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Water-Soluble Ionic Characteristics of Aerosols in the Marine Boundary Layer over the Yellow Sea during the KORUS-AQ Campaign

  • Joo Wan ChaEmail author
  • Beomchel Shin
  • Hee-Jung Ko
  • Yun Kyu Lim
  • Sang-Boom Ryoo
Open Access
Original Article
First Online:

Abstract

Major compositions of water-soluble ionic species in particulate matter less than 10 and 2.5 μm in diameter (PM10 and PM2.5, respectively) over the Yellow Sea were collected during the Korea–United States Air Quality (KORUS-AQ) campaign in 2016 onboard the research vessel Gisang 1. The secondary ionic species (NH4+, nss-SO42−, and NO3−) in PM10 and PM2.5 accounted for 84% and 89% of the total analyzed species. NH4+ was strongly correlated with non-sea salt (nss) SO42− (nss-SO42−) in PM10 and PM2.5; NO3− was closely correlated with Na+, Mg2+, and nss-Ca2+ in PM10 and NH4+ in PM2.5. High mass concentrations of methane sulfonic acid (MSA, CH3SO3−), the main source of natural sulfates over the Yellow Sea, were observed. The concentrations of MSA were found to show an increasing trend over the Yellow Sea in recent years. Biogenic sulfur contributions to the total nss-SO42− (MSA/nss-SO42− ratio) over the Yellow Sea ranged from 1.4% to 9.2% in PM10 and from 0.68% to 9.5% in PM2.5 during the cruise. Thus, biogenic nss-SO42− must be included, especially in the spring and early summer seasons, when biological activities are elevated in Northeast Asia. We classified the high aerosol mass concentration cases such as Asian dust and haze cases. In Asian dust cases, the ratio of NO3− to nss-SO42− in the aerosols showed that mobile (stationary) sources mainly affected PM10 (PM2.5). The major chemical species for Asian dust cases over the Yellow sea were CaCO3, Ca(NO3)2, Mg(NO3)2, Na(NO3)2, and sea salt. In haze cases over the Yellow sea, the contributions from stationary sources are high and the major species were (NH4)2SO4 and NH4NO3 in PM10 and PM2.5, respectively.

Keywords

Korea–United States air quality (KORUS-AQ) Yellow Sea Water-soluble ionic species PM10 PM2.5 Methane sulfonic acid (MSA CH3SO3−) 

1 Introduction

Recent economic development in Northeast Asia, especially in China, has resulted in frequent occurrences of high aerosol mass concentration events in the region (Ding and Liu 2014; Wang and Chen 2016). Aerosols originating over land, from natural and/or anthropogenic sources, are deposited into surrounding ocean surfaces, which become the main sources of continental aerosols (Arimoto et al. 1996; Zhang et al. 2004). The Gobi Desert and the Loess Plateau areas are key sources of mineral aerosols in Northeast Asia and the North Pacific Ocean (Zhang et al. 1993; Gao et al. 1997). Previous observations of aerosols over coastal seas and at a number of inland sites in Northeast Asia have focused on the spring and early summer seasons (Kim et al. 1998, 2009; Lee et al. 2002; Zhang et al. 2002). Northeast Asia has emerged as the world’s largest source of SO2 in recent years (Su et al. 2011). Furthermore, it has been reported that by 2020, NOx emissions in the region may increase five-fold compared to the levels in 1990 (Akimoto 2003). Recent some studies (Gu et al. 2013; Liu et al. 2016) reported the reduction in NOx emission trends over China. Although the reduction of NOx emission is found over China, the Northeastern Asia is the regions of highest NOx concentration in the world (Huang et al. 2017). Emissions from land sources in Northeastern Asia affect the aerosol field over the Yellow Sea, depending on atmospheric circulation patterns. Hence, the recent increase in anthropogenic emissions can alter aerosol compositions and their characteristics over the Yellow Sea.

The Korea-United States Air Quality (KORUS-AQ) campaign was an international, multi-organization mission to observe air quality across the Korean peninsula and surrounding waters. KORUS-AQ was conducted by the National Aeronautics and Space Administration (NASA) and their international partners from April to June 2016. National Institute of Meteorological Science (NIMS) of the Korea Meteorological Administration (KMA) measured atmospheric aerosols over the Yellow Sea, which was affected by aerosols originating in various regions of China and Korea during the campaign periods. KORUS-AQ integrated observations from ships, aircrafts, ground sites, and satellites, in conjunction with air quality models, to understand the factors governing air quality across urban, rural, and coastal interfaces in Northeast Asian (https://espo.nasa.gov/home/korus-aq).

Especially, MSA in the marine boundary layer is one of the major end products of the oxidation of dimethyl sulfide (DMS) produced by marine biota. MSA and DMS studies were conducted over the Yellows Sea by Chinese scientists to understand sulfur chemistry in the past (Chen et al. 2012). This study focuses on the NO3 radicals which play an important role as an oxidant in DMS decomposition in the regions of highest NOx concentration such as Northeastern Asia (Gao et al. 1996). China emits large amounts of SO2 and NOx (Han et al. 2005), which are transported to the Yellow Sea by prevailing westerlies and deposited there to enhance MSA production. For example, Gao et al. (1996) reported an MSA mass concentration of 0.029 μg m−3 in TSP (Total Suspended Particle) over the Yellow Sea and East China Sea. Zhang et al. (2015) reported an MSA mass concentration of 0.061 μg m−3 in TSP over the Yellow Sea in May 2012. Cha et al. (2016) reported an MSA mass concentration of 0.13 μg m−3 in PM10 over the Yellow Sea in April 2015 (Table 1).
Table 1

Statistical summary of important water-soluble species in aerosols sampled over the sea around Northeast Asia

 

MSA

SO42−

NO3−

Na+

NH4+

Mg2+

Ca2+

nss-SO42−

nss-K+

nss-Ca2+

Region/ date

Size-cut

Gao et al. (1996)

0.029

–

1.9

–

–

–

–

4.0

–

–

Yellow Sea, East China Sea/15–25 May 1992

TSP

Zhang et al. (2013)

0.011

13.0

3.5

1.8

4.6

0.59

1.0

12.0

0.94

0.97

North Yellow Sea /14–25 October 2007

TSP

Zhang et al. (2013)

0.0081

10.0

2.9

6.0

3.1

0.89

1.0

8.6

0.62

0.81

South Yellow Sea /2–24 November 2007

TSP

Yang et al. (2015)

0.0127

26.2

11.9

5.1

7.7

1.7

–

24.9

2.58

–

Bohai Sea, North Yellow Sea /21November - 1 December 2011

TSP

Zhang et al. (2015)

0.061

8.3

8.2

1.0

2.5

0.2

0.7

8.1

0.3

0.67

Bohai Sea, North –South Yellow Sea / 2–20 May 2012

TSP

Zhang et al. (2015)

0.012

6.2

6.5

2.5

2.5

0.5

1.0

5.7

0.4

0.88

Bohai Sea, North –South Yellow Sea /2–19 November 2012

TSP

Cha et al. (2016)

0.13

7.5

3.0

1.33

3.2

0.2

0.4

7.2

0.2

0.3

Yellow Sea /9–14 & 24–29 April and 1–5 May 2015

PM10

Boreddy and Kawamura (2015)

0.03

–

0.84

3.3

0.23

0.42

–

2.97

0.05

0.30

Western North Pacific sea/Spring

TSP

This study aims to understand the chemical characteristics of water-soluble aerosols over the Yellow Sea from shipborne sampling data of aerosols acquired during the KORUS-AQ campaign because the Yellow Sea is under the strongest air pollutant influence in the world (Wang and Chen 2016). Water-soluble ions are important constituents of atmospheric aerosols over ocean surfaces. In particular, heavy aerosol events, such as Asian dust and haze over the Yellow Sea, are mainly due to long-range aerosol transport from inland regions, such as China, Korea, and Japan. Therefore, this study focuses on analyzing the concentrations of the major water-soluble ionic compounds and methanesulfonic acid (MSA) in the high aerosol concentration cases observed during the KORUS-AQ campaign. The internal correlations between the chemical ionic species are examined in terms of the chemical species formation in these cases.

