Advertisement

Aerosol Science and Engineering

, Volume 2, Issue 4, pp 182–196 | Cite as

Aircraft Measurement of Chemical Characteristics of PM2.5 over the Yangtze River Area in China

  • Lihong Ren
  • Renjian Zhang
  • Xiaoyang Yang
  • Chunmei Geng
  • Wei Wang
  • Shiro Hatakeyama
  • Hong Li
  • Wen Yang
  • Zhipeng Bai
  • Akinori Takami
  • Hongjie Liu
  • Jianhua Chen
Original Paper
  • 492 Downloads

Abstract

To study the vertical distributions of PM2.5 mass and chemical components over the Yangtze River area, PM2.5 was sampled with filters over Changzhou, which is located in the eastern part of China, Shashi, which is located in the central part of China, and Xinjin, which is located in the western part of China, on the Yun-12 aircraft from August 21 to September 13, 2003. The samples were weighed for mass concentrations, and the chemical profiles of 8 inorganic ions (Cl, NO3, SO42−, Na+, NH4+, K+, Mg2+ and Ca2+), carbon fractions (organic carbon and elemental carbon) and 18 elements were analyzed in a laboratory. The mass concentrations at 400–1500 m were greater than those at 1600–3200 m, indicating the effect of ground surface sources. Similar PM2.5 compositions were found both at 400–1500 and 1600–3200 m. SO42− was the dominant ionic component, followed by NO3, NH4+, Ca2+, K+, Na+, Cl and Mg2+. Secondary inorganic ions (SO42−, NO3 and NH4+) contributed to 80–83% of the total ionic species, indicating that the role of secondary formation plays an important role in water-soluble ions. SO42− mainly existed as (NH4)2SO4. NH4+ was unable to completely neutralize SO42− and NO3, and the deficit was approximately 32%. More than 70% of the Ca2+ contribution was derived from anthropogenic sources, which was related to construction activities and cement manufacturing. K+ was predominantly derived from anthropogenic sources (72.2–74.0%) and crustal sources (approximately, 23.3–24.9%). The OC/EC ratios at 1600–2800 m were greater than those at 500–1200 m, which was probably due to the presence of secondary products that were produced by photochemical smog activities during the uplifting of air masses.

Keywords

Aircraft measurement PM2.5 Chemical characteristics Yangtze River area 

1 Introduction

PM2.5 (particulate matter with a diameter ≤ 2.5 µm) has received increasing attention for its effects on human health (Dockery et al. 1993), climate and visibility (Watson 2002; Gu et al. 2011). However, the impacts remain highly uncertain, which is mainly due to its complex chemical and microphysical properties and sources (Zhang et al. 2017; Khan et al. 2010; Chow et al. 2011). Because of the sources and vertical mixing, the lifetime and removal processes of aerosols are highly variable and uncertain, and the chemical compositions of aerosols show obvious vertical and spatial distribution characteristics. The chemical compositions of aerosols as a function of height are one of the important factors influencing direct and indirect aerosol radiative forcing (Padmakumari et al. 2013; Forster et al. 2007). The physical and chemical properties of aerosols in the atmospheric boundary layer may be different from those in the free troposphere because aerosols in the atmospheric boundary layer are often from local sources, whereas aerosols in the free troposphere may derive from long-range transport. The vertical distribution of aerosol chemical compositions is needed to validate the application of atmospheric chemistry models at the top of the atmospheric boundary layer and in the free troposphere. Although ground-based measurements of PM2.5 compositions are numerous, in situ vertical distributions of PM2.5 chemical species are seldom. Consequently, there is a need for high-quality measurement data of aerosol properties as a function of altitude.

Aircrafts have been proven to be an effective platform for studying the vertical and regional distribution of aerosols due to their high temporal and spatial resolution (Tu et al. 2004; Ren et al. 2012; Liang et al. 2004; Li et al. 2012; Ma et al. 2010). Since the 1990s, several aircraft observation campaigns at national and international scales have been conducted to investigate aerosol chemical properties in the atmospheric boundary layer and free troposphere and their effects on the climate, such as ACE-Asia, TRACE-P, and PEACE (Lee et al. 2003; Hatakeyama et al. 2011; Mayol-Bracero et al. 2002; Huebert et al. 2003; Jacob et al. 2003). Hatakeyama et al. (2011) studied the chemical composition of aerosols below 3000 m over the East China Sea by using aircraft measurements. Mayol-Bracero et al. (2002) found that the major components were particulate organic matter (POM, 35%), SO42− (34%), black carbon (14%) and NH4+ (11%) at altitudes up to 3.2 km during the Indian Ocean Experiment. These studies highlight the need to better understand the chemical composition of aerosols at high altitudes. However, many of the measurements mentioned above were conducted mainly over oceans, and little information is available for aerosol chemical properties over urban areas, especially over urban China, which experiences complex air pollution.

The Yangtze River region is currently the subject of intensive research because of the dramatically increased anthropogenic emissions due to rapid economic growth and population expansion. A number of studies have been carried out to understand the physicochemical properties of PM2.5 (Huang et al. 2013; Yang et al. 2011; Waldman et al. 1991; Yao et al. 2002; Yue et al. 2006). However, there is a lack of investigations on the vertical information of PM2.5 chemical characteristics over this region, which are important for regional pollution-control policies and decision making.

To study the chemical characteristics of PM2.5 at different heights, an aircraft measurement campaign was conducted from 7 Aug. to 13 Sep. in 2003. PM2.5 filter samples were collected, gaseous pollutants (SO2, NOX and O3), and the size distribution of particulate matter were measured online simultaneously. In this study, only PM2.5 filter samples were investigated to study the chemical characteristics and vertical distributions. These results could be an effective supplement to ground-based observations and provide data support for the assessment of long-range transport of air pollutants.

2 Methods

2.1 Flight Observations

This flight observation campaign was conducted from 7 Aug. to 13 Sep. in 2003 using a Yun-12 double engine light transport plane. The flight region covered the Yangtze River area from Changzhou (119.95°E, 31.78°N; located in Jiangsu Province) in the eastern part of China to Shashi (112.24°E, 30.32°N; located in Hubei Province) in the central part of China and Xinjin (103.78°E, 30.42°N; located in Sichuan Province) in the western part of China. A total of ten flights were conducted throughout the whole campaign, including five local flights and five transfer flights between cities. Due to higher and unstable flight speeds, the transfer flights are not discussed in this paper. The local flights were conducted mainly over Changzhou (CZ), Shashi (SS) and Xinjin (SJ) and utilized two flight patterns to sample different regions of the lower troposphere, including L-shaped and circle patterns at several altitudes, to quantify the mixing of aerosols and obtain more detailed information about the vertical distribution of particulate matter. The flight regions and flight tracks are shown in Figs. 1 and 2. More detailed flight information is presented in Table 1. Although there is engine exhaust from the aircraft, it is far away from the inlet; therefore, there was rarely any influence of engine exhaust on our observations. The effects of engine exhaust can be assessed from spiked and sudden enhancements in NOX, and the data affected by these enhancements have been removed in the following discussion.
Fig. 1

Flight regions

Fig. 2

Flight tracks

Table 1

Flight information

Flight no.

