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Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Environmental Sources Marine & Wildlife Sign in to save

Marine Litter Tracking System: A Case Study with Open-Source Technology and a Citizen Science-Based Approach

Sensors 2023 25 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 45 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Silvia Merlino, Marina Locritani, A. Guarnieri, Damiano Delrosso, Marco Bianucci, Marco Paterni

Summary

Researchers deployed GPS-tracked drifter devices in the Arno River using open-source hardware and citizen science approaches to track how plastic litter moves through river systems toward the ocean, providing empirical data on plastic transport dynamics that can improve models of river-to-ocean plastic flux.

Study Type Environmental

It is well established that most of the plastic pollution found in the oceans is transported via rivers. Unfortunately, the main processes contributing to plastic and debris displacement through riparian systems is still poorly understood. The Marine Litter Drifter project from the Arno River aims at using modern consumer software and hardware technologies to track the movements of real anthropogenic marine debris (AMD) from rivers. The innovative "Marine Litter Trackers" (MLT) were utilized as they are reliable, robust, self-powered and they present almost no maintenance costs. Furthermore, they can be built not only by those trained in the field but also by those with no specific expertise, including high school students, simply by following the instructions. Five dispersion experiments were successfully conducted from April 2021 to December 2021, using different types of trackers in different seasons and weather conditions. The maximum distance tracked was 2845 km for a period of 94 days. The activity at sea was integrated by use of Lagrangian numerical models that also assisted in planning the deployments and the recovery of drifters. The observed tracking data in turn were used for calibration and validation, recursively improving their quality. The dynamics of marine litter (ML) dispersion in the Tyrrhenian Sea is also discussed, along with the potential for open-source approaches including the "citizen science" perspective for both improving big data collection and educating/awareness-raising on AMD issues.

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