JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117, D16204, doi:10.1029/2011JD017220, 2012
Aerosol optical properties retrieved from Sun photometer measurements over Shanghai, China Qianshan He,1 Chengcai Li,2 Fuhai Geng,1 Hequn Yang,1 Peiren Li,3 Tingting Li,1 Dongwei Liu,1 and Zhen Pei3 Received 23 November 2011; revised 29 May 2012; accepted 3 July 2012; published 18 August 2012.
[1] Using a CIMEL Sun photometer, we conducted continuous observations over the urban area of Shanghai (31 14′N, 121 32′E) from 18 April 2007 to 31 January 2009. The aerosol optical depth (AOD), Angstrom wavelength exponent, single scattering albedo (w0), and aerosol particle size distribution were derived from the observational data. The monthly mean AOD reached a maximum value of 1.20 in June and a minimum value of 0.43 in January. The monthly averaged Angstrom wavelength exponent reached a minimum value of 1.15 in April and a maximum value of 1.41 in October. The frequencies of the AOD and Angstrom wavelength exponent presented lognormal distributions. The averaged w0 at 550 nm was 0.94 throughout the observation period, indicating that the aerosols over Shanghai are composed mainly of scattering particles. The concentrations of coarse mode and accumulation mode aerosols over Shanghai were highest in spring compared with other seasons, especially for those particles with radii between 1.0 and 2.0 mm. The median radius of monthly averaged accumulation mode aerosols increased with increasing AOD, and fine particles accounted for the majority of the aerosol volume concentration. The ratios of the monthly averaged volume concentration of accumulation mode and coarse mode aerosols (Vf/Vc) were over 0.6 for all months studied and reached up to 1.94 in August. The volumes of the two modes changed with AOD, but their correlations presented different sensitivities, that is, the volume concentration of accumulation mode aerosols was more sensitive to variations in AOD than that of coarse mode aerosols. The aerosol volume concentration decreased with increasing w0, indicating that the higher the volume concentration of aerosols, the higher the absorption in particle extinction properties. The increase in absorption was caused primarily by secondary species coated on black carbon (BC) and primary organic carbon (POC) particles. Citation: He, Q., C. Li, F. Geng, H. Yang, P. Li, T. Li, D. Liu, and Z. Pei (2012), Aerosol optical properties retrieved from Sun photometer measurements over Shanghai, China, J. Geophys. Res., 117, D16204, doi:10.1029/2011JD017220.
1. Introduction [2] Aerosols, which are solid or liquid particles suspended in the atmosphere, are important indicators of climate change [Intergovernmental Panel on Climate Change, 2007]. Comprehensive knowledge of the optical and microphysical properties of aerosols forms the foundation of research on the environmental and climatic effects of aerosols. Given that the 1
Shanghai Meteorological Bureau, Shanghai, China. Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China. 3 Shanxi Meteorological Bureau, Taiyuan, China. 2
Corresponding author: C. Li, Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China. (
[email protected]) ©2012. American Geophysical Union. All Rights Reserved. 0148-0227/12/2011JD017220
physical and chemical properties of aerosols are characterized by high spatial and temporal variability, the study of their influence over the environmental and climatic conditions depends on the determination of their spatial and temporal distributions, as well as on the accurate estimation of their optical properties. The optical properties of aerosols and the characteristics of their formation into cloud condensation nuclei are governed by aerosol chemical composition, particle size distribution, and particle hygroscopic ability. Emission sources and emission mechanisms indicate that different types of aerosols have varied optical and radiation characteristics [Eck et al., 1999; Dubovik et al., 2002; Kim et al., 2004]. Kiehl and Briegleb [1993] concluded that the average size and chemical characteristics of particles exert considerable effect on aerosol optical properties. To reduce uncertainty regarding such properties, adding to our knowledge of aerosols is essential. Increased data on aerosols is especially vital to enhancing our understanding of aerosol chemical compositions, including sulfate, nitrate, ammonium, dust, sea salt,
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secondary organic aerosol, and EC/OC, particle size distribution, and optical characteristics. Simulating the effect of aerosols on atmospheric radiation by solving the radiative transfer equation necessitates the determination of AOD (t(l)), single scattering albedo (w0(l)), and asymmetry parameter. The optical properties of natural and anthropogenic aerosols determine the different roles they play in radiative forcing, thereby resulting in varied contributions to climate change. Many studies indicate that w0 governs the positive or negative effects of radiative forcing, that is, heating or cooling; this effect is also related to surface reflectance. AOD determines the magnitude of radiative forcing [Hansen et al., 1997]. In addition, most aerosol radiative models calculate the radiative properties (e.g., t(l) and w0(l)) of aerosols by parameterization of their physical and chemical characteristics (e.g., particle size, shape, and composition) [Koepke et al., 1997; Hess et al., 1998]. The uncertainty in aerosol models (e.g., particle size, refractive index, w0, and sphericity) is one of the primary sources of error in the satellite remote sensing of atmospheric AOD [King et al., 1992; Kaufman and Sendra, 1988], for some prior aerosol models are usually used in the retrieval of AODs from satellite observations. Bergstrom and Russell [1999] estimated that when 1d uncertainty exists, the change in w0 (at 0.55 mm) of aerosols over the North Atlantic reaches up to 0.07, which means that the corresponding uncertainty of solar fluxes at the top of atmosphere will be about 21%. Elucidating atmospheric aerosol optical properties and their effect on radiation balance necessitates ascertaining the composition, size distribution, and total concentration of atmospheric aerosols [Santer et al., 1996]. Clarifying the mechanisms of aerosol radiative forcing depends on a thorough understanding of aerosol optical properties, as well as their spatial and temporal variations in different regions [Ackerman and Toon, 1981; Remer et al., 1997; Jacobson, 2001; Satheesh and Moorthy, 2005]. [3] Numerous observational studies on the aerosol optical properties over China or East Asia have been conducted. Mao et al. [1983] analyzed the characteristics and regularity of change in the AOD over Beijing, as well as its relationship with meteorological conditions. In recent years, several scientific experiments on the optical properties and radiative characteristics of aerosols have been conducted in East Asia; these include the East Asian Studies of Tropospheric Aerosols – An International Regional Experiment [Li et al., 2007a], Asian Atmospheric Particle Environmental Change Studies [Nakajima et al., 2003], and Asian Sky Radiation Observation Network Experiment [Kim et al., 2004]. The above mentioned initiatives focused on inland aerosols, which are closely related to dust aerosols and urban pollution in large cities. Significant progress has also been made with regard to ground-based remote sensing networks in China, such as the Aerosol Remote Sensing Network (CARSNET) developed by the China Meteorological Administration [Che et al., 2009], the Chinese Sun Hazemeter Network developed by the Chinese Academy of Sciences [Li et al., 2007a], and the Sun and Sky Radiometer Network development in China [Takamura et al., 2002]. However, research on aerosol optical properties over near coastal urban areas has yet to be conducted. The Yangtze River Delta is located in the middle of China’s eastern coast. It is a sub-tropical and temperate transition zone characterized by both monsoon and oceanic climates. The formation of atmospheric aerosols is
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influenced by the combined effects of continents and oceans. This region is also a densely populated and well-developed core economic area. Due to rapid development in recent years, high aerosol loading over this region has led to significant effects on the local crop production, ecological environment, and regional climate. Luo et al. [2001] computed and analyzed the variation characteristics of China’s AOD using data drawn from nearly 30 years’ historical measurements in 46 national solar radiation stations and found that the Yangtze River Delta (YRD) region has experienced a rapid increase in AOD. Xu et al. [2002] observed the physical, chemical, and radiative properties of aerosols over the Yangtze River Delta. Their study showed that the aerosol concentration over the agricultural areas is comparatively equal to that over any highly polluted agricultural region. Wang et al. [2006] found large ranges of daily variations and weak seasonal variations in AOD with heavy aerosol loading and steady aerosol modes in the YRD region. Chen et al. [2008] studied aerosol optical properties and their spatial and temporal variability in four sites in the Hangzhou region. Xia et al. [2007] retrieved and analyzed the characteristics of aerosols in the Taihu area based on Sun photometer and surface irradiance data. However, while all of these studies contribute to the understanding of aerosol optical properties over the YRD region, they mostly focus on either variations in AOD and Angstrom index in the YRD region or relatively few samples from several seasons. [4] We used Sun photometer measurements of the automated tracking CE318 (CIMEL Company) and combined them with aerosol data that were simultaneously gathered using in situ Aethalometer and GRIMM 180 air sampling equipment for black carbon (BC) and PM10/PM2.5 measurements, respectively, to determine the long-term characteristics of aerosol optical properties over a typical coastal urban region. The observational data were also used to analyze the optical and microphysical properties of aerosols over Shanghai. Parameters, such as AOD, Angstrom wavelength exponent, w0(l) at 550 nm, and volume distribution were derived from Sun photometer observations. [5] The rest of the paper is organized as follows: section 2 provides a description of the observation site and experimental equipment, section 3 describes the algorithm used in deriving the key optical and microphysical parameters of aerosols, section 4 discusses the mixed optical characteristics of urban and marine aerosols over Shanghai, and section 5 presents the conclusions.
