scholarly journals Thermomineral water of Nikolicevo Spa (Eastern Serbia)

2007 ◽  
pp. 91-96
Author(s):  
Petar Dokmanovic ◽  
Veselin Dragisic ◽  
Slavko Spadijer
Keyword(s):  
Water Quality ◽  
Chemical Composition ◽  
Water Temperature ◽  
Water Movement ◽  
Wide Spectrum ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Outflow Rate ◽  
Scaling Process ◽  
Water Outflow

New monitoring results (2000-2002) of the thermomineral water outflow and quality regime of the Nikolicevo Spa (eastern Serbia) show that, during 30 years, a scaling process occurred and decreased the well outflow by app. 80%, as a consequence of well deterioration and reservoir depletion. Consequently (slower water movement), the water temperature increased by 1,5-2?C. Stabile values of the outflow and water quality parameters, registered during new monitoring, show an insignificant influence of the annual meteorological cycle on the outflow and quality regime. According to its chemical composition, the water is sodiumbicarbonate- fluoride, oligomineral and isothermal and a wide spectrum of applications is available. The limit for an efficient exploitation and application of the water is the current low outflow rate, so the drilling of new wells is recommended.

scholarly journals Research and Design of Distributed IoT Water Environment Monitoring System Based on LoRa

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Wei Chen ◽  
Xiao Hao ◽  
JianRong Lu ◽  
Kui Yan ◽  
Jin Liu ◽  
...  
Keyword(s):  
Water Quality ◽  
Water Temperature ◽  
Real Time ◽  
Monitoring System ◽  
Water Environment ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Environment Monitoring ◽  
Long Distance ◽  
Internet Of Things Technology

In order to solve the problems of high labor cost, long detection period, and low degree of information in current water environment monitoring, this paper proposes a lake water environment monitoring system based on LoRa and Internet of Things technology. The system realizes remote collection, data storage, dynamic monitoring, and pollution alarm for the distributed deployment of multisensor node information (water temperature, pH, turbidity, conductivity, and other water quality parameters). Moreover, the system uses STM32L151C8T6 microprocessor and multiple types of water quality sensors to collect water quality parameters in real time, and the data is packaged and sent to the LoRa gateway remotely by LoRa technology. Then, the gateway completes the bridging of LoRa link to IP link and forwards the water quality information to the Alibaba Cloud server. Finally, end users can realize the water quality control of monitored water area by monitoring management platform. The experimental results show that the system has a good performance in terms of real-time data acquisition accuracy, data transmission reliability, and pollution alarm success rate. The average relative errors of water temperature, pH, turbidity, and conductivity are 0.31%, 0.28%, 3.96%, and 0.71%, respectively. In addition, the signal reception strength of the system within 2 km is better than -81 dBm, and the average packet loss rate is only 94%. In short, the system’s high accuracy, high reliability, and long distance characteristics meet the needs of large area water quality monitoring.

scholarly journals Assessment of Quality Parameters of a Pond Water: A Limnological Case Study in Bareilly District

2021 ◽  
Vol 07 (08) ◽  
Author(s):  
Vikas Jain ◽  
Keyword(s):  
Water Quality ◽  
Water Temperature ◽  
Pond Water ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Total Hardness ◽  
Standard Procedures ◽  
Calcium And Magnesium

The manuscript herewith presents the assessment of water quality parameters in the samples drawn in year 2014-15 from Akshar Vihar pond, located centrally in district Bareilly (U.P.), India. Analysis of check parameters chosen, was performed by employing standard procedures laid down in APHA. The minimum to maximum values recorded in each month of the experimental year for pH, total hardness, DO, BOD, COD, calcium and magnesium were 7.2-8.8, 380 - 486mg/L, 4.2-10.6 mg/L, 1.0-1.6 mg/L, 3.8-8.4 mg/L, 52.97-74.84 mg/L and 56.74-72.98 mg/L respectively. Significant correlation was observed for COD with pH (0.816), carbonate (0.875) and bicarbonate (0.927); that of total hardness with magnesium (0.954) as well as of DO inversely with water temperature (-0.821).

