Wuhan Ecological Environment Science and Technology Center,Wuhan 430015,Hubei,China
Show less
文章历史+
Received
Published
2022-04-15
2024-10-24
Issue Date
2026-07-23
PDF (569K)
摘要
以长江武汉段为研究区域,分别基于船舶自识别系统(automatic identification system,AIS)数据和船舶运输周转量数据,计算2019年长江武汉段船舶典型大气污染物排放量。结果显示,前者计算结果更符合武汉市真实情况。船舶大气污染物中SO2、NO x 、CO、PM10、PM2.5、VOCs(挥发性有机物)排放量分别为183.0、12 003.5、1 727.0、1 505.2、1 442.0、359.6 t;普通货船的污染物排放量分担率最高,SO2、NO x 、CO、PM10、PM2.5、VOCs排放量分别占船舶总排放的92.9%、90.8%、94.7%、85.1%、85.0%、87.6%;1月、8月和9月是高峰期。
Abstract
This study took Wuhan section of the Yangtze River as the research area, and established the typical air pollutants emissions inventory of ships in 2019. Two methods were adopted for the calculation of emissions, which were based on automatic identification system (AIS) data and ship turnover amount respectively. It was found that the former estimation results were more in line with the real situation of Wuhan. The emissions of SO2, NO x, CO, PM10, PM2.5 and VOCs from ships are 183.0, 12 003.5, 1 727.0, 1 505.2, 1 442.0 and 359.6 t respectively. The proportion of pollutant emissions from ordinary cargo ships is the highest, with SO2, NO x, CO, PM10, PM2.5, and VOCs accounting for 92.9%, 90.8%,94.7%, 85.1%, 85.0%, and 87.6% of the total emissions from ships, respectively. January, August and September are the peak periods for ship pollutants emissions.
近年来,由于我国船舶保有量和行驶里程大幅增长,其排放的大气污染物对人类健康、环境空气质量和气候变化产生了重要影响。因此,核算船舶的大气污染物排放清单具有重要意义。基于船舶自识别系统(automatic identification system,AIS)和船舶运输周转量数据,利用排放因子方法,苑帅等[1]计算了2017年长江江苏段船舶排放尾气中SO2、NO x 、PM10、PM2.5、CO和VOCs(挥发性有机物)分别为5.53、19.21、1.25、0.95、0.98、0.45万t;李明明等[2]计算得到,2018年珠海高栏港船舶排放的SO2、CO、HC、NO x 、PM2.5和PM10等6项大气污染物排放总量为1 140.70 t;曾凡涛等[3]研究发现,2018年厦门港的大气污染物最大贡献船型为集装箱船。以上研究表明,船舶排放已成为港口、江河沿岸城市大气污染的重要来源之一[4~8]。但目前关于船舶大气污染物排放清单和污染特征的分析主要集中在远洋船舶和沿海港口城市,而对内河船舶污染物排放清单及特征的研究较少[9,10]。
其中,Eij (g)为第i种类型船舶第j种污染物的排放量;VAN i (次)为第i种类型船舶在研究期间的抵港次数;Pi (kW)为第i种类型船舶的引擎功率;LF i 为第i种类型船舶的引擎负荷系数;Ai (h)为第i种类型船舶在航道中的活动时间;EF ij (g/(kW·h) )为第i种类型船舶第j种污染物的排放因子。
结合船舶燃料含硫量和表2中船舶排放因子,计算出长江武汉段的船舶大气污染物排放量,详见表8。结果表明,NO x 为最主要的污染物,全年共排放42 397.3 t;SO2排放量最少,全年共排放17.8 t。
4 结果对比与校验
基于AIS计算得到的NO x 、PM2.5、VOCs排放量占全社会总排放的比率分别为8.4%、2.6%、0.2%,而基于运输周转量计算得到的NO x 、PM2.5、VOCs排放量占全社会总排放的比率分别为29.6%、6.0%、3.4%,后者相较于前者普遍偏高,详见表9。船舶属于非道路移动源的一种[17],但采用基于运输周转量计算的污染物排放量占全社会总排放比率结果通常明显高于其他研究中非道路移动源排放量占全社会比率[21~23]。这主要是由于《城市大气污染物排放清单编制技术手册》[17]中未对柴油升级带来的除SO2外的其他污染物排放因子进行修正,从而导致船舶污染物排放量核算结果普遍偏高。另外,采用基于运输周转量的计算方法仅能得到研究区域内全部船舶的总排放量,而缺少不同船型的排放量结果。这主要是由于活动水平数据的处理是结合油耗系数推算出的船舶燃油消耗量,并未考虑到实际的船舶类型、出入港航行情况(是否超载、空载等)等。而采用AIS数据,考虑了多种类型船舶的航速、航行时间等动态因素和船舶尺寸类型、载重吨位等静态因素,得到的船舶污染物排放清单结果更加精准、细致[24,25]。因此,采用AIS数据计算结果更为合理。
