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Spatial and Seasonal Dynamics of Ship Emissions over the Yangtze River Delta and East China Sea and Their Potential Environmental Influence Qianzhu Fan,† Yan Zhang,*,† Weichun Ma,† Huixin Ma,‡ Junlan Feng,† Qi Yu,† Xin Yang,† Simon K. W. Ng,§ Qingyan Fu,∥ and Limin Chen† †

Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, P.R. China ‡ School of Computing Science, Fudan University, Shanghai 200433, P.R. China § Civic Exchange, 23/F, Chun Wo Commercial Centre, 23-29 Wing Wo Street, Central, Hong Kong ∥ Shanghai Environmental Monitoring Center, Shanghai, 200030, P.R. China S Supporting Information *

ABSTRACT: The Yangtze River Delta (YRD) port cluster is one of five major port clusters in China and is home to Shanghai port, the largest port worldwide. In this study, an automatic identification system-based model was built to estimate the ship exhaust emissions in the YRD and the East China Sea within 400 km of the coastline. In 2010, the total emissions of SO2, NOX, and PM2.5 were 3.8 × 105 tonnes/yr, 7.1 × 105 tonnes/yr, and 5.1 × 104 tonnes/yr, respectively. More than 60% and 85% of the ship emissions occurred within 100 km and 200 km of the coastline, respectively. Ship emissions also showed distinct seasonal variability. The emission of SO2 and NOX by ships in hot spots, such as ports and vessel traffic hubs was much higher than that on land, with maximum SO2 and NOX intensities from ships that were 36 times and 17 times greater, respectively, than the maximal landbased emissions. The potential impact of ship emissions at six hot spots on the surrounding atmospheric environment was estimated with the HYSPLIT model. Our study demonstrated that ship emissions have an important impact on both the entire YRD region and on greater East China.

1. INTRODUCTION

China and the East China Sea and their potential influences should be addressed scientifically as soon as possible. The establishment of a ship emission inventory is the basis of research on ship air pollution and its related issues. The first top-down ship emission inventory was performed for the Emission Database for Global Atmospheric Research, EDGAR,10,11 and was constructed from major global shipping routes and traffic intensities. Alternatively, the bottom-up method focuses on relatively precise shipping routes, and the emissions estimates are based on vessel activity. Wang et al.12 synthesized these two methods to build the Waterway Network Ship Traffic, Energy and Environment Model (STEEM) and suggested that the International Comprehensive OceanAtmosphere Data Set, ICOADS, exhibited bias when used on a small scale. The spatial and temporal resolutions in the ship emission inventories need to be improved. Automatic

Asian ports, which handle more than 50% of the world’s cargo throughput, developed rapidly from 2000 to 2009.1 Over the past two decades, the Pacific Northwest has played an important role in the volume of ocean transport around the world.2 The Yangtze River is called the “golden canal” to the East China Sea, playing a very important role in the waterway traffic in China. The Yangtze River Delta (YRD) port cluster, one of the five major clusters in China, is increasingly busy and features a dense distribution of ports and a large throughput. Simultaneously, China is now facing heavy regional air pollution and an increasing number of haze days.3 The influence of ship emissions on air quality has been identified worldwide.4−8 Zhao et al.9 researched Shanghai’s port and noted that the air pollution level of Yangshan Island was comparable with that of land. Influenced by ship emissions, the maximum concentration of ship-related pollutants is higher than the land’s average. Eastern China lies in the perennial monsoon region, and summer monsoons can help transmit ship pollutants to inland areas. The amount of ship emissions in East © 2015 American Chemical Society

Received: Revised: Accepted: Published: 1322

August 19, 2015 December 15, 2015 December 24, 2015 December 24, 2015 DOI: 10.1021/acs.est.5b03965 Environ. Sci. Technol. 2016, 50, 1322−1329

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Environmental Science & Technology

Figure 1. Sketch map of the study area (119°E to 125°E and 27°N to 36°N, R0, inland; R1, 100 km offline; R2, 100−200 km offline; R3, 200−300 km offline; R4 > 300 km offline).