2. Experimental Site and Instruments [6] The observation equipment used in this research was set on the roof of the Shanghai Pudong Meteorological Bureau building (31 14′N, 121 32′E; elevation = 14 m). Figure 1 shows the location of the observation equipment and the geographic features of Shanghai. Shanghai City is situated at the eastern tip of the Yangtze River Delta and halfway along China’s eastern coastline; that is, it is adjacent to the East China Sea. As one of the most developed regions in the country, Shanghai has a population of 18.58 million, as well as numerous industrial enterprises and vehicles (1 million, as reported by the 2007 Statistical Bulletin of Shanghai), which emit a substantial volume of pollutant gases and aerosol particles. Around Shanghai, there are
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Figure 1. Geographic location of the Shanghai aerosol observation site.
couple of densely populated cities, including Nanjing and Hangzhou, each of which has a population greater than 2 million, Suzhou, Wuxi, Ningbo, and Changzhou, each of which has population between 1 and 2 million, Nantong, Yangzhou, and Zhenjiang, each of which is inhabited by 0.5 to 1 million people, and Shaoxing, Taizhou, Huzhou, Jiaxing, and Zhousan, each of which has a population between 0.2 and 0.5 million. These cities are almost contiguous, constituting the Yangtze Delta Megalopolis. A vegetative region of about 10 km2 is located to the east of the observation site and the rest of the area is surrounded by commercial and residential buildings from all other directions. The observation site is located near a city traffic road and is exemplified by typical urban surface characteristics. [7] Observations of column aerosol optical properties were conducted with a CE318 Sun photometer during the daytime for the entire duration of the study period. The instrument is commonly used to measure direct solar radiation at 8 channels with wavelengths of 1020, 936, 870, 670, 500, 440, 380 and 340 nm. It is stable and easy to operate and can withstand a variety of severe weather conditions, requiring minimal maintenance under such situations. The Sun photometer mostly employs atmospheric window channels, except for the 936 nm channel, which measures the intensity at the strong water vapor absorption band. The 670 nm channel also measures the intensity in the atmospheric window
channel but presents weak ozone absorption. The bandwidth of 6 channels from 440 nm to 1020 nm is 10 nm, and the other two on 380 nm and 340 nm is 2 nm. CE318 runs automatically in accordance with scheduled procedures. It initiates observations in the morning when the air mass is 6.0 (the solar elevation angle is about 9 ), after which the air mass decreases with increasing solar elevation angle. It stops operating in the evening when the air mass reaches 6.0 again. Continuous observations were conducted from 18 April 2007 to 31 January 2009. The Sun photometer was first calibrated in 2006 at Izana Observatory, one of the remote stations of the European mainland, after which it was brought to Lin’an, Zhejiang, for Langley calibration experiments from 8 August to 17 August 2007 and then to Beijing from 1 January to 10 January 2008 by inter-comparison with the China Aerosol Remote Sensing Network (CARSNET) masters at the Chinese Academy of Meteorological Sciences; hence, the observations may have been interrupted. The master instrument was calibrated by Langley plot analysis at the Mauna Loa Observatory following the Aerosol Robotic Network (AERONET) calibration protocol. The AOD difference between the master and the recalibrated instrument ranged from 0.005 to 0.015. The calibration of diffuse radiance was done once a year with the standard laboratory integrating sphere, and its relative uncertainty was better than 4% to 5% [Holben et al., 1998; Eck et al., 1999]. The process of cloud
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Table 1. Sun Photometer Observation Samples From 18 April 2007 to 31 January 2009 Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total Days
5
10
24
15
20
20 41 23
24 37
32
21
272
filtering and data quality control for direct irradiance retrieval was based on Smirnov et al. [2000], while that for diffuse radiance retrieval was based on Holben et al. [1998]. We used SKYRAD (version 4.2) developed by Nakajima et al. [1996] and Dubovik and King [2000] to retrieve some parameters, such as w0(l) and volume size distribution. SKYRAD was developed for the PREDE Sun photometer, which is different from the CE318 Sun photometer, so transformation of CE-318 raw data to SKYRAD inputs was necessary. ASTPWin software was used to transform K7 format files from CE318 to ASCII files, after which the AODs were calculated. The ALL and ALR files produced included left and right scans of the almucantar plane, respectively. After cloud filtering and data quality control of the ASCII files, the validated data were re-arrayed according to the format of SKYRAD raw data. Preprocessing of input data used the altitude of the station to calculate the atmospheric pressure and the monthly TOMS satellite climatology data to calculate the ozone optical depth [Che et al., 2009]. The ground albedo was assumed to be 0.1 during analysis and remained unchanged except when the surface of the measurement site was extraordinary. The solid view angle of the CE318 Sun photometer was difficult to estimate because it cannot scan a small domain round the sun like the PREDE mode does. Therefore, the w0(l) and volume size distribution were retrieve through the AERONET algorithm from CE318 measurement data during the Lin’an calibration experiments. Then the solid view angle in SKYRAD was changed by iterative procedure using the above retrieved parameters from the AERONET algorithm. Finally, we obtained the converged solid view angle value that was used to calculate whole data sets, which represent aerosol observations through spring, summer, autumn, and winter. Valid sample numbers in each month are listed in Table 1. The mechanisms behind temporal variations in the aerosol optical properties were also analyzed.
