淡水生态系统重金属污染评估数据集。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Olha Biedunkova, Pavlo Kuznietsov
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引用次数: 0

摘要

重金属污染导致的水质恶化对人类健康和水生生态系统都构成了严重威胁。水生环境中重金属浓度的上升主要归因于人类活动。随着时间的推移,这些金属会在水体中不断累积,因此有必要进行严格监测,以准确评估污染水平。本研究主要是评估斯捷尔河(乌克兰)在核电厂排水前后的重金属污染情况。评估基于三个指数:重金属污染指数、重金属评估指数和污染程度。因此,本研究分析了重金属,包括锌 (Zn)、镉 (Cd)、铅 (Pb)、铜 (Cu)、镍 (Ni)、锰 (Mn)、砷 (As) 和铬 (Cr)。在五年内(2018-2022 年),每月在两个地点采集水样,随后使用电感耦合等离子体光发射光谱进行分析。结果表明,两个采样点的污染水平较低,表明整个研究区域的金属浓度稳定且均匀。此外,统计分析表明,某些金属与污染指数之间存在明显的关联,支持了这些指数在跟踪污染趋势和评估环境影响方面的实用性。该数据集强调了持续监测对于有效水质管理的重要性。
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 heavy metal pollution assessment in freshwater ecosystems.

Water quality degradation due to heavy metal contamination poses serious threats to both human health and aquatic ecosystems. The rise in the concentration of heavy metals in aquatic environments is largely attributable to anthropogenic activities. These metals accumulate over time in water bodies, necessitating rigorous monitoring to accurately assess pollution levels. The present study is concerned with the assessment of heavy metal pollution in the Styr River (Ukraine) before and after the discharge of water from a nuclear power plant. The assessment is based on three indices: the Heavy Metal Pollution Index, the Heavy Metal Evaluation Index, and the Degree of Contamination. Therefore, heavy metals, including zinc (Zn), cadmium (Cd), lead (Pb), copper (Cu), nickel (Ni), manganese (Mn), arsenic (As) and chromium (Cr), were analyzed in this study. Water samples were collected at two locations on a monthly basis over the course of five years (2018-2022) and subsequently analysed using inductively coupled plasma optical emission spectroscopy. The results indicates a low contamination level at both sampling sites, indicating stable and uniform concentrations of metals across the study area. Moreover, statistical analysis highlights significant associations between certain metals and pollution indices, supporting the indices' utility in tracking pollution trends and assessing environmental impacts. This dataset underscores the importance of ongoing monitoring for effective water quality management.

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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.
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