Atmospheric microplastic over the South China Sea and East Indian Ocean: abundance, distribution and source

Journal of Hazardous Materials xxx (xxxx) xxxx

Contents lists available at ScienceDirect

Journal of Hazardous Materials journal homepage: www.elsevier.com/locate/jhazmat

Atmospheric microplastic over the South China Sea and East Indian Ocean: abundance, distribution and source Xiaohui Wang, Changjun Li, Kai Liu, Lixin Zhu, Zhangyu Song, Daoji Li* State Key Laboratory of Estuarine and Coastal Research, East China Normal University, 500 Dongchuan Road, Shanghai, 200241, China

G R A P H I C A L A B S T R A C T

A R T I C LE I N FO

A B S T R A C T

Editor: Ok Yong Sik

At present, microplastic (MP) is pervasive globally and has a regional difference. Recent studies have identified MP in the terrestrial atmospheric environment. However, the connection between terrigenous atmospheric MP emissions and impacts over the ocean is not well known. Here, we present the distribution of atmospheric MP abundance over the ocean based on a transoceanic survey conducted across 21 sampling transects from the Pearl River Estuary (PRE) to the South China Sea (SCS) and then to the East Indian Ocean (EIO). The abundance of atmospheric MP over the PRE (4.2 ± 2.5 items/100 m3) was significantly higher than that over the EIO (0.4 ± 0.6 items/100 m3). However, the abundance of atmospheric MP in the SCS (0.8 ± 1.3 items/100 m3) was not significantly different from the EIO and PRE. This result revealed that MP undergoes long-range transport, more than 1000 km away, through the atmosphere, but atmospheric MP transmission as the main source of oceanic MP based on transoceanic studies is not a plausible assumption. Furthermore, backward trajectory model analysis of 21 sampling transects preliminary showed the potential sources of atmospheric MP over the PRE, SCS, and EIO.

Keywords: Microplastic Atmosphere East Indian Ocean South China Sea Pearl River Estuary

1. Introduction Millions of people die each year due to the effects of air contamination (Zhang et al., 2017). At present, air contamination is no longer a domestic or local phenomenon, but a regional and global

⁎

phenomenon (World Health Organization, 2016; Akimoto, 2003). Research shows that the dust flux transport across the ocean has potential health and environmental impact (Jimenez-Velez et al., 2009). Furthermore, the present research has shown that the resuspension and transportation of airborne dust and microplastic (MP) have common

Corresponding author. E-mail address: [email protected] (D. Li).

https://doi.org/10.1016/j.jhazmat.2019.121846 Received 17 September 2019; Received in revised form 5 December 2019; Accepted 6 December 2019 0304-3894/ © 2019 Elsevier B.V. All rights reserved.

Please cite this article as: Xiaohui Wang, et al., Journal of Hazardous Materials, https://doi.org/10.1016/j.jhazmat.2019.121846

Journal of Hazardous Materials xxx (xxxx) xxxx

X. Wang, et al.

Fig. 1. Sampling transects. The sampling transect numbers are marked along the cruise track.

atmospheric MP emissions and import over the ocean, a case study was performed across the Pearl River Estuary (PRE) to the South China Sea (SCS) and then to the East Indian Ocean (EIO) to provide a baseline dataset for a future risk assessment of atmospheric MP. The primary objective of this study was to provide a better understanding of the abundance, distribution, and source of the atmospheric MP from terrestrial areas to the open ocean. In this study, we present the characteristic distribution of atmospheric MP over the ocean. Furthermore, we show the potential sources of atmospheric MP over the ocean by a backward trajectory model.

mechanisms (Abbasi et al., 2019). In recent years, with the exponential increase in global production and use of synthetic plastics since their mass production in the 1950s (Geyer et al., 2017; PlasticsEurope, Plastics - The Facts, 2018), the impact of plastic contamination has attracted worldwide attention. The flux of waste into the environment increased by more than 100% between 1975 and 2010 (Jambeck et al., 2015). Global plastic production reached 350 million tons in 2017 (PlasticsEurope, Plastics - The Facts, 2018). These plastics will break down into MP (MP < 5 mm) due to physical factors, chemical factors, and biological factors. Studies found MP in water (Cózar et al., 2014; Law et al., 2010) and in organisms, such as fish (Lusher et al., 2015a; Zhang et al., 2019), shellfish (Zhao et al., 2018), and the inaccessible deep sea (Van Cauwenberghe et al., 2013; Bergmann et al., 2017), as well as in the polar regions (Isobe et al., 2019; Peeken et al., 2018). Research shows that the average concentration of MP in the Atlantic sub-surface Ocean is 1.15 ± 1.45 items/m3 (La Daana et al., 2017), that in the northeastern Pacific Ocean is 279 ± 178 items/m3 (Desforges et al., 2014), that in the Arctic surface water is 0.34 ± 0.31 items/ m3 (Lusher et al., 2015b), that in the surface water of the South China Sea is 2569 ± 1770 items/m3 (Cai et al., 2018), and that in the surface waters of the Ross Sea (Antarctica) is 0.17 ± 0.34 items/m3 (Cincinelli et al., 2017). It can be seen that MP is pervasive. These MPs may be transported through the food chain and food web eventually to the human body and even through the air. There are literature reviews exploring the effect of atmospheric MP on human health (Sharma and Chatterjee, 2017; Prata, 2018). Given that MPs have a small size, a low material density and high surface area, and various harmful additives on their surface, especially MP fibers, these MPs can easily enter the air through the effect of external factors, which have a potential impact on terrestrial and environments (Abbasi et al., 2019; Dris et al., 2016). Recent studies have shown that the distribution of MP in the atmosphere of Shanghai (China) ranges from 0 to 418 items/100 m3 (Liu et al., 2019a), that in the atmosphere of Paris (France) ranges from 30 to 150 items/100 m3 (Dris et al., 2017), and that in the atmosphere of the western Pacific Ocean ranges from 0 to 137 items/100 m3 (Liu et al., 2019b), whereas average remote mountain deposition is 36,500 ± 6900 items/100 m2/ day (Allen et al., 2019). Once in the atmosphere, suspended atmospheric MPs are transported passively by complex two-and three-dimensional physical winds, resulting in a very large variability over the land and surface ocean. However, less attention has been paid to the distribution and trans-regional transport of atmospheric MP over the ocean. In order to fully understand the connection between continental