In addition, the difference in emission factors between domestic vessels and ocean-going vessels, which is caused by the fuel sulfur content, is considered. Furthermore, by both comparing ship emissions with land-based emissions and analyzing airflow trajectories from key points in regions with intense ship emissions, this study shows the potential impact of ship emissions on the surrounding atmospheric environment. Through these analyses, we intend to better understand the implications of ship emissions in the Yangtze River Delta port cluster and East China Sea, which is one of the busiest shipping activity areas.

Identification System (AIS) installed on ships does regularly report instantaneous information such as their call numbers, location, speeds, and navigation status via radio.13−16 Using AIS data, the establishment of ship emission inventory has undergone a revolutionary change. Jalkanen et al.17 established the first ship emission inventory based on AIS data from the Baltic Sea. Goldsworthy et al.18 also built a ship emission model system based on AIS data and estimated the ship emission inventory for the seas surrounding Australia. The work on ship emission inventories in China has focused on the ports of Shanghai, Shenzhen, and Hong Kong. Yang et al.19 estimated an emission inventory for marine ships in the Shanghai Port in 2003 using annual activity rates multiplied by emission factors. Fu et al.20 calculated the total amount of air pollutant emissions in Shanghai port, and spatially gridded it into the routes defined by one-month AIS activity data with space to make full use of the AIS data. Li et al.21 roughly estimated the SO2 emissions in Shenzhen port and ship emissions in Hong Kong. Ng et al.22 established a ship emission inventory in Hong Kong based on AIS data. Earlier studies have focused on estimates of global-scale ship emissions and local-scale ship emissions such as a single port, but few studies have focused on regional port-cluster ship emission inventories.16,18 This topic remains unstudied in China. Additionally, the previous studies have done a lot of work in building the ship emission inventories, whereas few studies describe the detailed spatial and seasonal dynamics of ship emissions or their overall potential environmental influence. The focus of our work is on the spatial and seasonal dynamics of ship emissions and their potential environmental influence. In this study, the first high-resolution (1 × 1 km2) regionalscale ship emission inventory in Yangtze River Delta port cluster and East China Sea is reported. We have built an AISbased model to estimate the ship exhaust emissions in the YRD and the East China Sea within 400 km of the coastline. Lloyd’s database and the China Classification Society (CCS) database were used as the main sources of data on vessel characteristics.

2. METHODOLOGY AND MATERIALS 2.1. Study Area. The YRD region, including Shanghai, Jiangsu, and Zhejiang, is the core economic zone of China and also the confluence of the coastal shipping route and inland water transportation on the Yangtze River. The YRD coastal port cluster is composed of more than 15 ports, including Shanghai port, Ningbo-ZhouShan port, Zhenjiang port, Nantong port, Lianyungang port, Taizhou port, and Wenzhou port. As the center of the YRD port cluster, Shanghai port and Ningbo-ZhouShan port have served as the largest two container ports in the world since 2013. The throughput of these two ports also ranked among the largest ten ports in the world in 2014. The East China Sea with the YRD port cluster is one of the busiest ship lane regions in the world. Other studies from around the world have found that nearly 70% of ship emissions occur within 400 km of coastlines.5,23−25 Figure 1 is a sketch map of the study area in this research, from 119°E to 125°E and 27°N to 36°N (approximately 400 km of coastline). 2.2. AIS Activity Data and Ship Data. By analyzing the domestic and international ship emission inventory model, an AIS-based interpretation and analysis model was built. In this study, a 175-day AIS data set was chosen, and includes the months of February (28 days), April (30 days), June (30 days), September (30 days), November (30 days) and December (27 days) in 2010. The following types of exhaust emissions were 1323

