scholarly journals Sedimentation and Its Impacts/Effects on River System and Reservoir Water Quality: case Study of Mazowe Catchment, Zimbabwe

2018 ◽  
Vol 377 ◽  
pp. 57-66 ◽  
Author(s):  
Colleta Tundu ◽  
Michael James Tumbare ◽  
Jean-Marie Kileshye Onema
Keyword(s):  
Water Quality ◽  
Sediment Yield ◽  
Soil Loss ◽  
Quality Parameters ◽  
Water Sources ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Water Quality Data ◽  
Quality Of Water

Abstract. Sediment delivery into water sources and bodies results in the reduction of water quantity and quality, increasing costs of water purification whilst reducing the available water for various other uses. The paper gives an analysis of sedimentation in one of Zimbabwe's seven rivers, the Mazowe Catchment, and its impact on water quality. The Revised Universal Soil Loss Equation (RUSLE) model was used to compute soil lost from the catchment as a result of soil erosion. The model was used in conjunction with GIS remotely sensed data and limited ground observations. The estimated annual soil loss in the catchment indicates soil loss ranging from 0 to 65 t ha yr−1. Bathymetric survey at Chimhanda Dam showed that the capacity of the dam had reduced by 39 % as a result of sedimentation and the annual sediment deposition into Chimhanda Dam was estimated to be 330 t with a specific yield of 226 t km−2 yr−1. Relationship between selected water quality parameters, TSS, DO, NO3, pH, TDS, turbidity and sediment yield for selected water sampling points and Chimhanda Dam was analyzed. It was established that there is a strong positive relationship between the sediment yield and the water quality parameters. Sediment yield showed high positive correlation with turbidity (0.63) and TDS (0.64). Water quality data from Chimhanda treatment plant water works revealed that the quality of water is deteriorating as a result of increase in sediment accumulation in the dam. The study concluded that sedimentation can affect the water quality of water sources.

scholarly journals Assessment of Water Quality of Brahmani River using Correlation and Regression Analysis

2020 ◽  
Author(s):  
Saroj Nayak
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Correlation Coefficient ◽  
Surface Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Chemical Parameters ◽  
Water Quality Data

This work evaluates the surface water quality in terms of physico-chemical parameters of the Brahmani River, Odisha using statistical analysis involving the calculation of correlation coefficient and regression equation. Besides this, the work also highlights and draws attention towards the “Water Quality Index” in a simplified format which may be used at large and could represent the reliable picture of water quality. Surface water quality data is taken from OSPCB of various location i.e. Panposh D/S, Rourkela D/S, Rengali, Talcher U/S, Kamalanga D/S, Bhuban, Pattamundai and was assessed for summer, monsoon, winter for the years 2011, 2012, 2013, 2014 and 2015. Average of values, minimum of values and maximum of values of water quality parameters were obtained seasonally over the above mentioned years. Besides this, the standard deviation for the water quality parameters was also obtained for water quality parameters namely pH, Temperature, DO, TDS, Alkalinity, EC, Na+, Ca2+, Mg2+, K+, F-, Cl-, NO3-, SO42- and PO42-. Seasonal changes in various physical and chemical parameters were analysed.The values obtained were compared with the guideline values for drinking water by Bureau of Indian Standard (BIS). A systematic correlation and regression study is carried out for three seasons, showed linear relationship among different water quality parameters. This provides an easy and rapid method of monitoring water quality. Highly significant (0.8< r <1.0), moderately significant (0.6< r <0.8) and significant (0.5< r <0.6) correlations between the parameters have been worked out. High correlation coefficient has been observed between TDS,EC-Na+, Ca2+, Cl-, SO42- ; Na+- Cl-. From the collected quantities, certain parameters were selected to derive WQI for the variations in water quality of each designated sampling site. WQI of Brahmani River ranged from 36.7 to 44.1 which falls in the range of good quality of water.Panposh D/S and Rourkela D/S showed poor water quality in summer and winter season. It is shown that WQI may be a useful tool for assessing water quality and predicting trend of variation in water quality at differentlocations in the Brahmani River.

