Long-term variations of water quality in a reservoir in China

2012 ◽  
Vol 65 (8) ◽  
pp. 1454-1460 ◽  
Author(s):  
Y. Y. Chen ◽  
C. Zhang ◽  
X. P. Gao ◽  
L. Y. Wang
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Temporal Trends ◽  
Oxygen Demand ◽  
Temporal Variations ◽  
Quality Parameters ◽  
Quality Data ◽  
Water Quality Data ◽  
Yuqiao Reservoir

To study the spatial and temporal trends of water quality in the Yuqiao Reservoir (Ji County, Tianjin) in China, water quality data for ten physical and chemical parameters from three monitoring stations (S1, S2 and S3) was collected from 1989 to 2007 and from an other three stations (S4, S5 and S6) during the period of 1999–2007. A one-way ANOVA was employed to evaluate the spatial variation of water quality for each station. The results showed that there were statistically significant spatial differences for most water quality parameters except temperature and dissolved oxygen in the entire reservoir, and the concentrations of most parameters were higher in the uppermost part of the reservoir. The temporal trend study was conducted using the Seasonal–Kendall's test. The results revealed improving trends of water quality from 1989 to 2007, including a reduction of total phosphorous, temperature and biochemical oxygen demand and an increase of dissolved oxygen. High N:P ratios, ranging from 52.61 to 78.75, indicated that the reservoir was a phosphorous-limited environment. This study suggests long-term spatial and temporal variations of water quality in the Yuqiao Reservoir, which could be informative for water quality managers and scientists.

scholarly journals A Multivariate and Spatiotemporal Analysis of Water Quality in Code River, Indonesia

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Mochamad A. Pratama ◽  
Yan D. Immanuel ◽  
Dwinanti R. Marthanty
Keyword(s):  
Water Quality ◽  
Land Cover ◽  
Agricultural Land ◽  
Water Quality Management ◽  
Temporal Trends ◽  
Oxygen Demand ◽  
Principal Component ◽  
Water Quality Monitoring ◽  
Quality Data ◽  
Water Quality Data

The efficacy of a water quality management strategy highly depends on the analysis of water quality data, which must be intensively analyzed from both spatial and temporal perspectives. This study aims to analyze spatial and temporal trends in water quality in Code River in Indonesia and correlate these with land use and land cover changes over a particular period. Water quality data consisting of 15 parameters and Landsat image data taken from 2011 to 2017 were collected and analyzed. We found that the concentrations of total dissolved solid, nitrite, nitrate, and zinc had increasing trends from upstream to downstream over time, whereas concentrations of parameter biological oxygen demand, cuprum, and fecal coliform consistently undermined water quality standards. This study also found that the proportion of natural vegetation land cover had a positive correlation with the quality of Code River’s water, whereas agricultural land and built-up areas were the most sensitive to water pollution in the river. Moreover, the principal component analysis of water quality data suggested that organic matter, metals, and domestic wastewater were the most important factors for explaining the total variability of water quality in Code River. This study demonstrates the application of a GIS-based multivariate analysis to the interpretation of water quality monitoring data, which could aid watershed stakeholders in developing data-driven intervention strategies for improving the water quality in rivers and streams.

scholarly journals Anthropogenic and catchment characteristic signatures in the water quality of Swiss rivers: a quantitative assessment

2019 ◽  
Vol 23 (4) ◽  
pp. 1885-1904 ◽  
Author(s):  
Martina Botter ◽  
Paolo Burlando ◽  
Simone Fatichi
Keyword(s):  
Water Quality ◽  
Anthropogenic Impacts ◽  
Stream Water ◽  
Temporal Variations ◽  
Quality Data ◽  
Anthropogenic Factors ◽  
Water Quality Data ◽  
Catchment Characteristics