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to 1.5% and that of ocean-going vessels to 2.7% .The amount of SO2 emitted is directly affected by the sulfur content of the fuel; therefore, when ME emissions were estimated by the model, the emissions of domestic vessels should be amended correspondingly. The ME category was sorted into slow speed diesel (SSD), medium speed diesel (MSD), and high speed diesel (HSD) based on their RPM, and the largest AE category was MSD. The type of engine was judged first according to the RPM of the ME in Lloyd’s database. These criteria are discussed further in the SI. The emission factors of the different types of engines differ considerably. The ME emissions changed distinctly when the ME load factor was below 20%, and low load adjustment multipliers (LLAM) were applied to the ME emission factors. The LLAM are shown in the SI (Table S3).31,33 The control factors from the literature used in this research are provided in the SI.

calculated: SO2, NOX, CO, NMVOC, PM10, PM2.5, OC, EC, V, and Ni. The original AIS data obtained from the Marine Department were transmitted in the form of 6-bit ASCII compressed code, which cannot be read directly. Therefore, the original AIS data must be decoded and screened to obtain 27 different types of dynamic and static information. Depending on the speed and turning rate of the ship, the dynamic AIS information is recorded at intervals ranging from 2 s to 3 min, and the temporal resolution of the ship emission inventory can therefore reach the minute scale. In this study, we calculated the position of ships at 5 min intervals based on the AIS data and compiled a 1 × 1 km2 resolution grid of ship emission inventories for the East China Sea. We focused on the yearly and monthly scales to present the spatial patterns in the ship emissions. Furthermore, the higher temporal resolution ship emission inventory could be used to investigate ship plume cases around Shanghai port in future. The International Maritime Organization (IMO) requires AIS to be installed aboard international voyaging ships with a gross tonnage (GT) of 300 or more, and passenger ships of all sizes.26 Under the regulations of the China Maritime Safety Administration in 2010, Chinese vessels with a GT of 200 or more traveling in coastal waters or with a GT of 100 or more traveling on rivers, excluding fishery ships, public service ships, sports ships, and military ships, are required to have AIS equipment installed.27 This research involved 34 874 vessels, for which the following data were mainly obtained from Lloyd’s database: ship name, ship type, date of construction, flag name, revolutions per minute (RPM) of the main engine, speed, total kilowatts of the main engines (ME), total power of the auxiliary engines (AE), and gross tonnage. For some domestic ships unavailable in Lloyd’s database, the engine data are assumed to be 7000 kw, based on the combination of the East China Seagoing ships in Lloyd’s register (with an ME power mainly ranging from 11 000 kw to 14 000 kw) and domestic ships from the CCS (with an ME power mainly ranging from 4000 kw to 6000 kw). 2.3. Estimation of Ship Emissions. 2.3.1. Method. We built a marine vessels emission inventory using an activity-based method. The technical details are described in S.1 of the Supporting Information (SI). The actual speeds and operation times of the ships involved in the estimation of ship emissions can be obtained accurately from AIS data. The installed power of the ME, AE, and auxiliary boiler (AB) and the maximum speed of the ships, can be supplied from Lloyd’s database and other data sets. Emission factors, control factors and fuel correction factors are acquired through the literature. The details of these factors are listed in S.2−4, and the sources of uncertainty are discussed in S.5. 2.3.2. Emission Factors and Control Factor. The two most common fuel oils used in ships are residual oil (RO) and marine distillates (MD). In general, RO is used in the ME,27 and the fuel sulfur content is approximately 2.7%, MD is used in the AE, and the sulfur content is approximately 0.5%. Emission factors are shown in Table S1.13,18,28−32 On the basis of data on ships passing by the Port of Shanghai provided by the largest Chinese heavy fuel oil (HFO) supplier, China Marine Bunker (CMB), the sulfur content of the fuel used by the MEs in domestic vessels ranges from 0.2% to 2.0%, and the sulfur content of the fuel used by the MEs in ocean-going vessels ranges from 1.9% to 3.5%. In this study, we adjusted the sulfur content of the fuel used by the MEs in domestic vessels

3. RESULTS AND DISCUSSION 3.1. Spatial Distribution. 3.1.1. Distribution Characteristics of Ship Emissions. Using the AIS-based model, we produced a ship emission inventory with a resolution of 1 × 1 km2 for the YRD port cluster and the East China Sea in 2010. The spatial distributions of SO2 in ship emissions in 2010 are shown in Figure 2. For NOX, CO, NMVOC, PM10, and PM2.5, the emission maps are shown in Figure S1. The spatial distributions of OC, EC, V, and Ni in PM2.5 are shown in Figure S2.

