Size-segregated water-soluble N-bearing species in the land-sea boundary zone of East China

Atmospheric Environment 218 (2019) 116990

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Size-segregated water-soluble N-bearing species in the land-sea boundary zone of East China

T

Rui Lia, Lulu Cuia, Yilong Zhaoa, Hongbo Fua,b,c,∗, Qing Lia, Liwu Zhanga, Jianmin Chena,∗∗ a

Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Institute of Atmospheric Sciences, Fudan University, Shanghai, 200433, PR China b Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, 210044, PR China c Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, PR China

A R T I C LE I N FO

A B S T R A C T

Keywords: N-bearing species Size distribution Marine aerosol East China sea

Size-segregated aerosol samples were collected in the land-sea boundary zone of East China, the offshore area of East China Sea (ECS), to determine the concentrations of water-soluble inorganic nitrogen (WSIN), water-soluble organic nitrogen (WSON), urea, and free amino acids (FAAs), to quantify their sources, and to estimate their dry deposition fluxes. The results indicated that the mean concentrations of WSIN, WSON, urea, and FAAs in total suspended particle (TSP) were 547.14 nmol/m3, 258.64 nmol/m3, 12.69 nmol/m3, and 2589.03 pmol/m3, respectively. The WSIN, WSON, and urea concentrations in TSP showed remarkably spatial variation with the higher one during the shipping line (SL) 1 and SL4, while they exhibited the lower ones during SL3 and SL5. The biomass burning and fertilizer application in the continent provided important precursors for WSIN, WSON, and urea in the offshore areas. Besides, O3-related photochemical process also promoted the secondary formation of these species. In contrast, these anthropogenic sources played the minor roles on the relatively remote marine region (SL5). It was interesting to note that the total FAAs during SL5 did not show the lowest concentration, which might be contributed by the bubble bursting on the sea surface and the release of bacteria. The Positive Matrix Factorization (PMF) method identified fertilizer application and secondary formation were important sources for WSIN (35.54% and 27.53%) and WSON (43.07% and 40.46%), respectively. However, fertilizer application and combustion sources played the crucial important roles on urea (65.93% and 19.13%) and FAAs (51.35% and 16.35%). The mean dry deposition fluxes of WSIN, WSON, urea, and FAAs in the ECS offshore area were 206763.57 nmol m−2 d−1, 103763.07 nmol m−2 d−1, 4746.20 nmol m−2 d−1, and 1047.08 nmol m−2 d−1, respectively. The present study revealed that the ambient N-bearing particles in the land-sea boundary zone suffered from the combined effects of continental transport and the release of marine organisms.

1. Introduction Nitrogen (N) deposition has become a global issue following the rapid increase of fossil fuel consumption and agricultural emission (Liu et al., 2011). Minor N deposition often stimulates the phytoplankton growth, and thus enhances oceanic net primary production (NPP) (Chien et al., 2016; Cornell et al., 2003). In contrast, excess N deposition plays a negative role on the ecosystem health such as soil and oceanic acidification (Lawrence et al., 2015; Tian and Niu, 2015), loss of forest biodiversity (Sala et al., 2000; Stevens et al., 2004), and increase of soil respiration (Gao et al., 2014; Maaroufi et al., 2015). With

regard to the cloud chemistry, the organic nitrogen (ON), such as proteins and FAAs, not only serves as cloud condensation nuclei and ice nuclei (Almeida et al., 2013; Farmer et al., 2015), but also promotes the hygroscopic growth of aerosol particles (Ren et al., 2018), which ultimately affects the radiation balance and global climate change (Song et al., 2017). Besides, the abundant N-bearing functional groups (e.g., NO3−, NH4+, amines) in the atmosphere might affect the aerosol acidity (Laskin et al., 2009), which is closely associated with visibility degradation and haze pollution (Weber et al., 2016). Moreover, some N-bearing species such as water-soluble proteins and urea in the atmosphere can lead to adverse effects on human health especially in

∗

Corresponding author. Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Institute of Atmospheric Sciences, Fudan University, Shanghai, 200433, PR China. ∗∗ Corresponding author. E-mail addresses: [email protected] (H. Fu), [email protected] (J. Chen). https://doi.org/10.1016/j.atmosenv.2019.116990 Received 17 April 2019; Received in revised form 26 August 2019; Accepted 19 September 2019 Available online 20 September 2019 1352-2310/ © 2019 Elsevier Ltd. All rights reserved.

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anthropogenic emissions and natural contributions to N-bearing components. Here, size-segregated aerosol samples were collected in ECS during the summer of 2017 to determine the concentrations of WSIN, WSON, FAAs, and urea. The objectives of the study are (1) to investigate the spatial distribution of N-containing components; (2) to clarify the size distribution characteristics of organic N components; (3) to quantitatively identify the key sources of different N-bearing components in the land-sea boundary zone; (4) to assess the effect of continental transport on N-bearing species in the marine aerosol. To the best of our knowledge, this is the first report about the simultaneous determination of WSON, FAAs, and urea levels in the size-segregated particles of land-sea boundary zone. The results may deepen the current understanding of sources and fates of aerosol N-bearing components.

