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Journal articles on the topic 'Water Quality Parameters'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 September 2023

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1

Gonsor, Oksana. "SMART SYSTEM FOR MONITORING WATER QUALITY PARAMETERS." Measuring Equipment and Metrology 83, no. 4 (2022): 18–23. http://dx.doi.org/10.23939/istcmtm2022.04.018.

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Water is the most crucial factor for all living organisms, so it is essential to protect it. And water quality monitoring is one of the first steps required in the rational development and management of water resources. Smart systems used for real-time quality control and power consumption are rapidly developing. Their implementation in water quality assurance systems is essential and actual. The three-level smart system presented in this article involves the processing of water samples testing results from water supply sources, from the distribution network (consumers), test results of testing laboratories, and data from water consumption accounting systems. Transmission of the obtained results to consumers applying wireless communication technologies is an important system feature.

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2

Alam, Md J. B., M. R. Islam, Z. Muyen, M. Mamun, and S. Islam. "Water quality parameters along rivers." International Journal of Environmental Science & Technology 4, no. 1 (January 1, 2007): 159–67. http://dx.doi.org/10.1007/bf03325974.

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3

Addisie, Meseret B. "Evaluating Drinking Water Quality Using Water Quality Parameters and Esthetic Attributes." Air, Soil and Water Research 15 (January 2022): 117862212210750. http://dx.doi.org/10.1177/11786221221075005.

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This study assesses the quality of drinking water sources in the highlands of Ethiopia. The study considered a combination of users’ perceptions with the measured water quality parameters determined using the water quality index (WQI) tool. Data were collected using a cross-sectional research design for a household survey, and water quality samples were collected from improved and unimproved alternative sources. Nine physicochemical and two bacteriological analyses were performed. The result shows that esthetic water quality parameters had a potential interpretation of water quality as of the laboratory analysis. The taste was the dominant and easily detectable indicator as compared to odor and color. This is associated with the higher correlation between iron and manganese that deter the taste of water. Tap water was the only free source of bacteriological contamination. The WQI values show that one improved and three unimproved sources were found in the rank of unsuitable for drinking purposes. Unimproved sources are harmful for drinking, although they are used as an alternative source of water. Finally, the study suggests that due consideration of esthetic factors as measured parameters is fundamental for the sustainable use of drinking water infrastructures.

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Raman, Bai V., Reinier Bouwmeester, and S. Mohan. "Fuzzy Logic Water Quality Index and Importance of Water Quality Parameters." Air, Soil and Water Research 2 (January 2009): ASWR.S2156. http://dx.doi.org/10.4137/aswr.s2156.

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Determination of status of water quality of a river or any other water sources is highly indeterminate. It is necessary to have a competent model to predict the status of water quality and to advice for type of water treatment for meeting different demands. One such model (UNIQ2007) is developed as an application software in water quality engineering. The unit operates in a fuzzy logic mode including a fuzzification engine receiving a plurality of input variables on its input and being adapted to compute membership function parameters. A processor engine connected downstream of the fuzzification unit will produce fuzzy set, based on fuzzy variable viz. DO, BOD, COD, AN, SS and pH. It has a defuzzification unit operative to translate the inference results into a discrete crisp value of WQI. The UNIQ2007 contains a first memory device connected to the fuzzification unit and containing the set of membership functions, a secondary memory device connected to the defuzzification unit and containing the set of crisp value which appear in the THEN part of the fuzzy rules and an additional memory device connected to the defuzzification unit. More advantageously, UINQ2007 is constructed with control elements having dynamic fuzzy logic properties wherein target non-linearity can be input to result in a perfect evaluation of water quality. The development of the fuzzy model with one river system is explained in this paper. Further the model has been evaluated with the data from few rivers in Malaysia, India and Thailand. This water quality assessor probe can provide better quality index or identify the status of river with 90% perfection. Presently, WQI in most of the countries is referring to physic-chemical parameters only due to great efforts needed to quantify the biological parameters. This study ensures a better method to include pathogens into WQI due to superior capabilities of fuzzy logic in dealing with non-linear, complex and uncertain systems.

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Sakran, Thabet, Pessant Matooq, Gamal Shahawy, Maisa Shalaby, Hoda Sabry, and Mahmoud Gad. "Monitoring Water Quality Parameters in Egyptian Tap Water." Polish Journal of Environmental Studies 28, no. 4 (April 9, 2019): 2815–21. http://dx.doi.org/10.15244/pjoes/92820.

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6

., Garima Srivastava. "WATER QUALITY INDEX WITH MISSING PARAMETERS." International Journal of Research in Engineering and Technology 02, no. 04 (April 25, 2013): 609–14. http://dx.doi.org/10.15623/ijret.2013.0204035.

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7

Kim, Taeyun, and Y. P. Sheng. "Estimation of water quality model parameters." KSCE Journal of Civil Engineering 14, no. 3 (May 2010): 421–37. http://dx.doi.org/10.1007/s12205-010-0421-0.

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8

Vasudev, Shivam, Dr Bharat Nagar, and Mr Mukesh Choudhary. "Analysis Of Drinking Water Quality Parameters A Case Study Of Hanumangarh Town." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (August 31, 2018): 75–82. http://dx.doi.org/10.31142/ijtsrd15774.

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9

Erechtchoukova, Marina G., and Peter A. Khaiter. "Selecting Water Quality Parameters for Sustainable Water Resource Management." International Journal of Environmental, Cultural, Economic, and Social Sustainability: Annual Review 6, no. 1 (2010): 187–202. http://dx.doi.org/10.18848/1832-2077/cgp/v06i01/54716.

