Abstract

Aerosol complex refractive index (ACRI) has attracted intensive attentions due to its significance in modeling aerosol radiative effects. Determinations of ACRI from surface measurements of aerosol scattering and absorption coefficients as well as number size distributions during June, 2008 based on an iterative Mie algorithm were performed. The aim of our study was to introduce an inversion approach with the merits of high time-resolutions to retrieve the optically effective ACRI, especially its imaginary part. Based on simultaneous measurements of aerosol characteristics, mean ACRI value of 1.50 ( ± 0.34)–i0.025 ( ± 0.015) at 550 nm in Hefei in summer was deducted. The lower imaginary parts with higher single scattering albedos and lower scattering Angstrom exponents were obtained for haze periods compared with nonhaze conditions with similar air-mass back-trajectories, indicating more large and scattering particles contributing to the formation of haze episodes. The derived imaginary parts of ACRI related to agricultural biomass burning were in the range from 0.013 to 0.029 at 550 nm. Significant negative correlations between retrieved imaginary parts of ACRI and measured single scattering albedos indicate that our retrieval approach is a reasonable method for determining the imaginary parts of complex refractive indices of aerosol particles.

©2013 Optical Society of America

1. Introduction

Aerosol particles, emitted from various natural or anthropogenic sources [1,2], are ubiquitous in the atmosphere of the Earth, and affect the human health [3] and climate system [4]. Atmospheric aerosols influence the earth’s radiation budget directly by scattering and absorbing solar radiation [5,6], and indirectly by acting as condensation nuclei in cloud formation, thus affecting the radiative properties and lifetimes of clouds [7]. The largest uncertainty in estimating the effects of aerosols on climate stems from uncertainties in the determination of their microphysical properties, including the aerosol complex refractive index (ACRI), which in turn determines their optical and radiative properties.

The aerosol complex refractive index, which should be known for modeling the radiative effects of aerosols, is a crucial parameter governing both scattering and absorption properties. The magnitude of radiative forcing is very sensitive to ACRI and, in particular, the ACRI of biomass burning aerosols plays an important role in the radiative forcing at the top of atmosphere (TOA) because of its determination of the amount of absorption. The imaginary part of ACRI is essential to determine aerosol absorption properties, and the dependency of radiative forcing on the imaginary part is even more pronounced than the dependency on real part [8]. Liao and Seinfeld [9] introduced that for pure dust TOA net forcing was more sensitive to the variation in the imaginary part than surface cooling. Accurate determination of ACRI, especially the imaginary part, is therefore crucial for deducing optical properties and then the aerosol effects on climate.

The determination of ACRI is often provided from bulk chemical compositions and known values of the refractive indices of pure components with the application of volume mixing rules [10,11]. The choice of this traditional approach is driven by the high dependency of ACRI on the chemical compositions of aerosol particulates. Marley et al. [12] described preliminary results of ACRI for carbon soot samples generated in the laboratory and for standard diesel soot samples in the UV/visible spectral range. Aerosol models implemented in regional or global climate model tend to predict more and more details of the aerosol microphysics (size distribution, chemical composition, and mixing state), from which the aerosol optical and radiative properties can be calculated. Such a modeling approach requires new techniques to estimate an aerosol refractive index from observations, which could serve as a test for the models. Since determining the refractive index of each individual particle within an aerosol population is not feasible and provides wrong results, an equivalent or effective ACRI is normally used to represent the whole size distribution [13,14].

Several studies focused on the determination of the optically derived ACRI besides the chemically deduced ACRI. Petzold et al. [15] presented the retrieval technique for ACRI based on airborne in situ measurements of the particle size distributions and aerosol absorption coefficients by a three-wavelength Particle Soot Absorption Photometer (PSAP) combined with a Mie theory based data analysis scheme. Dubovik et al. [16,17] performed the application of a spheroidal dust model to deduce ACRI from Sun photometer observations with AERONET (Aerosol Robotic Network). Another inversion scheme based on Mie scattering computations using ground-based records of the aerosol number size distributions and absorption coefficients by the Spectral Optical Absorption Photometer (SOAP) was applied by Muller et al. [18]. Raut and Chazette [8,19] developed the methodology of two constrains for enabling both real and imaginary parts based on a Mie model with the size distribution utilizing a synergy between lidar, sunphotometer and in situ measurements. The two constrains of this method were either the extinction coefficient and backscatter-to-extinction ratio, or the scattering coefficient and single scattering albedo. The approach relying on simultaneous measurements of size distributions, scattering and absorption coefficients and the applicability of Mie theory with the assumption of spherical particles and chemical homogeneity of aerosol samples was also utilized to deduce the ACRI [2023]. However, the derived ACRI from different techniques only showed partly a reasonable agreement with distinct differences for the spectra of imaginary part remaining, according to the recent comparison by Muller et al. [24]. Despite numerous studies on the ACRI retrievals, the details of the derived imaginary part of ACRI based on the iterative Mie algorithm and combined measurements of aerosol characteristics still need more investigation.

In this paper, determination of ACRI from surface measurements of aerosol microphysical and optical properties based on an iterative Mie algorithm is carried out. A Scanning Mobility Particle Sizer, an aerodynamic particle sizer spectrometer, an integrating nephelometer and an aethalometer were used for the measurement of aerosol microphysical and optical properties during June 2008. The objective is to introduce an inversion approach with the merits of high time-resolutions to determine the optically effective ACRI, especially its imaginary part, which is of significant importance in modeling aerosol radiative effects. In Section 2 the methods including experiments, instruments and retrieval approach are described. Section 3 gives the results and discussions of the effective ACRI obtained by the retrieval method. The retrieved ACRI as well as aerosol optical and radiative properties with the air mass from different source regions over a medium city (Hefei) in central China in summer were also presented in Section 3. The findings are briefly summarized in Section 4.

2. Methods

2.1 Field experiments and instrumentations

Field experiments were scheduled between 16 and 25 June, 2008 and were done in Hefei (39.90° N, 117.19° E), the capital city of Anhui province with the climate of continental monsoon. Hefei is an inland city of China and had a population of 1.5 million in 2005. The measured aerosol data for six days including 16–20 June and 24 June, 2008 were utilized for the retrieval of ACRI. The instruments for this field work were located inside a construction trailer with inlet ports positioned through the roof. The experiment setup was designed to maximize counting statistics based on minimizing particle losses and measurement biases, with isokinetic sampling techniques and minimal bends in the tubing. A brief description of the instruments follows.

The number size distribution of aerosol particles was measured with a Scanning Mobility Particle Sizer (SMPS, TSI model 3936, mobility diameter range from 0.014 to 0.82 μm) and an Aerodynamic Particle Sizer (APS, TSI model 3321, aerodynamic diameter range from 0.523 to 19.81 μm). The aerodynamic diameter from APS was converted to the geometric diameter on the basis of the method described by Hand and Kreidenweis [25]. A complete particle size distribution might be determined in a matter of seconds or minutes based on the combination of SMPS and APS, making them a good choice for a broad range of applications [26,27]. The SMPS is composed of an electrostatic classifier (model 3080), a condensation particle counter (CPC, model 3022A), and a long differential mobile analyzer (LDMA) [28]. The concentration range of SMPS is from 2 to 108 cm−3 and the measurement cycle time of 5 min was selected in our experiments. The APS measured aerosol size distribution by determining the time-of-flight of individual particles in an accelerating flow field, which was calibrated by polystyrene gum elastic. Aerosol number size distribution of APS was measured in 51 size channels and measurement accuracy of number concentration was ± 10%. The APS sampling period was also conducted at 5-min intervals for measuring aerosol samples.

The aerosol scattering coefficients were measured by an integrating nephelometer at wavelengths of 450, 550 and 700 nm (TSI, model 3563). Calibration of the nephelometer was carried out by using CO2 as high span gas and filtered air as low span gas. This instrument drew the ambient air through a temperature-controlled inlet at flow rate of 30 L/min and the average sampling time ranged from 1 to 4096 s (300 s in our experiments). The drift was less than 2.0 × 10−7 m−1 at 30 s and the response time was less than 10 s. Calibrations were carried out with filtered particle-free air as the low span gas and CO2 as the high span gas. Zero baseline checks were performed at least once a week. The truncation errors, due to the geometrical blockage of scattered light for angle <7° and >170°, was corrected following the method described by Anderson and Ogren [29].

Aerosol absorption coefficients were obtained on a 5 min base at seven different wavelengths (370, 470, 520, 590, 660, 880 and 950 nm) using a Magee Scientific Aethalometer (model AE 31). The aethalometer yielded a change in optical attenuation by measuring the intensity of the light beam passing through a tare and loaded with particle filter tape. This specific cross-section is established for material trapped on the filter, which is not valid for particles in the ambient atmosphere, and the artifacts were corrected based on the calibrations described in Randriamiarisoa et al. [30]. This instrument was operating at a flow rate of 5 L/min in an automated mode, under which the filter tape advanced when the attenuation at 370 nm reached 75. The 880 nm channel is considered as the standard channel for BC measurement because BC is the principle absorber of light at this wavelength and other known aerosol components have negligible absorption at this wavelength. To be consistent with the nephelometer wavelengths, the absorption values at 450 nm were estimated from the values at 470 nm, and those at 550 nm were converted from measured values at 520 nm and 590 nm.

2.2 Back trajectories over Hefei

In order to establish the connections between aerosol microphysical and optical properties including complex refractive indices and synoptic air masses over Hefei, the 3-day back trajectories were analyzed using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT_4.9, version January 2010) model (http://ready.arl.noaa.gov/HYSPLIT.php). The meteorological data for the calculation of the trajectories came from the GDAS archive maintained by the Aerological Research Laboratory (ARL) (available at http://ready.arl.noaa.gov/gdas1.php). The trajectories were selected at three different height levels, namely, 10, 100, and 500 m above ground level. Trajectories described the visualization of not just the local air direction, but the transport over a continental scale.

2.3 Retrieval approach of aerosol complex refractive index

In our approach, the optically equivalent ACRI is defined as an ACRI that provided almost the same calculated scattering and absorption properties as those measured, on the basis of in situ size distribution for homogeneous, spherical particles. The derived effective ACRI was determined by an iterative Mie algorithm, a procedure using Mie codes iteratively, utilizing the simultaneous measurements of number size distributions, scattering and absorption coefficients. Exploiting all measurements, the designed inversion scheme to retrieve the wavelength dependent complex refractive index of the aerosol particulates was as follows. Based on a guess for the real, n, and imaginary part, k, of the ACRI at a given wavelength, two look-up tables were built from the database using the measured number size distribution from the integration of SMPS and APS. One look-up table contained the values of the scattering coefficient and the other encompassed the absorption coefficient values. In the calculation, the real part of refractive index varied from 1.1 to 2.5 with an equidistant space of 0.001 and the imaginary part changed from 10−6 to 1 with a logarithmic interval of 0.005. Then, the combined mean square deviations between the measured quantities from nephelometer and aethalometer, σsca,measured and σabs,measured, and the calculated values were computed with the formula shown in Eq. (1),

χ2(n,k)=1Ni=1N\[(σsca,calculated(n,k)σsca,measuredσsca,measured)2i+(σabs,calculated(n,k)σabs,measuredσabs,measured)2i]
where χ2(n,k) produced the fractional difference of the calculated scattering and absorption coefficients relative to the measured values, N was the number of measurements used in the retrieval. Equation (1) held for each wavelength step of the scattering and absorption measurements. Theχ2(n,k) value for scattering plus absorption was minimized by optimizing the initial guess values of the complex refractive index, yielding the effective complex refractive index at each wavelength. The absorption spectrum is mainly determined by the magnitude of k while the scattering property is primarily governed by the magnitude of n. Hence the minimization of χ2(n,k) value should deduce a unique retrieval result for both n and k.