2 Data and Methodology

The KORUS-AQ campaign involved three cruises conducted using the research vessel Gisang 1, operated by the National Institute of Meteorological Science (NIMS) of the Korea Meteorological Administration (KMA). The first cruise started on May 3, 2016, and ended on May 12, 2016; the second cruise started on May 18, 2016, and ended on May 29, 2016; and the third cruise started on June 5, 2016, and ended on June 13, 2016. The samples obtained during these cruises over the region 35.3–37.3°N and 123.2–125.2°E were selected for analysis in this study (Fig. 1).
Fig. 1

Ship track (black line) and target area (red dot-line box) over the Yellow Sea from May 2 to June 13, 2016

Five-minute average PM10 (particulate matter less than 10 μm in diameter) mass concentrations were measured using a PM10 suspended particulate analyzer (β-ray PM10 analyzer, Thermo Scientific Inc., FH62-C14; hereafter, β-ray PM10) on the basis of a β-ray absorption method. A total of 17 samples were collected using 47-mm Teflon filters and a particle measuring system (APM Inc., PMS-104; hereafter, PMS) equipped with a PM10 and PM2.5 (particulate matter less than 2.5 μm in diameter) separator. The study mainly focused on the characteristics of the samples collected through the PM10 and PM2.5 inlets. An aerodynamic particle sizer (TSI Inc., APS-3321; hereafter, APS) was used to observe the aerosol particle size distribution. The measurable concentration range of APS is 0–10,000 cm−3 and the observation range is 0.5–20 μm with 52-bin channels. The β-ray PM10 measures the PM10 mass concentration every 5 min, while the APS measures the size-segregated number concentration every 3 min. The PMS was placed on the deck of the ship at 8 m above sea level. Samples of PM10 and PM2.5 aerosols were collected for around 10 h during daytime. That is, the water-soluble ionic mass concentration obtains from PMS for PM10 and PM2.5 and the total PM10 mass concentration does from β-ray PM10. The size-segregated total PM mass concentration such as PM10 and PM2.5 is from APS.

Meteorological and oceanic parameters, such as temperature, wind direction, wind speed, and sea surface pressure, were observed every 5 min using automatic meteorological instruments installed on the ship. An aerosol observation container was installed on the bow of the ship to protect the instruments from marine hazards as well as to protect the ship from the effects of pollution sources, such as the smokestack. Figure 1 shows the main route of the research vessel over the Yellow Sea during the KORUS-AQ campaign. At the end of each cruise, aerosol samples collected on the filter were analyzed using an ion analyzer (ion chromatograph; hereafter, IC) to measure five types of cations (NH4+, Na+, K+, Ca2+, and Mg2+) and five types of anions (SO42−, NO3−, HCOO−, CH3COO−, and CH3SO3−). The detection limit and the coefficient of variation of IC vary from 0.29 to 7.48 μg L−1 and 0.19% to 7.33%, respectively, depending on the ion type.

3 Results and Discussion

3.1 Characteristics of Aerosol Ionic Species in Total Samples over the Yellow Sea

The major secondary aerosol mean mass concentrations for nss-SO42−, NO3−, and NH4+ were 10.195, 3.025, and 3.436 μg m−3, respectively, in PM10, and 8.823, 1.021 and 2.966 μg m−3, respectively, in PM2.5. Further, the diameters of nss-SO42− and NH4+ were mostly less than 2.5 μm; hereafter, size denotes diameter. The diameter of NO3− was mostly greater than 2.5 μm. Similar values of nss-SO42−, NH4+, and NO3− mean mass concentrations in TSP were reported by Zhang et al. (2015) over the Yellow Sea in 2012: 8.6, 3.1, and 2.9 μg m−3 for nss-SO42−, NH4+, and NO3−, respectively. In this study, these secondary ionic species accounted for 84% and 89% of the total analyzed species in PM10 and PM2.5, respectively. This implies that the aerosols collected over the Yellow Sea during the campaign were strongly affected by the anthropogenic emissions over land.

Non-sea salt potassium (nss-K+) is a good indicator of biomass burning (Simoneit 2002), and its diameter is less than 2.5 μm (Gaudichet et al. 1995). The nss-K+ mass concentration over the Yellow Sea in this study is decreased than that in previous studies (e.g. Zhang et al. 2013; Zhang et al. 2015). The nss-Ca2+ from soil (Kang et al. 2003) clearly shows a decreasing trend from land to ocean (Fig. 2). The nss-Ca2+ mean mass concentration in this study was 0.4 μg m−3 in PM10 and 0.1 μg m−3 in PM2.5. The difference between the nss-Ca2+ mean mass concentrations of PM10 and PM2.5 was 0.3 μg m−3 on average, and the nss-Ca2+ mean mass concentration of PM2.5 was 0.1 μg m−3 on average. Thus, most of the nss-Ca2+ was between 10 and 2.5 μm in diameter over the Yellow sea.
Fig. 2

Comparison between the observed ranges for water-soluble ionic mass concentrations using PMS in this study and those in previous studies over the sea near Northeast Asia

The correlation coefficient matrix was employed to analyze the internal relationships among different species. NH4+ is strongly correlated with nss-SO42− and the correlation coefficients between NH4+ and nss-SO42− are 0.84 and 0.97 for PM10 and PM2.5, respectively (Tables 5 and 6). This implies that most of the NH4+ was combined with nss-SO42−. The correlation coefficients between NO3− and Na+, Mg2+, and nss-Ca2+ for PM10 were 0.73, 0.74, and 0.72, respectively. Thus, NO3− in the PM10 samples existed as compounds of Na+, Mg2+, and nss-Ca2+. These results indicate that the marine and continental sources simultaneously affected the samples during the cruise.

The mean mass concentration of MSA in PM10 and PM2.5 was 0.318 and 0.239 μg m−3, respectively. The difference between the MSA mean mass concentrations of PM10 and PM2.5 was 0.079 μg m−3 on average and the MSA mean mass concentration of PM2.5 was 0.239 μg m−3 on average. Thus, most of the MSA was in PM2.5; the MSA mass concentration in PM10 and PM2.5 ranged from 0.096 to 0.61 μg m−3 and from 0.082 to 0.48 μg m−3, respectively. In conjunction with the previous measurements (Zhang et al. 2013, 2015; Cha et al. 2016), this study shows an increasing trend of the MSA mass concentration over the Yellow Sea in recent years and the MSA concentration in the central Yellow Sea is larger than that in other regions (see Fig. 2 and Table 1). The increase in MSA within aerosols in the Yellow Sea may be related to the changes in the inputs of the materials, which are related to the formation of MSA from dimethyl sulfide (DMS). High nutrient inputs from the Yellow River and the Yangtze River in China, which have rapidly increased in recent years (Wei et al. 2015), may have increased the formation of DMS and thus MSA. The deposition of mineral matter transported by air flow from inland deserts to the Yellow Sea (Hsu et al. 2009) can also affect the formation of DMS. The relationship among DMS, MSA, and marine productivity has been observed in many other regions (Calhoun 1992; Ayers et al. 1986; Park et al. 2017). Therefore, this study analyzed the detailed MSA mass concentration even though the MSA was not major water-soluble ions such as for nss-SO42−, NO3−, and NH4+.

