Impact of lockdowns and possible quantification of pollution sources on the water quality of the Yamuna River

References

• Agapiou, A., 2020. Evaluation of Landsat 8 OLI/TIRS level-2 and sentinel 2 level-1C fusion techniques intended for image segmentation of archaeological landscapes and proxies. Remote Sensing, 12 (3), 579. doi:10.3390/rs12030579
   Web of Science ®Google Scholar
• Anderson, M.C., et al., 2007. A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 2. Surface moisture climatology. Journal of Geophysical Research: Atmospheres, 112 (D11). doi:10.1029/2006JD007506
   Web of Science ®Google Scholar
• Anderson, D.M., Glibert, P.M., and Burkholder, J.M., 2002. Harmful algal blooms and eutrophication: nutrient sources, composition, and consequences. Estuaries, 25 (4), 704–726. doi:10.1007/BF02804901
   Google Scholar
• Awoke, A., et al., 2016. River water pollution status and water policy scenario in Ethiopia: raising awareness for better implementation in developing countries. Environmental Management, 58 (4), 694–706. doi:10.1007/s00267-016-0734-y
   PubMed Web of Science ®Google Scholar
• Bierman, P., et al., 2011. A review of methods for analysing spatial and temporal patterns in coastal water quality. Ecological Indicators, 11 (1), 103–114. doi:10.1016/j.ecolind.2009.11.001
   Web of Science ®Google Scholar
• Boretti, A. and Rosa, L., 2019. Reassessing the projections of the world water development report. NPJ Clean Water, 2 (1), 1–6. doi:10.1038/s41545-019-0039-9
   Google Scholar
• Carlson, T.N. and Ripley, D.A., 1997. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, 62 (3), 241–252. doi:10.1016/S0034-4257(97)00104-1
   Web of Science ®Google Scholar
• Chakraborty, B., et al., 2021a. Positive effects of COVID-19 lockdown on river water quality: evidence from River Damodar, India. Scientific Reports, 11 (1), 1–16. doi:10.1038/s41598-021-99689-9
   PubMed Web of Science ®Google Scholar
• Chakraborty, B., et al., 2021b. Cleaning the river Damodar (India): impact of COVID-19 lockdown on water quality and future rejuvenation strategies. Environment, Development and Sustainability, 23 (8), 11975–11989. doi:10.1007/2Fs10668-020-01152-8
   PubMed Web of Science ®Google Scholar
• Chakraborty, B., et al., 2021c. Eco-restoration of river water quality during COVID-19 lockdown in the industrial belt of eastern India. Environmental Science and Pollution Research, 28 (20), 25514–25528. doi:10.1007/s11356-021-12461-4
   PubMed Web of Science ®Google Scholar
• Chakraborty, B., et al., 2022. Effects of COVID-19 lockdown and unlock on the health of tropical large river with associated human health risk. Environmental Science and Pollution Research, 29 (24), 37041–37056. doi:10.1007/s11356-021-17881-w
   PubMed Web of Science ®Google Scholar
• Chaudhary, S., et al., 2019. Water quality–based environmental flow under plausible temperature and pollution scenarios. Journal of Hydrologic Engineering, 24 (5), 05019007. doi:10.1061/(ASCE)HE.1943-5584.0001780
   Web of Science ®Google Scholar
• Chaudhary, S., et al., 2020. Closure to “Water quality–based environmental flow under plausible temperature and pollution scenarios” by Shushobhit Chaudhary, CT Dhanya, Arun Kumar, and Rehana Shaik. Journal of Hydrologic Engineering, 25 (6), 07020005. doi:10.1061/(ASCE)HE.1943-5584.0001914
   Web of Science ®Google Scholar
• Chaudhary, S., Dhanya, C.T., and Kumar, A., 2018. Sequential calibration of a water quality model using reach-specific parameter estimates. Hydrology Research, 49 (4), 1042–1055. doi:10.2166/nh.2017.246
   Web of Science ®Google Scholar
• Chow, V.T., 1973. Open channel hydraulics. New York: McGraw-Hill.
   Google Scholar
• CPCB (Central Pollution Control Board), 2011. Polluted River stretches in India: criteria and status. Delhi, India: CPCB. Available from: https://cpcb.nic.in/wqm/RS-criteria-status.pdf [Accessed 12 Jun 2022].
   Google Scholar
• CPCB Report 2006, (ADSORBS/41/2006-07). Water quality status of Yamuna River (1999–2005). Available from: https://yamunariverproject.wp.tulane.edu/wp-content/uploads/sites/507/2021/01/cpcb_2006-water-quality-status.pdf [ Accessed 12 Jun 2022].
