Water Quality Characterization of Marusudar River in Chenab Sub-Basin of North-Western Himalaya Using Multivariate Statistical Methods

Bhat, Sami Ullah; Bhat, Aftab Ahmad; Jehangir, Arshid; Hamid, Aadil; Sabha, Inam; Qayoom, Umara

doi:10.1007/s11270-021-05394-8

Cited by 16 publications

(10 citation statements)

References 113 publications

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“…Satellite‐based assessments of geomorphology, topography, geology, structural controls, soil types, and the land use and land cover (Ingole et al, 2015; Romshoo & Muslim, 2011; Thakur et al, 2016) and the Surface Water and Ocean Topography (SWOT) analysis all provide critical datasets which are urgently needed for studying the changing Himalayan waterways (Pekel et al, 2016). While the predictive models on water quality and species‐environment relationships have markedly enhanced conservation efforts in the Himalayas (Bhat et al, 2021), artificial intelligence (AI) and machine learning (ML) has become a promising alternative to conventional statistical approaches, as AI is well suited with non‐linear datasets and geographic information systems that help enhance the accuracy of predicting future changes in waterways in the Himalayas (Joy & Death, 2004).…”

Section: Are There Adaptive Measures For Waterway Resilience In the A...mentioning

confidence: 99%

How resilient are waterways of the Asian Himalayas? Finding adaptive measures for future sustainability

et al. 2023

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The high‐mountain system, a storehouse of major waterways that support important ecosystem services to about 1.5 billion people in the Himalaya, is facing unprecedented challenges due to climate change during the 21st century. Intensified floods, accelerating glacial retreat, rapid permafrost degradation, and prolonged droughts are altering the natural hydrological balances and generating unpredictable spatial and temporal distributions of water availability. Anthropogenic activities are adding further pressure onto Himalayan waterways. The fundamental question of waterway management in this region is therefore how this hydro‐meteorological transformation, caused by climate change and anthropogenic perturbations, can be tackled to find avenues for sustainability. This requires a framework that can diagnose threats at a range of spatial and temporal scales and provide recommendations for strong adaptive measures for sustainable future waterways. This focus paper assesses the current literature base to bring together our understanding of how recent climatic changes have threatened waterways in the Asian Himalayas, how society has been responding to rapidly changing waterway conditions, and what adaptive options are available for the region. The study finds that Himalayan waterways are crucial in protecting nature and society. The implementation of integrated waterways management measures, the rapid advancement of waterway infrastructure technologies, and the improved governance of waterways are more critical than ever.This article is categorized under: Engineering Water > Sustainable Engineering of Water

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Section: Are There Adaptive Measures For Waterway Resilience In the A...mentioning

confidence: 99%

How resilient are waterways of the Asian Himalayas? Finding adaptive measures for future sustainability

et al. 2023

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“…Therefore, the data were standardized (mean value: 0, standard deviation value: 1; Z-Score) to improve the explanatory power of the statistical analysis results and reduce the related errors. Standardization reduces errors due to large fluctuations in water quality data (i.e., lower effects of a unit of measure and parameter variance), and satisfies the assumption of normality for statistical analysis [3].…”

Section: Data Treatments and Multivariate Statistical Techniquesmentioning

confidence: 99%

“…Water quality is a major global issue, as surface water is degraded due to the effects of land-cover changes, higher population density, livestock manure discharge, and point and nonpoint source pollution [2]. Rivers are created by natural water sources that are susceptible to anthropological, biological, and chemical impacts, and are important indicators for sustainable development in terms of human well-being and ecological and economic development [3]. In general, the water quality of rivers is greatly affected by natural process factors, such as soil erosion and weathering [4], oxidation of rock minerals [5], and seawater intrusion [6], and by currently unsustainable anthropogenic factors such as domestic and municipal sewage [7], livestock fertilizers [8], and agricultural and industrial wastewater [9].…”

Section: Introductionmentioning

confidence: 99%

Identification and Apportionment of Potential Pollution Sources Using Multivariate Statistical Techniques and APCS-MLR Model to Assess Surface Water Quality in Imjin River Watershed, South Korea

Cho

Choi

Lee

et al. 2022

Water

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Reliable water quality monitoring data, identifying potential pollution sources, and quantifying the corresponding potential pollution source apportionment are essential for future water resource management and pollution control. Here, we collected water quality data from seven monitoring sites to identify spatiotemporal changes in surface water in the Imjin River Watershed (IRW), South Korea, distinguish potential pollution sources, and quantify the source apportionment from 2018–2020. An analysis was performed based on multivariate statistical techniques (MST) and the absolute principal component score-multiple linear regression (APCS-MLR) model. Statistically significant groups were created based on spatiotemporally similar physicochemical water quality characteristics and anthropogenic activities: low-pollution (LP) and high-pollution (HP) regions, and dry season (DS) and wet season (WS). There were statistically significant mean differences in water quality parameters between spatial clusters, rather than between temporal clusters. We identified four and three potential factors that could explain 80.75% and 71.99% in the LP and HP regions, respectively. Identification and quantitative evaluation of potential pollution sources using MST and the APCS-MLR model for the IRW may be useful for policymakers to improve the water quality of target watersheds and establish future management policies.

