Integrating technology and environmental data to predict mismanaged plastic waste in a watershed

Lara M. Pinheiro, Diana Ita‐Nagy, Desireé G. Hidalgo, Daniela Flor, Andrea Osorio Baquero, Nicole Becerra, Inty Grønneberg, Ian Vázquez‐Rowe, Ramzy Kahhat, Ceri Lewis and Tamara S. Galloway

Journal of Industrial Ecology, 2025, vol. 29, issue 5, 1912-1926

Abstract: 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.

Date: 2025
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