their post-translational modified forms (Gruijthuijsen et al., 2006; Zhang et al., 2011). Currently, most of studies focus on the source apportionment and formation mechanism of inorganic N in the urban and coastal regions (Liu et al., 2016; Salameh et al., 2018). Yao et al. (2002) demonstrated that the ambient NO3− in some megacities of China was mainly originated from the mobile source emission (e.g., vehicle emission). Following this work, Wang et al. (2015) found that the gas-to-particle transformation from acidic NO2 to NO3− played a major role in the formation of this severe haze of Shanghai. Recently, Fu et al. (2018) confirmed that the inorganic N in East China Sea (ECS) was significantly affected by the continental transport. However, the distribution characteristics and major sources of different species in organic nitrogen (ON) were scarcely concerned. As an important fraction of N-bearing aerosol particles, ON generally accounts for 30–70% of total N in aerosol particles (Cape et al., 2011; Violaki et al., 2015). ON comprises of large number of species at trace level including proteins, organic amines, urea, and free amino acids (FAAs), which is often derived from secondary reactions(Ge et al., 2011; Kang et al., 2012). Recently, a growing body of studies have begun to investigate the levels of different species in water-soluble ON (WSON) and identified their potential sources in various regions, including urban, suburban, and marine. Wedyan et al. (2008) collected the aerosol samples in the middle of Atlantic Ocean and observed that the mean concentrations of FAAs reached 0.02 nmol/m3. Later on, Song et al. (2017) observed that the total FAAs in PM2.5 of Guangzhou reached 0.13 ± 0.05 μg/m3, accounting for 0.3 ± 0.1% of PM2.5. Very recently, Ren et al. (2018) determined the levels of fifteen hydrolyzed amino acids in a heavily polluted city (Beijing) and found the total amino acids concentrations ranged from 1.73 to 25.7 nmol/m3. Besides, Shi et al. (2010) reported that the urea level in the total suspended particles (TSP) of Qingdao varied between 0.50 and 53.9 nmol/m3. Although the concentrations of various ON species in some sites have been reported, few studies investigated the ambient N-bearing aerosols in the land-sea boundary zone, which arose from a variety of sources including both continental and oceanic sources. On the one hand, both of the bioaerosol release and the photochemical decomposition of humic materials in the ocean are often treated as the major sources of ON (e.g., FAAs) in the marine aerosol (Estillore et al., 2016; Milne and Zika, 1993). On the other hand, the anthropogenic activities in the coastal cities (e.g., biomass burning, fertilizer release) could lead to the accumulation of urea and FAAs for the offshore area (Di Filippo et al., 2014; Song et al., 2017). However, the separate contributions of continental and marine origins to N-bearing species in the land-sea boundary zone were not quantified yet. Furthermore, most of these studies focused on the ON components in PM2.5 and TSP, but they neglected the size distribution of these components (Verma et al., 2015; Xu et al., 2016). The size distribution of urea and FAAs could change greatly as a function of emission sources, meteorological factors, and atmospheric processes (Scalabrin et al., 2012). Thus, the N-bearing species in different sizes can reflect the contributions of sources and chemical processes. East China Sea (ECS) is located in the east of Yangtze River Delta (YRD) and North China Plain (NCP), the economically developed and densely populated regions. A great deal of vehicle emission, industrial activity, and biomass burning contribute to the higher levels of ozone and nitrous oxides (NOx) in these regions (Huang et al., 2011; Wang et al., 2014, 2017b). ECS is often struck by the air pollutants transported from YRD and NCP due to the effect of Northwest monsoon (Wang et al., 2016). Moreover, ECS is frequently affected by the ship emissions from many large container ship ports (e.g., Yangshan port, Luchao port) (Fu et al., 2018). Meanwhile, both of ECS and YRD are characterized with the subtropical monsoonal climate, and the abundant water vapor and heat are suitable for the phytoplankton growth and N-containing compounds release (Zechmeister-Boltenstern et al., 2015). Therefore, ECS is an ideal region to reveal the effects of

2. Materials and methods 2.1. The description of cruise and selected sampling sites The cruise campaign was performed onboard a rental vessel of 800 tons burden (25 m long, 5 m wide and 3 m high) along the predesigned path (29.7–31.7°N, 121.5–123.0°E) (Fig. 1). The campaign comprised of the cruises from Gongqing Port (GQP) to Yangshan Port (YSP) during June 2nd- June 3rd (Shipping line (SL) 1), YSP to Luchao Port (LCP) during June 5th-June 6th (SL2), LCP to Cezi Isles (CZI) during June 7thJune 8th (SL3), CZI to Beilun Port (BLP) during June 8th-June 9th (SL4), BLP to Dongji Isles (DJI) during 15th-June 16th (SL5) (Table 1). Besides, GQP was also selected as one of the sampling site before sailing. Meanwhile, the instruments for measuring the air pollutants (e.g., NO, NO2, and O3) and meteorological parameters (e.g., wind speed (WS), wind direction (WD)) were located on the bow of the ship. The hourly concentrations of air pollutants were determined using the chemiluminescence method, and the UV spectrophotometry method, respectively. 2.2. Aerosol sampling The size-segregated aerosol particles were collected on 47 mm quartz filters (PALLFLEX, USA) using 10-stage micro-orifice uniform deposit impactor (MOUDI, MSP Corp., USA; Model 110-R) with a flow rate of 30 l/min. All of the samples were collected onboard during the cruise over ECS during June 2-June 18, 2017. Furthermore, the samplers were installed on the upper deck of the ship at the site with 10 m height above sea level and switched off manually when the ship was anchored or the wind direction was not suitable for sampling (avoid contamination by the ship's exhaust). During each SL, two or three 10stage micro-orifice uniform deposit impactors were used to collect the repeated samples. The cascade impactor divides aerosols into 12 cut-off diameters including > 18 μm (stage 1), 10–18 μm (stage 2), 5.6–10 μm (stage 3), 3.2–5.6 μm (stage 4), 1.8–3.2 μm (stage 5), 1.0–1.8 μm (stage 6), 0.56–1.0 μm (stage 7), 0.32–0.56 μm (stage 8), 0.18–0.32 μm (stage 9), 0.10–0.18 μm (stage 10), 0.056–0.10 μm (stage 11), and < 0.056 μm (stage 12). The quartz fiber filters were pre-baked at 500 °C for 4 h in a muffle furnace to remove water and organic traces. The filters were weighed before and after sampling by an intelligent weighing system with at least 24 h of equilibration at 20 °C and a relative humidity (RH) of 40%. All of the samples and field blank filters were packed into the membrane filter boxes and stored in a freezer under −20 °C prior to analysis. At last, a total of 96 quartz filter samples were collected for the analysis of WSON and other chemical components. 2.3. Chemical analysis First of all, one fourth of each sample and the blank filter were both cut from the original one and placed into a 50 mL screw-cap vial. Two portions of 5 mL (a total of 10 mL) deionized water were added to the 2

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Fig. 1. The sampling sites, topographic maps, and land use types in the coastal region of ECS.

