Assessment of Water Quality of Different Water Bodies in and around Delhi, India

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Water quality plays a crucial role in sustaining ecosystems, supporting agriculture, and safeguarding human health. This study assessed the physico-chemical characteristics of water samples collected from three distinct water bodies in the Delhi-NCR region: The Yamuna River in Haryana, a natural pond at Sunder Nursery in Delhi, and a village pond in Greater Noida. Parameters including chemical oxygen demand (COD), dissolved oxygen (DO), free carbon dioxide, chloride, alkalinity, sulphate concentration, and total dissolved solids (TDS) were analyzed to evaluate pollution levels and their potential impacts. The results revealed marked site-specific variations, with Delhi water exhibiting the highest COD (48 mg/L), sulphate (7.1 mg/L), and TDS (1480 ppm), indicating heavy contamination from urban and industrial sources. The Yamuna sample showed critically low DO (0.125 mg/L) and elevated chloride (298 mg/L), reflecting the combined influence of sewage discharge, industrial effluents and agricultural runoff. Comparatively, the Noida Pond displayed relatively better water quality, although still impacted by moderate bicarbonate alkalinity and chloride levels. These findings highlight significant risks to agricultural productivity, as polluted irrigation water can alter soil chemistry, reduce crop yields, and lead to long-term soil salinity and toxicity. Overall, the study underscores the urgent need for improved wastewater management, stricter regulation of agricultural and industrial discharges, and community-level awareness to safeguard water quality, ensure agricultural sustainability and protect public health in the Delhi-NCR region.

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The quantity of water is as important as the quality, since these affect the quantity and the quality of available water, water quality is used to express the suitability of water and sustainable to various uses or processes. It is affected by a wide range of natural and human influences, the most important of the natural influences is hydrological influence. The water qualities study was carried out in Ibadan using three landuse (urban, peri urban and rural) in slum areas of Ibadan were evaluated for these physicochemical parameters: temperature, total dissolved solid (TDS), pH, and dissolved oxygen (DO), from well, borehole, water from vendor or pipeborne, from 8 localities and 3 LGA in Ibadan. The results showed variations in the water quality parameters the slum areas. Water pH is lowest in Awotan and Apete of Ido LGA water source which makeup rural slums landuse. with water pH of 5.7 well and 5.8 borehole which is acidic, there is highest value of conc of Lead in these areas of (0.259 mg/l) compared to other landuse and locality, water is of best quality among water from vendors. Using the sig value of 0.05, that is no sig difference between the water quality in slum areas and WHO standards. Using Anova with physico-chemical parameters of water temperature, pH, dissolved oxygen, total dissolved solids, conc of zinc, and iron also having no significant. But a significant spatial variation of water quality of Lead concentration with the sig value of 0.033 and F value of 2.983. There is significant variation in different water source of water pH, total dissolved solid and dissolved oxygen. a sig value of 0.028, 0.031, and 0.000 respectively.

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  • Cite Count Icon 10
  • 10.4236/ojf.2016.61002
Comparison of Water Quality Status of Disturbed and Undisturbed Mangrove Forest at Awat-Awat Lawas Sarawak
  • Dec 9, 2015
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  • Seca Gandaseca + 4 more

Water quality testing was compulsory for management of safe and reliable water sources. Various sources of pollution and destruction of mangrove forest decrease the quality of river water. Thus a study was conducted to determine the water quality status of mangrove forest river water of Awat-Awat Lawas Sarawak and compare the water quality status of disturbed and undisturbed mangrove forest river in that area. Samples from twelve sampling stations were collected from both mangrove forest river from October 2013 to March 2014. In-situ data collected (pH, temperature, dissolved oxygen (DO), salinity, turbidity, total dissolved solid (TDS), conductivity) and laboratory analysis (biochemical oxygen demand (BOD), chemical oxygen demand (COD), ammoniacal nitrogen (AN), total suspended solid (TSS)) were conducted according to the Standard method of Examination of Water and Wastewater AHPA 2005. Six water parameters: pH, DO, BOD, COD, AN and TSS value were used in calculating the Water Quality Index (WQI). Mean values for disturbed area were follows, pH (7.07), temperature (29.93°C), salinity (15.64 PSU), turbidity (55.13 NTU), DO (4.59 mg/L), BOD (0.73 mg/L), COD (10.16 mg/L), AN (0.14 mg/L), TSS (53.92 mg/L), TDS (23.14 mg/L) and conductivity (2.61 ms/m). Undisturbed area results were, pH (6.84), temperature (28.32°C), salinity (14.65 PSU), turbidity (35.41 NTU), DO (2.39 mg/L), BOD (0.55 mg/L), COD (15.82 mg/L), AN (0.13 mg/L), TSS (53.23 mg/L), TDS (22.82 mg/L) and conductivity (2.34 ms/m). There were no significant differences between two locations except for DO. Both water qualities of disturbed and undisturbed mangrove forest river were found under Class III, which describe that the water bodies are in moderate quality status.

  • Research Article
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مقایسه عملکرد مدل های هوش مصنوعی در تخمین پارامترهای کیفی آب رودخانه در دوره های کم آبی و پرآبی
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  • مجید منتصری + 1 more

وقوع متناوب دوره های کم آبی و پرآبی درحوضه آبریز زرینه رود علاوه بر تأثیر روی وضعیت کمی آب های سطحی، باعث تغییراتی در کیفیت آب این حوضه شده است. لذا، مدل بندی و پیش بینی پارامترهای کیفی آب رودخانه زرینه رود در دوره های کم آبی و پرآبی، یکی از ضرورت های تحقیقاتی در این رودخانه پرآب شمال غرب ایران بوده است. در این مطالعه، روش های شبکه های عصبی مصنوعی به ازای پنج الگوریتم آموزشی مختلف و سامانه استنتاجی عصبی-فازی تطبیقی مبتنی بر مدل دسته بندی تفریقی، جهت تخمین میزان جامدات محلول TDS به کار گرفته شدند. بدین منظور از داده های کیفیت آب هفت ایستگاه هیدرومتری در حوضه آبریز مذکور با طول دوره آماری 18 ساله (1389-1372) استفاده گردید. ابتدا دوره مطالعاتی مذکور بر اساس میزان جریان در رودخانه به دو دوره کم آبی و پرآبی تفکیک شده، سپس در یک آنالیز اولیه آماری، پارامترهای مؤثر اصلی در تخمین TDS تعیین و برای مدل بندی استفاده گردید. برای مدل بندی 75 درصد داده ها برای کالیبره کردن و 25 درصد برای ارزیابی مدل استفاده شده است. ارزیابی عملکرد مدل های به کار رفته بر اساس آزمون های آماری مختلف، ضریب همبستگی، ریشه میانگین مربعات خطا و میانگین قدر مطلق خطا انجام گرفت. نتایج حاصل حاکی از عملکرد قابل قبول هر دو روش شبکه های عصبی مصنوعی با الگوریتم آموزشی لونبرگ-مارگارت و سامانه استنتاجی عصبی-فازی تطبیقی در دوره های کم آبی و پرآبی بود. مقایسه عملکرد روش های به کار گرفته شده، نشان داد که عملکرد روش عصبی-فازی تطبیقی در هر دو دوره مطالعاتی بهتر از شبکه های عصبی مصنوعی می‏باشد.

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