Meteorol Atmos Phys DOI 10.1007/s00703-013-0268-2

ORIGINAL PAPER

The spatial–temporal variations in optical properties of atmosphere aerosols derived from AERONET dataset over China H. Chen • X. Gu • T. Cheng • Z. Li T. Yu

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Received: 6 September 2012 / Accepted: 23 May 2013 Ó Springer-Verlag Wien 2013

Abstract The spatial–temporal properties of aerosol types over China are studied using the radiance measurements and inversions data at four Aerosol Robotic Network (AERONET) stations in China. Based on a cluster analysis, five aerosol classes were identified including a coarse-sized dominated aerosol type (presumably dust) and four finesized dominated aerosol types ranging from non-absorbing to highly absorbing fine aerosols. The optical properties and seasonal variations of these aerosol types are investigated. The results of analysis show that: (1) the highly absorbing aerosols usually occur in winter, (2) nonabsorbing aerosols are frequently observed in summer; (3) coarse-sized dominated aerosols are frequently occurred in spring.

1 Introduction Aerosols play a significant role in climate change both through direct interactions with atmospheric radiation, and through indirect ones by modifying cloud optical properties and persistence. Due to the high spatial–temporal variability of their optical properties (e.g., aerosol optical thickness, single scattering albedo, asymmetry parameter, etc.,), aerosols are one of the largest uncertainties in climate forcing assessments (IPCC 2007). The sum of direct and indirect forcing by anthropogenic aerosols at the topof-atmosphere is likely to be negative and may be

Responsible editor: S. Trini Castelli. H. Chen  X. Gu  T. Cheng (&)  Z. Li  T. Yu Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China e-mail: [email protected]

comparable in magnitude to the positive forcing of about 2.4 Wm-2 by anthropogenic greenhouse gases (IPCC 2007). The atmospheric and climate response to the aerosol forcing are assessed by climate models regionally and globally under the past, present, and future conditions. However, large uncertainties exist because of incomplete knowledge concerning the distribution and the physical and chemical properties of aerosols as well as aerosol–cloud interactions. These uncertainties raise questions about the interpretation of the temperature record and complicate the assessment of aerosol impacts on surface–atmosphere interactions, the atmospheric boundary layer, global surface air temperatures, the hydrological cycle, photochemistry, and ecosystems (Yu et al. 2006). Reduction in these uncertainties requires long-term monitoring of detailed properties of different aerosol types. In many cases, mean values over a long-time range sorted by location or type to represent aerosol properties are used in radiative transfer models (Kiehl and Briegleb 1993; Penner et al. 1992; Tanre´ et al. 1999; Omar et al. 2005). According to Omar et al. (2005), there are significant shortcomings if a set of mean aerosol physical and chemical properties are assigned to a given location based on a long-term average, because the aerosol type can be variable on timescales as short as a few hours at any given location. Aerosol optical measurements must be made at short time interval and long-term scale to develop a large database which can be used to derive statistically significant correlations. To this purpose, the Aerosol Robotic Network (AERONET) is one of the most useful tools nowadays (Holben et al. 1998), which provides a database with a fine temporal resolution and enough information to establish globally a ground-based aerosol climatology. An extended set of physical and optical aerosol properties,

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such as detailed particle size distribution, complex refractive index, single scattering albedo, and particle shape information are provided for estimating aerosol effects on atmospheric radiation (Dubovik et al. 2002). China has experienced unprecedented economic growth over the past two decades characterized by the development of industries and anthropogenic activities. As a result, it has become one of the world’s most dense aerosol regions (Li et al. 2007; Duncan et al. 2003). In China, aerosols vary greatly in composition and size and have a significant effect on the atmospheric radiation budget. Although much attention has been paid to this issue, information on Chinese aerosol properties and their spatial and temporal variation is still limited. Thus, a systematic study is required to clarify the optical properties and improve our knowledge of China aerosol radiation effects. In this paper, the characteristics of aerosol types over China are studied via cluster analysis of optical and microphysical properties obtained from radiance measurements and inversions data at four AERONET stations in China. Based on the aerosol types identified via cluster analysis, the optical and seasonal properties of aerosol over China are investigated. The paper is organized as follows. In Sect. 2, the data and classification method used for discriminating aerosol types are described. In Sect. 3, the optical and microphysical properties of different aerosol types are discussed. In Sect. 4, the seasonal variations of aerosol types over China are analyzed. Conclusion is given in Sect. 5.