2. Materials and methods 2.1. Data collection During the investigation, suspended atmospheric particles were collected in an EIO voyage on March 20 and April 25 of 2019. The atmospheric MP was continuously sampled during the cruise of the R/V Shiyan No. 3. Previous studies adopted continuous sampling for monitoring fine particulate matter in the atmosphere over the ocean. For example, Jimenez-Velez et al. (2009) used a fine particulate sampler to collect PM2.5 air samples over the Atlantic Ocean within the portions of the ship trajectory. The limitation of weather conditions (i.e., rain) resulted in the interruption of some sampling transects during the voyage period. There were 21 monitoring transects across the PRE to the SCS and then to the EIO (Fig. 1). Samples were obtained at stations along the ship track using the KB120 F type intelligent middle flow total suspended atmospheric particulate sampler (Jinshida, Qingdao) with a sampling flow rate of 100 ± 0.1 L/min. Every sampling transect was conducted over period of 10–48 h with 53-259 m3 of air filtered per sample, depending on the weather conditions (Table S1). All sampling was conducted by drawing air through Whatman GF/A glass microfiber filters (1.6 μm pore size, 90 mm diameter) that were gently placed in an antistatic aluminum alloy separator on the upper part of the instrument before sampling. The instrument was placed horizontally on the roof of the ship at a height of about 1 m above the deck near the bow of the ship to avoid contamination from the ship. Upon collection, filters were gently transferred to a pre-cleaned air sampling cassette using stainless-steel tweezers, and then trapped with aluminum foil and stored horizontally in the buffer storage box until transfer to the laboratory for further analyses. The sampling date, sampling volume of filtered air, temperature and 2

Journal of Hazardous Materials xxx (xxxx) xxxx

X. Wang, et al.

along the cruise track by 5.78 ± 2.98 m/s, with a maximum recorded wind speed of 16.5 m/s (Figs. S1 and S2), and MPs were found more than one thousand kilometers from land in the present study (S13-S16), thereby allowing our computed trajectories to identify potential interregional air parcel transport pathways with each trajectory having a duration of 72 h (i.e., 3 days) (Chand et al., 2008; Prasad et al., 2010). Backward trajectories ending at each sampling transect for each hour on the episode day were computed by HYSPLIT. Each of the 21 sampling transects individually modeled the transport of air parcels from the beginning and end of each sampling transect. Each trajectory was collected by creating a back-trajectory map of trajectory potentials across the local area. After the HYSPLIT simulation was completed, back-trajectory maps were then imported into ArcGIS for the 21 sampling transects.

atmospheric pressure were recorded during the sampling period. Shared weather conditions (temperature, humidity, wind speed, wind direction and barometric pressure) in the area of sampling transects were also documented (Yang et al., 2015; Zeng et al., 2015), since the distribution of atmospheric MP can be easily influenced by these factors (Liu et al., 2019a). 2.2. Sample processing Sample processing was carried out in MP super-clean stainless laboratory. The filters were observed using a stereomicroscope (Leica M165 FC, Germany) equipped with a Leica DFC 450C camera. When observing filters, the microscope was set at 10× magnification, and photographs were taken in a “zigzag” pattern making sure all areas were covered (Peng et al., 2017; Cai et al., 2018). All particles were photographed, and the shape and color of the particles were recorded at the same time. The sizes of particles were measured by the ImageJ software program (version 1.51j8, National Institutes of Health, USA, http://imagej.nih.gov/ij) along their largest dimension. The chemical compositions of particles were identified by comparing their reference spectra with chemical spectra measured by a Micro Fourier Transform Infrared Spectrometer in transmission mode (Thermo Nicolet iN10, USA.). Samples with match values higher than 70% were accepted. To avoid contamination, the used filters were placed in a glass Petri dish, covered with aluminum foil, and then combusted in a muffle furnace at 450 °C for 4 h before use (Liu et al., 2019a). A procedural blank filter sample was taken for every sampling transect by manipulating an exposed filter through the sampling protocol without actually drawing air through it. A pure cotton laboratory coat and single-use nitrile gloves were worn during the analysis and sampling. The sampler was thoroughly wiped three times with alcohol solution prior to every use.

3. Results and discussion 3.1. Atmospheric MP distribution The atmospheric MPs were detected in 9 of the 21 sampling transects (S1–S3, S5, S8, S13–S16), with different characteristics (Table S1 and Fig. S1). Atmospheric MP was sparsely distributed in the sampling transects, ranging from 0 to 7.7 items/100 m3, with an average of 1.0 items/100 m3 (Fig. S3). Obviously, although the units were different, the characteristic abundance of atmospheric deposition was two and even three orders of magnitude higher than the suspended atmospheric MP (Table 1), which suggested that most of the airborne plastic cannot continuously be suspended in the atmosphere, especially plastic fragments (Abbasi et al., 2019). The mean abundance of atmospheric MP in the PRE was the highest (4.2 ± 2.5 items/100 m3, S1–S3) (Fig. 2). The average abundance of atmospheric MP in the EIO was the lowest (0.4 ± 0.6 items/100 m3, S11–S21). The high standard deviation in some sampling areas (EIO and SCS) was due to the consequence that MPs were not detected in some sampling transects (Fig. 2 and Table S1). Significant differences in abundance were found among atmospheric MPs in the PRE, SCS, and EIO (Kruskal-Wallis test, p = 0.028). In comparison, the abundance of atmospheric MP over the PRE was significantly higher than the abundance of atmospheric MP over the EIO (Mann-Whitney U test, p = 0.005; Fig. 2). The abundance of atmospheric MP in the SCS (0.8 ± 1.3 items/100 m3, S4-S7) was not significantly different from that in the EIO (Mann-Whitney U test, p = 1.000) and PRE (Mann-Whitney U test, p = 0.229). Intriguingly, in the Bay of Bengal (S17–S20) and the offshore field site of Sri Lanka (S21), only natural particles but no MP was found (Table S1). Compared with megacity atmospheric MP studies, the average abundance of these atmospheric MPs was two orders of magnitude lower than in Paris (Dris et al., 2017), Iran (Abbasi et al., 2019), and Shanghai (Liu et al., 2019a) (Table 1). Apparently, the average concentration of these atmospheric MPs was negligible, whereas the abundance of MP was considerable in the surface water of the PRE (Lin et al., 2018; Yan et al., 2019), SCS (Cai et al., 2018), Bay of Bengal (Eriksen et al., 2018) and Sri Lanka (Koongolla et al., 2018) (Table 2), providing proof that atmospheric MP based on transoceanic studies is an important source of oceanic MP (Liu et al., 2019c) but not the main MP source. The flux of plastic waste was estimated to range between 1.15 and 2.41 million tonnes from rivers to oceans each year worldwide (Lebreton et al., 2017). Rivers are widely recognized as a major pathway for plastic to the marine environment (Zhao et al., 2019; Lebreton et al., 2017; Schmidt et al., 2017). The highest abundance of suspended atmospheric MP was observed in the S2 (7.7 items/100 m3), followed by the S5 (3.1 items/100 m3) and S1 (3.0 items/100 m3), indicating that the contribution of terrestrial MP to the adjacent atmosphere was the highest. Interestingly, no MP was found in the sampling transects of S9, S10, and S21, which are close to the continent. The uneven distribution of atmospheric MP also suggested that the atmospheric MP interregional transport may be more

