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  • View in gallery

    Location of Shenyang station in northeastern Asia.

  • View in gallery

    (a) Monthly and (b) yearly variations in AOD and AE from 2004 to 2015.

  • View in gallery

    (a) Seasonal and (b) yearly variations in SSA, AAOD, and SAOD from 2004 to 2015.

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    (a) Seasonal and (b) yearly variations of radiative forcing from 2004 to 2015.

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    (a) Annual variations of GDP in agriculture, industry, and construction industry of northeastern China from 1990 to 2015 together with annual variations in AOD and AE from 2004 to 2015. (b) Relationships of annual-mean AOD, AAOD, SAOD, AOD/GDP per capita ratio, and GDP per capita (the dotted line is for better viewing the data for the corresponding year).

  • View in gallery

    Relationships among several aerosol optical parameters: (a) AE, AOD, and SSA, with the color bar representing SSA value; (b) SSA, AE, and AAOD, with the color bar representing AAOD value; (c) SAOD, TOA, and SSA, with the color bar representing SSA value; (d) AAOD, ATM, and SSA, with the color bar representing SSA value; (e) SAOD, SFC, and SSA, with the color bar representing SSA value.

  • View in gallery

    AE difference δAE [AE (440 675) − AE (675 870)] as a function of the AE (440 870) and AOD at 675 nm (color code) in four seasons.

  • View in gallery

    Cluster-mean back-trajectories result of the heavy industry zone in northeastern Asia (receptor site) in all four seasons.

  • View in gallery

    WPSCF and WCWT maps for AOD of the heavy industry zone in northeastern Asia (receptor site) in all four seasons: (a),(c),(e),(g) the results of PSCF analysis and (b),(d),(f),(h) the results of CWT analysis.

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Trends of Aerosol Optical Properties over the Heavy Industrial Zone of Northeastern Asia in the Past Decade (2004–15)

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  • 1 State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, Beijing, China
  • | 2 State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, Beijing, and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
  • | 3 State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, and Institute of Arid Meteorology, China Meteorological Administration, and Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province, and Key Laboratory of Arid Climatic Chance and Disaster Reduction, China Meteorological Administration, and Northwestern Regional Center of Numerical Weather Prediction, Lanzhou, China
  • | 4 Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
  • | 5 State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, and Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
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Abstract

The heavy industrial zone of northeastern Asia is dominated by year-round industrial scattering aerosols that undergo hygroscopic growth in summer. With the rapid economic development over the past decade, aerosol optical depth (AOD) has increased (6.35% yr−1) with an annual-mean AOD of 0.61 ± 0.13. Simultaneously, the aerosol particle size and aerosol scattering have increased, with an annual-mean scattering aerosol optical depth (SAOD) reaching 0.58 ± 0.15. However, considering that the annual AOD/gross domestic product (GDP) per capita decreased, the environmental degradation caused by aerosol emission is expected to reach a turning point based on the environmental Kuznets curve (EKC) hypothesis. In addition, annual-mean radiative forcing at the top, bottom, and interior of the atmospheric column reached −2.35 ± 2.33, −54.16 ± 7.26, and 51.81 ± 7.93 W m−2, respectively. The increase in unit SAOD contributes to the growth in net negative top-of-atmosphere (TOA) forcing and surface (SFC) forcing, and unit absorption aerosol optical depth (AAOD) increases together with atmosphere (ATM) forcing. Moreover, the cooling effect of aerosols on the Earth–atmosphere system showed an increase over the most recent 10 years related to the increase in scattering aerosol from development in the old industrial base. Except for local sources, under the western air masses, the circum–Bohai Sea economic zone was the potential source area of anthropogenic aerosols throughout the year with annual daily mean AOD, single-scattering albedo (SSA), TOA forcing, and SFC forcing values of 0.88, 0.93, −8.08, and −63.05 W m−2, respectively. The Mongolian Plateau was the potential natural dust source area under the northeastern air masses.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jinyuan Xin, xjy@mail.iap.ac.cn