Flight cities

Flight patterns

Date

Sampling time (UTC)

Flight altitude (m)

F1

Shashi

“L” shaped

21 Aug.

0:38–4:34

3000, 2000, 1000, 500

F2

Shashi

Circle

23 Aug.

0:45–5:11

2800, 2400, 2000, 1600, 1200, 800, 400

F3

Xinjin

“L” shaped

4 Sep.

0:30–3:33

3000, 2000, 1000, 600

F4

Xinjin

Circle

5 Sep.

0:48–4:47

2800, 2400, 2000, 1600, 1200, 800, 400

F5

Changzhou

Circle

13 Sep.

4:37–8:35

2800, 2400, 2000, 1600, 1200, 800, 400

Ambient air was introduced into the cabin of the aircraft by an isokinetic inlet set below the bottom of the aircraft and through a stainless tube with reduced transmission losses for particles. The schematic diagram of the sampling system can be obtained by referencing Wang et al. (2005). The particle sampling procedure was validated by calculating the transmission efficiencies of the bent and straight sections of the inlet. Particle density was assumed to be 1.0 g/cm3, and the transmission efficiencies were calculated using the methods from Pui et al. (1987) and Matsuki et al. (2003). The results show that the transmission efficiencies were 96, 96, 92 and 87% in the bent sections and 97, 93, 87 and 85% in the straight sections for particle diameters of 3.0, 5.0, 7.0 and 10.0 µm, respectively (Wang et al. 2005). This means that most of the ambient particles were sampled, and particle loss caused by deposition would not be obvious.

2.2 Sampling and Analysis

PM2.5 samples were collected on quartz fiber filters (47 mm, QR100) by a sampler at a flow rate of 78 L/min (China Geologic Device Factory). The sampling time varied between 90 and 150 min. Each flight was sampled with two filters: one filter sampled air at 400–1500 m, and another filter collected samples at 1600–3200 m.

Before sampling, all filters used were preheated for 2 h in a muffle furnace at 500 °C to eliminate organic species, equilibrated for 48 h at a temperature between 20 and 22 °C and a relative humidity (RH) between 40 and 50%, and weighed with a high-precision balance with a sensitivity of 10−5 g. After sampling, the exposed filter samples were kept chilled by using a refrigerator during the transportation from the observation sites to the laboratory and stored in a freezer at − 18 °C before analysis to prevent the evaporation of volatile components and avoid the destruction of components, particularly nitrate. In the laboratory, these filters were equilibrated and weighed under the same conditions as those before sampling. Each filter was weighed at least three times before and after sampling. In addition, the net mass was obtained by subtracting the average of the presampling weights from the average of the postsampling weights. Differences among the three weighings were less than 10 μg.

Each sample was divided into four parts for the analysis. One-fourth of the sample filter, which was reserved for the analysis of water-soluble ionic species (including Na+, NH4+, K+, Ca2+, Mg2+, F, Cl, NO3 and SO42−), was extracted by ultrasonic waves with 20 mL of deionized water for 40 min; then, the extracted solution was filtered by 0.47-μm microfilms made by the Beijing Chemical Technology School. These ionic concentrations were analyzed using DX-500 ion chromatography (DIONEX, USA). A 0.5-cm2 punch from another fourth of the filter was analyzed for OC and EC using a thermal/optical carbon analyzer (Sunset Lab Inc, RT-4). The detection limits for EC and OC were less than 0.2 µg/cm2 (Liu et al. 2002). The remainder of each filter was extracted with a mixture of acids using nitric acid (HNO3), hydrofluoric acid (HF) and perchlorate (HClO4); then, the residual solution was concentrated and analyzed for 18 elements (Al, As, B, Ba, Be, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Mo, Na, Ni, P, Pb, S, Sr, Ti, V, Zn and Zr) using inductively coupled plasma emission spectrometry (GE Company, USA).

Two field blanks were taken by exposing filters in the sampler without drawing air through. Field blanks were also treated and analyzed using the same procedure as for the actual samples. Blank filters were quite stable, with fluctuations in the mass concentrations and chemical species that were less than 5% of the PM samples.

2.3 The Backward Trajectories of Air Masses

To identify the potential influence of different source regions on aerosol compositions, 72 h backward trajectories at every flight altitude and sampling site were calculated by the HYSPLIT 4 (Hybrid Single Particle Lagrangian Integrated Trajectory) trajectory model to demonstrate the synoptic patterns and associated long-range transport routes of air masses. The model adopts meteorological data from the NCEP-FNL (Final Operational Global Analysis) as the input. The spatial resolution is 1° × 1°, and the temporal resolution is 3 h.

3 Results and Discussion

3.1 Vertical Distributions of PM2.5 Mass Concentrations

The vertical distributions of PM2.5 mass concentrations in different locations are shown in Fig. 3. PM2.5 mass concentrations increased with a decrease in altitude. The mass concentrations were 43–89 µg/m3 at altitudes of 400–1500 m and decreased to 21–40 µg/m3 at altitudes of 1600–3200 m, suggesting the effects of ground surface sources. It is likely that samples collected at altitudes of 400–1500 m were within the boundary layer, where air parcels are well mixed. Surface roughness, source strengths and locations, and meteorological conditions can determine the concentration of particulate matter in the atmospheric boundary layer. At altitudes of 1600–3200 m, the upper air meteorology can affect particulate matter concentrations.
Fig. 3

Vertical distribution of PM2.5 mass concentrations over different locations (XJ: Xinjin city, SS: Shashi city; YC: Yichang; CZ: Changzhou)

Air mass trajectories have an effect on PM2.5 concentrations. Although the flights conducted on 21 Aug. and 23 Aug, 2003 were sampled over the same city (Shashi), the mass concentrations observed on 23 Aug were obviously greater than those obtained on 21 Aug., which may have been caused by different air mass origins during these two flights. As shown in Fig. 4, the air masses that arrived at the observing location on 23 Aug were predominantly from the southeastern region of China and were ascending trajectories, which brought particulate matter up to the observation altitude and resulted in greater mass concentrations of PM2.5. However, the air trajectories observed on 21 Aug. were from the East China Sea and had less residual time over land, which could have caused lower PM2.5 concentrations.
Fig. 4

Backward trajectories of air mass at the observation city during the whole flight observation periods