2.3. Statistical analysis Data analysis was performed using SPSS 25.0 software, and graphs were generated by ArcGIS 10.5, Origin Pro 2018C, R 3.6.0 and Ocean Data View 5.1.5. Principal Component Analysis (PCA) and Pearson's correlation coefficients were used to exploring the relationship between the distribution of atmospheric MP and meteorological factors. The differences in atmospheric MP size were determined by one-way analysis of variance (ANOVA) followed by Tukey's HSD test (in the case of homogeneous variances). The differences in the atmospheric MP abundance were tested by the Kruskal-Wallis test followed by the Mann-Whitney U test. In all tests, statistical significance was accepted at p < 0.05. A technique for using kernel density estimates to approximate the population structure of suspended atmospheric MP size distribution (MPSD) by estimating the probability density function of MPSD. 2.4. Backward trajectory analysis The sources of atmospheric MP over the ocean are not well known because of complex transport mechanisms and an unknown wind-blow rate. In order to fully understand the potential source of atmospheric MP particles, a backward trajectory model was adopt using the web version of the HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) model developed by the Air Resources Laboratory of NOAA (National Oceanic and Atmospheric Administration) (Stein et al., 2015; Rolph et al., 2017). The HYSPLIT model is widely used to generate air mass backward trajectories for given starting locations (Su et al., 2015). The backward trajectory model has been used to analysis the remote atmospheric MP source (Allen et al., 2019; Liu et al., 2019b). The model was run using the meteorological data output from GDAS (Global Data Assimilation System) with a 0.5° horizontal resolution. The Surface Real-time wind (on the basis of shipboard observations) fluctuated 3

4

Atmosphere

Sri Lanka

N/A N/A N/A – PET, PE, PAN, PAA, EVA, EP, ALK, RY PE, PP, PS RY, PET, PU, PA PET, PE, PVC, PS

100-600a 30-150a 2900-28000b 30-110a

0-418a

N/A

0a

0a 0-0.8a 3-7.7a 0-0.8a 0-3.1a

PET, PE, PP, PS PET, EP, PE-PP, PS, PE, PVC, PR, ALK, RY, PMA, PA, PVA, PAN, and PP N/A PET PET, PP, PA, PEP PET, PP, PAN-AA, PR PP, PET, PEVA

36,500 ± 6900b 0-137a

3600 ± 700b – 0-60200 b

Polymer types

Abundance (items/100 m3 or items/100 m2/day)

Characterization of MP

N/A 382.15 288.2-1117.62 58.591-988.37 286.101861.78 N/A

– 16.142,086.69

– – –

23.07-9554.88

50-3250 50-1650 100-5000 –

Size (μm)

N/A

N/A Fibers Fibers Fibers (80%), fragments (20%) Fibers (75%), fragments (25%)

Fibers (67%), fragments (30%), granules (3%) Fibers, foams, fragments, films Fibers Fibers (> 95%), foams (< 1%), fragments (≈4%), films (< 1%) Fibers, films, fragments Fibers (60%), fragments (31%), granules (8%)

Fibers Fibers Fibers (90%), fragments (10%) Fiber, films, fragment, spherule

Shape

N/A

N/A Black Black, white, red, yellow, brown Yellow, black, blue Black, yellow, red

N/A N/A

N/A N/A N/A White-transparent, yellow-orange, red-pink, blue-green, black-grey Black, blue, red, transparent, brown, green, yellow, grey Blue, black, red yellow, pink, white N/A White, black, red, transparent

Color

study study study study study Present study

Present Present Present Present Present

Allen et al., 2019 Liu et al., 2019b

Cai et al., 2017 Dris et al., 2016 Zhou et al., 2017

Dris et al., 2017 Dris et al., 2017 Dris et al., 2015 Abbasi et al., 2019 Liu et al., 2019a

Reference

Note that: polyethylene terephthalate (PET), polyethylene (PE), polyacrylonitrile (PAN), poly(N-methyl acrylamide) (PAA), ethylene vinyl acetate (EVA), epoxy resin (EP), alkyd resin (ALK), rayon (RY), polyethylene (PE), polypropylene (PP), polystyrene (PS), polyurethane (PU), polyamide (PA), poly(ethylene-co-propylene) (PEP), poly(acrylonitrile-co-acrylic acid) (PAN-AA), phenoxy resin (PR), poly(ethylene-co-vinyl acetate) (PEVA), polyethylene-polypropylene (PE-PP), polyvinyl chloride (PVC), poly(N-methyl acrylamide) (PMA), poly (vinyl acetate) (PVA), and polyacrylonitrile (PAN). N/A: indicates no available information. a: items/100 m3. b: items/100 m2/day.

Atmosphere Atmosphere Atmosphere Atmosphere Atmosphere

Atmosphere Deposition Deposition

Dongguan Paris Yantai

Sunda Strait Karimata Strait Pearl River Estuary East Indian Ocean South China Sea

Atmosphere

Shanghai

Deposition Atmosphere

Atmosphere Atmosphere Deposition Atmosphere

Paris (indoor) Paris (outdoor) Paris Asaluyeh

Pyrenees West Pacific Ocean

Sample characteristic

Area

Table 1 Summary of characteristics of the atmospheric MP collected in similar studies.

