Characterization of water quality of bottom layer in the brackish Lake Nakanoumi using principal component analysis.

In order to effectively control pollution of sources of drinking water, improve the environmental quality of drinking water and guarantee the sanitation of drinking water, it is very important to assess water source quality. Main factors of drinking water were identified. Then principal component analysis was used to establish assessment model of drinking water, which could ensure that under the condition that the primitive data information was in the smallest loss, a small number of variables were used to replace the integrated multi-dimensional variables to simplify the data structure. The weightings of principal component were determinated as theirs pollution ratios. This paper was based on the theoretical study of principal component analysis, used the monitoring data on water quality of the main water resources in 2013 to evaluate and analyze the water quality of water resources. Analysis content included the main affecting factors, cause of pollution and the degree of pollution.The resulted showed that: the main affecting factors on water quality of Fo Si water source was CODMn, TP, fluoride.

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Water quality of the Chhoti Gandak River using principal component analysis, Ganga Plain, India

Journal of Earth System Science ◽

10.1007/s12040-010-0007-8 ◽

2010 ◽

Vol 119 (1) ◽

pp. 117-127 ◽

Cited By ~ 40

Author(s):

Vikram Bhardwaj ◽

Dhruv Sen Singh ◽

A K Singh

Keyword(s):

Water Quality ◽

Principal Component Analysis ◽

Principal Component ◽

Component Analysis ◽

Ganga Plain ◽

Chhoti Gandak ◽

Gandak River

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Water quality assessment of an urban river receiving tail water using the single-factor index and principal component analysis

Water Science & Technology Water Supply ◽

10.2166/ws.2018.107 ◽

2018 ◽

Vol 19 (2) ◽

pp. 603-609 ◽

Cited By ~ 4

Author(s):

Weiwei Lu ◽

Juan Wu ◽

Zhu Li ◽

Naxin Cui ◽

Shuiping Cheng

Keyword(s):

Water Quality ◽

Principal Component Analysis ◽

Principal Component ◽

Component Analysis ◽

Urban River ◽

Single Factor ◽

Sampling Points ◽

Factor Index ◽

Tail Water

Abstract Tail water from wastewater treatment plants (WWTP) serves as a major supplementary water source for scenic water bodies, whose water quality is one of the major focuses of public and scientific inquiries. This study investigated the temporal and spatial variations in water quality of Tangxihe River, a eutrophic urban river receiving tail water from a nearby WWTP in Hefei City, using the single-factor index (SFI) and principal component analysis (PCA). The results of SFI indicated that the most important parameters responsible for low water quality were total nitrogen (TN) and ammonia (NH4+-N). PCA showed that tail water from the WWTP greatly reduced water quality, as demonstrated by the significantly increased SFIs and integrated principal component values (F values) of the sampling points around the drain outlet of the WWTP (T3, T4 and T5). The sampling points located at the upstream of the river (T1) and up the water-gate of Chaohu Lake (T6) had negative F values, indicating relatively higher water quality. In addition, the season had a significant effect on the water quality of the river. Moreover, we discuss measures to improve the water quality of urban rivers in order to maintain their ecological functions.

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Assessment of Aquatic Ecological Health: A Case Study of Lake Rewalsar, Himachal Pradesh

Journal of AgriSearch ◽

10.21921/jas.v7i01.17627 ◽

2020 ◽

Vol 7 (01) ◽

Author(s):

RAMA KUMARI ◽

PARMANAND KUMAR

Keyword(s):

Water Quality ◽

Principal Component Analysis ◽

Water Quality Index ◽

Quality Index ◽

Management Strategies ◽

Oxygen Demand ◽

Principal Component ◽

Component Analysis ◽

Different Seasons

The present study was conducted for two years to analyze the water quality of the sacred lake Rewalsar. Water quality of different seasons was evaluated by water quality index. Various statistical techniques, such as correlation, principal component analysis were applied. Based on Water Quality Index, water quality of the lake was in the range of 33-80 in different seasons. Cluster analysis of similarity indicates the relationship intensity between the seasons as cluster ranged 80-100% during the study period. In the principal component analysis maximum variables (Conductivity, Alkalinity, Biochemical Oxygen Demand, Nitrates, Phosphates, and Chloride) shows maximum influence during the summer and monsoon. The outcome revealed that the major driving factors of water quality deterioration are the runoff of effluent from the domestic area and offering food materials to the fishes. So, it is necessary to implement effective management strategies for the conservation of the Rewalsarlake.

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Assessment of Anthropogenic Impacts on the Water Quality of Marreco River, Brazil, Based on Principal Component Analysis and Toxicological Assays

Water Air & Soil Pollution ◽

10.1007/s11270-016-3025-6 ◽

2016 ◽

Vol 227 (9) ◽

Cited By ~ 11

Author(s):

Soraya Moreno Palácio ◽

Fernando Rodolfo Espinoza-Quiñones ◽

Aline Roberta de Pauli ◽

Pitágoras Augusto Piana ◽

Caroline Bressan Queiroz ◽

...

Keyword(s):

Water Quality ◽

Principal Component Analysis ◽

Anthropogenic Impacts ◽

Principal Component ◽

Component Analysis

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Evaluation of Water Quality of Kaveri River in Tiruchirappalli District, Tamil Nadu by Principal Component Analysis

Current World Environment ◽

10.12944/cwe.11.1.12 ◽

2016 ◽

Vol 11 (1) ◽

pp. 89-95 ◽

Cited By ~ 1

Author(s):

Monikandon Sukumaran ◽

Kesavan Devarayan

Keyword(s):

Water Quality ◽

Principal Component Analysis ◽

Tamil Nadu ◽

Oxygen Demand ◽

Principal Component ◽

Component Analysis ◽

Quality Parameters ◽

Water Quality Parameters ◽

Linear Relationships

Principal component analysis is a unique technique for reducing the dimensionality of the data. In this study, ten water quality parameters of the river Kaveri observed at five different stations of Tiruchirappalli for six years were collected and subjected to principal component analysis. A computational program was prepared in order to process and understand the data as a cluster. At first necessary data for compiling the program were listed and then fed to the program. Then the outputs were analyzed and possible linear and non-linear relationships between the water quality parameters and the timeline. It is understood that biological oxygen demand and fecal coli had a linear relationship. Further, the results suggested for group of factors that influence the water quality in a particular year.

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