Figure 2. Spatial distributions of annual SO2 ship emission in 2010. Emissions are given as kg/cell (0.0091°, approximately 1 × 1 km2). Six distinct hot spots in the spatial distribution of ship emissionthree ports and three ship traffic hubs.

Statistically, the average intensities of SO2, NOX, and PM2.5 emissions were 1.0 tonnes/yr/km2, 1.9 tonnes/yr/km2, and 0.14 tonnes/yr/km2, respectively. Emission values greater than 8 tonnes/yr/km2 are common in the heavily frequented fairways of the East China Sea. These values are larger than the value reported for the Baltic Sea (6.3 tonnes/yr/km2, or 6.3 g/yr/m2).16 The highest regional emission intensities of SO2, NOX, and PM2.5 were 1.0 × 104 tonnes/yr/km2, 1.3 × 104 tonnes/yr/km2, and 1.1 × 103 tonnes/yr/km2, respectively, 1324

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Table 1. Statistics of Ship Emissions in Different Regions(Units: tonne/year) (R0, inland; R1, 100 km offline; R2, 100−200 km offline; R3, 200-300 km offline; R4 > 300 km offline) pollutants SO2 NOX CO NMVOC PM10 PM2.5 OC EC V Ni

R0 4.0 7.9 3.5 1.7 6.4 5.8 1.1 8.8 2.7 6.0

× × × × × × × × × ×

R1 104 104 103 103 103 103 103 101 102 101

2.1 3.9 1.7 7.5 3.2 2.9 5.1 4.4 1.3 3.0

× × × × × × × × × ×

R2 105 105 104 103 104 104 103 102 103 102

8.4 1.5 4.8 2.7 1.2 1.0 2.2 1.5 4.9 1.1

× × × × × × × × × ×

R3 104 105 103 103 104 104 103 102 102 102

3.4 6.3 1.9 1.1 4.8 4.3 8.9 6.0 2.0 4.4

× × × × × × × × × ×

R4 104 104 103 103 103 103 102 101 102 101

1.9 3.4 1.1 6.0 2.6 2.3 4.8 3.3 1.1 2.5

× × × × × × × × × ×

total 104 104 103 102 103 103 102 101 102 101

3.8 7.1 2.9 1.4 5.7 5.1 9.7 7.7 2.4 5.4

× × × × × × × × × ×

105 105 104 104 104 104 103 102 103 102

and 200 to 300 km from the coastline, respectively. R4 represents the area more than 300 km offshore. The emissions of SO2, NOX, and PM2.5 across the entire study area were 3.8 × 105 tonnes/yr, 7.1 × 105 tonnes/yr and 5.1 × 104 tonnes/yr, respectively. The ship emission estimates were likely lower than the actual emissions because they were calculated based on the emissions from the main engine (ME) and auxiliary engine (AE). The ship emissions in different regions are shown in Table 1. The amount of SO2 emitted in 2010 across the region of R1 was 2.1 × 105 tonnes, representing 55% of the total emissions. Within R2 and R3, the amount of SO2 emitted was 1.2 × 105 tonnes, representing 32% of the total emissions. More than 85% of the ship emissions occurred within 200 km of the coast, and more than 60% of the emissions occurred within 100 km of the coast. Goldsworthy et al.18 reported that vessels in transit within 200 km of the Australian coast represent approximately 72% of the total ship emissions. Thus, the emissions of transiting ships were quite large. In addition, the ship emissions in inland waters (R0) were also significant (Table 1). R0 was identified as the third largest emissions area among the five regions. The amount of SO2 emitted in the R0 area was 4.0 × 104 tonnes, accounting for 10% of the total ship emissions. The ship emissions in the Yangtze River and the Huangpu River were quite large, accounting for the majority part of emissions from inland waters in the YRD. On the basis of shipping visa data, Fu et al.20 determined that the total amounts of SO2, NOX, and PM2.5 in the vicinity of Shanghai port in 2010 were 3.5 × 104 tonnes/ yr, 4.7 × 104 tonnes/yr, and 3.7 × 103 tonnes/yr, respectively. Within the same geographic area in our study, the emissions of SO2, NOX, and PM2.5 surrounding Shanghai port were 8.2 × 104 tonnes/yr, 1.5 × 105 tonnes/yr, and 1.1 × 104 tonnes/yr, respectively, which are 2.3 times, 2.6 times, and 3.0 times greater than Fu’s estimates. These differences may be due to differences in ship activity between the visa data and the AIS data because we considered both visa ships and transit ships in our study. Moreover, transit ships passing by Shanghai without registration in the visa data were included in the AIS data, and their emissions were consequently included in our study. 3.2. Seasonal Variability in Ship Emissions. The ship emissions were analyzed for seasonal patterns based on data from the months of April (spring), June (summer), September, and November (autumn), and February and December (winter) in 2010. According to the 2010 data, the ship emissions in spring were the greatest, accounting for more than 26% of the annual ship emissions. No significant differences in the total emission quantities were observed among summer,

