Appraisal of spatial–temporal variation and pollution source estimation of Ganga River system through pollution indices and environmetrics in Upper Ganga basin

Abstract

National river of India, Ganga River, nurturing ecological, economic, and socio-cultural aspects defining the last long history of country. Forming the largest catchment, fulfil the needs of millions of people for fresh water used in domestic, agriculture, commercial and industrial sectors. Therefore, River Ganga is always the center of attraction to the administrative authorities, institutions, academicians and researchers for its quality issues. The periodically examination of the river water quality for its conservation, restoration or rejuvenation is essential and conducted by many researcher on different quality aspects. The aim of the present investigation is to estimates the spatio-temporal variability in the Ganga River System's hydrology in upstream regions in the Himalayan Region of Uttarakhand state, India. The result clearly explains considerable variation in water quality index (WQI: 29.39–71.60) screening seasonal variation, defining the deterioration of water quality (WQ) of Ganga River from good to moderate polluted level through the overall index of pollution (OIP). Moreover, principal component analysis (PCA) has implied to identify the pollution sources. The PCA generates seven components and contributes (85.1%) to influencing river water's hydrochemistry. These outcomes give detailed information to understand the water quality affecting factors in the Ganga River system. The management and remedial practices are an urgent task required to conserve WQ in the upstream region to check WQ's further deterioration in the future.

Impact of COVID-19 Nationwide Lockdowns and Unlock Phases in India on River Water Quality of Upper Part of the Ganga River

“…A total of 11 river water sampling stations were chosen for the current study (Figure 1). The study area receives wide pollutants from developmental and tourist activities including untreated sewage discharge, partially treated industrial effluent, and wastewater from commercial complexes [39][40][41]. At the same time, point sources of pollutants in the study area have not been well defined [42].…”

Specifying a cascade water demand forecasting model using time-series analysis: a case of Jordan

“…In addition, with uncontrolled population growth, industrialization, and urbanization, water scarcity faces many parts of the world. As a result, the situation worsens, leading to continued environmental degradation and climate change globally (Bond et al 2019 ; Matta et al 2022a ), which results in excessive freshwater demand, especially in emerging countries (Matta et al 2022b ). Moreover, severe problems affect societies, including water pollution, availability, and quality of freshwater for basic needs (Matta et al 2022b ).…”

“…As a result, the situation worsens, leading to continued environmental degradation and climate change globally (Bond et al 2019 ; Matta et al 2022a ), which results in excessive freshwater demand, especially in emerging countries (Matta et al 2022b ). Moreover, severe problems affect societies, including water pollution, availability, and quality of freshwater for basic needs (Matta et al 2022b ). In this regard, the United Nations (UN) encourages sustainable development, which is key to securing a sufficient quality of life for the coming generations without threatening the present population’s needs (WCED 1987 ).…”

“…Jordan is fully aware of the importance of providing drinking water for people while the gap between supply and demand is still evident, resulting in a significant water deficit. With its World Water Development Report 2020, the UN emphasized the current and future challenges and encouraged possible responses to climate change conditions that enable the adoption of water demand management practices (Matta et al 2022b ). As a result, the Jordanian government continues to adopt management practices to decrease this gap, involving several programs, projects, and managerial measures for conserving water, and reaching a high level of water supply for its citizens (Alissa 2007 ; MWI 2011 ).…”

Identification of driving factors for chlorophyll‐a in multi‐stable shallow lakes of China employing machine learning methods

“…Besides, process-based dynamic models often required lengthy calibration periods. Considering the complex interactions among physical, chemical and biological parameters in the process of algal growth and accumulation, the growth process of algae presents a non-linear state (Kumar et al, 2021;Li & Wu, 2023;Matta et al, 2022). The aforementioned models may struggle to capture the non-linear relationship and identify the main driving factors of Chl-a.…”