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10

Vartiainen, T., A. Liimatainen, P. Kauranen, and L. Hiisvirta. "Relations between drinking water mutagenicity and water quality parameters." Chemosphere 17, no. 1 (January 1988): 189–202. http://dx.doi.org/10.1016/0045-6535(88)90056-2.

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11

DIMPLE, HEMANT KUMAR MITTAL, PRADEEP KUMAR SINGH, K. K. YADAV, SITA RAM BHAKAR, and JITENDRA RAJPUT. "Groundwater quality parameters for irrigation utilization." Indian Journal of Agricultural Sciences 92, no. 7 (April 13, 2022): 803–10. http://dx.doi.org/10.56093/ijas.v92i7.114186.

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Globally, groundwater is utilized indiscriminately to fulfill the water demands of different sectors. This uncontrolled use has amounted to a downward trend in the groundwater table and the imbalance of this vital resource quantity and quality in the environment. Groundwater quality is crucial not only to human health but also to optimize crop yields. Human-induced contaminants andÂ insufficient correctiveÂ measures degrade groundwater quality. This article reviews various water quality parameters for irrigation and the impacts of low-quality water on crop yield and soil properties. Water quality is quantified and analyzed as per the water's intent. Indicators for the water quality evaluation include, in particular, the salinity hazards, sodium hazard, salt level, alkalinity hazards, permeability hazards, and ion-specific toxicity hazards. Improper irrigation water use has, in some cases, disordered soil and water quality. Proper attention must be given to manage low water quality and adequate drainage for soil affected by salts. This review paper illustrates the key parameters that affect crop yield and soil quality. This review article aims to enable the reader to comprehend better the effects of water quality on Soil and crops and guide in deciding appropriate possible solutions to address probable water quality threats that could lower yield under prevalent use conditions

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12

Usman, Surajo Aminu, Aminu Ahmed Aliko, and Abba Sabo. "Assessment of Temporal Changes in Water Quality on Selected Parameters in Cole Mere." International Journal of Environmental Science and Development 6, no. 2 (2015): 100–103. http://dx.doi.org/10.7763/ijesd.2015.v6.569.

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13

Jung, Kwang-Wook, Chun-Gyeong Yoon, Jae-Ho Jang, and Ji-Hong Jeon. "Water Quality and Correlation Analysis Between Water Quality Parameters in the Hwaong Watershed." Journal of The Korean Society of Agricultural Engineers 48, no. 1 (January 1, 2006): 91–102. http://dx.doi.org/10.5389/ksae.2006.48.1.091.

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14

Li, Yan, Zi Ming Wang, Long Jiang Zhang, and De Guan Wang. "Multi-Parameters Water Quality Model Uncertainty Analysis." Advanced Materials Research 807-809 (September 2013): 301–7. http://dx.doi.org/10.4028/www.scientific.net/amr.807-809.301.

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Based on the water quality index interaction of WASP model and the finite volume method, two-dimensional coupling model of water quantity and water quality was established. Then a random function module was added into the model having Generalized Likelihood Uncertainty Estimation (GLUE) function. Using GLUE method analyzes the uncertainty and sensitivity of the established model. The results show that organic sedimentation rateVS3is the most sensitive to total nitrogen changes, and its sensitive value range is 0.03-0.07m/d, while the influence of other parameters isnt obvious. By using the combinations of obtained sensitive parameters, the total nitrogen variation of Taihu Lake is simulated. The results are all within the 95% confidence interval, which explains that the model is reasonable.

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15

Worsfold, Paul J., J. Richard Clinch, and Harry Casey. "Spectrophotometric field monitor for water quality parameters." Analytica Chimica Acta 197 (1987): 43–50. http://dx.doi.org/10.1016/s0003-2670(00)84711-x.

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16

Goda, Takeshi. "Monitoring and measurement of water quality parameters." International Journal of Water Resources Development 4, no. 4 (December 1988): 270–75. http://dx.doi.org/10.1080/07900628808722401.

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17

Parmar, K. S., and R. Bhardwaj. "Water quality index and fractal dimension analysis of water parameters." International Journal of Environmental Science and Technology 10, no. 1 (August 10, 2012): 151–64. http://dx.doi.org/10.1007/s13762-012-0086-y.

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18

Semeniuk, N. "ECOLOGICAL WATER QUALITY." Ukrainian Journal of Natural Sciences, no. 2 (January 28, 2023): 45–56. http://dx.doi.org/10.35433/naturaljournal.2.2023.45-56.

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Water quality – is a description of water’s chemical and biological composition and physical properties, which characterize it as an abiotic component of aquatic ecosystem and determine its suitability for specific consumption purposes. Ecological water quality (environmental water quality) – refers to the ecological well-being of an aquatic ecosystem, with the main focus on protection of the aquatic environment and human life and health. It comprises a complex of physical, chemical, biological and other parameters reflecting specific features of abiotic and biotic components of aquatic ecosystems. The requirements for physical, chemical and biological properties of water are set in the water quality standards, which may be developed by particular countries or introduced by international organizations. Ecological water quality depends upon natural and human factors. Natural factors are in their turn divided into abiotic (for example, geological, meteorological, hydrological) and biotic (for example, the ratio of primary production and organic matter destruction). The main human factors affecting water quality include artificial modification of aquatic ecosystems’ hydrological conditions and their pollution with diverse chemical compounds. There are a lot of approaches to ecological water quality assessment according to both abiotic (physical and chemical) and biological parameters. Physical and chemical methods take into account such parameters as water transparency, suspended particulate matter concentration (turbidity), ion composition, water hardness, total dissolved salts content, nutrients and organic matter content, dissolved gases concentration, pH. Biological methods are based upon assessing the living organisms’ (biological indicators’) response to mineral and organic substances, present in water. Various living organisms can be used as biological indicators: algae, in particular – diatoms, higher aquatic plants, different species of aquatic invertebrates and fishes. While physical and chemical methods characterize water quality at the moment of sampling, biological methods provide an integral picture of water quality for a certain time period. Besides, biological methods are more informative, because they reflect the aquatic ecosystem’s response to pollution. On the whole, the most reliable data on ecological water quality can be obtained by combining physical, chemical and biological methods.