Investigation into the Trichoptera fauna of the Opa Stream in Ile-Ife, southwestern Nigeria

Zoosymposia ◽  
2016 ◽  
Vol 10 (1) ◽  
pp. 85-90
Author(s):  
BABATUNDE AMUSAN ◽  
SYLVESTER OGBOGU
Keyword(s):  
Water Quality ◽  
Species Composition ◽  
Water Temperature ◽  
Negative Relationship ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Southwestern Nigeria ◽  
Dry Seasons ◽  
Wet And Dry Seasons

The species composition and abundance of caddisflies in association with some water quality parameters (pH, water temperature and conductivity) in Opa Stream in Ile-Ife, Nigeria were investigated during October 2009–August 2010. One hundred and ninety adult caddisflies collected from the stream represent six species in six genera and three families. Hydropsychidae had three species, which is more than were found in other families. The caddisflies showed a relative mean abundance of 62% and 38.9% in the wet and dry seasons, respectively. Caddisfly abundance was positively correlated with pH and conductivity but there was a negative relationship between water temperature and the abundance of caddisflies in the stream.

scholarly journals Assessing the Effect of Changing Ambient Air Temperature on Water Temperature and Quality in Drinking Water Distribution Systems

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1916
Author(s):  
Yuchuan Lai ◽  
David A. Dzombak
Keyword(s):  
Water Quality ◽  
Water Temperature ◽  
Air Temperature ◽  
Distribution Systems ◽  
Water Distribution ◽  
Ambient Air ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Temperature Changes ◽  
Drinking Water Distribution Systems

Drinking water distribution systems (DWDS) are affected by climate change and this work aimed to assess the effect of changing ambient air temperature on the water temperature and various water quality parameters in DWDS. A water temperature estimation model was identified and evaluated at seven specific locations in the U.S. and water quality parameters were assessed with a case study for Washington D.C. Preliminary estimation of changes in water temperature and two temperature-related parameters (the chlorine decay rate and bacterial activity) were developed for 91 U.S. cities using local air temperature observations and projections. Estimated water temperature changes in DWDS are generally equivalent to air temperature changes on an annual average basis, suggesting modest changes for the assessed historical periods and possibly more intensified changes in the future with greater increase in air temperature. As higher water age can amplify the temperature effect and the effects of temperature on some water quality parameters can be inter-related, yielding an aggregated effect, evaluation of extreme cases for DWDS will be of importance. In responding to changing climate conditions, assessments of DWDS water temperature changes and resulting impacts on water quality merit more attention to ensure appropriate adaptation of DWDS design and management.

Deep learning for water quality prediction: the application of LSTM model to predict water quality in catchment scale

2021 ◽  
Author(s):  
Amir Sahraei ◽  
Lutz Breuer ◽  
Philipp Kraft ◽  
Tobias Houska
Keyword(s):  
Water Quality ◽  
Water Temperature ◽  
Mean Squared Error ◽  
Model Performance ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Bayesian Optimization ◽  
Optimization Approach ◽  
Quality Prediction ◽  
Water Quality Prediction

<p>The prediction of water quality is an efficient way for managing water resources and protecting ecosystems by providing an early warning against water quality deterioration. So far, the classical approach is to predict water quality by the utilization of complex process-based water quality models. However, these models are not easy to set up and require comprehensive input data. The local characteristics, detailed process understandings and eventually data from land users such as farmers are needed, to build up a valid model structure. Such constraints can end up in wrong scientific conclusions ranging from false alarms to unpredicted environmental pollution in practical water monitoring application. Long short-term memory (LSTM) algorithms are known to be able to overcome some of the typical constraints in hydrological model applications. However, their performance in water quality prediction has rarely been explored. In this study, we investigate the ability of a LSTM model to predict the complex, nonlinear behavior of water quality parameters in the Schwingbach Environmental Observatory (SEO), Germany.  We predict weekly nitrogen-nitrate concentrations, weekly stable isotopes of water concentrations (δ<sup>18</sup>O) and daily water temperature in six stream‑ and six groundwater sources with different landuse and hillslope conditions. We use meteorological forcing data and catchment attributes as input variables. To ensure an efficient model performance, we employ a Bayesian optimization approach to optimize the hyperparameters of the LSTM. The model performance is evaluated by the Root Mean Squared Error (RMSE). Our LSTM is robust in capturing the dynamics of the water quality parameters over time. The RMSE for the LSTM performance ranges from 0.27 to 3.38 mg/l, from 0.069 to 0.27 ‰ and from 1.3 to 2.1 °C for nitrogen‑nitrate, δ<sup>18</sup>O and water temperature, respectively. We compare the RMSE with statistical parameters of data. Results confirm that the LSTM is a promising tool for early risk assessment of water quality, particularly in view that only a minimal set of catchment information is needed to gain robust results.</p>