4. Results and Discussion

4.1 Temporal variations of in situ measured aerosol characteristics

Aerosol scattering and absorption coefficients as well as number size distribution were measured during June 2008 for ACRI on the basis of the retrieval algorithm discussed before. Figure 1 shows the temporal variations of the scattering coefficients and absorption coefficients of atmospheric aerosols collected during this experiment for wavelengths of 450, 550 and 700 nm. Between 450 and 700 nm, both scattering and absorption coefficients decreased gradually with the increasing of wavelength. For the wavelength of 550 nm, scattering coefficients and absorption coefficients ranged from 133 to 623 Mm−1, 28 to 107 Mm−1 (10th and 90th percentiles), respectively. The mean values of scattering and absorption coefficients at 550 nm were 314 ± 192 and 66 ± 30 Mm−1, respectively. The scattering coefficients in Hefei were similar to those measured in Quanzhou [31], which is also a medium city but in southeast China. The absorption coefficients over Hefei, however, were lower than those in Quanzhou, indicating relatively weaker pollutions emitted in Hefei.

 figure: Fig. 1

Fig. 1 Temporal variations of the measured scattering coefficients (a) and absorption coefficients (b) of atmospheric aerosols. The black solid squares, red open circles and blue dot-center triangles denote the wavelengths of 450, 550 and 700 nm, respectively.

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To examine the variation of number concentration as a function of particle size, Fig. 2 illustrates the particle number size distributions obtained from the connection of SMPS and APS in terms of mean, median, 10th, 25th, 75th and 90th percentile values. The number concentration plot (Fig. 2) was not normalized and reflected absolute differences in particle concentrations that were observed. This emphasized the very high concentration of accumulation particles (0.1–1 μm) and nucleation particulates (less than 0.1 μm) associated with fine fraction. The mean number size distribution (Fig. 2a) provides a trimodal distribution with modes around 30, 100 and 650 nm. It is anticipated that these temporal variations of the aerosol scattering coefficients, absorption coefficients and size distributions will manifest themselves in the time series of the complex refractive indices.

 figure: Fig. 2

Fig. 2 The aerosol number size distributions in terms of mean (a), median, 10th, 25th, 75th and 90th percentile (b) values, and the mean volume size distribution (c) measured from the combination of SMPS and APS during the whole sampling period.

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4.2. Retrieved complex refractive indices

The temporal variations of retrieved ACRI based on simultaneous measurements of aerosol size distributions, scattering and absorption coefficients are shown in Fig. 3. Generally speaking, a large variability from 1.1 to 2.5 for real part and a great variation up to 2 orders of magnitude for imaginary part were observed during the summer experiment. The real part of derived ACRI generally decreased with increasing wavelength as opposed to the increase of imaginary part with wavelength increasing. These phenomena can be explained by the physical mechanism of interaction between light and aerosols. The derived real parts of ACRI fluctuated wildly, especially for 450 nm, whereas the imaginary part performed consecutively. The mean values of retrieved real parts were 1.87 ± 0.36, 1.50 ± 0.34 and 1.33 ± 0.15 for wavelengths of 450, 550 and 700 nm, respectively. For the imaginary part, mean values of 0.021 ± 0.014, 0.025 ± 0.015 and 0.027 ± 0.017 were observed at 450, 550 and 700 nm, respectively. Dubovik et al. [16] reported the real part values of 1.39 for aerosol particles over the east coast of America, 1.4 for Creteil in France, 1.44 for Maldives, and 1.47 for Mexico City based on AERONET sun photometer observations. The AERONET appears to infer real parts that are consistently too low. This is probably due to that the AERONET method for retrieving ACRI requiring relatively high aerosol loadings. The relative humidity may be another factor that influences the low derived real parts, due to the uptake of water vapor.

 figure: Fig. 3

Fig. 3 Temporal variations of the deducted real part (a) and imaginary part (b) of aerosol complex refractive indices. The black solid squares, red open circles and blue dot-center triangles indicate the wavelengths of 450, 550 and 700 nm, respectively. Yellow vertical bars for S1, S2, S3 and S4 denote different source regions.

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Values of ACRI determined in this study are coherent with those retrieved from different approaches by other authors. The mean ACRI value of 1.51( ± 0.02)–i0.017( ± 0.003) at 532 nm over Paris area in July, 2000, was noted by Raut and Chazette [19]. Ebert et al. [32] presented a mean ACRI close to 1.6–i0.04 at 532 nm for urban aerosols with high real parts predominantly caused by the high abundance of metal oxide/hydroxide particles and large imaginary parts due to high abundances of soot. Muller et al. [33] deduced a mean wavelength-independent ACRI of 1.56–0.009i based on multi-wavelength backscatter and extinction lidar measurements. The determined mean ACRI at 550 nm of our study is also similar to the mean ACRI value of 1.52( ± 0.01)–i0.015( ± 0.004) in Brazil obtained from AERONET radiometers [34].

4.3. Sensitivity studies

In this section, we will first analyze the uncertainties in retrieving ACRI and then do several sensitivity studies which were performed to determine the parameters resulting in the largest uncertainties in ACRI from this retrieval method.

The uncertainties on retrieved ACRI are mainly attributed to uncertainties on measured size distributions, scattering and absorption coefficients as well as the retrieval method. The values of derived ACRI from measurements are highly sensitive to uncertainties in measured size distributions [22]. As shown by Randriamiarisoa et al. [30], the determination of the equivalent refractive index is indeed driven by the accumulation mode, which is the most optically efficient. The counting efficiencies of APS have some limitations with the range from 85% to 99% for solid particles [35]. The uncertainty contribution of the volumetric diameter from SMPS is determined considering the Electrostatic Classifier. In fact, the uncertainty on the fine size is only due to the misclassification of the particles through the DMA column. A relative uncertainty for every channel of ± 0.95% is assumed as a consequence of the results of Mulholland et al. [36] for 100 nm spheres. The average diffusion and CPC counting efficiency uncertainties are estimated as ± 10% and ± 8%, respectively [37].

The relative uncertainty on the scattering coefficients depends on the statistical fluctuation of number size distribution and scattering coefficient measured by the nephelometer, and it has been found to be close to 10%. The maximum uncertainty in the measured absorption coefficient from the aethalometer was as high as 20%, with the higher percentage of error being applicable to BC lower concentrations [38,39]. Jacobson [40] reported that the coating of BC was also a plausible physical configuration based on transmission electron microscopy, and the absorption coefficients of the core treatment were greater than those of BC particles externally mixed from water particles. Another relevant issue is the geometrical shape of aerosol particles, which is not necessarily spherical as assumed in our study. The coarse dust is generally non spherical particle and the retrieval code for spheroid particles has been utilized to calculate aerosol microphysical and optical properties [41,42]. All the above uncertainties can affect the accuracy of the derived ACRI values calculated from the iteration Mie procedure.

The sensitivity studies were intended to simulate the possible sources of errors and to quantify the errors in the retrieved ACRI. The case studies were divided into cases with measured errors due to (1) scattering coefficients only, (2) absorption coefficients only, (3) size distributions only, (4) conjunct impacts of scattering and absorption coefficients, and (5) all three quantities. This method yielded the errors in retrieved ACRI within 12% for the real part and within 9% for the imaginary part, provided that the errors in measured scattering coefficients were less than 10% and random in nature (case 1). The errors in deduced ACRI were less than 5% for the real part and less than 35% for the imaginary part in the case of a random 20% error in the aethalometer measurement (case 2). For the large errors in the input aerosol data, the ACRI retrieved appears to represent the circumstance accordingly. For example, an underestimate of measured aerosol scattering by 10% translated into an underestimate of the real part of ACRI by about 0.07, while an overestimate of 20% in absorption contributed to an overestimate of the imaginary part by near 0.015. For the uncertainties in the size distribution measurements, a measured 10% error at random led to the maximum errors of 13% in the derived real part and 9% in the imaginary part (case 3). For a fixed size distribution (case 4), the random noises of 10% for scattering coefficients and 20% for absorption coefficients resulted in the retrieval ACRI errors within 17% for the real part and less than 48% for the imaginary part. If the random errors of 10% in aerosol scattering, 20% in absorption and 10% in size distribution measurements were put into this retrieval routine, the errors in derived ACRI within 48% for the real part and within 59% for the imaginary part were induced (case 5). The sensitivity studies indicate that the uncertainties in retrieved ACRI seem to more sensitive to the uncertainties in the absorption coefficient measurements. Despite the retrieval routine appearing to perform well from the sensitivity studies, it still should be tested with other techniques, such as the laboratory generated aerosols.

4.4. Effectiveness of imaginary part of ACRI

Correlation analysis is able to shed some light on the effectiveness of the retrieved aerosol complex refractive indices. Figure 4 shows the correlations between measured single scattering albedos, ω0, and retrieved aerosol complex refractive indices. A poor correlation was found with a relatively large amount of variability in real part of derived ACRI compared to the variability in single scattering albedo (Figs. 4(a), 4(c) and 4(e)). Some extreme real parts were derived, and this may be due to the retrieved ACRI are called optically effective ACRI which is probably not the actual ACRI. Virkkula et al. [43] also reported some unreasonable low values (real part less than 1.3) and gave the possible explanations, such as inaccuracies in the retrieval method, non-analyzed biogenic particles, or non-spherical particles.

 figure: Fig. 4

Fig. 4 Correlation plots of single scattering albedo versus real part (left three) of derived complex refractive indices, and single scattering albedo versus imaginary part (right three). The black solid squares, green solid circles and blue solid triangles denote the wavelengths of 450, 550 and 700 nm, respectively.

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Significant correlations were profound to be obtained between derived imaginary parts of ACRI and measured single scattering albedos, which were the flattering results shown in Figs. 4b), 4(d) and 4(f). The single scattering albedo displayed decreasing when derived imaginary part increased with the negative linear correlation coefficients of 0.787, 0.793 and 0.724 for wavelengths of 450, 550 and 700 nm respectively. Atmospheric aerosols show different compositions and mixture of organic and inorganic compounds from different source regions. Therefore the particle optical properties may be quite different, since the major factors controlling aerosol characteristics may be different aerosol compositions. The single scattering albedo, defined as the ratio of particle scattering to particle extinction which is the sum of scattering and absorption coefficients, is dominated by the aerosol compositions. For instance, pure BC particles typically have the single scattering albedo value of 0.2 [44]. Absorption in urban pollution is thought to be dominated by BC while the scattering is attributed to the sulfates or organic particles. Thus the total aerosols may have the ω0 at 550 nm ranging from 0.8 to 0.95 depending on the BC contents, and lower ω0 values indicates that the aerosols are more absorptive. The ACRI is also associated with the aerosol compositions and different particulate components have different values of ACRI, especially the imaginary part. The larger imaginary part of ACRI is associated with the higher absorption coefficient, which also indicates that the particles are more absorptive. The good negative correlations between deduced imaginary parts of ACRI and measured single scattering albedos may imply that the imaginary parts of retrieved ACRI are effective, or at least this retrieval approach is self-consistent. This suggests that this retrieval approach is a reasonable method for determining the imaginary part of aerosol complex refractive indices, which is generally less well known than the real part.

4.5. Transport patterns

In this section we will discuss the retrieved ACRI as well as some other aerosol optical properties affected by different aerosol transport patterns, including those under haze episodes and clear weather conditions for similar source regions.