3. Methods [8] The SKYRAD method allows the size distribution, phase function, and w0(l) of the aerosols to be obtained from measurements of the direct solar irradiance (F) and diffuse sky radiance (E). When measured at ground level, these components are given by: F ðlÞ ¼ F0 ðlÞexp½m0 t ðlÞ
ð1Þ
EðQÞ ¼ Fm0 DW½wtPðQÞ þ qðQÞ
ð2Þ
where F0 is the extraterrestrial solar irradiance, w is the single scattering albedo, t aer(l) is the AOD, P(Q) is the total phase function at scattering angle Q, q(Q) is a multiple scattering term, and m0 is the optical air mass that can be
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approximated as m0 = 1/cosq0 as long as the zenith angle q0 ≤ 75 . DW is the solid view angle of the sky radiometer. [9] Nakajima et al. [1996] developed an optimized radiative transfer code for a plane-parallel atmosphere called the REDuced Multiple scattering program. To work with the diffuse component, the ratio R(Q) is defined: RðQÞ ¼
E ðQ Þ ¼ wtPðQÞ þ qðQÞ ¼ b ðQÞ þ qðQÞ Fm0 DW
ð3Þ
where b(Q) is the total scattering coefficient, which includes molecular and aerosol scattering. [10] The two quantities t(l) and b(Q) can be expressed as: Z t ðlÞ ¼ ð2p=lÞ
rM
Kext ðx; ˜mÞvðrÞd ln r
ð4Þ
K ðQ; x; ˜mÞvðrÞd ln r
ð5Þ
rm
Z b ðQÞ ¼ ð2p=lÞ
rM
rm
where Kext and K are kernel functions. x = 2pr/l, is the size parameter, v(r) is the columnar volume spectrum, which is defined as the volume of aerosol for an air column of unit cross section within a unit of logarithmic radius interval: v(r) = dV/d ln r (in cubic centimeters per square centimeters), rm and rM are minimum and maximum aerosol radii, respectively, and ˜m ¼ m ¼ ki is the aerosol complex refractive index. [11] The behaviors of Kext and K approximately determine the radius interval of reliable information of aerosol optical and physical features. Inspection of several kernels at refractive indices typical of the atmospheric aerosol shows that this interval ranges from 0.03 to 3 mm and 0.06 to 10 mm for only extinction and scattering data, respectively. When measurements of normalized diffuse sky flux are used together with the usual measurements of extinction, the radius interval ranges from 0.03 to 10 mm. [12] The idea of the method is to iteratively eliminate the multiple scattering term q(Q) from the data R(Q) to uncover the coefficient b(Q). In each of the steps, the code obtains the size distribution v(r) by the inversion of b(Q) and t(l). This distribution is used as input for the radiative transfer code for recalculating in turn R′(Q), which is compared with the experimental data to evaluate the average square difference ɛ(R). The process is repeated until the deviation is less than 10%. [13] Ångström [1964] provided a formula that expresses the spectral dependence of AOD on the wavelength of incident light: t aer ðlÞ ¼ bla
ð6Þ
where t aer(l) is the AOD, b is the Angstrom turbidity coefficient, which can be used to describe the general haziness of the atmosphere, and a denotes the Angstrom wavelength exponent, which can be obtained from the AODs of three wavelengths (i.e., 440, 670, and 870 nm) using the second order polynomial fit [Eck et al., 1999; Holben et al., 2001].
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Figure 2. Bar chart of the averaged monthly changes in the AOD (500 nm) and Angstrom exponent over Shanghai (error bar denotes standard deviation).
