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Keywords:

  • aerosol;
  • POLDER;
  • Aerosol Optical Depth

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Description of the Ground-Based and Satellite Data Set
  5. 3. Results and Discussion
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

[1] This paper is devoted to analysis of aerosol distribution and variability over East Asia based on PARASOL/POLDER-3 aerosol products over land. We first compared POLDER-3 Aerosol Optical Depth (AOD) with fine mode AOD (particles radius ≤ 0.30 μm) computed from AERONET (Aerosol Robotic Network) inversions over 14 sites. The rather good correlation (R ≈ 0.92) observed over land demonstrates the remarkable sensitivity of POLDER-3 retrievals to the smaller fraction of fine particles, mostly originating from anthropogenic sources. We analyzed the characteristics and seasonal variation of aerosol distribution over East Asia by considering 4 years of POLDER-3 Level 2 data (March 2005 to February 2009). Our study shows that the spatial distribution of fine-mode aerosols over East Asia, as retrieved from POLDER-3, is highly associated with human activities. Our work also evidenced a strong variability of seasonal fine-mode AOD patterns with geographical locations. Finally, the interannual variation during 2003–2009 periods of summer fine-mode AOD over North China, in particular the Beijing City region, was analyzed for the contribution to evaluating the regional impact of emission reduction enforced in Beijing during the 2008 Olympic Summer Games. We found that the summer average of fine-mode AOD exhibited relatively higher values in 2003, 2007, and 2008. The interannual variation patterns of monthly averaged AOD (June to August) shows that June generally exhibits the strongest variation and varies similarly to July, but differs from August. As a reference point, measured total AOD and fine-mode AOD computed from AERONET inversions in summer are also discussed for the Beijing City region.

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Description of the Ground-Based and Satellite Data Set
  5. 3. Results and Discussion
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

[2] Atmospheric aerosols, ubiquitous particles suspended in the atmosphere, play an important role in the climate system. They affect the Earth's radiative budget, both directly by scattering and absorbing solar radiation and indirectly by modifying properties of clouds such as their albedo and lifetime [Forster et al., 2007]. The effects of aerosols are thought to partially counterbalance global warming caused by greenhouse gases [Charlson et al., 1992; Forster et al., 2007]. Tropospheric aerosols, especially aerosols close to the Earth's surface, greatly influence air quality and thereby affect both the environment and human health. However, the large temporal and spatial variability of aerosols, as well as the variability of their physical properties, makes the estimation of their impact a challenging task. The lack of data characterizing the optical and physical properties of aerosols remains a primary source of uncertainty in quantifying both their climatological and environmental effects [Kaufman et al., 2002].

[3] As the first generation of large field-of-view polarimeter, POLDER (Polarization and Directionality of Earth's Reflectances) sensors [Deschamps et al., 1994] have flown on three different platforms. First, POLDER-1 was flown aboard the Japanese ADEOS-1 platform in operation from November 1996 to June 1997. Subsequently, POLDER-2 was flown aboard ADEOS-2 from April to October 2003. Finally, in December 2004, the third instrument, POLDER-3, was carried on the French microsatellite PARASOL, which was part of the A-Train up to the spring of 2010, and started routine observation in March 2005. POLDER has been designed to improve our knowledge of atmospheric aerosols and clouds by measuring the directionality and polarization of solar radiation reflected by the Earth atmosphere system. Over the land surface, spectral polarized radiances (Level 1 POLDER data) performed at 670 and 865 nm are used to derive the AOD over cloud-free regions (POLDER level 2). Thanks to its large field of view, POLDER provides daily global coverage. Moreover, while most other satellite sensors have difficulties in deriving aerosol properties over land because of the high surface reflectivity and spatial variability, POLDER overcomes the unknown surface contamination through the use of polarization information primarily due to atmospheric scattering processes [Deuzé et al., 2001].

[4] The aim of this study was to provide a comprehensive characterization of aerosols over East Asia based on more than 5 years of POLDER-3 retrievals over the land surface. First, we compared POLDER-3 AOD against AERONET over 14 sites located in this large area. Then we analyzed the characteristics and seasonal variations of aerosol distribution over East Asia using POLDER-3 aerosol retrievals over a 4-year period (March 2005–February 2009). Finally, considering all of the data available from POLDER-2 and POLDER-3, we analyzed summer AOD in the northern China region, especially the interannual evolution of summer aerosol content in Beijing City.

2. Description of the Ground-Based and Satellite Data Set

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Description of the Ground-Based and Satellite Data Set
  5. 3. Results and Discussion
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

[5] Our primary area of interest in this work was East Asia, a region that is considered to be one of the world's major sources of both natural and anthropogenic aerosols. In this study, “East Asia” is defined as the area with latitude ranging from 10°N to 55°N and longitude ranging from 70°E to 140°E. For the evaluation of POLDER-3 AOD, we used a data set covering the period between March 2005 and June 2008; however, in the analysis of aerosol distribution and variability over East Asia, we used POLDER-3 Level 2 AOD from March 2005 to March 2009. To broaden our database for the purpose of year-by-year evolution analysis during the summertime, we also included data from POLDER-2 in our work.

