A Novel Ship Fuel Sulfur Content Estimation Method Using Improved Gaussian Plume Model and Genetic Algorithms

Maritime transportation plays a vital role in global economic development but is also a significant contributor to air pollution, especially through emissions of SO2, NOx, and CO2. Identifying non-compliance with fuel sulfur content regulations is crucial for mitigating these environmental impacts, yet current methods face challenges, particularly in the absence of reliable CO2 concentration data. This study proposes a novel inverse calculation framework to estimate ship fuel sulfur content without relying on CO2 measurements. An improved Gaussian plume line source model was tailored to the dispersion characteristics of ship emissions, with influencing factors evaluated under varying wind field conditions. The emission source intensity inversion was formulated as an unconstrained multi-dimensional optimization problem, solved using genetic algorithms. By incorporating ship fuel consumption data derived from basic ship information, the sulfur content of ship fuels was effectively estimated. Experimental evaluations using 30 days of monitoring data revealed that the method successfully identified 2743 ships, with an overall detection rate of 82.72%. Among them, 131 ships were flagged as suspected of using high-sulfur fuel, and 111 were confirmed to be non-compliant via sampling and laboratory testing, achieving an accuracy of 84.73%. These results demonstrate that the proposed approach offers a reliable and efficient solution for real-time fuel sulfur content monitoring and enforcement under diverse atmospheric conditions, contributing to improved environmental management of maritime transport emissions.