[14] The w0(l) is retrieved at wavelengths of 440, 670, 870, and 1020 nm [He et al., 2010]. Therefore, the w0(l) at a wavelength of 550 nm could be determined by linear interpolation as follows: w0:55 ¼ 0:55
w
six parameters can be used to describe size distribution. These parameters are calculated as follows: Volume mean radius (logarithm of the average radius) Z
dV ðrÞ d ln r d ln r Z r max ; dV ðrÞ d ln r r min d ln r r max
ln r
w0:44 0:67 w0:67 0:44 0:44 w0:67 : þ 0:67 – 0:44 0:44 – 0:67 ð7Þ
ln rV ¼
r min
ð9Þ
Standard deviation of the volume mean radius [15] A distribution function must be introduced to quantitatively analyze the statistical characteristics of the retrieved size distribution of aerosol particles. Many studies show that the bimodal lognormal distribution function can accurately describe the actual size distribution of aerosol particles [Shettle and Fenn, 1979; Remer and Kaufman, 1998], and has been validated as being universal and available in the YRD region. One of the validating studies is that of Xia et al. [2007]: " 2 # ln r ln rf Cf dV ¼ pffiffiffiffiffiffi exp d ln r 2sf 2 2psf " # Cc ð ln r ln rc Þ2 þ pffiffiffiffiffiffi exp 2sc 2 2psc
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uZ r max dV ðrÞ u d ln r ð ln r ln rV Þ2 u d ln r u sV ¼ u r min Z r max ; t dV ðrÞ d ln r r min d ln r
ð10Þ
Volume concentration (mm3/mm2) Z
CV ¼
r max
r min
dV ðrÞ d ln r: d ln r
ð11Þ
4. Results and Discussions ð8Þ
where C is the volume concentration of the particles, r represents the median radius, s denotes the standard deviation, and the subscripts f and c represent the accumulation and coarse mode aerosols, respectively. [16] This function divides the size distribution of aerosol particles into coarse and accumulation mode aerosols so that
4.1. Aerosol Optical Depth and Particle Size 4.1.1. Seasonal Characteristics [17] The AOD and Angstrom exponent are basic parameters that characterize aerosol optical properties. They can be used to calculate the atmospheric aerosol content, determine aerosol mode and size distribution, and validate satelliteretrieved data related to aerosol optical properties. They are also key factors that determine the radiative and climatic effects of atmospheric aerosols [Hess et al., 1998].
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Figure 3. The averaged monthly changes in the days of dust and PM10/PM2.5 observed from the same location of the Sun photometer in Shanghai. [18] Figure 2 shows monthly averaged changes in the AOD and Angstrom exponent over the study period. The monthly averaged AOD was lowest in January (0.43) and its maximum value (1.20) was achieved in June, the only month when it exceeded 1.0. Pan et al. [2010] analyzed the characteristics of the AOD and Angstrom exponent using CIMEL Sun photometer data from 2007 to 2008 in five sites located in the YRD region and found results similar to those in the current work: the AOD over Shanghai is largest in the summer and reaches a maximum monthly average in June. In the present observational study, the Angstrom exponent was lowest from March to May, when the AOD was less than the one in the summer. The weather during autumn was always sunny, with rare precipitation and clear atmospheres. The monthly averaged Angstrom exponent was larger in autumn than in summer. The winter weather is dry and cold with stronger wind. Heavy wind is conducive to the dispersion of aerosols and the hydroscopic increasing effect of aerosol particles is weak due to lower relative humidity, so the average AOD was low during the winter. [19] The higher AOD and lower Angstrom exponent in spring compared with those in autumn and winter may be partly caused by dusty weather due to the long-distance transport of dust from the surrounding and remote areas. The YRD region could be affected by dust storms from North China, as well as local pollution sources in spring, when precipitation is relatively low [Gong et al., 2003; Cui et al., 2009]. As shown in Figure 3, the days of dust and PM10/ PM2.5 observed during the spring months significantly increased, especially in March, during which the location of interest experienced 3 days of dust weather, according to the weather record. In fact, while the main aerosols originated from local direct emissions (primary particles) and in situ nucleation (secondary particles), they mostly concentrated in the boundary layer [Yu et al., 2012]. The impact of dust particles extends to the middle troposphere, impacting the columnar averaged aerosol size and reducing the Angstrom exponent. [20] The extreme variability of Shanghai’s column aerosol optical properties in summer was caused mainly by regional static meteorological conditions and the regional transport of
