Characterization and source apportionment of marine aerosols over the East China Sea

Science of the Total Environment 651 (2019) 2679–2688

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Characterization and source apportionment of marine aerosols over the East China Sea Mingjie Kang a, Hao Guo b, Pengfei Wang b, Pingqing Fu c, Qi Ying d, Huan Liu e,f, Ye Zhao a,⁎, Hongliang Zhang b,⁎ a

State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803, USA Institute of Surface-Earth System Science, Tianjin University, Tianjin 300072, China d Department of Civil Engineering, Texas A&M University, College Station, TX 77845, USA e State Key Joint Laboratory of ESPC, School of Environment, Tsinghua University, Beijing 100084, China f State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China b c

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• PM2.5 and components over the East China Sea and offshore regions were investigated. • Model predictions agree well with observations over the ECS and along the coast. • Emissions from China have significant effects on marine aerosols over the ECS. • SO42− is the most abundant PM2.5 component over the ECS, followed by NH4+ and POA. • Industry and ship emissions are the top two contributors to total PM2.5.

a r t i c l e

i n f o

Article history: Received 11 September 2018 Received in revised form 9 October 2018 Accepted 12 October 2018 Available online 15 October 2018 Editor: Jianmin Chen Keywords: Marine aerosols PM2.5 Coastal Source apportionment CMAQ East China Sea

a b s t r a c t Awareness of the importance of marine atmosphere for accurately estimating global aerosol budget and climate impacts has arisen recently. However, studies are limited due to the difficulty and inconvenience in sampling as well as the diversity of sources. In this study, the Community Multiscale Air Quality (CMAQ) model was applied to investigate the fine particulate matter (PM2.5) and its chemical components over the East China Sea (ECS) and offshore regions. In spite of slight under-predictions, model predictions agree well with observations over the ECS and along the coast. PM2.5 and its major components in the mainland are higher than in marine area, suggesting Asian continent is a major emitter of marine aerosols. PM2.5 and its components in marine regions show higher abundance during daytime than nighttime, while it is opposite in continental regions. Aerosol phase SO42− is the most abundant component of PM2.5 over the ECS with an average concentration of 5.12 μg m−3, followed by NH4+ (1.02 μg m−3) and primary organic aerosol (POA) (0.92 μg m−3). Industry and ship emissions are the top two contributors to primary (PPM) and total PM2.5 over the ECS, while industry and agriculture sectors are major sources for secondary inorganic aerosols (SIA), followed by ship emissions. For terrestrial regions, industry and agriculture are predominant sources of PM2.5 and SIA, while industry and residential activities are

⁎ Corresponding authors. E-mail addresses: [email protected] (Y. Zhao), [email protected] (H. Zhang).

https://doi.org/10.1016/j.scitotenv.2018.10.174 0048-9697/© 2018 Published by Elsevier B.V.

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the top two contributors to PPM. This study improves the understanding of transport and accumulation of air pollutants over the ECS and adjacent regions, and provides useful information for designing efficient control strategies. © 2018 Published by Elsevier B.V.