3.2 Vertical Distribution of Ionic Components

Water-soluble ions are the major components of aerosol particles and have a potential effect on human health. The mass concentrations of various ionic species are shown in Table 2. During all flights, the sum of all detected water-soluble ions contributed 10.8–12.8% to the PM2.5 mass concentrations. The abundance of ionic components in PM2.5 was in the order of SO42− > NO3 >  NH4+ > Ca2+ > K+ > Na+ > Cl > Mg2+ (Fig. 5). The sum of SO42−, NO3 and NH4+ dominated the ionic species, comprising 80–83% of the sum of the ions, which indicated that secondary formation played an important role in water-soluble ions.
Table 2

Concentrations (µg/m3) of PM2.5 mass concentration and water-soluble ions

Component

Shashi

Xinjin

Changhzou

1600–3200 m

400–1500 m

1600–3200 m

400–1500 m

1600–3200 m

400–1500 m

Mass

33.30

58.02

33.50

44.00

26.40

89.00

Cl

0.18

0.30

0.15

0.25

0.12

0.43

NO3

0.91

1.90

0.66

1.58

0.71

2.54

SO42−

2.04

3.78

1.62

2.79

1.72

5.10

Na+

0.13

0.24

0.11

0.19

0.11

0.35

NH4+

0.70

1.32

0.58

0.98

0.59

1.85

K+

0.19

0.31

0.14

0.25

0.16

0.43

Ca2+

0.31

0.56

0.25

0.44

0.28

0.74

Mg2+

0.05

0.08

0.04

0.07

0.04

0.12

TI/mass (%)

11.33

12.13

12.37

12.41

11.98

10.82

SI/TI (%)

80.85

82.35

80.67

81.60

81.02

82.14

TI Total water-soluble ions, SI the sum of SO42+, NO3 and NH4+

Fig. 5

Ionic composition of PM2.5

Figures 6 and 7 show the vertical distributions of inorganic ions and sulfur dioxide (SO2) and nitrogen dioxide (NO2), respectively. It can be observed that SO42− decreased with increasing altitude and was mostly concentrated below 1600 m, which was similar to the vertical distribution of SO2, indicating that SO42− was mainly from local sources. Previous studies have also reported similar vertical distribution trends over land (Peltier et al. 2007; Warneck, 2000). NO3 was clearly concentrated below 1600 m, where most NOX was released. NO3 and NO2 have similar vertical distributions and were well correlated, with a correlation coefficient of 0.87. These correlations indicate that NO3 was formed by the oxidation of locally emitted nitric oxides (NOX) followed by the neutralization by ammonia (NH3). NH4+ was formed from its gaseous precursor, NH3, through gas-phase and aqueous-phase reactions with acidic species (e.g., H2SO4, HNO3 and HCl). NH4+ showed a similar vertical distribution to those of SO42− and NO3. Cl, Na+, K+, Ca2+ and Mg2+ mainly derived from the primary emissions of ground sources; therefore, these pollutants were concentrated at lower altitudes.
Fig. 6

Vertical distribution of inorganic ions

Fig. 7

Vertical distribution of SO2, NO2 and temperature

3.3 Correlation Between Inorganic Ions

The relationships between chemical species provided support to identify their sources (Lee et al. 2003). Figure 8 shows the correlations among inorganic ions during all flights. We can see that SO42− showed a good correlation with NH4+, with an R2 of 0.99 and a slope of 0.96 (Fig. 8), which suggested that particulate SO42− existed as ammonium salt. It is well known that the equivalent ratio of NH4+ to SO42− is 1.0 for (NH4)2SO4, and this ratio is 0.5 for NH4HSO4 (Seinfeld and Pandis 2006; Zhang et al. 2013). In this study, the average equivalent ratio of NH4+/SO42− was 0.96, which was less than 1.0, indicating that SO42− existed in the form of (NH4)2SO4 and NH4HSO4.
Fig. 8

Correlations among inorganic ions

NH4+ showed strong correlations with NO3, SO42− and the sum of NO3 and SO42−, suggesting that NH3 had common emission sources with its precursors (NO3 and SO42−). Perrino et al. (2001) reported that NH3 could be emitted from motor vehicles. The ratio of NH4+ to the sum of SO42− and NO3 was 0.68, indicating that NH4+ was insufficient to completely neutralize SO42− and NO3, and the deficit was approximately 32%. Because H2SO4 is a much stronger acid than HNO3, NH3 first reacted with H2SO4 to form SO42−. NO3 showed a good correlation with Ca2+, indicating that the rest of the nitrate could be neutralized by heterogeneous reactions of HNO3 in soil particles and mineral particles.

The mass ratio of NO3/SO42− has been used to analyze the contribution of vehicle emissions and coal burning to aerosols. If NO3/SO42− exceeds 1, it suggests that vehicle emission sources play an important role. When NO3/SO42− is below 1, it indicates that coal-burning sources are dominant (Yao et al. 2002). In this study, the ratio of NO3/SO42− was in the range of 0.38–0.58 with an average value of 0.52 (Fig. 8 and Table 3), suggesting that coal-burning sources (such as thermal power plants and industrial emissions) were still major sources contributing to PM2.5 over the Yangtze River region.
Table 3

NO3/SO42− in PM2.5 at 400–1500 and 1600–3200 m at different observation cities

Altitude (m)

F1

F2

F3

F4

F5

1600–3200

0.41

0.47

0.43

0.38

0.42

400–1500

0.53

0.49

0.55

0.58

0.50

Commonly, fine K+ is derived mainly from biomass/biofuel-burning emissions. However, in our study, good correlations were found when comparing K+ with Ca2+ and Mg2+, with R2 values of 0.99 and 0.98, respectively, indicating that soil materials were a source of K+. Formenti et al. (2003) also found that parts of fine-sized K+ were derived from soil materials.

Na+ and Cl were strongly correlated (R2 of ~ 0.99), with a mass ratio Cl/Na+ of 1.23, which was above the seawater ratio (1.17). The additional sources of Cl may be associated with urban pollution which can significantly influence the Cl/Na+. The urban source of Cl is related with coal combustion (Yao et al. 2002) and biomass burning (Li et al. 2007).

3.4 Source Assessment for Ionic Components

Ionic species of PM2.5 are mainly derived from crustal, sea salt and anthropogenic sources. For the assessment of crustal and sea salt contributions toward the formation of different chemical components, reference elements for crustal (Cr) and sea salt (Ss) fractions must be chosen.

