SALT Lofoten AS, Havneterminalen, Fiskergata 23, N-8301 Svolvær, Norway.
GRID-Arendal, Teaterplassen 3, N-4836 Arendal, Norway.
Mar Pollut Bull. 2019 Feb;139:117-126. doi: 10.1016/j.marpolbul.2018.12.025. Epub 2018 Dec 22.
Effective site selection is a key component of maximising debris removal during coastal cleanup actions. We tested a GIS-based predictive model to identify marine litter hotspots in Lofoten, Norway based on shoreline gradient and shape. Litter density was recorded at 27 randomly selected locations with 5 transects sampled in each. Shoreline gradient was a limiting factor to litter accumulation when >35%. The curvature of the coastline correlated differently with litter density at different spatial scales. The greatest litter concentrations were in small coves located on larger headlands. A parsimonious model scoring sites on a scale of 1-5 based on shoreline slope and shape had the highest validation success. Sites unlikely to have high litter concentrations were successfully identified and could be avoided. The accuracy of hotspot identifications was more variable, and presumably more parameters influencing litter deposition, such as shoreline aspect relative to prevailing winds, should be incorporated.
有效的场地选择是最大限度地清除沿海地区垃圾的关键组成部分。我们测试了一种基于 GIS 的预测模型，该模型根据海岸线的坡度和形状，在挪威罗弗敦群岛确定了海洋垃圾热点。在 27 个随机选择的地点记录了垃圾密度，每个地点有 5 个样条。当坡度>35%时，坡度成为垃圾堆积的限制因素。海岸线的曲率在不同的空间尺度上与垃圾密度的相关性不同。最大的垃圾浓度出现在位于较大岬角上的小海湾中。一个基于海岸线坡度和形状的 1-5 分制简单评分模型具有最高的验证成功率。成功识别出不太可能有高浓度垃圾的地点，可以避免这些地点。热点识别的准确性变化较大，可能需要纳入更多影响垃圾沉积的参数，例如与盛行风相比的海岸线方位。