5 结 语
本文基于AIS数据计算得出如下结论:2019年长江武汉段的内河船舶活动直接排放SO2 183.0 t、NO x 12 003.5 t、CO 1 727.0 t、PM10 1 505.2 t、PM2.5 1 442.0 t、VOCs 359.6 t,其中NO x 排放量占全社会总排放的8.4%;污染物排放的主要船舶类型为普通货船,其排放量占船舶总排放量的85.0%~94.7%;污染物排放峰值时间为1月、8月、9月,排放低谷时间为2月、6月。基于AIS数据的计算方法考虑了航速、航行时间等动态因素,同时还考虑了船舶尺寸类型、载重吨位等静态因素,计算结果更符合本地真实情况。
YUANS, FENGX J, ZHUY F. Rapid inventory of ship exhaust emissions for inland waterway: A case study in Jiangsu section of Yangtze River[J]. Transport Research, 2020, 6(2): 91-100. DOI: 10.16503/j.cnki.2095-9931.2020.02.011(Ch ).
LIM M, ZHOUZ J. Research on ship air pollutant emission list in Gaolan Port of Zhuhai[J]. China Maritime Safety, 2021(2): 54-56. DOI: 10.16831/j.cnki.issn1673-2278.2021.02.016(Ch ).
ZENGF T, LÜJ. Ship emission inventory and valuation of eco-efficiency in Xiamen Port[J]. China Environmental Science, 2020, 40(5): 2304-2311. DOI: 10.19674/j.cnki.issn1000-6923.2020.0264(Ch ).
[7]
LÜZ F, LIUH, YINGQ, et al. Impacts of shipping emissions on PM2.5 pollution in China[J]. Atmospheric Chemistry and Physics, 2018, 18(21): 15811-15824. DOI: 10.5194/acp-18-15811-2018 .
[8]
CHEND S, WANGX T, LIY, et al. High-spatiotemporal-resolution ship emission inventory of China based on AIS data in 2014[J]. Science of the Total Environment, 2017, 609: 776-787. DOI: 10.1016/j.scitotenv.2017.07.051 .
[9]
LIC, BORKEN-KLEEFELDJ, ZHENGJ Y, et al. Decadal evolution of ship emissions in China from 2004 to 2013 by using an integrated AIS-based approach and projection to 2040[J]. Atmospheric Chemistry and Physics, 2018, 18(8): 6075-6093. DOI: 10.5194/acp-18-6075-2018 .
LIUH, SHANGY, JINX X, et al. Review of methods and progress on shipping emission inventory studies[J]. Acta Scientiae Circumstantiae, 2018, 38(1): 1-12. DOI: 10.13671/j.hjkxxb.2017.0257(Ch ).
[12]
HUANGL, WENY Q, ZHANGY M, et al. Dynamic calculation of ship exhaust emissions based on real-time AIS data[J]. Transportation Research Part D: Transport and Environment, 2020, 80: 102277. DOI: 10.1016/j.trd.2020.102277 .
ZHUQ R, LIAOC H, WANGL, et al. Application of fine vessel emission inventory compilation method based on AIS data[J]. China Environmental Science, 2017, 37(12): 4493-4500. DOI:10.3969/j.issn.1000-6923.2017.12.011(Ch ).
