Assessment of atmospheric pollutant emissions with maritime energy strategies using bayesian simulations and time series forecasting

Abstract

With increasingly stringent regulations on emission criteria and environment pollution concerns, marine fuel oils (particularly heavy fuel oils) that are commonly used today for powering ships will no longer be allowed in the future. Various maritime energy strategies are now needed for the long-term upgrade that might span decades, and quantitative predictions are necessary to assess the outcomes of their implementation for decision support purpose. To address the technical need, a novel approach is developed in this study that can incorporate the strategic implementation of fuel choices and quantify their adequacy in meeting future environmental pollution legislations for ship emissions. The core algorithm in this approach is based on probabilistic simulations with a large sample size of ship movement in the designated port area, derived using a Bayesian ship traffic generator from existing real activity data. Its usefulness with scenario modelling is demonstrated with application examples at five major ports, namely the Ports of Shanghai, Singapore, Tokyo, Long Beach, and Hamburg, for assessment at Years 2020, 2030, and 2050 with three economic scenarios. The included fuel choices in the application examples are comprehensive, including heavy fuel oils, distillates, low sulphur fuel oils, ultra-low sulphur fuel oils, liquefied natural gas, hydrogen, biofuel, methanol, and electricity (battery). Various features are fine-tuned to reflect micro-level changes on the fuel choices, terminal location, and/or ship technology. Future atmospheric pollutant emissions with various maritime energy strategies implemented at these ports are then discussed comprehensively in details to demonstrate the usefulness of the approach.

Keywords: Emission forecasting; Fuel simulation; MCMC; Traffic scenarios.