scholarly journals Water Quality Monitoring with Emphasis on Estimation of Point and Diffuse Pollution Sources

2019 ◽  
Vol 21 (2) ◽  
pp. 163-171
Keyword(s):  
Water Quality ◽  
Organic Nitrogen ◽  
Nitrate Nitrogen ◽  
Anthropogenic Activities ◽  
Oxygen Demand ◽  
Water Quality Monitoring ◽  
Total Coliform ◽  
Ammonium Nitrogen ◽  
Quality Parameters ◽  
Water Quality Parameters

<p>Population growth, urbanization and anthropogenic activities are becoming a serious problem for water resources in Turkey, which necessitates their monitoring and maintenance of water quality. In this study, water quality was implemented in the Porsuk Stream in Inner Anatolia, Turkey. Water samples were collected at monthly intervals between the period of 2008-2010 at four selected stations. Twenty one water quality parameters were measured which are water temperature (T), pH, dissolved oxygen (DO), electrical conductivity (EC), salinity, turbidity, chloride, suspended solids, dissolved solids, organic nitrogen (Org-N), ammonium nitrogen (NH3-N), nitrite nitrogen (NO2-N), nitrate nitrogen (NO3-N), total organic carbon, biological oxygen demand (BOD), chemical oxygen demand (COD), total coliform, alkalinity, orthophosphate phosphorus (PO43--P), total phosphorus and chlorophyll-a. The monitoring was conducted to see how the water quality changed along the stream in response to various anthropogenic activities. Besides, a paired t-test was utilized to determine the concentration differences at stations above and below the single most important point source of pollutants (Eskişehir city). Moreover, a regression model was used to establish relations between water quality parameters and flow and to estimate nonpoint source loadings.</p>

scholarly journals EVALUATING SURFACE WATER QUALITY AND WATER MONITORING PARAMETERS IN THE TIEN RIVER, VIETNAMESE MEKONG DELTA

2021 ◽  
Vol 83 (3) ◽  
pp. 29-36
Author(s):  
Thanh Giao Nguyen ◽  
Vo Quang Minh
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Monitoring Program ◽  
Oxygen Demand ◽  
Surface Water Quality ◽  
Principal Component ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Monitoring ◽  
Water Quality Parameters

The study aimed to evaluate the surface water quality of the Tien River and identify water quality parameters to be monitored using the water quality monitoring data in the period of 2011 - 2019. The water samples were collected at five locations from Tan Chau to Cho Moi districts, An Giang province for three times per year (i.e., in March, June, and September). Water quality parameters included temperature (oC), pH, dissolved oxygen (DO), total suspended solids (TSS), nitrate (NO3--N), orthophosphate (PO43--P), biological oxygen demand (BOD), and coliforms. These parameter results were compared with the national technical regulation on surface water quality QCVN 08-MT: 2015/BTNMT, column A1. Principal component analysis (PCA) was used to identify the sources of pollution and the main factors affecting water quality. The results of this study showed that DO concentration was lower and TSS, BOD, PO43--P, coliforms concentrations in the Tien river exceeded QCVN 08-MT: 2015/BTNMT, column A1. pH, temperature, and NO3--N values were in accordance with the permitted regulation. The water monitoring parameters were seasonally fluctuated. DO, BOD, TSS, and coliforms concentrations were higher in the rainy season whereas NO3--N and PO43--P were higher in the dry season. The PCA results illustrated that pH, TSS, DO, BOD, PO43--P and coliforms should be included in the monitoring program. Other indicators such as temperature and NO3--N could be considered excluded from the program to save costs. 

scholarly journals A comparative evaluation of surface water quality standards for lakes and fish ponds from Bucharest and vicinity

2020 ◽  
Vol 2 (2) ◽  
pp. 30-37
Author(s):  
Gina Vasile Scaeteanu ◽  
Roxana Maria Madjar ◽  
Mala-Maria Stravescu-Bedivan
Keyword(s):  
Water Quality ◽  
Oxygen Demand ◽  
Surface Water Quality ◽  
Aquatic Organisms ◽  
Ammonium Nitrogen ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Total Hardness ◽  
Fish Ponds ◽  
Nitrogen And Phosphorus

Monitoring of lakes and ponds water quality parameters is important to evaluate the interactions between quality and effects on aquatic organisms’ growth and health. Even if each water parameter individually may not be relevant, several parameters together can reveal dynamic processes that occur in the water. For instance, unbalanced pH values may increase ammonia and hydrogen sulfide toxicity. Nitrogen and phosphorus are associated with plant and algae growth, although phosphorus is generally the limiting nutrient in freshwater bodies. Accordingly, it is recommended to monitor and assess water quality parameters based on routine analyses. Therefore, the aim of this study was to generate an overview of our researches related to the monitoring of water quality collected from lakes and fish ponds. The parameters on the basis of which was evaluated the quality of water were: pH, electrical conductivity (EC), total hardness (TH), chemical oxygen demand (COD), nitrate-nitrogen (N-NO3-), nitrite nitrogen (N-NO2-), ammonium nitrogen (N-NH4+), phosphate phosphorus (P-PO43-).

