4.5 Article

Contribution of point sources and non-point sources to nutrient and carbon loads and their influence on the trophic status of the Ganga River at Varanasi, India

Journal

ENVIRONMENTAL MONITORING AND ASSESSMENT
Volume 189, Issue 9, Pages -

Publisher

SPRINGER
DOI: 10.1007/s10661-017-6188-8

Keywords

Atmospheric deposition; DOC; Ganga River; Surface runoff; Point source; Trophic status

Funding

  1. Banaras Hindu University-University Grants Commission, New Delhi

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To determine the possible contributions of point and non-point sources to carbon and nutrient loading in the Ganga River, we analyzed N, P, and organic carbon (OC) in the atmospheric deposits, surface runoff, and in the river along a 37-km stretch from 2013 to 2015. We also assessed the trophic status of the river as influenced by such sources of nutrient input. Although the river N, P, and productivity showed a declining trend with increasing discharge, runoff DOC and dissolved reactive phosphorus (DRP) increased by 88.05 and 122.7% between the Adpr and Rjht sites, indicating contributions from atmospheric deposition (AD) coupled with land use where agriculture appeared to be the major contributor. Point source input led to increased river concentrations of NO3-, NH4+, DRP, and DOC by 10.5, 115.9, 115.2, and 67.3%, respectively. Increases in N, P, and productivity along the gradient were significantly negatively correlated with river discharge (p < 0.001), while river DOC and dissolved silica showed positive relationships. The results revealed large differences in point and non-point sources of carbon and nutrient input into the Ganga River, although these variations were strongly influenced by the seasonality in surface runoff and river discharge. Despite these variations, N and P concentrations were sufficient to enhance phytoplankton growth along the study stretch. Allochthonous input together with enhanced autotrophy would accelerate heterotrophic growth, degrading the river more rapidly in the near future. This study suggests the need for large-scale inter-regional time series data on the point and non-point source partitioning and associated food web dynamics of this major river system.

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