2.1. AERONET/PHOTONS Data Collection and Processing

[6] The AERONET program is a ground-based remote sensing aerosol network that covers more than 400 permanent and temporary sites worldwide [Holben et al., 1998, 2001]. The instrument used in AERONET is a CIMEL 318-E automatic Sun/sky radiometer. The purpose of this program is to provide long-term continuous measurements of aerosol optical, microphysical, and radiative properties for aerosol research as well as validation of satellite retrievals [Holben et al., 1998, 2001]. In the AERONET database, three different levels of data are available. The first level, termed Level 1.0, is available in real time. Level 1.0 data includes possible cloud contamination; however, in contrast, cloud contamination is automatically filtered from the second level of data, termed Level 1.5. For the third level of data, Level 2.0, data are cloud-screened and quality-assured and include final postdeployment calibrations. To obtain the largest amount of available ground-based data collocated by satellite retrievals, we use Level 1.5 AERONET AOD and inversion products for the evaluation of POLDER-3 retrievals at 865, 670, and 440 nm. When we performed this evaluation phase, we needed an aerosol record that would be as dense as possible. For many sites, Level 2 data were not available, and to get the most recent AERONET data records, we had to use Level 1.5 AOD and inversion products. Nevertheless, to keep the best quality data, we applied additional filters on the data, explained hereinafter. Figure 1 shows the area we focused on and the 14 AERONET sites (six sites in China; two sites in Thailand and Japan; and one site in India, Vietnam, South Korea, and Mongolia, respectively) considered in our analysis, providing more than 250 days with available data during the period from March 2005 to June 2008. More descriptive information for each site is presented in Table 1.

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Figure 1. Map of the study area, including the locations of the 14 AERONET sites (filled blue squares) in East Asia.

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Table 1. Geographic Sites of 14 AERONET/PHOTONS
SitesLatitude (°N)Longitude (°E)Elevation (m)Country
Beijing39.9116.490China
SACOL35.9104.11960China
Taihu31.4120.220China
Taipei_CWB25.0121.526China
XiangHe39.7116.936China
Xinglong40.4117.6970China
Anmyon36.5126.347South Korea
Bac_Giang21.3106.215Vietnam
Dalanzadgad43.6104.41470Mongolia
Kanpur26.580.2123India
Mukdahan16.6104.7166Thailand
Pimai15.2102.5220Thailand
Osaka34.6135.650Japan
Shirahama33.7135.410Japan

[7] In a recent study performed over Beijing and XiangHe AERONET sites, Fan et al. [2008] demonstrated good agreement between POLDER-3 and a fraction of the fine-mode AOD (particle radius ≤ 0.3 μm). On the basis of this previous work that was limited to two sites and a 15-month time period, we evaluated POLDER-3 AOD over land more comprehensively and systematically over 14 different sites located in East Asia over a period of 40 months from March 2005 to June 2008.

[8] In analyzing AERONET data, we adopted a methodology similar to that used by Fan et al. [2008]. Several filters were considered and applied to the data to detect and keep the more stable atmospheric conditions for comparing satellite and ground-based data. We first selected AERONET AOD Level 1.5 data matching satellite overpass time within ±30 minutes with four AOD observations available (AOD observation every 15 minutes). Then we computed the ratio between the temporal standard deviation, σ(τtotalAER), and the average AOD, τtotalAER, over the four values, keeping the information only when the ratio was lower than 0.20. Additionally, the sky error and the solar zenith angle of the almucantar were also considered to make further improvement in the data quality. We did not consider the AERONET inversion products when the sky radiance fitting error was larger than 5% and the solar zenith angle was less than 45 degrees. Finally, knowing that we would have to compute fine-mode AOD from the retrieved size distribution and refractive indexes, we considered the two following additional criteria. When calculating the fine-mode AOD from AERONET inversion products, we rejected data with the real part of the complex refractive index reaching the rather unrealistic value of 1.6 (maximum considered in AERONET retrievals). Moreover, we selected inversion products at times (inversion time) as close as possible to the satellite overpass time. However, time differences between the satellite overpasses and the first available inversion products were sometimes more than 2 hours. Therefore, to protect from the impact of a possible atmospheric change, we considered only inversion products when the Angström exponent, α, remained stable (Δα < 0.1). Moreover, to account for possible changes in AOD with stable α values, we introduced a specific correction given by equation (1).

[9] Fan et al. [2008] established for the Beijing AERONET site a 0.3 μm particle radius threshold for fine-mode definition consistent with POLDER retrievals. Assuming this value all over East Asia, we computed the fine-mode AOD, τfine(r≤0.3μm)inv, from size distribution and refractive index, assuming spherical particles. As mentioned previously, we accounted for the possible temporal variation of AOD between inversion and satellite overpass times using the following equation:

  • equation image

where τtotalAER is the total AOD averaged within ±30 min around the satellite overpass time, τtotalinv is the total AOD measured at the inversion time, τfine(r≤0.3μm)AER is the estimated fine-mode AOD truncated at 0.3 μm at overpass time, and τfine(r≤0.3μm)inv is the fine-mode AOD computed at inversion time.