1. Introduction Marine aerosols play an important role in the global budget of atmospheric aerosols (O'Dowd and de Leeuw, 2007; O'Dowd et al., 2004). It significantly affects the Earth's radiative forcing as well as the properties and lifetime of stratiform clouds via modifying abundance, chemical composition and size distribution of marine cloud condensation nuclei (CCN) (Andreae and Rosenfeld, 2008; Gantt and Meskhidze, 2013; Meskhidze and Nenes, 2006; Quinn and Bates, 2011; Quinn et al., 2017). Moreover, chemical species in marine aerosols, such as trace elements, nutrients, and hazardous components, are likely to be transported to ocean water through the interaction of marine atmosphere and sea (e.g., atmospheric deposition), affecting biogeochemistry and marine ecosystems and in turn have feedbacks on climate (Baker et al., 2003; Facchini et al., 2008; Hsu et al., 2010; Jickells et al., 2005; Kumar et al., 2012; Yuan and Zhang, 2006). Marine aerosols also exert a non-negligible effect on accurate model quantification of anthropogenic aerosol forcing of climate (Gantt and Meskhidze, 2013). Thus, detailed information about marine aerosols is needed for a better understanding of global aerosol budgets and their effects on radiative balance and cloud processes. Marine aerosols can directly originate from sea surface as a result of the wind-driven bubble bursting or sea spray process or formed by subsequent oxidation of biogenic gases released from marine biota (e.g., phytoplankton and organisms) particularly in biologically productive ocean regions (Meskhidze and Nenes, 2006; O'Dowd et al., 2004). Additionally, terrestrial aerosols can be transported to the marine air through long-range atmospheric transport or sea/land breeze circulation in coastal regions. These continental particles from soil dust, wildfires, biological particles (e.g., pollen, microbes and plant debris) and anthropogenic activities (Andreae, 2007; Fu et al., 2011; Kang et al., 2017) would be finally deposited into the ocean and participate in biogeochemical transformations in coastal and remote marine regions, exerting profound effects. It was reported that sea-salt particles in marine aerosols dominate primarily in the coarse mode, while organic matter and inorganic salts (e.g., NH4, NO3, nss-SO4) contribute significantly to fine particulate matter (PM2.5) particularly during plankton blooms (O'Dowd et al., 2004; O'Dowd et al., 1997). Fine particles have longer atmospheric lifetimes, and greater absorption and scattering efficiencies than coarse fractions (Bond et al., 2004). Therefore, it is important to understand the chemical composition and source apportionment of PM2.5 when studying marine aerosols. In recent years, with the rising awareness of the crucial role of marine aerosols in climate and the Earth system, a wide range of laboratory and modeling efforts in studying components, sources, formation and climatic impacts of oceanic aerosols have emerged (Fu et al., 2011; Kang et al., 2017; Miyazaki et al., 2011; Mochida et al., 2011; O'Dowd and de Leeuw, 2007; O'Dowd et al., 2004; Quinn et al., 2017; Roelofs, 2008). Yet due to the limited accessibility of marine aerosol samples, the nature and sources of chemicals in the marine air and relevant climatic effects remain poorly understood. As a strong complement of observation or sampling data, chemical transport models (CTMs) can give more detailed information about PM and gases species at high resolution on regional scale, overcoming the spatial and temporal limitations of traditional approaches based on fixed ambient monitors and field sampling campaigns (Bell, 2006; Bravo et al., 2012; Garaga et al., 2018; Zhang et al., 2014a). The Community Multiscale Air Quality (CMAQ) model is one of the most widely used regional CTMs (Kota

et al., 2018; Simon et al., 2012; Zhang et al., 2014a). To date, CMAQ model has been applied worldwide to clarify the spatial distribution, chemical composition, source origins, seasonal variations and formation mechanisms of PM2.5 (Guo et al., 2017; Hu et al., 2015; Zhang et al., 2014b). However, very few studies have been reported for simulating atmospheric particles with CMAQ model in marine and coastal regions despite its prominently globally climatic role (Gantt et al., 2010; Kelly et al., 2010). East China Sea (ECS) is a typical region for studying marine aerosols and estimating the influence of Asian dust and other continental air pollutants on the remote ocean (Hsu et al., 2010; Kang et al., 2017). Therefore, the CMAQ model was applied in this study to investigate the characterization and sources of PM2.5 and its major components over the ECS as well as the adjacent mainland and coastal regions during May to June 2014. This study provides useful information on differences between terrestrial and marine aerosols, and the impacts of continental emissions on the atmosphere over the western North Pacific Ocean. 2. Methodology 2.1. Model description This study applied the source-oriented CMAQ model version 5.0.1 with the SAPRC11 photochemical mechanism and the aerosol module version 6 (AERO6). Original CMAQ model was modified to improve its ability to predict secondary inorganic aerosols (SIA) and secondary organic aerosols (SOA). Specifically, the photochemical mechanism was modified to give more detailed treatment of isoprene oxidation chemistry (Ying et al., 2015), and pathways of SOA formation from surface uptake and additional heterogeneous reactions of SO42− and NO3− formation were included (Guo et al., 2017; Hu et al., 2016). The tagged non-reactive tracer method was used to estimate the contributions to primary PM (PPM) components. The emission rates of tracers were set to be 0.001% of total PM2.5 emissions from the corresponding source and they go through the same atmospheric processes. This fraction was small enough for not changing particle mass and size distribution (Hu et al., 2015). Then, the predicted PPM concentration from a given source is obtained by scaling the simulated tracer concentration by 105 (Guo et al., 2017). The source contributions to SIA were estimated using tagged reactive tracers. The aerosol modules and photochemical mechanism were expanded so that SO42−, NO3−, NH4+ and their precursors from different sources can be tracked separately throughout the model calculations. The below example provides a conceptual scheme of the sourceoriented nitrate formation through gas phase reactions of NO2 with hydroxyl radical (OH): NO2 i þ OH→HNO3 iðgÞ↔NO− 3 i ði ¼ 1; 2; …nÞ where NO2_i represents NO2 emitted from source sector i, HNO3_i and NO3−_i represent gas phase and particulate nitrate products of NO2_i, respectively. n is the total number of source types tracked. All SIA precursors, related gases and aerosol processes were treated in a similar way. More details can be found in previous studies (Guo et al., 2017; Qiao et al., 2015; Zhang et al., 2012, 2014b). It should be noted that sources of SOA was not tracked in this study as the predicted SOA concentration was low and with large uncertainties

