TROPOMI-Derived NOx Emissions from Sea Shipping: Estimates Along Major Lanes and for Individual Vessels

Maritime transport is a pillar of the global economy, accounting for 75% of the European Union (EU) external trade, for example. It also has considerable environmental impacts. In terms of atmospheric pollution, shipping was responsible for about 39% of transport-related nitrogen oxide (NO_x) emissions in the EU in 2022. 

The ESA-funded Earth Observation for Ship Emission Monitoring project (EO4SEM) aims to provide shipping greenhouse gas and atmospheric pollutant emissions estimates that can support EU emission regulations. Specifically, it explores the potential of satellite-based Earth Observation to complement the bottom-up inventories that are driven by the automatic ship-tracking system called Automatic Identification System (AIS). Within this project, we have been developing atmospheric inverse modeling methods to derive estimates of NO_x emissions from sea shipping by processing Sentinel-5P/TROPOMI NO₂ images over European seas for the period 2019–2023. Different scales have been targeted and are discussed in this presentation. First, a sophisticated Bayesian atmospheric 3D chemistry and transport inverse modelling approach allows us to derive emission budgets for large sea areas. Second, lighter data-driven techniques derive emissions along individual shipping lanes on the one hand and instant estimates for individual large ships on the other hand. The AIS-driven bottom-up estimation model STEAM from the Finnish Meteorological Institute is used to support the analysis and then as a reference for the evaluation of the results.

Monthly NO_x emission maps at 0.5° resolution and corresponding budgets were derived over large sea regions defined by adapted International Hydrographic Organization (IHO) boundaries, using an inverse modeling approach based on the assimilation of TROPOMI NO₂ observations into the CIF-CHIMERE model.

The derivation of emission estimates along shipping lanes relies on the “divergence method”. This method is applied to individual TROPOMI images at the instrumental ground pixel scale. It derives corresponding NO_x emission maps. Accounting for the temporally-varying spatial coverage and noise of the quality-filtered retrievals from TROPOMI, we aggregate the results as monthly-mean NO_x lineic emissions (in kg/km/month). First comparisons between monthly-mean TROPOMI-based and STEAM lineic emissions estimates show a strong consistency for isolated, high-traffic lanes. However, the quantification remains challenging in complex areas characterized by high lane-intersection density.

Our estimates of instant emissions from individual large vessels are based on two types of approaches. Both involve the detection and inversion of the NO_x enhancement plumes downwind the moving vessels. The plume-detection algorithm is cross-referenced with the AIS information from STEAM to ensure that the high-concentration patterns identified in the TROPOMI images correspond to large ships, and to infer the trajectory of the latter. We use traditional point-source data-driven quantification methods: the cross-sectional flux and local divergence methods, that we adapted to account for the motion of the ship. The resulting estimates are confronted to the STEAM continuous estimates for individual ships, showing good consistency on average, but high uncertainty for the individual TROPOMI-based results.