located in the vicinity of P5 (Figure 2). In addition, the greatest emission intensity of SO2 in our study was approximately 9.6 times and 52 times higher than the highest values reported for Hong Kong (∼1.1 × 103 tonnes/yr/km2) and Australia (∼2 × 102 tonnes/yr/km2), respectively.18,22 Wang et al.34 estimated global ship emission inventories in 2002 and found that ship emissions in the YRD were lower than in Hong Kong and Australia, illustrating the marked rise in shipping activities in the Yangtze River Delta since 2008. This increase has also been documented by Tournadre,2 who studied the growth of shipping traffic in the YRD through altimeter data analysis. Six distinct hot spots are present in the spatial distribution of the ship emissions in our study and include areas proximal to ports and traffic hubs. The three ports are those of Liangyun (P1), Shanghai (P2), and Ningbo (P3). The average respective emission intensities of SO2, NOX, and PM2.5 were 0.21 tonnes/ yr/km2, 0.42 tonnes/yr/km2, and 0.03 tonnes/yr/km2 for the P1 region; 24 tonnes/yr/km2, 40tonnes/yr/km2, and 3.1 tonnes/yr/km2 for the P2 region; and 13 tonnes/yr/km2, 26 tonnes/yr/km2, and 1.8 tonnes/yr/km2 for the P3 region. The traffic hubs were classified by their relative locations in the north−south shipping lanes along the coastlines relative to the Yangtze River. Thus, the three ship traffic hubs are the northern hub (P4), the intersection of north−south and east−west lanes (P5), and the southern hub (P6), as shown in Figure 2. Near the northern hub of the coastal shipping lanes (P4 in Figure 2), the average emission intensities of SO2, NOX, and PM2.5 were 8.7 tonnes/yr/km2, 22 tonnes/yr/km2, and 1.5 tonnes/yr/km2, respectively. At the intersection hub of the coastal shipping lanes and the Yangtze River Channel (P5 in Figure 2), the average emissions intensities of SO2, NOX, and PM2.5 were 7.1 tonnes/yr/km2, 17 tonnesyr/km2, and 1.2 tonnes/yr/km2, respectively. The greatest regional emission intensities occurred in the P5 region. At the south hub (P6 in Figure 2), the average emissions intensities of SO2, NOX, and PM2.5 were 1.3 tonnes/ yr/km2, 2.4 tonnes/yr/km2, and 0.16 tonnes/yr/km2, respectively. In the Baltic Sea, Hong Kong, and Australia, ship emission hot spots have been shown to be commonly concentrated at the ports.16,18,22 However, traffic between the ports also appears to have a significant effect on the regional variabilities, especially at the mouth of the Yangtze River Delta, where the “throat” of the golden channel to the sea is located. 3.1.2. Statistics on Ship Emissions at Different Distances from the Coastline. We divided the study area (approximately 400 km of coastline) into five regions according to the distance offshore (Figure 1). R0 represents the inland area adjacent to the coastline. R1 represents the marine space within 100 km of the coastline. The marine areas R2 and R3 represent 100 to 200 1325