2.2. POLDER Level 2 Data Collection and Processing

[10] POLDER Level 2 aerosol retrievals over land consist of AOD at 865 nm (τ865 nmSat) and Ångström exponent (αSat) at 20 km × 20 km spatial resolution. POLDER-1, POLDER-2 and POLDER-3 data were processed using the same algorithm described by Deuzé et al. [2001]. The inversion scheme is based on a “look-up table” approach, assuming spherical absorbing particles with monomodal size distribution and a given refractive index. Retrieval analysis demonstrated [Deuzé et al., 2001] that the refractive index cannot be retrieved reliably; therefore, an average refractive index of 1.47–0.01i estimated from Dubovik et al. [2002] climatology was considered. The retrieved aerosol parameters τ865 nmSat and αSat correspond to the aerosol model and average refractive index that give the best agreement between measured and simulated polarized radiances at 670 and 865 nm. Since the POLDER algorithm over land relies only on polarized radiances measured at 670 and 865 nm that are known to be mainly sensitive to small particles, the retrieved aerosol parameters are related to the smaller fraction of the accumulation mode. The Ångström exponent was not studied as such in our study, but was used to derive the AOD at shorter wavelength (670 and 440 nm) following equation (2):

  • equation image

[11] Because of calibration uncertainty in the POLDER-3 blue polarized channel, this channel cannot be used for retrieval. However, since the 440 nm channel has been shown to be useful for some air quality monitoring [Kacenelenbogen et al., 2006], we also derived AOD at 440 nm by extrapolation using equation (2), knowing that this extrapolation would bias the AOD because of the nonlinearity of the Angström exponent for fine particles, particularly for aged fine-mode pollution or smoke, which commonly occur in East Asia [Eck et al., 1999]. Hence, in our work, we use AERONET data to evaluate POLDER-3 retrievals first of all at 865 nm (τ865Sat) and 670 nm (τ670Sat), but we also considered the extrapolated AOD at 440 nm (τ440Sat) in this exercise. These parameters have been averaged over a 0.5° × 0.5°-size box centered on each Sun-photometer site. Moreover, the standard deviation within this box is provided to estimate the spatial variability around the Sun-photometer location. The AERONET and POLDER-3 aerosol retrievals are then matched in both time and space to evaluate POLDER-3 aerosol retrievals over East Asian land surfaces. The results of these comparisons are presented and discussed in the following section.

3. Results and Discussion

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Description of the Ground-Based and Satellite Data Set
  5. 3. Results and Discussion
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

3.1. Regional Validation of POLDER-3 Aerosol Retrievals

[12] Comparisons of POLDER-3 AOD against τfine(r≤0.3μm)AER are shown in Figures 2a, 2b, and 2c, respectively, for 865, 670, and 440 nm channels. The error bars on the y-axis represent the spatial standard deviation computed over the 0.5° × 0.5°-size window. Similar comparison for 440 nm is presented without error bars, since the standard deviation was very similar to that of 865 and 670 nm. Our results demonstrated overall good agreement between POLDER AOD and τfine(r≤0.3μm)AER. The correlation coefficients are 0.92, with the 95% confidence intervals (CI) from 0.90 to 0.94, for both of 865 and 670 nm. For 440 nm, the correlation coefficient is 0.908 and the corresponding 95% CI is 0.88–0.93. The slopes, calculated by the robust regression method on the basis of iteratively reweighted least squares, are 0.84, 0.81, and 0.7 for 865, 670, and 440 nm, respectively. The observed decrease of the slope with wavelength can very likely be explained by extrapolation validity as well as limitations in the retrieval assumptions such as departure between modeled and true absorption, particle shape, and residual surface contribution.

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Figure 2. Comparisons between POLDER-3 AOD and AERONET fine-mode AOD (r ≤ 0.3 μm) at (a) 865 nm, (b) 670 nm, and (c) 440 nm over 14 sites located in East Asia from March 2005 to June 2008. POLDER-3 AOD values at 670 and 440 nm were computed from POLDER-3 AOD at 865 nm and the corresponding Ångström exponent. Error bars in Figures 2a and 2b show spatial variation of POLDER pixels.

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Figure 2. (continued)

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[13] More detailed information (site by site) from robust regression is provided for the 865 nm channel in Table 2. To ensure the reliability of regression results, we skipped four sites having less than 5 days with available data, including Kanpur (4 days), Mukdahan (4 days), Pimai (4 days), and Taipei_CWB (2 days). Averaged AERONET and satellite AOD for each site are presented in the second and third columns in Table 2, along with the root mean square (RMS) indicating daily variation. All statistical data are arranged in order of increasing averaged AERONET AOD to partially reflect the aerosol pollution level for each site. For most of the sites, the averaged POLDER-3 AOD is rather comparable with the ground-based AOD. The results of robust regression analysis are also presented in Table 2, where R, together with its 95% CI in the fifth column, represents the correlation coefficient and N represents the number of days with both satellite and ground-based data. The SD provided in the sixth column of Table 2 is actually the residual standard deviation, suggesting the degree of dispersion from the actual data to the linear regression line.

