Water Quality Monitoring of Yamuna River by Using GIS  Based Water Quality Index in Delhi, India

Sharma, Shikha; Jha, Pawan Kumar; Ranjan, Manju Rawat; Singh, Umesh Kumar; Jindal, Tanu

doi:10.20546/ijcmas.2017.602.141

Cited by 12 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nitrate concentration depends on the activity of nitrifying bacteria which in turn get influenced by presence of dissolved oxygen (Ramesh and Krishnaiah, 2015;Phadatare and Gawande, 2016). Similar observations were earlier reported by Yogendra and Puttaiah (2008), Kumar and Dua (2009), Yadav et al (2010), Satone et al (2011), Sharma et al (2013), Wagh et al (2015), Gopal Krishan et al (2016), Sharma et al (2017Sharma et al ( , 2020, Behailu et al (2017) and Kinjal and Kapila (2020).…”

Section: Resultssupporting

confidence: 90%

Evaluation of Water Quality Index Using Various Physico-Chemical Parameters and Correlation Analysis of Gothivali Waterbody, Navi-Mumbai, Maharashtra, India

Singh¹,

Jadhav²

2023

Intern. J. Zool. Invest.

View full text Add to dashboard Cite

Water is a necessary component for the survival of all humans and animals, a finite resource in the natural world. Water quality is crucial not only for humans, but also for the plants and animals that rely on it to survive. As a result, water quality must be checked at regular intervals. This study focuses on determining the quality of water of Gothivali Waterbody, Navi-Mumbai using the Weighted Arithmetic Water Quality Index (WQI) and several physico-chemical factors i.e. Temperature, pH, Dissolved Oxygen (DO), Biochemical Oxygen Demand (B.O.D.), Chemical Oxygen Demand (C.O.D.), Alkalinity, Electrical Conductivity, Turbidity, Nitrite-Nitrogen, Nitrate-Nitrogen, and Hardness were analyzed in this investigation. The study was compiled into three seasons monsoon, post-monsoon, and pre-monsoon, with the Water Quality Index of the water body being 65.33, 72.20, and 75.06, respectively. WQI in all seasons are as prescribed by Indian Standards, and are acceptable for domestic use. The databases obtained were subjected to Pearson Correlation Matrix Correlation coefficients which revealed positive and significant correlations between the pairs of parameters in surface water. The present study however, is believed to serve as a baseline data for further studies. Future research is needed to provide more information on the seasonal variations of surface water quality.

show abstract

Section: Resultssupporting

confidence: 90%

Evaluation of Water Quality Index Using Various Physico-Chemical Parameters and Correlation Analysis of Gothivali Waterbody, Navi-Mumbai, Maharashtra, India

Singh¹,

Jadhav²

2023

Intern. J. Zool. Invest.

View full text Add to dashboard Cite

show abstract

“…The large metropolitan populations discharging loads of untreated wastewater, up to 1,104 × 10 6 tons yr −1 in the case of Delhi (Table ), may represent the primary anthropogenic perturbation to the production and consumption of GHGs in the Ganges. Untreated and poorly treated wastewater generated from the ever‐expanding population centers in India and Bangladesh can greatly impact downstream water quality by inducing surges in biochemical oxygen demand (BOD) and nutrients (CPCB 2013, Park et al., 2018; Sharma et al., 2017). Although reach‐specific data on wastewater is not available, the longitudinal patterns of GHGs in the Mekong and Yellow River can also be explained by increasing volumes of wastewater generated along the middle and lower reaches (Tables 1 and ).…”

Section: Discussionmentioning

confidence: 99%

Localized Pollution Impacts on Greenhouse Gas Dynamics in Three Anthropogenically Modified Asian River Systems

