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Item type:Publication, Prevalence of microplastics in the ocean in Latin America and the Caribbean(Elsevier, 2021-12-12)The release of microplastics to the ocean is an increasing global environmental concern. The specific characteristics of the Global South (e.g., widespread mismanaged waste and wastewater) make this an even greater challenge. The current study performed a critical review related to the prevalence of microplastics in the ocean in Latin America and the Caribbean, analyzing also the possible sources of microplastics release to the marine environment. A majority of the studies assessed point towards mismanaged waste, inland or offshore, as well as mismanaged wastewater as critical sources of plastic pollution into the ocean. However, there is a need to delve into the effects that these microplastics are generating on local biota and human health. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Analyzing the behavior of beachgoers in the city of Lima and their relationship with potential plastic emissions(Elsevier Ltd, 2024-12-01)Beach littering is a source of marine plastic waste accumulation. This is particularly so in overcrowded beaches in the Global South in which cleaning measures are scarce or sporadic and lack of waste management systems can increase plastic release. In the current study we focus on the importance of the behavior shown by beachgoers and how their conduct relates to the amount of plastic that potentially ends up entering littoral ecosystems. Transportation services to beaches, sports, food, and beverage containers are analyzed through a 24-question survey performed to 500 beachgoers in 4 beaches (i.e., Venecia, Punta Negra, Punta Hermosa and San Bartolo) located in the megacity of Lima, Peru, in February 2022. The data obtained were then processed to understand the differences in behavior across different beaches. Moreover, a K-means algorithm was used to identify representative beachgoer profiles. The results showed a dichotomous behavior between two groups of beaches, in which the size group of beachgoers, transportation mode, accommodation, food consumption patterns or the use of reusable containers are some of the main differences between the two groups. No major differences were identified in terms of age distribution across the different beaches, but group sizes were higher in Punta Negra and Villa El Salvador. The K-means algorithm suggests that the surveyed population can be grouped into three main categories, of which two correspond mainly to higher socioeconomic beachgoers in the beaches of Punta Hermosa and San Bartolo. Overall, single use plastic for food and beverages appears as one of the main sources of plastic pollution across beaches and groups, although other sources of plastic emission should not be underestimated. Finally, the three beachgoer profiles identified are useful to implement targeted policies to minimize the environmental impacts of these profiles. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The use of artificial intelligence algorithms to detect macroplastics in aquatic environments: A critical review(Elsevier B.V., 2024-10-01)The presence of macroplastic (MP) is having serious consequences on natural ecosystems, directly affecting biota and human wellbeing. Given this scenario, estimating MPs' abundance is crucial for assessing the issue and formulating effective waste management strategies. In this context, the main objective of this critical review is to analyze the use of machine learning (ML) techniques, with a particular interest in deep learning (DL) approaches, to detect, classify and quantify MPs in aquatic environments, supported by datasets such as satellite or aerial images and video recordings taken by unmanned aerial vehicles. This article provides a concise overview of artificial intelligence concepts, followed by a bibliometric analysis and a critical review. The search methodology aimed to categorize the scientific contributions through temporal and spatial criteria for bibliometric analysis, whereas the critical review was based on generating homogeneous groups according to the complexity of ML and DL methods, as well as the type of dataset. In light of the review carried out, classical ML techniques, such as random forest or support vector machines, showed robustness in MPs detection. However, it seems that achieving optimal efficiencies in multiclass classification is a limitation for these methods. Consequently, more advanced techniques such as DL approaches are taking the lead for the detection and multiclass classification of MPs. A series of architectures based on convolutional neural networks, and the use of complex pre-trained models through the transfer learning, are currently being explored (e.g., VGG16 and YOLO models), although currently the computational expense is high due to the need for processing large volumes of data. Additionally, there seems to be a trend towards detecting smaller plastic, which need higher resolution images. Finally, it is important to stress that since 2020 there has been a significant increase in scientific research focusing on transformer-based architectures for object detection. Although this can be considered the current state of the art, no studies have been identified that utilize these architectures for MP detection. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Integrating technology and environmental data to predict mismanaged plastic waste in a watershed(John Wiley and Sons, 2025)Comprehensive methods for estimating mismanaged waste accumulation in the environment are limited, especially in the Global South, and new technologies are urgently needed. Here, we applied the Azure system, a physical floating barrier designed to retain and extract river floating waste while providing observational data of mismanaged waste, comparing results with a modeling tool that uses material flow analysis to provide estimates of mismanaged waste, incorporating environmental and socioeconomic factors. The Azure system was installed at the Portoviejo River (Ecuador), and anthropogenic litter was removed, extracted, weighed, and classified. Approximately 13.8 tonnes (t) of litter were collected over 2 years of sampling, of which 87% were plastic bags containing domestic waste. About 45% of the total waste collected, that is, 6.2 t, was estimated to be plastic waste. In contrast, modeled mismanaged plastic waste estimates for the Portoviejo River varied between 148 and 1858 t per year, at least two orders of magnitude higher than field data. These results highlight the discrepancy that can occur between observational data and waste estimates. The factors that contribute to this are discussed here to help understand riverine waste sources and transport to the ocean. The results emphasize the need for a better understanding of socioeconomic and environmental aspects in the Global South to help the development of better modeling tools. Our findings of domestic deposition as a major source of riverine contamination in the Portoviejo watershed emphasize the importance of waste management for tackling river contamination. Effective monitoring tools, such as the Azure system, could help improve this.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Estimating carbon and plastic emissions of seafood products in trade routes between the European Union and South America(Pireo Editorial, 2024-06-01)International trade in fishery and aquaculture products is an important means of providing feed and food for different countries around the world. However, it is also responsible for multiple environmental impacts, namely climate change, as well as novel...1