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(Hu et al., 2017b; Ying et al., 2015; Zhang and Ying, 2011). SOA is regarded as a separate source (Guo et al., 2017). 2.2. Model application Fig. 1 shows the nested domains for the simulation in May and June 2014. The 36 km domain covers most part of East Asia, and the 12 km domain covers the ECS and coastal areas. Meteorology inputs were generated by the Weather Research & Forecasting model (WRF) v3.8.1, and anthropogenic emissions from transportation, residential activities, power, industry and agriculture were based on Multi-resolution Emission Inventory for China (MEIC) (http://www.meicmodel.org). Annual ship emission along the coast of China was obtained from Liu et al. (2016). Annual/monthly emissions were processed to hourly using temporal profiles specific to sources (Olivier et al., 2003; Streets et al., 2003; Wang et al., 2010). The Model for Emissions of Gases and Aerosols from Nature (MEGAN) was used for biogenic emissions, and open biomass burning emissions were generated from the Fire Inventory from NCAR (FINN) (Wiedinmyer et al., 2011). Emissions from windblown dust and sea salt were calculated inline (Hu et al., 2015). The default vertical distributions of concentrations that represent clean continental conditions were used for initial and boundary conditions of the 36 km domain, and predictions in the 36 km domain provide the boundary conditions for the 12 km domain. The first 3 days were used as spin-up and excluded from the analysis (Hogrefe et al., 2017). 2.3. Model evaluation Model predictions were evaluated against observational data from spring cruise sampling campaigns in the ECS and coastal cities in China. The ship track is shown in Fig. 1, and more details of the cruise sampling campaign can be found in a previous study (Kang et al., 2017). The total OC and EC in aerosol samples collected over the ECS were determined using thermal optical reflectance (TOR) following

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the Interagency Monitoring of Protected Visual Environments (IMPROVE) protocol on a DRI Model 2001 thermal/optical carbon analyzer. Filters aliquot of marine aerosol samples over the ECS were extracted with 20 mL ultrapure water under ultra-sonication for 30 min, then the solution was passed through a 0.22 μm filter head before Ion Chromatography (Dionex ICS900) analysis to get NH4+, NO3−, and SO42− concentrations. Hourly observations of PM2.5 and O3 from May to June 2014 at 53 stations in 8 coastal cities (see Fig. 1) were obtained from the publishing website (http://beijingair.sinaapp.com/). Observations at multiple sites in one city were averaged to calculate mean concentrations of the city. Statistical indexes of mean normalized bias (MNB) and mean normalized error (MNE) were used for O3 performance, while mean fractional bias (MFB) and mean fractional error (MFE) were utilized to evaluate PM (EPA, 2001; Hu et al., 2017a). 3. Results and discussion 3.1. Model validation 3.1.1. Meteorology The predicted meteorological factors including temperature (T2) and relative humidity (RH) at 2 m above surface, and wind speed (WS) and wind direction (WD) at 10 m above surface were compared with observations at 67 stations in the 12 km domain from National Climatic Data Center (NCDC) (see Table S1). In general, the meteorology model performance statistics in this paper is comparable to other previous reports using WRF in China (Hu et al., 2016; Zhang et al., 2012) and are similar to the recommended benchmarks by Emery et al. (2001). 3.1.2. Particulate pollutants in marine aerosols Total suspended PM (TSP) samples were collected on a cruise in the ECS and components were analyzed. In this study, the predicted PM2.5 components were scaled based on the ratios of TSP to PM2.5 from literature (Table S2). Median values of these ratios were used to calculate