The concentrations of OC and EC for all of the samples were corrected for the field blanks. 300 μL of the extracted samples were mixed with 100 μL of 0.1 N HCl for the FAA analysis. A total of 17 FAA species (e.g., aspartic acid (Asp), glutamic acid (Glu), cysteine (Cys), serine (Ser), glycine (Gly), histidine (His), arginine (Arg), threonine (Thr), alanine (Ala), proline (Pro), tyrosine (Tyr), valine (Val), methionine (Met), isoleucine (Iso), leucine (Leu), phenyalanine (Phe), and lysine (Lys)) were determined using high-performance liquid chromatography (HPLC) with a fluorescence detector following the precolumn derivatization of o-phthalaldehyde (OPA) and 9-fluorenylmethyl chloroformate (FMOC). The detailed mobile phase configuration is as follows: Mobile phase A: First of all, 1.4 g Na2HPO4 and 3.8 g Na2B4O7·10H2O were placed in the counting cup. After that, the deionized water and hydrochloric acid were mixed with those materials mentioned above, and transformed into a 1 L volumetric flask. The pH value should be adapted to 7.7. At last, 0.1% tetrahydrof uran was added in the volumetric flask, and filtered using the hydrophilic filter. Mobile phase B: The deionized water, acetonitrile, and methanol (volume ratio: 10:45:45) were added in the 1 L volumetric flask and then filtered using organic phase filter. The flow velocity should be kept at 0.7 mL/min, and the temperature of chromatographic column should remain at 35 °C. The method detection limits (MDLs) of 17 FAA species ranged from 0.045 nmol/mL to 0.093 nmol/mL. The precisions of the FAA samples

vial. All of the samples were extracted ultrasonically for 1 h, and then the 0.45 μm membrane were applied to filter the insoluble particles. At last, these extracted samples and blank filters were prepared for the analysis of inorganic ions including F−, Cl−, NO2−, NO3−, PO43−, SO42−, Na+, K+, Mg2+, Ca2+, and NH4+ by an ion chromatography (940 Professional IC, Switzerland). A separation column of Metrosep A supp 16–250 and a Metrosep C6 analytical column were used to determine the anion and cation concentrations, respectively. The relative standard deviation of all ions were less than 4% based on the reproducibility tests. NO2−, NO3−, and NH4+ were considered as the major WSIN species in the particles and the sum of three ions were treated as the WSIN level. In addition, the water-soluble total nitrogen (WSTN) concentrations in the samples were measured by a TOC/TN analyzer (TOC-L, Shimadzu, Kyoto, Japan) based on a thermo-catalytic oxidation approach. The detection limits and uncertainties of WSTN were 5 μg/L and 8.5%, respectively. The WSON was defined as the difference between WSTN and WSIN. The concentrations of OC and EC in the aerosols were determined by a DRI Model 2001 Thermal/Optical carbon analyzer (Atmoslytic Inc., Calabasas, CA, USA). A 0.506 cm2 punch of each sample was used to analyze the contents of eight carbon fractions (OC1, OC2, OC3, OC4, EC1, EC2, EC3, and OP (a pyrolyzed carbon fraction determined by transmittance)) using the IMPROVE thermal/optical reflectance (TOR) protocol. OC was estimated to be the sum of OC1, OC2, OC3, OC4, and OP, whereas EC was defined as the value of EC1 + EC2 + EC3 - OP.

Table 1 The detailed cruise and sampling information including meteorological data and air pollutants in ECS. Sample number

Start time

Terminal time

Sampling duration

T (°C)

WS (m/s)

WD

RH (%)

NO (ppb)

NO2 (ppb)

O3 (ppb)

GQP SL1 SL2 SL3 SL4 SL5

June. June. June. June. June. June.

June. June. June. June. June. June.

24 h 24 h 24 h 24 h 24 h 24 h

22.52 23.06 21.64 23.72 23.31 21.93

2.59 2.69 4.64 3.26 3.06 1.88

Northwest Northwest West West Northwest West

76.54 81.58 80.00 76.78 82.68 77.40

30.26 35.49 21.79 2.34 30.62 1.62

28.75 29.95 24.42 7.52 23.54 3.03

40.69 64.39 39.62 30.26 59.86 41.61

1st 2nd 5th 7th 8th 15th

2nd 3rd 6th 8th 9th 16th

3

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Table 2 Mass concentrations of WSIN (nmol/m3), WSON (nmol/m3), urea (nmol/m3), and FAAs (pmol/m3) in comparison to previous studies (arithmetic mean). Sampling site

Sampling period

PM size

WSIN

WSON

Urea

FAAs

References

East China Sea East China Sea East China Sea Xi'an Beijing Guangzhou Eastern Mediterranean Northwest Pacific Ocean Venice Erdemli Taiwan Qingdao Hawaiian

June 1–18, 2017 June 1–18, 2017 June 1–18, 2017 May 2008–February 2009 April 2012–May 2013 March 2012–February 2013 June–August 2007 May–July 2000 April–October 2007 March 2014–April 2015 January 2005–November 2006 May 2007–May 2008 July–August 1998

TSP PM10 PM1 PM2.5 PM2.5 PM2.5 PM2.5 PM2.5 PM2.5 PM10 TSP TSP PM1

547.14 449.28 207.85 377.14 – – – 13.57 – 107.14 179.28 – 10.71

259 203 102 300 103 – – – – 23.8 75.9 178 28.5

12.69 10.18 5.10 – – – – – – – – 14.8 4.50

2589 2042 1026 1320 – 1166 172 10.70 334 – – 2750 –

This study This study This study Ho et al. (2015) Ren et al. (2018) Song et al. (2017) Mandalakis et al. (2011) Matsumoto and Uematsu (2005) Barbaro et al. (2011) Nehir and Koçak (2018) Chen and Chen (2010) Shi et al. (2010) Cornell et al. (2001)

different directions. All of the back trajectory analysis was performed using Meteoinfo and ArcGIS 9.3 software (Draxler and Rolph, 2003).

were less than 8.5%. The recovery of bovine serum albumin (BSA) was 73.5%, and those of the individual FAAs varied between 70.7% for Met to 83.4% for Arg. The urea level in the aerosol was determined on the basis of the diacetyl monoxime colorimetric method (Cornell et al., 1998). The detection limit of this method was 0.4 μmol/L, and the relative standard deviation on replicate analysis of the standards was lower than 10%.