Na+ is assumed to mainly originate from sea salt, where it is found as NaCl, and the sea salt fraction of Na+ (SsNa+) in this study was chosen as the reference element for sea salt. SsNa+ can be calculated as SsNa+ = Na+ − (Na/Al)crust × Al due to parts of Na that are also present in various minerals, such as feldspar and smectite (Claquin et al. 1999). The sea salt fraction for these ionic species is computed as:
$${\text{Ss}}X = \left( {{{\left[ X \right]} \mathord{\left/ {\vphantom {{\left[ X \right]} {{\text{Na}}^{ + } }}} \right. \kern-0pt} {{\text{Na}}^{ + } }}} \right)_{\text{sea - salt}} \times {\text{SsNa}}^{ + } ,$$
(1)
where X represents the desired aerosol component that the sea salt fraction is to be calculated for. After calculating the sea salt fractions, we compute the crustal fraction for different chemical components by using CrMg2+ as a reference element for the crustal source. Assuming that Mg2+ is composed of sea salt and crustal sources only, CrMg2+ can be calculated as CrMg2+ = Mg2+ − SsMg2+. The crustal fraction was computed as:
$${\text{Cr}}X = \left( {{{\left[ X \right]} \mathord{\left/ {\vphantom {{\left[ X \right]} {\text{Mg}}}} \right. \kern-0pt} {\text{Mg}}}} \right)_{\text{crustal}} \times CrMg^{2 + } .$$
(2)
Finally, the anthropogenic fraction (Anthro) was calculated as:
$${\text{Anthro}}X = X - {\text{Ss}}X - {\text{CrS}} .$$
(3)
Figure 9 gives estimations for contributions from different sources to ionic species. From this figure, we can see that sea salt clearly dominated Cl, with a contribution of approximately 81.3–95.9% and the anthropogenic sources contributed approximately 4.0–18.6%. The major anthropogenic sources of Cl were coal combustion (Yao et al. 2002) and biomass burning (Li et al. 2007). The crustal sources contributed very little (< 0.2%). Na+ was mainly derived from sea salt, with contributions of ~ 57.1 to 71.1%.
Fig. 9

Source assessment of ionic components in PM2.5

Approximately, 98% of SO42− was derived from anthropogenic sources. High anthropogenic fractions were due to a large amount of coal consumption in China, not only from industrial activities but also from residential heating, which was consistent with large SO2 emissions (Cao et al. 2007). Sea salt contributed 0.9–1.3%. This shows the possibility of transporting sea salt SO42− from the Yellow Sea and East China Sea, as observed from the air masses back-trajectory analysis. However, the contribution from crustal sources was less than 0.2%, indicating the negligible occurrence of primary gypsum (CaSO4). NO3 and NH4+ were entirely derived from anthropogenic sources, with the crustal and sea salt sources having no contribution (Safai et al. 2010; Huang et al. 2008). Fossil fuel burning in industrial or vehicular exhaust may be the major contributor for NO3 and NH4+. Additionally, the use of fertilizers in fields and human and animal excreta are other important sources of NH4+.

K+ was predominantly derived from anthropogenic sources, with a contribution of ~ 69.5 to 78.6%, indicating that the impact of biomass burning was important for K+ in PM2.5. Crustal sources contributed ~ 19.2 to 28.5%. Formenti et al. (2003) also found that part of fine-sized K+ was derived from soil materials. The sea salt contribution was relatively small.

Generally, crustal sources are observed to be the dominant source for Ca2+. However, in this study, 77.7–91.1% of the contribution to Ca2+ was from anthropogenic sources, suggesting that anthropogenic activities, such as building activities and cement manufacturing, played a major role in aerosol Ca2+ content. Such results were also obtained in previous studies (Han et al. 2007; Safai et al. 2010). Crustal sources of Ca2+ dominated only 7.6–21.2%. Mg2+ was mainly from crustal sources, with a contribution of 71.2–83.1%. The rest of the fraction was from sea salt sources. The anthropogenic contribution was negligible for Mg2+.

3.5 Ionic Balance

The ionic balance in the observed aerosols offers insight into whether the major ionic species comprising the aerosol particles have been identified and quantified. Additionally, in the case of departure from neutrality, we may estimate the possible identities of the missing ionic species and their contributions. In addition, measurement reliability can also be evaluated through this inspection (Lee et al. 2003).

Figure 10 shows that equivalent concentrations of the total cations have a good correlation with the total anions (R2 of 0.99), with the cation-to-anion ratio in the range of 1.00 ± 0.25 (Table 4), indicating that the majority of ions in the PM2.5 samples were measured practically and ionic balance was achieved (Zhang et al. 2000). The average value of the cation-to-anion ratio was 1.05, indicating that anions had a deficit in some samples. A possible candidate for a missing anion was CO32−, which is known to be associated with crustal material-derived Ca2+.
Fig. 10

The equivalent ratios of anions to cations in PM2.5

Table 4

Cation-to-anion ratio in PM2.5 at 400–1500 and 1600–3200 m at different observation cities

Altitude (m)

Changzhou

Shashi

Xinjin

1600–3200

1.16

1.15

1.11

400–1500

1.10

1.07

1.07

3.6 Formation of Sulfate and Nitrate

Generally, sulfur oxidation ratios (SORs) and nitrogen oxidation ratios (NORs) are used to study the chemical transformations of SO2 to SO42− and NO2 to NO3 (Ohta and Okita 1990; Wang et al. 2006a, b, 2008). The SOR and NOR were determined by Eqs. (4) and (5), respectively, as follows:
$${\text{SOR}} = {{n - {\text{SO}}_{4}^{2 - } } \mathord{\left/ {\vphantom {{n - {\text{SO}}_{4}^{2 - } } {\left( {n - {\text{SO}}_{4}^{2 - } + n - {\text{SO}}_{2} } \right)}}} \right. \kern-0pt} {\left( {n - {\text{SO}}_{4}^{2 - } + n - {\text{SO}}_{2} } \right)}},$$
(4)
$${\text{NOR}} = {{n - {\text{NO}}_{3}^{ - } } \mathord{\left/ {\vphantom {{n - {\text{NO}}_{3}^{ - } } {(n - {\text{NO}}_{3}^{ - } + n - {\text{NO}}_{2} }}} \right. \kern-0pt} {(n - {\text{NO}}_{3}^{ - } + n - {\text{NO}}_{2} }}),$$
(5)
where n denotes the molar quantity of the chemical species.
If the SOR value and NOR value were greater than 0.10, it suggested that oxidation of SO2 and NOX would occur in the atmosphere, and secondary SO42− and NO3 could form (Ohta and Okita 1990). In this study, the SOR value was approximately 0.25–0.6 at altitudes of 400–1500 m, and decreased to 0.05–0.2 when the plane arrived at 1600–3200 m, which indicated the more intense secondary formation of sulfate at 400–1500 m than that at 1600–3200 m. The NOR values also showed similar vertical distribution characteristics, but the vertical difference was less than the corresponding SOR values. From the reports of published studies, the heterogeneous formation of sulfate and nitrate on pre-existing particles was much more efficient than the homogeneous formation in the atmosphere, and meteorological parameters have important influence on the heterogeneous conversion efficiency (Seinfeld and Pandis 2006). To study the influential factors on the formation of sulfate and nitrate, the vertical distributions of air temperature, SO2 and NOX were considered (Fig. 7). As shown in Fig. 11, the SOR had a similar vertical distribution to those of O3 and temperature, indicating that atmospheric oxidation and temperature influenced the SOR. Shen et al. (2008) also reported that SOR was positively correlated with temperature. Higher SO2 values at altitude of 400–1500 m also contributed to a higher SOR. The NOR showed the opposite vertical distribution compared with that of air temperature, which may be due to the increased evaporation of NO3 at higher temperatures.
Fig. 11