Fig. 1. Model domains with locations of coastal cities and the cruise track. The blue rectangle indicates the 12 km domain. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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Fig. 2. Predicted and observed concentrations of TSP components in marine aerosols during the day (left) and night (right) over the East China Sea (ECS) in the 12 km domain. The top and bottom of purple areas are scaled PM2.5 results using 91th and 9th percentiles of TSP/PM2.5 ratios, and MFB values are shown at upper-right. Observation data near coastal waters is shown as blue solid points. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

statistical indexes. The 9% and 91% percentiles of the ratios for each component serve as ratio ranges for showing uncertainties. Fig. 2 shows that the predicted values well capture the variation trend of observed PM and its components over ECS during day and night. CMAQ model worked relatively better in coastal waters compared to the remote ocean, where concentrations are generally underpredicted probably due to low concentration levels in open seas. Higher abundance of predicted PM, OC, EC, NO3−, NH4+, and SO42− appeared in samples collected near coastal areas, consistent with the observational data and previous reports, demonstrating the substantial influence of terrestrial emissions (Kang et al., 2017). Among all components, SO42− shows the best model performance with the smallest MFB values, followed by EC. However, sulfate is still under-estimated especially for the remote oceanic area probably due to lack of dimethyl sulfide (DMS) emissions from ocean (Ma et al., 2005; Yang et al., 2009). The predictions of PM, OC, NO3− and NH4+ are lower than observations especially in the regions far from the mainland (see Fig. 1 for ship track), suggesting potential emission underestimation. PM and OC share similar overall model performance. NO3− predictions are substantially lower than the relevant observations, probably related to the generally low concentrations of nitrate. Under-prediction of NOx emissions in the inventory may also play an important role (Hu et al., 2016, 2017a). The model performance also exhibits slight diurnal variations as suggested by MFB values. Except for NH4+ and SO42−, other species present a slightly better performance at night. 3.1.3. Model performance at coastal cities Fig. 3 illustrates the model performance on PM2.5 at coastal cities. Overall, the model predicts well PM2.5 concentrations in most coastal

cities with MFB less than ±0.60, although the predictions are slightly lower as indicated by negative MFB. Hourly trends in most coastal cities are generally well captured. The model performance at different coastal cities varies probably due to different emissions, meteorology and topography. Fig. S1 shows that the variation of O3 is generally well captured although some of the peaks in the observations are missed. The model performance for Wenzhou, Fuzhou and Quanzhou meets the O3 criteria recommended by U.S. EPA with MNB values well within ±0.15. However, the predicted O3 concentrations in other cities are relatively lower than the mean observations, likely attributable to the underestimation of emission inventories in these coastal cities and uncertainty in meteorology may play a role as well (Hu et al., 2016). 3.2. Temporal and spatial variations of PM2.5 and its components 3.2.1. Temporal distributions Fig. 4 illustrates the predicted PM2.5 and the ratios of major components to total at three locations in the north, middle and south areas of ECS during May to June 2014. PM2.5 abundance varies greatly with time in the north of ECS (N). Higher levels of PM2.5 from late May to early June occur in the middle (M) and south (S) of ECS. In general, the PM2.5 abundance in three locations shows decreasing trend from north to south of the ECS with mean concentrations of 27.0, 7.65 and 1.56 μg m−3, respectively, indicating a substantial influence of terrestrial emissions to the north of ECS. SO42− is the major species at three locations with fraction over 0.4 for most days, especially for site S in the southern ECS. Generally, SO42− ratio increased from north to south over the ECS. Moreover, the abundance of non-sea-salt SO42− (nss-SO42−) was