Abstract

The heavy industrial zone of northeastern Asia is dominated by year-round industrial scattering aerosols that undergo hygroscopic growth in summer. With the rapid economic development over the past decade, aerosol optical depth (AOD) has increased (6.35% yr−1) with an annual-mean AOD of 0.61 ± 0.13. Simultaneously, the aerosol particle size and aerosol scattering have increased, with an annual-mean scattering aerosol optical depth (SAOD) reaching 0.58 ± 0.15. However, considering that the annual AOD/gross domestic product (GDP) per capita decreased, the environmental degradation caused by aerosol emission is expected to reach a turning point based on the environmental Kuznets curve (EKC) hypothesis. In addition, annual-mean radiative forcing at the top, bottom, and interior of the atmospheric column reached −2.35 ± 2.33, −54.16 ± 7.26, and 51.81 ± 7.93 W m−2, respectively. The increase in unit SAOD contributes to the growth in net negative top-of-atmosphere (TOA) forcing and surface (SFC) forcing, and unit absorption aerosol optical depth (AAOD) increases together with atmosphere (ATM) forcing. Moreover, the cooling effect of aerosols on the Earth–atmosphere system showed an increase over the most recent 10 years related to the increase in scattering aerosol from development in the old industrial base. Except for local sources, under the western air masses, the circum–Bohai Sea economic zone was the potential source area of anthropogenic aerosols throughout the year with annual daily mean AOD, single-scattering albedo (SSA), TOA forcing, and SFC forcing values of 0.88, 0.93, −8.08, and −63.05 W m−2, respectively. The Mongolian Plateau was the potential natural dust source area under the northeastern air masses.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jinyuan Xin, xjy@mail.iap.ac.cn

1. Introduction

Aerosol is an important component of the atmosphere, and the study of aerosol has become a specific issue in basic and applied science (Hansen et al. 2000; Haywood and Boucher 2000; IPCC 2001; Lee et al. 2007b; Luo et al. 2000; Penner et al. 2001; Stocker et al. 2014). Large amounts of particulate matter can be inhaled by humans, resulting in health effects (Fujii et al. 2001). Furthermore, the direct radiation effect of aerosols drives important changes in which aerosols suspended in air absorb or scatter solar radiative energy and affect the radiative budgets at the top, bottom, and interior of the atmospheric column, further influencing global climate change (Charlson et al. 1992; Chou et al. 2002). As also reported in Tang et al. (2017), aerosol has a considerable effect on global radiation climatology over China on a decadal scale. Therefore, aerosol radiative forcing is an important parameter that must be analyzed. Besides, for a more detailed understanding of the aerosol effects on the environment, the analysis of aerosol optical characteristics is strongly needed, including aerosol optical depth (AOD), Ångström exponent (AE), single-scattering albedo (SSA), and their spatial and temporal distributions (Aoki and Fujiyoshi 2003). In the past several decades, the economic development of northeastern Asia has been remarkable, and one by-product of this growth is heavy pollution that has influenced regional cooperation with neighboring countries because of the unique geographical environment (Wang and Zhang 2015). Therefore, several studies of northeastern Asian aerosols have been performed. Zhao et al. (2013) investigated the aerosol optical characteristics of four industrial cities in northeastern China based on data from a sun photometer and showed that unique “intercity” pollution exists between certain industrial cities. Xin et al. (2011) researched and analyzed the aerosol optical properties of the Bohai Rim region and supplied data validation for the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. Xia et al. (2007) analyzed 1 year of data and observed that dust activity could enhance the weight of coarse aerosols in the atmosphere. Xin et al. (2014a) used the data obtained from MODIS satellites and ground-based observation (2009–11) to research the relationships between AOD and particulate matter with diameter of less than 2.5 μm (PM2.5) in northeastern China and observed that the errors vary over seasons. Lee et al. (2007a) found that the AOD values in northeastern Asia were contributed by five portions of this region, including northeastern China, the Yellow Sea, Korea, the East Sea, and the South Sea, along with Japan, based on 1 year of data from MODIS satellites. Eck et al. (2005) studied the aerosol optical characteristics using Aerosol Robotic Network (AERONET) observations in East Asia, including several sites in China. Most research on aerosol in northeastern Asia has been performed based on short-term data.