Vertical distribution of SOR and NOR

3.7 Carbonaceous Aerosols

Generally, OC could be derived from both the primary emissions of fossil fuel combustion and biomass burning (Arhami et al. 2010) and secondary formation via photochemical reactions (Verma et al. 2009). EC is a primary pollutant emitted during combustion processes, such as biomass burning and fossil fuel combustion (Salma et al. 2004; Zhang et al. 2015). If OC and EC have a good correlation, it may indicate that they have a common source. In our study, the correlation between OC and EC was weak (R2 = 0.65), with an intercept of 7.81 (Fig. 12), which suggested that OC included not only primary OC that was produced directly from combustion processes but also secondary OC that was produced by gas-to-particle conversions (Wang et al. 2015).
Fig. 12

Scatter plot of OC and EC concentrations in PM2.5 over the Yangtze River region

The OC/EC ratio has been used to determine the origin of carbonaceous aerosols (Turpin and Huntzicker 1995; Chow et al. 1993). Cao et al. (2006) reported that the OC/EC ratio was 1.0–2.0 for power plants, 1.4–3.0 for transportation, 0.21–2.42 for industries and 2.93–6.05 for biomass burning. During this study, the OC/EC ratios ranged from 7.33 to 20.5 (with an average of 12.8), which were much greater than the ratios obtained from primary emission sources. These results indicated that a significant fraction of OC was derived from secondary oxidation.

Figure 13 shows the vertical distributions of OC, EC and OC/EC. It can be found that the OC and EC concentrations at 400–1500 m were greater than those at 1600–3200 m, which was similar to the results found for PM2.5 mass concentrations and ionic species. It was suggested that OC and EC were also mainly influenced by ground sources. The OC/EC ratios at 1600–3200 m varied from 12 to 20, which were greater than those at 400–1500 m (7.3–20). Elevated OC/EC ratios at 1600–3200 m were probably due to the presence of secondary products produced by photochemical smog activities during the uplifting and long-range transport of air masses (Zhou et al. 2012).
Fig. 13

Vertical distribution of OC and EC concentration and OC/EC

3.8 Trace Elements

In this study, some of the crustal and anthropogenic trace elements (as mentioned in Sect. 2.2) were collected to determine their levels in the atmosphere of the Yangtze River. The concentrations of these elements followed the order of S > Ca > Fe > K > Al > Na > P > Mg > Zn > Pb > Cu > V > Ni > Cr > Ti > Ba > Mn > As (Fig. 14a). The crustal elements showed greater values compared to the anthropogenic elements (except for S), indicating that the resuspension of soil-derived dust had a strong effect on the chemical composition of atmospheric aerosols. S was the predominant anthropogenic element, with concentrations from 0.5 to 1.9 μg/m3 (accounting for 31–34% of the 15 elements), followed by Zn (35–70 ng/m3 at 1600–3200 m and 79–130 ng/m3 at 400–1500 m) and Pb (34–58 ng/m3 at 1600–3200 m and 67–107 ng/m3 at 400–1500 m). The highest S concentration usually indicated the influence of coal-burning activities on PM2.5.
Fig. 14

Average concentrations (a) of elements and their enrichment factors (b) over the three sampling locations

The enrichment factor (EF) can be used to estimate the degree of enrichment for a given element in the atmosphere compared to the relative abundance of that element in crustal materials (Tang et al. 2006). The selection of a reference element to calculate the EF of an element included Al, Ce, Sc, Si and Ti; in the present study, Ti was chosen as the reference element. Thus, the EF was calculated as EF = (Cx/Ti)sample/(Cx/Ti)crust, where (Cx/Ti)sample represents the ratio between the concentration of element x to that in the sample, and (Cx/Ti)crust represents the same ratio but in crustal materials, which was obtained from Taylor (1964). If the EF is less than 10, it indicates that these elements have a significant crustal source, but if the EF is greater than 10, a significant proportion of an element has anthropogenic source.

Figure 14b shows the average EF values for elements over the Yangtze River Delta. It can be found that Ca, Fe, K, Al, Na, Mg and Mn had EFs less than 10, indicating that they were mainly derived from crustal sources. Ba, P, Cr, V and Ni had EFs of ~ 10 to 100, suggesting that both crustal and anthropogenic sources were important for these elements. The EFs of As, Cu, Zn, S, Pb and Zn were in the range of 100–1000, and they were mainly derived from anthropogenic sources, such as coal combustion and vehicle/industry emissions. Previous studies have showed that such enriched elements can originate from a wide variety of anthropogenic sources. The enrichment of Cr was possibly derived from fossil fuel combustion, steel industries or solid waste dumping (Pacyna 1984), and Ni could be emitted from fuel burning and vehicular emissions (Pacyna 1984). Cu in atmospheric particles around the globe was derived from the combustion of fossil fuels, industrial metallurgical process and waste incineration (Nriagu and Pacyna 1988). Zn may also be derived from similar sources as those of Cu or other traffic-related sources. Pb could be derived from industrial processes or low-tech coal combustion (Widory et al. 2010; Li et al. 2008).

4 Summary and Conclusion

A Yun-12 aircraft was used to study the physical and chemical properties of PM2.5 over the Yangtze River area from 7 Aug. to 13 Sep. in 2003. The results indicated that PM2.5 mass concentrations increased with a decrease in flight altitude, suggesting the effect of ground surface sources. Secondary inorganic ions (i.e., the sum of SO42−, NO3 and NH4+) dominated the ionic species, with contributions up to 80–83% for all flights. The equivalent ratios of the total cations to anions were 1.07–1.16. SO42− mainly existed as (NH4)2SO4. The equivalent ratio of NO3/SO42− was in the range of 0.30–0.45, suggesting that stationary sources such as power plants, petrochemical factories and other industrial emissions were still major sources of airborne ions over the Yangtze River region.