2.6. Dry deposition flux The dry deposition flux of individual N-bearing species (F nmol m−2 d ) was estimated to be the product of its average concentration in TSP (C nmol m−3) and the corresponding dry deposition velocity (Vd cm s−1). −1

2.4. Source apportionment method

F = C × Vd

Positive Matrix Factorization (PMF) 5.0 model version, a typical receptor-based source identification model, was applied to identify the main origins of the WSON species. Briefly, the objective of PMF is to decompose the input matrix into factor contribution and factor profile, as shown in Eq. (1). Meanwhile, the contribution of each source for individual species should be non-negative because no sample showed a negative source apportionment. Therefore, the least object function Q was calculated based on Eq. (2) with regard to the non-negative gik matrix (Taghvaee et al., 2018; Manousakas et al., 2017).

Vd was determined by the mean mass percentage for individual species (Ri) in each stage of the size-segregated samples multiplying the corresponding deposition velocity (Vi). In the present study, the Williams’ model was applied to predict Vi (Qi et al., 2005). The detailed calculation equation is as follows: 12

Vd =

∑ gik fkj

+ eij

n

Q=

m

∑∑ [ i=1 j=1

p

x ij − ∑k = 1 gik fkj uij

(4)

Although the method proposed by Qi et al. (2005) displayed the lower error compared with the traditional method (Zufall et al., 1998), the method showed some weaknesses in the correction of deposition velocity. For instance, the method was more suitable to the coastal region with the higher RH rather than the arid regions.

(1)

k=1

∑ Ri × Vi i=0

p

x ij =

(3)

]2 (2)

where xij and uij represent the concentration and uncertainty of j species, respectively. gik denotes the contribution of kth source to i sample, fkj is the ratio of j species in kth source, and eij indicates the residual of j species in the i sample. Uncertainties linked with factor profiles were assessed using three error estimation methods including displacement (DISP) analysis, bootstraps (BS) method, and the combination method of DISP and BS (BS–DISP). For the BS method, 100 runs were employed and the solution was demonstrated to be valid because all of the factors had a mapping of above 90%. DISP analysis suggested that the solution was believed to be stable because the observed drop in the Q value was less than 0.1% and no factor swap was observed. For the BS–DISP analysis, the solution was considered to be useful because the observed drop in the Q value was below 0.5%. The results from BS and BS-DISP did not suggest any asymmetry or rotational ambiguity for five factors.

3. Results and discussion 3.1. The concentrations of WSIN, WSON, urea, and FAAs in aerosol particles The mean values of WSIN, WSON, urea, and FAAs in six sampling sites are summarized in Table 2. The WSIN, WSON, urea, and FAAs concentrations in TSP were 547.14 nmol/m3, 258.64 nmol/m3, 12.69 nmol/m3, and 2589.03 nmol/m3, respectively. The WSIN, WSON, urea, and FAAs levels in PM10 were 449.28 nmol/m3, 203.22 nmol/m3, 10.18 nmol/m3, and 2042.35 pmol/m3, respectively. In the fine particles (PM1), the WSIN, WSON, urea, and FAAs concentrations reached 207.85 nmol/m3, 102.05 nmol/m3, 5.10 nmol/m3, and 1026.54 pmol/ m3, respectively. As shown in Table 2, remarkably concentration differences of N-bearing components were observed among the inland, coastal, and marine regions. The WSON concentrations in fine particles of ECS were 66% and 1% lower than those in Xi'an and Beijing (Ho et al., 2015; Ren et al., 2018), respectively. The FAAs concentrations in ECS were 28.66% lower than those in Xi'an (Ho et al., 2015). Compared with the N-containing components of coastal cities, the WSIN concentrations in coarse particles of ECS were about 4.20 times of those in Erdemli, Turkey (Nehir and Koçak, 2018). The WSON concentrations in ECS were 8.53 and 1.46 times of those in Erdemli and Qingdao (Nehir and Koçak, 2018; Shi et al., 2010). However, the urea concentration

2.5. Back trajectory analysis To assess the effects of air masses from different sources on the aerosol particles, 24-h backward trajectories of air masses arriving at six sampling sites at the height of 1000 m above the sea during the sampling periods were calculated using the HYSPLIT mode. The meteorological data were obtained from National Oceanic and Atmospheric Administration (NOAA) Global Reanalysis Data. Besides, the cluster analysis was applied to determine the major air masses from 4

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and Anastasio, 2001). Besides, Arg, Ala, and Ser showed abundant concentrations because widespread bacteria, pollen, and spores were major sources of these protein-bearing particles, which also have been found in the biological aerosols of Beijing (Cao et al., 2014). Met in the aerosol particles displayed the lowest concentration among all of the FAA species, though it was a universal constituent of proteins in nearly all of the marine organisms. It was assumed that it was inclined to be oxidized to the methionine sulfoxide (MetSO) with a short half-life less than 2.5 h (Scalabrin et al., 2012).