et al. 2021

View full text Add to dashboard Cite

Despite growing research on greenhouse gas (GHG) emissions from inland waters, few systematic efforts have been made to assess the regional-scale GHG emissions from Asian rivers under increasing anthropogenic stress. We examined factors controlling longitudinal and seasonal variations in the partial pressure of CO 2 (pCO 2 ), and CH 4 and N 2 O concentrations in the Ganges, Mekong, and Yellow River by simultaneously measuring gas concentrations and stable C isotopes, and optical properties of dissolved organic matter (DOM) from 2016 to 2019. The levels of pCO 2 and CH 4 were distinctively higher in polluted tributaries and affected reaches of the Ganges and Mekong than in the Yellow River. The highest levels of N 2 O were found in the Ganges, followed by the Yellow River and Mekong. Across these basins, dry-season mean concentrations of CO 2 , CH 4 , and N 2 O were 1.6, 2, and 7 times higher than those measured in the monsoon season, respectively. This seasonality was consistent with that of δ 13 C-CO 2 , while δ 13 C-CH 4 showed an opposite pattern. GHG concentrations exhibited significant positive relationships with DOM concentrations and optical properties including fluorescence index and proteinlike fluorescence, implying the contribution of anthropogenic, labile DOM to production of GHGs in the polluted reaches. Graphical mixing models of δ 13 C-CO 2 and δ 13 C-CH 4 support the stronger impact of wastewater on the Ganges and Mekong than on the Yellow River. The overall results suggest that neglecting localized pollution impacts on GHG emissions from increasingly urbanized river basins can result in a substantial underestimation of global riverine GHG emissions.Plain Language Summary Rapid urbanization and poor wastewater infrastructure are turning many large rivers in Asia into significant sources of greenhouse gases (GHGs). We examined controlling factors for the concentrations of CO 2 , CH 4 , and N 2 O in the Ganges, Mekong, and Yellow River by simultaneously measuring gas concentrations and stable carbon isotopes, and optical properties of dissolved organic matter (DOM). The concentrations of CO 2 and CH 4 were distinctively higher in polluted tributaries and affected sections of the Ganges and Mekong than in the Yellow River. N 2 O concentration was highest in the Ganges, followed by the Yellow River and Mekong. Across these basins, the concentrations of the three gases were generally higher during dry periods than in the monsoon season. GHG concentrations were positively related to DOM optical properties indicating the contribution of easily degradable DOM from pollution sources. The stable carbon isotope ratios of CO 2 and CH 4 support the stronger impact of wastewater on the Ganges and Mekong than on the Yellow River. The overall results suggest that neglecting local pollution impacts on GHG emissions from increasingly urbanized river basins can result in a substantial underestimation of global riverine GHG emissions. BEGUM ET AL.

show abstract

“…(Bhardwaj and Verma, 2017). Paliwal et al (2007), Sharma et al (2009), Phadatare and Gawande (2016) and Sharma et al (2017). During the monsoon nitrate and nitrite concentrations recorded were 0.44 µg/L and 0.47 µg/L, and during post-monsoon it was 0.89 µg/L and 0.68 µg/L, respectively (Fig.…”

Section: Hardnessmentioning

confidence: 88%

Assessment of Quality Index of Water by Using Different Physico-Chemical Parameters in Rabale Waterbody, Navi-Mumbai, Maharashtra, India

Pooja¹,

S²

2021

IJZI

View full text Add to dashboard Cite

Water is a must for all living things. Reservoir and lake water is used for a variety of purposes, including drinking water, agricultural, commercial, recreational, and aquaculture. However, due to rising population pressures, which has resulted in rapid urbanization, industrialization, and modern agricultural practices, water pollution has become a major issue in recent years. The water quality index is a single number that reflects overall water quality at a specific place and time. To compute, water quality index (WQI) we used Temperature, pH, Dissolved Oxygen (DO), Biochemical Oxygen Demand (B.O.D), Chemical Oxygen Demand (C.O.D), Alkalinity, Electrical Conductivity, Turbidity, and Chemical Oxygen Demand (C.O.D), Nitrite-Nitrogen, Nitrate-Nitrogen and Hardness were the physico-chemical parameters investigated in this study. The water quality index is to transform complicated water quality data into information that the general public can use. The Water Quality Index was calculated using the Weighted Arithmetic Water Quality Index (WQI). The measured WQI was then used to assess the water quality in Rabale water body. The water quality index (WQI) of the lake was 49.813, 53.483, and 53.045 during the pre-monsoon, monsoon, and post-monsoon periods, respectively. When comparing seasonal variations, WQI values show that water status is fairly good during the pre-monsoon but is low during the monsoon and postmonsoon seasons, thus water from Rabale water body may be used for a variety of industrial purposes.

show abstract

Water Quality Monitoring of Yamuna River by Using GIS Based Water Quality Index in Delhi, India

Cited by 12 publications

References 18 publications

Evaluation of Water Quality Index Using Various Physico-Chemical Parameters and Correlation Analysis of Gothivali Waterbody, Navi-Mumbai, Maharashtra, India

Evaluation of Water Quality Index Using Various Physico-Chemical Parameters and Correlation Analysis of Gothivali Waterbody, Navi-Mumbai, Maharashtra, India

Localized Pollution Impacts on Greenhouse Gas Dynamics in Three Anthropogenically Modified Asian River Systems

Assessment of Quality Index of Water by Using Different Physico-Chemical Parameters in Rabale Waterbody, Navi-Mumbai, Maharashtra, India

Contact Info

Product

Resources

About