X. Wang, et al.

Journal of Hazardous Materials xxx (xxxx) xxxx

Journal of Hazardous Materials xxx (xxxx) xxxx

X. Wang, et al.

Fig. 3. Principal Component Analysis (PCA) plots indicate the relationship among the abundance of atmospheric MP at the SCS and EIO and the meteorological condition. The vectors show the direction and strength of the environmental variable relative to the overall distribution. The colored symbols correspond to the SCS and EIO community cluster defined in this study. The colored ellipses represent the 95% confidence coefficient.

Fig. 2. Atmospheric MP abundance in different sampling areas. Bars that do not share the same letters are significantly different (p < 0.05).

episodic than other air pollutants such as ozone. The uneven distribution probably depends on seasonality and climate conditions such as wind speed, wind direction, and rain (Prata, 2018). Atmospheric MPs were also observed in the continuous sampling transects of S13–S16, which are far from land-based sources, indicating that terrigenous MP may be lifted into the free troposphere, reaching more than one thousand kilometers away through the atmosphere. The transport of terrigenous MP from source regions to remote areas could have a potential effect on the global atmospheric environment. According to the backward trajectories (Fig. 6) and previous studies (Zelinsky et al., 2019; Meenu et al., 2007), the Intertropical Convergence Zone (ITCZ) migrated roughly between 10 °S and 2 °N in the EIO during sampling. Interestingly, the atmospheric MP of the EIO (S11-S21) was mainly distributed in the region of ITCZ (S13-S16). The ITCZ is a semi-permanent band of clouds that form where the trade winds converge near the equator (Zelinsky et al., 2019). The ITCZ not only plays an important role in the vertical and horizontal transport of mass, energy, and momentum in the troposphere but is also one of the most prominent features of the tropical troposphere (Meenu et al., 2007). The research results showed that the average concentration of atmospheric MP was negligible in the north of the ITCZ (0 items/100 m3, S17–S21), whereas it was 1.0 ± 0.5 items/100 m3 over the ITCZ (S13–S16). We concluded that the ITCZ may be the central recipient for the southnorth transport of atmospheric MP over the ocean, but whether atmospheric MP may accumulate in the ITCZ just like the Great Pacific Garbage Patch (Lebreton et al., 2018) deserves further exploration. The relationships between the abundance of atmospheric MP in the SCS (S4–S7) and EIO (S11–S18, S21) and meteorological factors were determined by Principal Component Analysis (PCA) (Fig. 3). PC1 and PC2 explained 63.2% of the total variance. The main contribution of

PC2 (24.7%) was from the pressure (0.51), whereas the contribution of PC1 (38.5%) was dominated by the wind speed (0.54), humidity (0.45) and gust velocity (0.57). Thus, it can be seen that four meteorological factors limited the distribution of atmospheric MP: (1) wind speed. The long-distance transmission of atmospheric MP requires wind. (2) Gust velocity is necessary to blow up MP from the ground. (3) Humidity and pressure are essential restrictive factors with respect to the remote transmission of atmospheric MP. Relative humidity (0.50), barometric pressure (0.33) and wind speed (0.23) were the main influential factors of atmospheric MP distribution in a similar study (Liu et al., 2019a). In Fig. 3, the acute angle between the vectors represents a positive correlation, while the obtuse angle indicates a negative correlation. Although an acute angle between the wind direction and atmospheric MP abundance was found, the wind direction had no significant correlation with the abundance of atmospheric MP (Pearson's correlation, R = 0.316, P = 0.293). However, Liu et al. (2019a) reported that the wind direction had a significant correlation with the abundance of megacity atmospheric MP (Pearson's correlation, R = 0.78, P = 0.01). This is could be because the sampling area was different. We can see that the distance of different sampling transects was very short in Fig. 3, which confirmed the present result that the abundance of atmospheric MP between the SCS and EIO was not significantly different. 3.2. Sample characteristics Spectral analysis revealed that the atmospheric particles consisted of 18 different polymers (Fig. S4), of which natural particles constituted 76.84% of all observed particles, followed by plastic materials (21.05%)

Table 2 The abundance of MP in surface seawater from similar areas. Area

Sampling method

Aperture (μm)

Environment matrix

Abundance (items/m3 or items/km2)

Reference

South China Sea Sri Lanka Bay of Bengal Pearl River Estuary Pearl River Estuary

Pump Plankton nets Manta trawl and AVANI trawl Water sampler Water sampler

44 80 335 20 50

Surface Surface Surface Surface Surface

2569 ± 1770a 6.8 ± 9.59a 16,107 ± 47,077.63b 379–7924a 7850–10,950a

Cai et al., 2018 Koongolla et al., 2018 Eriksen et al., 2018 Lin et al., 2018 Yan et al., 2019

a: items/m3. b: items/km2. 5

water water water water water

Journal of Hazardous Materials xxx (xxxx) xxxx

X. Wang, et al.

et al., 2012), whereas the PP is used in textiles, packaging, and reusable products with the least dense of all particles (Allen et al., 2019; Hidalgo-Ruz et al., 2012). Compared with other similar studies, PET and PP were also found in the atmospheric deposition samples (Allen et al., 2019; Zhou et al., 2017; Dris et al., 2016) (Table 1). If these atmospheric MP falls into the ocean by external factors, it may be ingested by marine organisms because their size renders them accessible to a wide range of organisms at least as small as zooplankton, with potential for physical and toxicological harm (Law and Thompson, 2014). Furthermore, atmospheric MP sources are increasing (e.g., synthetic clothing) with rapid economic development, which may lead to an increase in MP types and environmental concentrations. Further research should try to focus on detecting and quantifying atmospheric MP in different areas and conditions. The collected atmospheric samples were composed of fibrous and fragmented MPs. The majority of suspended atmospheric MPs were fibers (88.89%) with only a very low number of fragments identified (11.11%). A total of two MP fragments from all sampling transects were only found in the SCS (S5) and EIO (S13) (Fig. 5B), which is the PR and PP, respectively. The MP fibers correspond to purely synthetic polymer fiber, mainly PET and PP, which is similar to the result of atmospheric deposition of MP in a remote mountain (Allen et al., 2019). The atmospheric MPs over the PRE were all MP fibers, and a high abundance of MP fiber was also observed in the SCS (75%) and EIO (80%) (Fig. 5B). Similarly, a high abundance of MP fiber was also found in the atmosphere (Liu et al., 2019a, b) and the atmospheric deposition (Allen et al., 2019; Dris et al., 2015; Zhou et al., 2017). In fact, MP fibers are also prevalent in marine organisms (Zhao et al., 2018), arctic snow (Bergmann et al., 2019) and the coastal environment (Zhao et al., 2019; Peng et al., 2017). Atmospheric MPs consisted of five colors (Fig. 5C), with the most common color being black in the PRE (37.5%), SCS (50%) and EIO (60%). Similarly, previous studies indicate that black MPs were among the highest in a megacity atmosphere (Liu et al., 2019a). However,