displayed the different distribution characteristics that the urea levels in the coarse particles of ECS were slightly lower than those in Qingdao (Shi et al., 2010). The FAAs concentrations in ECS were lower than those in Chinese coastal cities such as Guangzhou and Qingdao (Song et al., 2017; Shi et al., 2010), whereas they were significantly higher than some cities such Venice (Barbaro et al., 2011). Compared with the marine regions, the concentrations of N-bearing components were one order of magnitude higher than those measured from some regions such as Eastern Mediterranean (Mandalakis et al., 2011), Northwest Pacific Ocean (Matsumoto and Uematsu, 2005), and Hawaiian (Cornell et al., 2001). In the present study, nearly all of the N-bearing compounds in ECS displayed notably higher values than those in the remote or pristine marine environments, all of which were less affected by the anthropogenic source. Moreover, the anthropogenic emissions of N-bearing precursors (e.g., NOx and NH3) in Europe and North America were significantly lower than those in China since 1990s (Huang et al., 2017, 2018). The small contributions of non-land-based emissions such as algal blooms triggered the lower N levels. In contrast, the sampling sites in the present study were concentrated on the coastal waters of China and close to the large ports (e.g., YSP and BLP), suggesting the remarkable contributions of continental sources. Furthermore, biomass burning in June could play an important role on the elevation of the Nbearing compounds in the atmosphere (Ahern et al., 2016; Zhou et al., 2017). The urea and total FAAs contributed 3.91% (2.64–5.04%) and 0.81% (0.60–1.10%) to WSON, respectively. The ratio of total FAAs to WSON in ECS was comparable to the reported results in urban Xi'an (0.44%) (Ho et al., 2015) and Qingdao (1.54%) (Shi et al., 2010). However, the urea/WSON value was significantly lower than those in Qingdao (8.31%) (Shi et al., 2010) and Hawaii (15.79%) (Cornell et al., 2001). Amongst the investigated FAAs, Gly, Arg, and Ala were major FAA species, contributing to 26.45%, 26.45%, and 20.32% to the total FAAs (Fig. 2a–c), respectively. Subsequently, Ser, Thr, Val, and Cys accounted for 6.07%, 2.97%, 2.82%, and 2.67% of the total FAAs, respectively. The minor species such as Pro, Met, Tyr together accounted for only 12.25% of the total FAAs. The proportion of individual FAA species in PM10 and PM1 was similar to that in TSP. The compositions of FAAs showed slight variations with the geographical location. Song et al. (2017) observed that Gly, Met, Val, and Phe were main species in Guangzhou. Ho et al. (2015) found that Gly, Cys, Ala, and Val were dominant species in the aerosols of Xi'an. Generally, Gly has been accepted as a key species in many regions because it was a basic component of the fibrous proteins in the biological organisms (Mandalakis et al., 2011; Shi et al., 2010). In addition, elastin and certain keratins were also frequently observed in the environment (Song et al., 2017), which might contribute to the higher occurrence of Gly. Furthermore, Gly generally displayed a long half-life (> 2000 h) in the atmosphere and the strong stability led to the higher Gly concentration (McGregor

3.2. The spatial distribution of N-bearing species in ECS The spatial variations of N-bearing components in coarse (TSP and PM10) and fine (PM1) particles are depicted in Fig. 3. WSIN concentration in ECS showed similar spatial characteristics in coarse and fine particles with the highest one during SL1 and the lowest one during SL5 (Fig. 3a), respectively. GQP (NO2: 28.75 ppb) and YSP (29.95 ppb) were located in the offshore area of ECS, and they showed the higher NO2 compared with other sampling sites. It was widely believed that the higher NO2 could be transformed into nitrate with the appropriate meteorological condition, which was the major component of WSIN (Liang et al., 2017). Therefore, WSIN showed the higher concentration in the offshore area of ECS. WSON presented the higher concentrations in GQP and SL1 (Fig. 3b), followed by those during SL4, SL2, SL3, and the lowest one during SL5. It was well known that both of GQP and YSP were major freight ports of China and the strong shipping emission contributed to the higher WSON in the cruises (Fan et al., 2016; Song, 2014). In addition, the GQP and SL1 were close to the Southern part of Pudong district, which were markedly influenced by anthropogenic emission such as fossil fuel combustion and industrial emission compared with other cruises. Moreover, many farmland were located in the south of Pudong district. As shown in Fig. S1 and Fig. 3, fertilizer release and biomass burning have been widely observed in the south of Shanghai during the sampling period. It was well known that K+ was treated as a typical fingerprint for aerosols released from biomass burning (Coggon et al., 2016). Pio et al. (2008) developed a novel equation to calculate the K-biomass concentration (K+ concentration induced by biomass burning) (Pio et al., 2008). In the present study, we tried to explore the relationship between WSON concentration in the aerosol particles and K-biomass concentration, and found that crop residues combustion played a crucial role on ambient WSON in these regions (r = 0.58, p < 0.05) (Table 3). Nehir and Kocak (2018) also confirmed that manmade biomass burning were responsible for the ambient WSON. Apart from GQP and SL1, SL4 showed the higher WSON concentration due to the effect of BLP. Tong et al. (2017) found that the sampling site close to BLP displayed the higher NOx concentration compared with other regions in Ningbo due to the dense emissions from shipping, gas plants, and automobile factory. Yu et al. (2017) confirmed that shipping emission could be an important source of WSON because crude oil

Fig. 2. The average percentage of individual amino species accounting for the total FAAs in TSP (a), PM10 (b), and PM1 (c). 5

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Fig. 3. The spatial distribution of WSIN (a), WSON (b), urea (c), and FAAs (d) in ECS.

level might be originated from the small contributions of the sea-salt and algal blooms (Nehir and Koçak, 2018). Additionally, the intense rainfall during June 12–14th might dilute the long-range transported WSON concentration in the atmosphere.

generally contained N species. It can be seen that the lowest WSON level was observed during SL5 from BLP to DJI. It was assumed that the region was far away from the continent compared with other cruises and it was less contributed by the continental sources. The lower WSON Table 3 The correlation coefficients of different species in the aerosol particles of ECS. WSON WSON WSIN Urea FAAs F− Cl− NO2− NO3− PO43− SO42− Na+ NH4+ K+ Mg2+ Ca2+ OC EC