Assessement of Water Quality Parameters in Some Potential Pollutant Areas from Romania

2018 ◽  
Vol 69 (8) ◽  
pp. 2045-2049
Author(s):  
Catalina Gabriela Gheorghe ◽  
Andreea Bondarev ◽  
Ion Onutu
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Surface Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Chemical Parameters ◽  
Water Quality Data ◽  
Pollutant Sources ◽  
Water Quality Forecasting

Monitoring of environmental factors allows the achievement of some important objectives regarding water quality, forecasting, warning and intervention. The aim of this paper is to investigate water quality parameters in some potential pollutant sources from northern, southern and east-southern areas of Romania. Surface water quality data for some selected chemical parameters were collected and analyzed at different points from March to May 2017.

Making Sense of Water Quality: Multispecies Encounters on the Mystic River

2013 ◽  
Vol 17 (2) ◽  
pp. 150-160 ◽  
Author(s):  
Caterina Scaramelli
Keyword(s):  
Water Quality ◽  
Laboratory Data ◽  
Quality Data ◽  
Human Beings ◽  
Water Lily ◽  
Water Quality Data ◽  
Water Flows ◽  
Making Sense ◽  
Quality Of Water

This paper takes water quality as an ethnographic subject. It looks at how water quality monitors in Boston make sense of the quality of water through mundane engagement with three non-human beings who they encounter during their monitoring activities: herring, bacteria and water lily. Each of these organisms suggests a different understanding of water quality for the monitors and poses a dilemma. Water quality monitors who contribute to the production of water quality data come to know water quality as through direct interactions with these beings, mediated by both sensorial experience and laboratory data. These experiences, at the same time, confuse and redraw relationships between science, water flows, non-human vitality, including that of invasive species, and people.

Generation of geolocated and radiometrically corrected true reflectance surfaces in the visible portion of the electromagnetic spectrum over large bodies of water using images from a sUAS

2020 ◽  
Vol 8 (3) ◽  
pp. 172-185
Author(s):  
Juan G. Arango ◽  
Brandon K. Holzbauer-Schweitzer ◽  
Robert W. Nairn ◽  
Robert C. Knox
Keyword(s):  
Water Quality ◽  
Near Infrared ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Electromagnetic Spectrum ◽  
Water Quality Data ◽  
Ground Control Points ◽  
Bodies Of Water

The focus of this study was to develop true reflectance surfaces in the visible portion of the electromagnetic spectrum from small unmanned aerial system (sUAS) images obtained over large bodies of water when no ground control points were available. The goal of the research was to produce true reflectance surfaces from which reflectance values could be extracted and used to estimate optical water quality parameters utilizing limited in-situ water quality analyses. Multispectral imagery was collected using a sUAS equipped with a multispectral sensor, capable of obtaining information in the blue (0.475 μm), green (0.560 μm), red (0.668 μm), red edge (0.717 μm), and near infrared (0.840 μm) portions of the electromagnetic spectrum. To develop a reliable and repeatable protocol, a five-step methodology was implemented: (i) image and water quality data collection, (ii) image processing, (iii) reflectance extraction, (iv) statistical interpolation, and (v) data validation. Results indicate that the created protocol generates geolocated and radiometrically corrected true reflectance surfaces from sUAS missions flown over large bodies of water. Subsequently, relationships between true reflectance values and in-situ water quality parameters were developed.

scholarly journals Investigation of water quality from Damanga estuary through the water quality indexing Gujarat

2021 ◽  
Vol 9 (2) ◽  
Author(s):  
Shefaliben Sureshbhai Patel ◽  
Susmita Sahoo
Keyword(s):  
Heavy Metals ◽  
Water Quality ◽  
Anthropogenic Activities ◽  
River Estuary ◽  
Poor Quality ◽  
Quality Parameters ◽  
Quality Data ◽  
Water Quality Data ◽  
Investigation Area

The seasonal investigation about the water quality from Damanganga river estuary on two habitats downstream and upstream was carried out from January to December 2019 containing three major seasons: winter, summer and monsoon. For this monitoring activity total 29 parameters (24 physico-chemical parameters and 5 heavy metals) were analyzed. Multivariate analyses suggested inter dependency among these studied parameters. Water Quality Index is computed based on the major fluctuated and affected parameters. The calculated values of WQI for all three seasons ranged from 122.84 to 173.82 which suggested poor water quality of the water body. WQI values of the investigation area proposed that the estuarine water quality is deteriorated due to high value of presented heavy metals (Aluminum, Iron, Manganese, Boron and Zinc), Chloride, Ammonium and Sulfate in water sample. In this case, the downstream station is having accessional pollutant contaminations while the upstream station is having diminutive pollutant contaminants. Temporally, the dominant frailty found during the winter followed by summer and monsoon. This study field exhibited poor quality of water; the reason behind this might be the impressive surrounding industrial zone as well as other anthropogenic activities. There is quite normal probability distribution expressed by the represented water quality data at the both habitats. The Bray-Curtis cluster analysis shows different percentage similarity level between the water quality parameters.  