anthropogenic fine pollution particles from inland areas. Figure 4 shows the spatial distribution of 850 hPa seasonal average wind vectors and geopotential height over East Asia. This distribution was derived from NCEP/NCAR reanalysis data from June 2007 to January 2009. In summer, the atmosphere of Shanghai was dominated by continental air masses from the southwest and the clean sea air had no effect on the city, such that a large quantity of aerosols emitted from the local YRD region accumulated under static atmospheric conditions. The presence of intense solar radiation in the summer promoted the gas-particle transformation effect in the atmosphere, thereby producing more fine aerosol particles. Buzorius et al. [2004] analyzed the mechanisms of secondary aerosol formation over the Asian continent by airborne, shipborne, and ground-based observations in the ACE-Asia experiment. From the ground-based observation of aerosol size distribution, the authors found that Aitken nuclei particles increase with the rise in nucleation mode aerosols. They also found that newly formed particles are composed primarily of ammonium sulfate. These airborne observations indicate that the increasing concentration of aerosols is often accompanied by increasing concentrations of SO2. Yamaji et al. [2006] found that from May to June, the O3 concentration of the boundary layer over East Asia is at its maximum, 30% to 60% of which originates from the regional anthropogenic emissions produced by photochemical effects. In addition, the relative humidity of the lower atmosphere of Shanghai in June is high, and the growth of hydrophilic aerosol particles increases the AOD. Researchers found that the atmosphere over East Asia contains a substantial amount of sulfate and ammonium, both of which are highly sensitive to gas-particle transformations and hygroscopic growth. Aerosols in Asia exhibit more pronounced hygroscopic growth effects than those in Europe and the eastern United States. Frequent biomass burning in eastern China in June also contributes extensively to the increase in AOD. Tian et al. [2002] analyzed the consumption of biomass fuels in 1990s in rural China and found that straw is the main source of biomass emission. Cao et al. [2005] studied the emission factor of biomass consumption in Chinese provinces and cities in 2000 and found that the biomass consumption in
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Figure 4. Spatial distribution of the East Asia seasonal average 850 hPa wind vectors and geopotential height from the NCEP/NCAR reanalysis data for the observation period: (a) Spring, (b) Summer, (c) Autumn, and (d) Winter. eastern China is mainly induced by the open burning of straw. The optical properties of the aerosols produced by biomass burning are related to the age of the particles. Coagulation and gas-to-particle transformation increase the aerosol size such that the particles are transformed into accumulation mode aerosols with peaks near 0.2 mm [Eck et al., 2003]. [21] In summary, the AOD over the Shanghai area during the spring and summer is large, indicating that the atmosphere throughout the two seasons is turbid, and this turbidity may be attributed to different factors. In spring, atmospheric aerosols include a mixture of dust brought about by northerly or northwesterly winds and local emissions, whereas aerosols in the summer are comprised of local urban/industrial aerosols when heavy wind days do not occur frequently, which could cause the accumulation of local pollutants due to stable weather [Duan and Mao, 2007]. High aerosol loading in summer is typically caused by hygroscopic growth under high humidity conditions, gas-particle transformations under high temperature, and high levels of radiation. This result is consistent with that of Zhou and Xiang [1994], who viewed considerable urban air pollution as the direct cause of the increase in atmospheric turbidity. 4.1.2. Frequency Characteristics of AOD and Particle Size [22] During the observation period, the AOD ranged from 0.0 to 3.0. This range was divided by an interval of 0.2, after
which the frequency of the AOD was computed at each interval. The Angstrom exponent ranged from 0.0 to 2.0. The frequency of the Angstrom exponent at each 0.2 interval was calculated using the same method employed in the AOD calculations. The frequencies of the two aerosol parameters are plotted in Figure 5. The two frequencies are of lognormal distribution, that is, the average value of the data sets can represent the most likely value at each observation. The distribution of the AOD was positively skewed, whereas that of the Angstrom exponent was negatively skewed. The solid and dashed lines in Figure 5 represent the fitting curves of the frequency of the Angstrom exponent and AOD, respectively. F, x, and s represent the peak, mean and standard deviation of lognormal distribution, respectively. The AODs were mostly concentrated at 0.2 to 1.0, accounting for 80% of the total value. Low cases of AOD (AOD 1.0) accounted for 20%. AODs greater than 1.6 rarely occurred, accounting for 8% of the total value. The most common AODs were those that ranged from 0.4 to 0.6, accounting for 32% of the total value. [23] The frequency distribution of those samples with the Angstrom exponent (a) ranging from 1.4 to 1.6 accounting for 38% of the total samples. Those samples with a > 1.8 and a < 0.6 accounted for 0.3% and 1% of the total, respectively. The total samples are consistent with the lognormal
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Figure 5. Frequency distribution of the AOD and Angstrom exponent during the observation period. distribution (mean, 1.29; median, 1.34), which is consistent with the results of a previous study on the characteristics of aerosols located in the YRD region by Pan et al. [2010]. This asymmetry is caused by the occasional occurrence of largeparticle aerosols that affect Shanghai. Basically, aerosols over Shanghai are fine particles that originate from urban/ industrial and biomass burning aerosols. On the basis of their average value, we deduce that aerosols over Shanghai are mainly continental aerosols. Such types of aerosols occur because locally or regionally transported dust particles, hygroscopic growth, and the combined growth of fine particles