3.2 High PM Cases

3.2.1 Classification of High PM Cases

High aerosol mass concentration cases during the experiment were classified using the β-ray PM10 and APS. The differential mass concentration of a given aerodynamic diameter (dMDae) for each channel is calculated as
$$ {\mathrm{dM}}_{\mathrm{D}\mathrm{ae}}={\mathrm{dN}}_{\mathrm{D}\mathrm{ae}}\frac{\uppi}{6}{\mathrm{D}}_{\mathrm{ve}}^3{\uprho}_{\mathrm{p}} $$
where Dae is the aerodynamic diameter, dNDae is the differential number concentration for a given aerodynamic diameter, ρp is the density of the particle (1 g cm−3, Katrib et al. 2005), and Dve is the volumetric equivalent diameter, all of which were obtained from the APS. PM10 and PM2.5 mass concentrations were calculated from the summation of channels 1–42 and 1–23, respectively. The unit for the differential mass concentration is μg m−3. Seventeen-day data were analyzed after eliminating anchoring periods due to severe weather, fuel supply, and buoy release, as well as obvious contamination from the research vessel.
We performed a mass-frequency distribution analysis for the PM10 hourly series measured on the ship and in Seoul during the study period (Fig. 3). The average PM10 mass concentrations over the Yellow Sea and in Seoul during the study period were around 40 and 48 μg m−3, respectively. The measurements at the two sites were similar despite differences in the station type: the ship (Gisang 1) is a background station located on the sea, whereas Seoul is a megacity affected by many types of aerosol sources. The peak aerosol mass concentration was 30–40 μg m−3 on the ship and 40–50 μg m−3 at the Seoul station, and the frequency above 50 μg m−3 at Seoul was higher than that on the ship. Further, the frequency above 100 μg m−3 was higher than the frequency for the range 70–100 μg m−3 both on the ship and at Seoul station. It shows that both sites have similar frequency of mass concentration distribution. The cases above 100 μg m−3 during the campaign was from same high mass concentration episodes (the Asian dust and haze cases) at both site (not shown). That is, long-range aerosol transport from Chinese sources.
Fig. 3

Histogram of PM10 mass concentration by PM10 analyzer (β-ray application) in research vessel Gisang 1 (square box) and Seoul (dashed line) from May 2 to June 13, 2016

Classification of the dust and haze events over the Yellow Sea in this study follows the work of Cho et al. (2013): for “Asian dust” (“haze”), the hourly-mean PM2.5/PM10 ratio was below 40% (above 80%). Figure 4 shows the method for the classification of Asian dust and haze event by flowchart. Figure 5a shows the PM10 mass concentration from the β-ray PM10, and Fig. 5b shows the ratios of PM2.5/PM10 over the Yellow Sea. In Fig. 5b, the blue (red) regions indicate ratios below (above) 80% (40%). The dust and haze events were selected when the PM10 concentration exceeded 50 μg m−3, i.e., the value corresponding to the sum of the mean and one standard deviation of the PM10 mass concentration over the Yellow Sea. On the basis of this criterion, one Asian dust event (Case I: May 7, 2016) and three haze events (Case II: May 12, 2016; Case III: May 21, 2016; and Case IV: May 29, 2016)) were identified (Table 2). The aerosol chemical compositions were sampled in Cases I, II, and III for PM10, and in Cases I, II, and IV for PM2.5. The cases were selected if more than 50 μg m−3 was measured at least once using the β-ray PM10. The cumulative PM10 mass concentrations collected using 47-mm Teflon filters using PMS during each case day were 73, 47, and 52 μg m−3 in Cases I, II, and III, respectively, and the PM2.5 mass concentrations were 22, 30, and 52 μg m−3 in Cases I, II, and IV, respectively. The aerosol chemical compositions were not sampled in Case IV for PM10 and Case III for PM2.5 during the cruise.
Fig. 4

Flowchart for classification of Asian dust and haze using PM10 mass concentration by PM10 analyzer (β-ray application) and the ratio of PM2.5/PM10 by APS in research vessel Gisang 1

Fig. 5

a PM10 mass concentration by PM10 analyzer (β-ray) and b classification of Asian dust and haze by the ratio of PM2.5/PM10 by APS over the Yellow Sea from May 2 to June 13, 2016

Table 2

Aerosol sampling information using PMS for the four high aerosol mass concentration cases over the Yellow Sea during KORUS-AQ 2016

Case

LST

Area

No. of Samples

Remark

Start Time

End Time

Longitude (N)

Latitude (E)

PM10

PM2.5

Case I

2016-05-07 09:00

2016-05-07 18:13

37.31 → 35.33

124.28 → 124.28

1

1

Asian Dust

Case II

2016-05-12 08:00

2016-05-12 17:55

37.33 → 35.35

124.28 → 124.28

1

1

Haze & Mist

Case III

2016-05-21 07:59

2016-05-21 20:02

35.34 → 36.27

124.28 → 125.75

1

–

Haze

Case IV

2016-05-29 08:01

2016-05-29 16:24

37.32 → 35.32

124.28 → 124.32

–

1

Haze

3.2.2 Origins of High PM Cases

The HYSPLIT 4 model developed at the National Oceanic and Atmospheric Administration/Air Resources Laboratory was used to estimate the upstream path of air flow over the Yellow Sea during the KORUS-AQ campaign. The HYSPLIT simulations were run using the Unified Model–Global Data Assimilation and Prediction System (UM–GDAPS) weather data from KMA for 72 h prior to each case, at 500 m above the center of the vessel observation route (36.16°N, 124.29°E).

Figure 6 shows the 72-h backward trajectories for the selected high PM10 mass concentration cases from the HYSPLIT simulations. In Case I (Asian dust case; hereafter, AD), air from the Inner Mongolia region passed through the main source regions of Asian dust to affect the sampling region. In Case II, i.e., the haze and mist from Northeast China (HMNC) case, air from Liaoning province in Western Manchuria stayed over the Yellow Sea for extended periods after leaving the land. The classification criterion of mist and haze is 75% RH: Mist ≥75% RH and Haze <75% RH (http://www.kma.go.kr). In Case III, i.e., the haze from the Korean Peninsula (HKP) case, air pollutants from South Korea affected the sampling area. In Case IV, i.e., the haze from the Shandong Peninsula in China (HSPC) case, air flows originating in inland China passed through the Shandong Peninsula, an area of recent rapid industrialization, and stayed over the Yellow Sea for extended periods before arriving at the sampling site. Table 2 provides detailed information on the observations for the four cases.
Fig. 6

Backward trajectories for high concentration cases from May 2 to June 13, 2016 (AD: Asian dust case; HMNC: haze and mist from Northeast China case; HKP: haze from the Korean Peninsula case; HSPC: haze from the Shandong Peninsula in China case)

3.2.3 Characteristics of Aerosol Ionic Species in High PM Cases

The mass concentration of nss-SO42− is larger than that of the other ions, even though PM10 and PM2.5 were not sampled in HSPC and HKP, respectively (Fig. 7). The mass concentration varies in the order HSPC > HMNC > HKP > AD. Thus, most of the nss-SO42− over the Yellow Sea in May 2016 came from the Shandong Peninsula and Northeast China. The NH4+ mass concentration varies nearly identically to that of nss-SO42−, except for the differences in the mass concentrations. This suggests that the NH4+ over the Yellow Sea came from nearly the same sources as nss-SO42−, mainly as nss-SO42− compounds in PM2.5. Figure 7 also suggests that the nss-SO42− and NH4+ over the Yellow Sea were affected by sources in South Korea, although China is the main source of these two ions. The mass concentration of NO3− varies in the order HSPC > AD > HKP > HMNC. For the four cases, NO3−, Na+, Mg2+, and nss-Ca2+ were mostly contained in PM10. These results suggest that NO3− is mostly combined with Na+, Mg2+, and nss-Ca2+ in PM10 during high aerosol mass concentration events over the Yellow Sea. This implies that ions such as Mg2+ and nss-Ca2+ in PM10 among dust particles during AD reacted more with nitrates than with sulfates from anthropogenic sources in China. The nss-K+ from biomass burning and combustion were mostly in PM2.5 with the largest mass concentration in HSPC (see Fig. 7 and Table 3). These results show that during the campaign, the Shandong Peninsula (HSPC) was the largest anthropogenic source of the ions over the Yellow Sea.
Fig. 7

