Roadmap for Long-Term Macroplastic Monitoring in Rivers

Macroplastic pollution in and around rivers negatively impacts human livelihood, and aquatic ecosystems. Monitoring data are crucial for better understanding and quantifying this problem, and for the design of effective intervention strategies. However, current monitoring efforts are often of short duration, or study single river compartments. We present a “Roadmap” to overcome the challenges related to the design and implementation of long-term riverine macroplastic monitoring strategies. This “Roadmap” can help accelerating the process of achieving structural monitoring through providing a stepwise approach, which links monitoring goals and research questions to the data and methods required to answer them. We identify four monitoring goals: 1) policy, 2) knowledge development, 3) operations, and 4) solutions. Linked to these, we provide a non-exhaustive list of 12 globally common research questions that are important to answer to reach these goals. The “Roadmap” takes these questions and links them to development levels of monitoring methods for each river compartment: 1) method development, 2) baseline assessment, and 3) long-term monitoring. At each level, specific questions can only be answered if the level is achieved for specific river compartments. For questions at higher levels, the previous levels need to be achieved first. This creates a clear stepwise approach to solve open challenges. With the “Roadmap”, we provide a new tool to support decision-making and planning of specific projects by policy makers. The “Roadmap” is a clear and stepwise, yet flexible framework that allows to add and remove elements based on new insights, available resources, and other relevant changes

Deep learning for detecting macroplastic litter in water bodies: A review

Plastic pollution in water bodies is an unresolved environmental issue that damages all aquatic environments, and causes economic and health problems. Accurate detection of macroplastic litter (plastic items &gt;5 mm) in water is essential to estimate the quantities, compositions and sources, identify emerging trends, and design preventive measures or mitigation strategies. In recent years, researchers have demonstrated the potential of computer vision (CV) techniques based on deep learning (DL) for automated detection of macroplastic litter in water bodies. However, a systematic review to describe the state-of-the-art of the field is lacking. Here we provide such a review, and we highlight current knowledge gaps and suggest promising future research directions. The review compares 34 papers with respect to their application and modeling related criteria. The results show that the researchers have employed a variety of DL architectures implementing different CV techniques to detect macroplastic litter in various aquatic environments. However, key knowledge gaps must be addressed to overcome the lack of: (i) DL-based macroplastic litter detection models with sufficient generalization capability, (ii) DL-based quantification of macroplastic (mass) fluxes and hotspots and (iii) scalable macroplastic litter monitoring strategies based on robust DL-based quantification. We advocate for the exploration of data-centric artificial intelligence approaches and semi-supervised learning to develop models with improved generalization capabilities. These models can boost the development of new methods for the quantification of macroplastic (mass) fluxes and hotspots, and allow for structural monitoring strategies that leverage robust DL-based quantification. While the identified gaps concern all bodies of water, we recommend increased efforts with respect to riverine ecosystems, considering their major role in transport and storage of litter.</p

Deep learning for detecting macroplastic litter in water bodies: A review

Plastic pollution in water bodies is an unresolved environmental issue that damages all aquatic environments, and causes economic and health problems. Accurate detection of macroplastic litter (plastic items &gt;5 mm) in water is essential to estimate the quantities, compositions and sources, identify emerging trends, and design preventive measures or mitigation strategies. In recent years, researchers have demonstrated the potential of computer vision (CV) techniques based on deep learning (DL) for automated detection of macroplastic litter in water bodies. However, a systematic review to describe the state-of-the-art of the field is lacking. Here we provide such a review, and we highlight current knowledge gaps and suggest promising future research directions. The review compares 34 papers with respect to their application and modeling related criteria. The results show that the researchers have employed a variety of DL architectures implementing different CV techniques to detect macroplastic litter in various aquatic environments. However, key knowledge gaps must be addressed to overcome the lack of: (i) DL-based macroplastic litter detection models with sufficient generalization capability, (ii) DL-based quantification of macroplastic (mass) fluxes and hotspots and (iii) scalable macroplastic litter monitoring strategies based on robust DL-based quantification. We advocate for the exploration of data-centric artificial intelligence approaches and semi-supervised learning to develop models with improved generalization capabilities. These models can boost the development of new methods for the quantification of macroplastic (mass) fluxes and hotspots, and allow for structural monitoring strategies that leverage robust DL-based quantification. While the identified gaps concern all bodies of water, we recommend increased efforts with respect to riverine ecosystems, considering their major role in transport and storage of litter.Sanitary Engineerin