scholarly journals ANALYSIS OF POTENTIAL PHYSICO CHEMICAL WATER QUALITY PARAMETERS OF ELENGENA BEEL, JAGIROAD, MORIGAON, ASSAM

YMER Digital ◽  
2021 ◽  
Vol 20 (10) ◽  
pp. 100-106
Author(s):  
JAYANTA KUMAR BORA ◽  
◽  
MD. Y HASSAN ◽  
M BURAGOHAIN ◽  
◽  
...  
Keyword(s):  
Water Quality ◽  
Water Temperature ◽  
Oxygen Demand ◽  
Paper Mill ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Total Alkalinity ◽  
Total Hardness ◽  
Physico Chemical ◽  
Chemical Water

The study was made to investigate the potential physico-chemical water quality of Elengena beel. In this study 40 nos water samples were collected from 4 sampling sites (10 from each) of Elengena beel and had been analyzed for some water quality parameters and ranges of results were found as - water temperature, transperancy, depth, pH, dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), total dissolved solids (TDS), total suspended solids (TSS), total solids (TS), total alkalinity (TA), total hardness (TH), chloride (Cl-) and fluoride (F-). silicates (SiO2), free carbondioxide (FCO2), nitrate (NO3-), phosphate (PO43-), colour and odour. Nutrients were determined by following the standard procedures outlined in the American Public Health Association (APHA). The result showed that water temperature, transperancy, depth, pH, DO, BOD, COD, TA, TH, TS, TDS, SiO2 , F-CO2 , Cl- , NO3- , and PO43-were 19.9 ±0.28; 21.5 ± 0.71; 1.0 ± 0.23; 6.8 ± 0.15; 4.1 ± 0.34; 51.8 ± 2.32; 58.61 ± 3.22; 156.2 ± 1.2; 210.5 ± 0.2; 153.8 ±0.90; 170.2 ±0.60; 49.2 ± 0.85; 2.87 ± 0.02; 10.91 ± 1.32; 0.20 ± 0.01 and 0.10 ± 0.1 mg/L respectively. All the measured parameters were within the standard values of WHO. In general the present investigation found that the maximum parameters were not at a level of pollution. In order to stop further deterioration of Elengena beel water quality and to eventually restore the beneficial uses of the beel, management of effluents of Nagaon paper mill in the beel watershed should be given urgent priority.

scholarly journals Water quality parameters and incidence of fish diseases in some water bodies in Natore, Bangladesh

1970 ◽  
Vol 2 (2) ◽  
pp. 27-30 ◽  
Author(s):  
M Delwar Hossain ◽  
M Kabil Hossain ◽  
M Habibur Rahman
Keyword(s):  
Water Quality ◽  
Stress Response ◽  
Water Temperature ◽  
Physicochemical Parameters ◽  
Quality Parameters ◽  
Water Bodies ◽  
Water Quality Parameters ◽  
Total Hardness ◽  
Fish Diseases ◽  
Monthly Variations

