Water Quality Assessment using Environmetrics and Pollution Indices in a Tropical River, Kerala, SW Coast of India

Envirometrics and pollution indices are proxies to assess water quality of a wetland ecosystem. Hence, the present study is focused on establishing water quality and elucidating the pollution status of Karamana River (KR) in Kerala, SW coast of India. The Karamana River Basin – KRB (n=6th; L= 68 km, A=695 km2), is the main source of water for domestic and drinking purpose in Thiruvananthapuram city. The Killi River (n= 5th; L= 24 km, A= 102 km2), the largest tributary of KR, carry heavy load of pollutants mainly from city and joins KR towards its downstream side. For this study, about 12 sampling stations were selected along the KR from upstream to downstream (interval= ~3km), and water samples (n=12x2= 24) were collected during non-monsoon (NON) and monsoon (MON) of 2015 to assess the variability and sourcing of key hydrochemical variables. Environmetric methods, viz., Pearson Correlation and Principal Component Analysis (PCA) were applied for apportionment of pollution sources significantly responsible for the surface water quality. It was found that sewage effluents and seawater intrusion were the primary factors deteriorating water quality in downstream. Further, the results of water quality analyses and Pollution Indices, viz., Organic Pollution Index (OPI), Eutrophication Index (EI) and Comprehensive Pollution Index (CPI) indicate that lower reaches (L= ~4 km) of KR is seriously polluted. A distinct Zone of Pollution Influence (ZPI) has been delineated based on the indices and this attempt is first of its kind in KR. The present study provides several noteworthy contributions to the existing knowledge on the factors influencing surface water quality and serves as a baseline data for watershed managers and administrators.

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Surface water quality assessment in the semi-arid area by a combination of heavy metal pollution indices and statistical approaches for sustainable management

Environmental Challenges ◽

10.1016/j.envc.2021.100230 ◽

2021 ◽

pp. 100230

Author(s):

Diani Khadija ◽

Amar Hicham ◽

Azmi Rida ◽

Elbelrhiti Hicham ◽

Nouayti Nordine ◽

...

Keyword(s):

Water Quality ◽

Heavy Metal ◽

Surface Water ◽

Sustainable Management ◽

Surface Water Quality ◽

Water Quality Assessment ◽

Arid Area ◽

Pollution Indices ◽

Statistical Approaches ◽

Semi Arid

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Using Principal Components Analysis and IDW Interpolation to Determine Spatial and Temporal Changes of Surface Water Quality of Xin’anjiang River in Huangshan, China

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17082942 ◽

2020 ◽

Vol 17 (8) ◽

pp. 2942 ◽

Cited By ~ 2

Author(s):

Wenjie Yang ◽

Yue Zhao ◽

Dong Wang ◽

Huihui Wu ◽

Aijun Lin ◽

...

Keyword(s):

Water Pollution ◽

Water Quality ◽

Surface Water ◽

Safety Management ◽

Temporal Distribution ◽

Surface Water Quality ◽

Principal Component ◽

Organic Pollution ◽

Sampling Point

This study was aimed at assessing the spatial and temporal distribution of surface water quality variables of the Xin’anjiang River (Huangshan). For this purpose, 960 water samples were collected monthly along the Xin’anjiang River from 2008 to 2017. Twenty-four water quality indicators, according to the environmental quality standards for surface water (GB 3838-2002), were detected to evaluate the water quality of the Xin’anjiang River over the past 10 years. Principal component analysis (PCA) was used to comprehensively evaluate the water quality across eight monitoring stations and analyze the sources of water pollution. The results showed that all samples could be analyzed by three main components, which accounted for 87.24% of the total variance. PCA technology identified important water quality parameters and revealed that nutrient pollution and organic pollution are major latent factors which influence the water quality of Xin’anjiang River. It also showed that agricultural activities, erosion, domestic, and industrial discharges are fundamental causes of water pollution in the study area. It is of great significance for water quality safety management and pollution control of the Xin’anjiang River. Meanwhile, the inverse distance weighted (IDW) method was used to interpolate the PCA comprehensive score. Based on this, the temporal and spatial structure and changing characteristics of water quality in the Xin’anjiang River were analyzed. We found that the overall water quality of Xin’anjiang River (Huangshan) was stable from 2008 to 2017, but the pollution of the Pukou sampling point was of great concern. The results of IDW helped us to identify key areas requiring control in the Xin’anjiang River, which pointed the way for further delicacy management of the river. This study proved that the combination of PCA and IDW interpolation is an effective tool for determining surface water quality. It was of great significance for the control of water pollution in Xin’anjiang River and the reduction of eutrophication pressure in Thousand Island Lake.

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Combining Water Quality Indices and Multivariate Modeling to Assess Surface Water Quality in the Northern Nile Delta, Egypt

Water ◽

10.3390/w12082142 ◽

2020 ◽

Vol 12 (8) ◽

pp. 2142 ◽

Cited By ~ 2

Author(s):

Mohamed Gad ◽

Salah Elsayed ◽

Farahat S. Moghanm ◽

Mohammed H. Almarshadi ◽

Abdullah S. Alshammari ◽

...

Keyword(s):

Water Quality ◽

Surface Water ◽

Nile Delta ◽

Major Ions ◽

Surface Water Quality ◽

Principal Component ◽

Fundamental Aspect ◽

Pollution Indices ◽

Least Squares Regression ◽

Multivariate Statistical

Assessing surface water quality for drinking use in developing countries is important since water quality is a fundamental aspect of surface water management. This study aims to improve surface water quality assessments and their controlling mechanisms using the drinking water quality index (DWQI) and four pollution indices (PIs), which are supported by multivariate statistical analyses, such as principal component analysis, partial least squares regression (PLSR), and stepwise multiple linear regression (SMLR). Twenty-two physicochemical parameters were analyzed using standard analytical methods for 55 surface water sites in the northern Nile Delta, Egypt. The DWQI results indicated that 33% of the tested samples represented good water, and 67% of samples indicated poor to unsuitable water for drinking use. The PI results revealed that surface water samples were strongly affected by Pb and Mn and were slightly affected by Fe and Cr. The SMLR models of the DWQI and PIs, which were based on all major ions and heavy metals, provided the best estimations with R2 = 1 for the DWQI and PIs. In conclusion, integration between the DWQI and PIs is a valuable and applicable approach for the assessment of surface water quality, and the PLSR and SMLR models can be used through applications of chemometric techniques to evaluate the DWQI and PIs.

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