Major chemical ionic species in a PM10 and b PM2.5 using PMS for high aerosol mass concentration cases over the Yellow Sea during the KORUS-AQ campaign, 2016

Table 3

Comparison the mass concentrations of water-soluble species in aerosols sampled using PMS in High PM cases and in total period of the campaign over the Yellows sea

 

MSA

SO42−

NO3−

Na+

NH4+

Mg2+

Ca2+

nss- SO42−

nss- K+

nss- Ca2+

Size-cut

Asian Dust cases (AD, Case I)

0.212

3.906

8.268

1.022

1.937

0.389

1.352

3.650

0.208

1.312

PM10

0.197

3.226

1.134

0.214

1.250

0.055

0.142

3.172

0.145

0.134

PM2.5

Haze & Mist case (HMNC, Case II

0.609

18.326

4.044

1.579

5.536

0.32

0.426

17.929

0.266

0.366

PM10

0.477

13.088

0.119

0.243

3.900

0.103

0.113

13.027

0.200

0.104

PM2.5

Haze case (HKP, Case III)

0.614

10.541

7.705

0.115

5.393

0.105

0.399

10.512

0.179

0.395

PM10

–

–

–

–

–

–

–

–

–

–

PM2.5

Haze Case (HSPC, Case IV)

–

–

–

–

–

–

–

–

–

–

PM10

0.173

20.891

8.151

0.230

8.714

0.100

0.178

20.833

0.344

0.170

PM2.5

Average of total samples

0.319

10.195

3.025

0.659

3.436

0.244

0.475

10.029

0.229

0.450

PM10

0.239

8.823

1.021

0.145

2.966

0.077

0.137

8.787

0.214

0.188

PM2.5

To identify the main sources of the sampled water-soluble ions, we calculated the Enrichment factor (EF) in aerosols using their contents in seawater and the soil crust. In this method, Na+ and nss-Ca2+ are regarded as conservative elements of the marine and crustal sources (Nishikawa et al. 1991; Millero 2006). The EFs of an ion X are defined as follows:
$$ {\mathrm{EF}}_{\mathrm{seawater}}={\left(\mathrm{X}/{\mathrm{Na}}^{+}\right)}_{\mathrm{aerosol}}/{\left(\mathrm{X}/{\mathrm{Na}}^{+}\right)}_{\mathrm{seawater}} $$
(1)
and
$$ {\mathrm{EF}}_{\mathrm{crust}}={\left(\mathrm{X}/\mathrm{nss}-{\mathrm{Ca}}^{2+}\right)}_{\mathrm{aerosol}}/{\left(\mathrm{X}/\mathrm{nss}-{\mathrm{Ca}}^{2+}\right)}_{\mathrm{crust}} $$
(2)
where the subscript “aerosol” denotes the mass concentration ratio between X and Na+ or nss-Ca2+ in the aerosol samples. The subscripts “seawater” and “crust” denote the ratio of X to Na+ in seawater and X to nss-Ca2+ in the crust, respectively. The Na+ and nss-Ca2+ mass concentrations for seawater and crust obtain from Nishikawa et al. (1991).
The EFs of the secondary ions (nss-SO42−, NO3−, and NH4+) for crustal sources are high in all the four cases (Table 4), implying that these ions are not from crustal sources. The EF of NO3− in PM2.5 in HMNC for crustal sources is smaller than the EFs of nss-SO42− and NH4+, suggesting that the NO3− in HMNC is more affected by crustal sources compared to nss-SO42− and NH4+. The EFs of nss-SO42− for seawater source ranged from 13 in PM10 in AD to 83 in PM2.5 in HSPC. This implies that nss-SO42− is from non-sea salt sources. The nss-SO42− in AD is smaller than those in the haze cases (HMNC, HKP, and HSPC), suggesting that aerosols over the Yellow Sea in AD are affected more by the sulfates from seawater than in the other three cases. The EF of K+ and nss-Ca2+ in the four cases varies as follows: 5.1–10.0 (4.2–9.5) for K+ in PM10 (PM2.5) and 10.0–33.8 (2.8–4.5) for nss-Ca2+ in PM10 (PM2.5). The EF of K+ for crustal source lies in the range of 0.3–0.5 (0.2–0.5) in PM10 (PM2.5), indicating that for the four cases, the K+ and nss-Ca2+ in the samples are mostly from the crustal source.
Table 4

Enrichment factors for high aerosol mass concentration cases over the Yellow Sea during KORUS-AQ 2016

 

PM10

PM2.5

Crust

Seawater

Crust

Seawater

AD

HMNC

HKP

HSPC

AD

HMNC

HKP

HSPC

AD

HMNC

HKP

HSPC

AD

HMNC

HKP

HSPC

nss-SO42−

912

4482

2628

N/A

14

72

42

N/A

793

3257

N/A

5208

13

52

NA

83

NO3−

2067

1011

1348

N/A

–

–

–

–

283

30

N/A

5208

–

–

–

–

NH4+

484

1384

1348

N/A

–

–

–

–

312

974

N/A

2178

–

–

–

–

Na+

1.3

2.1

0.15

N/A

1.0

1.0

1.0

N/A

0.3

0.3

N/A

0.3

1.0

1.0

N/A

1.0

Mg2+

1.2

1.0

0.3

N/A

3.2

2.7

0.9

N/A

0.2

0.3

N/A

0.3

0.5

0.9

N/A

0.8

K+

0.4

0.5

0.3

N/A

6.8

10.0

5.1

N/A

0.2

0.3

N/A

0.5

4.2

5,8

N/A

9.5

nss-Ca2+

1.0

1.0

1,0

N/A

33.8

10.6

10.0

N/A

1.0

1.0

N/A

1.0

3.6

2.8

N/A

4.5

N/A, Missing data, –, Not calculated

In recent years, Northeast Asia has emitted large amounts of SO2 and NOx (Han et al. 2005). The oxidation of SO2 and NOx can contribute to high levels of secondary ions, such as SO42− and NO3−. NH3 from agricultural activity and livestock farming (Galloway et al. 1996) and mineral dust transported from desert areas are rich in Ca2+ ions, and they play an important role in neutralizing acid aerosols. To understand the relationship between the cations (NH4+and nss-Ca2+) and the anions (nss-SO42− and NO3−), these two groups of ionic species were converted to equivalent concentrations for comparison. Figure 8 shows the correlation between the sum of the acidic ionic species (nss-SO42− and NO3−) and the sum of the alkaline ionic species (NH4+ and Ca2+) in the form of equivalent concentration, in aerosols sampled over the Yellow Sea. These two groups are strongly correlated, with a slope of 1.2 for PM10 and 1.1 for PM2.5. This finding is similar to that of Zhang et al. (2013) over the Yellow Sea. This implies that most of the acidic ionic species were neutralized by the alkaline species over the Yellow Sea. Thus, nss-SO42− and NO3− from anthropogenic SO2 and NOx are adsorbed onto wet sand aerosol particles, and are then combined with Ca2+ by replacing CO32− in CaCO3 from crustal sources during the long-range transport (Zhang et al. 2013). In this study, nss-Ca2+ showed a good correlation with NO3− (Tables 5 and 6). Thus, the reaction processes of Ca2+ play significant roles in reducing acidic aerosols over the Yellow Sea.
Fig. 8