Monthly variations of the physicochemical parameters in some selected water bodies (12 Beels and 210 ponds) in Natore during July 2006 and June 2007 have been studied. The highest values for water temperature, alkalinity, ammonia, free CO2, DO, pH and total hardness in Beel waters were 31.5°C (May '07), 180ppm (January and February '07), 2.5ppm (September '06), 9.6ppm (April '07), 7.5ppm (January '07), 8.6 (December '06) and 190ppm (February '07), respectively and the lowest values were 15°C (February '07), 35ppm (October '06), 0.5ppm (December '06), 6.3ppm (January '07), 4.8ppm (April '07), 6.8 (September '06) and 50ppm (September '06), respectively. The highest and lowest values of these parameters in pond waters were 33°C (May '07), 200ppm (March'07), 2.3ppm (July '06), 9.3ppm (April '07), 7.5ppm (January '07), 8.6 (November '06) and 200ppm (February '07) respectively, and 17°C (December '06), 50ppm (October '06), 0.6ppm (December '06 and January '07), 6.4ppm (January '07), 5.0ppm (May '07), 6.2 (April '07) and 40ppm (September '06) respectively. Changing in water quality parameters resulted in a stress response in the fishes, making them more susceptible to parasitic attacks and diseases, many of them being fatal. Key words: Beels; ponds; water quality parameters; fish diseases DOI: 10.3329/jles.v2i2.7493 J. Life Earth Sci., Vol. 2(2) 27-30, 2007

scholarly journals A Water Quality Prediction Method Based on the Deep LSTM Network Considering Correlation in Smart Mariculture

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1420 ◽  
Author(s):  
Zhuhua Hu ◽  
Yiran Zhang ◽  
Yaochi Zhao ◽  
Mingshan Xie ◽  
Jiezhuo Zhong ◽  
...  
Keyword(s):  
Water Quality ◽  
Water Temperature ◽  
Prediction Accuracy ◽  
Prediction Method ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Prediction ◽  
Short Term ◽  
Water Quality Prediction ◽  
Term Prediction

An accurate prediction of cage-cultured water quality is a hot topic in smart mariculture. Since the mariculturing environment is always open to its surroundings, the changes in water quality parameters are normally nonlinear, dynamic, changeable, and complex. However, traditional forecasting methods have lots of problems, such as low accuracy, poor generalization, and high time complexity. In order to solve these shortcomings, a novel water quality prediction method based on the deep LSTM (long short-term memory) learning network is proposed to predict pH and water temperature. Firstly, linear interpolation, smoothing, and moving average filtering techniques are used to repair, correct, and de-noise water quality data, respectively. Secondly, Pearson’s correlation coefficient is used to obtain the correlation priors between pH, water temperature, and other water quality parameters. Finally, a water quality prediction model based on LSTM is constructed using the preprocessed data and its correlation information. Experimental results show that, in the short-term prediction, the prediction accuracy of pH and water temperature can reach 98.56% and 98.97%, and the time cost of the predictions is 0.273 s and 0.257 s, respectively. In the long-term prediction, the prediction accuracy of pH and water temperature can reach 95.76% and 96.88%, respectively.

Changing Climatic Conditions Affect Surface Water Quality in Southwestern Louisiana in the United States

2021 ◽  
Author(s):  
Katherine Eddings ◽  
Durga D Poudel ◽  
Timothy W. Duex ◽  
Robert Miller ◽  
J. Calvin Berry
Keyword(s):  
Climate Change ◽  
Water Quality ◽  
Surface Water ◽  
Water Temperature ◽  
Air Temperature ◽  
Quality Parameters ◽  
Climatic Conditions ◽  
Water Quality Parameters ◽  
Surface Water Temperature ◽  
Temperature And Precipitation

Climate change impacts on rising temperatures, changes on rainfall patterns, drought, flooding, sea level rise, glacier melts, and incidence of diseases and parasites are reported worldwide in recent decades. This study investigates the effects of changing climatic conditions – particularly air temperature and precipitation, on surface water temperatures and other water quality parameters, such as the conductivity, dissolved oxygen (DO), pH, and turbidity. A statistical analysis was performed on air temperature and precipitation data from 1980 to 2005 to determine the changing climatic conditions. The water quality data for four waterbodies in southwestern Louisiana was also analyzed to examine trends between the air temperature and surface water temperatures, precipitation and surface water temperatures, and precipitation and water quality parameters. There was an unexpected increase in surface water temperature with an increase in precipitation. As the precipitation and air temperature increased, so did the surface water temperature. This increase in surface water temperature was correlated with decrease in DO levels. The increase in precipitation also correlated with an increase in pH and turbidity in Bayou Plaquemine Brule. This study’s findings could be utilized in a dynamic climate modeling system to provide more accurate predictions of climate change in southwestern Louisiana.

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