A Systematic Approach to Identify Shipping Emissions Using Spatio-Temporally Resolved TROPOMI Data

Abstract

Stringent global regulations aim to reduce nitrogen dioxide (NO2) emissions from maritime shipping. However, the lack of a global monitoring system makes compliance verification challenging. To address this issue, we propose a systematic approach to monitor shipping emissions using unsupervised clustering techniques on spatio-temporal georeferenced data, specifically NO2 measurements obtained from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Copernicus Sentinel-5 Precursor satellite. Our method involves partitioning spatio-temporally resolved measurements based on the similarity of NO2 column levels. We demonstrate the reproducibility of our approach through rigorous testing and validation using data collected from multiple regions and time periods. Our approach improves the spatial correlation coefficients between NO2 column clusters and shipping traffic frequency. Additionally, we identify a temporal correlation between NO2 column levels along shipping routes and the global container throughput index. We expect that our approach may serve as a prototype for a tool to identify anthropogenic maritime emissions, distinguishing them from background sources.

Keywords

• TROPOMI
• nitrogen dioxide
• maritime traffic monitoring
• data processing
• unsupervised learning