LAUSR

The maritime transport industry is recognised as one of the cleanest modes of global transport. It is important to measure engine exhaust emissions to maintain its ecological superiority over road, rail, and other forms of transport. Emission inventories are needed to estimate emissions. Current inventories need to review the emission factors (EFs) they currently employ, which generally yield over- or under-estimations. There is a need to consider more relevant measurements that will enhance the accuracy of emission prediction models. There is also a need to consider different mathematical approaches, to find better ways to manage the many changeable parameters of fuel consumption and engine specifications used to estimate emissions. In this study, new sets of EF equations are developed to take into consideration real-time emission measurements during 11-d emission measurements on-board of two ocean-going vessels at berth and during sailing. They were tested on two ocean-going vessels, running on slow speed diesel main engines at berth while manoeuvring and cruising. Both vessels ran on heavy diesel fuel. Regression analysis, along with a consideration of fuel consumption and engine parameters, was used to develop the equations. The results show a better prediction of emission quantity than current inventories for different engine types, in in-port and at-sea activities, with the sum of primary emissions coming closest to the actual sea emission calculations and also to the smallest standard values. This should be helpful when upgrading environmental policies.