SO42− and NO3 increased with a decrease in altitude and were mostly concentrated at 400–1500 m, suggesting that the production of SO42− and NO3 from the oxidation of locally emitted SO2 and NOy was a dominant process. The equivalent ratio of NH4+/SO42− had no obvious vertical distribution. The average equivalent ratios of NO3/Ca2+ were 0.89 and 1.12 at altitude of 1600–3200 m and 400–1500 m, respectively, which showed that particulate matter was considerably more hygroscopic at an altitude of 400–1500 m than at 1600–3200 m.

The source assessment indicated that 4.0–18.6% of Cl was derived from anthropogenic sources, such as coal combustion and biomass burning. The anthropogenic contributions of Ca2+ were 70.9–76.4%, which showed the possible effects of construction activities and cement manufacturing. K+ was predominantly derived from anthropogenic sources, and the crustal contribution was approximately 21.5–25.6%.

The OC and EC concentrations at an altitude of 400–1500 m were greater than those at an altitude of 1600–3200 m. However, the OC/EC ratios at 1600–3200 m (which varied from 12 to 20) were greater than those at an altitude of 400–1500 m. The elevated OC/EC ratios at an altitude of 1600–3200 m were probably due to the presence of secondary products produced by photochemical smog activity during the uplifting and long-range transport of air masses.

The sum of the concentrations of crustal elements accounted for approximately 40% of the total element concentration, suggesting that the contribution of crustal sources to PM2.5 was non-negligible. Zn and Pb were the most abundant heavy metals in PM2.5. The EFs of As, Cu, Zn, S, Pb and Zn were in the range of 100–1000, and they were mainly derived from anthropogenic sources.

Notes

Acknowledgements

This research was partially supported by funds from a project supported by the central government, Scientific Research Institute for Basic R&D (special business fund; 2016YSKY-023, JY-41375133), the National Natural Science Foundation (41705136), and the National Key Research and Development Program of China (2016YFC0206001). The authors wish to thank Baohui Yin and Mingzhi Xu for participating in the flight measurement, and they express their heartfelt gratitude to Professor Merched Azz for the valuable revision suggestions.