Recently, there are also many scholars who focus on the aerosol variation on a decadal scale. Based on long-term (2007–13) ground-based observations, the urban area in central China exhibited high values of AOD and AE (Wang et al. 2015). Guo et al. (2014) researched the trend analysis of the aerosol properties over China based on 9-yr (2002–10) fusion data of MODIS and Multiangle Imaging SpectroRadiometer (MISR) AOD data and obtained an obvious increasing trend of the AOD in Yangtze River delta, where human activities may be the main source of the increasing AOD. The trend of aerosol optical depth (AOD550nm) and aerosol type were investigated over China’s circum–Bohai Sea economic zone, and AOD increased by 0.07 and AE decreased by 0.18 during 2004–10, presumably because of rapid economic growth in the special zone (Xin et al. 2011). Yoon et al. (2011) compared the variation trends of AOD555nm (1997–2008) in Europe and in southern China and found that aerosol loading decreased by −2.35% yr−1 over European region, opposite to that in Pearl River delta with the average annual growth rate of +1.15%. Kudo et al. (2010) investigated the trends in aerosol loading in Japan (1998–2008) and found the surface global irradiance has increased because of an increase of SSA, which may be related to the reduced emission of black carbon in East Asia. Li et al. (2014) found that Beijing, China, had an increase in SSA and a reduction in absorption aerosol optical depth (AAOD), while most European and North American sites showed positive SSA and negative AAOD trends during 2000–13.

Much progress has been made and much knowledge has been made gained from trend analysis in several studies. However, given that few studies included long-term monitoring and reference to discussions of aerosol in certain heavily polluted regions in northeastern Asia, the objective of this study is to fill this gap. In this paper, the Shenyang site was selected to represent the aerosol properties of the heavy industrial zone in northeastern Asia, which is the center of northeastern Asia and is also an important industrial base. Revitalization of the old northeastern industry has resulted in a massive emission of anthropogenic aerosols (Lu et al. 2010), leading to serious environmental degradation in northeastern Asia (Xin et al. 2007). As also reported in Che et al. (2015), the AODs in the urban area of northeastern China were as high (AODs > 0.6) as those in the north China plain because of heavy industrial and other anthropogenic emissions. Thus, it is necessary to discuss and analyze the long-term changes and trends in aerosol optical properties in the heavy industrial zone of northeastern Asia and propose selected recommendations for management of the atmospheric environment. Meanwhile, the long-term change of aerosol optical radiation characteristics in the heavy industrial zone can be elucidated in a more systematic and comprehensive way while providing some references for regional climate change and the accuracy of satellite products.

2. Observation site and data

Figure 1 shows the location of Shenyang station (41.52°N, 123.63°E; 31 m), which is one component of the Campaign on Atmospheric Aerosol Research Network of China (CARE-CHINA) and the Chinese Sun Hazemeter Network (CSHNET) (Xin et al. 2007, 2015). The optical parameters used in this paper were obtained from the long-term ground-based observations in Shenyang for a period of up to 10 years (2004–15) and were used in conjunction with MODIS satellite data to obtain the single-scattering albedo and the direct radiative forcing effect of the aerosol. The data from 2004 to 2011 were obtained by handheld light-emitting diode (LED) hazemeters, and Microtops II solar photometers were used to observe the optical parameters in the 2012–15 period. Both instruments collected measurements from 1000 and 1400 local time (LT) when the sky was covered by few clouds or less than half covered by clouds for efficient reduction of the cloud effect (Xin et al. 2011, 2015). According to the Beer–Lambert–Bouguer law, the AOD of the total atmosphere can be obtained (Morys et al. 2001) and the AE can be estimated from the AOD of multiple wavelengths (Ångström 1930). The algorithm took Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) (Levy et al. 2007) as the core model to calculate the radiative forcing parameters. Multiple sets of SSA and backscattering coefficient were calculated based on Mie theory (Dubovik 2002) and albedo obtained from MODIS to calculate radiative forcing at the top of atmosphere (TOA). The results were combined with MODIS observations, and the results that have the lowest deviation were defined as the actual parameters of aerosols. This set of parameters was used to calculate the radiative forcing at the top, bottom, and interior of the atmospheric column. There were some more detailed descriptions in our previous work (Gong et al. 2017; J. Xin et al. 2016). Our previous work proved that retrieved SSA in Beijing and AERONET results have high consistency and credibility and that the bias of SSA was larger in the spring and lower in winter (Gong et al. 2014). The simulation results in northwestern China also showed the same heating effect as the results from other studies in the periphery (J. Xin et al. 2016). In addition, the uncertainty of the aerosol optical data and the lack of satellite data have been enhanced in the algorithm (Gong et al. 2017). The AAOD and scattering aerosol optical depth (SAOD), which, respectively, indicate the aerosol absorption and scattering degree in the atmospheric column, can be derived from specific equations: AAOD = (1 − SSA) × AOD and SAOD = SSA × AOD. To facilitate comparison, AOD and SSA are at 500 nm, and the AE is calculated with the AOD from 440 to 870 nm, accompanied by radiative forcing accumulated from 0.25 to 4.0 μm (Gong et al. 2017).