Correlation between the sum of acidic ions (nss-SO42− and NO3−) and the sum of alkaline ions (NH4+ and Ca2+), in the form of equivalent concentration in aerosols sampled using PMS over the Yellow Sea during the KORUS-AQ campaign, 2016

Table 5

Correlation coefficient matrix aerosol samples in PM10 using PMS over the Yellow Sea during KORUS-AQ 2016

 

MSA

nss-SO42−

NO3−

NH4+

Na+

Mg2+

nss-K+

nss-Ca2+

MSA

1.00

       

nss-SO42−

0.24

1.00

      

NO3−

0.19

−0.01

1.00

     

NH4+

0.34

0.84

0.47

1.00

    

Na+

0.04

0.06

0.73

0.28

1.00

   

Mg2+

0.00

−0.05

0.74

0.15

0.86

1.00

  

nss-K+

−0.05

0.29

0.56

0.39

0.46

0.59

1.00

 

nss-Ca2+

0.01

−0.21

0.72

0.08

0.44

0.76

0.44

1.00

Correlation coefficients with a statistical significance of p < 0.01 are listed in bold

Table 6

Correlation coefficient matrix aerosol samples in PM2.5 using PMS over the Yellow Sea during KORUS-AQ 2016

 

MSA

nss-SO42−

NO3−

NH4+

Na+

Mg2+

nss-K+

nss-Ca2+

MSA

1.00

       

nss-SO42−

0.17

1.00

      

NO3−

−0.02

0.54

1.00

     

NH4+

0.14

0.97

0.72

1.00

    

Na+

0.06

0.05

0.56

0.12

1.00

   

Mg2+

0.29

0.25

0.34

0.21

0.54

1.00

  

nss-K+

0.24

0.46

0.71

0.52

0.64

0.77

1.00

 

nss-Ca2+

0.39

0.22

0.35

0.22

0.36

0.84

0.78

1.00

Correlation coefficients with a statistical significance of p < 0.01 are listed in bold

NH4+ has a strong correlation with nss-SO42− and NO3− (Tables 5 and 6). This study applied the method of Rogula-Kozłowska et al. (2014) for estimating (NH4)2SO4 and NH4NO3. Figure 9 shows the estimated (NH4)2SO4 and NH4NO3. Most of the (NH4)2SO4 is in PM2.5, and the mass concentration in HSPC is extremely high. The mass concentration of (NH4)2SO4 varies in the order HSPC > HMNC > HKP > AD (Fig. 9), i.e., in the high aerosol mass concentration cases during the campaign, most of the (NH4)2SO4 was from China. NH4NO3 does not appear in HMNC, and HKP shows large amounts of NH4NO3, indicating that the amount of NH4NO3 is substantially reduced, while air flows travel over the sea surface for long periods after leaving the land, and vice versa.
Fig. 9

Mass concentration of (NH4)2SO4 and NH4NO3 using PMS for high aerosol mass concentration cases over the Yellow Sea during the KORUS-AQ campaign, 2016

The NO3− to nss-SO42− ratio in aerosols can be used to track the relative contribution of stationary and mobile sources to the secondary aerosols (Park and Lim 2006). Ko et al. (2017) estimated a ratio of 0.01–13.72 for PM10 and 0.00–0.92 for PM2.5 over the Yellow Sea. The ratios were 2.27, 0.23, and 0.73 for PM10 in AD, HMNC, and HKP, respectively, and 0.36, 0.009, and 0.39 for PM2.5 in AD, HNMC, and HSPC, respectively. The contributions from stationary sources are high for HMNC. Mobile (stationary) sources had a greater effect on the PM10 (PM2.5) in AD. These results show that during the campaign, stationary sources mostly contributed to high aerosol mass cases, and mobile sources affected PM10 more than PM2.5 over the Yellow Sea.

3.2.4 MSA and Contribution of Biogenic SO42− in High PM Cases

MSA in the marine boundary layer is one of the major end products of the oxidation of dimethyl sulfide (DMS) produced by marine biota. The formation of MSA from DMS oxidation is affected by the concentrations of OH and NO3 radicals and temperature (Gao et al. 1996). The MSA mass concentrations are 0.21, 0.61, and 0.61 μg m−3 in PM10 in AD, HMNC, and HKP, respectively, and 0.20, 0.48, and 0.17 μg m−3 in PM2.5 in AD, HMC, and HSPC, respectively. The MSA mass concentration varies in the order HMNC > HKP > AD > HSPC, while the NO3− mass concentration varies in the order HSPC > AD > HKP > HMNC. MSA can be used to track the contribution of biogenic SO42− to the total nss-SO42−; the ratio MSA/nss-SO42 represents the biogenic contribution. The ratio ranges from 1.4% to 9.2% for PM10 and from 0.68% to 9.5% for PM2.5 collected during the campaign. The ratio is 5.4%, 3.3%, and 5.8% for PM10 in AD, HMNC, and HKP, respectively, and 6.1%, 3.6%, and 0.83% for PM2.5 in AD, HMC, and HSPC, respectively. The ratios reported in previous studies vary widely according to the seasons and geographical locations. Chen et al. (2012) reported a ratio of 0.2%–6% in tropical regions, 6%–12% in unpolluted mid-latitudes, and 15%–93% near coastal Antarctica. Gao et al. (1996) observed a biogenic contribution of 10%–19% over the East China Sea for March–Jun. Arimoto et al. (1996) reported that marine biogenic sources accounted for 3.6% and 10.9% at the eastern and western sides of Jeju Island around the Yellow Sea, respectively. Zhang et al. (2013) also estimated the contribution of biogenic nss-SO42− to be 12% over the North Yellow Sea in the spring season (April 23–May 5, 2007). The differences between this study and previous ones may be explained by the differences in the sampling periods and locations as well as the seasonal variation in MSA concentration, at least partially. For example, Mukai et al. (1995) found that the maximum (minimum) biogenic SO42− occurred in the spring and early summer (winter) seasons. Even though the observation regions over the Yellow Sea were substantially affected by anthropogenic pollutants from Asia, the local biogenic nss-SO42− cannot be ignored, especially in the spring and early summer seasons, when biological activities are elevated. In addition, estimations of biogenic contributions include considerable uncertainties, as the MSA formation in aerosols from DMS must consider a number of factors, such as the formation time of DMS by nutrient-fed phytoplanktons in the Yellow Sea, and the reaction processes of NO3 radicals after leaving terrestrial anthropogenic sources. For more quantitative estimates of the MSA trend over the Yellow Sea, future studies should consider additional variables related to these relevant factors.

3.2.5 Aerosol Size Distribution (ASD) and Chemical Species in High PM Cases

The APS observed the aerosol volume number concentration (Nvc) during the cruise (Fig. 10). Figure 10a shows that dust particles in AD increase the Nvc in the diameter range above 2.5 μm. Asian dust mostly affects the particle above 2.5 μm and Haze does it below 2.5 μm in Korea (Cho et al. 2013). In Fig. 10a, the increased the Nvc above 2.5 μm is from Asian dust. Figure 10b shows the ASD in HMNC. The Nvc rapidly increased in the diameter range below 1 μm owing to the haze effects, while the Nvc increased slightly in the diameter range of 1–4 μm owing to the sea salt effects. The sea salt concentration depends on the wind speed (Jaeglé et al. 2011). The wind speed in HNMC is the highest among the four cases, with a maximum (minimum) of 12 m s−1 (10.8 m s−1). Thus, the strong winds during the sampling period contributed to the formation of atmospheric sea salt over the Yellow Sea in HMNC. Figure 10c shows the ASD in HKP. The Nvc below the diameter range of 1 μm increased because of the haze. The haze had the strongest influence on the observation in HSPC among the four cases (Fig. 10d).
Fig. 10

Aerosol size distribution (dV/dLog) using APS for the four cases during the KORUS-AQ campaign, 2016