Abstract. The hydrological and biogeochemical response of rivers carries information about solute sources, pathways, and transformations in the catchment. We investigate long-term water quality data of 11 Swiss catchments with the objective to discern the influence of major catchment characteristics and anthropic activities on delivery of solutes in stream water. Magnitude, trends, and seasonality of water quality samplings of different solutes are evaluated and compared across catchments. Subsequently, the empirical dependence between concentration and discharge is used to classify the solute behaviors. While the anthropogenic impacts are clearly detectable in the concentration of certain solutes (i.e., Na+, Cl−, NO3, DRP), the influence of single catchment characteristics such as geology (e.g., on Ca2+ and H4SiO4), topography (e.g., on DOC, TOC, and TP), and size (e.g., on DOC and TOC) is only sometimes visible, which is also because of the limited sample size and the spatial heterogeneity within catchments. Solute variability in time is generally smaller than discharge variability and the most significant trends in time are due to temporal variations of anthropogenic rather than natural forcing. The majority of solutes show dilution with increasing discharge, especially geogenic species, while sediment-bonded solutes (e.g., total phosphorous and organic carbon species) show higher concentrations with increasing discharge. Both natural and anthropogenic factors affect the biogeochemical response of streams, and, while the majority of solutes show identifiable behaviors in individual catchments, only a minority of behaviors can be generalized across the 11 catchments that exhibit different natural, climatic, and anthropogenic features.

scholarly journals A Long-Term Water Quality Prediction Method Based on the Temporal Convolutional Network in Smart Mariculture

Water ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 2907
Author(s):  
Yuexin Fu ◽  
Zhuhua Hu ◽  
Yaochi Zhao ◽  
Mengxing Huang
Keyword(s):  
Water Quality ◽  
Prediction Method ◽  
Quality Parameters ◽  
Quality Data ◽  
Convolutional Network ◽  
Water Quality Data ◽  
Water Quality Prediction ◽  
Term Prediction ◽  
Long Term Prediction

In smart mariculture, traditional methods are not only difficult to adapt to the complex, dynamic and changeable environment in open waters, but also have many problems, such as poor accuracy, high time complexity and poor long-term prediction. To solve these deficiencies, a new water quality prediction method based on TCN (temporal convolutional network) is proposed to predict dissolved oxygen, water temperature, and pH. The TCN prediction network can extract time series features and in-depth data features by introducing dilated causal convolution, and has a good effect of long-term prediction. At the same time, it is predicted that the network can process time series data in parallel, which greatly improves the time throughput of the model. Firstly, we arrange the 23,000 sets of water quality data collected in the cages according to time. Secondly, we use the Pearson correlation coefficient method to analyze the correlation information between water quality parameters. Finally, a long-term prediction model of water quality parameters based on a time domain convolutional network is constructed by using prior information and pre-processed water quality data. Experimental results show that long-term prediction method based on TCN has higher accuracy and less time complexity, compared with RNN (recurrent neural network), SRU (simple recurrent unit), BI-SRU (bi-directional simple recurrent unit), GRU (gated recurrent unit) and LSTM (long short-term memory). The prediction accuracy can reach up to 91.91%. The time costs of training model and prediction are reduced by an average of 64.92% and 7.24%, respectively.

scholarly journals Assessment of Spatio-temporal Variations of Selected Water Quality Parameters of Lake Ziway, Ethiopia Using Multivariate Techniques

2021 ◽  
Author(s):  
Dessie Tibebe ◽  
Feleke Zewge ◽  
Brook Lemma ◽  
Yezbie Kassa
Keyword(s):  
Water Quality ◽  
River Mouth ◽  
Temporal Variations ◽  
Quality Parameters ◽  
Dry Season ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Nutrient Concentrations ◽  
Wet Season ◽  
Water Quality Data