Air mass transport pathways arriving at the sampling site were utilized to assess the impact of air mass histories and source regions on pollutions of atmospheric particles for different episodic cases. On the basis of 3-day air trajectories the aerosol data of this sampling period have been mainly separated into four cases labeled as S1, S2, S3 and S4. The details of these four typical main source regions, of which S1 and S2 cases were influenced by intense wild fires of biomass burning based on MODIS images of fire spots (not shown), are summarized in Table 1. The retrieved ACRI values grouped with respect to air mass origins are marked in Fig. 3. The air quality of the sampling area is undoubtedly affected by these air masses and the representative air trajectories are illustrated in Fig. 5 based on the HYSPLIT model. In order to characterize the ACRI under different weather conditions of pollution, we also grouped the measured sampling data into haze episodes and nonhaze periods, which was also listed in Table 1. The criteria of haze episodes are set as that the atmospheric horizontal visibility monitored by the Hefei station of Chinese Meteorological Bureau is not more than 5 km [45], since no fog events happened in this experiment. Under such conditions, the aerosol number concentrations are generally high especially for the fine particles [46].

Tables Icon

Table 1. Details of the four representative source regions.

 figure: Fig. 5

Fig. 5 Representative HYSPLIT backward trajectories for S1 (a), S2 (b), S3 (c), S4 (d) over Hefei. Red, Blue and green denote backward trajectories arriving at 10, 100 and 500 m heights above ground level, respectively.

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Table 2 summarizes some of the important parameters describing aerosol optical and radiative properties and retrieved values of ACRI in terms of mean, 10th and 90th percentile values. In general, higher scattering and absorption coefficients were measured on haze episodes compared to non-haze periods, indicating increased aerosol loading and heavier pollution on hazy days. Scattering Angstrom exponent, αs, was estimated from the nephelometer measurements at 450 nm and 700 nm using

αs=log[σs(λ1)/σs(λ2)]/log(λ1/λ2).
The lower mean αs values in haze periods compared with the non-haze aerosols with the air mass from similar source regions, indicate the larger particle size for the haze plume. Between the wavelengths of 450 and 700 nm, ω0 for all groups decreased gradually with the increase of wavelength, suggesting the scattering decreases faster than absorption of aerosol particles. The largest ω0 values were observed for haze groups of S1 and S2, followed by those for S1 and S2 nonhaze groups and nonhaze group S3 and S4 had the lowest ω0 values. The larger values of ω0 for S1 and S2 than those for S3 and S4 are attributed to the agricultural biomass burning with the organic mass by carbon’s contributions to aerosol particles. The observed ω0 values of S1 grouping were lower than those of S2 for both haze and nonhaze periods, indicating that the aerosols from South Korea and Japan may be more absorptive in comparison with particle pollutions from the medium and small-sized cities in China. The higher ω0 values as well as lower αs values were obtained for haze episodes, indicative of more large and scattering particles contributing to the formation of haze episodes, consistent with the previous findings described by Zhang et al. [45]. The ω0 values obtained from ground-based measurements are generally lower than the lidar retrievals, from which Noh et al. [47,48] presented the values of ω0 larger than 0.90 for smoke aerosols over East Asia. As noted by Dubovik et al. [34], the values of ω0 retrieved through the Aerosol Robotic Network (AERONET) for the northern hemisphere ranged from 0.85 to 0.95. The ω0 values in this study are also lower than AERONET retrievals, in agreement with the findings of Li et al. [49].

Tables Icon

Table 2. Summary of measured aerosol optical properties and retrieved complex refractive indicesa

The real parts of derived ACRI decreased with the wavelength increasing for all air mass types, in contrast to the imaginary parts. The derived ACRI showed dependence on the types of air mass according to the source regions. Since the imaginary part of retrieved ACRI seems to be reasonable, we only analyze the imaginary part for different transport patterns. With respect to the imaginary part of retrieved ACRI, its obtained mean values at 550 nm were 0.017, 0.013, 0.029, 0.017, 0.032 and 0.039 for haze groups of S1 and S2, nonhaze groups of S1, S2, S3 and S4, respectively. The air masses associated with the plumes of agricultural biomass burning (group S1 and S2) appeared to have low values of imaginary part, coherent with the high values of ω0. For the biomass burning, lower mean values of imaginary part related to haze episodes were observed than those of nonhaze periods, probably due to higher percentage of scattering particles. The retrieved imaginary parts of biomass burning plumes from our approach are also in accordance with the value of 0.021 reported by Dubovik et al. [34] in Africa savannah in Zambia. Haywood et al. [50] also reported the imaginary parts of 0.025 for fresh biomass-burning aerosols and 0.018 in aged biomass burning plumes at 550 nm. The derived imaginary parts for urban pollutions (S3 and S4 groups) are consistent with those assessed in Paris with the values of 0.034 at 355 nm and 0.040 at 532 nm [51].

5. Conclusion

The microphysical and optical properties of ambient aerosols in the boundary layer were measured during June, 2008 in an inland city of central China (Hefei) to retrieve complex refractive indices based on an iterative Mie algorithm.

According to the retrieval results, the mean value of ACRI retrieved from simultaneous measurements of aerosol size distributions, scattering and absorption coefficients was 1.50 ( ± 0.34)–i0.025 ( ± 0.015) at 550 nm over Hefei in summer. Significant negative correlations between derived imaginary parts of ACRI and measured single scattering albedos indicate that our retrieval approach is a reasonable method for determining the imaginary part of complex refractive indices of aerosol particles. The lower imaginary parts of derived ACRI along with higher ω0 values and lower αs values were observed for haze episodes in comparison with nonhaze periods with similar air trajectories, indicative of more large and scattering particles contributing to the haze formation. Mean values of retrieved imaginary part of ACRI associated with agricultural biomass burning ranged from 0.013 to 0.029 at 550 nm for distinct source regions. The imaginary part of ACRI, which is extensively less well known than the real part, retrieved from our plausible approach is an important parameter for the assessment of climate forcing of aerosols highlighted by Intergovernmental Panel on Climate Change (IPCC).

Acknowledgments

This work was supported by National Basic Research Program of China (973 Program) under Grant No. 2013CB955802 and National Natural Science Foundation of China (NSFC) under Grant No. 40905009. We thank Researcher Wei Heli for the illuminating discussions and suggestions of the manuscript. We also gratefully appreciate the supports from Center for Computational Science, CASHIPS.

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9. H. Liao and J. H. Seinfeld, “Radiative forcing by mineral dust aerosols: sensitivity to key variables,” J. Geophys. Res. 103(D24), 31637–31645 (1998). [CrossRef]  

10. P. Chazette and C. Liousse, “A case study of optical and chemical apportionment for urban aerosols in Thessaloniki,” Atmos. Environ. 35(14), 2497–2506 (2001). [CrossRef]  

11. K. Kandler, L. Schutz, C. Deutscher, M. Ebert, H. Hofmann, S. Jackel, R. Jaenicke, P. Knippertz, K. Lieke, A. Massling, A. Petzold, A. Schladitz, B. Weinzierl, A. Wiedensohler, S. Zorn, and S. Weinbruch, “Size distribution, mass concentration, chemical and mineralogical composition and derived optical parameters of the boundary layer aerosol at Tinfou, Morocco, during SAMUM 2006,” Tellus B Chem. Phys. Meterol. 61(1), 32–50 (2009). [CrossRef]  

12. N. A. Marley, J. S. Gaffney, J. C. Baird, C. A. Blazer, P. J. Drayton, and J. E. Frederick, “An empirical method for the determination of the complex refractive index of size-fractionated atmospheric aerosols for radiative transfer calculations,” Aerosol Sci. Technol. 34, 535–549 (2001).

13. J. Redemann, R. P. Turco, K. N. Liou, P. B. Russell, R. W. Bergstrom, B. Schmid, J. M. Livingston, P. V. Hobbs, W. S. Hartley, S. Ismail, R. A. Ferrare, and E. V. Browell, “Retrieving the vertical structure of the effective aerosol complex index of refraction from a combination of aerosol in situ and remote sensing measurements during TARFOX,” J. Geophys. Res. 105(D8), 9949–9970 (2000). [CrossRef]  

14. P. Guyon, O. Boucher, B. Graham, J. Beck, O. L. Mayol-Bracero, G. C. Roberts, W. Maenhaut, P. Artaxo, and M. O. Andreae, “Refractive index of aerosol particles over the Amazon tropical forest during LBA-EUSTACH 1999,” J. Aerosol Sci. 34(7), 883–907 (2003). [CrossRef]  

15. A. Petzold, K. Rasp, B. Weinzierl, M. Esselborn, T. Hamburger, A. Dornbrack, K. Kandler, L. Schutz, P. Knippertz, M. Fiebig, and A. Virkkula, “Saharan dust absorption and refractive index from aircraft-based observations during SAMUM 2006,” Tellus B Chem. Phys. Meterol. 61(1), 118–130 (2009). [CrossRef]  

16. O. Dubovik, B. N. Holben, T. Lapyonok, A. Sinyuk, M. I. Mishchenko, P. Yang, and I. Slutsker, “Non-spherical aerosol retrieval method employing light scattering by spheroids,” Geophys. Res. Lett. 29(10), 1415 (2002). [CrossRef]  

17. O. Dubovik, A. Sinyuk, T. Lapyonok, B. N. Holben, M. Mishchenko, P. Yang, T. F. Eck, H. Volten, O. Munoz, B. Veihelmann, W. J. van der Zande, J. F. Leon, M. Sorokin, and I. Slutsker, “Application of spheroid models to account for aerosol particle nonsphericity in remote sensing of desert dust,” J. Geophys. Res. 111(D11), D11208 (2006). [CrossRef]  

18. T. Müller, A. Schladitz, A. Massling, N. Kaaden, K. Kandler, and A. Wiedensohler, “Spectral absorption coefficients and imaginary parts of refractive indices of Saharan dust during SAMUM-1,” Tellus B Chem. Phys. Meterol. 61(1), 79–95 (2009). [CrossRef]  

19. J. C. Raut and P. Chazette, “Vertical profiles of urban aerosol complex refractive index in the frame of ESQUIF airborne measurements,” Atmos. Chem. Phys. 8(4), 901–919 (2008). [CrossRef]  

20. A. A. Riziq, C. Erlick, E. Dinar, and Y. Rudich, “Optical properties of absorbing and non-absorbing aerosols retrieved by cavity ring down (CRD) spectroscopy,” Atmos. Chem. Phys. 7(6), 1523–1536 (2007). [CrossRef]  

21. G. Schkolnik, D. Chand, A. Hoffer, M. O. Andreae, C. Erlick, E. Swietlicki, and Y. Rudich, “Constraining the density and complex refractive index of elemental and organic carbon in biomass burning aerosol using optical and chemical measurements,” Atmos. Environ. 41(5), 1107–1118 (2007). [CrossRef]  

22. L. A. Mack, E. J. T. Levin, S. M. Kreidenweis, D. Obrist, H. Moosmuller, K. A. Lewis, W. P. Arnott, G. R. McMeeking, A. P. Sullivan, C. E. Wold, W. M. Hao, J. L. Collett Jr, and W. C. Malm, “Optical closure experiments for biomass smoke aerosols,” Atmos. Chem. Phys. 10(18), 9017–9026 (2010). [CrossRef]  

23. M. Stock, Y. F. Cheng, W. Birmili, A. Massling, B. Wehner, T. Muller, S. Leinert, N. Kalivitis, N. Mihalopoulos, and A. Wiedensohler, “Hygroscopic properties of atmospheric aerosol particles over the Eastern Mediterranean: implications for regional direct radiative forcing under clean and polluted conditions,” Atmos. Chem. Phys. 11(9), 4251–4271 (2011). [CrossRef]  

24. D. Müller, B. Weinzierl, A. Petzold, K. Kandler, A. Ansmann, T. Muller, M. Tesche, V. Freudenthaler, M. Esselborn, B. Heese, D. Althausen, A. Schladitz, S. Otto, and P. Knippertz, “Mineral dust observed with AERONET Sun photometer, Raman lidar, and in situ instruments during SAMUM 2006: Shape-independent particle properties,” J. Geophys. Res. 115(D7), D07202 (2010). [CrossRef]  

25. J. L. Hand and S. M. Kreidenweis, “A new method for retrieving particle refractive index and effective density from aerosol size distribution data,” Aerosol Sci. Technol. 36(10), 1012–1026 (2002). [CrossRef]  

26. T. M. Peters and D. Leith, “Concentration measurement and counting efficiency of the aerodynamic particle sizer 3321,” J. Aerosol Sci. 34(5), 627–634 (2003). [CrossRef]  

27. J. Shi, R. M. Harrison, and D. Evans, “Comparison of ambient particle surface area measurement by epiphaniometer and SMPS/APS,” Atmos. Environ. 35(35), 6193–6200 (2001). [CrossRef]  

28. S. Shen, P. A. Jaques, Y. Zhu, M. D. Geller, and C. Sioutas, “Evaluation of the SMPS–APS system as a continuous monitor for measuring PM2.5, PM10 and coarse (PM2.5−10) concentrations,” Atmos. Environ. 36(24), 3939–3950 (2002). [CrossRef]  

29. T. L. Anderson and J. A. Ogren, “Determining aerosol radiative properties using the TSI 3563 integrating nephelometer,” Aerosol Sci. Technol. 29(1), 57–69 (1998). [CrossRef]  

30. H. Randriamiarisoa, P. Chazette, P. Couvert, J. Sanak, and G. Megie, “Relative humidity impact on aerosol parameters in a Paris suburban area,” Atmos. Chem. Phys. 6(5), 1389–1407 (2006). [CrossRef]  

31. X. Zhang, Y. Huang, and R. Rao, “Aerosol characteristics including fumigation effect under weak precipitation over the southeastern coast of China,” J. Atmos. Sol-Terr. Phy. 84–85, 25–36 (2012).

