, b), suggesting that the local aerosol contribution was distinct (Wang et al. 2010b).

When EAODc values were larger than 0.2, the results (Fig. 2f) were similar to those of EAODf. Furthermore, a highly significant linear relationship (0.97 at four wavelengths) between the two sites was found, indicating that the deviation between the two groups of data was small. The mean EAODc increased with increasing wavelength, but was smaller than its EAODf equivalent. This indicated that the fine mode aerosol particles were the dominant EAOD contributor. The mean EAODc values between two sites were the same at four wavelengths, ranged from 0.15 ± 0.11 to 0.18 ± 0.13.

The linear fitting curve of REFR was higher than the y = x line at 1020 nm, with a strong correlation of R = 0.85, but the situation was more complicated at other three wavelengths, with the correlation coefficients ranging from 0.63 (440 nm) to 0.84 (870 nm) (Fig. 2g). The deviation between the two groups of data was larger at 440 nm. The aerosol properties at shorter wavelengths have much greater sensitivity due to high scattering properties (Eck et al. 1999). Therefore, the larger deviation at 440 nm may be due to the greater sensitivity at shorter wavelengths. The mean REFR values were the same at each wavelength between the two sites.

Figure 2h shows that the scatter of REFI was relatively concentrated compared with the scatter of REFR. When REFIs were larger than 0.01, the linear fitting curves were lower than the y = x line, at all four wavelengths. The correlation coefficients between the REFI at 440, 675, 870 and 1020 nm were 0.89, 0.84, 0.82 and 0.85, with slopes of 0.86, 0.82, 0.82 and 0.85, respectively. The mean REFI values were 0.01 for four wavelengths at both IAP and CAMS.

Similar to REFI, the SSA patterns were scattered with correlation coefficients ranging from 0.81 to 0.89 in the linear fit scatter plots (Fig. 2i). SSA is related to refractive index. The scatter patterns of REFR, REFI and SSA shows that the chemical components of the aerosol particles were different, suggesting that the impact of local emissions cannot be ignored. The mean SSA values were same at 440, 870 and 1020 nm, while a subtle difference of 1.08% was found at 675 nm.

Figure 2j shows that correlation coefficients of ASYM are larger than 0.84. However, the linear fit pattern of ASYM at 440 nm is scattered, with a correlation coefficient and slope of 0.84 and 0.81, respectively. For symmetrical scattering, Rayleigh scattering, the ASYM is considered to be 0, and for a purely forward scattering aerosol the value is taken as 1. For a cloudless atmosphere, the ASYM ranges from 0.1 in very clean conditions to 0.75 in polluted conditions (Zege et al. 1991). In the present study, the mean ASYMs ranged from 0.63 ± 0.03 (1020 nm) to 0.71 ± 0.03 (440 nm) and from 0.63 ± 0.04 (1020 nm) to 0.71 ± 0.03 (440 nm) at IAP and CAMS, respectively. The differences between the two sites were less than 2%, at four wavelengths.

Figure 3 shows the mean aerosol volume size distributions with standard deviation at IAP and CAMS. The mean values differed little between the two sites. One can see that the size distributions simultaneously showed a bimodal logarithm normal structure: fine mode with radius <0.6 μm and a coarse mode with radius >0.6 μm (Dubovik et al. 2002). As shown in Fig. 3, two peak volumes are at radii of 0.15 and 2.94 μm and with volume size spectra (dV/dln r) of 0.09 ± 0.05 and 0.12 ± 0.07 μm3/μm2 at IAP and 0.09 ± 0.04 and 0.11 ± 0.07 μm3/μm2 at CAMS. Thus, it can be inferred that the extinction at Beijing could be associated with fine mode aerosols and coarse mode aerosols. The volume concentration of coarse particles in Beijing was higher than those of Mexico, Paris, and Hangzhou, China, but the volume concentration of fine particles were similar (Dubovik et al. 2002; Qi et al. 2016). This means that there were more coarse aerosol loading in Beijing among these urban sites (Zhang et al. 2012). The mean effective radius of the fine mode was 0.17 ± 0.03 and 0.16 ± 0.03 μm at IAP and CAMS, respectively. The mean effective radius of the coarse mode was 2.25 ± 0.34 and 2.21 ± 0.30 μm, respectively. The differences in effective radius of the fine mode and coarse mode between the two sites were 6.25 and 1.81%, respectively. The larger difference in effective radius of the fine mode may be due to the different physical and chemical properties of fine mode particles at two closed sites.
Fig. 3