   Google Scholar
• CPCB Report 2020, (MINARS/38/2020-21). Assessment of impact of lockdown on water quality of major rivers. Available from: https://cpcb.nic.in/upload/Assessment-of-Impact-Lockdown-WQ-MajorRivers.pdf [Accessed 12 Jun 2022].
   Google Scholar
• Currell, M.J. and Han, D., 2017. The global drain: why China’s water pollution problems should matter to the rest of the world. Environment: Science and Policy for Sustainable Development, 59 (1), 16–29. doi:10.1080/00139157.2017.1252605
   Web of Science ®Google Scholar
• Dogliotti, A.I., et al., 2015. A single algorithm to retrieve turbidity from remotely-sensed data in all coastal and estuarine waters. Remote Sensing of Environment, 156, 157–168. doi:10.1016/j.rse.2014.09.020
   Web of Science ®Google Scholar
• Dogliotti, A.I., et al., 2018. Detecting and quantifying a massive invasion of floating aquatic plants in the Rio de la Plata turbid waters using high spatial resolution ocean color imagery. Remote Sensing, 10 (7), 1140. doi:10.3390/rs10071140
   Web of Science ®Google Scholar
• DPCC, 2020. Assessment of Yamuna river water quality Delhi during lockdown period, Delhi pollution control committee. Available from: https://yamuna-revival.nic.in/wp-content/uploads/2020/04/Water-Quality-of-River-Yamuna-during-the-Lockdown.21.04.2020.pdf [ Accessed 12 Jun 2022].
   Google Scholar
• Dwivedi, S., Mishra, S., and Tripathi, R.D., 2018. Ganga water pollution: a potential health threat to inhabitants of Ganga basin. Environment International, 117, 327–338. doi:10.1016/j.envint.2018.05.015
   PubMed Web of Science ®Google Scholar
• FAO, 2017. Water pollution from agriculture: a global review. rome, food and agriculture organization of the United Nations (FAO). Available from: Water pollution from agriculture: a global review - Executive summary (fao.org) [ Accessed 12 Jun 2022].
   Google Scholar
• Gautam, S.K., et al., 2013. A study of the effectiveness of sewage treatment plants in Delhi region. Applied Water Science, 3 (1), 57–65. doi:10.1007/s13201-012-0059-9
   Google Scholar
• Glasgow, H.B., et al., 2004. Real-time remote monitoring of water quality: a review of current applications, and advancements in sensor, telemetry, and computing technologies. Journal of Experimental Marine Biology and Ecology, 300 (1–2), 409–448. doi:10.1016/j.jembe.2004.02.022
   Web of Science ®Google Scholar
• Hafeez, S., et al., 2019. Comparison of machine learning algorithms for retrieval of water quality indicators in case-II waters: a case study of Hong Kong. Remote Sensing, 11 (6), 617. doi:10.3390/rs11060617
   Web of Science ®Google Scholar
• Hindustan Times. 910 industries in Delhi fined by government for toxic effluents in drains. Available from: https://www.hindustantimes.com/delhi-news/in-a-first-910-industries-in-delhi-fined-by-government-for-toxic-effluents-in-drains/story-BUbRDtCHO5eqE6lUeDESZN.html [ Accessed 12 Jun 2022].
   Google Scholar
• Ilori, C.O., Pahlevan, N., and Knudby, A., 2019. Analyzing performances of different atmospheric correction techniques for Landsat 8: application for coastal remote sensing. Remote Sensing, 11 (4), 469. doi:10.3390/rs11040469
   Web of Science ®Google Scholar
• India-WRIS: Rainfall. Available from: India-WRIS (indiawris.gov.in) [ Accessed 12 Jun 2022].
   Google Scholar
• Jafar-Sidik, M., et al., 2017. The relationship between suspended particulate matter and turbidity at a mooring station in a coastal environment: consequences for satellite-derived products. Oceanologia, 59 (3), 365–378. doi:10.1016/j.oceano.2017.04.003
   Web of Science ®Google Scholar
• Jaiswal, R.K., 2007. Ganga action plan–a critical analysis. Kanpur: Eco Friends. Available from: Mimeo(https://ecofriends.org/main/eganga/images/critical%20analysis%20of%20gap.pdf) [Accessed 14 Jun 2022].