Fig. 4. Polymer types of particles identified in atmospheric samples. Polypropylene (PP), poly(ethylene-co-vinyl acetate) (PEVA), polyethylene terephthalate (PET), poly(ethylene-co-propylene) (PEP), poly(acrylonitrile-co-acrylic acid) (PAN-AA), phenoxy resin (PR).

and artificial fibers (4.21%). The natural particles were mainly cotton (68.49%) and cellulose (12.33%), while the artificial fibers were composed of rayon (50%) and cellophane (50%). The atmospheric MP particles were composed of polymers such as polyethylene terephthalate (PET, 50.00%), polypropylene (PP, 22.22%), and other polymers (27.78%) including phenoxy resin (PR), poly(acrylonitrile-coacrylic acid) (PAN-AA), poly(ethylene-co-propylene) (PEP), poly(ethylene-co-vinyl acetate) (PEVA), and polyamide (PA) (Fig. 4). These natural materials may be derived from plants and agricultural emissions, while the MP particles and artificial fibers may be derived from synthetic textiles and city dust (Prata, 2018). Fig. 5A displays the distribution of MP types in different sampling areas. The PET was common in the PRE (62.5%) and EIO (40%), whereas the PP was common in the SCS (50%). The PET is widely used in the textile industry (Hidalgo-Ruz

Fig. 5. Polymer types (A), shape (B), color (C) and size (D) of atmospheric MP particles in different sampling areas. 6

Journal of Hazardous Materials xxx (xxxx) xxxx

X. Wang, et al.

Fig. 6. The backward trajectory of the air parcel along all sampling transects. The dotted line represents the three-day backward trajectories of air parcels in different sampling areas.

and backward trajectory models can be used to explore the potential source. The MP fiber may originate from textile material (Liu et al., 2019a; Dris et al., 2016), while MP fragments may originate from the breakdown, abrasion, and weathering of packaging and reusable products. The highest proportion of PET fiber (50.00%) was probably from the drop out and breakdown of clothes fabricated with synthetic fibers. The relatively high proportion of PP (22.22%) with a low density in the SCS was probably from packaging and textile products. The backward trajectory model analyses preliminary revealed the potential relationship between the distribution of atmospheric MP and long-distance air parcel transport (Fig. 6). In the offshore field site of the PRE (S1-S3), backward trajectories of air parcels are derived from the regional ocean. We thus infer that continental atmospheric MP might not originate from adjacent continent MP emission but also from the adjacent oceanic atmosphere polluted by other continental atmospheric MP emissions. The transport model simulations suggest that atmospheric MP of the SCS (S3-S7) is mainly derived from the Philippines. The backward trajectory (S3-S7) revealed the pathway that continental atmospheric MP can be transported to the atmosphere over the ocean through the atmosphere. This also proves the latest conclusion that terrigenous atmospheric MP is consistently transported to the ocean through the atmosphere (Liu et al., 2019b). According to the backward trajectory model analysis, the air parcels in the region of the ITCZ (S13-S16) originated from the regional ocean, so the atmospheric MP of the ITCZ might also be derived from the adjacent oceanic atmosphere polluted by continental MP emissions. Compared with the backward trajectory in the other sampling areas, the backward trajectories in the ITCZ change slightly during the duration of 72 h, so the atmospheric MP transmission passing the region of the ITCZ is probably stay for a moment. Furthermore, precipitation in the ITCZ is frequent, so atmospheric MP at higher altitudes will be easier to deposit, explaining the relatively high concentration of atmospheric MP in the ITCZ. However, these calculated backward trajectories indicated that the synoptic situation and general origin of the air mass sampled, because of uncertainties (diabatic effects, interpolation, observational errors,

white-transparent MPs were dominant in industrial (70%) and urban settings (53%) (Abbasi et al., 2019). Fig. 5D shows the size distribution of the collected atmospheric MP from different sampling areas. The size of atmospheric MP ranged from 58.59 μm to 2251.54 μm (851.09 ± 578.30 μm), of which MP fragments ranged in diameter from a minimum of 58.59 μm to a maximum of 286.10 μm, while MP fibers measured between 288.20 μm and 2251.54 μm (935.94 ± 556.63 μm) in length. No significant differences in particle size were observed among atmospheric MP in the PRE, SCS, and EIO (ANOVA, p = 0.547). Nevertheless, the average size of atmospheric MP in the SCS (953.00 ± 730.10 μm; median = 1117.94 μm) and PRE (917.38 ± 563.60 μm; median = 807.27 μm) was significant larger than that in the EIO (643.13 ± 319.40 μm; median = 739.00 μm; Fig. 5D). This could be because the size of MP limits the transmission distance. The longest MP particle identified as an MP fiber in the PRE was 2251.54 μm (S2). Previous studies found atmospheric plastic of 23.07–9555 μm in a megacity, with an average of 582.2 μm (Liu et al., 2019a). The size range of MP was from 100 μm to 5000 μm in atmospheric deposition samples (Allen et al., 2019; Zhou et al., 2017; Dris et al., 2015 and 2016) (Table 1). A decrease in the number of atmospheric MP towards the large sizes was noted (Fig. S5A). The distribution of MP counts versus size distribution and kernel density estimation showed that the predominant MP in atmospheric samples was 400–1000 μm (55.56%) in length (Fig. S5B). There may be even smaller MPs in the atmosphere. However, efficient identification is a serious challenge for quantifying MP loads, especially with decreasing size (Law and Thompson, 2014; Abbasi et al., 2019). Spectroscopic analysis can detect the individual fragments of common plastics as small as 10–20 μm in diameter (Law and Thompson, 2014; Song et al., 2015). However, the potential for harm from MP could increase with decreasing MP size (Law and Thompson, 2014).