scholarly journals Appraisal of Drinking Water Quality in Lahore Residence, Pakistan

2017 ◽  
Vol 60 (1) ◽  
pp. 34-41
Author(s):  
Khalid Mahmood ◽  
Muhammad Asim
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Underground Water ◽  
Poor Quality ◽  
Drinking Water Quality ◽  
Quality Data ◽  
Southeastern Part ◽  
Water Quality Data ◽  
Quality Of Water

A comprehensive study for the spatial distribution of drinking water quality had been conductedfor residential area of Lahore, Pakistan. The study had made use of the geographic information system(GIS) for geographical representation and spatial analysis of groundwater quality. Physicochemicalparameters including electric conductivity, pH, TDS, Cl, Mg, Ca, alkalinity and bicarbonates from 73 ofthe water samples had been included in the analysis. Water quality data had been geo-referenced followedby its interpolation using inverse distance weighted (IDW) for each of the parameters. Very high alkalinityand bicarbonates values were observed in most parts of the area. For the comprehensive view, water qualityindex map had been prepared using weighted overlay analysis (WOA). The water quality index map wasclassified into five zones of excellent, good, poor, very poor and unfit for drinking as per WHO standardsof drinking water. 21% region had excellent quality of the underground water and 50% was found goodfor drinking. Poor quality of water was found in southeastern part, covering 27% of the study area. Only2% of the area was found under the very poor and unfit water quality conditions for drinking.

scholarly journals Implementation of Long-Short Term Memory Neural Network (LSTM) for Predicting The Water Quality Parameters in Sungai Selangor

2021 ◽  
Vol 6 (4) ◽  
pp. 40-49
Author(s):  
Nur Natasya Mohd Anuar ◽  
Nur Fatihah Fauzi ◽  
Huda Zuhrah Ab Halim ◽  
Nur Izzati Khairudin ◽  
Nurizatul Syarfinas Ahmad Bakhtiar ◽  
...  
Keyword(s):  
Water Quality ◽  
Short Term Memory ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Short Term ◽  
Term Memory ◽  
Quality Of Water ◽  
Long Short Term Memory

Predictions of future events must be factored into decision-making. Predictions of water quality are critical to assist authorities in making operational, management, and strategic decisions to keep the quality of water supply monitored under specific criteria. Taking advantage of the good performance of long short-term memory (LSTM) deep neural networks in time-series prediction, the purpose of this paper is to develop and train a Long-Short Term Memory (LSTM) Neural Network to predict water quality parameters in the Selangor River. The primary goal of this study is to predict five (5) water quality parameters in the Selangor River, namely Biochemical Oxygen Demand (BOD), Ammonia Nitrogen (NH3-N), Chemical Oxygen Demand (COD), pH, and Dissolved Oxygen (DO), using secondary data from different monitoring stations along the river basin. The accuracy of this method was then measured using RMSE as the forecast measure. The results show that by using the Power of Hydrogen (pH), the dataset yielded the lowest RMSE value, with a minimum of 0.2106 at station 004 and a maximum of 1.2587 at station 001. The results of the study indicate that the predicted values of the model and the actual values were in good agreement and revealed the future developing trend of water quality parameters, showing the feasibility and effectiveness of using LSTM deep neural networks to predict the quality of water parameters.

scholarly journals The Fluctuation of Water Quality of Ci Lutung flow areas in Majalengka and Sumedang.

