基于gis的喀拉拉邦坎努尔地下水质量和重金属公共健康风险评估

Thangavelu Arumugam , Sapna Kinattinkara , Karpagamani Vellingiri , Anjana C
{"title":"基于gis的喀拉拉邦坎努尔地下水质量和重金属公共健康风险评估","authors":"Thangavelu Arumugam ,&nbsp;Sapna Kinattinkara ,&nbsp;Karpagamani Vellingiri ,&nbsp;Anjana C","doi":"10.1016/j.clwat.2025.100113","DOIUrl":null,"url":null,"abstract":"<div><div>This study was conducted on groundwater quality in the Kannur region and surrounding areas using multivariate statistical techniques combined with Geographic Information Systems (GIS). We have collected and analysed 37 groundwater samples from different locations for physicochemical and heavy metal properties between January and May 2023. These properties were assessed according to WHO (2016) standards. GIS was employed to analyze spatial patterns and relationships among geographic features, supported by multivariate statistical methods for interpretation. The water quality index (WQI) and heavy metal pollution index (HMPI) were used to combine multiple water quality parameters into single scores for easier assessment. The main findings are as follows: (1) WQI results showed that most sampling sites had water suitable for drinking, except Anthoor and Ulikkal, where water quality was relatively poor; (2) HMPI results indicated that Edat, Peravoor, and Ramanthali had the highest levels of lead; and (3) combining GIS with multivariate statistics proved effective in identifying spatial patterns and pollution hotspots. The study highlights the importance of ongoing monitoring, site-specific actions, and preventive measures to ensure safe drinking water and safeguard public health.</div></div>","PeriodicalId":100257,"journal":{"name":"Cleaner Water","volume":"4 ","pages":"Article 100113"},"PeriodicalIF":0.0000,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A GIS-based assessment of groundwater quality and public health risks of heavy metals in Kannur, Kerala\",\"authors\":\"Thangavelu Arumugam ,&nbsp;Sapna Kinattinkara ,&nbsp;Karpagamani Vellingiri ,&nbsp;Anjana C\",\"doi\":\"10.1016/j.clwat.2025.100113\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This study was conducted on groundwater quality in the Kannur region and surrounding areas using multivariate statistical techniques combined with Geographic Information Systems (GIS). We have collected and analysed 37 groundwater samples from different locations for physicochemical and heavy metal properties between January and May 2023. These properties were assessed according to WHO (2016) standards. GIS was employed to analyze spatial patterns and relationships among geographic features, supported by multivariate statistical methods for interpretation. The water quality index (WQI) and heavy metal pollution index (HMPI) were used to combine multiple water quality parameters into single scores for easier assessment. The main findings are as follows: (1) WQI results showed that most sampling sites had water suitable for drinking, except Anthoor and Ulikkal, where water quality was relatively poor; (2) HMPI results indicated that Edat, Peravoor, and Ramanthali had the highest levels of lead; and (3) combining GIS with multivariate statistics proved effective in identifying spatial patterns and pollution hotspots. The study highlights the importance of ongoing monitoring, site-specific actions, and preventive measures to ensure safe drinking water and safeguard public health.</div></div>\",\"PeriodicalId\":100257,\"journal\":{\"name\":\"Cleaner Water\",\"volume\":\"4 \",\"pages\":\"Article 100113\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-08-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cleaner Water\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2950263225000511\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cleaner Water","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2950263225000511","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

利用多元统计技术结合地理信息系统(GIS)对坎努尔地区及周边地区的地下水水质进行了研究。在2023年1月至5月期间,我们收集并分析了37个不同地点的地下水样本的物理化学和重金属特性。根据世卫组织(2016)标准对这些属性进行了评估。利用地理信息系统分析地理特征之间的空间格局和关系,并辅以多元统计方法进行解释。采用水质指数(WQI)和重金属污染指数(HMPI)将多个水质参数合并为单个分数,便于评价。主要发现如下:(1)WQI结果表明,除Anthoor和Ulikkal水质较差外,其余采样点水质均适宜饮用;(2) HMPI结果显示,Edat、Peravoor和Ramanthali的铅含量最高;(3)将GIS与多元统计相结合,可有效识别空间格局和污染热点。该研究强调了持续监测、具体地点行动和预防措施的重要性,以确保安全饮用水和保障公众健康。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A GIS-based assessment of groundwater quality and public health risks of heavy metals in Kannur, Kerala
This study was conducted on groundwater quality in the Kannur region and surrounding areas using multivariate statistical techniques combined with Geographic Information Systems (GIS). We have collected and analysed 37 groundwater samples from different locations for physicochemical and heavy metal properties between January and May 2023. These properties were assessed according to WHO (2016) standards. GIS was employed to analyze spatial patterns and relationships among geographic features, supported by multivariate statistical methods for interpretation. The water quality index (WQI) and heavy metal pollution index (HMPI) were used to combine multiple water quality parameters into single scores for easier assessment. The main findings are as follows: (1) WQI results showed that most sampling sites had water suitable for drinking, except Anthoor and Ulikkal, where water quality was relatively poor; (2) HMPI results indicated that Edat, Peravoor, and Ramanthali had the highest levels of lead; and (3) combining GIS with multivariate statistics proved effective in identifying spatial patterns and pollution hotspots. The study highlights the importance of ongoing monitoring, site-specific actions, and preventive measures to ensure safe drinking water and safeguard public health.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信