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19

Φωτίου, E., and N. Κολοβός. "EVALUATION OF BOTTLED WATER QUALITY." Bulletin of the Geological Society of Greece 36, no. 4 (January 1, 2004): 2087. http://dx.doi.org/10.12681/bgsg.16708.

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The investigation and the evaluation of the bottled water parameters and their relation to the human health is the main target of this paper. Thirty three samples of bottled water were investigated and the quality parameters were compared according to the standards introduced by EC (instruction 80/778 - 15/07/80) of the drinking water. The physicochemical parameters include pH, conductivity, CI, S04, Si02, Ca, Mg, Na, Κ, F, total hardness and alkalinity.

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20

Feng, Tian Heng, Ping Jie Huang, Di Bo Hou, and Guang Xin Zhang. "Study on Methods of Water Quality Analysis Based on Conventional Water Quality Monitoring Parameters." Applied Mechanics and Materials 316-317 (April 2013): 686–89. http://dx.doi.org/10.4028/www.scientific.net/amm.316-317.686.

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Water contamination events of urban water supply often show the characteristics of abruptness and variety, and the on-line water quality monitoring parameters usually are difficult to cover various contaminant parameters. With the help with routine detection and on-line water quality monitoring, it is possible to detect and judge whether a water quality event has happened or not and to identify its type indirectly, and obtain the relevant information of qualitative and quantitative analysis. These will be significant to the decision making by the concerning sectors and units. Taking the events of ammonium iron sulfate in the distribution system for example, the relationship of water between contamination concentration and conventional water quality monitoring parameters is investigated, and the quantitative method based on D-S evidence theory is given.

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21

Kongprajug, Akechai, Namfon Booncharoen, Kanyaluck Jantakee, Natcha Chyerochana, Skorn Mongkolsuk, and Kwanrawee Sirikanchana. "Sewage-specific enterococcal bacteriophages and multiple water quality parameters for coastal water quality assessment." Water Science and Technology 79, no. 5 (October 31, 2018): 799–807. http://dx.doi.org/10.2166/wst.2018.460.

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Abstract Coastal water quality is deteriorating worldwide. Water quality monitoring is therefore essential for public health risk evaluation and the management of water bodies. This study investigated the feasibility of using bacteriophages of Enterococcus faecalis as sewage-specific faecal indicators, together with physicochemical (dissolved oxygen, pH, temperature and total suspended solids) and biological parameters, to assess coastal water quality using multivariate analysis incorporating non-detects. The principal component and cluster analyses demonstrated that coastal water quality was mostly influenced by biological parameters, including Escherichia coli and total coliforms, which were found in all 31 sampling sites, and enterococci, which was found in all but two sampling sites. The enterococcal bacteriophages AIM06 and SR14 were detected in 17 and 18 samples at concentrations up to 1,815 and 2,790 PFU/100 mL, respectively. Both bacteriophages co-presented in approximately 80% of phage-positive samples, and the concentrations at each site were not significantly different. Overall, either bacteriophage could be used to differentiate high- and low-level coastal water pollution, as grouped by cluster analysis. This study is the first to investigate the suitability of sewage-specific bacteriophages of E. faecalis for monitoring coastal water quality and emphasises the importance of a multivariate analysis with non-detects to facilitate coastal water quality monitoring and management.

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22

Kothari, Vinod, Suman Vij, SuneshKumar Sharma, and Neha Gupta. "Correlation of various water quality parameters and water quality index of districts of Uttarakhand." Environmental and Sustainability Indicators 9 (February 2021): 100093. http://dx.doi.org/10.1016/j.indic.2020.100093.

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23

Ahmed, Sarah, Ali Abedulwahab, Rehab Ahmed, Mohamed Najemalden, and Omer Taha. "Assessment of Lower Zab river water quality using both Canadian Water Quality Index Method and NSF Water Quality Index Method." Przegląd Naukowy Inżynieria i Kształtowanie Środowiska 29, no. 2 (July 6, 2020): 155–71. http://dx.doi.org/10.22630/pniks.2020.29.2.14.

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Rivers are considered the most important sources of surface water on Earth. They are play a significant role in all human activities and the quality of river water is needed. Therefore, the importance of the water quality index is arising through providing data base about quality of the water source, and explain the change in the water quality over a period of time continually. This study involved determination of physicochemical and biological parameters of Lower Zab river in Kirkuk city at two different points. The objectives of the study are to assess the present water quality, through analysis of some selected water quality parameters like pH, TDS, BOD, dissolved oxygen, turbidity, EC, alkalinity, and salinity etc. and to compare the results with the Canadian Council of Ministers of the Environment and National Sanitation Foundation Water Quality Indices. Raw water samples were collected from the Lower Zab river twice a month by one sample every 15 days from each station. The water quality data include 16 different parameters. Tests were carried out following the American Public Health Association standard methods. The results show that all parameters values were within the standards of drinking water proposed by the CCME standards and Iraqi standards or the World Health Organization standards for drinking purpose, except turbidity, DO, nitrate, calcium, which were mostly higher than the standards and sometimes BOD and potassium. The results of WQI showed that the water quality at LZ3 station is lower than LZ2 station due to the polluting activity of the Lower Zab river. Furthermore, for the years 2014, 2015, and 2016, the water quality was degraded due to the ISIS war. Also, it was noted in the 2013 year that the water quality degraded more in fall and winter seasons due to that the earth has exposed to the long-dried season and then suddenly exposed to a high rainfall season which in turns leads to increase some parameters very high (i.e. turbidity). Finally, the Lower Zab river water cannot be use for drinking directly. However, a pretreatment is needed before the drinking use.