3.3. Atmospheric MP source The sources of atmospheric MP are hard to trace, but the morphological characteristics and polymer types of atmospheric MP (Fig. 5) 7

Journal of Hazardous Materials xxx (xxxx) xxxx

X. Wang, et al.

and subgrid-scale processes), anyone trajectory should not be interpreted as the guaranteed path traversed by an air parcel (Rhoads et al., 1997; Lobert and Harris, 2002).

Jaafarzadeh, N., 2019. Distribution and potential health impacts of microplastics and microrubbers in air and street dusts from Asaluyeh County, Iran. Environ. Pollut. 244, 153–164. https://doi.org/10.1016/j.envpol.2018.10.039. Bergmann, Melanie, Mützel, Sophia, Primpke, Sebastian, Tekman, Mine Banu, Trachsel, J.ürg, Gerdts, Gunnar, 2019. Microplastic in snow from European and Arctic sites. Sci. Adv. 5 (8). https://doi.org/10.1126/sciadv.aax1157. Bergmann, M., Wirzberger, V., Krumpen, T., Lorenz, C., Primpke, S., Tekman, M.B., Gerdts, G., 2017. High quantities of microplastic in Arctic deep-sea sediments from the HAUSGARTEN observatory. Environ. Sci. Technol. 51 (19), 11000–11010. https://doi.org/10.1021/acs.est.7b03331. Cózar, A., Echevarría, F., González-Gordillo, J.I., Irigoien, X., Úbeda, B., Hernández-León, S., et al., 2014. Plastic debris in the open ocean. Proc. Natl. Acad. Sci. 111 (28), 10239–10244. https://doi.org/10.1073/pnas.1314705111. Cai, M., He, H., Liu, M., Li, S., Tang, G., Wang, W., et al., 2018. Lost but can’t be neglected: huge quantities of small microplastics hide in the South China Sea. Sci. Total Environ. 633, 1206–1216. https://doi.org/10.1016/j.scitotenv.2018.03.197. Cai, L., Wang, J., Peng, J., Tan, Z., Zhan, Z., Tan, X., Chen, Q., 2017. Characteristic of microplastics in the atmospheric fallout from Dongguan city, China: preliminary research and first evidence. Environ. Sci. Pollut. Res. - Int. 24 (32), 24928–24935. https://doi.org/10.1007/s1135. Chand, D., Jaffe, D., Prestbo, E., Swartzendruber, P.C., Hafner, W., Weiss-Penzias, P., et al., 2008. Reactive and particulate mercury in the Asian marine boundary layer. Atmos. Environ. 42 (34), 7988–7996. https://doi.org/10.1016/j.atmosenv.2008.06. 048. Cincinelli, A., Scopetani, C., Chelazzi, D., Lombardini, E., Martellini, T., Katsoyiannis, A., et al., 2017. Microplastic in the surface waters of the Ross Sea (Antarctica): occurrence, distribution and characterization by FTIR. Chemosphere 175, 391–400. https://doi.org/10.1016/j.chemosphere.2017.02.024. Dris, R., Gasperi, J., Saad, M., Mirande, C., Tassin, B., 2016. Synthetic fibers in atmospheric fallout: a source of microplastics in the environment? Mar. Pollut. Bull. 104 (1-2), 290–293. https://doi.org/10.1016/j.marpolbul.2016.01.006. Dris, R., Gasperi, J., Mirande, C., Mandin, C., Guerrouache, M., Langlois, V., Tassin, B., 2017. A first overview of textile fibers, including microplastics, in indoor and outdoor environments. Environ. Pollut. 221, 453–458. https://doi.org/10.1016/j.envpol. 2016.12.013. Dris, R., Gasperi, J., Rocher, V., Saad, M., Renault, N., Tassin, B., 2015. Microplastic contamination in an urban area: a case study in Greater Paris. Environ. Chem. 12 (5), 592–599. https://doi.org/10.1071/en14167. Desforges, J.P.W., Galbraith, M., Dangerfield, N., Ross, P.S., 2014. Widespread distribution of microplastics in subsurface seawater in the NE Pacific Ocean. Mar. Pollut. Bull. 79 (1–2), 94–99. https://doi.org/10.1016/j.marpolbul.2013.12.035. Eriksen, M., Liboiron, M., Kiessling, T., Charron, L., Alling, A., Lebreton, L., et al., 2018. Microplastic sampling with the AVANI trawl compared to two neuston trawls in the Bay of Bengal and South Pacific. Environ. Pollut. 232, 430–439. https://doi.org/10. 1016/j.envpol.2017.09.058. Geyer, R., Jambeck, J.R., Law, K.L., 2017. Production, use, and fate of all plastics ever made. Sci. Adv. 3 (7), e1700782. https://doi.org/10.1126/sciadv.1700782. Hidalgo-Ruz, V., Gutow, L., ThoMPon, R.C., Thiel, M., 2012. Microplastics in the marine environment: a review of the methods used for identification and quantification. Environ. Sci. Technol. 46 (6), 3060–3075. https://doi.org/10.1021/es2031505. Isobe, A., Iwasaki, S., Uchida, K., Tokai, T., 2019. Abundance of non-conservative microplastics in the upper ocean from 1957 to 2066. Nat. Commun. 10 (1), 417. https:// doi.org/10.1038/s41467-019-08316-9. Jambeck, J.R., Geyer, R., Wilcox, C., Siegler, T.R., Perryman, M., Andrady, A., et al., 2015. Plastic waste inputs from land into the ocean. Science 347 (6223), 768–771. https://doi.org/10.1126/science.1260352. Jimenez-Velez, B., Detrés, Y., Armstrong, R.A., Gioda, A., 2009. Characterization of African dust (PM2. 5) across the Atlantic Ocean during AEROSE 2004. Atmos. Environ. 43 (16), 2659–2664. https://doi.org/10.1016/j.atmosenv.2009.01.045. Koongolla, J.B., Andrady, A.L., Kumara, P.T.P., Gangabadage, C.S., 2018. Evidence of microplastics pollution in coastal beaches and waters in southern Sri Lanka. Mar. Pollut. Bull. 137, 277–284. https://doi.org/10.1016/j.marpolbul.2018.10.031. Law, K.L., Thompson, R.C., 2014. Microplastics in the seas. Science 345 (6193), 144–145. https://doi.org/10.1126/science.1254065. Law, K.L., Morét-Ferguson, S., Maximenko, N.A., Proskurowski, G., Peacock, E.E., Hafner, J., Reddy, C.M., 2010. Plastic accumulation in the North Atlantic subtropical gyre. Science 329 (5996), 1185–1188. https://doi.org/10.1126/science.1192321. Lebreton, L.C., Van der Zwet, J., Damsteeg, J.W., Slat, B., Andrady, A., Reisser, J., 2017. River plastic emissions to the world’s oceans. Nat. Commun. 8, 15611. https://doi. org/10.1038/ncomms15611. Lusher, A.L., O’Donnell, C., Officer, R., O’Connor, I., 2015a. Microplastic interactions with North Atlantic mesopelagic fish. Ices J. Mar. Sci. 73 (4), 1214–1225. https://doi.org/ 10.1093/icesjms/fsv241. Lusher, A.L., Tirelli, V., O’Connor, I., Officer, R., 2015b. Microplastics in Arctic polar waters: the first reported values of particles in surface and sub-surface samples. Sci. Rep. 5, 14947. https://doi.org/10.1038/srep14947. Liu, K., Wang, X., Fang, T., Xu, P., Zhu, L., Li, D., 2019a. Source and potential risk assessment of suspended atmospheric microplastics in Shanghai. Sci. Total Environ. 675, 462–471. https://doi.org/10.1016/j.scitotenv.2019.04.110. Liu, K., Wu, T., Wang, X., Song, Z., Zong, C., Wei, N., Li, D., 2019b. Consistent transport of terrestrial microplastics to the ocean through atmosphere. Environ. Sci. Technol. 53 (18), 10612–10619. https://doi.org/10.1021/acs.est.9b03427. Lebreton, L., Slat, B., Ferrari, F., Sainte-Rose, B., Aitken, J., Marthouse, R., et al., 2018. Evidence that the great pacific garbage patch is rapidly accumulating plastic. Sci. Rep. 8 (1), 4666. https://doi.org/10.1038/s41598-018-22939-w. Lobert, J.M., Harris, J.M., 2002. Trace gases and air mass origin at Kaashidhoo, Indian