Fig. 1.
Fig. 1.

Location of Shenyang station in northeastern Asia.

Citation: Journal of the Atmospheric Sciences 75, 6; 10.1175/JAS-D-17-0260.1

3. Results and discussion

a. Aerosol optical properties analysis

Monthly variation of AOD exhibited a unimodal structure, with the monthly mean AOD reaching a peak value of 0.88 ± 0.19 in June and a low value of 0.40 ± 0.07 in December. The monthly variation in AE was different, displaying a minimum (1.02 ± 0.20) in May and a maximum (1.25 ± 0.27) in November (Fig. 2a). These seasonal variations were consistent with what was reported in Xia et al. (2016). There was a great majority of AOD > 0.60 and AE > 1.00, which indicates the heavy aerosol loadings in this region with dominant fine-mode aerosols (Che et al. 2015). The different seasonal variations between AOD and AE was possibly due to frequent dust particulate transmission and soil aerosol emission in spring, followed by aerosol hygroscopic growth in summer (Deng et al. 2012; Y. F. Wang et al. 2011; Xin et al. 2007; Yoon and Kim 2006), increasing smoke and soot aerosol from fossil fuel and biomass combustion in autumn (Bing et al. 2011; Yan et al. 2006), and coarse aerosol emission constrained to winter (Xin et al. 2007). The annual-mean AOD, reaching 0.61 ± 0.13, was much larger than that observed at the background stations like the Longfeng and Tongyu Mountains in northeastern China (AOD ~ 0.3) (Che et al. 2015). As seen in Table 1, the annual-mean AOD increased from 0.43 ± 0.06 to 0.81 ± 0.18 with an average annual growth rate of 6.35%, and AE decreased gradually since 2004, followed by a slow increase after 2012, together with an opposite tendency of the dominant aerosol modal, with annual-mean value of 1.13 ± 0.12.

Fig. 2.
Fig. 2.

(a) Monthly and (b) yearly variations in AOD and AE from 2004 to 2015.

Citation: Journal of the Atmospheric Sciences 75, 6; 10.1175/JAS-D-17-0260.1

Table 1.

The annual and seasonal values of aerosol optical parameters from 2004 to 2015.

Table 1.

The annual-mean values of SSA, AAOD, and SAOD reached 0.91 ± 0.04, 0.04 ± 0.01, and 0.58 ± 0.15, with seasonal ranges from 0.85 ± 0.04 to 0.95 ± 0.02, 0.03 ± 0.01 to 0.06 ± 0.01, and 0.44 ± 0.15 to 0.78 ± 0.23, respectively (see Fig. 3a). The highest value of SSA occurred in summer, which was likely caused by larger amount of production of secondary particles with weak absorption, consistent with what was analyzed in Xia et al. (2016). Except in the winter, SSA exceeded 0.9 together with an AAOD of less than 0.05 and SAOD close to the AOD. And for the fine-dominated aerosols, the majority of SSAs are within 0.85–0.95 (Xia et al. 2016). All of these points mean that industrial scattering aerosol is dominant all year round, such as sulfate aerosol and nitrate aerosol discharged in the industrial zone (Yue et al. 2010). SSA and SAOD represented a slight increasing trend in the most recent 10 years (y = 0.0003x + 0.88, R2 = 0.07; and y = 0.003x + 0.37, R2 = 0.25, respectively) corresponding to the little change in AAOD (y = −0.000 02x + 0.04, R2 = 0.01) (see Fig. 3b). Obviously, the effect from scattering aerosol caused by economic and industrial development was increasingly significant over the decade.

Fig. 3.
Fig. 3.

(a) Seasonal and (b) yearly variations in SSA, AAOD, and SAOD from 2004 to 2015.