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Fig. 3. Predicted and observed PM2.5 (μg m−3) in the coastal cities from May to June 2014. The purple line indicates predication and green dots represents observation. MFB values of PM2.5 are shown at upper-right. PM model performance criteria are in the range of ±0.60 for MFB according to U.S. EPA suggestion. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

calculated by subtracting sea-salt sulfate (ss-SO42−) from the total sulfate using the typical sulfate-to sodium mass ratio of 0.252 in seawater (Yang et al., 2015). Episode averaged nss-SO42− are much larger than ss-SO42−

for the three locations, agreeing well with previous results in the Bohai Sea and northern Yellow Sea (Yang et al., 2015). Both nss-SO42− and ss-SO42− ratios display an increasing trend from north to south of ECS. 150

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Fig. 4. Ratios of major components to total PM2.5 (left y-axis), concentrations of PM2.5 (right y-axis), and episode averaged ratios (pie graphs) for site N in the north (a), site M in the middle (b), and site S in the south (c) of ECS during May to June 2014.

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Interestingly, EC ratio slightly decreased from north to south, and relatively higher ratios primarily occur in June for north and middle of ECS (N and M), but in May for the southern location (S). Compared to the north and middle of ECS, relatively higher SOA proportions occur in the south of ECS (site S), where SOA ratios are basically N0.05. NO3− ratio showed a significantly decreased trend from north to south similar to PM2.5 and EC. NH4+ and primary organic aerosol (POA) are very close on most of the days in all three sites. 3.2.2. Spatial distributions Fig. 5 shows the predicted regional distribution of PM10, PM2.5 and its components, in the 12 km domain during the day and night. Both PM10 and PM2.5 have higher concentrations in the Yangtze River Delta (YRD) and North China Plain (NCP). EC accounts for 4.50% of total PM2.5, showing higher levels in the YRD. High POA levels are mainly distributed in the NCP. SOA concentrations (b3 μg m−3) are generally low compared to other components, with high levels evenly distributed in the Yangtze Plain, including Anhui, northern Jiangxi and western Zhejiang Province. POA is more abundant (12.3%) than SOA (3.70%), but lower than secondary inorganic aerosols (SIA) (i.e., total of SO42−, NO3− and NH4+) (59.5%), reflecting that SIA is the most significant

Daytime

components of PM2.5 in this region. High SIA concentrations mainly occur in NCP and YRD. Specifically, NO3− and NH4+ exhibits larger abundance in NCP and YRD, and SO42− presents significantly higher levels in YRD. SO42− is the most abundant species among SIA accounting for 40.9% of predicted total PM2.5 and 68.8% of total SIA mass, and the highest SO42− concentrations are N20 μg m−3 in YRD, comparable to a previous report (Hu et al., 2017a). NO3− and NH4+ account for 6.53% and 12.0% of total PM2.5 predictions, respectively. Concentrations of these constituents along the east coast are usually low, consistent with previous modeling work in China (Zhang et al., 2012). Moreover, both PM and its components exhibit higher concentrations in the mainland than in the ocean, and their concentrations decrease with the distance from the continent, indicating outflow of terrestrial air masses to the marine air. Except for SOA and SO42− without significant diurnal change, higher concentrations of PM10, PM2.5 and its other components in 12 km domain occur at night, probably associated with descent of planetary boundary layer (PBL) (Liu et al., 2018; Qu et al., 2017), and the land/sea breeze effect especially at coastal locations may also makes contributions (Fung et al., 2005; Li et al., 2017). Fig. 6 shows regional distribution of PM10, PM2.5 and its components along with the averaged wind vectors for the day with relatively higher

Nighttime

Fig. 5. Predicted daytime (left) and nighttime (right) PM10, PM2.5 and its components (μg m−3) during May to June 2014.