1.00

WSIN 0.41 1.00

b

Urea a

0.36 0.53b 1.00

FAAs a

0.32 0.36a 0.61b 1.00

F− −0.11 −0.09 −0.23 −0.14 1.00

Cl− 0.23 0.39b 0.21 0.09 0.04 1.00

NO2− 0.23 0.31a 0.04 0.08 0.50 −0.05 1.00

NO3−

PO43-

b

0.48 0.53b 0.49b 0.31a −0.20 0.24 −0.01 1.00

0.08 0.02 0.15 0.21 −0.02 −0.02 0.03 −0.13 1.00

a: p < 0.05. b: p < 0.01. 6

SO420.11 0.22 0.29a 0.14 0.09 0.70b 0.03 0.22 0.10 1.00

Na+ 0.07 −0.06 0.05 0.37a 0.25 0.14 0.04 −0.05 −0.08 0.30 1.00

NH4+ a

0.33 0.51b 0.35a 0.34a −0.18 −0.01 0.01 0.33a 0.17 0.18 −0.16 1.00

K+ a

0.32 0.20 0.29a 0.20 0.28a 0.62b 0.04 0.10 0.12 0.83b 0.33a 0.10 1.00

Mg2+

Ca2+

OC

EC

0.10 0.39a 0.21 0.11 −0.15 0.69b −0.10 0.27a −0.08 0.15 −0.08 0.13 0.06 1.00

0.11 −0.05 0.10 0.04 0.34a 0.55b −0.02 −0.01 0.03 0.84b 0.43b −0.07 0.79b −0.08 1.00

0.11 0.28a 0.37b 0.22 −0.02 0.26a −0.13 0.45b −0.21 0.16 0.23 0.11 0.07 0.25 0.16 1.00

0.03 −0.05 −0.03 −0.08 0.14 0.01 −0.07 −0.02 −0.07 −0.02 0.19 −0.13 0.09 −0.10 0.21 0.14 1.00

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3.3. The size distribution of N-bearing components in ECS

The urea concentration showed slightly different spatial distribution from WSIN and WSON. The higher urea in the aerosol particles were observed during SL1 and SL4. However, no notably higher urea level was concentrated on GQP (Fig. 3c). It was widely recognized that urea was a major industrial product to be utilized as the agricultural fertilizer and the cattle-feed supplement (Cornell et al., 1998). Constant and Sheldrick (1992) estimated that the total urea manufacturing capacity around the world increased from 2 × 1010 to 5 × 1010 g N/yr and most of the urea were consumed in China. As shown in Fig. 3c, SL1 and SL4 were adjacent to Shanghai and Ningbo, respectively. It can be seen that paddy field and cropland were widely distributed in the southern of Shanghai and the northern of Ningbo. The application of fertilizer and livestock manures combined with the dense biomass burning in June contributed to the higher urea concentration in these regions (Cornell et al., 1998). However, GQP was surrounded by the urban land and unused land, which was less affected by biomass burning and fertilizer evaporation compared with SL1 and SL4. Although SL5 (8.48, 6.63, and 3.43 nmol/m3) was far away from the continent, the urea concentration during SL5 approached to that in GQP and SL3. It was supposed that urea was a major nitrogenous end-product of mammalian metabolism, and it was also a key metabolite excreted by the marine organisms (Antia et al., 1991). Besides, the decomposition of proteinaceous matter or other N-bearing organic compounds was another source of urea in the atmosphere (Schmidt et al., 2011). In Table 3, the urea concentration presented the significant correlation with the WSON concentration in the aerosol particles, suggesting that bubble-bursting fractionation might promote the transfer of urea between the sea surface and atmosphere (Cornell et al., 1998). The total FAAs exhibited the higher concentration during SL1 and SL4 (Fig. 3d), followed by those in SL5, SL2, GQP, and the lowest one in SL3. The enhancements of total FAAs in SL1 and SL4 were mainly associated with human activities. For instance, the biomass or biofuel burning in the harvest season could be responsible for the higher total FAAs in these cruises. It has been inferred by the higher K+ concentration observed in SL1 and SL4. In addition, the contributions of other sources to FAAs could not be ruled out because of the significant relationship between FAAs and NH4+ (p < 0.05). It indicated that the application of chemical fertilizer contributed to the elevation of FAAs. Apart from the contribution of man-made emission, the natural source might play an important role on the FAAs generation. Milne and Zika (1993) inferred that FAAs in the remote marine atmosphere could be produced by direct photolysis, photochemical hydrolysis, and enzymebased hydrolysis of high molecular weight proteinaceous materials, which was mainly sourced from the bursting bubbles at the sea-air interface. To confirm the potential effect of photochemical hydrolysis of proteinaceous matter, the correlation analysis was employed to test the correlation between FAAs and ozone (O3) concentration. In the present study, the FAAs displayed remarkably relationship with the O3 level (r = 0.82, p < 0.05) despite the few sample data. It suggested that O3 could be involved in the process of FAAs formation. The impact of O3 on FAAs formation is shown in the following three aspects: At first, O3 tended to promote the release of protein from the pollens (Beck et al., 2013), thereby increasing the precursor (e.g., protein) levels. On the other hand, O3 can also induce the photochemical degradation of high molecular weight proteinaceous materials (McGregor and Anastasio, 2001). At last, O3 could affect the FAAs accumulation through the hydrolysis of urea (Cornell et al., 1998), which has been verified by the significant correlation between urea and FAAs concentrations. As a whole, the dominant species of FAA showed the similarly spatial distribution with the total FAAs (Fig. 4a–c). Gly, Arg, and Ser displayed the higher levels during SL1 and SL4, indicating the effects of terrestrial emissions. However, it should be noted that the highest Ala concentration was observed during SL5, which was attributable to the release of marine organisms (Barbaro et al., 2015).