32. M. Ebert, S. Weinbruch, P. Hoffmann, and H. M. Ortner, “The chemical composition and complex refractive index of rural and urban influenced aerosols determined by individual particle analysis,” Atmos. Environ. 38(38), 6531–6545 (2004). [CrossRef]  

33. D. Müller, A. Ansmann, F. Wagner, K. Franke, and D. Althausen, “European pollution outbreaks during ACE 2: Microphysical particle properties and single-scattering albedo inferred from multiwavelength lidar observations,” J. Geophys. Res. 107(D15), 4248 (2002). [CrossRef]  

34. O. Dubovik, B. Holben, T. F. Eck, A. Smirnov, Y. J. Kaufman, M. D. King, D. Tanré, and I. Slutsker, “Variability of absorption and optical properties of key aerosol types observed in worldwide locations,” J. Atmos. Sci. 59(3), 590–608 (2002). [CrossRef]  

35. J. Volckens and T. M. Peters, “Counting and particle transmission efficiency of the aerodynamic particle sizer,” J. Aerosol Sci. 36(12), 1400–1408 (2005). [CrossRef]  

36. G. W. Mulholland, N. P. Bryner, and C. Croarkin, “Measurement of the 100 nm NIST SRM 1963 by Differential Mobility Analysis,” Aerosol Sci. Technol. 31(1), 39–55 (1999). [CrossRef]  

37. G. W. Mulholland, M. K. Donnelly, C. R. Hagwood, S. R. Kukuck, V. A. Hackley, and D. Y. H. Pui, “Measurement of 100 nm and 60 nm particle standards by Differential Mobility Analysis,” J. Res. Natl. Inst. Stand. Technol. 111(4), 257–312 (2006). [CrossRef]  

38. C. E. Corrigan, V. Ramanathan, and J. J. Schauer, “Impact of monsoon transition on the physical and optical properties of aerosols,” J. Geophys. Res. 111(D18), D18208 (2006). [CrossRef]  

39. K. K. Moorthy, S. S. Babu, and S. K. Satheesh, “Temporal heterogeneity in aerosol characteristics and the resulting radiative impact at a tropical coastal station—Part 1: Microphysical and optical properties,” Ann. Geophys. 25(11), 2293–2308 (2007). [CrossRef]  

40. M. J. Jacobson, “A physically-based treatment of elemental carbon optics: implications for global direct forcing of aerosols,” Geophys. Res. Lett. 27(2), 217–220 (2000). [CrossRef]  

41. I. Veselovskii, O. Dubovik, A. Kolgotin, T. Lapyonok, P. Di Girolamo, D. Summa, D. N. Whiteman, M. Mishchenko, and D. Tanre, “Application of randomly oriented spheroids for retrieval of dust particle parameters from multi-wavelength lidar measurements,” J. Geophys. Res. 115(D21), D21203 (2010). [CrossRef]  

42. A. Papayannis, R. E. Mamouri, V. Amiridis, E. Remoundaki, G. Tsaknakis, P. Kokkalis, I. Veselovskii, A. Kolgotin, A. Nenes, and C. Fountoukis, “Optical-microphysical properties of Saharan dust aerosols and composition relationship using a multi-wavelength Raman lidar, in situ sensors and modelling: a case study analysis,” Atmos. Chem. Phys. 12(9), 4011–4032 (2012). [CrossRef]  

43. A. Virkkula, I. K. Koponen, K. Teinila, R. Hillamo, V. M. Kerminen, and M. Kulmala, “Effective real refractive index of dry aerosols in the Antarctic boundary layer,” Geophys. Res. Lett. 33(6), L06805 (2006). [CrossRef]  

44. M. Schnaiter, H. Horvath, O. Mohler, K. H. Naumann, H. Saathoff, and O. W. Schock, “UV-VIS-NIR spectral optical properties of soot and soot-containing aerosols,” J. Aerosol Sci. 34(10), 1421–1444 (2003). [CrossRef]  

45. X. Zhang, Y. Huang, W. Zhu, and R. Rao, “Aerosol characteristics during summer haze episodes from different source regions over the coast city of North China Plain,” J. Quant. Spectrosc. Radiat. Transf. 122, 180–193 (2013). [CrossRef]  

46. Z. Z. Deng, C. S. Zhao, N. Ma, P. F. Liu, L. Ran, W. Y. Xu, J. Chen, Z. Liang, S. Liang, M. Y. Huang, X. C. Ma, Q. Zhang, J. N. Quan, P. Yan, S. Henning, K. Mildenberger, E. Sommerhage, M. Schafer, F. Stratmann, and A. Wiedensohler, “Size-resolved and bulk activation properties of aerosols in the North China plain,” Atmos. Chem. Phys. 11(8), 3835–3846 (2011). [CrossRef]  

47. Y. M. Noh, D. Muller, D. H. Shin, H. L. Lee, J. S. Jung, K. H. Lee, M. Cribb, Z. Li, and Y. J. Kim, “Optical and microphysical properties of severe haze and smoke aerosol measured by integrated remote sensing techniques in Gwangju, Korea,” Atmos. Environ. 43(4), 879–888 (2009). [CrossRef]  

48. Y. M. Noh, D. Muller, I. Mattis, H. Lee, and Y. J. Kim, “Vertically resolved light-absorption characteristics and the influence of relative humidity on particle properties: Multiwavelength Raman lidar observations of East Asian aerosol types over Korea,” J. Geophys. Res. 116(D6), D06206 (2011). [CrossRef]  

49. C. Li, L. T. Marufu, R. R. Dickerson, Z. Li, T. Wen, Y. Wang, P. Wang, H. Chen, and J. W. Stehr, “In situ measurements of trace gases and aerosol optical properties at a rural site in northern China during East Asian Study of Tropospheric Aerosols: An International Regional Experiment 2005,” J. Geophys. Res. 112(D22), D22S04 (2007). [CrossRef]  

50. J. M. Haywood, S. R. Osborne, P. N. Francis, A. Keil, P. Formenti, M. O. Andreae, and P. H. Kaye, “The mean physical and optical properties of regional haze dominated by biomass burning aerosol measured from the C-130 aircraft during SAFARI 2000,” J. Geophys. Res. 108, (2003). [CrossRef]  

51. J. C. Raut and P. Chazette, “Retrieval of aerosol complex refractive index from a synergy between lidar, sunphotometer and in situ measurements during LISAIR experiment,” Atmos. Chem. Phys. 7(11), 2797–2815 (2007). [CrossRef]  

