For developing national maritime traffic routes through the coastal waters of Korea, the customary maritime traffic flow must be accurately identified and quantitatively evaluated. In this study, the occupancy time of ships in cells was calculated through a density analysis based on automatic identification system data. The density map was statistically created by logarithmically transforming the density values and adopting standard deviation-based stretch visualization to increase the normality of the distribution. Many types of traffic routes such as open-sea, coastal, inland, and coastal access routes were successfully identified; moreover, the stretch color ramp ratio was reduced to identify routes having relatively high density. Adopting a single standard deviation and demonstrating the top 25% of color ramps, the analysis afforded the main routes through which customary traffic flows. This novel density analysis method and statistical visualization method is expected to be used for developing national maritime traffic routes and should ultimately contribute to maritime safety. Moreover, it provides a scientific means and simulator for determining the navigation area and analyzing conflicts with other activities in marine spatial planning.

The aim of this paper is to apply a new lane separation methodology for the maritime sector emissions attributed to the different vessel types and marine traffic loads in the Mediterranean and the Black Sea defined via the European Marine and Observation Data network (EMODnet), developed in 2016. This methodology is implemented for the first time on the Copernicus Atmospheric Monitoring Service Global Shipping (CAMS-GLOB-SHIP v2.1) nitrogen oxides (NO_X) emissions inventory, on the Sentinel-5 Precursor Tropospheric Monitoring Instrument (TROPOMI) nitrogen dioxide (NO₂) tropospheric vertical column densities, and on the LOTOS-EUROS (Long Term Ozone Simulation—European Operational Smog) CTM (chemical transport model) simulations. By applying this new EMODnet-based lane separation method to the CAMS-GLOB-SHIP v2.1 emission inventory, we find that cargo and tanker vessels account for approximately 80% of the total emissions in the Mediterranean, followed by fishing, passenger, and other vessel emissions with contributions of 8%, 7%, and 5%, respectively. Tropospheric NO₂ vertical column densities sensed by TROPOMI for 2019 and simulated by the LOTOS-EUROS CTM have been successfully attributed to the major vessel activities in the Mediterranean; the mean annual NO₂ load of the observations and the simulations reported for the entire maritime EMODnet-reported fleet of the Mediterranean is in satisfactory agreement, 1.26 ± 0.56 × 10¹⁵ molecules cm⁻² and 0.98 ± 0.41 × 10¹⁵ molecules cm⁻², respectively. The spatial correlation of the annual maritime NO₂ loads of all vessel types between observation and simulation ranges between 0.93 and 0.98. On a seasonal basis, both observations and simulations show a common variability. The wintertime comparisons are in excellent agreement for the highest emitting sector, cargo vessels, with the observations reporting a mean load of 0.98 ± 0.54 and the simulations of 0.81 ± 0.45 × 10¹⁵ molecules cm⁻² and correlation of 0.88. Similarly, the passenger sector reports 0.45 ± 0.49 and 0.39 ± 0.45 × 10¹⁵ molecules cm⁻² respectively, with correlation of 0.95. In summertime, the simulations report a higher decrease in modelled tropospheric columns than the observations, however, still resulting in a high correlation between 0.85 and 0.94 for all sectors. These encouraging findings will permit us to proceed with creating a top-down inventory for NO_x shipping emissions using S5P/TROPOMI satellite observations and a data assimilation technique based on the LOTOS-EUROS chemical transport model.