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Fig. 6. Predicted daytime (left) and nighttime (right) PM10, PM2.5 and its components (μg m−3) and wind vectors on June 4, 2014.

concentrations based on field samples far from mainland, which reflects the remarkable influence of inland air masses on the atmosphere in southeastern China and the remote sea as indicated by wind patterns. It shows that a tropical cyclone passed this region and caused this significant off-land transport. Besides, the high concentrations are evidently associated with low wind speed unfavorable to pollutant dispersion. In particular, the outflows of SOA, SO42− and NH4+ to the ocean are very significant, demonstrating that continental emission sources of these species make tremendous contributions to the marine air likely ascribed to their universally secondary nature. However, NO3− of continental origin only poses a lesser impact to marine air compared to other secondary components, possibly suggesting that regional transport effect of NO3− is not that strong. Previous studies also found that continental aerosols greatly affected the marine atmosphere in western North Pacific region through long-range atmospheric transport (Kang et al., 2017; Nakamura et al., 2005; Uematsu et al., 2010; Zhou et al., 1990). 3.3. Source apportionment of marine aerosols Fig. 7 shows the emission distribution of each source sector to total PM2.5 during the whole simulation period in 12 km domain. Higher

residential emissions are in NCP with a large population density. Transportation, agricultural activities, and power plants show large emissions mainly in YRD. Industry sector, the largest contributor, has a significant contribution in YRD and NCP, suggesting that the reduction of industry source emissions is essential for improving the air quality of these regions. Ship emission mainly contributes to the coastal and offshore atmosphere with the highest concentration over coastal waters near YRD, reflecting the heavy traffic to and from the busy ports in YRD region especially during daytime (Fig. S2). Not surprisingly, sea salt sector makes a greater contribution in marine areas, while other emission intensity decreases with distance from the mainland. Wildfire sector has high concentrations in NCP. SOA sector distributes evenly in the eastern Yangtze Plain (i.e., Anhui, northern Jiangxi and western Zhejiang Province). Except for ship emission and SOA, other sectors generally exhibit larger emissions at night (Fig. S2), probably on account of less mixing due to lower PBL heights and increasing emissions at night. Fig. S3 shows the episode averaged contributions of different sources to PM2.5 in the 12 km domain. Industry sector contributes most to total PM2.5 (36.7%), followed by agriculture (10.6%), residential activities (8.2%), power plants (7.1%), ship emission (6.3%) and SOA (5.2%). The contributions from transportation (4.1%), sea salt (1.1%) and wildfire

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Fig. 7. PM2.5 source apportionment in the 12 km domain during May to June 2014. Units are μg m−3.

(2.4%) to total PM2.5 are rather small. The contributions to PPM and SIA are shown in Figs. S4–S10. Industry sector is the dominant source accounting for 54.4% of total PPM mass followed by residential activities (19.6%). Industry is also a dominant source for SIA (28.5%), followed by agricultural emission (18.6%), which contains plentiful NH3 and NO2, precursors of secondary NH4+ and NO3− in SIA (Guo et al., 2017; Paulot and Jacob, 2014). Moreover, industry is the largest contributor to secondary sulfate (32.8%), followed by power plants (9.3%) and ship emissions (8.8%); industry sector is also a major contributor to secondary nitrate (43.1%), followed by transportation (22.5%), power plants (20.9%) and ship emissions (7.6%). Agriculture is the predominant origin of secondary ammonium (89.7%), followed by industry sector (3.2%). 3.4. Comparison of continent and ocean The differences in concentrations and sources of terrestrial and marine aerosols were investigated to see if different control measures are needed. As shown in Fig. 8a, striking difference between continental and oceanic aerosols exists for average concentrations of PM2.5 and its major components, particularly for POA, NO3−, NH4+ and OC with land to ocean ratios of 4.5, 7.7, 3.7 and 4.6, respectively. The land/ocean ratios for SOA and SO42− are 2.9 and 1.9, respectively, slightly lower than other components. Such high land/ocean ratios illustrate mainly terrestrial