To identify the main chemical forms in the four cases, we compared the correlation of the ionic species with the ASD. Basically, Asian dust mostly affects the particle above 2.5 um and Haze affects it below 2.5 um in Korea (Cho et al. 2013) and then the chemical forms of these ionic species were estimated on the basis of their correlation coefficients and concentrations. In Beijing in 2007, Wang et al. (2005) reported that the main chemical forms are CaCO3, CaSO4, and CaNO3 in dust events, and (NH4)2SO4 and NH4NO3 in haze events. We also calculated the CO3−, which was the main chemical form in AD. The CO3− was not directly analyzed by the IC in the collected samples. We used the method of Kchih et al. (2015) for estimating the CO3− as follows:
$$ \left[{{\mathrm{CO}}_3}^{-}\right]=2.5\left[{\mathrm{Mg}}_{\left(\mathrm{s}\right)}\right]+1.5\left[{\mathrm{Ca}}_{\left(\mathrm{s}\right)}\right] $$
(3)
CaCO3 and MgCO3 are calculated by the equivalent molar concentrations of Ca2+, Mg2+, and CO32−, which are from continental sources. The nss-CO3− is estimated using Eq. (3) with nss-Ca2+ and nss-Mg2+. In Tables 5 and 6, the nss-SO42− is very weakly correlated with the nss-Ca2+, indicating that during the KORUS-AQ campaign, CaSO4 was not the main chemical over the Yellow Sea and the most of the NO3− is contained in PM10 (See Fig. 9), and that nss-CO3− is closely correlated with Na+, Mg2+, and nss-Ca2+ in PM10. Thus, during the KORUS-AQ campaign, NO3− was present in PM10 over the Yellow Sea. Table 7 summarizes the relationships between the major ions and the concentrations of the main chemical species in the PM10 and PM2.5 samples over the Yellow Sea. CaCO3 and MgCO3 are the clearly dominant species for AD. In addition, coarse nitrate particles in PM10 can be produced via the reaction of gaseous nitric acid with mineral aerosols, because NO3− is well correlated with Na+, Mg2+, and nss-Ca2+, and weakly correlated with NH4+ in PM10. Thus, the Nvc increases in the diameter range above 2.5 μm in AD.
Table 7

Relationships between the major ions and the concentration of the main chemical species in PM10 and PM2.5 using PMS over the Yellow Sea during KORUS-AQ 2016

  

PM10

PM2.5

   

Correlation coefficients

nss-Ca2+-nss-CO32−

0.99

0.97

nss-Mg2+-nss-CO32−

0.89

0.93

NH4+-NO3−

0.47

0.72

NH4+-SO42−

0.84

0.97

Observation cases

 

AD

HMNC

HKP

HSPC

AD

HMNC

HKP

HSPC

Concentration (μgm−3)

CaCO3

3.28

0.91

0.98

N/A

0.33

0.26

N/A

0.42

MgCO3

0.92

0.45

0.32

N/A

0.10

0.25

N/A

0.25

NH4NO3

2.56

–

6.56

N/A

0.30

–

N/A

4.23

(NH4)2SO4

5.04

24.74

14.51

N/A

4.38

17.98

N/A

28.75

Correlation coefficients with a statistical significance of p < 0.01 are listed in bold

N/A, Missing data, –, Not calculated

In HMNC, RH ranges from 83% to 90%. The increased Nvc in the diameter range below 1 μm was due to haze and mist. When haze occurred over land, the air polluted with haze induced mist formations over the Yellow Sea. In such an environmental condition, the secondary inorganic aerosols (NH4+, nss-SO42−, and NO3−) exist mainly in the form of (NH4)2SO4 and NH4NO3 in the diameter range below 1 μm (Rogula-Kozłowska et al. 2014). The (NH4)2SO4 mass concentration was 24 μg m−3, and NH4NO3 was not present in HMNC (Fig. 9). Thus, when RH and the mass concentration of nss-SO42− are large, (NH4)2SO4 is the main component of the particles having a diameter of less than 1 μm (Song et al. 2008).

We used 3.27 × Na+ for estimation of the sea salt mass concentration for the four cases, as shown in Fig. 11 (Ohta and Okita 1984). The mass concentration of sea salt is the highest in HNMC among the four cases. Sea salt is generally formed as NaCl. The sea salt size distribution ranges from 0.005 to 10 μm. Thus, (NH4)2SO4 and sea salt were the main chemical species in HNMC. The sea salts enhanced the mist in the observation site over the Yellow Sea, because they are hydrophilic particle in high RH (83% to 90%) environments.
Fig. 11

Mass concentration of sea salt using PMS for high aerosol mass concentration cases over the Yellow Sea during the KORUS-AQ campaign, 2016

In HKP and HSPC, the Nvc rapidly increased in the diameter range below 1 μm owing to the haze. The main species in both the cases were (NH4)2SO4 and NH4NO3. The mass concentration of (NH4)2SO4 is higher than that of NH4NO3. In particular, NH4+ in PM2.5 is better correlated with NO3− than that in PM10 (Tables 5 and 6). The reactions in which HNO3 replaces water-soluble particulates, such as formate, acetate, and oxalate, are also important formation pathways for fine nitrate particles (Tabazadeh et al. 1998). Organic anions may be abundant in fine particles, such as those from biomass burning (Talbot et al. 1988; Andreae et al. 1988). In the future, to understand the generation of fine nitrate particles over the Yellow Sea in the presence of the reaction of gaseous nitric acid with gaseous ammonia, we will require additional details regarding NH4NO3 chemical reactions of the sampled gaseous nitric acid with gaseous ammonia.

4 Summary and Conclusions

This study investigated the water-soluble ionic characteristics of atmospheric aerosols collected over the Yellow Sea during the KORUS-AQ campaign. High aerosol mass concentration cases during the campaign were classified in terms of the aerosol size distribution and mass concentration: “Asian dust” and “haze” for the hourly mean PM2.5/PM10 ratio below 40% and over 80%, respectively. Backward trajectories corresponding to each case were analyzed using the HYSPLIT model. On the basis of these criteria and trajectory analyses, the high aerosol concentration cases during the campaign were classified into four groups: AD, HMNC, HKP, and HSPC. After the classification, we analyzed the water-soluble ions in the samples over the Yellow sea.

In four high PM cases, the mass concentration of nss-SO42− varies in the order HSPC > HMNC > HKP > AD. Thus, most of the nss-SO42− over the Yellow Sea in May 2016 came from the Shandong Peninsula and Northeast China. The NH4+ mass concentration varies nearly identically to that of nss-SO42−, except for the differences in the mass concentrations. The mass concentration of NO3− varies in the order HSPC > AD > HKP > HMNC. For the four cases, NO3−, Na+, Mg2+, and nss-Ca2+ were mostly contained in PM10. These results suggest that NO3− is mostly combined with Na+, Mg2+, and nss-Ca2+ in PM10 during high aerosol mass concentration events over the Yellow Sea. This implies that ions such as Mg2+ and nss-Ca2+ in PM10 among dust particles during AD reacted more with nitrates than with sulfates from anthropogenic sources in China. In AD case, the ratio of NO3− to nss-SO42− in the aerosols showed that mobile (stationary) sources mainly affected PM10 (PM2.5). The major chemical species for Asian dust cases over the Yellow sea were CaCO3, Ca(NO3)2, Mg(NO3)2, Na(NO3)2, and sea salt. In haze cases (HSPC, HKP, and HMNC) over the Yellow sea, the contributions from stationary sources are high and the major species were (NH4)2SO4 and NH4NO3 in PM10 and PM2.5, respectively.

