a case study of pollution in river arkavathy

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• Review Article
• Published: 12 September 2023

Global river water quality under climate change and hydroclimatic extremes

• Michelle T. H. van Vliet   ORCID: orcid.org/0000-0002-2597-8422 1 ,
• Josefin Thorslund   ORCID: orcid.org/0000-0001-6111-4819 1 , 2 ,
• Maryna Strokal   ORCID: orcid.org/0000-0002-8063-7743 3 ,
• Nynke Hofstra   ORCID: orcid.org/0000-0002-0409-5145 3 ,
• Martina Flörke   ORCID: orcid.org/0000-0003-2943-5289 4 ,
• Heloisa Ehalt Macedo   ORCID: orcid.org/0000-0002-3430-1480 5 ,
• Albert Nkwasa   ORCID: orcid.org/0000-0002-8685-8854 6 , 7 ,
• Ting Tang 8 ,
• Sujay S. Kaushal 9 ,
• Rohini Kumar   ORCID: orcid.org/0000-0002-4396-2037 10 ,
• Ann van Griensven 6 ,
• Lex Bouwman 11 , 12 &
• Luke M. Mosley 13  

Nature Reviews Earth & Environment volume  4 ,  pages 687–702 ( 2023 ) Cite this article

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• Environmental chemistry

Climate change and extreme weather events (such as droughts, heatwaves, rainstorms and floods) pose serious challenges for water management, in terms of both water resources availability and water quality. However, the responses and mechanisms of river water quality under more frequent and intense hydroclimatic extremes are not well understood. In this Review, we assess the impacts of hydroclimatic extremes and multidecadal climate change on a wide range of water quality constituents to identify the key responses and driving mechanisms. Comparison of 965 case studies indicates that river water quality generally deteriorates under droughts and heatwaves (68% of compiled cases), rainstorms and floods (51%) and under long-term climate change (56%). Also improvements or mixed responses are reported owing to counteracting mechanisms, for example, increased pollutant mobilization versus dilution during flood events. River water quality responses under multidecadal climate change are driven by hydrological alterations, rises in water and soil temperatures and interactions among hydroclimatic, land use and human drivers. These complex interactions synergistically influence the sources, transport and transformation of all water quality constituents. Future research must target tools, techniques and models that support the design of robust water quality management strategies, in a world that is facing more frequent and severe hydroclimatic extremes.

River water quality is generally deteriorating under droughts and heatwaves (68% of case studies), rainstorms and floods (51%) and multidecadal historical and future climate change (56%), although improvements and mixed responses are also reported.

Droughts and heatwaves result in lower dissolved oxygen and increased river temperature, algae, salinity and concentrations of pollutants (such as pharmaceuticals) from point sources owing to lower dilution. By contrast, low flow during these events leads to reduced pollutant transport from agricultural and urban surface runoff, contributing to lower concentrations.

Rainstorms and floods generally increase the mobilization of plastics, suspended solids, absorbed metals, nutrients and other pollutants from agricultural and urban runoff, although high flow can dilute concentrations for salinity and other dissolved pollutants. The sequence of extreme events (such as droughts followed by floods) also impacts the magnitude and drivers of river water quality responses.

Multidecadal climate change is causing water temperatures and algae to generally increase, partly causing a general decrease in dissolved oxygen concentrations. Nutrient and pharmaceutical concentrations are mostly increasing under climate change, whereas biochemical oxygen demand, salinity, suspended sediment, metals and microorganisms show a mixture of increasing and decreasing trends.

The main driving mechanisms for multidecadal water quality changes in response to climate change include hydrological alterations, rises in water and soil temperatures and interactions of hydroclimatic drivers with land use. These impacts are compounded with other human-induced drivers.

Our findings stress the need to improve understanding of the complex hydroclimatic–geographic–human driver feedbacks; water quality constituent fate, transport, interactions and thresholds; and to develop technologies and water quality frameworks that support the design of robust water quality management strategies under increasing hydroclimatic extremes.

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Acknowledgements

The authors kindly acknowledge J. Banken of Wageningen University and K. Schweden of Ruhr University Bochum for their assistance with collecting water quality literature. The authors thank M. Stoete of Utrecht University for her assistance in designing some figures. The authors also acknowledge the World Water Quality Alliance (WWQA), ISI-MIP and EU COST-Action PROCLIAS initiatives. M.T.H.v.V. was financially supported by the European Union (ERC Starting Grant, B-WEX, Project 101039426) and Netherlands Scientific Organisation (NWO) by a VIDI grant (VI.Vidi.193.019). M.S. was supported by the Netherlands Scientific Organisation (NWO) by a VENI grant (016.Veni.198.001). J.T. was financially supported by The Swedish Research Council Formas (Project No. 2018-00812).