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  16. O. Dubovik, B. N. Holben, T. Lapyonok, A. Sinyuk, M. I. Mishchenko, P. Yang, and I. Slutsker, “Non-spherical aerosol retrieval method employing light scattering by spheroids,” Geophys. Res. Lett. 29(10), 1415 (2002).
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  18. T. Müller, A. Schladitz, A. Massling, N. Kaaden, K. Kandler, and A. Wiedensohler, “Spectral absorption coefficients and imaginary parts of refractive indices of Saharan dust during SAMUM-1,” Tellus B Chem. Phys. Meterol. 61(1), 79–95 (2009).
    [Crossref]
  19. J. C. Raut and P. Chazette, “Vertical profiles of urban aerosol complex refractive index in the frame of ESQUIF airborne measurements,” Atmos. Chem. Phys. 8(4), 901–919 (2008).
    [Crossref]
  20. A. A. Riziq, C. Erlick, E. Dinar, and Y. Rudich, “Optical properties of absorbing and non-absorbing aerosols retrieved by cavity ring down (CRD) spectroscopy,” Atmos. Chem. Phys. 7(6), 1523–1536 (2007).
    [Crossref]
  21. G. Schkolnik, D. Chand, A. Hoffer, M. O. Andreae, C. Erlick, E. Swietlicki, and Y. Rudich, “Constraining the density and complex refractive index of elemental and organic carbon in biomass burning aerosol using optical and chemical measurements,” Atmos. Environ. 41(5), 1107–1118 (2007).
    [Crossref]
  22. L. A. Mack, E. J. T. Levin, S. M. Kreidenweis, D. Obrist, H. Moosmuller, K. A. Lewis, W. P. Arnott, G. R. McMeeking, A. P. Sullivan, C. E. Wold, W. M. Hao, J. L. Collett, and W. C. Malm, “Optical closure experiments for biomass smoke aerosols,” Atmos. Chem. Phys. 10(18), 9017–9026 (2010).
    [Crossref]
  23. M. Stock, Y. F. Cheng, W. Birmili, A. Massling, B. Wehner, T. Muller, S. Leinert, N. Kalivitis, N. Mihalopoulos, and A. Wiedensohler, “Hygroscopic properties of atmospheric aerosol particles over the Eastern Mediterranean: implications for regional direct radiative forcing under clean and polluted conditions,” Atmos. Chem. Phys. 11(9), 4251–4271 (2011).
    [Crossref]
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    [Crossref]
  25. J. L. Hand and S. M. Kreidenweis, “A new method for retrieving particle refractive index and effective density from aerosol size distribution data,” Aerosol Sci. Technol. 36(10), 1012–1026 (2002).
    [Crossref]
  26. T. M. Peters and D. Leith, “Concentration measurement and counting efficiency of the aerodynamic particle sizer 3321,” J. Aerosol Sci. 34(5), 627–634 (2003).
    [Crossref]
  27. J. Shi, R. M. Harrison, and D. Evans, “Comparison of ambient particle surface area measurement by epiphaniometer and SMPS/APS,” Atmos. Environ. 35(35), 6193–6200 (2001).
    [Crossref]
  28. S. Shen, P. A. Jaques, Y. Zhu, M. D. Geller, and C. Sioutas, “Evaluation of the SMPS–APS system as a continuous monitor for measuring PM2.5, PM10 and coarse (PM2.5−10) concentrations,” Atmos. Environ. 36(24), 3939–3950 (2002).
    [Crossref]
  29. T. L. Anderson and J. A. Ogren, “Determining aerosol radiative properties using the TSI 3563 integrating nephelometer,” Aerosol Sci. Technol. 29(1), 57–69 (1998).
    [Crossref]
  30. H. Randriamiarisoa, P. Chazette, P. Couvert, J. Sanak, and G. Megie, “Relative humidity impact on aerosol parameters in a Paris suburban area,” Atmos. Chem. Phys. 6(5), 1389–1407 (2006).
    [Crossref]
  31. X. Zhang, Y. Huang, and R. Rao, “Aerosol characteristics including fumigation effect under weak precipitation over the southeastern coast of China,” J. Atmos. Sol-Terr. Phy. 84–85, 25–36 (2012).
  32. M. Ebert, S. Weinbruch, P. Hoffmann, and H. M. Ortner, “The chemical composition and complex refractive index of rural and urban influenced aerosols determined by individual particle analysis,” Atmos. Environ. 38(38), 6531–6545 (2004).
    [Crossref]
  33. D. Müller, A. Ansmann, F. Wagner, K. Franke, and D. Althausen, “European pollution outbreaks during ACE 2: Microphysical particle properties and single-scattering albedo inferred from multiwavelength lidar observations,” J. Geophys. Res. 107(D15), 4248 (2002).
    [Crossref]
  34. O. Dubovik, B. Holben, T. F. Eck, A. Smirnov, Y. J. Kaufman, M. D. King, D. Tanré, and I. Slutsker, “Variability of absorption and optical properties of key aerosol types observed in worldwide locations,” J. Atmos. Sci. 59(3), 590–608 (2002).
    [Crossref]
  35. J. Volckens and T. M. Peters, “Counting and particle transmission efficiency of the aerodynamic particle sizer,” J. Aerosol Sci. 36(12), 1400–1408 (2005).
    [Crossref]
  36. G. W. Mulholland, N. P. Bryner, and C. Croarkin, “Measurement of the 100 nm NIST SRM 1963 by Differential Mobility Analysis,” Aerosol Sci. Technol. 31(1), 39–55 (1999).
    [Crossref]
  37. G. W. Mulholland, M. K. Donnelly, C. R. Hagwood, S. R. Kukuck, V. A. Hackley, and D. Y. H. Pui, “Measurement of 100 nm and 60 nm particle standards by Differential Mobility Analysis,” J. Res. Natl. Inst. Stand. Technol. 111(4), 257–312 (2006).
    [Crossref]
  38. C. E. Corrigan, V. Ramanathan, and J. J. Schauer, “Impact of monsoon transition on the physical and optical properties of aerosols,” J. Geophys. Res. 111(D18), D18208 (2006).
    [Crossref]
  39. K. K. Moorthy, S. S. Babu, and S. K. Satheesh, “Temporal heterogeneity in aerosol characteristics and the resulting radiative impact at a tropical coastal station—Part 1: Microphysical and optical properties,” Ann. Geophys. 25(11), 2293–2308 (2007).
    [Crossref]
  40. M. J. Jacobson, “A physically-based treatment of elemental carbon optics: implications for global direct forcing of aerosols,” Geophys. Res. Lett. 27(2), 217–220 (2000).
    [Crossref]
  41. I. Veselovskii, O. Dubovik, A. Kolgotin, T. Lapyonok, P. Di Girolamo, D. Summa, D. N. Whiteman, M. Mishchenko, and D. Tanre, “Application of randomly oriented spheroids for retrieval of dust particle parameters from multi-wavelength lidar measurements,” J. Geophys. Res. 115(D21), D21203 (2010).
    [Crossref]
  42. A. Papayannis, R. E. Mamouri, V. Amiridis, E. Remoundaki, G. Tsaknakis, P. Kokkalis, I. Veselovskii, A. Kolgotin, A. Nenes, and C. Fountoukis, “Optical-microphysical properties of Saharan dust aerosols and composition relationship using a multi-wavelength Raman lidar, in situ sensors and modelling: a case study analysis,” Atmos. Chem. Phys. 12(9), 4011–4032 (2012).
    [Crossref]
  43. A. Virkkula, I. K. Koponen, K. Teinila, R. Hillamo, V. M. Kerminen, and M. Kulmala, “Effective real refractive index of dry aerosols in the Antarctic boundary layer,” Geophys. Res. Lett. 33(6), L06805 (2006).
    [Crossref]
  44. M. Schnaiter, H. Horvath, O. Mohler, K. H. Naumann, H. Saathoff, and O. W. Schock, “UV-VIS-NIR spectral optical properties of soot and soot-containing aerosols,” J. Aerosol Sci. 34(10), 1421–1444 (2003).
    [Crossref]
  45. X. Zhang, Y. Huang, W. Zhu, and R. Rao, “Aerosol characteristics during summer haze episodes from different source regions over the coast city of North China Plain,” J. Quant. Spectrosc. Radiat. Transf. 122, 180–193 (2013).
    [Crossref]
  46. Z. Z. Deng, C. S. Zhao, N. Ma, P. F. Liu, L. Ran, W. Y. Xu, J. Chen, Z. Liang, S. Liang, M. Y. Huang, X. C. Ma, Q. Zhang, J. N. Quan, P. Yan, S. Henning, K. Mildenberger, E. Sommerhage, M. Schafer, F. Stratmann, and A. Wiedensohler, “Size-resolved and bulk activation properties of aerosols in the North China plain,” Atmos. Chem. Phys. 11(8), 3835–3846 (2011).
    [Crossref]
  47. Y. M. Noh, D. Muller, D. H. Shin, H. L. Lee, J. S. Jung, K. H. Lee, M. Cribb, Z. Li, and Y. J. Kim, “Optical and microphysical properties of severe haze and smoke aerosol measured by integrated remote sensing techniques in Gwangju, Korea,” Atmos. Environ. 43(4), 879–888 (2009).
    [Crossref]
  48. Y. M. Noh, D. Muller, I. Mattis, H. Lee, and Y. J. Kim, “Vertically resolved light-absorption characteristics and the influence of relative humidity on particle properties: Multiwavelength Raman lidar observations of East Asian aerosol types over Korea,” J. Geophys. Res. 116(D6), D06206 (2011).
    [Crossref]
  49. C. Li, L. T. Marufu, R. R. Dickerson, Z. Li, T. Wen, Y. Wang, P. Wang, H. Chen, and J. W. Stehr, “In situ measurements of trace gases and aerosol optical properties at a rural site in northern China during East Asian Study of Tropospheric Aerosols: An International Regional Experiment 2005,” J. Geophys. Res. 112(D22), D22S04 (2007).
    [Crossref]
  50. J. M. Haywood, S. R. Osborne, P. N. Francis, A. Keil, P. Formenti, M. O. Andreae, and P. H. Kaye, “The mean physical and optical properties of regional haze dominated by biomass burning aerosol measured from the C-130 aircraft during SAFARI 2000,” J. Geophys. Res. 108, (2003).
    [Crossref]
  51. J. C. Raut and P. Chazette, “Retrieval of aerosol complex refractive index from a synergy between lidar, sunphotometer and in situ measurements during LISAIR experiment,” Atmos. Chem. Phys. 7(11), 2797–2815 (2007).
    [Crossref]

2013 (1)

X. Zhang, Y. Huang, W. Zhu, and R. Rao, “Aerosol characteristics during summer haze episodes from different source regions over the coast city of North China Plain,” J. Quant. Spectrosc. Radiat. Transf. 122, 180–193 (2013).
[Crossref]

2012 (2)

A. Papayannis, R. E. Mamouri, V. Amiridis, E. Remoundaki, G. Tsaknakis, P. Kokkalis, I. Veselovskii, A. Kolgotin, A. Nenes, and C. Fountoukis, “Optical-microphysical properties of Saharan dust aerosols and composition relationship using a multi-wavelength Raman lidar, in situ sensors and modelling: a case study analysis,” Atmos. Chem. Phys. 12(9), 4011–4032 (2012).
[Crossref]

X. Zhang, Y. Huang, and R. Rao, “Aerosol characteristics including fumigation effect under weak precipitation over the southeastern coast of China,” J. Atmos. Sol-Terr. Phy. 84–85, 25–36 (2012).

2011 (4)

M. Stock, Y. F. Cheng, W. Birmili, A. Massling, B. Wehner, T. Muller, S. Leinert, N. Kalivitis, N. Mihalopoulos, and A. Wiedensohler, “Hygroscopic properties of atmospheric aerosol particles over the Eastern Mediterranean: implications for regional direct radiative forcing under clean and polluted conditions,” Atmos. Chem. Phys. 11(9), 4251–4271 (2011).
[Crossref]

V. Sivaprakasam, H. B. Lin, A. L. Huston, and J. D. Eversole, “Spectral characterization of biological aerosol particles using two-wavelength excited laser-induced fluorescence and elastic scattering measurements,” Opt. Express 19(7), 6191–6208 (2011).
[Crossref] [PubMed]

Z. Z. Deng, C. S. Zhao, N. Ma, P. F. Liu, L. Ran, W. Y. Xu, J. Chen, Z. Liang, S. Liang, M. Y. Huang, X. C. Ma, Q. Zhang, J. N. Quan, P. Yan, S. Henning, K. Mildenberger, E. Sommerhage, M. Schafer, F. Stratmann, and A. Wiedensohler, “Size-resolved and bulk activation properties of aerosols in the North China plain,” Atmos. Chem. Phys. 11(8), 3835–3846 (2011).
[Crossref]

Y. M. Noh, D. Muller, I. Mattis, H. Lee, and Y. J. Kim, “Vertically resolved light-absorption characteristics and the influence of relative humidity on particle properties: Multiwavelength Raman lidar observations of East Asian aerosol types over Korea,” J. Geophys. Res. 116(D6), D06206 (2011).
[Crossref]

2010 (3)

I. Veselovskii, O. Dubovik, A. Kolgotin, T. Lapyonok, P. Di Girolamo, D. Summa, D. N. Whiteman, M. Mishchenko, and D. Tanre, “Application of randomly oriented spheroids for retrieval of dust particle parameters from multi-wavelength lidar measurements,” J. Geophys. Res. 115(D21), D21203 (2010).
[Crossref]

D. Müller, B. Weinzierl, A. Petzold, K. Kandler, A. Ansmann, T. Muller, M. Tesche, V. Freudenthaler, M. Esselborn, B. Heese, D. Althausen, A. Schladitz, S. Otto, and P. Knippertz, “Mineral dust observed with AERONET Sun photometer, Raman lidar, and in situ instruments during SAMUM 2006: Shape-independent particle properties,” J. Geophys. Res. 115(D7), D07202 (2010).
[Crossref]

L. A. Mack, E. J. T. Levin, S. M. Kreidenweis, D. Obrist, H. Moosmuller, K. A. Lewis, W. P. Arnott, G. R. McMeeking, A. P. Sullivan, C. E. Wold, W. M. Hao, J. L. Collett, and W. C. Malm, “Optical closure experiments for biomass smoke aerosols,” Atmos. Chem. Phys. 10(18), 9017–9026 (2010).
[Crossref]

2009 (4)

K. Kandler, L. Schutz, C. Deutscher, M. Ebert, H. Hofmann, S. Jackel, R. Jaenicke, P. Knippertz, K. Lieke, A. Massling, A. Petzold, A. Schladitz, B. Weinzierl, A. Wiedensohler, S. Zorn, and S. Weinbruch, “Size distribution, mass concentration, chemical and mineralogical composition and derived optical parameters of the boundary layer aerosol at Tinfou, Morocco, during SAMUM 2006,” Tellus B Chem. Phys. Meterol. 61(1), 32–50 (2009).
[Crossref]

A. Petzold, K. Rasp, B. Weinzierl, M. Esselborn, T. Hamburger, A. Dornbrack, K. Kandler, L. Schutz, P. Knippertz, M. Fiebig, and A. Virkkula, “Saharan dust absorption and refractive index from aircraft-based observations during SAMUM 2006,” Tellus B Chem. Phys. Meterol. 61(1), 118–130 (2009).
[Crossref]