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variability in the spatial distribution. In spring (Figure 3a), ship emissions in coastal areas around Jiangsu, located in the vicinity of P4 (Figure 2), were approximately 2 times higher than the average. Emissions around P2, P3, and P5 (Figure 2) were approximately 1.5 times higher than the average. In summer (Figure 3b), significant emissions occurred in water traffic lanes far from land due to long-distance ships; these emissions were approximately 4 times higher than the average. Similarly, emissions around P4 (Figure 2) were approximately 2 times higher than the average. In autumn (Figure 3c), emissions around P2, P3, P5, and P6 (Figure 2) were approximately 2 times higher than the average. In winter (Figure 3d), ship emissions in the inshore area around P1 (Figure 2) were 2 or 3 times higher than the average, and the emissions around P6 (Figure 2) were approximately 2 times higher than the average. The higher ship emissions in the P4 region in spring and summer will have a clear impact on the air quality in Shanghai. Although the variation in ship emissions among different seasons is small, Figure 3 shows a distinct seasonal variability in the spatial distribution. All of the spatial and seasonal distributions of ship emissions together exert a combined influence on the regional coastal air quality. The variability in spatial distribution affects the accuracy of the numerical simulation results. 3.3. Emissions from Different Ship Types and Activity Conditions. During the development of the model, the pollutants were sorted by type, ship emission source, ship type, and navigational status. The ship types can be divided into five categories: container cargo ship, noncontainer cargo ship, oil tankers, passenger ships, and other types of ships. Figure S3 shows the contributions of the different types of ship. The container cargo ships emitted 1.4 × 105 tonnes, 2.3 × 105 tonnes, and 1.7 × 104 tonnes of SO2, NOX, and PM2.5, respectively, and the noncontainer cargo ships emitted 1.2 × 105 tonnes, 2.5 × 105 tonnes, and 1.8 × 104 tonnes of SO2, NOX, and PM2.5, respectively. In terms of total emitted SO2, NOX, and PM2.5, container cargo ships emitted 33−38%, noncontainer cargo ships 32−36%, oil tankers 24−26%, passenger ships 1−2%, and other types of ships 4−6%. Approximately 69% of the ship emissions were produced by container and noncontainer cargo ships, and 35% of these emissions were produced by noncontainer cargo ships, which

autumn and winter, each of which contributed 23−25% of the annual emissions. This pattern is generally consistent with other seasonal studies. Corbett et al.23 reported that, globally, seasonal variations in emissions are small. Similarly, Jalkanen et al.16 reported that, in the Baltic Sea, the variation in total emissions among different seasons is small, although ship emissions are greatest during summer. The seasonal variability in ship emissions was estimated based on the six-month average value as the baseline and using SO2 as the example. Figure 3 represents the spatial distribution

Figure 3. Spatial distribution maps for the seasonal variability of ship emissions based on the monthly average values. Parts (a), (b), (c), and (d) represent spring, summer, autumn, and winter, respectively.

maps for the ratio of ship SO2 emissions in spring, summer, autumn, and winter. This figure shows a distinct seasonal

Table 2. Total, Mean and Maximum Ship Emission Fluxes in Land-Based Jiangsu, Shanghai, and Zhejiang Provinces and the Marine Regiona land-based

SO2 Jiangsu

total mean max

Shanghai

total mean max

Zhejiang

total mean max

water-based

SO2 inland water and east China sea

a

NOX

1.3 × 106 1.4 × 101 1.5 × 102 SO2 2.2 × 105 3.3 × 101 2.8 × 102 SO2 6.4 × 105 8.4 9.5 × 101 total mean max

3.8 × 105 1.0 1.0 × 104

PM2.5

2.2 × 106 2.3 × 101 2.0 × 102 NOX 5.1 × 105 7.7 × 101 7.7 × 102 NOX 1.5 × 106 1.3 × 101 1.5 × 102 NOX 7.1 × 105 1.9 1.3 × 104

