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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, Developing a methodology to quantify mismanaged plastic waste entering the ocean in coastal countries(John Wiley and Sons Inc, 2022-12-01)Marine plastic accumulation has gained international attention in recent years. Sources, pathways, and environmental impacts are being currently studied to understand the complex interactions during waste, especially plastic, transportation to the sea. Rivers have been identified as debris corridors allowing transportation of mismanaged waste. However, there is also evidence of waste accumulation in river basins, suggesting they can also act as sinks. Thus, assuming a uniform and continuous transportation of waste through rivers towards the ocean may signify an oversimplification. This study proposes a methodology to estimate plastic release to the ocean, considering a more detailed characterization of each river basin, including natural attributes and manmade constructions that may act as barriers or boosters for this release. The methodology is exemplified using a case study for the Region of Piura, Peru, and estimating a range of 4.2 to 13.9 kg/person/year of plastic waste reaching the Pacific coast during 2018. These results, when compared with the existing literature, demonstrate more conservative estimations. This methodology is presented as a useful tool that can be easily applied to develop more accurate mismanaged waste dissipation along different compartments. Supplementary Information: The online version of this article (doi:10.1111/jiec.13349) contains supplementary material, which is available to authorized users. - 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, Marine Litter Colonization: Methodological Challenges and Recommendations(Frontiers Media S.A., 2023-02-02)Marine litter colonization by marine invertebrate species is a major global concern resulting in the dispersal of potentially invasive species has been widely reported. However, there are still several methodological challenges and uncertainties in this field of research. In this review, literature related to field studies on marine litter colonization was compiled and analyzed. A general overview of the current knowledge is presented. Major challenges and knowledge gaps were also identified, specifically concerning: 1) uncertainties in species identification, 2) lack of standardized sampling methodologies, 3) inconsistencies with the data reported, and 4) insufficient chemical-analytical approaches to understand this phenomenon. Aiming to serve as a guide for future studies, several recommendations are provided for each point, particularly considering the inaccessibility to advanced techniques and laboratories.3