correspond to the best fit of both measured AOT and sky radiances. The latest retrieval scheme (Dubovik et al. 2006) assumes that aerosol is a mixture of spherical and nonspherical aerosol components and estimates the fraction that is nonspherical. The modeling is performed using kernel lookup tables of quadrature coefficients employed in the numerical integration of spheroid optical properties over size and shape (see the work of Dubovik et al. 2006). Retrievals from both Sun and Sky AERONET measurements are controlled by rigorous calibration and cloud screening processes. The results are also constrained by specific criteria identified in sensitivity studies (Dubovik and King 2000). The AERONET data are provided in three categories. As discussed by Dubovik et al. (2002), Level 2 of AERONET products are accurate retrieval results and can be used to assess the aerosol properties and provide the ground truth for satellite-based datasets. Within this paper, the quality assured, ‘‘Level 2 Inversion All Points’’ data are used. The following 22 parameters comprising optical and physical properties retrieved from AERONET inversion algorithms are used in our clustering analysis: 1. 2.

3. 4.

2 Methodology of aerosol classification 2.1 The AERONET instruments and data The AERONET (Holben et al. 1998, 2001) is a wellestablished network of over 700 stations and provides standardized high quality aerosol measurements. The ‘‘Sun’’ products are retrievals of spectral Aerosol Optical Thickness (AOT) at several wavelengths (0.34, 0.38, 0.44, 0.67, 0.87, and 1.02 lm). The indirect ‘‘sky’’ products are retrievals of aerosol optical properties and aerosol size distributions at four wavelengths (0.44, 0.67, 0.87, and 1.02 lm) (Holben et al. 1998). Measurements of AOT and sky radiances observed in the solar almucantar are used for estimating detailed aerosol properties (such as aerosol size distribution, complex refractive index, phase function, absorption properties, etc.,). The retrieval algorithm approximates aerosol as an ensemble of polydisperse spheres (Dubovik and King 2000) or randomly oriented spheroids (Dubovik et al. 2002) and provides the volume distribution for 22 radius size bins and spectral complex refractive index that

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Complex refractive index at 440, 676, 869 and 1,020 nm (8*). Aerosol size distribution parameters: fine/coarse mean radius, fine/coarse standard deviation, fine/coarse mode total volume (6). Single scattering albedo at 440, 676, 869 and 1,020 nm (4). Asymmetry factor at 440, 676, 869, 1,020 nm (4).

*The numbers in parentheses denote the number of variables for the given property. We applied additional filters to rule out the observations with less stable atmospheric conditions: (a)

The sky error was considered to make further improvement in the data quality. AERONET inversion products with the sky radiance fitting error larger than 5 % were not used. (b) Data with the real part of the complex refractive index reaching the rather unrealistic value of 1.6 (maximum considered in AERONET retrievals) were removed. In addition, the fine mode fraction (FMF) is also obtained from AERONET measurements, but not used in the clustering. FMF is used to determine the dominant size mode and provides quantitative information for each fineand coarse-mode aerosol. It is defined as the ratio of finemode AOT to total AOT. AERONET retrieves FMF using two methods: the Dubovik inversion of sky radiances (Dubovik and King 2000) and the O’Neill inversion of spectral Sun measurements (O’Neill et al. 2003). The Dubovik inversion of 22 parameters (refractive index size

The spatial–temporal variations in optical properties of atmosphere aerosols

Xianghe is a rural site near Beijing. The air pollution is very serious at Xianghe and is influenced by the pollution from Beijing. Taihu Lake site is located in the suburb of Wuxi City, surrounded with farmland and cottages. SACOL is situated at the Gansu province in Northwest China. It is a typical dust activity center and has a relatively high surface reflectance. 2.3 Methodology

