With the advancement of technology and the arrival of miniaturized environmental sensors that offer greater performance, the idea of building mobile network sensing for air quality has quickly emerged to increase our knowledge of air pollution in urban environments. However, with these new techniques, the difficulty of building mathematical models capable of aggregating all these data sources in order to provide precise mapping of air quality arises. In this context, we explore the spatio-temporal geostatistics methods as a solution for such a problem and evaluate three different methods: Simple Kriging (SK) in residuals, Ordinary Kriging (OK), and Kriging with External Drift (KED). On average, geostatistical models showed 26.57% improvement in the Root Mean Squared Error (RMSE) compared to the standard Inverse Distance Weighting (IDW) technique in interpolating scenarios (27.94% for KED, 26.05% for OK, and 25.71% for SK). The results showed less significant scores in extrapolating scenarios (a 12.22% decrease in the RMSE for geostatisical models compared to IDW). We conclude that univariable geostatistics is suitable for interpolating this type of data but is less appropriate for an extrapolation of non-sampled places since it does not create any information.

Liquefied natural gas (LNG) is increasingly used as a marine fuel due to its capacity to significantly reduce emissions of particulate matter, sulfur oxides (SO_x), and nitrogen oxides (NO_x), compared to conventional fuels. In addition, LNG combustion produces less carbon dioxide (CO₂) than conventional marine fuels, and the use of non-fossil LNG offers further potential for reducing greenhouse gas emissions. However, this benefit can be partially offset by methane slip—the release of unburned methane in engine exhaust—which has a much higher global warming potential than CO₂. This study presents an experimental evaluation of methane emissions from a RoPax vessel powered by low-pressure dual-fuel four-stroke engines with a direct mechanical propulsion system. Methane slip was measured directly during onboard testing and combined with a year-long analysis of engine operation using an Engine Load Monitoring (ELM) method. The yearly average methane slip coefficient (C_slip) obtained was 1.57%, slightly lower than values reported in previous studies on cruise ships (1.7%), and significantly lower than the default values specified by the FuelEU (3.1%) Maritime regulation and IMO (3.5%) LCA guidelines. This result reflects the ship’s operational profile, characterized by long crossings at high and stable engine loads. This study provides results that could support more representative emission assessments and can contribute to ongoing regulatory discussions.

Nanoparticles applications, whether for pharmaceuticals, for environmental assessment or for evaluation of global climatic has led to the use of CFD tools to improve the understanding of their dynamical behaviour (transport, deposition and coagulation). Due to small particle sizes and low Stokes numbers, nanoparticles are typically considered to deposit at the wall as a combined result of Brownian motion and turbulent dispersion. To simulate these mechanisms in this work, the two-phase flow is computed using a RANS model (Reynolds Averaged Navier-Stokes) for the mean fluid properties, and a Lagrangian tracking approach for the dispersed phase in which the fluctuating fluid velocity at particle location, that ensures the particle turbulent dispersion, is predicted through a user implemented Langevin-based dispersion model. When a temperature gradient is present, the aerosol particles experience a thermophoretic force in addition to the drag and the Brownian forces. Depending on the temperature gradient and particle size, the thermophoretic force could become the predominant deposition mechanism. This size dependence makes it important to appropriately choose the turbulent dispersion model in wall-bounded turbulent flows. Actually, in most commercial codes, the turbulent dispersion of particles is predicted using the so-called Eddy Interaction Model (EIM), whose major drawback is that it cannot account for turbulence non-homogeneity, thus leading to some unphysical accumulation of low-inertia particles near the wall and therefore to an overestimation of the deposition velocity which is accentuated by thermophoresis. This study shows that turbulent and thermophoretic depositions are not completely independent, since thermophoresis enhances the deposition of particles which are sensitive to the turbulent dispersion model.