Seasonal hotspots of beach litter in the North-East Atlantic linked to aquaculture and river runoff

Abstract

Macroplastic pollution is a pervasive global environmental challenge, adversely affecting marine ecosystems, wildlife and human health. Understanding temporal variations is crucial for identifying pollution sources and developing effective mitigation policies. However, in-situ data from beach surveys are often irregular, both spatially and temporally, and highly variable, complicating robust statistical conclusions. Here we employ a Bayesian machine learning framework to investigate seasonal variations, identify regional hotspots and elucidate their anthropogenic drivers. Using data from 3866 surveys across 168 western European beaches, we leverage a spatial log-Gaussian Cox Process to enhance statistical inference by integrating information from nearby beaches. Distinct seasonal patterns emerge, with winter and spring exhibiting the highest pollution levels, while pronounced regional differences highlight seasonal pollution hotspots in the western Iberian Peninsula, French coastline, Irish Sea and Skagerrak region. These peaks are attributed to riverine emissions and aquaculture activities, highlighting the potential impact of these sources on beach pollution. Our findings advocate for enhanced, time-specific monitoring to effectively manage litter hotspots, emphasizing the importance of aquaculture-related plastic emissions. Seasonal variations in beach litter on North East Atlantic coastlines are driven by riverine and aquaculture inputs, and are likely to be exacerbated by adverse weather conditions in the future, according to a machine learning framework informed by beach litter survey data.