A novel GIS-based tool for predicting coastal litter accumulation and optimising coastal cleanup actions. (February 2019)

• Record Type: Journal Article
• Authors: Haarr, Marthe Larsen; Westerveld, Levi; Fabres, Joan; Iversen, Kriss Rokkan; Busch, Kjersti Eline Tønnessen
• Is Part Of: Marine pollution bulletin. Volume 139(2019)
• Page Start: 117
• Page End: 126
• Publication Date: 2019-02
• Subjects: Marine litter -- Coastal cleanup actions -- Predictive modeling -- Planning tool -- Beach geomorphology; Marine pollution -- Periodicals; Marine Biology -- Periodicals; Water Pollution -- Periodicals; Mer -- Pollution -- Périodiques; Publications périodiques; Pollution des mers; Lutte antipollution; Electronic journals; 363.7394
• DOI: 10.1016/j.marpolbul.2018.12.025
• Languages: English
• ISSNs: 0025-326X

Abstract

Abstract: 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. Highlights: Shore gradients >35% limit the accumulation of marine litter. Small coves situated on larger headlands accumulate the most litter. A simple model considering shoreline geomorphology can identify clean shorelines.