Table 2. Statistics and Robust Regression Analysis Between PARASOL AOD and Fine-Mode AOD from AERONET Measurements at 865 nm (March 2005 to June 2008)
SiteMean ValueRobust Regression
AERONETPARASOLR95% CI for RSDslopeinterceptN
Dalanzadgad0.01 ± 0.010.02 ± 0.010.16(−0.42, 0.65)0.010.380.0113
Anmyon0.04 ± 0.010.04 ± 0.010.84(0.40, 0.97)0.010.770.019
Osaka0.05 ± 0.020.05 ± 0.030.61(0.12, 0.86)0.020.970.0014
Shirahama0.05 ± 0.030.05 ± 0.040.72(0.46, 0.88)0.030.980.0025
Xinglong0.06 ± 0.040.04 ± 0.040.93(0.73, 0.98)0.010.78−0.0110
SACOL0.07 ± 0.030.03 ± 0.020.67(0.44, 0.82)0.020.56−0.0137
XiangHe0.13 ± 0.130.11 ± 0.110.96(0.93, 0.98)0.030.850.0032
Beijing0.13 ± 0.130.11 ± 0.100.93(0.89, 0.96)0.040.730.0152
Taihu0.14 ± 0.060.17 ± 0.090.87(0.60, 0.97)0.051.270.0012
Bac_Giang0.15 ± 0.140.17 ± 0.140.97(0.78, 1.00)0.030.950.036

[14] For 9 of 10 sites, the correlation coefficients (R) are better than 0.6 and the majority of slopes are close to 1. Nevertheless, there are three sites with 95% CI for R wider than 0.5, which probably were induced by the few available data.

[15] For Dalanzadgad site, the correlation coefficient of 0.16 shows no strong correlation between POLDER-3 retrievals and AERONET inversions. The main explanation may be attributed to the very low level of AOD over this site, which is located in the Gobi areas of Mongolia, together with restrictively limited AOD range. The atmosphere is generally quite clear and clean, except during dust events that are estimated to occur 30 times per year [Mandakh and Khaulenbek, 2002; Qian et al., 2006]. The multiannual AERONET total AOD at 865 nm measured over Dalanzadgad was only 0.07; this is much lower than the total AOD recorded over Beijing, which was 0.41. In most cases, dust events generating mainly coarse, nonspherical particles are very weakly polarizing light, therefore making these particles almost nondetectable with POLDER current retrievals. The distribution of blue dots in Figure 2 also partly reflects the particular characteristic of aerosols over this site.

[16] Overall, our validation results show that POLDER-3 aerosol retrievals over land are very consistent with the ground-based fine-mode AOD for most of the AERONET sites of East Asia, when the fine mode is defined with a maximum radius value of 0.30 μm. We observed that 9 of 10 AERONET stations have correlation coefficients above 0.6 and four sites are above 0.9.

[17] These results indicate that POLDER aerosol products over land are quite relevant for analyzing spatial and temporal distributions of fine particles over East Asia. This analysis is developed in the next section.

3.2. Characteristics of POLDER AOD Distribution Over East Asia

[18] East Asia is an important source of both natural and anthropogenic aerosols because of its geographical characteristics and the rapid growth of its economy. However, our knowledge of the spatiotemporal distributions of aerosols is still limited because of the lack of long-term and large-coverage observations [Choi et al., 2009; Li et al., 2002; Xu et al., 2004; Park et al., 2005; Li et al., 2008]. We therefore considered POLDER-3 retrievals from March 2005 to February 2009 to characterize aerosol patterns over East Asia as well as seasonal variability.

[19] To provide a first overview of the aerosol distribution over this very large region, we show in Figure 3a a 4-year POLDER-3 averaged AOD. Information on the percentage of days with available aerosol retrievals is also given in Figure 3b. Percentages lower than 100% are due to cloud cover (e.g., the whole area, especially Southeast Asia and the southern part of China), to topographic features of the underlying surface (e.g., the Tibetan Plateau), or to missing data (the white color in the inland areas). Moreover, to aid interpretation, the locations of several typical areas mentioned below are marked in Figure 3c.

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Figure 3. (a) Map of averaged AOD at 865 nm and (b) pixel frequency based on POLDER-3 observation over a 4-year period (March 2005–February 2009). The frequency is calculated as the number of days with successful PARASOL aerosol retrievals in clear sky divided by the total number of calendar days during the study period. (c) Illustration of typical areas mentioned in this paper, with the Sichuan Basin presented as a shadow. Country names are given in red, including red “1” and “2” for Laos and Bangladesh, respectively, while the names of provinces in China (shaded boundaries) are written in light green.

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[20] As shown in Figure 3a, the highest average AOD mostly appears in the Sichuan basin and the southeastern region of China, particularly around the middle and lower valleys of the Yangtze River, the Delta of the Pearl River, and the North China Plain, which are densely populated, highly industrialized, and economically developed. Additionally, there are also several other areas with high AOD value, such as the southern Himalaya region in India and the areas around the border between China and Laos. According to our study, the maximum of POLDER-3 AOD value over East Asia has been estimated as 0.25, whereas the mean value is 0.05. Excluding the very clear regions over the Tibetan Plateau and the northwest part of East Asia, which are beyond the scope of this study, the low AOD values primarily appear over northeast China, including Heilongjiang (HLJ), Jilin (JL) Province, and Inner Mongolia (IM).

[21] Similar patterns have also been demonstrated by Luo et al. [2001] on the basis of the analyses of total AOD retrieved from a network of 46 solar radiation stations as well as with MODIS total AOD within the August 2000 to April 2003 period [Li et al., 2003b].