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24

Jadhav, S. D., and M. S. Jadhav. "Analysis of Water Quality Using Physico-chemical Parameters of Mula-Mutha River, Pune Maharashtra." International Journal of Trend in Scientific Research and Development Volume-1, Issue-6 (October 31, 2017): 250–56. http://dx.doi.org/10.31142/ijtsrd2509.

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25

El-Zanfaly, H. T., and A. M. Shabaan. "Applying Bacteriological Parameters for Evaluating Underground Water Quality." Water Science and Technology 20, no. 11-12 (November 1, 1988): 425–28. http://dx.doi.org/10.2166/wst.1988.0316.

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A total of 111 water samples were drawn from 15 wells. These samples were subjected to bacteriological examinations to determine the suitability of the monitored wells as source for drinking water. Significant levels of bacterial pollution indicators were detected in the underground water samples during the period of study. Total bacterial counts ranged between 10-104CFU/ml. Faecal streptococci was the most common and detected in 9 6% of water samples. Lower percentages of samples (92, 55 and 45%) were contaminated with total coliforms, faecal coliforms and total clostridia, respectively. Faecal coliforms: faecal streptococci was less than 0.7 in 86% of the examined samples. Anaerobic bacteria were detected in the examined well water samples. Only 101 water samples were tested for anaerobic spore-formers and all showed positive results. Most examined wells do not meet the water quality standards for drinking water. Accordingly, the necessity of controllin bacterial pollution in ground-water through disinfection was clearly confirmed. Testing the underground water for more than one indicator represents an important approach for water quality assurance.

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26

To, Thuy Chau. "WATER QUALITY ASSESSMENT OF SAIGON RIVER FOR PUBLIC WATER SUPPLY BASED ON WATER QUALITY INDEX." Vietnam Journal of Science and Technology 58, no. 5A (November 12, 2020): 85. http://dx.doi.org/10.15625/2525-2518/58/5a/15203.

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Water Quality Index (WQI) is a dimensional number that aggregates information from many water quality parameters according to a defined method. WQI is accepted as an efficient tool for water quality management. In this study, WQI of Saigon river for public water supply was calculated from nine water quality parameters including pH, suspended solids (SS), dissolved oxygen (DO), chemical oxygen demand (COD), nitrite, ammonia, phosphate, total dissolved iron and total coliform based on water quality data obtained monthly from January 2016 to December 2019 at three sampling sites. The results showed that most of WQI values belonged to class III (medium water quality with the WQIs of 35 – 64) and class IV (poor water quality with the WQIs of 11 – 34) and a deteriorating trend was observed from upstream to downstream of Saigon river. The river water quality could not be used for public water supply.

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27

Pathak, Akhil. "Assessment of Water Quality Parameters of Gomti River." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 25, 2021): 1977–82. http://dx.doi.org/10.22214/ijraset.2021.36786.

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The Gomti originates from Gomat Taal (Fulhaar jheel) near Madho Tanda, Pilibhit, India. It extends 960 km through Uttar Pradesh and meets the Ganges near Saidpur, Kaithi, 27 km from Varanasi district. The Earth is facing issues with a large kind of pollutants. Healthy soil, clean water, and air are very much necessary to live properly. In today’s world, Soil, water, and air are not any longer clean and pure however create human health risks. Gomti receives immense quantities of untreated waste, agricultural runoff, brings heaps of pesticides, fertilizer, street washout's transportation oil, asphalt, sediment, and lots of sorts of serious metals from different sources. The study area covers the Gomti River in part of Lucknow city of Uttar Pradesh lies between 80.899893 to 80.968180 N latitude 26.886799 to 26.833321 E longitude. To thoroughly investigate the Physico- Chemical Parameters in the Gomti River of Lucknow city, a total of eight sampling sites were selected between Guaghat upstream and Piparaghat. Samples are collected and analyzed from the proposed locations. The Physico-chemical parameters in the water of river Gomti were assessed to know about the water quality of the river. Parameters like temperature, TSS, TDS, pH, Hardness, Turbidity, Dissolved Oxygen (DO), Nitrate, Chlorine, Alkalinity, Calcium, Magnesium, BOD, COD were determined. After analysis of the parameters in the laboratory, variation of the parameters at a different location is analyzed. We present this variation through the graphs. Variation of many of the parameters is within limit according to the standards limit but some parameters show the excess pollution in the river. Dissolved Oxygen is zero at three sites and other three sites it is below 3 PPM which shows the pollution in water. It could be due to the high discharge of water from the catchment area, industries, and various drains. We also analyzed the correlation between the parameters. It will help to know the variation in any parameters by changing the value of other parameters.

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28

Laksono Putro, Primadi Gayuh, H. Hadiyanto, and Amirudin. "Water Quality Parameters of Tofu Wastewater: A Review." IOP Conference Series: Materials Science and Engineering 1156, no. 1 (June 1, 2021): 012018. http://dx.doi.org/10.1088/1757-899x/1156/1/012018.

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29

Bharath Singh, J., S. Vijayalakshmi, and S. Sushmitha. "Cloud based analysis of Drinking Water quality parameters." Journal of Physics: Conference Series 1979, no. 1 (August 1, 2021): 012053. http://dx.doi.org/10.1088/1742-6596/1979/1/012053.

