Evaluation of spatio-temporal variation of water quality and source identification of conducive parameters in Damodar River, India

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作者
Souvanik Maity
Ramkrishna Maiti
Tarakeshwar Senapati
机构
[1] Vidyasagar University,Department of Geography
[2] Vidyasagar University,Department of Environmental Science, Directorate of Distance Education (DDE)
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Factor analysis; Cluster analysis; Discriminant analysis; CCME-WQI; Damodar River;
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摘要
The lower course of the Damodar River in West Bengal is one of the most polluted stretches in the Ganga River basin. There is a lack of research along the whole course of the Damodar, and parameter level analysis receives little attention. Eleven monitoring sites were chosen based on the potential sources of pollution for 6 years (2014–2019). Multivariate statistical techniques (factor analysis (FA), cluster analysis (CA), and discriminate analysis (DA)) evaluate the spatial and temporal variation of Damodar River water quality by considering 24 parameters. Factor analysis extracts the most influential seasonal parameters, and stepwise DA extracts ammonia, DO, potassium, temperature, total coliform, TFS, and turbidity, which are the most responsible parameters for seasonal variation of the water quality. CA classify sampling stations into three groups helping to identify the spatial variation of water quality. Ammonia, BOD, calcium, chloride, conductivity, DO, sodium, sulfate, temperature, Alkalinity, TDS, hardness, TSS, and turbidity are the most influential variables for spatial variation extracted through stepwise DA. Monsoon season shows a higher pollution level due to the contribution from both point and non-point sources. Due to high-density urban areas and large-scale industries, the middle course is more polluted. The Canadian Council of Ministers of the Environment (CCME) water quality index (WQI) accesses the water quality in temporal and spatial scales. The resultant water quality pattern is matched with the derived result from multivariate analysis. Poor water quality is regular at all sample sites in all seasons.
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