origin of these chemical species. SO42− is the predominant PM2.5 species for both marine (51.5%) and continental (33.8%) aerosols with average concentrations of 5.12 and 9.69 μg m−3, respectively, followed by POA (0.92 and 4.12 μg m−3), and NH4+ (1.02 and 3.79 μg m−3). Besides, PM2.5 and its components in continental aerosols consistently present higher levels at night (Table S3), basically in agreement with diurnal variations of the whole domain. In contrast, these constituents in marine aerosols show more abundant concentration during the daytime. Fig. 8b shows the contributions of different source sectors to continental and marine aerosols. Except for sea salt and ship emission, other sectors have much larger emissions in continental regions with generally higher land/ocean ratios, particularly for residential, transportation and wildfire sector with ratios N 9.4. The land/ocean ratio for ship emission is only 0.6, suggesting this sector mainly affects the marine atmosphere, especially over coastal areas as indicated by its spatial concentration distribution (Fig. 7). Ship emission accounts for 13.8% of total PM2.5 in marine aerosols, much larger than its contribution of 2.4% to the continental region (Fig. S11). Not surprisingly, sea salt emission has the lowest land/ocean ratio of 0.2, contributing 2.8% to marine aerosols but only 0.1% to continental air. Also, it can be noticed that industry (40.5%), agriculture (11.9%) and residential activities (10.6%) are three major contributors to terrestrial PM2.5, while industry (29.4%), ship emission (13.8%) and agriculture (8.2%) are main emission sectors for marine PM2.5, exhibiting considerable influences of continental

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SIA in marine aerosols over the ECS, followed by ship emissions. Interestingly, continental aerosols show higher nighttime concentrations of PM2.5 and its components, while marine aerosols exhibit higher daytime levels. Similarly, higher concentrations of total PM2.5, PPM and SIA contributed by each source sector occur at night in continental regions, while all source sectors present higher emissions during daytime in marine areas. Besides, ship emission and sea salt contribute more to marine aerosols compared to terrestrial aerosols, but other sources show larger contributions to terrestrial regions. The study demonstrates the ability of CMAQ model to predict PM2.5 and its components over the ECS and adjacent regions and improves the understanding of abundances of major PM constituents as well as source contributions to marine aerosols in the western North Pacific Ocean. This study helps understand the transport and accumulation of air pollutants over the ECS and adjacent regions, which would provide valuable information for reducing adverse effects of aerosols. Acknowledgments Portions of this research were conducted with high performance computing resources provided by Louisiana State University (http:// www.hpc.lsu.edu). This research was supported by the China Scholarship Council (File No. 201606040106). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2018.10.174. References

Fig. 8. Comparison of components (a) and sources (b) of PM2.5 over continental and oceanic regions in the 12 km domain during May to June 2014.

origins to marine air. On the whole, sea salt and ship emission contribute more to marine aerosols, while other sources make larger contributions to continental aerosols. Interestingly, each source sector of continental PM2.5 has larger emissions at night, while in marine region they tend to contribute more during daytime (Table S4). Similar to PM2.5, SIA and PPM have much higher levels in continental aerosols than marine aerosols (Fig. S12). Industrial and residential activities are two major sources for continental PPM, while industrial and ship emissions are two main contributors to marine PPM. For SIA, industry and agriculture are the dominant emission sources in both terrestrial and marine regions, but ship emission contributes significantly to marine SIA as well. PPM and SIA in terrestrial and marine aerosols contributed by different source sectors share the same diurnal variations as total PM2.5 (Table S5). 4. Conclusion Characteristics and sources of PM2.5 over the ECS were investigated using a source-oriented version of CMAQ. Model predictions generally agree well with observations from coastal cities and the ECS. Higher levels of PM2.5 and its components mainly occur in the mainland (e.g., NCP and YRD) rather than oceanic regions, suggesting outflows of terrestrial aerosols to the remote ocean through long-range atmospheric transport. SO42− is the most significant component of PM2.5 over the ECS with an average concentration of 5.12 μg m−3, followed by NH4+ (1.02 μg m−3) and POA (0.92 μg m−3). The industry sector is the dominant source of PM2.5 and PPM over the ECS, followed by ship emissions. Industry and agriculture sectors are two major sources for

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