The WSIN concentration showed the mono-modal distribution with the highest value at the coarse mode (5.6–10 μm) (Fig. 5a). It was widely recognized that NO3− and NH4+ usually peaked in the fine mode (~0.43 μm), both of which were generally formed through the heterogeneous reaction of gaseous precursors (e.g., NH3, NO2) and the acidic species (Li et al., 2013). However, the higher WSIN was also observed in the coarse mode of the marine aerosol (Fu et al., 2018), which might be derived from the sea-salt or crustal particles. Based on the correlation analysis, WSIN was closely linked to Mg2+ (p < 0.05), but was not associated with Na+ (p > 0.05), indicating that crustal source transported from the continent played an important role on the coarse-mode WSIN in aerosol particles. The WSON concentration also showed the mono-modal distribution with the highest value in the coarse mode (3.2–5.6 μm) (Fig. 5b). Montero-Martínez et al. (2014) found that the WSON in San Pietro Capofiume exhibited the highest concentration in the coarse mode (1.2–3.5 μm), which was in good agreement with the results of our study. Nevertheless, the urea and total FAAs concentrations exhibited the bimodal distribution with the peak in the fine particles (< 1 μm) and the coarse particles (> 3.2 μm) (Fig. 5c and d). Scalabrin et al. (2012) collected the size-segregated aerosol particles in Arctic, and found that nearly all of the FAAs were concentrated on the fine particles (< 0.95 μm). Mace et al. (2003) also analyzed the size distribution of FAAs and found that they showed the bimodal distribution with the higher concentrations between 3.57.2 μm and 0–0.95 μm. The urea and FAAs in the coarse modes were mainly derived from the bacteria growing on aerosol media in the ocean. However, the presence of FAAs in the fine modes were due to the gas-to-particle conversions of biogenic emissions (Leck and Bigg, 1999). 3.4. Source identification of N-containing compounds and the impact of long-range transport PMF model was applied to identify the major sources of N-bearing components combined with the water-soluble ions and carbonaceous aerosols. Factor 1 revealed that Cl− (68.18%), SO42− (65.87%), and K+ (58.68%) showed the higher loadings compared with other components (Fig. 6a). K+ was often treated as a useful tracer of biomass burning (Zhang et al., 2015), and SO42− generally reflected the contribution of coal combustion (Chen et al., 2017). Although Cl− was usually originated from the sea-salt aerosol (Vogt et al., 1996), the Cl− did not display the marked relationship with Na+ in the present study, and the mean Na+/Cl− ratio (9.42) in this study was significantly higher than the seawater value (0.56) (Keene et al., 1998). Furthermore, Corsini et al. (2017) verified that a large portion of Cl− in the sea were probably derived from the combustion sources through the long-range transport (e.g., coal combustion, biomass burning). Therefore, the factor 1 was identified as the combustion source. Factor 2 was distinguished by high loadings of WSON (43.07%), WSIN (35.54%), Urea (65.93%), FAAs (51.35%), NO3− (40.30%), and NH4+ (35.51%) (Fig. 6b). It was well documented that urea served as an important tracer for the fertilizer application (Wang et al., 2017a). Besides, NH4+ in the aerosol particles, as the protonation product of NH3, was tightly associated with the processes of fertilizer use and livestock excreta. Thus, the factor 2 could be treated as the agricultural source based on the higher loadings of N-bearing compounds. Factor 3 exhibited the higher loadings of WSON (40.46%), WSIN (27.53%), NO3− (41.06%), NH4+ (33.50%), and OC (70.32%). It has been reported that the higher OC/EC ratio reflected the contribution of secondary organic aerosol (SOA) to the carbonaceous aerosol (Xu et al., 2016; Yan et al., 2015). In the present study, all of OC/EC ratios (3.56) in the samples were significantly higher than 1.0, indicating that the OC could be mainly sourced from the SOA formation. Additionally, the photochemical decomposition of dissolved humic materials also promoted the WSON generation (Tarr et al., 2001). Thus, the factor 3 was regarded as the 7

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Fig. 4. The concentrations of amino species in TSP (a), PM10 (b), and PM1 (c) during the cruise campaign.

Fig. 5. The size distribution of WSIN (a), WSON (b), urea (c), and FAAs (d) in the aerosol particles (the mean values for GQP and five cruises).

dust. Although the F− in the atmosphere was closely linked to the fossil fuel combustion, the crustal source could play a crucial role on the F− accumulation (Ding et al., 2017a; Ozbek et al., 2016). Factor 5 was mainly comprised of the higher loadings of ocean-derived ions

secondary formation source. Factor 4 seemed to represent the crustal source because it showed the higher loadings of F− (67.90%) and Ca2+ (94.56%). Fu et al. (2014) found that Ca2+ in the marine aerosol might be sourced from the long-range transport of Asian dust or construction 8

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Fig. 6. The PMF source profile: the contribution ratio of each source to the concentration of each species.

including Na+ (40.39%) and PO43− (72.61%), and thus the factor 5 served as the marine/biological source. Based on the results of PMF analysis, we quantified the contributions of five sources to N-bearing components including WSIN, WSON, urea, and FAAs (Fig. 7). The fertilizer application and photochemical decomposition were regarded as the major source of WSON and WSIN, accounting for 43.07%, and 40.46% for WSON, 35.54% and 27.53% for WSIN, respectively (Fig. 7). The aqueous-phase process associated with the enhanced relative humidity could promote the transformation from NO2 to nitrate (Wang et al., 2015), and thus played a significant role on the WSIN generation. Additionally, it was widely recognized that the urea cycle of organisms could be a major source of proteinaceous

materials, which could be transformed into WSON and small FAA fractions through photochemical hydrolysis (Song et al., 2017). For urea, the fertilizer use served as the dominant source (65.93%), followed by combustion source (19.13%), crustal source (8.26%), ocean source (4.41%), and secondary formation (2.28%). It was assumed that that fertilizer use and biomass burning could release biomolecules, such as cholesterol, proteins, and urea (Rogge et al., 1991; Scalabrin et al., 2012). However, the fertilizer application (51.35%) and ocean source (20.22%) were considered as the major sources of FAAs. The higher contribution of ocean source to FAAs was tightly linked with the release of marine organism (Matsumoto et al., 2005). Soil and fugitive dusts were also found to be associated with the urea accumulation was due to

Fig. 7. The source contributions of WSIN (a), WSON (b), urea (c), and FAAs (d). 9