The MSA mass concentrations are 0.21, 0.61, and 0.61 μg m−3 in PM10 in AD, HMNC, and HKP, respectively, and 0.20, 0.48, and 0.17 μg m−3 in PM2.5 in AD, HMC, and HSPC, respectively. The MSA mass concentration varies in the order HMNC > HKP > AD > HSPC, while the NO3− mass concentration varies in the order HSPC > AD > HKP > HMNC. High mass concentrations of methane sulfonic acid (MSA, CH3SO3−), the main source of natural sulfates over the Yellow Sea, were observed. The concentrations of MSA were found to show an increasing trend over the Yellow Sea in recent years. Biogenic sulfur contributions to the total nss-SO42− (MSA/nss-SO42− ratio) over the Yellow Sea ranged from 1.4% to 9.2% in PM10 and from 0.68% to 9.5% in PM2.5 during the cruise. Thus, biogenic nss-SO42− must be included, especially in the spring and early summer seasons, when biological activities are elevated in Northeast Asia.

In the future, we need to analyze element species such as Al and Fe in the aerosols because the element species such as Fe involves in production of DMS as the source of MSA and understand the more detail changes in various chemical species over the Yellow Sea.

Notes

Acknowledgements

This research was funded by the Korea Meteorological Administration Research and Development Program “ Development of Asian Dust and Haze Monitoring and Prediction Technology “ under Grant (1365003013).