T. Müller, A. Schladitz, A. Massling, N. Kaaden, K. Kandler, and A. Wiedensohler, “Spectral absorption coefficients and imaginary parts of refractive indices of Saharan dust during SAMUM-1,” Tellus B Chem. Phys. Meterol. 61(1), 79–95 (2009).
[Crossref]

Y. M. Noh, D. Muller, D. H. Shin, H. L. Lee, J. S. Jung, K. H. Lee, M. Cribb, Z. Li, and Y. J. Kim, “Optical and microphysical properties of severe haze and smoke aerosol measured by integrated remote sensing techniques in Gwangju, Korea,” Atmos. Environ. 43(4), 879–888 (2009).
[Crossref]

2008 (2)

J. C. Raut and P. Chazette, “Vertical profiles of urban aerosol complex refractive index in the frame of ESQUIF airborne measurements,” Atmos. Chem. Phys. 8(4), 901–919 (2008).
[Crossref]

J. C. Raut and P. Chazette, “Radiative budget in the presence of multi-layered aerosol,” Atmos. Chem. Phys. 8(22), 6839–6864 (2008).
[Crossref]

2007 (6)

M. I. Mishchenko and I. V. Geogdzhayev, “Satellite remote sensing reveals regional tropospheric aerosol trends,” Opt. Express 15(12), 7423–7438 (2007).
[Crossref] [PubMed]

A. A. Riziq, C. Erlick, E. Dinar, and Y. Rudich, “Optical properties of absorbing and non-absorbing aerosols retrieved by cavity ring down (CRD) spectroscopy,” Atmos. Chem. Phys. 7(6), 1523–1536 (2007).
[Crossref]

G. Schkolnik, D. Chand, A. Hoffer, M. O. Andreae, C. Erlick, E. Swietlicki, and Y. Rudich, “Constraining the density and complex refractive index of elemental and organic carbon in biomass burning aerosol using optical and chemical measurements,” Atmos. Environ. 41(5), 1107–1118 (2007).
[Crossref]

K. K. Moorthy, S. S. Babu, and S. K. Satheesh, “Temporal heterogeneity in aerosol characteristics and the resulting radiative impact at a tropical coastal station—Part 1: Microphysical and optical properties,” Ann. Geophys. 25(11), 2293–2308 (2007).
[Crossref]

J. C. Raut and P. Chazette, “Retrieval of aerosol complex refractive index from a synergy between lidar, sunphotometer and in situ measurements during LISAIR experiment,” Atmos. Chem. Phys. 7(11), 2797–2815 (2007).
[Crossref]

C. Li, L. T. Marufu, R. R. Dickerson, Z. Li, T. Wen, Y. Wang, P. Wang, H. Chen, and J. W. Stehr, “In situ measurements of trace gases and aerosol optical properties at a rural site in northern China during East Asian Study of Tropospheric Aerosols: An International Regional Experiment 2005,” J. Geophys. Res. 112(D22), D22S04 (2007).
[Crossref]

2006 (5)

G. W. Mulholland, M. K. Donnelly, C. R. Hagwood, S. R. Kukuck, V. A. Hackley, and D. Y. H. Pui, “Measurement of 100 nm and 60 nm particle standards by Differential Mobility Analysis,” J. Res. Natl. Inst. Stand. Technol. 111(4), 257–312 (2006).
[Crossref]

C. E. Corrigan, V. Ramanathan, and J. J. Schauer, “Impact of monsoon transition on the physical and optical properties of aerosols,” J. Geophys. Res. 111(D18), D18208 (2006).
[Crossref]

A. Virkkula, I. K. Koponen, K. Teinila, R. Hillamo, V. M. Kerminen, and M. Kulmala, “Effective real refractive index of dry aerosols in the Antarctic boundary layer,” Geophys. Res. Lett. 33(6), L06805 (2006).
[Crossref]

H. Randriamiarisoa, P. Chazette, P. Couvert, J. Sanak, and G. Megie, “Relative humidity impact on aerosol parameters in a Paris suburban area,” Atmos. Chem. Phys. 6(5), 1389–1407 (2006).
[Crossref]

O. Dubovik, A. Sinyuk, T. Lapyonok, B. N. Holben, M. Mishchenko, P. Yang, T. F. Eck, H. Volten, O. Munoz, B. Veihelmann, W. J. van der Zande, J. F. Leon, M. Sorokin, and I. Slutsker, “Application of spheroid models to account for aerosol particle nonsphericity in remote sensing of desert dust,” J. Geophys. Res. 111(D11), D11208 (2006).
[Crossref]

2005 (2)

J. Volckens and T. M. Peters, “Counting and particle transmission efficiency of the aerodynamic particle sizer,” J. Aerosol Sci. 36(12), 1400–1408 (2005).
[Crossref]

Y. J. Kaufman, I. Koren, L. A. Remer, D. Rosenfeld, and Y. Rudich, “The effect of smoke, dust, and pollution aerosol on shallow cloud development over the Atlantic Ocean,” Proc. Natl. Acad. Sci. U.S.A. 102(32), 11207–11212 (2005).
[Crossref] [PubMed]

2004 (1)

M. Ebert, S. Weinbruch, P. Hoffmann, and H. M. Ortner, “The chemical composition and complex refractive index of rural and urban influenced aerosols determined by individual particle analysis,” Atmos. Environ. 38(38), 6531–6545 (2004).
[Crossref]

2003 (4)

P. Guyon, O. Boucher, B. Graham, J. Beck, O. L. Mayol-Bracero, G. C. Roberts, W. Maenhaut, P. Artaxo, and M. O. Andreae, “Refractive index of aerosol particles over the Amazon tropical forest during LBA-EUSTACH 1999,” J. Aerosol Sci. 34(7), 883–907 (2003).
[Crossref]

T. M. Peters and D. Leith, “Concentration measurement and counting efficiency of the aerodynamic particle sizer 3321,” J. Aerosol Sci. 34(5), 627–634 (2003).
[Crossref]

M. Schnaiter, H. Horvath, O. Mohler, K. H. Naumann, H. Saathoff, and O. W. Schock, “UV-VIS-NIR spectral optical properties of soot and soot-containing aerosols,” J. Aerosol Sci. 34(10), 1421–1444 (2003).
[Crossref]

J. M. Haywood, S. R. Osborne, P. N. Francis, A. Keil, P. Formenti, M. O. Andreae, and P. H. Kaye, “The mean physical and optical properties of regional haze dominated by biomass burning aerosol measured from the C-130 aircraft during SAFARI 2000,” J. Geophys. Res. 108, (2003).
[Crossref]

2002 (6)

S. Shen, P. A. Jaques, Y. Zhu, M. D. Geller, and C. Sioutas, “Evaluation of the SMPS–APS system as a continuous monitor for measuring PM2.5, PM10 and coarse (PM2.5−10) concentrations,” Atmos. Environ. 36(24), 3939–3950 (2002).
[Crossref]

J. L. Hand and S. M. Kreidenweis, “A new method for retrieving particle refractive index and effective density from aerosol size distribution data,” Aerosol Sci. Technol. 36(10), 1012–1026 (2002).
[Crossref]

D. Müller, A. Ansmann, F. Wagner, K. Franke, and D. Althausen, “European pollution outbreaks during ACE 2: Microphysical particle properties and single-scattering albedo inferred from multiwavelength lidar observations,” J. Geophys. Res. 107(D15), 4248 (2002).
[Crossref]

O. Dubovik, B. Holben, T. F. Eck, A. Smirnov, Y. J. Kaufman, M. D. King, D. Tanré, and I. Slutsker, “Variability of absorption and optical properties of key aerosol types observed in worldwide locations,” J. Atmos. Sci. 59(3), 590–608 (2002).
[Crossref]

D. A. Ligon, A. E. Wetmore, and P. S. Gillespie, “Simulation of the passive infrared spectral signatures of bioaerosol and natural fog clouds immersed in the background atmosphere,” Opt. Express 10(18), 909–919 (2002).
[Crossref] [PubMed]

O. Dubovik, B. N. Holben, T. Lapyonok, A. Sinyuk, M. I. Mishchenko, P. Yang, and I. Slutsker, “Non-spherical aerosol retrieval method employing light scattering by spheroids,” Geophys. Res. Lett. 29(10), 1415 (2002).
[Crossref]

2001 (3)

N. A. Marley, J. S. Gaffney, J. C. Baird, C. A. Blazer, P. J. Drayton, and J. E. Frederick, “An empirical method for the determination of the complex refractive index of size-fractionated atmospheric aerosols for radiative transfer calculations,” Aerosol Sci. Technol. 34, 535–549 (2001).

P. Chazette and C. Liousse, “A case study of optical and chemical apportionment for urban aerosols in Thessaloniki,” Atmos. Environ. 35(14), 2497–2506 (2001).
[Crossref]

J. Shi, R. M. Harrison, and D. Evans, “Comparison of ambient particle surface area measurement by epiphaniometer and SMPS/APS,” Atmos. Environ. 35(35), 6193–6200 (2001).
[Crossref]

2000 (2)

J. Redemann, R. P. Turco, K. N. Liou, P. B. Russell, R. W. Bergstrom, B. Schmid, J. M. Livingston, P. V. Hobbs, W. S. Hartley, S. Ismail, R. A. Ferrare, and E. V. Browell, “Retrieving the vertical structure of the effective aerosol complex index of refraction from a combination of aerosol in situ and remote sensing measurements during TARFOX,” J. Geophys. Res. 105(D8), 9949–9970 (2000).
[Crossref]

M. J. Jacobson, “A physically-based treatment of elemental carbon optics: implications for global direct forcing of aerosols,” Geophys. Res. Lett. 27(2), 217–220 (2000).
[Crossref]

1999 (1)

G. W. Mulholland, N. P. Bryner, and C. Croarkin, “Measurement of the 100 nm NIST SRM 1963 by Differential Mobility Analysis,” Aerosol Sci. Technol. 31(1), 39–55 (1999).
[Crossref]

1998 (2)

T. L. Anderson and J. A. Ogren, “Determining aerosol radiative properties using the TSI 3563 integrating nephelometer,” Aerosol Sci. Technol. 29(1), 57–69 (1998).
[Crossref]

H. Liao and J. H. Seinfeld, “Radiative forcing by mineral dust aerosols: sensitivity to key variables,” J. Geophys. Res. 103(D24), 31637–31645 (1998).
[Crossref]

1997 (1)

J. M. Haywood and K. P. Shine, “Multi-spectral calculations of the direct radiative forcing of tropospheric sulphate and soot aerosols using a column model,” Quarterly Journal of the Royal Meteorological Society—Part A 123(543), 1907–1930 (1997).
[Crossref]

Althausen, D.

D. Müller, B. Weinzierl, A. Petzold, K. Kandler, A. Ansmann, T. Muller, M. Tesche, V. Freudenthaler, M. Esselborn, B. Heese, D. Althausen, A. Schladitz, S. Otto, and P. Knippertz, “Mineral dust observed with AERONET Sun photometer, Raman lidar, and in situ instruments during SAMUM 2006: Shape-independent particle properties,” J. Geophys. Res. 115(D7), D07202 (2010).
[Crossref]

D. Müller, A. Ansmann, F. Wagner, K. Franke, and D. Althausen, “European pollution outbreaks during ACE 2: Microphysical particle properties and single-scattering albedo inferred from multiwavelength lidar observations,” J. Geophys. Res. 107(D15), 4248 (2002).
[Crossref]

Amiridis, V.

A. Papayannis, R. E. Mamouri, V. Amiridis, E. Remoundaki, G. Tsaknakis, P. Kokkalis, I. Veselovskii, A. Kolgotin, A. Nenes, and C. Fountoukis, “Optical-microphysical properties of Saharan dust aerosols and composition relationship using a multi-wavelength Raman lidar, in situ sensors and modelling: a case study analysis,” Atmos. Chem. Phys. 12(9), 4011–4032 (2012).
[Crossref]

Anderson, T. L.