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emissions from agricultural fields (Song et al., 2017). Although PMF method showed many advantages to identify the main sources for N-bearing components, the result of PMF model still showed some uncertainties. The uncertainty of each source has been assessed by base model displacement (DISP), bootstrap (BS) and bootstrap displacement (BS-DISP) methods. As shown in Table S1, fertilizer release showed the highest uncertainty among all of the sources, followed by secondary formation, and other three sources remained relatively stable for BS and BS-DISP tests. The higher uncertainties for fertilizer release and secondary formation suggested that a number of peak events could influence these factors. However, combustion source, crustal source, and marine source were not sensitive to resampling and displacement. In order to examine the impact of long-range transport on the Nbearing components in the marine aerosols, 24-h backward trajectories of air masses reaching the ECS offshore area at a height of 1000 m are depicted in Fig. S2. One can see that most of air masses were derived from North China, Southeast China, and ocean (ECS and Pacific). First of all, the air masses derived from ECS accounted for 60.00% of all the air masses, suggesting that the marine air masses played the significant roles on the accumulation of N-bearing aerosols. It verified that both of the bubble bursting on the sea surface and the release of pollen and spores from marine organisms actually promoted the FAAs elevation near the Dongji Island (Barbaro et al., 2011; Leck and Keith Bigg, 2008). Following the marine air masses, the air masses originated from North China and Southeast China accounted for 18.33% and 21.67% of the total air masses, respectively. It was assumed that the biomass burning in June contributed to the substantial N-bearing aerosols in the ECS offshore region (Kang et al., 2017; Wang et al., 2019) (Figs. S2a–b), which has been verified by the fire points in Fig. S1. Besides, it should be noted that some air masses were originated from the coastal ports (e.g., BLP), which demonstrated that the local industrial emission and anchored shipping emission could contribute to the higher concentrations of N-containing species in the region (Ding et al., 2017b; Wang et al., 2016).

neighboring ocean especially for the P-limited ocean (Chien et al., 2016). In addition, the higher input of N-containing components could affect the phytoplankton community structure and even cause the algae blooms. Moreover, the N deposition could promote the carbon export to depth and increase the carbon uptake capacity of ocean (Toit, 2018). 4. Conclusions The size-segregated aerosol samples were collected in the ECS offshore region to investigate the spatial distributions of N-containing particles, to quantify their sources, and to estimate their dry deposition fluxes. Apart from FAAs, most of N-bearing components displayed the higher concentrations in the offshore area (GQP, SL1 and SL4), while the lower values were often observed in the remote area (SL5), which was mainly contributed by the anthropogenic emission and secondary formation. However, the higher total FAAs in SL5 might be due to the bubble bursting on the sea surface and the release of bacteria. The WSIN and WSON concentrations exhibited the mono-modal distribution with the highest value at the coarse mode, the urea and total FAAs concentrations displayed the bimodal distribution with the peak in the fine particles (< 1 μm) and the coarse particles (> 3.2 μm). The Nbearing components in the coarse modes were mainly derived from the bacteria growing on aerosol media, while those in fine modes were originated from gas-to-particle conversions of biogenic emissions. The PMF model identified five sources and demonstrated that fertilizer application and secondary formation served as the major source for WSIN and WSON. Apart from the effect of fertilizer application, combustion source and marine source also played the significant role on urea and FAAs, respectively. The dry deposition fluxes of WSIN, WSON, and urea showed the higher values near the port, whereas FAAs displayed the higher deposition flux in the remote island. The N-bearing components displayed significantly higher concentrations near the coastal port compared with other regions. In view of the potential damage to alveolar macrophages and respiratory epithelial tissue by N-containing species, it was highly imperative to decrease the shipping and agricultural emissions near these large ports. In addition, it was urgently needed to assess the health effects of N-bearing species near the large ports and implement some prevention measures.

3.5. The dry deposition of N-bearing components in ECS The estimated dry deposition fluxes of N-bearing components are summarized in Table S2. The dry deposition fluxes of WSIN in ECS ranged from 140360.71 to 304815.71 nmol m−2 d−1 with the highest one during SL4, followed by those in GQP, SL2, SL1, SL5, and the lowest one during SL3. The deposition fluxes of WSIN in the present study were 1.15–2.50 and 3.70–6.67 times of those in the western North Pacific (122571 nmol m−2 d−1) and Huang Sea (45714 nmol m−2 d−1), respectively (Fu et al., 2018; Shi et al., 2010). It was mainly contributed by the higher WSIN concentration in the ECS offshore zone compared with the remote seas. The dry deposition fluxes in the ECS offshore showed the significantly spatial variation. Although SL1 displayed the highest WSIN concentration, the dry deposition flux of WSIN during SL1 was significantly lower than that of SL4, GQP, and SL2. It was assumed that the lowest Vd during SL1 contributed to the medium WSIN concentration among these cruises. The dry deposition fluxes of WSON varied between 70163.27 and 161485.48 nmol m−2 d−1 with the highest one during SL1, followed by those during SL3, GQP, SL2, SL4, and SL5. As a whole, the dry deposition flux was in good agreement with the concentration for WSON except SL4. The lower Vd during SL4 was attributed to the lower mass percentage in the coarse particles. The dry deposition fluxes of urea and FAAs ranged from 2956.43 to 6811.59 nmol m−2 d−1 and from 662.95 to 1658.96 nmol m−2 d−1, respectively. Both of the urea and FAAs exhibited the higher dry deposition fluxes during SL1 and SL4 due to the higher mass concentration and Vd. Based on the observation result of the present study, the higher Nbearing component deposition fluxes were concentrated near large ports, which might pose great damage to the marine productivity in the

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This work was supported by National Natural Science Foundation of China (Nos. 91744205, 21777025, 21577022, 21177026) and Shanghai Tongji Gao Tingyao Environmental Science & Technology Development Foundation (STGEF). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.atmosenv.2019.116990. References Ahern, A.T., Subramanian, R., Saliba, G., Lipsky, E.M., Donahue, N.M., Sullivan, R.C., 2016. Effect of secondary organic aerosol coating thickness on the real-time detection and characterization of biomass-burning soot by two particle mass spectrometers. Atmos. Meas. Tech. 9, 6117–6137. Almeida, J., Schobesberger, S., Kürten, A., Ortega, I.K., Kupiainen-Määttä, O., Praplan, A.P., Adamov, A., Amorim, A., Bianchi, F., Breitenlechner, M., 2013. Molecular understanding of sulphuric acid–amine particle nucleation in the atmosphere. Nature 502, 359.

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