Citation: Journal of the Atmospheric Sciences 75, 6; 10.1175/JAS-D-17-0260.1

The seasonal and yearly variations of aerosol radiative forcing in the atmosphere (ATM), at the TOA, and at the bottom of atmosphere [surface (SFC)] are shown in Fig. 4. Seasonal and yearly averages were calculated based on the daily average value of aerosol radiative forcing. ATM forcing and SFC forcing had annual means of 51.81 ± 7.93 and −54.16 ± 7.26 W m−2, respectively, with seasonal changes from 45.12 ± 4.65 to 62.57 ± 9.95 W m−2 and −64.87 ± 10.33 to −49.11 ± 9.12 W m−2, respectively. The annual-mean TOA forcing is −2.35 ± 2.33 W m−2 and displayed a net positive in spring (0.53 ± 4.15 W m−2), likely because of dust and minor absorption aerosol increases, and a net negative in other seasons, with the strongest cooling effect in autumn (−5.16 ± 4.56 W m−2), which explains the dominant scattering aerosols like sulfate, nitrate, and organic aerosols over the heavy industrial zone (Fig. 4a). In the multiyear trend (Fig. 4b), the cooling effect of aerosol on the ground (dependent variable y) and the Earth–atmosphere system (dependent variable y) showed a small increase over the most recent 10 years (independent variable x): y = −0.09x −47.08, R2 = 0.11; and y = −0.06x + 2.19, R2 = 0.18, respectively. It is related to the increase in industrial scattering aerosols from development of the old industrial base. This trend is expected to enhance the atmospheric stability and restrain pollutant diffusion, causing a more severe pollution cycle.

Fig. 4.
Fig. 4.

(a) Seasonal and (b) yearly variations of radiative forcing from 2004 to 2015.

Citation: Journal of the Atmospheric Sciences 75, 6; 10.1175/JAS-D-17-0260.1

b. Relationship between aerosol emission and economic development

The relationship between aerosol optical properties and gross domestic product (GDP) is shown in Fig. 5. Together with a development lag in agriculture and the construction industry, industry was rather slow to develop during the end of the twentieth century. Since 2004, the GDPs in agriculture and the construction industry have increased (Fig. 5a). Commonly, people in northeastern China burn straw and firewood for food preparation and heating, particularly in the villages (Bai et al. 2013), and urban–industrial pollution was not heavy in past decades. The dominant aerosols were fine particulates composed of local natural aerosol and carbon aerosols, corresponding to a high value of AE. In 2003, the state adopted relevant policies to encourage and support vitalization of the northeastern China industrial base, with the primary task of promoting agriculture. The revitalization of this industry drove the development of the construction industry, and the production model of the grain base has been transformed from small-scale peasant production to regionalization and collectivization of agriculture (Guo and Xu 2007), accompanied by emissions of mine dust (Lu et al. 2010) and a decline in AE. Since 2012, reestablishment of the industry base has gradually ceased, and the production model of agriculture has been converted to a modern agricultural production system with decreasing emissions of coarse aerosols. However, industrial development is still rapid, and the anthropogenic influence on the regional atmosphere condition is more significant, together with increasing discharge of fine particulates. Therefore, AE has increased slowly. Since the revitalization of the old industrial base, large particulates have been discharged into the air, especially particulate matter such as ash or soot, causing an increase in AOD.

Fig. 5.
Fig. 5.

(a) Annual variations of GDP in agriculture, industry, and construction industry of northeastern China from 1990 to 2015 together with annual variations in AOD and AE from 2004 to 2015. (b) Relationships of annual-mean AOD, AAOD, SAOD, AOD/GDP per capita ratio, and GDP per capita (the dotted line is for better viewing the data for the corresponding year).

Citation: Journal of the Atmospheric Sciences 75, 6; 10.1175/JAS-D-17-0260.1

As shown in Fig. 5b, since the rejuvenation of northeast industrial base in 2004, GDP per capita increased together with an unavoidable increase in AOD, coinciding with an increase in SAOD. AAOD showed no obvious change. These examples suggest that economic development could contribute to a significant increase in scattering aerosols, which primarily originate from the prosperous industrial base. It is highly possible that this process belongs to the growth portion of the environmental Kuznets curve (EKC), which describes a hypothesized relationship between environmental degradation and income per capita, taking the form of an inverted U, named by Kuznets (1955). The EKC hypothesis means that the environmental damage first increases with income and subsequently declines over a turning point (income level), which was confirmed for several different countries with different turning points (Stern et al. 1996) and indicates certain implications for balancing the relationship between the environment and development, under certain limits. In the early years, because of a focus on economic development, pollution control was always ignored, and most enterprises applied an extensive production model of high investment, production, and pollution in the heavy industrial zone of northeastern Asia (Xiang et al. 2006), reaching a high AOD/GDP per capita of 0.30. Later, although the AOD increased over the years, the AOD/GDP per capita ratio exhibited a slow decline, which means that in this region and period, the synchronization of economic development and environmental governance was gradually taking place, with expectations of reaching the shift in income level at which environmental degradation begins to decline, although with increasing AOD. Because the EKC does not guarantee that the environmental degradation decline is automatic with time and economic growth, policies designed to achieve sustainable development must be added as an important incentive for reducing environmental degradation. It appears that the current atmospheric condition is not good, but current pollution control measures are meaningful, and environmental improvement over time is possible if the measures are followed.

