http://scholars.ntou.edu.tw/handle/123456789/21557
Title: | Quantity and type of coastal debris pollution in Taiwan: A rapid assessment with trained citizen scientists using a visual estimation method | Authors: | Yen, Ning Hu, Chieh-Shen Chiu, Ching-Chun Walther, Bruno A. |
Keywords: | ANTHROPOGENIC MARINE DEBRIS;BEACH LITTER;SCIENCE;OCEAN;MICROPLASTICS;ACCUMULATION;SHORELINES;ABUNDANCE;DYNAMICS;TRENDS | Issue Date: | 20-May-2022 | Publisher: | ELSEVIER | Journal Volume: | 822 | Source: | SCI TOTAL ENVIRON | Abstract: | Ongoing monitoring of the distribution and composition of coastal debris is a prerequisite for efficient management and cleanups. Therefore, we conducted a rapid assessment of coastal debris along the 1210 km coastline of Taiwan using a visual estimation method. Forty-nine citizen scientists were intensively trained to correctly identify the volume and types of debris. At 121 sampling locations randomly placed along Taiwan's coastline, the citizen scientists recorded the pollution level and the three most abundant debris types within a 100-m transect during four surveys in 2018-2019. Averaging over the four surveys, the mean amount of coastal debris was estimated to be 406.6 kg/km, and the three most abundant debris types were plastic bottles, foamed plastics, and fishing nets and ropes. Using a statistical test which avoids spatial pseudoreplication, we showed that north-facing coastlines had significantly higher pollution levels than the other coastlines, which we suggest is deposited there during strong winter winds. We also showed that fishery-related debris was a much more important part of coastal debris when the volume of it was determined instead of just the number of items. Mean pollution levels were further associated with wind speed, coastline type, and the distance to presumed pollution sources. Our results compare well with similar surveys conducted in Japan and South Korea. In each country, the debris was highly aggregated, which means it was concentrated in a few highly polluted localities. Therefore, the visual estimation method can effectively guide cleanup efforts to the most polluted areas and also reliably generate long-term monitoring data. |
URI: | http://scholars.ntou.edu.tw/handle/123456789/21557 | ISSN: | 0048-9697 | DOI: | 10.1016/j.scitotenv.2022.153584 |
Appears in Collections: | 11 SUSTAINABLE CITIES & COMMUNITIES 14 LIFE BELOW WATER |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.