7.3 × 105 8.4 4.2 × 101 PM2.5 8.0 × 104 1.5 × 101 6.6 × 101 PM2.5 3.5 × 105 3.9 3.7 × 101 PM2.5 5.1 × 104 0.14 1.1 × 103

Total emissions are given as tonne/yr; and the average values and maximum values are given as tonne/yr/km2. 1326

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Figure 4. Potential effect of ship emissions on the surrounding atmospheric environment in summer season, the affecting duration for each grid is estimated by airflow retention time (hours) starting from ports and ship emission hub points.

were primarily concentrated in inland waterways. Ng et al.22 reported that approximately 80% of ship emissions were produced by container vessels in Hong Kong. Jalkanen et al.16 reported an interesting feature of the ship traffic in the Baltic Sea area: RoPax ships and oil tankers featured large emissions shares, in association with the heavy use of RoRO and RoPax ships, which are larger than the container feeder vessels. Evidently, different port types cause differences in the emission shares. The operation mode can be sorted into four categories: hotelling, manoeuvring, slow cruise and fairway cruise. The proportions of ship emissions associated with different operating conditions are shown in Figure S4). Approximately 70% to 80% of emissions occurred during the fairway cruise. Ng et al.22 found that a significant amount of emissions were produced during hotelling, slow cruise, and fairway cruise, accounting for approximately 30%, 32%, and 29%, respectively. In Australia, Goldsworthy et al.18 sorted the operation mode as follows: at anchor, at berth, maneuvering, and transit. The largest ship emissions that these authors observed occurred in transit. In contrast, low-load/maneuvering, anchorage and berth ship emissions represented 10%, 9%, and 9% of total ship emissions, respectively. We only considered emissions from the main and auxiliary machinery because ship emissions are primarily generated by the main engine, which accounts for approximately 90% to 99% of the total emissions. However, Ng et al.22 reported that the AB contributed approximately 2% to

22% of ship emissions in Hong Kong. Emissions at berth are from auxiliary engines used to generate ship electricity and boilers used for heating purposes. In our study, emissions from hotelling did not consider boiler burning, and the emissions occurring during hoteling were underestimated. 3.4. Potential Influence of Ship Emissions on Surrounding Regions. 3.4.1. Comparison with LandBased Emissions Intensities. The total, mean, and maximum emissions in Jiangsu, Shanghai, and Zhejiang are shown in Table 2 in detail. The database used for land-based emissions was the combination of the Multiresolution Emission Inventory for China (MEIC),35,36 the emission inventories developed by the Shanghai Environmental Monitoring Center (SEMC),35 and the refined Baoshan emission inventory.35,37 Land-based emissions are estimated for major anthropogenic sources, considering thermal power plant, industrial, transportation, residential, and agricultural sources. The general method is the integration of top-down and bottom-up approaches based on anthropogenic activities multiplying emission factors. In the whole land domain, the MEIC emission inventory was used. But we locally updated the emission inventory to make it more realistic. For Shanghai, we updated the emission inventory using a 1 × 1 km2 resolution developed by the SEMC in 2009. However, for the Baoshan district (a district with heavy industry) in Shanghai, a refined emission inventory has been established using a completely bottom-up approach.37,38 The land-based emissions were comparable with other studies in the 1327

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Table 3. Percentage of Airflow Retention Time Starting from Ports and Ship Emission Hub Points in February and June 2010a starting

a

Winter

Summer

(Feb.)

(June)

point

Shanghai

Jiangsu

Zhejiang

other land

sea

Shanghai

Jiangsu

Zhejiang

other land

sea

P1 P2 P3 P4 P5 P6

0% 6% 1% 1% 0% 0%

14% 13% 3% 7% 2% 0%

1% 10% 13% 3% 10% 9%

35% 16% 13% 12% 8% 10%

50% 55% 70% 77% 80% 81%

0% 6% 1% 2% 1% 0%

8% 23% 2% 18% 3% 1%

0% 2% 28% 0% 16% 6%

39% 16% 10% 10% 8% 6%

53% 52% 59% 69% 72% 87%

Bold ones represents values in land area more than 10%.

