河流系统中不同丰度塑料碎片的高光谱反射率数据库

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-11-20 DOI:10.1038/s41597-024-03974-x
Mohammadali Olyaei, Ardeshir Ebtehaj, Christopher R Ellis
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引用次数: 0

摘要

塑料碎片污染由河流系统输送到湖泊和海洋,已成为一个重大的环境问题,对生态系统、食物网和人类健康造成了不利影响。遥感技术为加强拦截和清除工作提供了一种具有成本效益的方法。然而,与海洋环境不同,人们对淡水中塑料废弃物的光学特性仍然知之甚少。本研究旨在通过提供一个开放式的高光谱反射率数据库,在受控实验室实验中对河流系统中发现的漂浮风化塑料碎片和原始塑料碎片进行研究,从而弥补这一空白。数据库利用密西西比河的天然水域,在亚临界水流条件和不同悬浮沉积物浓度下,通过部署在水力水槽上的遥感数据采集系统进行组装。测量包括从紫外线(UV,350 纳米)到短波红外线(SWIR,2500 纳米)波长的高光谱漫射光反射率。以网络通用数据格式(NetCDF)和逗号分隔值(CSV)存档的数据库为更好地了解淡水生态系统中不同丰度的漂浮塑料碎片的关键光谱特征提供了宝贵的见解。
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A Hyperspectral Reflectance Database of Plastic Debris with Different Fractional Abundance in River Systems.

Plastic debris pollution transported by river systems to lakes and oceans has emerged as a significant environmental concern with adverse impacts on ecosystems, food webs, and human health. Remote sensing presents a cost-effective approach to bolster interception and removal efforts. However, unlike marine environments, the optical properties of plastic debris in fresh waters remain poorly understood. This study aims to address this gap by providing an open-access hyperspectral reflectance database of floating weathered and virgin plastic debris found in river systems under controlled laboratory experiments. Utilizing natural waters from the Mississippi River, the database was assembled using a remote sensing data acquisition system deployed over a hydraulic flume operating under subcritical flow conditions and varying suspended sediment concentrations. The measurements encompass hyperspectral diffused light reflectance from ultraviolet (UV, 350 nm) to shortwave infrared (SWIR, 2500 nm) wavelengths. The database archived in Network Common Data Form (NetCDF) and Comma-separated values (CSV), offers valuable insights for better understanding key spectral signatures indicative of floating plastic debris, with different fractional abundance, in freshwater ecosystems.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
期刊最新文献
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