Evaluating methods for marine fuel sulfur content using microsensor sniffing systems on ocean-going vessels.

Establishing emission control areas (ECAs) can effectively reduce air pollution from marine emissions, making efficient monitoring of the fuel sulfur content (FSC) of ocean-going vessels (OGVs) key to ECA policy enforcement. Various FSC detection approaches, including oil sample analysis, sniffing method, and optical remote sensing, have been proposed, each with its benefits and drawbacks. Among these, the sniffing method appears promising but requires further improvement in field operation protocol and data analysis processes. This study aims to develop a comprehensive methodology, including sensor calibration, field operations, and data analysis, to enhance the performance of an Unmanned Aerial Vehicle (UAV)-based Microsensor Sniffing System (MSS) for real-time FSC monitoring. Hong Kong has a cap of 0.5 % m/m FSC for OGVs, and hence Hong Kong waters served as the "real-world" monitoring location to evaluate the MSS system through land-based and sea-based measurements. Three different FSC calculation methods were employed and verified against bunker delivery note (BDN) data through blind testing. Results confirm that the MSS is effective in field settings, though it has an underestimation tendency, demonstrating an absolute error of 0.06 % m/m, 0.11 % m/m, and 0.10 % m/m for the Crest, Slope, and Area methods, respectively, compared to BDN data. However, high errors were possible with low CO and SO peak heights, and single-peak samples compared to multi-peak samples. Over 16 successful trips, the FSC of 125 valid OGVs (Mean FSC = 0.39 % m/m) exhibited a lognormal distribution pattern, with the distribution tail approaching the 0.5 % m/m regulatory cap. This investigation highlights the potential of a UAV-based MSS for monitoring and enforcing FSC regulations within ECAs, providing a systematic protocol to guide future research direction and enforcement practices.