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30

Cornwell, David, Richard Brown, and Nancy McTigue. "Controlling Lead and Copper Rule Water Quality Parameters." Journal - American Water Works Association 107, no. 2 (February 2015): E86—E96. http://dx.doi.org/10.5942/jawwa.2015.107.0011.

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31

Abu Bakar, Norshila, N. Othman, Z. M. Yunus, Zawawi Daud, Nur Salsabila Norisman, and Muhammad Haziq Hisham. "Physico-Chemical Water Quality Parameters Analysis on Textile." IOP Conference Series: Earth and Environmental Science 498 (June 16, 2020): 012077. http://dx.doi.org/10.1088/1755-1315/498/1/012077.

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32

Verma, A. K., and T. N. Singh. "Prediction of water quality from simple field parameters." Environmental Earth Sciences 69, no. 3 (September 14, 2012): 821–29. http://dx.doi.org/10.1007/s12665-012-1967-6.

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33

Beqaj, Blerina. "Evaluation of Water Quality Parameters for Narta Lagoon." European Journal of Engineering and Technology Research 8, no. 1 (January 19, 2023): 41–45. http://dx.doi.org/10.24018/ejeng.2023.8.1.2945.

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The coastal area of Albania is characterized by specific constructions, parts of which are the coastal wetlands and its lagooning. Lagoons are very specific bodies of water that acquire an interesting status along with the indisputable values they have. The value of knowing the physico-chemical properties of the water of the Narta Lagoon is related to many processes that occur within the lagoon and in the environments around it. The study was carried out on the Narta Lagoon to identify the chemical and physical characteristics of the water. For the determination of the physico-chemical properties, a certain methodology has been followed, which includes the establishment of a network of five stations for sampling the water of the lagoon and the determination of the level of certain physico-chemical parameters. The physico-chemical parameters that were determined are: temperature (T), pH, dissolved oxygen (O2), BOD5, S%, Cl-, ECw, NH4+, NO2-, P-PO43-, hardness. The most pronounced differences of the physico-chemical levels are those that belong to the data of the south-east station. According to the level of different components for all the stations, a degree of homogeneity in the physico-chemical composition is observed. Only the physico-chemical feature of the water at south-eastern part of the lagoon makes a difference, which is related to the influence of the urban discharge channel of the city of Vlora. The physico-chemical parameters that interpret pollution such as: dissolved oxygen (O2), BOD5, NH4+, NO2-, NO3-, PO43-, hardness is higher than in other stations of the lagoon.

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34

Uudeberg, Kristi, Age Aavaste, Kerttu-Liis Kõks, Ave Ansper, Mirjam Uusõue, Kersti Kangro, Ilmar Ansko, Martin Ligi, Kaire Toming, and Anu Reinart. "Optical Water Type Guided Approach to Estimate Optical Water Quality Parameters." Remote Sensing 12, no. 6 (March 13, 2020): 931. http://dx.doi.org/10.3390/rs12060931.

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Currently, water monitoring programs are mainly based on in situ measurements; however, this approach is time-consuming, expensive, and may not reflect the status of the whole water body. The availability of Multispectral Imager (MSI) and Ocean and Land Colour Instrument (OLCI) free data with high spectral, spatial, and temporal resolution has increased the potential of adding remote sensing techniques into monitoring programs, leading to improvement of the quality of monitoring water. This study introduced an optical water type guided approach for boreal regions inland and coastal waters to estimate optical water quality parameters, such as the concentration of chlorophyll-a (Chl-a) and total suspended matter (TSM), the absorption coefficient of coloured dissolved organic matter at a wavelength of 442 nm (aCDOM(442)), and the Secchi disk depth, from hyperspectral, OLCI, and MSI reflectance data. This study was based on data from 51 Estonian and Finnish lakes and from the Baltic Sea coastal area, which altogether were used in 415 in situ measurement stations and covered a wide range of optical water quality parameters (Chl-a: 0.5–215.2 mg·m−3; TSM: 0.6–46.0 mg·L−1; aCDOM(442): 0.4–43.7 m−1; and Secchi disk depth: 0.2–12.2 m). For retrieving optical water quality parameters from reflectance spectra, we tested 132 empirical algorithms. The study results describe the best algorithm for each optical water type for each spectral range and for each optical water quality parameter. The correlation was high, from 0.87 up to 0.93, between the in situ measured optical water quality parameters and the parameters predicted by the optical water type guided approach.

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35

Kruszyński, Wojciech. "COMPUTER MODELING OF SELECTED WATER QUALITY PARAMETERS IN WATER DISTRIBUTION SYSTEMS." Inżynieria Ekologiczna, no. 48 (2016): 125–29. http://dx.doi.org/10.12912/23920629/63283.

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36

Berman, T., A. Nishri, A. Parparov, B. Kaplan, S. Chava, M. Schlichter, and U. Pollingher. "Relationships between water quality parameters and water levels in Lake Kinneret." SIL Proceedings, 1922-2010 26, no. 2 (December 1997): 671–74. http://dx.doi.org/10.1080/03680770.1995.11900800.

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Payakka, Ampon, and Taeng-On Prommi. "Aquatic Insects Biodiversity and Water Quality Parameters of Receiving Water body." Current World Environment Journal 9, no. 1 (April 28, 2014): 53–58. http://dx.doi.org/10.12944/cwe.9.1.08.

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Ratnaningsih, D., E. L. Nasution, N. T. Wardhani, O. D. Pitalokasari, and R. Fauzi. "Water pollution trends in Ciliwung River based on water quality parameters." IOP Conference Series: Earth and Environmental Science 407 (December 17, 2019): 012006. http://dx.doi.org/10.1088/1755-1315/407/1/012006.

