An Overview of Emissions Monitoring and Inventory Techniques for Shipping Operations

Abstract

Ship pollution contributes to global anthropogenic emissions, and they have a significant impact on human health as
well as climate change. Thus, this paper aims to review recent studies that used various methodologies to measure and estimate ship emissions. It provides a comprehensive review of different techniques for monitoring ship emissions, including measurement and estimation methods, to identify each technique’s features, strengths and limitations. Three main methodologies for ship emission measurement are investigated, including the on-board measurement, the in-situ measurement for sniffer method, and optical remote-sensing techniques such as Differential Optical Absorption Spectroscopy (DOAS), Light Detection and Ranging (LiDAR), and UV-CAM-based monitoring. Direct measurements are not always possible due to time and human resource restrictions, and the difficulty of installing measurement devices. Therefore, emission inventories are the most commonly applied method to estimate ship emissions. Estimation methods, including inventory approaches and the application of machine learning techniques, are also reviewed here. Among the reviewed approaches, direct measurements provide high accuracy but may be costly and operationally challenging, whereas inventory and data-driven methods offer broader applicability for large-scale assessments. Future work should focus on integrating measurement, inventory, and machine-learning approaches to improve reliability, scalability, and standardization in ship-emission monitoring.