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M.T.H.v.V. designed and led the study and manuscript effort. J.T. contributed to the design of the literature review. J.T., M.S., N.H., M.F., H.E.M., A.N., T.T. and M.T.H.v.V. collected literature for the analyses and wrote reports for specific water quality constituents for the supplementary information. L.M.M., S.S.K. and R.K. contributed to the writing of specific sections. All authors contributed to the writing of the manuscript.

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van Vliet, M.T.H., Thorslund, J., Strokal, M. et al. Global river water quality under climate change and hydroclimatic extremes. Nat Rev Earth Environ 4 , 687–702 (2023). https://doi.org/10.1038/s43017-023-00472-3

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Estimating turbidity concentrations in highly dynamic rivers using Sentinel-2 imagery in Google Earth Engine: Case study of the Godavari River, India

• Research Article
• Published: 01 May 2024

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• Meena Kumari Kolli 1 , 2 &
• Pennan Chinnasamy   ORCID: orcid.org/0000-0002-3184-2134 1 , 2 , 3 , 4 , 5 , 6  

Turbidity is an essential biogeochemical parameter for water quality management because it shapes the physical landscape and regulates ecological systems. It varies spatially and temporally across large water bodies, but monitoring based on point-source field observations remains a difficult task in developing countries due to the need for logistics and costs. In this study, we present a novel semi-analytical approach for estimating turbidity from remote sensing reflectance \(({R}_{{\text{rs}}})\) in moderate to highly turbid waters in the lower part of the Godavari River (i.e., locations near Rajahmundry). The proposed method includes two sub-algorithms—Normalized Difference Turbidity Index (NDTI) and semi-empirical single-band turbidity ( \({T}_{{\text{s}}}\) ) algorithm—to retrieve spectral reflectance information corresponding to the study locations for turbidity modeling. Sentinel-2 Multi-Spectral Imager data have been used to quantify the turbidity in the Google Earth Engine (GEE) platform. The correlation analysis was observed between spectral reflectance values and in situ turbidity data using cubic polynomial regression equations. The results indicated that the \({T}_{{\text{s}}}\) , which uses the only red-edge wavelength, identified turbidity as the most accurate across all locations (highest R 2  = 0.91, lowest RMSE = 0.003), followed by NDTI (highest R 2  = 0.85, lowest RMSE = 0.05), respectively. The remote sensing data application provides a better way to monitor turbidity at large spatio-temporal scales in attaining the water quality standards of the Godavari River.

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Acknowledgements

This work is partially supported by the Institute Post-doctoral Fellowship at IIT Bombay. The authors would like to thank the European Space Agency for free access to the Sentinel-2 data. We are grateful to Google for the GEE platform, which provided an efficient and powerful computing platform.

This research has been supported by the Water Productivity Improvement in Practice (Water-PIP) project, which is funded by the IHE Delft Water and Development Partnership Programme (WDPP) under the programmatic cooperation between the Directorate General for International Cooperation (DGIS) of the Ministry of Foreign Affairs of the Netherlands and IHE Delft (ID: DGIS Activity DME0121369).

Award Number: 111349 (WATERPIP project) | Recipient: Pennan Chinnasamy, Ph.D.

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Centre for Technology Alternatives for Rural Areas, Indian Institute of Technology (IITB), Powai, Mumbai, Maharashtra, 400076, India

Meena Kumari Kolli & Pennan Chinnasamy

Rural Data Research and Analysis Lab (RuDRA), IIT Bombay, Mumbai, India

Interdisciplinary Programme in Climate Studies (IDPCS), IIT Bombay, Mumbai, India

Pennan Chinnasamy

Centre for Machine Intelligence and Data Science(C‑MInDS), IIT Bombay, Mumbai, India

Ashank Desai Centre for Policy Studies, IIT Bombay, Mumbai, India

Nebraska Water Center, University of Nebraska, Lincoln, NE, USA

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Ideation, discussion, data sources identification, draft preparation, editing, and finalization—Prof. Pennan Chinnasamy.

Data collection, GIS, analysis, draft preparation, writing, and maps—Meena Kumari Kolli.

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Kolli, M.K., Chinnasamy, P. Estimating turbidity concentrations in highly dynamic rivers using Sentinel-2 imagery in Google Earth Engine: Case study of the Godavari River, India. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-33344-4

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DOI : https://doi.org/10.1007/s11356-024-33344-4

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