5年(2018-2022年)珊瑚三角区万鸦老湾(印度尼西亚苏拉威西岛北部)海滩垃圾量化数据集

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES
Veibe Warouw , Jane M. Mamuaja , Markus T. Lasut
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引用次数: 0

摘要

数据显示了宏观和中观尺寸,重量,以及收集自印度尼西亚苏拉威西岛北部万鸦老湾的各种海滩垃圾的数量,该海湾位于珊瑚三角内。这些原始和部分处理的数据是在5年(2018年至2022年)期间使用监测海洋垃圾的国际标准方法收集的,印度尼西亚已采用该方法。该分类基于9种材料类型:(1)塑料(PL),(2)泡沫塑料(FP),(3)布(CL),(4)玻璃和陶瓷(GC),(5)金属(ME),(6)其他类型的垃圾(OT),(7)纸和纸板(PC),(8)橡胶(RB)和(9)木材(WD),并进一步细分为子类别。这些数据显示出垃圾的增加趋势,塑料瓶(2l)的重量达到峰值1125.46克(2020年)和807.68克(2022年),服装垃圾飙升至6088.80克(2022年)。这些数据与珊瑚三角其他地区以及世界其他地区收集的海洋垃圾污染数据一致。我们预计这些将对研究人员和其他利益攸关方有价值,为监测和决策目的提供有关海洋垃圾的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dataset on quantifying the beach litter from Manado Bay (northern Sulawesi, Indonesia), which lies in the Coral Triangle, over a 5-year period (2018-2022)
Data is presented on the macro and meso size, weight, and number of items for a variety of beach litter types collected from Manado Bay, Northern Sulawesi, Indonesia, which lies within the Coral Triangle. The data, both raw and partly processed, were collected over 5 years (2018 to 2022) using the internationally standard method for monitoring marine debris, which has been adopted by Indonesia. The classification is based on 9 material types: (1) plastics (PL), (2) foamed plastics (FP), (3) cloth (CL), (4) glass and ceramics (GC), (5) metal (ME), (6) other type of litter (OT), (7) paper and cardboard (PC), (8) rubber (RB), and (9) wood (WD), and further broken down into subcategories. These data show an increasing trend of waste, with plastic bottles (<2 L) reaching peak weights of 1,125.46 g (2020) and 807.68 g (2022), and clothing waste soaring to 6,088.80 g (2022). These data are compatible with data collected on marine litter pollution in other parts of Coral Triangle, as well as the rest of the world. We anticipate that these will be of value for researchers and other stakeholders, providing information on marine litter for monitoring and decision making purposes.
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来源期刊
Data in Brief
Data in Brief MULTIDISCIPLINARY SCIENCES-
CiteScore
3.10
自引率
0.00%
发文量
996
审稿时长
70 days
期刊介绍: Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.
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