   Google Scholar
• Jamwal, P., Mittal, A.K., and Mouchel, J.M., 2011. Point and non-point microbial source pollution: a case study of Delhi. Physics and Chemistry of the Earth, Parts A/B/C, 36 (12), 490–499. doi:10.1016/j.pce.2008.09.005
   Web of Science ®Google Scholar
• Ji, L., Zhang, L., and Wylie, B., 2009. Analysis of dynamic thresholds for the normalized difference water index. Photogrammetric Engineering and Remote Sensing, 75 (11), 1307–1317. doi:10.14358/PERS.75.11.1307
   Web of Science ®Google Scholar
• Kour, G., et al., 2021. Impact assessment on water quality in the polluted stretch using a cluster analysis during pre-and COVID-19 lockdown of Tawi river basin, Jammu, North India: an environment resiliency. Energy, Ecology and Environment, 1-12. doi:10.1007/s40974-021-00215-4
   Google Scholar
• Lacaux, J.P., et al., 2007. Classification of ponds from high-spatial resolution remote sensing: application to Rift Valley Fever epidemics in Senegal. Remote Sensing of Environment, 106 (1), 66–74. doi:10.1016/j.rse.2006.07.012
   Web of Science ®Google Scholar
• Liu, D., et al., 2022. COVID-19 lockdown improved river water quality in China. Science of the Total Environment, 802, 149585. doi:10.1016/j.scitotenv.2021.149585
   PubMed Web of Science ®Google Scholar
• McFeeters, S.K., 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17 (7), 1425–1432. doi:10.1080/01431169608948714
   Web of Science ®Google Scholar
• Meybeck, M., 2002. Riverine quality at the anthropocene: propositions for global space and time analysis, illustrated by the Seine River. Aquatic Sciences, 64 (4), 376–393. doi:10.1007/PL00012593
   Web of Science ®Google Scholar
• Misra, A.K., 2010. A river about to die: Yamuna. Journal of Water Resource and Protection, 2 (5), 489. doi:10.4236/jwarp.2010.25056
   Google Scholar
• Nechad, B., Ruddick, K.G., and Park, Y., 2010. Calibration and validation of a generic multisensor algorithm for mapping of total suspended matter in turbid waters. Remote Sensing of Environment, 114 (4), 854–866. doi:10.1016/j.rse.2009.11.022
   Web of Science ®Google Scholar
• Niroumand-Jadidi, M., et al., 2020. Physics-based bathymetry and water quality retrieval using planetscope imagery: impacts of 2020 COVID-19 lockdown and 2019 extreme flood in the Venice Lagoon. Remote Sensing, 12 (15), 2381. doi:10.3390/rs12152381
   Web of Science ®Google Scholar
• Oki, T. and Kanae, S., 2006. Global hydrological cycles and world water resources. Science, 313 (5790), 1068–1072. doi:10.1126/science.1128845
   PubMed Web of Science ®Google Scholar
• Patel, P.P., Mondal, S., and Ghosh, K.G., 2020. Some respite for India’s dirtiest river? Examining the Yamuna’s water quality at Delhi during the COVID-19 lockdown period. Science of the Total Environment, 744, 140851. doi:10.1016/j.scitotenv.2020.140851
   PubMed Web of Science ®Google Scholar
• Peterson, K.T., et al., 2018. Suspended sediment concentration estimation from landsat imagery along the lower Missouri and middle Mississippi Rivers using an extreme learning machine. Remote Sensing, 10 (10), 1503. doi:10.3390/rs10101503
   Web of Science ®Google Scholar
• Peterson, K.T., Sagan, V., and Sloan, J.J., 2020. Deep learning-based water quality estimation and anomaly detection using Landsat-8/Sentinel-2 virtual constellation and cloud computing. GIScience & Remote Sensing, 57 (4), 510–525. doi:10.1080/15481603.2020.1738061
   Web of Science ®Google Scholar
• Postel, S.L., 2000. Entering an era of water scarcity: the challenges ahead. Ecological Applications, 10 (4), 941–948. doi:10.1890/1051-0761(2000)010[0941:EAEOWS]2.0.CO;2
   Web of Science ®Google Scholar
• Poudel, D.D., Jeong, C.Y., and DeRamus, A., 2010. Surface run-off water quality from agricultural lands and residential areas. Outlook on Agriculture, 39 (2), 95–105. doi:10.5367/000000010791745394
   Web of Science ®Google Scholar
• Robert, E., et al., 2017. Analysis of suspended particulate matter and its drivers in Sahelian ponds and lakes by remote sensing (Landsat and MODIS): Gourma region, Mali. Remote Sensing, 9 (12), 1272. doi:10.3390/rs9121272
   Web of Science ®Google Scholar
• Roy, D.P., et al., 2014. Landsat-8: science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154–172. doi:10.1016/j.rse.2014.02.001
   Web of Science ®Google Scholar
• Said, S. and Hussain, A., 2019. Pollution mapping of Yamuna River segment passing through Delhi using high-resolution GeoEye-2 imagery. Applied Water Science, 9 (3), 1–8. doi:10.1007/s13201-019-0923-y
   Web of Science ®Google Scholar
• Sarkar, S., et al., 2021. Effects of COVID-19 lockdown and unlock on health of Bhutan-India-Bangladesh trans-boundary rivers. Journal of Hazardous Materials Advances, 4, 100030. doi:10.1016/j.hazadv.2021.100030
   Google Scholar
• Schmidt, A., et al., 2005. Investigations to reduce sedimentation upstream of a barrage on the river Rhine. WIT Transactions on Ecology and the Environment, 80. Available from: https://www.witpress.com/Secure/elibrary/papers/WRM05/WRM05015FU.pdf [Accessed 14 Jun 2022].
