{"title":"淡水生态系统重金属污染评估数据集。","authors":"Olha Biedunkova, Pavlo Kuznietsov","doi":"10.1038/s41597-024-04116-z","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1241"},"PeriodicalIF":5.8000,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dataset on heavy metal pollution assessment in freshwater ecosystems.\",\"authors\":\"Olha Biedunkova, Pavlo Kuznietsov\",\"doi\":\"10.1038/s41597-024-04116-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":21597,\"journal\":{\"name\":\"Scientific Data\",\"volume\":\"11 1\",\"pages\":\"1241\"},\"PeriodicalIF\":5.8000,\"publicationDate\":\"2024-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scientific Data\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41597-024-04116-z\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-024-04116-z","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
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.
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.