Mean aerosols volume size distributions with standard deviation at IAP and CAMS

3.2 Aerosol Volume Size Distributions with a Tri-Modal Pattern

Aerosol volume size distributions with a tri-modal pattern were picked out from 126 data pairs at the two sites, as shown in Fig. 4. There were two peaks at the radii of about 0.10–0.20 and 0.30–0.50 μm for the fine mode and one peak at a radius of about 1.0–4.0 μm for the coarse mode in January and April 2013 (Fig. 4a–c, e). According to Che et al. (2014), 10–16 January, 2013 was the most intense haze period and the relative humidity was more than 60%. This phenomenon that occurred in winter and spring probably reflected the hygroscopic characteristics of fine particles (Che et al. 2014). According to the results reported by Zhang et al. (2013a), the mean measured non-refractory submicron particle mass concentration at IAP is composed of organics (49.8%), sulphate (21.4%), nitrate (14.6%), ammonium (10.4%), and chloride (3.8%) in January 2013. These hygroscopic compositions could be one of the major reasons for the tri-modal size distribution. Another possible reason of the bi-mode submicron size distribution could be associated with the haze-fog and cloud processing (Li et al. 2011; Eck et al. 2012). Because the distance between IAP and CAMS is less than 6 km, CAMS had a similar tri-modal size distribution. And Che et al. (2014) also found the similar tri-modal pattern at CAMS during the same period. However, there were one peak at a radius of about 0.10–0.15 μm for the fine mode and two peaks at radii of about 1.30–1.80 and 3.80–3.90 μm for the coarse mode in April and May 2013 (Fig. 4d, f). On 22 May 2013, visibility was good, wind speed was low, and there was no haze. However, the CWV was 1.84 cm at IAP and 2.09 cm at CAMS at the moment featured in Fig. 4d and f. Therefore, the phenomenon of one peak for the fine mode and two peaks for the coarse mode was associated with the coagulation and hygroscopic growth of aerosol particles.
Fig. 4

Aerosol volume size distributions with a tri-modal pattern at IAP and CAMS

3.3 A case of Instantaneous EAOD440 nm Differing by More than 0.2 Between the Two Sites

A case (on 23 June 2013) in which the EAODs at 440 nm differed by more than 0.2 for all the instantaneous data between the two sites (|EAODIAP − EAODCAMS| >0.2), including the corresponding CWV and EAE values, is analyzed. Figure 5a shows the daily averaged EAOD440 nm, CWV and EAE at two sites for the day. And the instantaneous data of the three parameters are also displayed in Fig. 5b, c and d, respectively. The daily averaged values are 0.84 and 0.67 for EAOD440 nm, 2.68 and 3.00 for CWV, and 1.35 and 1.60 for EAE at IAP and CAMS, respectively. This indicates that the daily averaged EAOD440 nm of IAP was larger than that of CAMS and the aerosol particles at IAP were larger than those at CAMS. This could have been associated with the fact that IAP is located at the side of a road, and the different chemical composition of aerosols (Zhang et al. 2013a). The surface-observed data and weather conditions on 23 June 2013 indicate that visibility was good, wind speed was low and there was no haze.
Fig. 5

Daily mean EAOD440 nm, CWV and EAE and all simultaneous instantaneous data for the three parameters on 23 June 2013 at IAP and CAMS