Impact of anthropogenic activities on water quality parameters of glacial lakes from Rodnei mountains, Romania

2020 ◽  
Vol 182 ◽  
pp. 109136
Author(s):  
Oana Mare Roșca ◽  
Thomas Dippong ◽  
Monica Marian ◽  
Cristina Mihali ◽  
Lucia Mihalescu ◽  
...  
Keyword(s):  
Water Quality ◽  
Anthropogenic Activities ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Glacial Lakes

scholarly journals Cladoceran abundance in relation to water quality parameters of Chakki Talab, Bodhan, Telangana

2018 ◽  
Vol 8 (01) ◽  
Author(s):  
Vasudha Lingampally ◽  
V.R. Solanki ◽  
D. L. Anuradha ◽  
Sabita Raja
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Oxygen Demand ◽  
Total Dissolved Solids ◽  
Quality Parameters ◽  
Biological Oxygen Demand ◽  
Water Quality Parameters ◽  
Total Alkalinity ◽  
Total Hardness ◽  
One Year

In the present study an attempt has been made to evaluate water quality and related density of Cladocerans for a period of one year, October 2015 to September 2016. Water quality parameters such as temperature, PH, total dissolved solids, dissolved oxygen, biological oxygen demand, total alkalinity, total hardness, chlorides, phosphates, and nitrates are presented here to relate with the abundance of Cladocerans. The Cladoceran abundance reflects the eutrophic nature of the Chakki talab.

Ecological assessment of water quality for different water uses: the upstream sub-basin of the Dnieper and Desna rivers

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
V Strokal ◽  
◽  
A Kovpak ◽  
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
Oxygen Demand ◽  
Agricultural Runoff ◽  
Ammonium Nitrogen ◽  
Quality Parameters ◽  
Harmful Algae ◽  
Main Research ◽  
Average Temperature ◽  
The Status

Novelties of this study include a synthesis of water quality parameters for the upstream sub-basin of the Dnieper River. This upstream sub-basin includes the Desna River. The synthesis revels new insights on the sources of the water pollution and the status of the water quality for different purposes such as drinking, aquaculture and recreation. The main research objective was to identify the main sources of water pollution and how those sources could decrease the water quality. As a result of our analysis, we conclude the following. The levels of ammonium-nitrogen and nitrite-nitrogen in the Desna River (upstream sub-basin) are by 2-43 times and up to 53 times higher than the water quality thresholds, respectively. This poses a risk for recreational activities since too much nutrients often lead to blooms of harmful algae. We also find an increased level of biological oxygen demand in the river for drinking purposes. For aquaculture, decreased levels of dissolved oxygen are found. Climate change has an impact on water quality. For example, extreme floods caused by too much precipitation can bring pollutants to nearby waters. Monthly average temperature has increased by +2.7 degrees contributing to increased microbiological processes that could stimulate blooms of harmful algae. Main sources of water pollution are sewage discharges in cities, agricultural runoff and erosion activities after floods.

scholarly journals Use of Artificial Neural Networks and Multiple Linear Regression Model for the Prediction of Dissolved Oxygen in Rivers: Case Study of Hydrographic Basin of River Nyando, Kenya

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-23 ◽  
Author(s):  
Yashon O. Ouma ◽  
Clinton O. Okuku ◽  
Evalyne N. Njau
Keyword(s):  
Water Quality ◽  
Linear Regression ◽  
Dissolved Oxygen ◽  
Multiple Linear Regression ◽  
Pearson Correlation ◽  
Multiple Linear Regression Model ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Monitoring

The process of predicting water quality over a catchment area is complex due to the inherently nonlinear interactions between the water quality parameters and their temporal and spatial variability. The empirical, conceptual, and physical distributed models for the simulation of hydrological interactions may not adequately represent the nonlinear dynamics in the process of water quality prediction, especially in watersheds with scarce water quality monitoring networks. To overcome the lack of data in water quality monitoring and prediction, this paper presents an approach based on the feedforward neural network (FNN) model for the simulation and prediction of dissolved oxygen (DO) in the Nyando River basin in Kenya. To understand the influence of the contributing factors to the DO variations, the model considered the inputs from the available water quality parameters (WQPs) including discharge, electrical conductivity (EC), pH, turbidity, temperature, total phosphates (TPs), and total nitrates (TNs) as the basin land-use and land-cover (LULC) percentages. The performance of the FNN model is compared with the multiple linear regression (MLR) model. For both FNN and MLR models, the use of the eight water quality parameters yielded the best DO prediction results with respective Pearson correlation coefficient R values of 0.8546 and 0.6199. In the model optimization, EC, TP, TN, pH, and temperature were most significant contributing water quality parameters with 85.5% in DO prediction. For both models, LULC gave the best results with successful prediction of DO at nearly 98% degree of accuracy, with the combination of LULC and the water quality parameters presenting the same degree of accuracy for both FNN and MLR models.

