2019
DOI: 10.1016/j.marpolbul.2018.12.025
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A novel GIS-based tool for predicting coastal litter accumulation and optimising coastal cleanup actions

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Cited by 46 publications
(26 citation statements)
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“…Based on our qualitative analysis and on the results of and Haarr et al (2019), we suggest a general division of deposition zones at beaches (Figure 3). We adhere to this division when describing our results.…”
Section: General Deposition Zones In the Studied Beachesmentioning
confidence: 64%
See 1 more Smart Citation
“…Based on our qualitative analysis and on the results of and Haarr et al (2019), we suggest a general division of deposition zones at beaches (Figure 3). We adhere to this division when describing our results.…”
Section: General Deposition Zones In the Studied Beachesmentioning
confidence: 64%
“…Onshore accumulation of marine plastic is controlled by ocean currents, tidal movements, wind and coastal shape. Important geographical properties that control deposition include shape and orientation of the bay, shoreline slope and substratum (Haarr et al, 2019). Plastic accumulation in western Norway is localized into bays and coves .…”
Section: Physical Factors Controlling Plastic Accumulation In Western Norwaymentioning
confidence: 99%
“…In e.g. Haarr et al (2019) and Hardesty et al (2017), it was reported that large scale headlands tend to enhance catchment of litter compared to large scale sheltered areas. This is in line with our findings, with an increasing n grid •n leading to more predicted litter (see Appendix B5).…”
Section: Regression Analysismentioning
confidence: 99%
“…The accelerated increase of mismanaged plastic waste into the global ocean gives rise to the need of effective cleanup strategies (Ogunola et al, 2018). In order to minimize the negative impact of plastic pollution on the environment, cleanup strategies need to be optimized to target the most impacted areas while limiting the economic costs (Haarr et al, 2019;Newman et al, 2015).…”
Section: Introductionmentioning
confidence: 99%
“…Citizen science data collection presents numerous potential sources of error such as: observer bias, poor and inconsistent tagging of items, lack of standardisation of collection methods, regional biases in effort, and temporal variation in effort 43,44 . However, observed trends within and between nations in relation to national legislation and the WHO's declarations are evident, supporting the use of citizen science in understanding widespread geographic and temporal events.…”
Section: Long Termmentioning
confidence: 99%