Fig. 1 Geographical locations of the selected AERONET sites

distribution, SSA, ASY) is used in the cluster analysis. For consistency, the Dubovik inversion of FMF at the wavelength 675 nm is employed in the modified paper, which yields the mean FMFs of 0.83, 0.73, 0.80, 0.75 and 0.32 for five classes, respectively. 2.2 AERONET sites description The geographical location of the Chinese AERONET sites selected for the analysis is shown in Fig. 1 and relevant information for those sites is given in Table 1. Four sites are selected as both representative of the major different climatic types of China (e.g., semi-desert region, urban area, rural area), and based on the availability of statistically significant, quality assured datasets in the period 2006–2011. The mean AOT at 440 nm is used to give a more complete picture of the aerosol loading at the four studied sites during 2006–2011. The sites with highest average AOTs are Beijing and Xianghe (1.00 and 0.98), followed by Taihu (0.85), and SACOL (0.58) being the lowest. Beijing is a major commercial and political center in northern China. It has complicated landscape, and is affected by long-range transported dust in the spring, coal burning in the winter, strong local emissions from heavy traffic, and industrial factories all over the year (He et al. 2001; Sun et al. 2004).

Cluster analysis is a statistical tool for grouping large datasets into several categories using predefined variables. This method was applied globally and regionally by Omar et al. (2005), Qin and Mitchell (2009), Levy et al. (2007), and (2010) to derive statistically significant groupings in aerosol optical properties. In this study, an agglomerative complete-link clustering algorithm of the hierarchical class (Kotsiantis and Pintelas 2004) is used to categorize the AERONET dataset. The Manhattan distance is used to represent the distance Pn between two points, defined as i¼1 jai  bi j where a and b denote two records with n parameters each. A brief description of the algorithm follows. Initially, each single record is treated as a class. The distances between each pair of classes are calculated. The distance between two classes, A and B, is defined to be the maximum distance between any one of class A records and any one of class B records (the so-called complete-link). The two closest classes are then merged as one class. The last two steps are repeated until the minimum distance among the paired classes reaches a prescribed criterion. This criterion is experimental and controls the number of classes. Before the cluster analysis, each of the 22 parameters was normalized by the standard deviation. This step ensures that the relative contribution of the parameters to the distance calculation is balanced. By applying the clustering analysis, the AERONET records were classified into five classes. Five clusters provided the largest number of clusters in which each had a reasonable number of records. The size distribution, SSA, and asymmetry parameter of the five clustered aerosol

Table 1 Geographical coordinates of four AERONET sites used for clustering and relevant information Site name

Lon/lat

Observing period

Ecosystem/geographic region

Number of records employed (total and per season) in the analysis

Beijing

116.381/39.977

2006.1–2011.12

Urban

1,526, 479/211/335/501

SACOL

104.137/35.946

2006.8–2011.12

A rural site under the influence of Asian dust transport

1,193, 249/144/260/540

Taihu

120.215/31.421

2006.1–2011.12

Freshwater lake/urban

1,446, 547/106/324/469

XiangHe

116.962/39.754

2006.1–2011.12

Suburban/rural

2,029, 592/299/556/582

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H. Chen et al. Fig. 2 Size distributions, single scattering albedo and asymmetry parameter of the five aerosol classes at their densityweighted centers. The vertical bars represent the standard deviation

types are shown in Fig. 2, while corresponding refractive index and SSA values are given in Table 2. The mean FMF of each class is also investigated. The first four classes show a mean FMF of 0.81, 0.76, 0.83 and 0.76, respectively, which indicate aerosol types dominated by fine particles. The last class shows a mean FMF of 0.37, indicating an aerosol type dominated by coarse particles. Based on the above results, the five classes are labeled as fine0-MA (moderately absorbing fine-sized dominated aerosols, SSA ranges from 0.87 to 0.89 in the wavelength 440–1,020 nm), fine1-MA (SSA ranges from 0.89 to 0.91 in the wavelength 440–1,020 nm), fine2-NA (non-absorbing fine-sized dominated aerosols, SSA ranges from 0.93 to 0.95 in the wavelength 440–1,020 nm), fine3-HA (highly absorbing fine-sized dominated aerosols, SSA ranges from 0.84 to 0.87 in the wavelength 440–1,020 nm), and coarse dust (coarse-sized dominated aerosols, SSA ranges from 0.89 to 0.96 in the wavelength 440–1,020 nm, presumably dust), respectively. The distances between the five classes reported in Table 3 are defined as the maximum distance between a pair of records, one in one cluster and one in the other, DðX; YÞ ¼ maxðdðx; yÞÞ Where 1. 2.

dðx; yÞ is the distance between elements x 2 X and y2Y X and Y are two sets of records (clusters)

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If X = Y, the distance is defined as the diameter of cluster X/Y (the maximum distance between a pair of records in a cluster). Table 3 shows that five classes are well separated from each other, demonstrated by much smaller cluster diameters compared to inter-cluster distances.