3.4 Aerosol Characterization Inferred by Absorption Properties for All Inversions

EAE is a good indicator of aerosol particle size, while AAE is an intrinsic property closely related to aerosol chemical components (Giles et al. 2012). The fine mode fraction of EAOD [η = EAODf/(EAODf + EAODc)] is also an aerosol size proxy. According to Giles et al. (2011, 2012), the relationship of aerosol absorption and size information [e.g., EAE or η 675 nm (η at 675 nm)] could be used to categorize the dominant aerosol type or optical mixture. Therefore, both the relationships of instantaneous AAE vs. EEA (hereafter defined as “AAE/EAE”) and AAE vs. η 675 nm (hereafter defined as “AAE/η 675 nm”) were analyzed with AERONET Version 2, Level 1.5 EAOD and almucantar retrievals for IAP and CAMS in Fig. 6. The green ellipses denote commonly used aerosol classifications: dust, mixed, and U/I&BB (urban/industrial and biomass-burning). The values of AAE, EAE, and η 675 nm for different aerosol types were selected based on the literature (Giles et al. 2011, 2012) and characteristics of the stations. For IAP and CAMS, the AAE/EAE and AAE/η 675 nm relationships show a nonlinear dependence over the aerosol size ranges, whereas the sphericity fraction has a strong transition from non-spherical to spherical particles around EAE of ~1.3 or η 675 nm of ~0.66. The “Mostly Dust” category is characterized by EAE ≤ 0.5 (η 675 nm ≤0.33), sphericity fraction <0.2 and AAE >2.0. The EAE > 0.8 (η 675 nm >0.66), and sphericity fraction ≥0.2 associated with 1.0 < AAE ≤ 2.0 represents the “U/I&BB” aerosol type. The “Mixed” category is classified as 0.5 < EAE < 0.8 (0.33 < η 675 nm < 0.66) and 1.0 < AAE ≤ 2.0. For the AERONET data, AAE <1.0 may be related to black carbon coating by coarse particles such as dust and organic carbon (Dubovik et al. 2000; Eck et al. 2010). At IAP (Fig. 6a) and CAMS (Fig. 6b), the main aerosol type is “U/I&BB”, accounting for 59.87 and 57.43%, respectively; and the “Mostly Dust” category accounting for 2.12 and 1.56%, respectively. Such a large number of fine mode aerosol loading is associated with cooking emission, traffic emissions, sporadic coal combustion emissions, nearby industrial emissions, and biomass-burning (Sun et al. 2016; Zhang et al. 2013b).
Fig. 6

The absorption Ångström exponent (AAE) and sphericity fraction as a function of extinction Ångström exponent (EAE) and fine mode fraction of EAOD at 675 nm (η 675 nm; from the almucantar inversions) using AERONET version 2, Level 1.5 data of IAP and CAMS. AAE is plotted from 0.0 to 3.5 (red) and sphericity fraction is plotted from 0.0 to 1.0 (blue). The green ellipses represent probable aerosol mixture categories. AAE of 1.0 indicates λ−1 dependence, and a sphericity fraction of 1.0 indicates a 100% spherical particle (color figure online)

The volume size distribution retrievals were binned based on AAE in Fig. 7. The size distributions presented bi-modal patterns for both IAP and CAMS: fine mode (a peak between 0.1 and 0.2 μm) and coarse mode (a peak between 2 and 4 μm). As AAE increased to 2.0, coarse mode particles became dominant.
Fig. 7

Aerosol volume size distribution by AAE bins for IAP and CAMS. Averages in which n < 25 were removed from the plots (n represents the sample size)

