Wasted shores: Using drones to monitor the spatio-temporal evolution of debris accumulation hotspots on South Africa's Umgeni River

无人机 碎片 海岸 地理 热点(地质) 环境科学 地质学 海洋学 气象学 地震学 生物 遗传学
作者
Tadiwanashe Gutsa,Cristina Trois,Robin de Vries,Thomas Mani
出处
期刊:Science of The Total Environment [Elsevier]
卷期号:: 176791-176791
标识
DOI:10.1016/j.scitotenv.2024.176791
摘要

Rivers are major contributors of plastic waste to the oceans. Running through the 1.3 million-inhabitants in the northern part of the City of Durban, South Africa, the Umgeni River is estimated to flush in the order of tens to hundreds of tonnes of plastic waste into the Indian Ocean every year. The riverbanks are lined with plastic and other macro-waste accumulation zones formed due to direct littering and occasional deposition of river debris loads. This study presents the use of Unmanned Aerial Vehicles (UAVs) and hydro-meteorological sensors to (1) identify, quantify and monitor such anthropogenic macro-waste hotspots; and (2) investigate the influence of rainfall, river water level and a major flood event on the spatio-temporal evolution of the hotspots, evidencing the debris' availability to leak into the Indian Ocean. The one-year aerial monitoring (2021-2022) of waste hotspots shows that extreme hydrometeorological events have an immediate but short-term effect on the erosion of debris stocks in riverine systems. We observe that reduction in mean index changes of hotspot surface area after flooding were 2-5 times higher than in non-flood conditions. Despite visual evidence of seasonality in debris erosion between the wet and dry season, only one type of hotspot showed a significant comparison. Our findings support reported inconsistencies of macro-debris erosion with hydrological factors. Although the data contributes a baseline for macro-debris erosion in the Umgeni River, future ground truth sampling and finer monitoring scales are important to fully understand debris transfer in river systems. The mapping of waste hotspots and understanding their spatio-temporal transfer dynamics supports policymakers in planning and timing to mitigate environmental pollution.
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