3 Classification interpretation 3.1 Classes fine0-MA and fine1-MA Class fine0-MA and fine1-MA are fine-sized dominated aerosols. They are the dominant aerosols at all four sites. The single scattering albedo of class fine0-MA and fine1MA is 0.87–0.92 in the wavelength 440–1,020 nm (Fig. 2), which is consistent with measurements of urban–industrial aerosol by Dubovik et al. (2002). The fine0-MA and fine1-MA aerosols are mainly distinguished by the mean SSAs and refractive indexes at four wavelengths (440, 676, 869, 1,020 nm). The SSAs for fine0-MA particles at four wavelengths are 0.88, 0.89, 0.88 and 0.87, respectively. fine1-MA particles are less absorbing, with mean SSAs being 0.89, 0.91, 0.91, and 0.91 at four wavelengths. The real parts of the refractive index for fine0-MA aerosols are 1.47–1.50 over the wavelength range 440–120 nm, while that for fine1-MA are 1.44–1.46. Class fine1-MA is the most common of all, with about 54 % of all the records being classified in this

The spatial–temporal variations in optical properties of atmosphere aerosols Table 2 Summary of the real part of refractive index and single scattering albedo of the cluster analysis results Cluster Fine0MA

Fine1MA

Fine2NA

Fine3HA

Coarse dust

mr440

1.47

1.44

1.40

1.48

1.50

mr676

1.49

1.46

1.42

1.51

1.53

mr869

1.51

1.46

1.44

1.52

1.52

mr1020

1.50

1.46

1.44

1.53

1.51

SSA440

0.88

0.89

0.94

0.84

0.90

SSA676

0.89

0.91

0.95

0.87

0.95

SSA869

0.88

0.91

0.95

0.85

0.96

SSA1020

0.87

0.91

0.95

0.84

0.96

The values are the centers of each cluster. ‘‘mr’’ is the real part of refractive index

Table 3 The distances between classes Class

Fine0MA

Fine1MA

Fine2NA

Fine3HA

Coarse ust

Fine0-MA

0.84

4.98

4.16

3.46

5.39

Fine1-MA

4.98

0.85

4.65

4.53

5.52

Fine2-NA

4.16

4.65

0.93

4.19

4.78

Fine3-HA

3.46

4.53

4.19

0.71

5.09

Coarse dust

5.39

5.52

4.78

5.09

0.84

Comparing the particle size distribution to class fine0–2, it is noted that the fine-mode particle volume concentration of class fine2-NA is larger (0.14 lm3/lm2) than that for fine0-MA (0.11 lm3/lm2) and fine1-MA (0.08 lm3/lm2). As shown in Table 3, the real parts of the refractive index for fine2-NA aerosols are the lowest ones (1.40–1.44) over the wavelength range 440–120 nm. According to Dubovik (2002), urban–industrial aerosols usually show higher SSA correlated with lower real part of the refractive index. This correlation likely appears due to geophysical reasons. Aerosols with lower real parts of the refractive index are possibly associated with high relative humidity and resultant aerosol hygroscopic growth. Analyzing AERONET ‘Sun’ data, Gobbi et al. (2007) also inferred an important role of hygroscopic and/or coagulation growth of an aging fine mode at the Beijing site. According to our analysis, fine2-NA aerosols are usually observed in the summer season when the rainy days are frequent and the air humidity is larger than in other seasons. To support this argument, the AERONET-derived precipitable water (PW) parameter of the five aerosol types is analyzed. Class fine2-NA shows the highest mean value of PW (1.65), the other four classes showing average PW of 1.39 (fine0-MA), 0.99 (fine1-MA), 0.58 (fine3-HA), and 0.78 (coarse), respectively. 3.3 Class fine3-HA