[22] However, there are some areas with high AOD derived from MODIS in the western part of Tibet, the Tarim Basin of Xianjiang (XJ), and the Qaidam Basin in Qinghai Province (QH) mentioned by Li et al. [2003b] that are not highlighted by POLDER. This difference is likely related to the specificity of the POLDER-3 retrievals over land, which are not sensitive to the coarse mode fraction characterizing dust particles occurring in these regions. There is another visible feature in the Sichuan Province, where the highest AOD, appearing in the east, contrasts sharply with the lowest values retrieved in the western part of the province. This remarkable difference is likely due to the unique topography of Sichuan Province. The elevation in the west part is almost 10 times higher than the elevation in the east. The terrain with higher elevation certainly limits aerosol transport and therefore exhibits lower AOD.

[23] A more precise examination of the seasonal variation of aerosol patterns is presented in Figure 4, in which the four seasons, spring (MAM), summer (JJA), autumn (SON), and winter (DJF), are separated. The white color in the inland regions again represents areas in which the valid retrievals were not obtained.

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Figure 4. Spatial distribution characteristics of POLDER-3 AOD at 865 nm over East Asia for (a) spring (MAM), (b) summer (JJA), (c) autumn (SON), and (d) winter (DJF).

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[24] In spring (Figure 4a), the highest AOD values mostly appear in the Sichuan Basin, the Pearl River Delta in China, the areas around Stanovoy Range in Russia (the north of Heilongjiang Province in China) and Southeast Asia, especially in the northern part of Laos and its surrounding areas in Burma (B), Thailand (T), and Vietnam (V), which nearly link up with the high AOD value area in the southern Guangxi Province (GX) of China. The high AOD values in Sichuan Basin seem consistent with its high population density and its unique topography benefiting the retention and accumulation of aerosol pollutants [Li et al., 2003a], while the high values over Southeast Asia and Russia are very likely due to slash-and-burn farming and forest fires [Kim et al., 2004; Murdiyarso and Lebel, 2007; Padoch et al., 2007; Damoah et al., 2004; Lee et al., 2005; Kanaya et al., 2003]. Along with local aerosol emissions, fine-mode particles generated by the biomass burning in Southeast Asia may be transported to the south of China following the dominant westerly wind during the spring; this is likely the cause of the high AOD in Guangxi Province and the Pearl River Delta [Li et al., 2003b; Kim et al., 2007].

[25] In summer (Figure 4b), though, the AOD values significantly decrease around Sichuan Basin. We observed an increase of AOD in the eastern part of China, especially around the border of Shandong and Hebei, likely due to anthropogenic pollution. The decreasing AOD trends in the Sichuan Basin are possibly associated with the wet deposition caused by the frequent rainfall, which washes out the majority of ambient respirable suspended particulates and shortens their lifetime. High AOD values detected in spring in Southeast Asia are barely visible in summer because of the reduced amount of aerosol retrievals resulting from the heavy cloud cover during the monsoon period. Additionally, the strong rainfall, together with the high cloud cover over Southeast Asia during summer, prevents the biomass burning and also washes out pollution. In contrast, most of southern Guangxi Province still has high AOD during this time because of the aerosol emissions from the heavy industrialized areas and the relatively stagnant conditions over this region.

[26] In autumn (Figure 4c), AOD slightly increases in eastern and southern China, the Sichuan Basin, and the region from the north part of India to the south of the Himalayan Mountains, but decreases in Guangxi Province of China.

[27] During the winter (Figure 4d), AOD values increase and reach their maximum in the Sichuan Basin, the northeastern part of India along the Himalayan Mountains and Bangladesh. In the Sichuan Basin, the observed AOD increase can be explained by poor dispersion conditions and heavy local industrial and vehicle emissions. Near the base of the Himalayan Mountains in northern India and Bangladesh, a large AOD, possibly related to the pollution accumulation due to temperature inversion in the boundary layer, is observed. During the winter, cold air flows from the mountains down to the plains, making the air near the ground cooler than the air above it. This probably traps aerosols from agricultural fires and cities near the ground [Marshall, 2005; Di Girolamo et al., 2004].

[28] On the basis of the above analysis, we conclude that aerosol patterns detected by POLDER-3 over East Asia are clearly linked with human activities. Most of the regions characterized by large AOD values are located in southeastern China and northeastern India, areas characterized by developed industries and rapid economic growth. The north part of Southeast Asia and the Stanovoy Range in Russia only exhibit a high AOD during spring, when representative aerosol emissions are dominated by biomass burning activities.

[29] Seasonal fluctuations in POLDER-3 AOD vary between different geographical locations. For example, south-central China and the northeastern region of India along the Himalayan Mountains reach their maximum AOD during winter, while the maximal AOD in Southeast Asia and the Stanovoy Range in Russia occurs during spring. The North China Plain reaches its maximum of AOD in summer, while in southern China (the middle and lower valley of the Yangtze River), the highest AOD occurs in autumn. The main factors controlling regional variability are the differences in both local emissions and seasonal meteorological conditions in the different geographical regions [Kaufman et al., 2002].