under heavy pollution affect the balance of the total number of samples. 4.2. Aerosol Single Scattering Albedo [24] In the present study, the column average w0 at 550 nm, with values ranging from 0.81 to 0.97 (Figure 6), was derived using two-year Sun photometer observations as bases. The observed w0 imposed a wide range of variations in the atmosphere over Shanghai and is potentially related to the burning of materials in the city, the type of aerosol formation, long-distance transport, and environmental and
Figure 6. Change in aerosol single scattering albedo (w0) at 550 nm derived from the Sun photometer during the observation period. The circle is the day average of w0, while the line is the average of all the samples. 8 of 17
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Figure 7. Monthly average changes in w0 during the observation period (error bars denote standard deviation, middle line denotes monthly average value, horizontal levels outside the rectangle denote maximum and minimum values of the month). meteorological conditions, among others. Thus far, no measurement or retrieval of w0 has been conducted in the Shanghai area; however, ground-based observation data gathered using the Aethalometer indicate that changes in BC concentration exhibit a trend opposite that of the w0 recorded by the Sun photometer. Transportation near the observation site is the largest source of pollution. The exhaust gas from numerous vehicles is one of the main factors that influence aerosol formation. The BC from burned oil causes the aerosols over Shanghai to be particularly absorptive. The long-distance transport of dust from the surrounding and remote areas also affects the properties of aerosols over the city. During the observation period, the average w0 was 0.94 0.02, close to the value (0.93) observed in Lin’an [Xu et al., 2002], which is a rural site and about 200 km away from Shanghai, but far from the value (0.90 in 440 nm and decreasing slightly with wavelength) observed in Beijing [Xia et al., 2006]. Specifically, the w0 of aerosols over Shanghai was higher than that in Beijing, indicating that the aerosol source and formation mechanism in these areas are somewhat different. Beijing is located in the north. The burning of coal during the winter for warmth produces large amounts of BC, further compounded by the presence of heavy industries in the surrounding areas of the city. As well, the terrain of Beijing is conducive to aerosol accumulation. In contrast, Shanghai is located near the sea. Marine aerosols are more strongly diluted by air pollution, and the absorption of these marine aerosols is very weak. Therefore, the aerosols over Shanghai have less absorption capacity than those in the northern cities of China. The aerosols over Shanghai are mainly generated from combustion processes induced by human activities. These processes include oil combustion, biomass burning, and a mixture of various industrial processes. In recent years, the implementation of industrial transformation,
motor restrictions, and other measures has been beneficial to the reduction of aerosol absorption. Levy et al. [2007] classified aerosols as follows: urban/industrial aerosols are nonabsorbing aerosols, with a representative w0 value of 0.95; general, forest smoke, and aerosol emissions in developing countries are classified as moderately absorbing aerosols, with a representative w0 value of 0.90; and smoke aerosols from prairie fires are high-absorbing aerosols, with a representative w0 value of 0.85. According to this classification standard, they considered aerosols over Southeast Asia as consisting primarily of non-absorbing aerosols. Observations of Shanghai aerosols in the current work confirm this conclusion. In addition, a recent study by Eck et al. [2005] verified that the aerosols over Southeast Asia are essentially non-absorbent. [25] Figure 7 plots the monthly average w0 (550 nm) derived from the Sun photometer. The figure shows that the minimum (0.92) of monthly averaged values was reached in March; the minimum of all individual observations for a given year was also observed in the same month. A low w0 generally represents the absorption properties of dust aerosols and biomass burning aerosols [Torres et al., 2005]. Zhang et al. [2005] found that the w0 in East Asia during the spring significantly decreases under the influence of frequent dust storms and long-distance dust transmission. Using TOMS to measure the global UV-band column average of w0, Hu et al. [2007] also found that the w0 in East Asia during the spring is at its lowest compared with other seasons. For visible bands, however, dust aerosols tend to be more scattering and less absorptive. The w0 in Beijing during dust periods was observed to increase significantly from 0.90 to 0.93 compared with anthropogenic air pollution episodes [Xia et al., 2005]. We suppose that the dust transported to Shanghai from remote deserts in Northwest China traveled a long distance over many industrial and urban areas of China.