class. Fine0-MA particles are rarely observed with about 5 % of all the records being classified in this class. Class fine1-MA is the most common of all, with about 54 % of all the records being classified in this class. Fine0-MA particles are rarely observed with about 5 % of all the records being classified in this class. Wide variability of SSA for the urban locations probably can be explained by differences in fuel types, emission conditions, long-range transport and environmental and meteorological conditions (Dubovik et al. 2002). Aerosol dominated by fine particles result in the asymmetry factor g strongly decreasing with increasing wavelength. According to Mie theory (e.g., Bohren and Huffmann 1983), light in the considered spectral range (440–1,020 nm) is more effectively scattered by particles of fine-mode sizes (r \ 0.6 lm) than coarse-mode particles (r [ 0.6 lm). These changes in asymmetry factor are consistent with the overlap between classes 0 and 1. Class fine0-MA and fine1-MA are believed to have significant urban–industrial pollution components.

Class fine3-HA is of interest in view of its strong absorption, with class-center single scattering albedos from 0.84 to 0.86 over the wavelength range 440–1,020 nm (Fig. 2). The size distribution of class fine3-HA is comparable to that of class fine0-1 aerosols (industrial–urban pollution). SSA of class fine3-HA (0.84 at 440 nm) is comparable to the measurements from the Indian Ocean Experiment (INDOEX) and from industrial regions in China (Mu¨ller et al. 2003). The real parts of the refractive index for fine3HA aerosols are in the range 1.48–1.53 over the wavelength range 440–120 nm (Table 3). According to studies of Dubovik et al. (2002) and Menon et al. (2002), higher real part of refractive index combined with lower SSA are possibly associated with high concentrations of black carbon in the atmosphere. In fact, China is generally considered as a major global anthropogenic source for carbonaceous aerosols (Cooke and Wilson 1996; Streets et al. 2003; Bond et al. 2004; Menon et al. 2002; Cao et al. 2006), where high rates of usage of coal and biofuels, are primarily responsible for high carbon emissions.

3.2 Class fine2-NA 3.4 Class coarse Class fine2-NA is significantly non-absorbing as the mean single scattering albedos varies from 0.94 to 0.95 over the wavelength range 440–1,020 nm (Fig. 2).

The coarse class shows relatively low FMFs (mean FMF is 0.37) and high asymmetry factors compared to the four fine

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classes (Fig. 2). The mean single scattering albedo is higher than the fine classes with the only exception of class fine 2-NA in the shortest wavelengths. In addition, the mean SSA of the coarse class exhibits a distinct feature in the spectral distribution of single scattering albedo, with a depression at 440 nm followed by a gradual increase at longer wavelengths, as shown in Fig. 1. This feature is typically observed in desert dust condition (Dubovik et al. 2002; Levy et al. 2007; Qin and Mitchell 2009) and is likely due to absorption in the blue spectral region by the iron oxide hematite. Efforts on incorporating mineralogical composition into modeling radiative properties of dust (Claquin et al. 1998, 1999; Sokolik and Toon 1999) emphasize that the way the hematite is mixed with quartz or clay is complicated and strongly impacts the resulting absorption. Considering its optical properties (Fig. 2) and temporal distribution (usually observed in spring season, see Sect. 4), class coarse is presumably dust aerosol. The frequency of the dust in China results from the AERONET stations’ proximity to the desert source region. Dust from the Taklamakan and Gobi deserts often affects west and northwest of China in spring every year (Kim et al. 2004).

4 Seasonal variation of aerosol types at selected sites The clustering algorithm also provides a useful tool to examine the seasonal variation of aerosol types over China. Illustrated in Fig. 3 are the monthly distributions of the occurrence of the different aerosol types identified (over 6 years of data in the four AERONET sites). Figure 3 shows that the non-absorbing aerosols (fine2NA) are frequently observed in summer which can be attributed to the aerosol hygroscopic growth in this season. Conversely, the proportion of high absorbing aerosols (fine3-HA) is high from autumn to early spring and low from late spring to summer, which is likely caused by the increased use of fossil fuel combustion in the cold season.