References

  1. Arhami M, Minguillon MC, Polidori A, Schauer JJ, Delfino RJ, Sioutas C (2010) Organic compound characterization and source apportionment of indoor and outdoor quasi-ultrafine particulate matter in retirement homes of the Los Angeles Basin. Indoor Air 20:17–30CrossRefGoogle Scholar
  2. Cao G, Zhang X, Zheng F (2006) Inventory of black carbon and organic carbon emissions from China. Atmos Environ 40:6516–6527CrossRefGoogle Scholar
  3. Cao JJ, Lee SC, Chow JC, Watson JG, Ho KF, Zhang RJ, Jin ZD, Shen ZX, Chen GC, Kang YM, Zou SC, Zhang LZ, Qi SH, Dai MH, Cheng Y, Hu K (2007) Spatial and seasonal distributions of carbonaceous aerosols over China. J Geophys Res 112:1–9CrossRefGoogle Scholar
  4. Chow JC, Watson JG, Lowenthal DH, Solomon PA, Magliano KL, Ziman SD, Richards LW (1993) PM10 and PM2.5 compositions in California’s San Joaquin valley. Aerosol Sci Technol 18:105–128CrossRefGoogle Scholar
  5. Chow JC, Watson JG, Lowenthal DH, Chen LWA, Motallebi N (2011) PM2.5 source profiles for black and organic carbon emission inventories. Atmos Environ 45:5407–5414CrossRefGoogle Scholar
  6. Claquin T, Schulz M, Balkanski YJ (1999) Modeling the mineralogy of atmospheric dust sources. J Geophys Res 104:22243–22256CrossRefGoogle Scholar
  7. Dockery DW, Pope CA, Xu X, Spengler JD, Ware JH, Fay ME, Ferris BG Jr, Speizer FE (1993) An association between air pollution and mortality in six US cities. N Engl J Med 329(24):1753–1759CrossRefGoogle Scholar
  8. Formenti P, Elbert W, Maenhaut W, Haywood J, Osborne S, Andreae MO (2003) Inorganic and carbonaceous aerosols during the Southern African Regional Science Initiative (SAFARI 2000) experiment: chemical characteristics, physical properties, and emission data for smoke from African biomass burning. J Geophys Res 108(D13):8488.  https://doi.org/10.1029/2002JD002408 CrossRefGoogle Scholar
  9. Forster P, Ramaswamy V, Artaxo P, Berntsen T, Betts RW, Fahey D, Haywood J, Lean J, Lowe D, Myhre G, Nganga J, Prinn R, Raga G, Schulz M, Van Dorland R (2007) Changes in atmospheric constituents and in radiative forcing, climate change 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  10. Gu J, Pitz M, Schnelle-Kreis J, Diemer J, Reller A, Zimmermann R, Soentgen J, Stoelzel M, Wichmann HE, Peters A, Cyrys J (2011) Source apportionment of ambient particles: comparison of positive matrix factorization analysis applied to particle size distribution and chemical composition data. Atmos Environ 45:1849–1857CrossRefGoogle Scholar
  11. Han LH, Zhuang GS, Cheng SY, Wang Y, Li J (2007) Characteristics of re-suspended road dust and its impact on the atmospheric environment in Beijing. Atmos Environ 41:7485–7499CrossRefGoogle Scholar
  12. Hatakeyama S, Hanaoka S, Ikeda K, Watanabe I, Arakaki T, Sadanaga Y, Bandow H, Kato S, Kajii Y, Sato K (2011) Aerial Observation of aerosols transported from East Asia-chemical composition of aerosols and layered structure of an air mass over the East China Sea. Aerosol Air Qual Res 11:497–507CrossRefGoogle Scholar
  13. Huang K, Zhuang G, Xu C, Wang Y, Tang A (2008) The chemistry of the severe acidic precipitation in Shanghai, China. Atmos Res 89:149–160CrossRefGoogle Scholar
  14. Huang X, Xue L, Tian X, Shao W, Sun T, Gong Z, Ju W, Jiang B, Hu M, He L (2013) Highly time-resolved carbonaceous aerosol characterization in Yangtze River Delta of China: composition, mixing state and secondary formation. Atmos Environ 64:200–207CrossRefGoogle Scholar
  15. Huebert BJ, Bates T, Russell PB, Shi G, Kim YJ, Kawamura K, Carmichael G, Nakajima T (2003) An overview of ACE-Asia: strategies for quantifying the relationships between Asian aerosols and their climatic impacts. J Geophys Res 108:8633CrossRefGoogle Scholar
  16. Jacob DJ, Crawford JH, Kleb MM, Connors VS, Bendura RJ, Raper JL, Sachse GW, Gille JC, Emmons L, Heald CL (2003) Transport and chemical evolution over the Pacific (TRACE-P) aircraft mission: design, execution, and first results. J Geophys Res 108(10):1029Google Scholar
  17. Khan MF, Shirasuna Y, Hirano K, Masunaga S (2010) Characterization of PM2.5, PM2.5–10 and PM10 in ambient air, Yokohama, Japan. Atmos Res 96:159–172CrossRefGoogle Scholar
  18. Lee YN, Weber R, Ma Y, Orsini D, Maxwell-Meier K, Blake D, Meinardi S, Sachse G, Harward C, Chen T-Y, Thornton D, Tu F-H, Bandy A (2003) Airborne measurement of inorganic ionic components of fine aerosol particles using the particle-into-liquid sampler coupled to ion chromatography technique during ACE-Asia and TRACE-P. J Geophys Res.  https://doi.org/10.1029/2002jd003265 CrossRefGoogle Scholar
  19. Li XG, Wang SX, Duan L, Hao J, Li C, Chen YS, Yang L (2007) Particulate and trace gas emissions from open burning of wheat straw and corn stover in China. Environ Sci Technol 41(6052–6058):2007.  https://doi.org/10.1021/es0705137 CrossRefGoogle Scholar
  20. Li YW, Liu XD, Li B, Yang HX, Dong SP, Hang ZT, Guo J (2008) Source apportionment of aerosol lead in Beijing using absolute principal component analysis. Environ Sci (Chinese Edition) 29:3310–3319Google Scholar
  21. Li C, Stehr JW, Marufu LT, Li Z, Dickerson RR (2012) Aircraft measurements of SO2 and aerosols over northeastern China: vertical profiles and the influence of weather on air quality. Atmos Environ 62:492–501CrossRefGoogle Scholar
  22. Liang Q, Jaeglé L, Jaffe DA, Weiss Penzias P, Heckman A, Snow JA (2004) Long-range transport of Asian pollution to the northeast Pacific: seasonal variations and transport pathways of carbon monoxide. J Geophys Res.  https://doi.org/10.1029/2003jd004402 CrossRefGoogle Scholar
  23. Liu XM, Shao M, Zeng LM, Zhang YH (2002) Study on EC and OC compositions of ambient particles in Pearl River Delta Region. Environ Sci 23(Sup.):54–59Google Scholar
  24. Ma J, Chen Y, Wang W, Yan P, Liu H, Yang S, Hu Z, Lelieveld J (2010) Strong air pollution causes widespread haze-clouds over China. J Geophys Res.  https://doi.org/10.1029/2009JD013065 CrossRefGoogle Scholar
  25. Matsuki A, Iwasaka Y, Osada K, Matsunaga K, Kido M, Inomata Y, Trochkine D, Nishita C, Nezuka T, Sakai T (2003) Seasonal dependence of the long-range transport and vertical distribution of free tropospheric aerosols over east Asia: on the basis of aircraft and lidar measurements and isentropic trajectory analysis. J Geophys Res 108(D23):8663.  https://doi.org/10.1029/2002JD003266 CrossRefGoogle Scholar
  26. Mayol-Bracero OL, Gabriel R, Andreae MO, Kirchstetter TW, Novakov T, Ogren J, Sheridan P, Streets DG (2002) Carbonaceous aerosols over the Indian Ocean during the Indian Ocean Experiment (INDOEX): chemical characterization, optical properties, and probable sources. J Geophys Res.  https://doi.org/10.1029/2000JD000039 CrossRefGoogle Scholar
  27. Nriagu JO, Pacyna JM (1988) Quantitative assessment of worldwide contamination of air, water and soils by trace metals. Nature 320:735–738Google Scholar
  28. Ohta S, Okita T (1990) A chemical characterization of atmospheric aerosol in Sapporo. Atmos Environ 24:815–822CrossRefGoogle Scholar
  29. Pacyna JM (1984) Estimation of atmospheric emissions of trace elements from anthropogenic sources in Europe. Atmos Environ 18:41–50CrossRefGoogle Scholar
  30. Padmakumari B, Maheskumar RS, Harikishan G, Morwal SB, Prabha TV, Kulkarni JR (2013) In situ measurements of aerosol vertical and spatial distributions over continental india during the major drought year 2009. Atmos Environ 80:107–121CrossRefGoogle Scholar
