This work presents a methodology to identify the fuel sulphur content (FSC) violations of ongoing vessels, using CFD RANS modelling by implementing the SST k-omega turbulence parameterization to predict the CO2 and SO2 concentrations of plume dispersion. Gaseous pollutants measurements and meteorological data from a ship emission monitoring station in the approach to the port of Hamburg, Germany, have been used for the evaluation of the methodology. Gaseous pollutants concentration, meteorology, identity, and position of passing ships (AIS signal) are the main parameters of the modelling approach. Five ships have been selected to demonstrate and evaluate the method developed. The vessels had different installed power, geometry, and fuel sulphur content. A virtual monitoring station is created for collecting concentration data from the simulated plume and producing concentration time-series. The comparison between modelling and measurements is being performed with integrated numerical values that were produced from the concentration time-series. Modelling, in comparison to measurements for all case studies, provides results for CO2 and SO2 that are in the same order of magnitude, which constitutes the main criterion of validity. In the methodology, the uncertainties are separated into three categories. The first one is related to ambient conditions such as turbulence and temperature profiles. The second concerns ships’ funnel exit characteristics considering the estimation of funnel exit concentration and exhaust mass flow. The third considers the uncertainties that are related to measurements. All details above were used to produce expressions considering the relationship between the FSC and SO2 measuring instruments. The outcome of this work can contribute in finding optimum monitoring location and in estimating plume dispersion close to the water level, e.g. for studying pollutants exchange in the air-water interface.