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HE ET AL.: AEROSOL MEASURED FROM SUNPHOTOMETER
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Table 2. Average, Maximum, and Minimum w0 at Four Bands for the Different Seasons 440 nm
670 nm
870 nm
1020 nm
Season
w
wmax
wmin
w
wmax
wmin
w
wmax
wmin
w
wmax
wmin
Spring Summer Autumn Winter
0.935 0.95 0.944 0.942
0.968 0.968 0.968 0.969
0.802 0.849 0.842 0.868
0.925 0.944 0.934 0.934
0.968 0.968 0.967 0.967
0.818 0.829 0.821 0.84
0.92 0.938 0.928 0.929
0.965 0.967 0.966 0.963
0.823 0.815 0.815 0.828
0.917 0.933 0.923 0.925
0.962 0.966 0.964 0.961
0.825 0.813 0.817 0.826
The process may cause the dust particles were severely polluted and contaminated with BC [Jacobson, 2001]. Seinfeld et al. [2004] also demonstrated that aerosols in mixed states (i.e., with addition of BC and other aerosols to the mineral particles) can change dust aerosol radiative effects in many ways. In this study, the maximum monthly averaged w0 (0.95) was achieved in August. A high w0 generally corresponds to water-soluble aerosols under relatively high humidity conditions [Shettle and Fenn, 1979; Koepke et al., 1997], but many studies have found that the w0 of urban/ industrial aerosols continues to exhibit low values under relative humidity (RH > 90%). Hess et al. [1998] obtained w0 (550 nm) = 0.82, while Shettle and Fenn [1979] obtained w0 (550 nm) = 0.84. The lowest w0 value in August in the present study was 0.85. This monthly mean value in the summer is consistent with observations of Taihu of the YRD region [Xia et al., 2007]. However, compared with the w0 of other urban/industrial aerosols presented in the references, such as those from Mexico City with w0 (550 nm) = 0.90 [Hess et al., 1998], Beijing, and Xianghe [Qiu et al., 2004; Xia et al., 2006; Li et al., 2007b], our results are higher. Shettle and Fenn [1979] reported that w0 (550 nm) = 0.79 under the same humidity conditions. When the relative humidity drops, w0 decreases. Thus, for the relative humidity of Shanghai, which is generally high, the w0 should not be very low. BC emissions in China, as observed by Streets et al. [2003], exhibit a seasonal trend similar to that of the w0 in the present work. [26] Table 2 shows the seasonal average of w0 at different wavelengths inverted from the Sun photometer measurements. The average w0 at each band was consistently higher than 0.91 and showed a decreasing trend with increasing wavelength in the visible bands. The scattering extinctions of winter aerosols at each band were almost the same. The range of w0 tended to be larger with increasing wavelength in all other seasons except spring, during which dust outbreaks play a critical role in the formation of more coarse mode particles. For each band, the scattering capacity of aerosol particles was weakest in the spring but their absorption capacity during the same season was strongest compared with all other seasons. In contrast, the scattering capacity of aerosol particles was strongest in summer, but their absorption capacity during the same season was weakest compared with all other seasons. [27] Figure 8 illustrates the frequency distribution of w0 for different seasons. As shown in the figure, the w0 with the highest frequency in spring, autumn, and winter was typically about 0.94; in summer, it was usually 0.96, accounting for 58% of the total number of samples. These results indicate that summer aerosol particles have the strongest scattering capacity. Spring and autumn exhibited a
wide distribution range, but this result was especially pronounced in spring. The two seasons are alternate periods of warm and cold and more varied airflow activities can bring about even more aerosol sources, hence a higher probability of change in the scattering and absorption properties of aerosols exists in these seasons. Some samples for the two seasons had a w0 less than 0.85. These absorption aerosols would impose a significant heating effect on the regional climate. The w0 values of the summer and winter aerosols were concentrated at 0.86 to 0.96, and the values of 96% of these samples were concentrated at 0.92 to 0.96, indicating that the aerosol properties of the two seasons in Shanghai are stable and that their sources are relatively simple. 4.3. Aerosol Size Distribution [28] The retrieved volume size distributions during the study period presented various modes in different samples, including monomodal, bimodal and trimodal. However, most aerosol spectra can be distinguished in the bimodal distribution, and volume concentrations usually reached minima when particle radii ranged between 0.2 and 5 mm. Inversion results for the volume distribution showed that the shape of each mode was relatively close to the lognormal distribution. However, some volume distributions deviated from the lognormal distribution. Research shows that this kind of bias yields only a minimal effect on radiation. In calculating the distribution of two aerosol modes, therefore, minimum volume concentrations between 0.2 mm and 5 mm were selected as the cut-off point for fine mode and coarse mode particles. Figure 9 shows the inversion of the average volume distribution for the four seasons. The form dV(r)/d ln r (the particle volume concentration per unit area per log radius for a vertical column of air) was adopted to denote the aerosol particle size distribution and facilitate graphical representation. The median radius and standard deviation of the particle volume concentrations for each mode of volume size distribution were calculated. Particles produced from coal burning are usually concentrated in areas smaller than 1 mm, whereas particles generated from vehicle exhaust are concentrated in areas smaller than 2 mm. Some observational data revealed that vehicle emissions are the main source of Aitken aerosol pollution (diameter =