References

  1. Akimoto, H.: Global air quality and pollution. Science. 302, 1716–1719 (2003)CrossRefGoogle Scholar
  2. Andreae, M.O., Browell, E.V., Garstang, M., Gregory, G.L., Harriss, R.C., Hill, G.F., Jacob, D.J., Pereira, M.C., Sachse, G.W., Setzer, A.W., Silva Dias, P.L., Talbot, R.W., Torres, A.L., Wofsy, S.C.: Biomass-burning emissions and associated haze layers over Amazonia. J. Geophys. Res. 93, 1509–1527 (1988)CrossRefGoogle Scholar
  3. Arimoto, R., Duce, R.A., Savoie, D.L., Prospero, J.M., Talbot, R., Cullen, J.D., Tomza, U.N., Lewis, F., Ray, B.J.: Relationships among aerosol constituents from Asia and the North Pacific during PEM-west A. Journal of Geophysical Research: Atmospheres. 101, 2011–2023 (1996)CrossRefGoogle Scholar
  4. Ayers, G.P., Ivey, J.P., Goodman, H.S.: Sulfate and methanesulfonate in the maritime aerosol at cape grim, Tasmania. J. Atmos. Chem. 4, 173–185 (1986)CrossRefGoogle Scholar
  5. Boreddy, S. K. R, and K. Kawamura: A 12-year observation of water-soluble ions in TSP aerosols collected at a remote marine location in the western North Pacific: an outflow region of Asian dust. Atmospheric Chemistry and Physics. 15, (2015).  https://doi.org/10.5194/acp-15-6437-2015 CrossRefGoogle Scholar
  6. Calhoun, J.A.: Chemical and isotopic methods for understanding the natural marine sulfur cycle. Ph.D. thesis, Univ. of Washington, Seattle. 86 pp (1992)Google Scholar
  7. Cha, J.W., Ko, H.J., Shin, B., Lee, H.J., Kim, J.E., Ahn, B., Ryoo, S.B.: Characteristics of aerosol mass concentration and chemical composition of the yellow and South Sea around the Korean peninsula using a Gisang 1 research vessel. ATMOSPHERE-KOREA. 26, 357–372 (2016)CrossRefGoogle Scholar
  8. Chen, L., Wang, J., Gao, Y., Xu, G., Yang, X., Lin, Q., Zhang, Y.: Latitudinal distributions of atmospheric MSA and MSA/nss-SO42− ratios in summer over the high latitude regions of the southern and northern hemispheres. Journal of Geophysical Research: Atmospheres. 117, D10306 (2012).  https://doi.org/10.1029/2011JD016559 CrossRefGoogle Scholar
  9. Cho, C., Park, G., Kim, B.: An effectiveness of simultaneous measurement of PM10, PM2.5, and PM1.0 concentrations in Asian dust and haze monitoring. Journal of Environmental Science International. 22, 651–666 (2013)CrossRefGoogle Scholar
  10. Ding, Y., Liu, Y.: Analysis of long-term variations of fog and haze in China in recent 50 years and their relations with atmospheric humidity. Sci. China Earth Sci. 57, 36–46 (2014)CrossRefGoogle Scholar
  11. Galloway, J.N., Schlesinger, W.H., Levy, H., Michaels, A., Schnoor, J.L.: Nitrogen fixation: Anthropogenic enhancement-environmental response. Glob. Biogeochem. Cycles. 9, 235–252 (1996)CrossRefGoogle Scholar
  12. Gao, Y., Arimoto, R., Duce, R.A., Chen, L.Q., Zhou, M.Y., Gu, D.Y.: Atmospheric non-sea-salt sulfate, nitrate and methanesulfonate over the China Sea. Journal of Geophysical Research: Atmospheres. 101, 12601–12611 (1996)CrossRefGoogle Scholar
  13. Gao, Y., Arimoto, R., Duce, R.A., Zhang, X.Y., Zhang, G.Y., An, Z.S., Chen, L.Q., Zhou, M.Y., Gu, D.Y.: Temporal and spatial distributions of dust and its deposition to the China Sea. Tellus B: Chemical and Physical Meteorology. 49, 172–189 (1997)CrossRefGoogle Scholar
  14. Gaudichet, A., Echalar, F., Chatenet, B., Quisefit, J.P., Malingre, G., Cachier, H., Buat-Menard, P., Artaxo, P., Maenhaut, W.: Trace elements in tropical African savanna biomass burning aerosols. J. Atmos. Chem. 22, 19–39 (1995)CrossRefGoogle Scholar
  15. Gu, D., Wang, Y., Smeltzer, C., Liu, Z.: Reduction in NOx emission trends over China: regional and seasonal variations. Environ. Sci. Technol. 47, 12912–12919 (2013)CrossRefGoogle Scholar
  16. Han, Z., Ueda, H., Matsuda, K.: Model study of the impact of biogenic emission on regional ozone and the effectiveness of emission reduction scenarios over eastern China. Tellus B: Chemical and Physical Meteorology. 57, 12–27 (2005)CrossRefGoogle Scholar
  17. Hsu, S.C., Liu, S.C., Arimoto, R., Liu, T.H., Huang, Y.T., Tsai, F., Lin, F.J., Kao, S.J.: Dust deposition to the East China Sea and its biogeochemical implications. Journal of Geophysical Research: Atmospheres. 114, D15304 (2009).  https://doi.org/10.1029/2008JD011223 CrossRefGoogle Scholar
  18. Huang, T., Zhu, X., Zhong, Q., Yun, X., Meng, W., Li, B., Ma, J., Zeng, E.Y., Tao, S.: Spatial and temporal trends in global emissions of nitrogen oxides from 1960 to 2014. Environ. Sci. Technol. 51, 7992–8000 (2017)CrossRefGoogle Scholar
  19. Jaeglé, L., Quinn, P.K., Bates, T.S., Alexander, B., Lin, J.T.: Global distribution of sea salt aerosols: new constraints from in situ and remote sensing observations. Atmos. Chem. Phys. 11, 3137–3157 (2011)CrossRefGoogle Scholar
  20. Kang, C.H., Kim, W.H., Lee, W.: Chemical composition characteristics of precipitation at two sites in Jeju Island. Bull. Kor. Chem. Soc. 24, 363–368 (2003)CrossRefGoogle Scholar
  21. Katrib, Y., Martin, S.T., Rudich, Y., Davidovits, P., Jayne, J.T., Worsnop, D.R.: Density changes of aerosol particles as a result of chemical reaction. Atmos. Chem. Phys. 5, 275–291 (2005)CrossRefGoogle Scholar
  22. Kchih, H., Perrino, C., Cherif, S.: Investigation of desert dust contribution to source apportionment of PM10 and PM2. 5 from a southern Mediterranean coast. Aerosol Air Qual. Res. 15, 454–464 (2015)CrossRefGoogle Scholar
  23. Kim, Y.P., Lee, J.H., Baik, N.J., Kim, J.Y., Shim, S.G., Kang, C.H.: Summertime characteristics of aerosol composition at Cheju Island. Korea. Atmospheric Environment. 32, 3905–3915 (1998)CrossRefGoogle Scholar
  24. Kim, J.H., Yum, S.S., Lee, Y.G., Choi, B.C.: Ship measurements of submicron aerosol size distributions over the Yellow Sea and the East China Sea. Atmos. Res. 93, 700–714 (2009)CrossRefGoogle Scholar
  25. Ko, H.J., Kang, C.H., Cha, J.W., Ryoo, S.B.: Concentration and pollution characteristics of secondary aerosol components over the Yellow Sea by ship-borne observation in spring, 2015. ATMOSPHERE-KOREA. 27, 29–40 (2017)CrossRefGoogle Scholar
  26. Lee, S.B., Bae, G.N., Moon, K.C., Kim, Y.P.: Characteristics of TSP and PM2.5 measured at Tokchok Island in the Yellow Sea. Atmos. Environ. 36, 5427–5435 (2002)CrossRefGoogle Scholar
  27. Liu, F., Zhang, Q., Ronald J. d., Zheng, B., Tong, D., Liu Y, Zheng, Y. and He, K.: Recent reduction in NOx emissions over China: synthesis of satellite observations and emission inventories. Environ. Res. Lett. 11, 114002 (2016)CrossRefGoogle Scholar
  28. Millero, F.J.: Chemical Oceanography. 3rd ed. CRC Press. 496 pp (2006)Google Scholar
  29. Mukai, H., Yokouchi, Y., Suzuki, M.: Seasonal variation of methanesulfonic acid in the atmosphere over the oki islands in the sea of Japan. Atmos. Environ. 29, 1637–1648 (1995)CrossRefGoogle Scholar
  30. Nishikawa, M., Kanamori, S., Kanamori, N., Mizoguchi, T.: Kosa aerosol as eolian carrier of anthropogenic material. Sci. Total Environ. 107, 13–27 (1991)CrossRefGoogle Scholar
  31. Ohta, S., Okita, T.: Measurements of particulate carbon in urban and marine air in Japanese areas. Atmos. Environ. 18, 2439–2445 (1984)CrossRefGoogle Scholar
  32. Park, J.Y., Lim, H.J.: Characteristics of water soluble ions in fine particles during the winter and spring in Daegu. J. Korean Soc. Atmos. Environ. 22, 627–641 (2006)Google Scholar
  33. Park, K.T., Jang, S., Lee, K., Yoon, Y.J., Kim, M.S., Park, K., Cho, H.J., Kang, J.H., Udisti, R., Lee, B.Y., Shin, K.H.: Observational evidence for the formation of DMS-derived aerosols during Arctic phytoplankton blooms. Atmos. Chem. Phys. 17, 9665–9675 (2017)CrossRefGoogle Scholar
  34. Rogula-Kozłowska, W., Klejnowski, K., Rogula-Kopiec, P., Ośródka, L., Krajny, E., Błaszczak, B., Mathews, B., et al.: Air Qual. Atmos. Health. 7, 41–58 (2014)CrossRefGoogle Scholar
  35. Simoneit, B. R. T.: Biomass burning - A review of organic tracers forsmoke from incomplete combustion, Appl. Geochem., 17, 129–162(2002)CrossRefGoogle Scholar
  36. Song, C.H., Park, M.E., Lee, K.H., Ahn, H.J., Lee, Y., Kim, J.Y., Han, K.M., Kim, J., Ghim, Y.S., Kim, Y.J.: An investigation into seasonal and regional aerosol characteristics in East Asia using model-predicted and remotely-sensed aerosol properties. Atmos. Chem. Phys. 8, 6627–6654 (2008)CrossRefGoogle Scholar
  37. Su, S., Li, B., Cui, S., Tao, S.: Sulfur dioxide emissions from combustion in China: from 1990 to 2007. Environ. Sci. Technol. 45, 8403–8410 (2011)CrossRefGoogle Scholar
  38. Tabazadeh, A., Jacobson, M.Z., Singh, H.B., Toon, O.B., Lin, J.S., Chatfield, R.B., Thakur, A.N., Talbot, R.W., Dibb, J.E.: Nitric acid scavenging by mineral and biomass burning aerosols. Geophys. Res. Lett. 25, 4185–4188 (1998)CrossRefGoogle Scholar
  39. Talbot, R.W., Andreae, M.O., Andreae, T.W., Harriss, R.C.: Regional aerosol chemistry of the Amazon Basin during the dry season. Journal of Geophysical Research: Atmospheres. 93, 1499–1508 (1988)CrossRefGoogle Scholar
  40. Wang, H.J., Chen, H.P.: Understanding the recent trend of haze pollution in eastern China: roles of climate change. Atmos. Chem. Phys. 16, 4205–4211 (2016)CrossRefGoogle Scholar
  41. Wang, Y., Zhuang, G., Sun, Y., An, Z.: Water-soluble part of the aerosol in the dust storm season-evidence of the mixing between mineral and pollution aerosols. Atmos. Environ. 39, 7020–7029 (2005)CrossRefGoogle Scholar
  42. Wei, Q., Yao, Q., Wang, B., Wang, H., Yu, Z.: Long-term variation of nutrients in the southern Yellow Sea. Cont. Shelf Res. 111, 184–196 (2015)CrossRefGoogle Scholar
  43. Yang, G.-P., Zhang, S.-H., Zhang, H.-H., Yang, J., Liu, C.-Y.: Distribution of biogenic sulfur in the Bohai Sea and northern Yellow Sea and its contribution to atmospheric sulfate aerosol in the late fall. Mar. Chem. 169, 23–32 (2015)CrossRefGoogle Scholar
  44. Zhang, J., Liu, S.M., Lü, X., Huang, W.W.: Characterizing Asian wind-dust transport to the Northwest Pacific Ocean. Direct measurements of the dust flux for two years. Tellus B: Chemical and Physical Meteorology. 45, 335–345 (1993)CrossRefGoogle Scholar
  45. Zhang, J., Wu, Y., Liu, C.L., Shen, Z.B., Zhang, Y.: Major components of aerosols in North China: desert region and the Yellow Sea in the spring and summer of 1995 and 1996. J. Atmos. Sci. 59, 1515–1532 (2002)CrossRefGoogle Scholar
  46. Zhang, J., Yu, Z.G., Raabe, T., Liu, S.M., Starke, A., Zou, L., Gao, H.W., Brockmann, U.: Dynamics of inorganic nutrient species in the Bohai seawaters. J. Mar. Syst. 44, 189–212 (2004)CrossRefGoogle Scholar
  47. Zhang, H.H., Yang, G.P., Liu, C.Y., Su, L.P.: Chemical characteristics of aerosol composition over the Yellow Sea and the East China Sea in autumn. J. Atmos. Sci. 70, 1784–1794 (2013)CrossRefGoogle Scholar
  48. Zhang, Y., Zhang, H.H., Yang, G.P., Liu, Q.L.: Chemical characteristics and source analysis of aerosol composition over the Bohai Sea and the Yellow Sea in spring and autumn. J. Atmos. Sci. 72, 3563–3573 (2015)CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Applied Meteorology Research DivisionNational Institute of Meteorological ScienceJejuRepublic of Korea
  2. 2.Earth system Research DivisionNational Institute of Meteorological ScienceJejuRepublic of Korea

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