Abstract Lake Ziway is shallow freshwater located in Northern part of Ethiopian Rift Valley. It is characterized as semi-arid to sub-humid type of climate. Expansions of the flower industry, widespread fisheries, intensive agricultural activities, fast population growth lead to deterioration of water quality and depletion of aquatic biota. In this study, spatial and temporal variations of selected water quality parameters of Lake Ziway were evaluated using multivariate statistical techniques in the study periods of 2014 and 2015. Monthly water quality data were collected from nine sampling stations during dry and wet seasonal basis for analysis of 15 water quality parameters. Mean nutrient concentrations showed increasing trend and were higher in Floriculture effluent (Fb) and Floriculture effluent after mixing in the lake (Fa) in all seasons. These sites were also characterized by high electrical conductivity (EC) and total dissolved solid (TDS). All the nine sampling sites were categorized into three pollution levels according to their water quality features using cluster analysis (CA). Accordingly, sampling sites Fb and Ketar River (Kb) are highly and moderately polluted in both seasons, respectively. On the other hand, sampling sites at the center (C), Meki river mouth (Ma), Ketar river mouth (Ka), Meki River (Mb), Korekonch (Ko) and Fa in dry season and Ka, C, Ma, Ko, Bulbula river mouth (B) and Fa during wet season were less polluted. Principal component analysis (PCA) analysis also showed the pollutant sources were mainly from Fb during dry season Mb and Kb during wet season. The values of comprehensive pollution index illustrated the lake is moderately and slightly polluted in dry and wet seasons, respectively. Comparatively, the pollution status of the lake is high around floriculture effluent discharge site and at the two feeding rivers (Kb and Mb) due to increasing trends in agrochemical loads. This may lead to long term ecological changes in the lake unless possible measures taken.

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.

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.

scholarly journals Chemometric analysis of drinking water quality parameters of Sagar city, Madhya Pradesh, India

2020 ◽  
Vol 31 (2) ◽  
pp. 99-105
Author(s):  
Hemant Pathak
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Quality Parameters ◽  
Chemometric Analysis ◽  
Madhya Pradesh ◽  
Drinking Water Quality ◽  
Quality Data ◽  
Multivariate Linear Regression Analysis ◽  
Water Quality Data ◽  
Entire Study

AbstractThe present study uses numerous chemometric techniques to evaluate and interpret a water quality data obtained from the drinking water resources namely municipal water (supplied by Rajghat dam on Bewas River), bore well, ground water of Sagar city, a divisional headquarter of Madhya Pradesh, India. Data was collected from May 2018 to June 2019 for 10 parameters used to assess the status of the water quality. Water quality was monitored at 15 sampling stations along the entire district. The data were analyzed using chemometric analysis such as principal component analysis, correlation matrix, multivariate linear regression analysis and hierarchical cluster analysis that reduced the data dimensions for better interpretation. Results of statistical analysis expressed that slightly higher value of BOD in some areas due to sewage contamination, need of chlorination treatment required at those places. This study also presents the value of diverse statistical methods for assessment and analysis of drinking water quality data for the reason of monitoring the effectiveness of water resource management. The study indicated that the maximum quality parameters of drinking water is in permissible limits of WHO and IS: 10500 guidelines on entire study places.

scholarly journals THE CONTRIBUTION OF GIS TO DISPLAY AND ANALYZE THE WATER QUALITY DATA COLLECTED BY A WIRELESS SENSOR NETWORK: CASE OF BOUREGREG CATCHMENT, MOROCCO

2017 ◽  
Vol IV-4/W4 ◽  
pp. 331-334
Author(s):  
S. Boubakri ◽  
H. Rhinane
Keyword(s):  
Water Quality ◽  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Geographic Information Systems ◽  
Real Time ◽  
Quality Parameters ◽  
Wireless Sensor ◽  
Quality Data ◽  
Catchment Scale ◽  
Water Quality Data

The monitoring of water quality is, in most cases, managed in the laboratory and not on real time bases. Besides this process being lengthy, it doesn’t provide the required specifications to describe the evolution of the quality parameters that are of interest. This study presents the integration of Geographic Information Systems (GIS) with wireless sensor networks (WSN) aiming to create a system able to detect the parameters like temperature, salinity and conductivity in a Moroccan catchment scale and transmit information to the support station. This Information is displayed and evaluated in a GIS using maps and spatial dashboard to monitor the water quality in real time.

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