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S, MOHANDAS, and MARIMUTHU R. "Effect of sea water intrusion on quality parameters of ground water." Madras Agricultural Journal 89, March (2002): 22–25. http://dx.doi.org/10.29321/maj.10.a00173.

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A study was carried out in coastal Ramanathapuram district to find out the influence of sea water intrusion on quality of irrigation water. The analytical results indicated that the ground water in the study area was sodic. The Residual Sodium Corbonate (RSC) value was negative in ninety three percent of the samples analysed. The Sodium Adsorption Ratio (SAR) of irrigation water was found to be positively associated with SAR of the Soil Water Extract (SWE). A wide variations in the other quality parameters viz. potential salinity, permeability index, sodium ratio and adjusted SAR were observed due to the intrusion of sea water in to the ground fresh water aquifers of the coastal belt at various degrees. Significant positive correlation was noticed between SAR of irrigation water and the SWE of irrigated surface soils (r=0.231) No relationship existed between SAR and ESP of the coarse taxtured soils in the coastal areas.

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Misman, Nurafifa Akmal, Mohd Faez Sharif, Ahmed Jalal Khan Chowdhury, and Nur Hafizah Azizan. "Water pollution and the assessment of water quality parameters: a review." DESALINATION AND WATER TREATMENT 294 (2023): 79–88. http://dx.doi.org/10.5004/dwt.2023.29433.

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Rodrigues Junior, Jocimar Coutinho, Anderson Luiz Ribeiro de Paiva, Fabricio Motteran, Leidjane Maria Maciel de Oliveira, José Adalberto da Silva Filho, and Jonas Onis Pessoa. "Variância de parâmetros de qualidade da água e análise de agrupamentos na bacia hidrográfica do rio Goiana, Pernambuco." Revista Brasileira de Geografia Física 15, no. 6 (2022): 3013–31. http://dx.doi.org/10.26848/rbgf.v15.6.p3013-3031.

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The quality of surface water is a subject of interest to the scientific community, the management bodies and all civil society, mainly due to the speed of degradation of water bodies. Thus, the aim of this study was to analyze some of the main water quality parameters in the Goiana river basin. The parameters analyzed (pH, dissolved oxygen, biochemical oxygen demand, turbidity, ammonia, total phosphorus, thermotolerant coliforms and electrical conductivity) correspond to sampling points in seven locations within the Goiana river watershed, distributed over six distinct periods, considering the rainy season and the dry season. The data were submitted to variance evaluation and Fisher's test, with a significance level of p ≤ 0.05, as well as cluster analysis. Considering the effects of seasonality, the statistical tests performed allowed us to conclude that in the rainy season, the parameters pH (p = 0.0097) and dissolved oxygen (p = 0.0352) presented means with statistically different values, whereas in the dry season, this occurred with turbidity (p = 0.0058). Thus, it can be seen that there may be differences in water quality at certain points, between the seasons of the year, as a result of the behavior of the local rainfall. In relation to the groupings established, there were some differences, considering the seasonality, obtaining 2 or 3 groupings for the points under study, showing different levels of pollution, especially near urban areas. Therefore, the results obtained are essential to assist in the development of public policies for the mitigation of environmental impacts.

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Berndt, William L. "Salinity Affects Quality Parameters of ‘SeaDwarf’ Seashore Paspalum." HortScience 42, no. 2 (April 2007): 417–20. http://dx.doi.org/10.21273/hortsci.42.2.417.

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Irrigating salt-tolerant grasses with nonpotable water, like salt water, conserves fresh water resources. Advertising suggests that ‘SeaDwarf’ seashore paspalum (Paspalum vaginatum O. Swartz) is salt-tolerant and that it resists the effects of salinity on growth typically observed when irrigating other turf types with salty water. As a result, it is now being used on golf courses and home lawns in an effort to help conserve fresh water. Commensurate with the use of nonpotable irrigation, however, would be an expectation of high turf quality. This study was done to determine if the quality of ‘SeaDwarf’ seashore paspalum was affected by irrigating it with nonpotable water having high levels of salinity. Seven irrigation water sources created by blending tap water and ocean water and ranging in salinity from 0.52 to 49.40 dS·m−1 were used to flood-irrigate containerized ‘SeaDwarf’ seashore paspalum once daily for 50 consecutive days. Turf quality gradually decreased as salinity increased but improved with time except at the highest level of salinity. Effect of water source on turf quality was attributed to salinity-induced changes in quality parameters, including leaf texture, color, stolon growth, and shoot yield. The observed effect of salinity on quality parameters likely resulted from osmotic stress associated with high levels of salinity. The salt tolerance of ‘SeaDwarf’ seashore paspalum in this study was moderately good, but irrigating it with water having lower levels of salinity resulted in better quality turf.

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Lachhab, Ahmed, Matthew Beren, and Brian Zuidervliet. "Middle Creek Water Assessment Using Water Quality Index (WQI)." Journal of the Pennsylvania Academy of Science 88, no. 1 (March 1, 2014): 4–12. http://dx.doi.org/10.5325/jpennacadscie.88.1.0004.

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ABSTRACT Water quality Index (WQI) provides a single value to express overall quality of water which is calculated based on a number of measurable parameters. WQI was calculated based on 10 parameters that were determined during the period between June and July of 2012 and July of 2013 in an effort to assess the water quality of Middle Creek (MC) (Snyder, CO. PA). The Middle Creek (MC) is a 2nd order tributary stream flowing into Penn's Creek before joining the Susquehanna River. In this study, eleven sites were selected along MC between the head waters and its confluence with Penn's Creek. Physical properties were measured in the field, and grab-samples were tested for P, NO2−, NO3−, NH3, biochemical oxygen demand (BOD), chemical oxygen demand (COD) and total suspended solids (TSS). The Composite WQI for all sites along MC was calculated to be 89.71, indicating that MC is in good condition regarding water quality and is capable of buffering nutrient and chemical runoff. Results have shown that certain parameters, such as temperature (T°), COD or BOD, can be used as proxies for predicting the WQI. Due to good correlation with WQI values, T°, COD or BOD can be used to assess water quality within a short period of time. In this study, T° and COD were found to give the best forecasting parameters, followed by BOD. The relationships found between WQI and these three parameters, allow for a simple test to provide an adequate assessment of the water quality of a given stream system.