The percentages of coarse-mode aerosols (dust) are small in summer (avg. 4 %) and in autumn (avg. 2 %), and increase rapidly from winter (avg. 7 %) to spring (avg. 26 %). Figure 4 illustrates the monthly distribution of the different aerosol types at the sites of Beijing (4a), SACOL (4b), and Taihu (4c). It is worth mentioning that records in summer season (July to August) are fewer than in other seasons due to the prevailing cloudy and rainy days (Table 1). The monthly distribution of different aerosol types at Xianghe site is not being represented in Fig. 4 for that Xianghe site is near from Beijing (about 70 km southeast of Beijing) and the optical properties at Xianghe are nearly the same as over Beijing (Eck et al. 2005; Xia et al. 2005). Aerosols in Beijing are more absorptive in autumn, winter, and spring than in summer, due to the large proportion of mid-absorptive (class fine0–1 aerosols, avg. proportion of 54 %) and high-absorptive (class fine2-HA aerosols, avg. proportion of 23 %) aerosol types in these seasons. SACOL shows significantly larger proportion of coarsemode aerosols (avg. proportion of 57 % in spring) than other two sites. According to Fig. 4, fine2-NA is high in autumn at SACOL. The water vapor information at SACOL site in summer season is obtained from AERONET data. The average water vapor is 1.85 and standard deviation is 0.42. According to (Du et al. 2012), SACOL site has a semi-arid continental temperate monsoon climate. The annual precipitation range is 381.8 mm, a maximum of precipitation observed in August. The annual evaporation is 1528.5 mm and the relative humidity is 63 %. In Taihu, coarse-sized dominated aerosols (dust) are mainly observed in spring (shown an averaged proportion of 18 %) which is consistent to the results by Tsai et al. (2008) and Liu et al. (2011). In spring dust, aerosols are in fact transported out of Northwest China and can increase the aerosol loading over the south-eastern part of the country. Strong absorptive aerosols also show a relatively

Fig. 3 Seasonal distributions of the four different aerosol types identified over China (over 6 years of data collected from four AERONET sites)

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Fig. 4 Seasonal distributions of different aerosol types at the three selected sites over China

large proportion in winter (avg. 19 %), indicating serious aerosol pollution in this season. Although Taihu site is surrounded by farmyards and cottages, it represents roughly the geometric center of a cluster of big cities in the Yangtze delta, e.g., Wuxi, Suzhou, Shanghai, Hangzhou, and Nanjing, where the pollution is mainly caused by sulfate aerosols from industrial emissions. Taihu shows a large component of fine0-MA aerosols (Fig. 4).

5 Conclusion The spatial–temporal properties of different aerosol types over the China region are investigated via cluster analysis

of optical and microphysical properties obtained from the radiance measurements and inversions data at four AERONET stations in China. This analysis identified five aerosol types: four fine-sized dominated aerosol types (moderately absorbing, non-absorbing and highly absorbing) and one coarse-sized dominated aerosol type. The four fine-dominated aerosol types differ mainly by their absorption capabilities, ranging from ‘‘non-absorbing’’ (SSA *0.94 at 440 nm) to ‘‘highly absorbing’’ (SSA *0.84 at 440 nm). The coarse-dominated aerosol type differs from fine-mode aerosols by the low FMF (0.37), size distribution, asymmetry parameters, and values of SSA. Based on the aerosol types identified via cluster analysis, the mean optical properties of different aerosol types in

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China and their seasonal variations were investigated. The identified ‘fine0-MA’ and ‘fine1-MA’ classes are believed to have significant urban–industrial pollution components. Class ‘fine2-NA’ is frequently observed in summer. Class ‘fine3-HA’ is possibly associated with high concentrations of black carbon and is frequently observed in winter. The coarse-mode aerosol class (dust) is frequently observed in spring. Acknowledgments This research was supported by the National Basic Research Program of China (973 Program) (Grant No: 2010CB950800), the Strategic Priority Research Program of the Chinese Academy of Sciences, Climate Change: Carbon Budget and Relevant Issues (Grant No. XDA05100201), the Funds of the Chinese Academy of Sciences for Key Topics in Innovation Engineering (Grant No: KZCX2-EW-QN311), and the National Natural Science Foundation of China (Grant No: 41001207). We thank the PIs and their staff for establishing and maintaining the AERONET sites used in this study.

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