c. Relationship among aerosol optical parameters

Figure 6 shows the relationship among several aerosol optical parameters. As seen in Fig. 6a, most data with high SSA values (>0.9) are concentrated on the region where AOD > 0.5 and AE ≈ 1.0 over increasing AOD, indicating a high aerosol extinction primarily caused by industrial scattering particulates, similar to the result in Wu et al. (2015). Absorption aerosol with low SSA covered a wide-ranging AE. It is necessary to add AE and AAOD integrated by SSA to categorize the aerosols into different absorption aerosol types with different sizes (Russell et al. 2015), shown in Fig. 6b. Most data with high AAOD and low SSA are concentrated in the area where AE ≈ 1.5 and AE < 1.0, most likely coinciding with the characteristics of biomass-burning aerosol and dust aerosol, respectively (Bibi et al. 2016; Smirnov et al. 2002). Different aerosols have different influences on radiative forcing. Notably high relationships are found between TOA forcing and SAOD, SFC forcing and SAOD, and ATM forcing and AAOD classified by SSA, with correlation coefficients of 0.54, 0.53, and 0.81, respectively. As also reported in Xia et al. (2016), AOD and SSA determine >94% and >87% of aerosol direct radiation effect variability at the TOA forcing and SFC forcing. Seen in Fig. 6c, SSA increased together with SAOD (independent variable x) and decreasing TOA forcing (dependent variable y) (y = −22.53x + 10.45). Once SSA crossed the value of 0.9, SAOD exceeded 0.5 and even reached as high as 2.5, and TOA forcing shifted sharply from positive to negative. This result showed a significant influence of industrial scattering aerosol on the radiation balance, considering that most data fall in the region of TOA forcing < 0, SAOD > 0.5 and SSA > 0.9. The cooling effect of SFC forcing (dependent variable y) also increased together with scattering aerosol or SAOD (independent variable x) (y = −35.82x − 31.42) (see Fig. 6e). These mean that the dominant nonabsorption aerosol primarily from an industry base intensifies the cooling effect on the ground and the Earth–atmosphere system. ATM forcing (dependent variable y), which represents a warming effect of aerosol on the atmosphere, had a large positive correlation with absorption aerosol or AAOD (independent variable x) (y = 967.90x + 12.34) (see Fig. 6d), illustrating that absorption aerosol has a potentiation effect on warming the atmosphere.

Fig. 6.
Fig. 6.

Relationships among several aerosol optical parameters: (a) AE, AOD, and SSA, with the color bar representing SSA value; (b) SSA, AE, and AAOD, with the color bar representing AAOD value; (c) SAOD, TOA, and SSA, with the color bar representing SSA value; (d) AAOD, ATM, and SSA, with the color bar representing SSA value; (e) SAOD, SFC, and SSA, with the color bar representing SSA value.

Citation: Journal of the Atmospheric Sciences 75, 6; 10.1175/JAS-D-17-0260.1

d. Seasonal variation on aerosol mixtures of multiple aerosols

As analyzed in Tian et al. (2017), dust plays an important role in spring, and anthropogenic pollution dominates in summer over northern China. The analysis method described by Gobbi et al. (2007) was applied for tracking mixtures of multiple aerosols in the heavy industrial zone of northeastern Asia, and more detailed information can be observed in Gobbi et al. (2007). The dataset of AOD (675 nm), AE (440 870), and δAE [AE (440 675) − AE (675 870)] was used to analyze and build the graphical framework displayed in Fig. 7. The black full lines indicate the contour lines of fine mode size Rf with values of 0.05, 0.10, 0.15, 0.20, 0.30, 0.40, and 0.50 μm. The black imaginary lines represent the fractional contributions to the total AOD of fine mode fraction η, with values of 1%, 10%, 30%, 50%, 70%, 90%, and 99%.