4. Conclusion Here, we showed not only the spatial and temporal characteristic distribution of atmospheric MP over the ocean but also the potential sources of atmospheric MP, based on a combination of backward trajectory modeling and transoceanic surveys conducted across the PRE to the SCS and then to the EIO. The ITCZ may be a central recipient for the south-north transport of atmospheric MP over the ocean. The result revealed that atmospheric MP over the ocean might originate not only from the adjacent continent MP emissions but also from the adjacent oceanic atmosphere polluted by continental MP emissions. The rapid growth of plastic usage and production, particularly in developing economies, and continuing industrialization and urbanization, coupled with inefficient or careless waste management and disposal practices, enhances the emission of MP into the air. However, few monitoring and mitigation measures have been implemented regarding these recently discovered atmospheric MPs. Therefore, in order to more accurately access the impacts of atmospheric MP, long-term measurements should be conducted around the world. CRediT authorship contribution statement Xiaohui Wang: Investigation, Data curation, Writing - original draft. Changjun Li: Visualization, Investigation. Kai Liu: Writing review & editing, Software. Lixin Zhu: Conceptualization, Validation. Zhangyu Song: Writing - review & editing. Daoji Li: Supervision, Resources, Methodology. Declaration of Competing Interest The authors and co-author declare that there is no actual or potential conflict of interests regarding the publication of this article. Acknowledgments This study was financially supported by the National Key Research and Development Program (2016YFC1402205) and the National Natural Science Fund of China (41676190). Sincere thanks are extended to the Intergovernmental Oceanographic Commission for the Western Pacific (IOC-WESTPAC) project and ECNU Academic Innovation Promotion Program for Excellent Doctoral Students (YBNLTS2019-007). The author and co-authors are grateful to the “Shiyan 3” crew administered by the South China Sea Institute of Oceanology, the Chinese Academy of Sciences. The authors and coauthors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model and READY website (http://www.ready.noaa.gov) used in this publication. In addition, we would like to thank the editor and the anonymous reviewers for their useful comments on the manuscript. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.jhazmat.2019.121846. References Akimoto, H., 2003. Global air quality and pollution. Science 302 (5651), 1716–1719. https://doi.org/10.1126/science.1092666. Allen, S., Allen, D., Phoenix, V.R., Le Roux, G., Jiménez, P.D., Simonneau, A., et al., 2019. Atmospheric transport and deposition of microplastics in a remote mountain catchment. Nat. Geosci. 12 (5), 339. https://doi.org/10.1038/s41561-019-0335-5. Abbasi, S., Keshavarzi, B., Moore, F., Turner, A., Kelly, F.J., Dominguez, A.O.,