3.3. Year-by-Year Evolution of Summer Aerosol Loads in the North China Region

[30] China, centrally located in East Asia, shows higher emissions of both natural and anthropogenic aerosols. The mean aerosol total AOD in China is about twice the global continental mean value [Li et al., 2002]. Beijing, the political and cultural center of China, is even recognized as one of the world's 10 most polluted cities and also shares with Mexico City the distinction of being the world's most polluted capital. During the past 30 years, Beijing has experienced severe aerosol pollution predominantly caused by rapid economic development, population expansion, and urbanization, as well as some secondary problems such as increasing traffic density, a high consumption of coal, flourishing construction activities, and dust storms from deserts [Sun et al., 2004; Chan and Yao, 2008]. As an indicator of air quality, Aerosol Optical Depth in Beijing has been revealed to reach its maximum during summer because of atmospheric stagnation events that are typically associated with high temperatures [Fan et al., 2006; Xia et al., 2006, 2007]. According to the previous studies, the severe aerosol pollution in Beijing is a result of not only internal emission sources within Beijing but also the atmospheric pollution of surrounding provinces [Xu et al., 2002, 2003, 2005, 2006]; this is particularly true during summer as a result of a combination of southerly winds and the topography of the surrounding regions [He et al., 2009]. Numerical simulations have also suggested that 34% of PM2.5 [Streets et al., 2007] and 40% of PM10 [Chen et al., 2007] in summer over urban Beijing could be generated by regional emissions. To improve the air quality in Beijing and ensure a healthier atmosphere for athletes and spectators during the 2008 Summer Olympic Games, China introduced extensive provisions and enforced emission reductions in Beijing and its surrounding areas before and during the event. Such an effort provided a unique opportunity to study the anthropogenic contribution to the atmospheric aerosol loads. Therefore, we now focus our analysis on aerosol variability during the summer in Beijing and its surrounding provinces, which are termed “North China” in this article. In the following analysis, we investigate interannual AOD variation from 2003 to 2009 as seen using POLDER instruments. More precisely, our area of interest, North China, includes all regions of China within the limits of 32°N–42°N in latitude and 110°E–120°E in longitude (Figure 5a), including Beijing (population 11.5 million) and Tianjin (9.3 million) municipalities [National Bureau of Statistics of China (NBS), 2004]. Surrounding provinces, including Hebei, Shandong, Shanxi, and Henan, which are heavily populated, urbanized, and industrialized, are also included. In the areas surrounding Beijing, emission controls on stationary sources and vehicles are not as stringent as in Beijing itself, and emission rates are therefore higher. Rural biomass burning has also been indentified as an important contributor to fine PM concentrations in Beijing [Duan et al., 2004; Streets et al., 2007]. Emissions from these nearby sources, as well as more distant ones, are subject to chemical reactions during transport on prevailing winds, forming secondary species that enter the entire region and are added to the local pollution of Beijing [Han et al., 2005; Hatakeyama et al., 2005; Luo et al., 2000; Mauzerall et al., 2000].

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Figure 5. (a) Study area referred to as “North China” in this paper and (b) the map of Beijing showing main urban districts (shaded area), interior suburban districts (dark shaded boundary), the far north districts and counties (light shaded boundary), and the locations of Beijing and XiangHe sites (black points). The box in Figure 5b highlights the Beijing City defined and analyzed in this paper.

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[31] Utilizing both POLDER-2 (2003) and POLDER-3 (2005–2009), we averaged, over the North China area, the AOD at 865 nm from June to August for each available year. The results are presented in Figures 6a6f. Moreover, complementary statistical parameters such as median, mean, standard deviation, first and third quartiles, as well as the maximum and minimum, are given in Figure 7.

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Figure 6. Spatial distribution characteristics of POLDER AOD at 865 nm over North China (a) in the summer of 2003 and (b–f) from 2005 to 2009.

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Figure 7. Boxplots of averaged POLDER AOD at 865 nm in the summer of 2003 and 2005–2009 over North China. In each box, the central bar is the median, and the lower and upper limits are the first and the third quartiles, respectively. The error bars on the y-axis indicate 1.5 times the spatial variation (SD).The associated maxima and minima are indicated by asterisks. The square symbols indicate mean values.

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[32] A significant interannual variation in AOD can be observed in both Figures 6 and 7. The maximum AOD was observed in 2003 (0.19), whereas the minimum AOD, which was roughly half of the maximum (0.10), occurred in 2006. The summer of 2008 appears to have been a moderately polluted season and was followed in 2009 by a new minimum AOD (0.08).

[33] A similar trend was observed upon investigation of the regions covered with high AOD values (Figures 6a6f). During the summer of 2003, high AOD values covering almost the entire Shandong Province were linked with those measured over the Southern Hebei Province, Beijing and Tianjin municipality, the northeast region of Henan Province, as well as the northern Anhui and Jiangsu Province. The strip of plains in Shanxi Province between the Taihang (East) and Lüliang (West) Mountains also shows a higher AOD in the summertime. During the summers of 2005 and 2006, the area covered with AOD values greater than 0.20 decreased significantly and was mainly localized along the border of Hebei and Shandong provinces, Tianjin municipality, and the border area between Shandong and Jiangsu provinces. However, the surface covered with higher AOD levels expanded greatly into the southwest regions during the summer of 2007, indicating another heavily polluted season. In the seasons following, emission control strategies were enforced in Beijing and its surrounding provinces to ensure better air quality for the 2008 Olympic Games. The area covered with AOD values larger than 0.20 decreased slightly in the summer of 2008 when compared to 2007. The area covered with high AOD values decreased again in the summer of 2009, when high AOD values only appeared over the north part of Anhui and Jiangsu provinces.