Fig. 7.
Fig. 7.

AE difference δAE [AE (440 675) − AE (675 870)] as a function of the AE (440 870) and AOD at 675 nm (color code) in four seasons.

Citation: Journal of the Atmospheric Sciences 75, 6; 10.1175/JAS-D-17-0260.1

In summer, with AOD increasing, the data fall nearly perpendicular to the black full line, into the larger-size fine mode and fine fraction in the region (η > 70%, 0.75 < AE < 1.5, δAE < −0.2). Gobbi et al. (2007) mentioned that even with the existence of coarse aerosols, high extinctions are linked to a hygroscopic or aging condition of aerosols in many cases. Therefore, the abovementioned region indicates the significant situation of aerosol [like secondary inorganic salt aerosols discharged from industries (Myhre et al. 2004)] hygroscopic growth in the heavy industry zone of northeastern Asia due to the near nonexistence of the coarse mode (η > 70%) and a significant growth of the fine mode size (Rf changed from 0.15 to 0.25 μm). This situation was not observed in other seasons and could explain the high extinction that occurs in summer. In spring, the dataset in the region (η < 70%, AE < 1, −0.2 < δAE < 0.2) falls nearly parallel to the black full line without a distinct variation of the fine mode size but an apparent increase in AOD. The values of η and δAE gradually tended toward zero with increasing AOD, and the fine mode size was relatively unchanged with Rf ≈ 0.17 μm. Obviously, the increasing effect of coarse particulates (consisting mainly of sand and mine dust aerosols) primarily occurred and was most significant in spring, with the most data being observed in the coarse mode region. The region (η < 30%, AE < 0.75, −0.2 < δAE < 0.2) with high AOD values (AOD > 1.0) could represent the typical coarse mode condition. In winter, similar to the result in Zhao et al. (2013), the most significant effect was likely biomass burning for heating and offers a clear example of cloud contamination, with an approximately coherent fine mode of 0.15–0.20 μm corresponding to an increase in AOD.

e. Transport pathway and potential source area analysis

We combine the back trajectories with potential source contribution function (PSCF) and concentration-weighted trajectory (CWT) analysis to more clearly examine the different influences of the main air masses from different directions or different outside source areas. The trajectory data were obtained from the National Oceanic and Atmospheric Administration (NOAA) HYSPLIT_4 and are reflected in Fig. 8. All of the 48-h back trajectories arriving at 500 m above the ground of Shenyang station were calculated every 12 h (UTC) on each day from 2004 to 2015 (Draxler and Hess 1998). Specific details are shown in Tables 2 and 3. The results of PSCF and CWT analysis are shown in Fig. 9, and additional detailed information can be found in Hsu et al. (2003), Wang et al. (2006, 2009), and Zeng and Hopke (1989), accompanied by the suitable weight functions chosen with reference to previous work for minimization of uncertainty (Y. Xin et al. 2016). The limits were the seasonal-mean values of AOD, reaching 0.56, 0.89, 0.53, and 0.50 from spring to winter, respectively.

Fig. 8.
Fig. 8.

Cluster-mean back-trajectories result of the heavy industry zone in northeastern Asia (receptor site) in all four seasons.

Citation: Journal of the Atmospheric Sciences 75, 6; 10.1175/JAS-D-17-0260.1

Table 2.

Percentage and polluted percentage, along with daily mean AOD and polluted daily mean AOD values (in parentheses), for each cluster. (The polluted daily mean AOD is the averaged value of daily AOD corresponding to the trajectories for each cluster, which is higher than the limit value, similar to the percentage of polluted trajectories.)

Table 2.
Table 3.

Daily mean values of radiative forcing, along with SSA (in parentheses), for each cluster.

Table 3.
Fig. 9.
Fig. 9.

WPSCF and WCWT maps for AOD of the heavy industry zone in northeastern Asia (receptor site) in all four seasons: (a),(c),(e),(g) the results of PSCF analysis and (b),(d),(f),(h) the results of CWT analysis.