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Ahmed, Mehreen, Rafia Mumtaz, and Zahid Anwar. "An Enhanced Water Quality Index for Water Quality Monitoring Using Remote Sensing and Machine Learning." Applied Sciences 12, no. 24 (December 13, 2022): 12787. http://dx.doi.org/10.3390/app122412787.

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Water quality deterioration is a serious problem with the increase in the urbanization rate. However, water quality monitoring uses grab sampling of physico-chemical parameters and a water quality index method to assess water quality. Both processes are lengthy and expensive. These traditional indices are biased towards the physico-chemical parameters because samples are only collected from certain sampling points. These limitations make the current water quality index method unsuitable for any water body in the world. Thus, we develop an enhanced water quality index method based on a semi-supervised machine learning technique to determine water quality. This method follows five steps: (i) parameter selection, (ii) sub-index calculation, (iii) weight assignment, (iv) aggregation of sub-indices and (v) classification. Physico-chemical, air, meteorological and hydrological, topographical parameters are acquired for the stream network of the Rawal watershed. Min-max normalization is used to obtain sub-indices, and weights are assigned with tree-based techniques, i.e., LightGBM, Random Forest, CatBoost, AdaBoost and XGBoost. As a result, the proposed technique removes the uncertainties in the traditional indexing with a 100% classification rate, removing the necessity of including all parameters for classification. Electric conductivity, secchi disk depth, dissolved oxygen, lithology and geology are amongst the high weighting parameters of using LightGBM and CatBoost with 99.1% and 99.3% accuracy, respectively. In fact, seasonal variations are observed for the classified stream network with a shift from 55:45% (January) to 10:90% (December) ratio for the medium to bad class. This verifies the validity of the proposed method that will contribute to water management planning globally.

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Sailaja, V. Hema, and M. Anji Reddy. "Investigation of Physico-Chemical Parameters to Assess the Water Quality of Himayat Sagar Lake, Hyderabad." International Journal of Scientific Research 2, no. 5 (June 1, 2012): 279–81. http://dx.doi.org/10.15373/22778179/may2013/93.

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Mohammed, Shahad Esmaeel, and Khalid Adel Abdulrazzaq. "Developing Water Quality Index to Assess the Quality of the Drinking Water." Civil Engineering Journal 4, no. 10 (October 30, 2018): 2345. http://dx.doi.org/10.28991/cej-03091164.

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In the present study, an attempt has been to develop a new water quality index (WQI) method that depends on the Iraqi specifications for drinking water (IQS 417, 2009) to assess the validity of the Euphrates River for drinking by classifying the quality of the river water at different stations along its entire reach inside the Iraqi lands. The proposed classifications by this method are: Excellent, Good, Acceptable, Poor, and Very poor. Eight water quality parameters have been selected to represent the quality of the river water these are: Ion Hydrogen Concentration (pH), Calcium (Ca), Magnesium (Mg), Sodium (Na), Chloride (Cl), Sulphate (SO_4), Nitrate (NO_3), and Total Dissolved Solids (TDS). The variation of the water quality parameters along the river have been represented by graphs using Excel.2013 software. The results revealed that the quality of the Euphrates River ranges from “Good” to “Poor”, it enters the Iraqi borders with “Good” water quality and gradually its quality begins to decrease after it receives pollution from many sources such as domestic sewage and different industrial effluents until its quality becomes “Poor” according to the proposed classification. Finally the proposed WQI can be used as a tool to assess the quality of the river with both place and time.

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Supardiono, Supardiono, Gito Hadiprayitno, Jono Irawan, and Lalu Adi Gunawan. "Analysis of River Water Quality Based on Pollution Index Water Quality Status, Lombok District, NTB." Jurnal Penelitian Pendidikan IPA 9, no. 3 (March 31, 2023): 1602–8. http://dx.doi.org/10.29303/jppipa.v9i3.4591.

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Leneng River is one of the rivers in Praya City, Central Lombok Regency, which receives domestic, agricultural, and household industrial waste. Various community activities along the banks of the Leneng River have affected river water quality. Some communities on the banks of the Leneng River, from upstream to downstream, still use river water for their daily needs. This study aims to 1) analyze the water quality of the Leneng River in terms of environmental parameters (Physical, chemical, and biological) and 2) Determine the level of pollution of the Leneng River water using the Pollution Index (IP) method. River water quality measurements were carried out at 3 (three) sample points. Parameters measured and observed were physical parameters, namely Total Solid Suspend (TSS); chemical parameters, namely pH, Total Phosphate (T.PO4), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), and biological parameters namely Total coliform and Fecal Coliform. This type of research is survey research with a quantitative descriptive approach. The results showed that the TSS value at sample point 1 (upstream) exceeded the required quality standard; the highest TSS concentration value was 54 mg/L, and the lowest at sample point 2 (middle section) was 2.50 mg/L. Value parameter concentration pH 7.7-8.7, T.PO4 value 0.05 mg/L-0.29 mg/L, BOD value 0.17-0.42 mg/L, COD concentration value 8 mg/L -9mg/L, and DO value of 6.07 mg/L-7.57 mg/L, this shows that the concentration for chemical parameters still meets the class I water quality standards according to PP 22 of 2021, while the total coliform parameter value and Facel coliform have exceeded class I water quality standards. The total coliform concentration value is 10,000 - 20,000 MPN/100 mL, and the Fecal coliform value is 600 MPN/100 mL – 2,500 MPN/100 mL, while based on the Pollution Index (IP) quality status water in the upstream with a value of 2.84, the middle part of 1.97, and the downstream part of 2.84, this shows a decrease in water quality from upstream to downstream of the river, with a level of pollution in the lightly polluted category.