T. L. Anderson and J. A. Ogren, “Determining aerosol radiative properties using the TSI 3563 integrating nephelometer,” Aerosol Sci. Technol. 29(1), 57–69 (1998).
[Crossref]

Andreae, M. O.

G. Schkolnik, D. Chand, A. Hoffer, M. O. Andreae, C. Erlick, E. Swietlicki, and Y. Rudich, “Constraining the density and complex refractive index of elemental and organic carbon in biomass burning aerosol using optical and chemical measurements,” Atmos. Environ. 41(5), 1107–1118 (2007).
[Crossref]

P. Guyon, O. Boucher, B. Graham, J. Beck, O. L. Mayol-Bracero, G. C. Roberts, W. Maenhaut, P. Artaxo, and M. O. Andreae, “Refractive index of aerosol particles over the Amazon tropical forest during LBA-EUSTACH 1999,” J. Aerosol Sci. 34(7), 883–907 (2003).
[Crossref]

J. M. Haywood, S. R. Osborne, P. N. Francis, A. Keil, P. Formenti, M. O. Andreae, and P. H. Kaye, “The mean physical and optical properties of regional haze dominated by biomass burning aerosol measured from the C-130 aircraft during SAFARI 2000,” J. Geophys. Res. 108, (2003).
[Crossref]

Ansmann, A.

D. Müller, B. Weinzierl, A. Petzold, K. Kandler, A. Ansmann, T. Muller, M. Tesche, V. Freudenthaler, M. Esselborn, B. Heese, D. Althausen, A. Schladitz, S. Otto, and P. Knippertz, “Mineral dust observed with AERONET Sun photometer, Raman lidar, and in situ instruments during SAMUM 2006: Shape-independent particle properties,” J. Geophys. Res. 115(D7), D07202 (2010).
[Crossref]

D. Müller, A. Ansmann, F. Wagner, K. Franke, and D. Althausen, “European pollution outbreaks during ACE 2: Microphysical particle properties and single-scattering albedo inferred from multiwavelength lidar observations,” J. Geophys. Res. 107(D15), 4248 (2002).
[Crossref]

Arnott, W. P.

L. A. Mack, E. J. T. Levin, S. M. Kreidenweis, D. Obrist, H. Moosmuller, K. A. Lewis, W. P. Arnott, G. R. McMeeking, A. P. Sullivan, C. E. Wold, W. M. Hao, J. L. Collett, and W. C. Malm, “Optical closure experiments for biomass smoke aerosols,” Atmos. Chem. Phys. 10(18), 9017–9026 (2010).
[Crossref]

Artaxo, P.

P. Guyon, O. Boucher, B. Graham, J. Beck, O. L. Mayol-Bracero, G. C. Roberts, W. Maenhaut, P. Artaxo, and M. O. Andreae, “Refractive index of aerosol particles over the Amazon tropical forest during LBA-EUSTACH 1999,” J. Aerosol Sci. 34(7), 883–907 (2003).
[Crossref]

Babu, S. S.

K. K. Moorthy, S. S. Babu, and S. K. Satheesh, “Temporal heterogeneity in aerosol characteristics and the resulting radiative impact at a tropical coastal station—Part 1: Microphysical and optical properties,” Ann. Geophys. 25(11), 2293–2308 (2007).
[Crossref]

Baird, J. C.

N. A. Marley, J. S. Gaffney, J. C. Baird, C. A. Blazer, P. J. Drayton, and J. E. Frederick, “An empirical method for the determination of the complex refractive index of size-fractionated atmospheric aerosols for radiative transfer calculations,” Aerosol Sci. Technol. 34, 535–549 (2001).

Beck, J.

P. Guyon, O. Boucher, B. Graham, J. Beck, O. L. Mayol-Bracero, G. C. Roberts, W. Maenhaut, P. Artaxo, and M. O. Andreae, “Refractive index of aerosol particles over the Amazon tropical forest during LBA-EUSTACH 1999,” J. Aerosol Sci. 34(7), 883–907 (2003).
[Crossref]

Bergstrom, R. W.

J. Redemann, R. P. Turco, K. N. Liou, P. B. Russell, R. W. Bergstrom, B. Schmid, J. M. Livingston, P. V. Hobbs, W. S. Hartley, S. Ismail, R. A. Ferrare, and E. V. Browell, “Retrieving the vertical structure of the effective aerosol complex index of refraction from a combination of aerosol in situ and remote sensing measurements during TARFOX,” J. Geophys. Res. 105(D8), 9949–9970 (2000).
[Crossref]

Birmili, W.

M. Stock, Y. F. Cheng, W. Birmili, A. Massling, B. Wehner, T. Muller, S. Leinert, N. Kalivitis, N. Mihalopoulos, and A. Wiedensohler, “Hygroscopic properties of atmospheric aerosol particles over the Eastern Mediterranean: implications for regional direct radiative forcing under clean and polluted conditions,” Atmos. Chem. Phys. 11(9), 4251–4271 (2011).
[Crossref]

Blazer, C. A.

N. A. Marley, J. S. Gaffney, J. C. Baird, C. A. Blazer, P. J. Drayton, and J. E. Frederick, “An empirical method for the determination of the complex refractive index of size-fractionated atmospheric aerosols for radiative transfer calculations,” Aerosol Sci. Technol. 34, 535–549 (2001).

Boucher, O.

P. Guyon, O. Boucher, B. Graham, J. Beck, O. L. Mayol-Bracero, G. C. Roberts, W. Maenhaut, P. Artaxo, and M. O. Andreae, “Refractive index of aerosol particles over the Amazon tropical forest during LBA-EUSTACH 1999,” J. Aerosol Sci. 34(7), 883–907 (2003).
[Crossref]

Browell, E. V.

J. Redemann, R. P. Turco, K. N. Liou, P. B. Russell, R. W. Bergstrom, B. Schmid, J. M. Livingston, P. V. Hobbs, W. S. Hartley, S. Ismail, R. A. Ferrare, and E. V. Browell, “Retrieving the vertical structure of the effective aerosol complex index of refraction from a combination of aerosol in situ and remote sensing measurements during TARFOX,” J. Geophys. Res. 105(D8), 9949–9970 (2000).
[Crossref]

Bryner, N. P.

G. W. Mulholland, N. P. Bryner, and C. Croarkin, “Measurement of the 100 nm NIST SRM 1963 by Differential Mobility Analysis,” Aerosol Sci. Technol. 31(1), 39–55 (1999).
[Crossref]

Chand, D.

G. Schkolnik, D. Chand, A. Hoffer, M. O. Andreae, C. Erlick, E. Swietlicki, and Y. Rudich, “Constraining the density and complex refractive index of elemental and organic carbon in biomass burning aerosol using optical and chemical measurements,” Atmos. Environ. 41(5), 1107–1118 (2007).
[Crossref]

Chazette, P.

J. C. Raut and P. Chazette, “Vertical profiles of urban aerosol complex refractive index in the frame of ESQUIF airborne measurements,” Atmos. Chem. Phys. 8(4), 901–919 (2008).
[Crossref]

J. C. Raut and P. Chazette, “Radiative budget in the presence of multi-layered aerosol,” Atmos. Chem. Phys. 8(22), 6839–6864 (2008).
[Crossref]

J. C. Raut and P. Chazette, “Retrieval of aerosol complex refractive index from a synergy between lidar, sunphotometer and in situ measurements during LISAIR experiment,” Atmos. Chem. Phys. 7(11), 2797–2815 (2007).
[Crossref]

H. Randriamiarisoa, P. Chazette, P. Couvert, J. Sanak, and G. Megie, “Relative humidity impact on aerosol parameters in a Paris suburban area,” Atmos. Chem. Phys. 6(5), 1389–1407 (2006).
[Crossref]

P. Chazette and C. Liousse, “A case study of optical and chemical apportionment for urban aerosols in Thessaloniki,” Atmos. Environ. 35(14), 2497–2506 (2001).
[Crossref]

Chen, H.

C. Li, L. T. Marufu, R. R. Dickerson, Z. Li, T. Wen, Y. Wang, P. Wang, H. Chen, and J. W. Stehr, “In situ measurements of trace gases and aerosol optical properties at a rural site in northern China during East Asian Study of Tropospheric Aerosols: An International Regional Experiment 2005,” J. Geophys. Res. 112(D22), D22S04 (2007).
[Crossref]

Chen, J.

Z. Z. Deng, C. S. Zhao, N. Ma, P. F. Liu, L. Ran, W. Y. Xu, J. Chen, Z. Liang, S. Liang, M. Y. Huang, X. C. Ma, Q. Zhang, J. N. Quan, P. Yan, S. Henning, K. Mildenberger, E. Sommerhage, M. Schafer, F. Stratmann, and A. Wiedensohler, “Size-resolved and bulk activation properties of aerosols in the North China plain,” Atmos. Chem. Phys. 11(8), 3835–3846 (2011).
[Crossref]

Cheng, Y. F.

M. Stock, Y. F. Cheng, W. Birmili, A. Massling, B. Wehner, T. Muller, S. Leinert, N. Kalivitis, N. Mihalopoulos, and A. Wiedensohler, “Hygroscopic properties of atmospheric aerosol particles over the Eastern Mediterranean: implications for regional direct radiative forcing under clean and polluted conditions,” Atmos. Chem. Phys. 11(9), 4251–4271 (2011).
[Crossref]

Collett, J. L.

L. A. Mack, E. J. T. Levin, S. M. Kreidenweis, D. Obrist, H. Moosmuller, K. A. Lewis, W. P. Arnott, G. R. McMeeking, A. P. Sullivan, C. E. Wold, W. M. Hao, J. L. Collett, and W. C. Malm, “Optical closure experiments for biomass smoke aerosols,” Atmos. Chem. Phys. 10(18), 9017–9026 (2010).
[Crossref]

Corrigan, C. E.

C. E. Corrigan, V. Ramanathan, and J. J. Schauer, “Impact of monsoon transition on the physical and optical properties of aerosols,” J. Geophys. Res. 111(D18), D18208 (2006).
[Crossref]

Couvert, P.

H. Randriamiarisoa, P. Chazette, P. Couvert, J. Sanak, and G. Megie, “Relative humidity impact on aerosol parameters in a Paris suburban area,” Atmos. Chem. Phys. 6(5), 1389–1407 (2006).
[Crossref]

Cribb, M.

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L. A. Mack, E. J. T. Levin, S. M. Kreidenweis, D. Obrist, H. Moosmuller, K. A. Lewis, W. P. Arnott, G. R. McMeeking, A. P. Sullivan, C. E. Wold, W. M. Hao, J. L. Collett, and W. C. Malm, “Optical closure experiments for biomass smoke aerosols,” Atmos. Chem. Phys. 10(18), 9017–9026 (2010).
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I. Veselovskii, O. Dubovik, A. Kolgotin, T. Lapyonok, P. Di Girolamo, D. Summa, D. N. Whiteman, M. Mishchenko, and D. Tanre, “Application of randomly oriented spheroids for retrieval of dust particle parameters from multi-wavelength lidar measurements,” J. Geophys. Res. 115(D21), D21203 (2010).
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Swietlicki, E.

G. Schkolnik, D. Chand, A. Hoffer, M. O. Andreae, C. Erlick, E. Swietlicki, and Y. Rudich, “Constraining the density and complex refractive index of elemental and organic carbon in biomass burning aerosol using optical and chemical measurements,” Atmos. Environ. 41(5), 1107–1118 (2007).
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Tanre, D.

I. Veselovskii, O. Dubovik, A. Kolgotin, T. Lapyonok, P. Di Girolamo, D. Summa, D. N. Whiteman, M. Mishchenko, and D. Tanre, “Application of randomly oriented spheroids for retrieval of dust particle parameters from multi-wavelength lidar measurements,” J. Geophys. Res. 115(D21), D21203 (2010).
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Tanré, D.

