a U.S. Environmental Protection Agency National Vehicle and Fuel Emissions Laboratory , Oak Ridge Institute for Science and Education research participant , Ann Arbor , MI , USA.
b National Vehicle and Fuel Emissions Laboratory , U.S. Environmental Protection Agency , Ann Arbor , MI , USA.
J Air Waste Manag Assoc. 2019 Jun;69(6):752-763. doi: 10.1080/10962247.2019.1580229. Epub 2019 Apr 16.
Maritime greenhouse gas emissions are projected to increase significantly by 2050, highlighting the need for reliable inventories as a first step in analyzing ship emission control policies. The impact of ship power models on marine emissions inventories has garnered little attention, with most inventories employing simple, load-factor-based models to estimate ship power consumption. The availability of more expansive ship activity data provides the opportunity to investigate the inventory impacts of adopting complex power models. Furthermore, ship parameter fields can be sparsely populated in ship registries, making gap-filling techniques and averaging processes necessary. Therefore, it is important to understand of the impact of averaged ship parameters on ship power and emission estimations. This paper examines power estimation differences between results from two complex, resistance-based and two simple, load-factor-based power models on a baseline inventory with unique ship parameters. These models are additionally analyzed according to their sensitivities toward average ship parameters. Automated Identification System (AIS) data from a fleet of commercial marine vessels operating over a 6-month period off the coast of the southwestern United States form the basis of the analysis. To assess the inventory impacts of using averaged ship parameters, fleet-level carbon dioxide (CO) emissions are calculated using ship parameter data averaged across ship types and their subtype size classes. Each of the four ship power models are used to generate four CO emissions inventories, and results are compared with baseline estimates for the same sample fleet where no averaged values were used. The results suggest that a change in power model has a relatively high impact on emission estimates. They also indicate relatively little sensitivity, by all power models, to the use of ship characteristics averaged by ship and subtype. : Commercial marine vessel emissions inventories were calculated using four different models for ship engine power. The calculations used 6 months of Automated Identification System (AIS) data from a sample of 248 vessels as input data. The results show that more detailed, resistance-based models tend to estimate a lower propulsive power, and thus lower emissions, for ships than traditional load-factor-based models. Additionally, it was observed that emission calculations using averaged values for physical ship parameters had a minimal impact on the resulting emissions inventories.
到 2050 年，预计海洋温室气体排放量将大幅增加，这凸显了可靠清单作为分析船舶排放控制政策的第一步的重要性。船舶动力模型对海洋排放清单的影响尚未得到充分关注，大多数清单采用简单的基于负荷因子的模型来估算船舶动力消耗。更广泛的船舶活动数据的可用性为采用复杂动力模型对清单的影响提供了调查机会。此外，船舶参数字段在船舶登记处可能很少，这使得填补空白技术和平均过程成为必要。因此，了解平均船舶参数对船舶动力和排放估算的影响很重要。本文研究了在基线清单中，使用独特的船舶参数时，两种复杂的阻力模型和两种简单的基于负荷因子的功率模型之间的功率估算差异。还根据它们对平均船舶参数的敏感性来分析这些模型。分析基于美国西南部沿海 6 个月期间商业船舶船队的自动识别系统 (AIS) 数据。为了评估使用平均船舶参数对清单的影响，使用跨船型和子型尺寸级别的船舶参数平均值计算船队的二氧化碳 (CO) 排放量。使用四种船舶动力模型中的每一种生成四个 CO 排放清单，并将结果与同一船队的基线估计进行比较，该船队在没有使用平均值的情况下使用。结果表明，动力模型的变化对排放估算的影响相对较大。它们还表明，所有动力模型对船舶和子型平均使用船舶特征的敏感性相对较低。：使用四种不同的船舶发动机功率模型计算商业船舶排放清单。该计算使用了 248 艘船舶的 6 个月自动识别系统 (AIS) 数据作为输入数据。结果表明，与传统的基于负荷因子的模型相比，更详细的阻力模型往往会估算船舶的推进功率较低，从而排放较低。此外，还观察到使用物理船舶参数的平均值进行排放计算对最终排放清单的影响最小。
