distinction is noteworthy as it illustrates that while the OGVs in Danish water exhibited higher absolute non-compliance rates, the level of the FSC exceedances was higher for the OGVs in Belgian waters. Spatial sulfur compliance trends. The temporal analyses reveal notable disparities in non-compliance rates between ?xed stations (1.0%), typically situated near ports, and airborne measurements (5.3%), typically conducted in the territorial waters (12 nm) and the Exclusive Economic Zones (EEZ) (200 nm) (Fig. 1). Similar patterns were observed for the other cutoff levels (Supplementary Fig. 3A, B). These differences in non-compliance rates between airborne measurements and ?xed stations were statistically sig- ni?cant for all cutoff levels (P < 0.001). Although based on the same methodology (Supplementary Methods 1), ?xed and airborne measurements use different operational methods, which can partly explain the differing non- compliance rates. Airborne platforms try to avoid redundant measurements of the same OGVs, while ?xed stations take a non- selective approach and measure all passing OGVs. This may therefore lead to a slight underestimation of the determined non- compliance rate by ?xed stations if compliant OGVs like compliant ro-ro ferries are overrepresented in these datasets. Furthermore, aerial remote measurements may tend to focus on OGVs with a higher risk pro?le, and, to some extent, avoid OGVs operating only in the SECA, or smaller coasters, i.e., small to medium-sized cargo OGVs designed for transportation along coastlines or in relatively calm waters. This may overestimate the overall non-compliance rate by airborne measurements. Never- theless, these ?ndings indicate a clear pattern of adaptive non- compliant behavior among OGVs. A comparison of non-compliance trends between the various measurement campaigns revealed a high consistency (Supple- mentary Tables 3 and 4). It was observed that locations in closer proximity to the SECA border have higher non-compliance rates. Measurements taken at the border by the MUMM and Chalmers University27 demonstrated an average non-compliance rate of approximately 30%. When plotting the non-compliance data against the distance from the border, it followed an exponential decreasing curve, with a high goodness of ?t (Fig. 2A). In order to mitigate the in?uence of the high compliance rate in ports, the combined airborne data from RPAS, helicopter, and aircraft was utilized (Fig. 2B). In this case, an excellent goodness of ?t was also observed. Given the signi?cant disparity between non-compliance rates observed in ports compared to those at sea, the relationship between compliance and the distance from port was determined (Fig. 2C). Similar patterns were observed for the other FSC cutoff levels (Supplementary Fig. 4A–C). The ?tting constants and correlation factors (R?) of the curve ?ttings for all cutoff levels are provided in Supplementary Tables 5 and 6. This spatial analysis provided valuable insights into the distribution of non-compliance risks along the SECA border. Notably, the analysis revealed that the highest risk for non- compliance was observed within the ?rst 300–450 km from the SECA border. The results indicate that compliance rates at sea, beyond a distance of 900 km, were 1.4% for the 0.15% FSC cutoff level. Furthermore, these ?ndings indicate that non-compliance begins to notably increase at approximately 70–90 km from the port. At a distance of 180 km from the port, the proximity to the port stops in?uencing non-compliance behavior. It must be acknowledged that the number of points for these ?ttings was, in particular for the non-compliance in function of the distance from the port, very low. To obtain a better understanding of these relationships it is recommended that a dedicated more in-depth analysis based on the raw measurement data is conducted. Upon comparing the Baltic Sea and the North Sea, noticeable differences in non-compliance rates were observed. In general, the Baltic Sea exhibited higher non-compliance rates, with an overall non-compliance rate of 2.2%, compared to 1.3% for the North Sea for the 0.15% FSC cutoff level (Fig. 2D). Similarly for the other cutoff levels the Baltic Sea demonstrated higher non- compliance rates. Importantly, for all cutoff levels, the differences were determined to be statistically signi?cant (P < 0.001). When comparing the airborne results, for the North Sea a higher non- compliance rate is observed for the 0.15% FSC and the 0.20% FSC cutoff levels. However, the Baltic Sea showed a higher non- compliance rate for the 0.13% cutoff level. This indicates that non-compliant OGVs at sea in the North Sea tend to have higher absolute FSC levels compared to those in the Baltic Sea, whereas in the Baltic Sea, low FSC exceedances appear to occur more often. NOx emission control area. For this study, it was not feasible to compare the Belgian NOx non-compliance data with other Fig. 1 FSC non-compliance for remote monitoring locations in the SECA. Non-compliance rates for the different monitoring locations for the 0.15% FSC cutoff level. Measurements with ?xed wing aircraft are displayed with full lines and diamond icons, measurements using RPAS and helicopters have full lines and circle icons, ?xed sniffer measurements are displayed with dotted line and triangular icons. COMMUNICATIONS EARTH & ENVIRONMENT | https://doi.org/10.1038/s43247-023-01050-7 ARTICLE COMMUNICATIONS EARTH & ENVIRONMENT | (2023) 4:391 | https://doi.org/10.1038/s43247-023-01050-7 | www.nature.com/commsenv 3