2018 ◽  
Vol 73 ◽  
pp. 04013
Author(s):  
Deddy Caesar Agusto ◽  
Eko Kusratmoko
Keyword(s):  
Water Quality ◽  
Point Source ◽  
Agricultural Land ◽  
Quality Data ◽  
Water Quality Data ◽  
Water Quality Indicators ◽  
Quality Of Water ◽  
The Moment ◽  
The Impact

The river is the main source of water in Indonesia, which at the moment, this quality tends to get worse and is no longer worth consuming for various needs. The cause of the pollution is the entry of pollutants both point source (industrial waste) and non-point source (residential and agricultural land). Rainfall can be a non-point source pollutant agent from a watershed to a water body. The impact of rainfall on increasing concentrations of pollutants is very significant, especially the high intensity rainfall that falls after the long dry season. In this study, water quality data is obtained from river outlets located in Damkamun taken every 30 minutes during the rainfall event so that fluctuation in water quality can be seen. Water quality indicators studied in this research are TDS, DHLNitrate, Phosphate and Ph. The author, in analyzing, using rainfall Himawari 8 which is obtained every 10 minutes. The result shows that rainfall is directly related to the water flow and the fluctuation of the discharge affects the water quality. From the calculations, the chemical quality of water is also influenced by the use of land in the watershed. Nitrate value increases when the occurrence of rain occurs in land use while phosphate experiences a high value during the event.

Standardization of Water Quality Information in a Tidal River

1986 ◽  
Vol 18 (4-5) ◽  
pp. 43-52 ◽  
Author(s):  
Tetsuya Kusuda ◽  
Tohru Futawatari ◽  
Youichi Awaya ◽  
Kenichi Koga ◽  
Katsuhiro Furumoto
Keyword(s):  
Water Quality ◽  
Time Lag ◽  
High Tide ◽  
Tidal River ◽  
Quality Data ◽  
Water Quality Data ◽  
Monitoring Method ◽  
Quality Of Water ◽  
Tidal Rivers

The objectives of this study were to clarify the defects of the current tidal river monitoring method and to propose a better method to obtain water quality data of high quality for tidal rivers. In Japan, the Water Quality Standards for rivers also apply to tidal rivers. The method indicates that water should be sampled from 20% of the water depth below the water surface at an arbitrary time once a month. Since this method was apparently inappropriate to understand the dynamics and water profiles in tidal rivers, field surveys were conducted at different times in the River Rokkaku, which is well mixed. The results showed that the turbidity maximum moved up- and down-stream more than 10 km due to the tide. Based on this fact, a new monitoring method was proposed, which required water samples to be taken with a certain time lag from a high tide at a station. This newly proposed method improved the quality of information on water quality and made data available to ascertain long term trends. Modifications to the new method are suggested to further improve the quality of water quality data for tidal rivers.

Effects of sampling frequency on estimation accuracies of annual loadings for water quality parameters in different sized watersheds

2020 ◽  
Vol 55 (3) ◽  
pp. 261-277
Author(s):  
Lin Gao ◽  
Junyu Qi ◽  
Sheng Li ◽  
Glenn Benoy ◽  
Zisheng Xing ◽  
...  
Keyword(s):  
Water Quality ◽  
Suspended Solids ◽  
Nitrate Nitrogen ◽  
Sampling Frequency ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Estimation Errors ◽  
Water Quality Data ◽  
Novel Approach

Abstract Potential errors or uncertainties of annual loading estimations for water quality parameters such as suspended solids (SS), nitrate-nitrogen (NO3-N), ortho-phosphorus (Ortho-P), potassium (K), calcium (Ca), and magnesium (Mg) can be greatly affected by sampling frequencies. In this study, annual loading estimation errors were assessed in terms of the coefficient of variation, relative bias, and probability of potential errors that were estimated with statistical samples taken at a series of sampling frequencies for a watershed in northwestern New Brunswick, Canada, and one of its sub-watersheds. Results indicate that annual loading estimation errors increased with decreasing sampling frequency for all water quality parameters. At the same sampling frequencies, the estimation errors were several times greater for the smaller watershed than those for the larger watershed, possibly due to the flushing nature of streamflows in the smaller watershed. We also found that low sampling frequency tended to underestimate the annual loadings of water quality parameters dominated by stormflow events (SS and K) and overestimate water quality parameters dominated by baseflow (Mg and Ca). These results can be used by hydrologists and water quality managers to determine sampling frequencies that minimize costs while providing acceptable estimation errors. This study also demonstrates a novel approach to assess potential errors when analyzing existing water quality data.

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