[34] Overall, our analysis indicates that the fine-mode AOD in North China is quite variable with the extreme values separated by a factor of nearly 2 between years. In addition, our results suggest that the summer of 2008 was a moderately polluted season.

[35] Furthermore, the differences observed between the third and the first quartiles in Figure 7, as well as the standard deviations, are quite large, indicating the high level of spatial variability of AOD.

[36] As shown in Figures 6a6f, the high AOD values appear predominantly over the southeast plains of our analyzed area, where many large urban centers with dense populations and developed industries are distributed, whereas the plateau and mountains in the northwest typically exhibit AOD values lower than 0.10. The plateau and mountains with high elevations presumably restrict the spreading of aerosol pollution. The same explanation can be given for the low AOD in the northwest part of Beijing City, which contrasts with the high values in the main southeast urban regions.

[37] To obtain a more detailed picture of the aerosol variability in North China during the summer months, we investigated the interannual evolutions of monthly mean of AOD at 865 nm for June, July, and August separately in Figure 8. The results in Figure 8 demonstrate that June is the month with the highest AOD value, as well as demonstrating the highest level of AOD variability. In July, the variability of AOD generally appears to be similar to that of June, but with reduced amplitude. It should be noted that AOD for both June and July start increasing in 2006 and do not decrease until 2009. In 2008, AOD for both June and July still show an increase, whereas a decreasing trend was observed for the summer as a whole, as shown in Figure 7. August shows a significantly different interannual AOD variation. The monthly mean AOD of August increases continuously from 2003 until 2008, when it appears strongly decreased. Moreover, in contrast to June and July, the mean AOD increased in August 2009. The interannual contrasts observed over the North China region for June, July, and August can be explained by the relative contributions of complex processes such as stagnant synoptic meteorological patterns, secondary aerosol formation, and hygroscopic growth of aerosols and smoke aerosols by regional biomass burning [Kim et al., 2007].

image

Figure 8. Interannual evolution of monthly POLDER Aerosol Optical Depth at 865 nm in June (hollow triangle), July (filled triangle), and August (filled circle) over the whole of North China from 2003 to 2009.

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[38] Finally, we focused on smaller-scale analysis, specifically in Beijing City, which is defined by a 0.5° × 0.5°-size area centered on the Beijing AERONET site (latitude 39.98°N, longitude 116.38°E).

[39] At this new scale of analysis, ground-based total AOD and fine-mode AOD as measured and derived from AERONET data again served as a reference (Figure 9). Considering the sparse available data obtained in the summer of 2008 over the Beijing site, we also include the XiangHe AERONET site (latitude 39.75°N, longitude 116.96°E), which has been in operation since 2005 and is located about 70 km southeast of Beijing. This site is classified as a rural area with respect to surface cover; however, AOD and other aerosol properties (size distribution, absorption) over the XiangHe site are nearly the same as those over the Beijing site [Eck et al., 2005; Xia et al., 2005], though the surface properties are different. Combining these two sites enhances the reliability of our comparison. Our study area, covering the core city districts of Beijing, is highlighted by a box in Figure 5b, along with the locations of AERONET sites of Beijing and XiangHe. As described previously, we use AERONET Level 1.5 products to obtain more data. The ground-based parameters are plotted in Figure 9 result from an averaging of the XiangHe and Beijing sites. The mean AOD from each site is weighted by the number of available observation from each site. As shown in Figure 9, both POLDER AOD values and ground-based fine-mode AOD generally follow very similar year-to-year evolution to total AOD provided by AERONET. A very slight decrease in the fine mode AERONET AOD can, however, be observed in 2008, whereas the POLDER-3 AOD slightly increases along with the total AOD.

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Figure 9. Year-to-year evolution of AOD at 865 nm from POLDER observations (filled pentagram) and AERONET measurements around Beijing areas considering both Beijing and XiangHe ground-based sites during summer months, where the total and fine-mode AOD are represented by filled circles and hollow pentagrams, respectively.

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[40] The good agreement between AOD variability as detected by POLDER and the ground-based one demonstrates the potential of POLDER for monitoring aerosols. Moreover, the combination of POLDER, demonstrated to be sensitive to a fraction of the fine-mode aerosol, and MODIS, known to be sensitive to the entire size distribution [Remer et al., 1996, 2005], could enable derivation of aerosol coarse-mode optical depth in the context of the A-Train observatory.

[41] More detailed information for June, July, and August over Beijing City are presented separately in Figure 10 for both total AOD and fine-mode AOD. As shown in Figure 10, monthly POLDER AOD, as well as monthly AERONET total AOD and fine-mode AOD, varies strongly. Specifically regarding AERONET, the maximum total AOD was detected in June 2008, while the minimum was detected in August 2004 (the next is June 2009, more recently). The maximum fine-mode AOD appeared in June 2002 (June 2007 next), and the minimum appeared in June 2009.

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Figure 10. Interannual evolution of monthly averaged AOD at 865 nm from POLDER (inner bar) observations in June (light shaded), July (dark shaded), and August (black) as well as the associated total (hollow bar) and fine-mode AOD (short line) from AERONET measurements for the available years from 2002 to 2009 over Beijing City.