The SSA retrievals binned by AAE were depicted in Fig. 8. And the two urban closed sites showed the similar variation trends. SSA transitioned from spectra representing dust (i.e., strong absorption in the blue wavelength region and relatively weak absorption in the near-infrared) to U/I&BB pollution (i.e., stronger absorption in longer wavelengths); the interpretation of these SSA spectra is consistent with results reported by Giles et al. (2011), Prasad and Singh (2007) and Derimian et al. (2008). However, as AAEs decrease to 1.25, the mean SSA values at IAP were less than those at CAMS for all wavelengths. AAE ~1.0 is often associated with black carbon, either with or without coatings (Gyawali et al. 2009). It suggests that there were more black carbon aerosols at IAP which is a roadside site (Song et al. 2013; Yan et al. 2014). Figure 9 demonstrated mean SSA for all the retrievals and SSA data were further partitioned based on the EAE intervals of 0.0–0.8 and 0.8–2.0 and η 675 nm intervals of 0.0–0.33, 0.33–0.66, and 0.66–1.0 at two sites. Figure 10 showed the mean AAOD, mean EAODf and mean EAODc for all retrievals at two sites. Strong absorption was noted at 440 nm relative to longer wavelengths for all SSA retrievals (Fig. 9a), SSA with EAE >0.8 (Fig. 9c), and SSA with η > 0.66 (Fig. 9f), indicating aerosols were dominated by fine mode particles both at IAP and CAMS. However, the SSA values at IAP were lower than those at CAMS for the cases of the above, suggesting there are more absorbing aerosol particles at IAP (Song et al. 2013; Yan et al. 2014). The lower SSA values at IAP were consistent with the higher AAODs and higher EAODfs (Fig. 10). Strong absorption at 440 nm and strong scattering at longer wavelengths for SSA with AAE ≤0.8 (Fig. 9b) and SSA with η < 0.33 (Fig. 9d) represented large dust particles (Giles et al. 2011). And the higher SSA values at IAP except at 440 nm were consistent with the higher EAODcs (Fig. 10). This may be associated with the dust absorbing particles and mixed particles.
Fig. 8

SSA averaged by AAE bins for IAP and CAMS. Averages in which n < 25 were removed from the plots

Fig. 9

Mean SSA and SSA data were partitioned based on EAE and η 675 nm using IAP and CAMS AERONET. a Mean SSA for all the SSA retrievals at two sites. b The case for large particle-dominated conditions (i.e., EAE is ≤0.8); c the case for small particle-dominated conditions (i.e., EAE is >0.8); d mainly coarse mode particles (η 675 nm ≤ 0.33); e mixed size particles (0.33 < η 675 nm ≤ 0.66); and f mainly fine mode particles (η 675 nm > 0.66)

Fig. 10

a Mean AAOD for all retrievals, b mean EAODf for all retrievals, c mean EAODc for retrievals at two sites

4 Conclusions

The aerosol optical properties were compared and discussed at two neighboring urban sites of Beijing between January 2013 and July 2015, including optical parameters for simultaneous observations and absorption properties for all the inversions. The study yielded the following conclusions:
  1. (1)

    High correlations were found, with correlation coefficients larger than or equal to 0.98, for the extinction aerosol optical depth, extinction Ångström exponent, columnar water vapor; correlation coefficients were smaller than or equal to 0.90 for absorption aerosol optical depth, real parts of the refractive index, imaginary parts of the refractive index, and single scattering albedo. The percentage differences were almost less than 2.0%, but greater than 4.0% for extinction aerosol optical depth at 675 nm and extinction aerosol optical depth of fine particles at four wavelengths. The mean volume size distributions simultaneously showed bi-modal patterns, with two peaks at radii of 0.15 and 2.94 μm and with volume size spectra (dV/dln r) of 0.09 ± 0.05 and 0.12 ± 0.07 at IAP and 0.09 ± 0.04 and 0.11 ± 0.07 at CAMS.

     
  2. (2)

    Cases of two kinds of tri-peak model were analyzed during the study period: one with two peaks at radii of about 0.10–0.20 and 0.30–0.50 μm for the fine mode and one peak at a radius of about 1.0–4.0 μm for the coarse mode in January and April 2013, possibly reflecting the hygroscopic characteristics of fine particles; and another with one peak at a radius of about 0.10–0.15 μm for the fine mode and two peaks at radii of about 1.30–1.80 and 3.80–3.90 μm for the coarse mode in April and May 2013, probably due to coagulation and hygroscopic growth of aerosol particles. A case when extinction aerosol optical depths at 440 nm differed by more than 0.20 for simultaneously instantaneous data between the two sites reflected the effect of local aerosol pollution.