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“…Pode ser uma ferramenta de comunicação poderosa para simplificar um conjunto complexo de parâmetros, cuja interpretação individual pode ser difícil, em um único índice que representa a qualidade geral da água (ANDRADE COSTA et al, 2020). É um método eficaz de medir a qualidade da água que é comumente usado entre pesquisadores e gestores de qualidade da água (HORTON, 1965;KUMAR et al, 2019;BHAT et al, 2021). O IQA é calculado pelo produto ponderado das qualidades de água correspondentes às variáveis que integram o índice (Equação 1).…”

Section: Cálculo Do íNdice De Qualidade Da áGua (Iqa)unclassified

“…É importante ressaltar que a qualidade da água é um pré-requisito para o saneamento sustentável, de acordo com o Objetivo de Desenvolvimento Sustentável (ODS) no. 6, e é igualmente importante para vários outros objetivos do Desenvolvimento Sustentável relacionados à saúde, segurança alimentar e biodiversidade (BHAT, 2021). Esta questão está diretamente relacionada com o crescimento populacional, a urbanização e a mudança no estilo de vida da população, que levam a uma mudança na qualidade e no volume das águas residuais nas cidades, com o potencial de desencadear riscos nutricionais e biológicos nos sistemas aquáticos (GUPTA et al, 2018), até mesmo para populações ribeirinhas.…”

Section: Introductionunclassified

Influência do ribeirão dos Pinheiros na qualidade de água do rio Atibaia, sub-bacia do rio Atibaia, São Paulo, Brasil

Venancio¹,

Borba²

2022

Geochim. Bras.

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Esta pesquisa teve como objetivo avaliar o impacto da qualidade da água do ribeirão Pinheiros na qualidade das águas do rio Atibaia, por meio de análise estatística e análise da concentração espaço-temporal dos dados de monitoramento de parâmetros físico-químicos e microbiológicos pré-existentes. Foi realizada pesquisa documental de dados de monitoramento de parâmetros físicos, químicos e biológicos disponibilizados pela CETESB, através do Sistema de Informação InfoÁguas. Após análise temporal e espacial foram utilizadas 3 estações de monitoramento para avaliação da qualidade das águas superficiais através da série histórica de dados no rio Atibaia (estações ATIB02035 e ATIB02065) e do ribeirão dos Pinheiros (PINO03900), entre anos de 2009 a 2019. Os valores dos parâmetros das amostras foram analisados considerando sua equivalência às classes estabelecidas na Resolução CONAMA nº 357/2005. Para o Índice de Qualidade das Águas (IQA) foram selecionadas amostras das estações de monitoramento de qualidade de água estudadas, as quais contém dados dos 9 parâmetros utilizados no cálculo. O Índice de Qualidade das Águas (IQA) anual médio do ribeirão dos Pinheiros (estação PINO03900) é aceitável, enquanto o índice do rio Atibaia (estações ATIB02035 e ATIB02065) é bom, tanto para as águas antes do ribeirão dos Pinheiros (Estação ATIB02035) quanto para as águas após o desague do ribeirão dos Pinheiros (Estação ATIB02065). Após análise generalizadas, destaca-se que os parâmetros nas águas do ribeirão dos Pinheiros que mais influenciam nas águas do rio Atibaia são coliformes termotolerantes, demanda bioquímica de oxigênio, nitrogênio, fósforo, resíduo total e oxigênio dissolvido.

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Water Quality Characterization of Marusudar River in Chenab Sub-Basin of North-Western Himalaya Using Multivariate Statistical Methods

Cited by 16 publications

References 113 publications

How resilient are waterways of the Asian Himalayas? Finding adaptive measures for future sustainability

How resilient are waterways of the Asian Himalayas? Finding adaptive measures for future sustainability

Identification and Apportionment of Potential Pollution Sources Using Multivariate Statistical Techniques and APCS-MLR Model to Assess Surface Water Quality in Imjin River Watershed, South Korea

Influência do ribeirão dos Pinheiros na qualidade de água do rio Atibaia, sub-bacia do rio Atibaia, São Paulo, Brasil

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