O. Dubovik, B. Holben, T. F. Eck, A. Smirnov, Y. J. Kaufman, M. D. King, D. Tanré, and I. Slutsker, “Variability of absorption and optical properties of key aerosol types observed in worldwide locations,” J. Atmos. Sci. 59(3), 590–608 (2002).
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A. Virkkula, I. K. Koponen, K. Teinila, R. Hillamo, V. M. Kerminen, and M. Kulmala, “Effective real refractive index of dry aerosols in the Antarctic boundary layer,” Geophys. Res. Lett. 33(6), L06805 (2006).
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D. Müller, B. Weinzierl, A. Petzold, K. Kandler, A. Ansmann, T. Muller, M. Tesche, V. Freudenthaler, M. Esselborn, B. Heese, D. Althausen, A. Schladitz, S. Otto, and P. Knippertz, “Mineral dust observed with AERONET Sun photometer, Raman lidar, and in situ instruments during SAMUM 2006: Shape-independent particle properties,” J. Geophys. Res. 115(D7), D07202 (2010).
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A. Papayannis, R. E. Mamouri, V. Amiridis, E. Remoundaki, G. Tsaknakis, P. Kokkalis, I. Veselovskii, A. Kolgotin, A. Nenes, and C. Fountoukis, “Optical-microphysical properties of Saharan dust aerosols and composition relationship using a multi-wavelength Raman lidar, in situ sensors and modelling: a case study analysis,” Atmos. Chem. Phys. 12(9), 4011–4032 (2012).
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O. Dubovik, A. Sinyuk, T. Lapyonok, B. N. Holben, M. Mishchenko, P. Yang, T. F. Eck, H. Volten, O. Munoz, B. Veihelmann, W. J. van der Zande, J. F. Leon, M. Sorokin, and I. Slutsker, “Application of spheroid models to account for aerosol particle nonsphericity in remote sensing of desert dust,” J. Geophys. Res. 111(D11), D11208 (2006).
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O. Dubovik, A. Sinyuk, T. Lapyonok, B. N. Holben, M. Mishchenko, P. Yang, T. F. Eck, H. Volten, O. Munoz, B. Veihelmann, W. J. van der Zande, J. F. Leon, M. Sorokin, and I. Slutsker, “Application of spheroid models to account for aerosol particle nonsphericity in remote sensing of desert dust,” J. Geophys. Res. 111(D11), D11208 (2006).
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A. Papayannis, R. E. Mamouri, V. Amiridis, E. Remoundaki, G. Tsaknakis, P. Kokkalis, I. Veselovskii, A. Kolgotin, A. Nenes, and C. Fountoukis, “Optical-microphysical properties of Saharan dust aerosols and composition relationship using a multi-wavelength Raman lidar, in situ sensors and modelling: a case study analysis,” Atmos. Chem. Phys. 12(9), 4011–4032 (2012).
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I. Veselovskii, O. Dubovik, A. Kolgotin, T. Lapyonok, P. Di Girolamo, D. Summa, D. N. Whiteman, M. Mishchenko, and D. Tanre, “Application of randomly oriented spheroids for retrieval of dust particle parameters from multi-wavelength lidar measurements,” J. Geophys. Res. 115(D21), D21203 (2010).
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A. Petzold, K. Rasp, B. Weinzierl, M. Esselborn, T. Hamburger, A. Dornbrack, K. Kandler, L. Schutz, P. Knippertz, M. Fiebig, and A. Virkkula, “Saharan dust absorption and refractive index from aircraft-based observations during SAMUM 2006,” Tellus B Chem. Phys. Meterol. 61(1), 118–130 (2009).
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A. Virkkula, I. K. Koponen, K. Teinila, R. Hillamo, V. M. Kerminen, and M. Kulmala, “Effective real refractive index of dry aerosols in the Antarctic boundary layer,” Geophys. Res. Lett. 33(6), L06805 (2006).
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J. Volckens and T. M. Peters, “Counting and particle transmission efficiency of the aerodynamic particle sizer,” J. Aerosol Sci. 36(12), 1400–1408 (2005).
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O. Dubovik, A. Sinyuk, T. Lapyonok, B. N. Holben, M. Mishchenko, P. Yang, T. F. Eck, H. Volten, O. Munoz, B. Veihelmann, W. J. van der Zande, J. F. Leon, M. Sorokin, and I. Slutsker, “Application of spheroid models to account for aerosol particle nonsphericity in remote sensing of desert dust,” J. Geophys. Res. 111(D11), D11208 (2006).
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D. Müller, A. Ansmann, F. Wagner, K. Franke, and D. Althausen, “European pollution outbreaks during ACE 2: Microphysical particle properties and single-scattering albedo inferred from multiwavelength lidar observations,” J. Geophys. Res. 107(D15), 4248 (2002).
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A. Petzold, K. Rasp, B. Weinzierl, M. Esselborn, T. Hamburger, A. Dornbrack, K. Kandler, L. Schutz, P. Knippertz, M. Fiebig, and A. Virkkula, “Saharan dust absorption and refractive index from aircraft-based observations during SAMUM 2006,” Tellus B Chem. Phys. Meterol. 61(1), 118–130 (2009).
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C. Li, L. T. Marufu, R. R. Dickerson, Z. Li, T. Wen, Y. Wang, P. Wang, H. Chen, and J. W. Stehr, “In situ measurements of trace gases and aerosol optical properties at a rural site in northern China during East Asian Study of Tropospheric Aerosols: An International Regional Experiment 2005,” J. Geophys. Res. 112(D22), D22S04 (2007).
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Wetmore, A. E.

Whiteman, D. N.

I. Veselovskii, O. Dubovik, A. Kolgotin, T. Lapyonok, P. Di Girolamo, D. Summa, D. N. Whiteman, M. Mishchenko, and D. Tanre, “Application of randomly oriented spheroids for retrieval of dust particle parameters from multi-wavelength lidar measurements,” J. Geophys. Res. 115(D21), D21203 (2010).
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Z. Z. Deng, C. S. Zhao, N. Ma, P. F. Liu, L. Ran, W. Y. Xu, J. Chen, Z. Liang, S. Liang, M. Y. Huang, X. C. Ma, Q. Zhang, J. N. Quan, P. Yan, S. Henning, K. Mildenberger, E. Sommerhage, M. Schafer, F. Stratmann, and A. Wiedensohler, “Size-resolved and bulk activation properties of aerosols in the North China plain,” Atmos. Chem. Phys. 11(8), 3835–3846 (2011).
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T. Müller, A. Schladitz, A. Massling, N. Kaaden, K. Kandler, and A. Wiedensohler, “Spectral absorption coefficients and imaginary parts of refractive indices of Saharan dust during SAMUM-1,” Tellus B Chem. Phys. Meterol. 61(1), 79–95 (2009).
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L. A. Mack, E. J. T. Levin, S. M. Kreidenweis, D. Obrist, H. Moosmuller, K. A. Lewis, W. P. Arnott, G. R. McMeeking, A. P. Sullivan, C. E. Wold, W. M. Hao, J. L. Collett, and W. C. Malm, “Optical closure experiments for biomass smoke aerosols,” Atmos. Chem. Phys. 10(18), 9017–9026 (2010).
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Z. Z. Deng, C. S. Zhao, N. Ma, P. F. Liu, L. Ran, W. Y. Xu, J. Chen, Z. Liang, S. Liang, M. Y. Huang, X. C. Ma, Q. Zhang, J. N. Quan, P. Yan, S. Henning, K. Mildenberger, E. Sommerhage, M. Schafer, F. Stratmann, and A. Wiedensohler, “Size-resolved and bulk activation properties of aerosols in the North China plain,” Atmos. Chem. Phys. 11(8), 3835–3846 (2011).
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O. Dubovik, A. Sinyuk, T. Lapyonok, B. N. Holben, M. Mishchenko, P. Yang, T. F. Eck, H. Volten, O. Munoz, B. Veihelmann, W. J. van der Zande, J. F. Leon, M. Sorokin, and I. Slutsker, “Application of spheroid models to account for aerosol particle nonsphericity in remote sensing of desert dust,” J. Geophys. Res. 111(D11), D11208 (2006).
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O. Dubovik, B. N. Holben, T. Lapyonok, A. Sinyuk, M. I. Mishchenko, P. Yang, and I. Slutsker, “Non-spherical aerosol retrieval method employing light scattering by spheroids,” Geophys. Res. Lett. 29(10), 1415 (2002).
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X. Zhang, Y. Huang, and R. Rao, “Aerosol characteristics including fumigation effect under weak precipitation over the southeastern coast of China,” J. Atmos. Sol-Terr. Phy. 84–85, 25–36 (2012).

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M. Stock, Y. F. Cheng, W. Birmili, A. Massling, B. Wehner, T. Muller, S. Leinert, N. Kalivitis, N. Mihalopoulos, and A. Wiedensohler, “Hygroscopic properties of atmospheric aerosol particles over the Eastern Mediterranean: implications for regional direct radiative forcing under clean and polluted conditions,” Atmos. Chem. Phys. 11(9), 4251–4271 (2011).
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A. Virkkula, I. K. Koponen, K. Teinila, R. Hillamo, V. M. Kerminen, and M. Kulmala, “Effective real refractive index of dry aerosols in the Antarctic boundary layer,” Geophys. Res. Lett. 33(6), L06805 (2006).
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X. Zhang, Y. Huang, and R. Rao, “Aerosol characteristics including fumigation effect under weak precipitation over the southeastern coast of China,” J. Atmos. Sol-Terr. Phy. 84–85, 25–36 (2012).

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D. Müller, A. Ansmann, F. Wagner, K. Franke, and D. Althausen, “European pollution outbreaks during ACE 2: Microphysical particle properties and single-scattering albedo inferred from multiwavelength lidar observations,” J. Geophys. Res. 107(D15), 4248 (2002).
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Figures (5)

Fig. 1
Fig. 1 Temporal variations of the measured scattering coefficients (a) and absorption coefficients (b) of atmospheric aerosols. The black solid squares, red open circles and blue dot-center triangles denote the wavelengths of 450, 550 and 700 nm, respectively.
Fig. 2
Fig. 2 The aerosol number size distributions in terms of mean (a), median, 10th, 25th, 75th and 90th percentile (b) values, and the mean volume size distribution (c) measured from the combination of SMPS and APS during the whole sampling period.
Fig. 3
Fig. 3 Temporal variations of the deducted real part (a) and imaginary part (b) of aerosol complex refractive indices. The black solid squares, red open circles and blue dot-center triangles indicate the wavelengths of 450, 550 and 700 nm, respectively. Yellow vertical bars for S1, S2, S3 and S4 denote different source regions.
Fig. 4
Fig. 4 Correlation plots of single scattering albedo versus real part (left three) of derived complex refractive indices, and single scattering albedo versus imaginary part (right three). The black solid squares, green solid circles and blue solid triangles denote the wavelengths of 450, 550 and 700 nm, respectively.
Fig. 5
Fig. 5 Representative HYSPLIT backward trajectories for S1 (a), S2 (b), S3 (c), S4 (d) over Hefei. Red, Blue and green denote backward trajectories arriving at 10, 100 and 500 m heights above ground level, respectively.

Tables (2)

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Table 1 Details of the four representative source regions.

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Table 2 Summary of measured aerosol optical properties and retrieved complex refractive indicesa

Equations (2)

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χ 2 (n,k)= 1 N i=1 N \ [ ( σ sca ,calculated (n,k) σ sca ,measured σ sca ,measured ) 2 i + ( σ abs, calculated (n,k) σ abs, measured σ abs, measured ) 2 i ]
α s =log[ σ s ( λ 1 )/ σ s ( λ 2 ) ]/log( λ 1 / λ 2 ).

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