     
  3. (3)

    Aerosol absorption and size relationships showed a nonlinear dependence over the aerosol size ranges and allowed for determination of dominant absorbing aerosol types (urban/industrial and biomass-burning). The main aerosol types are urban/industrial and biomass-burning, accounting for nearly 60% at two sites. But the “Mostly Dust” category accounted for about 2% at two sites. These relationships along with mean single scattering albedo spectra were used to categorize black carbon and dust as dominant absorbers and identify a third category where both black carbon and dust dominate absorption. As absorption Ångström exponents increased to 2.0, coarse mode particles became dominant. Further, single scattering albedo transitioned from spectra representing dust to urban/industrial and biomass-burning. As absorption Ångström exponents decrease to 1.25, the mean single scattering albedo values at Institute of Atmospheric Physics were less than those at Chinese Academy of Meteorological Sciences for all wavelengths. This could be related to the fact that Institute of Atmospheric Physics is a roadside site and with more black carbon aerosols. Strong absorptions were noted at 440 nm relative to longer wavelengths and higher values occurred at Institute of Atmospheric Physics for mean single scattering albedo and single scattering albedo with extinction Ångström exponent >0.8, and single scattering albedo with the fine mode fraction of extinction aerosol optical depth >0.66, indicating aerosols were dominated by fine mode particles at two sites and Institute of Atmospheric Physics had more absorbing particles. Strong absorption at 440 nm and strong scattering at longer wavelengths for single scattering albedo with absorption Ångström exponents ≤0.8 and single scattering albedo with the fine mode fraction of extinction aerosol optical depth <0.33 represented large dust particles. And the higher single scattering albedo values at Institute of Atmospheric Physics except at 440 nm may be associated with the dust absorbing particles and mixed particles.

     

Notes

Acknowledgements

This work was financially supported by the National Key R&D Program (2016YFA0601900), the NSFC Project of Nos. 41375153 and 41590874, CAMS Basic Research Project (2014R17), the Climate Change Special Fund of the CMA (CCSF201504), and Jiangsu Collaborative Innovation Center of Climate Change.

Author Contributions

Jie Yu and Huizheng Che designed and conceived the study. Hongbin Chen, Philippe Goloub, Quanliang Chen, Ke Gui, and Yu Zheng analyzed data. Hong Wang, Yaqiang Wang, Linchang An, Tianze Sun and Xiaoye Zhang significantly contributed to the manuscript’s revision. Jie Yu analyzed the data and wrote the paper.

Compliance with Ethical Standards

Conflicts of Interest

The authors declare no conflict of interest.

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Copyright information

© Institute of Earth Environment, Chinese Academy Sciences 2017

Authors and Affiliations

  • Jie Yu
    • 1
  • Huizheng Che
    • 1
  • Quanliang Chen
    • 2
  • Hongbin Chen
    • 3
  • Philippe Goloub
    • 4
  • Ke Gui
    • 1
    • 2
  • Yu Zheng
    • 5
  • Hong Wang
    • 1
  • Yaqiang Wang
    • 1
  • Linchang An
    • 6
  • Tianze Sun
    • 1
  • Xiaoye Zhang
    • 1
  • Renjian Zhang
    • 7
  • Mingkai Dai
    • 8
  1. 1.State Key Laboratory of Severe Weather (LASW) and Institute of Atmospheric CompositionChinese Academy of Meteorological Sciences CMABeijingChina
  2. 2.Plateau Atmospheric and Environment Key Laboratory of Sichuan Province, College of Atmospheric SciencesChengdu University of Information TechnologyChengduChina
  3. 3.Laboratory for Middle Atmosphere and Global Environment Observation, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  4. 4.Laboratoire d’Optique AmosphériqueUniversité des Sciences et Technologies de LilleVilleneuve d’AscqFrance
  5. 5.Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological AdministrationNanjing University of Information Science and TechnologyNanjingChina
  6. 6.National Meteorological Center, CMABeijingChina
  7. 7.Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  8. 8.Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric SciencesLanzhou UniversityLanzhouChina

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