scholarly journals IoT for Monitoring and Control of Water Quality Parameters

2020 ◽  
Vol 14 (16) ◽  
pp. 4
Author(s):  
Fouzi Lezzar ◽  
Djamel Benmerzoug ◽  
Ilham Kitouni
Keyword(s):  
Water Quality ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Monitoring And Control ◽  
Cloud Processing ◽  
Data Collection Process ◽  
Standard Values ◽  
Processing And Storage ◽  
And Control

<p class="0abstract"><span lang="EN-US">This work presents an Internet of Things (IoT) solution to facilitate real time water quality monitoring by enabling the management of collected data from electronic sensors. Firstly, we present in detail problems encountered during the used data collection process. We discuss after the requirements from the water monitoring quality standpoint, data acquisition, cloud processing and data visualization to the end user. We designed a solution to minimize technicians’ visits to isolated water tower, we designed sensors achieving a lifespan of several years. The solution will be capable of scaling the processing and storage resources. This combination of technologies can cope with different types of environments. The system also provides a notification to a remote user, when there is a non-conformity of water quality parameters with the pre-defined set of standard values.</span></p>

scholarly journals Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing

Water ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 22
Author(s):  
Qi Cao ◽  
Gongliang Yu ◽  
Shengjie Sun ◽  
Yong Dou ◽  
Hua Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Real Time ◽  
Training Model ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Hyperspectral Remote Sensing ◽  
Water Quality Parameters ◽  
Quality Monitoring ◽  
Haihe River

The Haihe River is a typical sluice-controlled river in the north of China. The construction and operation of sluice dams change the flow and other hydrological factors of rivers, which have adverse effects on water, making it difficult to study the characteristics of water quality change and water environment control in northern rivers. In recent years, remote sensing has been widely used in water quality monitoring. However, due to the low signal-to-noise ratio (SNR) and the limitation of instrument resolution, satellite remote sensing is still a challenge to inland water quality monitoring. Ground-based hyperspectral remote sensing has a high temporal-spatial resolution and can be simply fixed in the water edge to achieve real-time continuous detection. A combination of hyperspectral remote sensing devices and BP neural networks is used in the current research to invert water quality parameters. The measured values and remote sensing reflectance of eight water quality parameters (chlorophyll-a (Chl-a), phycocyanin (PC), total suspended sediments (TSS), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH4-N), nitrate-nitrogen (NO3-N), and pH) were modeled and verified. The results show that the performance R2 of the training model is above 80%, and the performance R2 of the verification model is above 70%. In the training model, the highest fitting degree is TN (R2 = 1, RMSE = 0.0012 mg/L), and the lowest fitting degree is PC (R2 = 0.87, RMSE = 0.0011 mg/L). Therefore, the application of hyperspectral remote sensing technology to water quality detection in the Haihe River is a feasible method. The model built in the hyperspectral remote sensing equipment can help decision-makers to easily understand the real-time changes of water quality parameters.

scholarly journals Near real time water quality monitoring of Chivero and Manyame lakes of Zimbabwe

2018 ◽  
Vol 378 ◽  
pp. 85-92 ◽  
Author(s):  
Ronald Muchini ◽  
Webster Gumindoga ◽  
Sydney Togarepi ◽  
Tarirai Pinias Masarira ◽  
Timothy Dube
Keyword(s):  
Water Quality ◽  
Water Quality Monitoring ◽  
Remotely Sensed ◽  
Quality Parameters ◽  
Point Sources ◽  
Water Quality Parameters ◽  
Quality Monitoring ◽  
Quality Information ◽  
Web Portal

Abstract. Zimbabwe's water resources are under pressure from both point and non-point sources of pollution hence the need for regular and synoptic assessment. In-situ and laboratory based methods of water quality monitoring are point based and do not provide a synoptic coverage of the lakes. This paper presents novel methods for retrieving water quality parameters in Chivero and Manyame lakes, Zimbabwe, from remotely sensed imagery. Remotely sensed derived water quality parameters are further validated using in-situ data. It also presents an application for automated retrieval of those parameters developed in VB6, as well as a web portal for disseminating the water quality information to relevant stakeholders. The web portal is developed, using Geoserver, open layers and HTML. Results show the spatial variation of water quality and an automated remote sensing and GIS system with a web front end to disseminate water quality information.

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