[15]
ZHANGY Q, FUNGJ C H, CHANJ W M, et al. The significance of incorporating unidentified vessels into AIS-based ship emission inventory[J]. Atmospheric Environment, 2019, 203: 102-113. DOI: 10.1016/j.atmosenv.2018.12.055 .
Wuhan Municipal Statistics Bureau, State Statistical Bureau Wuhan Investigation Team. 2020 Wuhan Statistical Yearbook[M]. Wuhan: China Statistics Press, 2020(Ch).
[18]
YANGL, ZHANGQ J, ZHANGY J, et al. An AIS-based emission inventory and the impact on air quality in Tianjin Port based on localized emission factors[J]. Science of the Total Environment, 2021, 783: 146869. DOI: 10.1016/j.scitotenv.2021.146869 .
[19]
HUANGL, WENY Q, ZHANGY M, et al. Dynamic calculation of ship exhaust emissions based on real-time AIS data[J]. Transportation Research Part D: Transport and Environment, 2020, 80: 102277. DOI: 10.1016/j.trd.2020.102277 .
[20]
United States Environmental Protection Agency. Office of Transportation and Air Quality, Energy and Environmental Analysis, inc. Analysis of Commercial Marine Vessels Emissions and Fuel Consumption Data [R]. Washington, DC: Office of Transportation and Air Quality, U.S. Environmental Protection Agency, 2000.
[21]
NGS K W, LOH C, LINC B, et al. Policy change driven by an AIS-assisted marine emission inventory in Hong Kong and the Pearl River Delta[J]. Atmospheric Environment, 2013, 76: 102-112. DOI: 10.1016/j.atmosenv.2012.07.070 .
Guangdong Provincial Environmental Monitoring Center, Environmental Protection Department of the Hong Kong Special Administrative Region Government. Compilation Manual of Air Pollutant Emission Inventory in the Pearl River Delta[R]. Guang Zhou: Guangdong Environmental Protection Bureau, 2005(Ch).
[24]
贺克斌. 城市大气污染物排放清单编制技术手册[R]. 北京: 生态环境部, 2015.
[25]
HEK B. Technical Manual for Compiling Urban Air Pollutant Emission Inventory[R]. Beijing: Ministry of Ecology and Environment, 2015(Ch).
ZHUY F, LEIZ Y, FENGX J, et al. River based on AIS big data[J]. Environmental Science and Technology, 2019, 32(4): 41-46. DOI: 10.3969/j.issn.1674-4829.2019.04.009(Ch ).
FUQ Y, SHENY, ZHANGJ. Research on the inventory of air pollutant emissions from ships in Shanghai Port[J]. Journal of Safety and Environment, 2012, 12(5): 57-64. DOI: 10.3969/j.issn.1009-6094.2012.05.013(Ch ).
[30]
YAOX, MOUJ M, CHENP F, et al. Ship emission inventories in estuary of the Yangtze River using terrestrial AIS data[J]. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, 2016, 10(4): 633-640. DOI: 10.12716/1001.10.04.13 .
XIAOK, ZHAOQ. Study of VOCs emission nventory of anthropogenic sources in a typical city in china: A case study of Wuhan City[J]. Guangdong Chemical Industry, 2019, 19(46):143-145. DOI: 1007-1865(2019)19-0143-03(Ch ).
ZHOUJ R, HUANGY, QIU P Pet al. Air pollutant emission inventory and distribution characteristics in Wuhan city[J]. Journal of Nanjing University of Information Science and Technology (Natural Science Edition), 2018, 10(5):599-605. DOI: 10.13878 /j.cnki.jnuist.2018.05.010 (Ch ).
[37]
LIC, YUANZ B, OUJ M, et al. An AIS-based high-resolution ship emission inventory and its uncertainty in Pearl River Delta region, China[J]. Science of the Total Environment, 2016, 573: 1-10. DOI: 10.1016/j.scitotenv.2016.07.219 .
[38]
WANZ, ZHANGQ, XUZ P, et al. Impact of emission control areas on atmospheric pollutant emissions from major ocean-going ships entering the Shanghai Port, China[J]. Marine Pollution Bulletin, 2019, 142: 525-532. DOI: 10.1016/j.marpolbul.2019.03.053 .