   Google Scholar
• Schwarzenbach, R.P., et al., 2010. Global water pollution and human health. Annual Review of Environment and Resources, 35 (1), 109–136. doi:10.1146/annurev-environ-100809-125342
   Web of Science ®Google Scholar
• Sharma, D. and Kansal, A., 2011. Water quality analysis of River Yamuna using water quality index in the national capital territory, India (2000–2009). Applied Water Science, 1 (3), 147–157. doi:10.1007/s13201-011-0011-4
   Google Scholar
• Soni, V., Shekhar, S., and Singh, D., 2014. Environmental flow for the Yamuna river in Delhi as an example of monsoon rivers in India. Current Science, 558–564. Available from: https://www.jstor.org/stable/24100063 [Accessed 14 Jun 2022].
   Web of Science ®Google Scholar
• Tokatlı, C. and Varol, M., 2021. Impact of the COVID-19 lockdown period on surface water quality in the Meriç-Ergene River Basin, Northwest Turkey. Environmental Research, 197, 111051. doi:10.1016/j.envres.2021.111051
   PubMed Web of Science ®Google Scholar
• Vermote, E., et al., 2016. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment, 185, 46–56. doi:10.1016/j.rse.2016.04.008
   PubMed Web of Science ®Google Scholar
• Vermote, E., et al., 2018. LaSRC (Land Surface Reflectance Code): overview, application and validation using MODIS, VIIRS, LANDSAT and Sentinel 2 data’s. In: IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain. IEEE, 8173–8176. Available from: https://ieeexplore.ieee.org/document/8517622 [Accessed 3 Jul 2023].
   Google Scholar
• Visitacion, M.R., et al., 2019. Detection of algal bloom in the coastal waters of boracay, Philippines using normalized difference vegetation index (ndvi) and floating algae index (FAI). International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, XLII-4/W19, 479–486. doi:10.5194/isprs-archives-XLII-4-W19-479-2019
   Google Scholar
• Wagh, P., et al., 2020. Indicative lake water quality assessment using remote sensing images-effect of COVID-19 lockdown. Water, 13 (1), 73. doi:10.3390/w13010073
   Web of Science ®Google Scholar
• Walling, B., et al., 2017. Estimation of environmental flow incorporating water quality and hypothetical climate change scenarios. Environmental Monitoring and Assessment, 189 (5), 225. doi:10.1007/s10661-017-5942-2
   PubMed Web of Science ®Google Scholar
• Wang, X., Zhang, F., and Ding, J., 2017. Evaluation of water quality based on a machine learning algorithm and water quality index for the Ebinur Lake Watershed, China. Scientific Reports, 7 (1), 1–18. doi:10.1038/s41598-017-12853-y
   PubMed Web of Science ®Google Scholar
• Xing, Y., et al., 2005. A spatial temporal assessment of pollution from PCBs in China. Chemosphere, 60 (6), 731–739. doi:10.1016/j.chemosphere.2005.05.001
   PubMed Web of Science ®Google Scholar
• Xu, H., 2006. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27 (14), 3025–3033. doi:10.1080/01431160600589179
   Web of Science ®Google Scholar
• YMC, 2019. Yamuna monitoring committee report. Available from: https://yamuna-revival.nic.in/wp-content/uploads/2019/02/Pollution-of-the-River-Yamuna.pdf [ Accessed 12 Jun 2022].
   Google Scholar
• YMC, 2020. Yamuna monitoring committee report. Available from: Final-Report-of-YMC-29.06.2020.pdf (Yamuna-revival.nic.in) [ Accessed 12 Jun 2022].
   Google Scholar
• Yunus, A.P., Masago, Y., and Hijioka, Y., 2020. COVID-19 and surface water quality: improved lake water quality during the lockdown. Science of the Total Environment, 731, 139012. doi:10.1016/j.scitotenv.2020.139012
   PubMed Web of Science ®Google Scholar
• Zhao, S., et al., 2005. The 7-decade degradation of a large freshwater lake in Central Yangtze River, China. Environmental Science & Technology, 39 (2), 431–436. doi:10.1021/es0490875
   PubMed Web of Science ®Google Scholar