利用主成分分析监测地下水质量

IF 2.3 Q2 REMOTE SENSING
Manaswinee Patnaik, Chhabirani Tudu, Dilip Kumar Bagal
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引用次数: 0

摘要

对于没有常年地表水源的地区,地下水可能被视为仅次于地表水的第二大饮用水源。然而,地下水极易受到污染,因为地下水库是由地表水流入地下形成的;在其适当的运动过程中,可能会溶解任何可能的污染物,如农用化学品、垃圾填埋场沥滤液、地下管道溢出的油和下水道废物,并进一步将受污染的水输送到一些地下水含水层,从那里再将水抽出供人类饮用。因此,在地下水可饮用之前,应对其水质进行评估,以确定是否含有碱度、硬度、不良矿物质和重金属。布巴内斯瓦尔中央地下水委员会(CGWB)在卡拉汉迪地区的 61 个站点收集了 15 个理化参数的数据,包括 pH 值、碳酸氢盐、硬度、硫酸盐、Cl-、总溶解固体、Mg++、K+、Na+、总碱度、硝酸盐、氟化物、碳酸盐、电导率和钙,以评估地下水的质量。其目的是确定影响水质的主要元素,并了解地下水质量指标的区域分布情况。作为研究的一部分,我们收集了中央地下水委员会(CGWB)的数据,并使用 PCA 方法确定了主要的影响元素。为了进一步减少数据集的多维性,我们采用了主成分分析法。前三个主要成分加在一起,解释了 76.64% 的总体变异性。前两个主因子本身可解释总变异的 56.9%。这三个主因子分别表示地下水的盐度、硬度、相对碱度和酸度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Monitoring groundwater quality using principal component analysis

For areas without perennial surface water sources, groundwater might be considered the second-largest source of drinking water after surface water. However, groundwater is highly prone to contamination as the groundwater reservoir is formed by the movement of surface water into the subsoil; in its due course of motion, it may dissolve any probable contaminants such as agrochemicals, landfill leachates, the oil spill from underground pipelines, and sewer waste and further convey the contaminated water to join some groundwater aquifers from where the water is again pumped out for human consumption. Therefore, prior to its potable applicability, the quality of groundwater should be evaluated for the presence of alkalinity, hardness, and undesirable and heavy minerals. The Central Ground Water Board (CGWB), Bhubaneswar, collects data on 61 stations in the Kalahandi District for 15 physiochemical parameters, including pH, bicarbonate, hardness, sulphate, Cl, total dissolved solids, Mg++, K+, Na+, total alkalinity, nitrate, fluoride, carbonate, electrical conductivity, and calcium, to assess the quality of the groundwater. The goals were to pinpoint the major elements influencing water quality and comprehend the groundwater quality measures’ regional distribution. Data from the Central Groundwater Board (CGWB) were collected as part of our research, and PCA was used to identify the major impacting elements. To further minimize the dataset’s multidimensionality, a principal component analysis is used. Together, the first three major components explain 76.64% of the overall variability. The first two principal factors themselves explain about 56.9% of the total variance. The three principal factors indicate salinity, hardness, and relative alkalinity and acidity, respectively, in the groundwater.

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来源期刊
Applied Geomatics
Applied Geomatics REMOTE SENSING-
CiteScore
5.40
自引率
3.70%
发文量
61
期刊介绍: Applied Geomatics (AGMJ) is the official journal of SIFET the Italian Society of Photogrammetry and Topography and covers all aspects and information on scientific and technical advances in the geomatics sciences. The Journal publishes innovative contributions in geomatics applications ranging from the integration of instruments, methodologies and technologies and their use in the environmental sciences, engineering and other natural sciences. The areas of interest include many research fields such as: remote sensing, close range and videometric photogrammetry, image analysis, digital mapping, land and geographic information systems, geographic information science, integrated geodesy, spatial data analysis, heritage recording; network adjustment and numerical processes. Furthermore, Applied Geomatics is open to articles from all areas of deformation measurements and analysis, structural engineering, mechanical engineering and all trends in earth and planetary survey science and space technology. The Journal also contains notices of conferences and international workshops, industry news, and information on new products. It provides a useful forum for professional and academic scientists involved in geomatics science and technology. Information on Open Research Funding and Support may be found here: https://www.springernature.com/gp/open-research/institutional-agreements
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