YRD.20,39 Comparing the ship emissions with land-based emissions showed that ship emissions along most shipping routes were lower than the land-based emissions. The total amounts of land-based SO2, NOX, and PM2.5 emissions were 2.3 × 106 tonnes/yr, 4.5 × 106 tonnes/yr, and 1.3 × 106 tonnes/yr, as 6.2 times, 6.3 times and 26 times greater, respectively, than the marine ship emissions in our study area. The average intensities of the ship emissions of SO2 and NOX were only 16% those of the average land-based emissions intensities. The average land-based emission intensities of SO2, NOX, and PM2.5 were larger than the marine-based intensities, and their ratios ranged from approximately 106 to 7. However, the marine emission maximums were greater than the landbased emissions in most regions. Figure S5 presents the map of annual land-based and marine SO2, NOX, and PM2.5 emissions. In contrast, the intensities of the SO2 and NOX emissions in ship traffic hub areas were close to those on land. The maximum SO2 and NOX emissions of the water area were 37 times and 17 times greater than those of land, respectively. Ship emissions in hot spot areas with exceptionally high emission densities have a much greater influence on the surrounding atmospheric environment. 3.4.2. Forward Trajectory of Airflow from the Six Hot Spots in the Ship Emissions Map. Eastern China lies in the perennial monsoon region, and summer monsoons can carry ship pollutants to inland areas. The HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model is one of the most frequently used atmospheric transport and dispersion models in large-scale research. In the study area, we selected three key spots around the port areas and three ship emissions hub points using the HYSPLIT model to estimate the potential impact of areas with high ship emissions on the surrounding atmospheric environment. We calculated forward trajectories of airflow from the six hot spots every 3 days with a duration of 72 h in February and June 2010. Forward trajectories starting at heights of 500 m were simulated with the HYSPLIT model. Then we counted the number of track points at a 0.2° × 0.2° resolution grid using ArcGIS to get the retention hours in this grid. The potential influence of ship emissions on the surrounding atmospheric environment in June 2010 is shown in Figure 4. To show the seasonal variation, the airflow duration map for February is shown in Figure S6. Under winter and summer monsoon conditions, ship emissions were delivered to the local area of the Yangtze River Delta. In contrast, under summer monsoon conditions, the emissions were more easily transported to the Yangtze River Delta region, with most land areas receiving airflow passing shipping-lane areas. Table 3 presents the percentage of airflow retention time starting from ports and ship emission hub points in February and June 2010.

For airflow starting from the ports, the P1, P2, and P3 retention time percentages on land were 50%, 45%, and 30%, respectively, in winter. In summer, the P1, P2, and P3 retention time percentages on land were 47%, 48%, and 41%, respectively. In winter, the retention times of the ship emissions hub points P4, P5, and P6 ranged from 19% to 23%. In summer, the retention time percentages ranged from 13% to 31%. Ship activity could result in higher concentrations of ship emission related air pollutants in the air in and around major ports.18,40 Ship emissions have a significant impact on the entire Yangtze River Delta region and on greater East China. In future research, we will use an air quality model to more systematically evaluate the ship emission inventory and its impacts. This work will also provide references for the establishment of an emission control area (ECA) in the East China Sea area in the future.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.5b03965. Additional information regarding materials, methods, emissions factors, defaults, and additional figures and tables (PDF)

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AUTHOR INFORMATION

Corresponding Author

*Phone: +86-21-65642530; fax: +86-21-65643597; e-mail: [email protected] (Y.Z.). Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (41005076) and the Science & Technology Commission of Shanghai Municipality (Grant No. 14ZR1402800, No.10JC1402000, and No.15DZ1205404). Thanks are extended to the Maritime Safety Administration of Shanghai for providing the AIS data. The authors also wish to thank China Marine Bunker for supplying sulfur content data for HFO samples and Mr. Fengping Dai from Hangzhou Allhigh Technology Co., Ltd for technical communications.

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REFERENCES

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