A Ship Emission Diffusion Model Based on Translation Calculation and Its Application on Huangpu River in Shanghai

Xianhua Wu ()
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Xianhua Wu: Shanghai Maritime University, School of Economics and Management

Chapter Chapter 4 in Environmental Disaster in China, 2026, pp 79-105 from Springer

Abstract: Abstract Under global warming, how to accurately measure and reduce ship emissions has become an important issue concerned by the transportation sector and the whole society. The diffusion concentration of continuous ship emissions is calculated by the emission concentrations at the time of observation. With the increase of observation time window, diffusion distance, and calculation accuracy, the calculation time of traditional algorithms and the requirements for computing resources increase exponentially. Therefore, this paper proposes a fast algorithm based on data translation (matrix translation and point translation algorithms), which can calculate the emission diffusion concentration of multiple ships, long distances and long duration while ensuring the accuracy. The time complexity of the traditional algorithm and the fast algorithm are O(kT2) and O(kT) respectively, and the fast algorithm can greatly improve the computing efficiency and save the computing time. The matrix translation algorithm is suitable for small-scale calculations with a fixed observation window, the amount of data is relatively small, and the calculation speed is the fastest. The point translation algorithm is suitable for large-scale calculations with a moving observation window and can adjust the calculation accuracy flexibly, but the data size is large and difficult to predict. Finally, in the numerical simulation on the Huangpu River, millions of emission concentrations data, based on trajectory information in 12 hours over eight vessels, were calculated only in several minutes, which proves the high efficiency of the fast algorithm.

Keywords: Ship emission; Diffusion model; AIS big data; Translation calculation; Huangpu River; Shanghai (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-981-92-0910-1_4

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