8

Journal of Hazardous Materials xxx (xxxx) xxxx

X. Wang, et al.

Stein, A.F., Draxler, R.R., Rolph, G.D., Stunder, B.J., Cohen, M.D., Ngan, F., 2015. NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull. Am. Meteorol. Soc. 96 (12), 2059–2077. https://doi.org/10.1175/BAMS-D-14-00110.1. Song, Y.K., Hong, S.H., Jang, M., Han, G.M., Rani, M., Lee, J., Shim, W.J., 2015. A comparison of microscopic and spectroscopic identification methods for analysis of microplastics in environmental samples. Mar. Pollut. Bull. 93 (1-2), 202–209. https:// doi.org/10.1016/j.marpolbul.2015.01.015. Van Cauwenberghe, L., Vanreusel, A., Mees, J., Janssen, C.R., 2013. Microplastic pollution in deep-sea sediments. Environ. Pollut. 182, 495–499. https://doi.org/10.1016/ j.envpol.2013.08.013. World Health Organization, 2016. Ambient Air Pollution: A Global Assessment of Exposure and Burden of Disease. https://apps.who.int/iris/handle/10665/250141. Yang, L., Wang, D., Huang, J., Wang, X., Zeng, L., Shi, R., et al., 2015. Toward a mesoscale hydrological and marine meteorological observation network in the South China Sea. Bull. Am. Meteorol. Soc. 96 (7), 1117–1135. https://doi.org/10.1175/bams-d-1400159.1. Yan, M., Nie, H., Xu, K., He, Y., Hu, Y., Huang, Y., Wang, J., 2019. Microplastic abundance, distribution and composition in the Pearl River along Guangzhou city and Pearl River estuary, China. Chemosphere 217, 879–886. https://doi.org/10.1016/j. chemosphere.2018.11.093. Zhang, Q., Jiang, X., Tong, D., Davis, S.J., Zhao, H., Geng, G., et al., 2017. Transboundary health impacts of transported global air pollution and international trade. Nature 543 (7647), 705. https://doi.org/10.1038/nature21712. Zhao, S., Ward, J.E., Danley, M., Mincer, T.J., 2018. Field-based evidence for microplastic in marine aggregates and mussels: implications for trophic transfer. Environ. Sci. Technol. 52 (19), 11038–11048. https://doi.org/10.1021/acs.est.8b03467. Zhao, S., Wang, T., Zhu, L., Xu, P., Wang, X., Gao, L., Li, D., 2019. Analysis of suspended microplastics in the Changjiang Estuary: implications for riverine plastic load to the ocean. Water Res. 161, 560–569. https://doi.org/10.1016/j.watres.2019.06.019. Zhang, F., Wang, X., Xu, J., Zhu, L., Peng, G., Xu, P., Li, D., 2019. Food-web transfer of microplastics between wild caught fish and crustaceans in East China Sea. Mar. Pollut. Bull. 146, 173–182. https://doi.org/10.1016/j.marpolbul.2019.05.061. ZHOU, Q., TIAN, C., LUO, Y., 2017. Various forms and deposition fluxes of microplastics identified in the coastal urban atmosphere. Chin. Sci. Bull. 62 (33), 3902–3910. https://doi.org/10.1360/n972017-00956. Zeng, L., Wang, Q., Xie, Q., Shi, P., Yang, L., Shu, Y., et al., 2015. Hydrographic field investigations in the Northern South China Sea by open cruises during 2004–2013. Sci. Bull. 60 (6), 607–615. https://doi.org/10.1007/s11434-015-0733-z. Science Bulletin, 60(6), 607-615. Zelinsky, R.C., Zhang, C., Liu, C., 2019. The relationship between the ITCZ and MJO initiation over the Indian Ocean. J. Atmos. Sci. 76 (8), 2275–2294. https://doi.org/ 10.1175/JAS-D-18-0327.1.

Ocean. J. Geophys. Res. Atmos. 107 (D19). https://doi.org/10.1029/2001jd000731. INX2-12. Lin, L., Zuo, L.Z., Peng, J.P., Cai, L.Q., Fok, L., Yan, Y., et al., 2018. Occurrence and distribution of microplatics in an urban river: a case study in the Pearl River along Guangzhou City, China. Sci. Total Environ. 644, 375–381. https://doi.org/10.1016/j. scitotenv.2018.06.327. La Daana, K.K., Officer, R., Lyashevska, O., Thompson, R.C., O’Connor, I., 2017. Microplastic abundance, distribution and composition along a latitudinal gradient in the Atlantic Ocean. Mar. Pollut. Bull. 115 (1–2), 307–314. https://doi.org/10.1016/j. marpolbul.2016.12.025. Meenu, S., Rajeev, K., Parameswaran, K., Suresh Raju, C., 2007. Characteristics of the double intertropical convergence zone over the tropical Indian Ocean. J. Geophys. Res. Atmos. 112 (D11). https://doi.org/10.1029/2006jd007950. Peng, G., Zhu, B., Yang, D., Su, L., Shi, H., Li, D., 2017. Microplastics in sediments of the Changjiang Estuary, China. Environ. Pollut. 225, 283–290. https://doi.org/10.1016/ j.envpol.2016.12.064. Prata, J.C., 2018. Airborne microplastics: consequences to human health? Environ. Pollut. 234, 115–126. https://doi.org/10.1016/j.envpol.2017.11.043. PlasticsEurope, Plastics - The Facts, 2018. An Analysis of European Plastics Production, Demand and Waste Data (PlasticsEurope, 2018). https://www.plasticseurope.org/ en/resources/publications/619-plastics-facts-2018. Peeken, I., Primpke, S., Beyer, B., Gütermann, J., Katlein, C., Krumpen, T., et al., 2018. Arctic sea ice is an important temporal sink and means of transport for microplastic. Nat. Commun. 9 (1), 1505. https://doi.org/10.1038/s41467-018-03825-5. Prasad, A.K., El-Askary, H., Kafatos, M., 2010. Implications of high altitude desert dust transport from Western Sahara to Nile Delta during biomass burning season. Environ. Pollut. 158 (11), 3385–3391. https://doi.org/10.1016/j.envpol.2010.07.035. Rhoads, K.P., Kelley, P., Dickerson, R.R., Carsey, T.P., Farmer, M., Savoie, D.L., Prospero, J.M., 1997. Composition of the troposphere over the Indian Ocean during the monsoonal transition. J. Geophys. Res. Atmos. 102 (D15), 18981–18995. https://doi.org/ 10.1029/97jd01078. Rolph, G., Stein, A., Stunder, B., 2017. Real-time environmental applications and display system: READY. Environ. Model. Softw. 95, 210–228. https://doi.org/10.1016/j. envsoft.2017.06.025. Schmidt, C., Krauth, T., Wagner, S., 2017. Export of plastic debris by rivers into the sea. Environ. Sci. Technol. 51 (21), 12246–12253. https://doi.org/10.1021/acs.est. 7b02368. Sharma, S., Chatterjee, S., 2017. Microplastic pollution, a threat to marine ecosystem and human health: a short review. Environ. Sci. Pollut. Res. - Int. 24 (27), 21530–21547. https://doi.org/10.1007/s11356-017-9910-8. Su, L., Yuan, Z., Fung, J.C., Lau, A.K., 2015. A comparison of HYSPLIT backward trajectories generated from two GDAS datasets. Sci. Total Environ. 506, 527–537. https://doi.org/10.1016/j.scitotenv.2014.11.072.

9