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[42] The lack of available POLDER data in 2002 and 2004 may partially bias the comparison of year-to-year evolution with AERONET AOD. Nevertheless, the monthly POLDER AOD in summertime showed a maximum in June 2003 and a minimum in June 2009.

[43] However, for the fine-mode AOD, the aerosol data did not show such a clear pattern. Focusing on the years around the Olympic Games (2007–2009), we observed that the AERONET total AOD in August varied in a trend opposite to that of June and July. The total AERONET AOD decreased very slightly from 2007 to 2008 and then increased from 2008 to 2009. The decrease of total AOD in 2008 has also been observed with MODIS [Cermak and Knutti, 2009]. For the fine-mode fraction, the agreement between POLDER and AERONET are good for June and July 2008, whereas our results show that POLDER AOD were an overestimation of the AERONET values in August. Finally, both ground-based and satellite fine-mode AOD values are consistent and clearly detect the strong AOD decrease in 2009 compared to 2008.

4. Conclusions

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Description of the Ground-Based and Satellite Data Set
  5. 3. Results and Discussion
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

[44] In this study, we addressed the distribution and variability of aerosols over East Asia based on the POLDER-2 (2003) and POLDER-3 (2005–2009) data sets. Special attention was paid to AOD variability during the summer months over North China and Beijing City.

[45] In preliminary work, we evaluated POLDER-3 against AERONET AOD values on the basis of 14 AERONET/PHOTONS sites located over several countries of East Asia. The results show a good agreement between space- and ground-based fine-mode AOD (particle radii less than or equal to 0.30 μm). POLDER retrievals are shown to underestimate this fine-mode AOD by 16%, 20%, and 30% for 865, 670, and 440 nm, respectively. Nevertheless, good correlations were observed over most of the AERONET sites considered in this paper, thus confirming the high quality of the POLDER aerosol retrievals, as well as their capacity to detect the fine particles of aerosol loads. The analysis of POLDER-3 AOD from March 2005 to February 2009 highlights the characteristics and seasonal variations of aerosol distribution over East Asia, strongly suggesting that human activities may be the main source of fine-mode aerosols. Indeed, all areas with relatively high values of AOD are regions characterized by dense population and rapid growth of economic, such as the Sichuan Basin, the middle and lower valleys of the Yangtze River, and the North China Plain in China and the northern India. Significant seasonal variation of aerosol distribution was clearly observed by POLDER-3. The occurrence of high spots with heavy fine-mode aerosol loads over Southeast Asia and the forest zone in Russia were found only during spring seasons. For south-central China (i.e., the Sichuan Basin) and northeastern India along the Himalayan Mountains, the maximum AOD occurs in winter, while the maximum AOD of the North China Plain occurs in summer. However, Southern China (specifically the middle and lower valleys of the Yangtze River) presents its highest AOD in autumn.

[46] On the basis of 6 years of observations from POLDER-2 (2003) and POLDER-3 (2005–2009), we analyzed the year-to-year evolution of fine-mode aerosol loads in the region of North China and Beijing City, with a special interest in the impact of emission control strategies implemented over Beijing and its surrounding areas for the 2008 Summer Olympic Games. In the North China region, the level of fine-mode aerosol load observed by POLDER decreased slightly during summer 2008 compared with 2007 and then further decreased strongly in the summer 2009. However, POLDER data showed a different variability over Beijing City during the summer of 2008, with slightly higher levels of AOD than in 2007. The most striking feature shown by the POLDER data set for both North China and Beijing is that the lowest level of fine-mode aerosol loads was recorded in the summer of 2009. This characteristic was confirmed over the Beijing area through analysis of ground-based aerosol data.

[47] Here we show that June is, in general, the most polluted month of the summer and is characterized by the largest amplitude of interannual variations. A similar year-to-year trend is shown for July; aerosol loads in August, however, vary differently.

[48] Finally, our analysis over East Asia demonstrates the potential of the POLDER data set for monitoring fine-mode AOD and complements other aerosol records from both ground and space.

Acknowledgments

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Description of the Ground-Based and Satellite Data Set
  5. 3. Results and Discussion
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

[49] This work is mainly supported by the National Basic Research Program of China (2010CB950804) and the French Embassy and Ministry of Foreign affairs. The assistance of the colleagues in LOA, Isabelle Jankowiak, Thierry Podvin, Luc Blarel, and Romain De Filippi are gratefully acknowledged. Special thanks to Jean-Luc Deuzé and Oleg Doubovik for their helpful comments. The authors thank the AERONET/PHOTONS team and the ICARE center for their support in processing the high-quality AERONET inversions and POLDER Level 2 data used in this study. We also thank the PI site managers of all AERONET sites used and are very grateful to the anonymous reviewers for their helpful comments and suggestions.

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  2. Abstract
  3. 1. Introduction
  4. 2. Description of the Ground-Based and Satellite Data Set
  5. 3. Results and Discussion
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Description of the Ground-Based and Satellite Data Set
  5. 3. Results and Discussion
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information
FilenameFormatSizeDescription
jgrd16547-sup-0001-t01.txtplain text document1KTab-delimited Table 1.
jgrd16547-sup-0002-t02.txtplain text document1KTab-delimited Table 2.

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