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Maharjan, Sujan, Tista Prasai Joshi, and Sujen Man Shrestha. "Poor Quality of Treated Water in Kathmandu: Comparison with Nepal Drinking Water Quality Standards." Tribhuvan University Journal of Microbiology 5 (September 26, 2018): 83–88. http://dx.doi.org/10.3126/tujm.v5i0.22319.

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Objectives: In order to evaluate the quality assurance of drinking water in Kathmandu valley, this study analyzed selected physiochemical and microbial parameters of treated water samples and compared with Nepal Drinking Water Quality Standards (NDWQS). Methods: Treated water samples were collected from all over the Kathmandu valley and analyzed in terms of physicochemical and microbiological parameters over the period of one year from July 2017 to July 2018. The physio-chemical parameters of water samples were performed according to standard methods for the examination of water and wastewater. The total coliforms were enumerated by standard membrane filtration technique. Results: We report that microbiological aspect of treated water was the major problem as 66% of the water samples crossed the guideline value for total coliform count. Above 92% of jar water samples, 77% of tanker water samples and 69% of filtered water samples had the total coliform count exceeding the NDWQS. Moreover, 20% of bottled water was contaminated by coliform bacteria. Iron and ammonia content were found to be higher than the guideline values in 16% and 21% of the total treated water samples respectively. Analyzing the types of treated water samples showed that 35% and 15% of tanker water samples had higher ammonia and iron content respectively, and the same parameters were higher in 23% and 19% in the filtered water samples respectively than the standard criteria recommended by NDWQS. Conclusion: The treated water samples exceed the standard values set by NDWQS and hence had poor quality. The presence of faecal pollution indicating coliform bacteria was the key problem for treated drinking water of Kathmandu valley. Therefore, monitoring and proper treatment of water should be conducted to prevent dissemination of waterborne diseases.

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Aldrees, Ali, Mohsin Ali Khan, Muhammad Atiq Ur Rehman Tariq, Abdeliazim Mustafa Mohamed, Ane Wai Man Ng, and Abubakr Taha Bakheit Taha. "Multi-Expression Programming (MEP): Water Quality Assessment Using Water Quality Indices." Water 14, no. 6 (March 17, 2022): 947. http://dx.doi.org/10.3390/w14060947.

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Water contamination is indeed a worldwide problem that threatens public health, environmental protection, and agricultural productivity. The distinctive attributes of machine learning (ML)-based modelling can provide in-depth understanding into increasing water quality challenges. This study presents the development of a multi-expression programming (MEP) based predictive model for water quality parameters, i.e., electrical conductivity (EC) and total dissolved solids (TDS) in the upper Indus River at two different outlet locations using 360 readings collected on a monthly basis. The optimized MEP models were assessed using different statistical measurements i.e., coefficient-of-determination (R2), root-mean-square error (RMSE), mean-absolute error (MAE), root-mean-square-logarithmic error (RMSLE) and mean-absolute-percent error (MAPE). The results show that the R2 in the testing phase (subjected to unseen data) for EC-MEP and TDS-MEP models is above 0.90, i.e., 0.9674 and 0.9725, respectively, reflecting the higher accuracy and generalized performance. Also, the error measures are quite lower. In accordance with MAPE statistics, both the MEP models shows an “excellent” performance in all three stages. In comparison with traditional non-linear regression models (NLRMs), the developed machine learning models have good generalization capabilities. The sensitivity analysis of the developed MEP models with regard to the significance of each input on the forecasted water quality parameters suggests that Cl and HCO3 have substantial impacts on the predictions of MEP models (EC and TDS), with a sensitiveness index above 0.90, although the influence of the Na is the less prominent. The results of this research suggest that the development of intelligence models for EC and TDS are cost effective and viable for the evaluation and monitoring of the quality of river water.

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Rohini A. and Manikya Reddy P. "Assessment of Water Quality in Safilguda Lake, Hyderabad using Water Quality Index." Ecology, Environment and Conservation 28 (2022): 101–6. http://dx.doi.org/10.53550/eec.2022.v28i07s.017.

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In the present study,Water Quality Index (WQI) was used to assess the quality of water in Safilguda lake. Safilguda lake was the source of drinking for the Nawabs and British authorities and was also the source of irrigation to the farmers. But today the agricultural fields have turned into housing colonies and multistorey complexes which led to the deterioration of lake water by direct entry of sewage into the lake. Three sampling stations were selected within the lake for the collection of surface water samples at monthly intervals for a period of one year. The water samples were analyzed for various physico-chemical parameters by following the standard procedures. For calculating WQI, ten physico-chemical parameters were considered such as pH, alkalinity, chlorides, total hardness, calcium, magnesium, nitrates, sulphates, total dissolved solids and dissolved oxygen. From the water quality index, the lake water quality was determined. On the basis of WQI classification, the water quality status ratings clearly indicates that the Safilguda lake has poor water quality due to the discharge of untreated domestic waste water from the surrounding residents and religious activities like immersion of Ganesh idols made up of plaster of paris and the lakewater is unsuitable for drinking, domestic and recreational purposes. The water quality rating study also exhibits eutrophic condition of the lake.

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