Citation: Journal of the Atmospheric Sciences 75, 6; 10.1175/JAS-D-17-0260.1

In the PSCF analysis, the region with warmer color indicates higher weighted PSCF (WPSCF) values and ratios of the potential source area (PSA). Similarly, in the CWT analysis, the warmer region denotes a more significant effect on the receptor site with higher weighted CWT (WCWT) values. The results of PSCF and CWT analysis must be analyzed with a dialectical view; region A mainly represents the circum–Bohai Sea economic zone. In all seasons, region A was considered to be the uppermost PSA and made the most significant contribution to the daily mean AOD level of the receptor site with the highest WPSCF and WCWT and warmest color. According to back-trajectory analysis (see Fig. 8 and Table 2), the pathway where the western and southwestern air masses originate in or pass through region A and arrive at the receptor site was considered to be the main perennial pollution transport pathway (clusters 2 and 4 in spring, cluster 2 in summer and autumn, and clusters 3 and 5 in winter). This phenomenon was defined because of the occupation of a relatively higher proportion, polluted percentage, and maximum corresponding daily mean AOD (greater than the limit). It means that this type of air mass picked up heavy aerosols from region A, coinciding with the highest daily mean AOD in the receptor site and reaching 0.89, 1.11., 0.72, and 0.79 for spring, summer, autumn, and winter, respectively. At the same time, under the influence of these air masses, the aerosol radiative forcing effect in the receptor site was strong, with the highest daily mean SSA, net negative TOA forcing, and SFC forcing values of 0.96, −5.89, and −60.37 W m−2 in spring; 0.96, −6.83, and −59.57 W m−2 in summer; 0.93, −11.53, and −58.01 W m−2 in autumn; and 0.89, −8.08, and −74.26 W m−2 in winter. This result indicated that these air masses passing region A carried masses of anthropogenic aerosol with strong scattering and polluted the receptor site with high AOD. Currently, anthropogenic and secondary aerosols discharged from the Shandong site and Liaodong Peninsula (with the fast-growing industrial and city modernization construction) are increasing (Xin et al. 2014b). The Beijing–Tianjin–Hebei region also experiences heavy pollution, mostly due to human activities. Obviously, region A became the most significant potential source area of anthropogenic pollution for the receptor site with the western air masses. In spring, region B (eastern Mongolian Plateau) was also an important PSA with WCWT values nearly greater than the limit (0.56), likely causing the high daily mean AOD in the receptor site. Under the influence of the northern and northwestern air masses, which accounted for 46.38% (clusters 1 and 6), TOA forcing and SFC forcing reached the maximum values of 4.26 and −47.12 W m−2, respectively, and SSA reached the minimum of 0.91. These values demonstrate that absorption aerosols (dust and continental aerosols) carried by the northern air masses blowing through region B seriously polluted the receptor site and were responsible for the occurrence of the decrease in aerosol scattering and the cooling effect on the Earth–atmosphere system. Therefore, region B was the important potential natural dust source area where a large amount of half-fixed and half-mobile sand dunes and the semiarid region occur. It is worth noting that in winter region B was covered by snow with minimal natural dust emission and low WCWT (less than the limit) in most areas (Xin et al. 2007). The northern air masses passing through region B were strong and clean with the lowest daily mean AOD of 0.43 (clusters 1, 4, and 6), exerting a washing effect on the receptor site (Xin et al. 2012). With the increase in black carbon aerosols from heating in the Mongolian Plateau and northeastern China (Y. Wang et al. 2011), the northern air masses corresponded to the lowest SSA and aerosol cooling effect of the receptor site.

4. Conclusions

With the revitalization of the industrial zone, the strong influence of anthropogenic aerosol on the aerosol optical and radiative properties increased during the past decade (2004–15). The AOD, SSA, and SAOD increased together and led to a cooling effect on the ground and the Earth–atmosphere system in the course of the past decade. The dominant aerosol mode size has changed as the economic structure has changed. This period represented a growth component of the EKC, but the AOD/GDP per capita ratio was observed to decrease, meaning that current pollution control measures were meaningful, and a decline in atmospheric degradation is expected in the future with policies for sustainable development. Meanwhile, the impact from outside source areas cannot be ignored. The western and southwestern air masses carried anthropogenic aerosols with significant scattering, and the northern and northwestern air masses carried dust and sand. Therefore, solving the air pollution problem in the circum–Bohai Sea economic zone would improve the atmospheric condition in the heavy industry zone of northeastern Asia.

Acknowledgments

This study was partially supported by the National Basic Research Program of China (2016YFC0202001 and 2014CB441200) and the National Natural Science Foundation of China (41375036). The authors are grateful for services rendered by the National Oceanic and Atmospheric Administration (NOAA) and the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC).

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