  31. Peltier RE, Sullivan AP, Weber RJ, Brock CA, Wollny AG, Holloway JS, Gouw JAD, Warneke C, Science A, Systems E, Collins F (2007) Fine aerosol bulk composition measured on WP-3D research aircraft in vicinity of the Northeastern United States—results from NEAQS. Atmos Chem Phys 7:3231–3247CrossRefGoogle Scholar
  32. Perrino C, Ramirez D, Allegrini I (2001) Monitoring acidic air pollutants near Rome by means of diffusion lines: development of a specific quality control procedure. Atmos Environ 35:331–341CrossRefGoogle Scholar
  33. Pui DYH, Francisco RN, Liu BYH (1987) Experimental study of particles deposition in bends of circular cross section. Aerosol Sci Technol 7:301–315CrossRefGoogle Scholar
  34. Ren LH, Zhang RJ, Bai ZP, Chen JH, Liu HJ, Zhang MG, Yang XY, Zhang LM (2012) Aircraft measurements of ionic and elemental components in PM2.5 over Eastern Coastal Area of China. Aerosol Air Qual Res 12:1237–1246CrossRefGoogle Scholar
  35. Safai PD, Budhavant KB, Rao PSP, Ali K, Sinha A (2010) Source characterization for aerosol constituents and changing roles of calcium and ammonium aerosols in the neutralization of aerosol acidity at a semi-urban site in SW India. Atmos Res 98(1):78–88CrossRefGoogle Scholar
  36. Salma I, Chi X, Maenhaut W (2004) Elemental and organic carbon in urban canyon and background environments in Budapest, Hungary. Atmos Environ 38:27–36CrossRefGoogle Scholar
  37. Seinfeld HJ, Pandis NS (2006) Atmospheric chemistry and physics: from air pollution to climate change. Wiley, OxfordGoogle Scholar
  38. Shen ZX, Arimoto R, Cao JJ, Zhang RJ, Li XX, Du N, Tomoaki O, Shunsuke N, Shigeru T (2008) Seasonal variations and evidence for the effectiveness of pollution controls on water-soluble inorganic species in total suspended particulates and fine particulate matter from Xi’an, China. J Air Waste Manag Assoc 58:1560–1570CrossRefGoogle Scholar
  39. Tang XY, Zhang YH, Shao M (2006) Atmospheric environment chemistry. Higher Education Press, BeijingGoogle Scholar
  40. Taylor SR (1964) Abundance of chemical elements in the continental crust: a new table. Geochim Cosmochim Acta 28:1273–1285CrossRefGoogle Scholar
  41. Tu FH, Thornton DC, Bandy AR, Carmichael GR, Tang Y, Thornhill KL, Sachse GW, Blake DR (2004) Long-range transport of sulfur dioxide in the central Pacific. J Geophys Res 109:D15CrossRefGoogle Scholar
  42. Turpin BJ, Huntzicker JJ (1995) Identification of secondary organic aerosol episodes and quantitation of primary and secondary organic aerosol concentrations during SCAQS. Atmos Environ 29:3527–3544CrossRefGoogle Scholar
  43. Verma RL, Sahu LK, Kondo Y, Takegawa N (2009) Temporal variation of elemental carbon in Guangzhou, china, in summer 2006. Atmos Chem Phys 9(6):6471–6485Google Scholar
  44. Waldman JM, Lioy PJ, Zelenka M, Jing L, Lin YN, He QC, Qian ZM, Chapman R, Wilson WE (1991) Wintertime measurements of aerosol acidity and trace elements in Wuhan, a city in central China. Atmos Environ 25:113–120CrossRefGoogle Scholar
  45. Wang W, Liu H, Yue X, Li H, Chen J, Tang D (2005) Study on size distributions of airborne particles by aircraft observation in the spring over eastern coastal areas of China. Adv Atmos Sci 22:328–336CrossRefGoogle Scholar
  46. Wang W, Liu HJ, Yue X, Li HJ, Chen JH, Ren LH, Tang DG, Hatakeyama S, Takami A (2006a) Study on acidity and acidic buffering capacity of particulate matter over Chinese eastern coastal areas in spring. J Geophys Res 111:D18207.  https://doi.org/10.1029/2005jd006753 CrossRefGoogle Scholar
  47. Wang Y, Zhuang G, Zhang X, Huang K, Xu C, Tang A, Chen J, An Z (2006b) The ion chemistry, seasonal cycle, and sources of PM2.5 and TSP aerosol in Shanghai. Atmos Environ 40:2935–2952CrossRefGoogle Scholar
  48. Wang W, Ren LH, Zhang YH, Chen JH, Liu HJ, Bao LF, Fan SJ, Tang DG (2008) Aircraft measurements of gaseous pollutants and particulate matter over Pearl River Delta in China. Atmos Environ 42:6187–6202CrossRefGoogle Scholar
  49. Wang P, Cao JJ, Shen ZX, Han YM, Lee SC, Huang Y, Zhu CS, Wang QY, Xu HM, Huang RJ (2015) Spatial and seasonal variations of PM2.5 mass and species during 2010 in Xi’an, China. Sci Total Environ 508:477–487CrossRefGoogle Scholar
  50. Warneck P (2000) Chemistry of the natural atmosphere, 2nd edn. Academic Press, New YorkGoogle Scholar
  51. Watson JG (2002) Visibility: science and regulation. J Air Waste Manag Assoc 52:628–713CrossRefGoogle Scholar
  52. Widory D, Liu X, Dong S (2010) Isotopes as tracers of sources of lead and strontium in aerosols (TSP & PM2.5) in Beijing. Atmos Environ 44:3679–3687CrossRefGoogle Scholar
  53. Yang F, Tan J, Zhao Q, Du Z, He K, Ma Y, Duan F, Chen G, Zhao Q (2011) Characteristics of PM2.5 speciation in representative megacities and across China. Atmos Chem Phys 11:5207–5219CrossRefGoogle Scholar
  54. Yao XH, Chan CK, Fang M, Cadle S, Chan TI, Mulawa P, He KB, Ye BM (2002) The water-soluble ionic composition of PM2.5 in Shanghai and Beijing, China. Atmos Environ 36:4223–4234CrossRefGoogle Scholar
  55. Yue W, Li X, Liu J, Li Y, Yu X, Deng B, Wan T, Zhang G, Huang Y, He W, Hua W, Shao L, Li W, Yang S (2006) Characterization of PM2.5 in the ambient air of Shanghai City by analyzing individual particles. Sci Total Environ 368:916–925CrossRefGoogle Scholar
  56. Zhang J, Chen N, Yu Zh, Zhang J (2000) Ion balance and composition of atmospheric wet deposition (precipitation) in Western Yellow Sea. Mar Environ Sci 19(2):10–13Google Scholar
  57. Zhang R, Jing J, Tao J, Hsu S, Wang G, Cao J, Lee CSL, Zhu L, Chen Z, Zhao Y, Shen Z (2013) Chemical characterization and source apportionment of PM2.5 in Beijing: seasonal perspective. Atmos Chem Phys 13:7053–7074CrossRefGoogle Scholar
  58. Zhang Q, Shen Z, Cao J, Zhang R, Zhang L, Huang RJ, Zheng C, Wang L, Liu S, Xu H, Zheng C, Liu P (2015) Variations in PM2.5, TSP, BC, and trace gases (NO2, SO2, and O3) between haze and non-haze episodes in winter over Xi’an, China. Atmos Environ 112:64–71CrossRefGoogle Scholar
  59. Zhang Y, Tang L, Croteau PL, Favez O, Sun Y, Canagaratna MR, Wang Z, Couvidat F, Albinet A, Zhang H (2017) Field characterization of the PM2.5 aerosol chemical speciation monitor: insights into the composition, sources and processes of fine particles in Eastern China. Atmos Chem Phys 17:1–52CrossRefGoogle Scholar
  60. Zhou S, Wang Z, Gao R, Xue L, Yuan C, Wang T, Gao X, Wang X, Nie W, Xu Z, Zhang Q, Wang W (2012) Formation of secondary organic carbon and long-range transport of carbonaceous aerosols at Mount Heng in South China. Atmos Environ 63:203–212CrossRefGoogle Scholar

Copyright information

© Institute of Earth Environment, Chinese Academy Sciences 2018

Authors and Affiliations

  • Lihong Ren
    • 1
    • 2
  • Renjian Zhang
    • 2
  • Xiaoyang Yang
    • 1
  • Chunmei Geng
    • 1
  • Wei Wang
    • 1
  • Shiro Hatakeyama
    • 3
  • Hong Li
    • 1
  • Wen Yang
    • 1
  • Zhipeng Bai
    • 1
  • Akinori Takami
    • 4
  • Hongjie Liu
    • 1
  • Jianhua Chen
    • 1
  1. 1.State Key Laboratory of Environmental Criteria and Risk AssessmentChinese Research Academy of Environmental SciencesBeijingChina
  2. 2.Key Laboratory of Regional Climate-Environment for East Asia, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  3. 3.Institute of AgricultureTokyo University of Agriculture and TechnologyFuchuJapan
  4. 4.Center for Regional Environmental Research, National Institute for Environmental StudiesTsukubaJapan

Personalised recommendations