Interpretation of seasonal water quality variation in the Yeongsan Reservoir, Korea using multivariate statistical analyses

2009 ◽  
Vol 59 (11) ◽  
pp. 2219-2226 ◽  
Author(s):  
Kyung Hwa Cho ◽  
Yongeun Park ◽  
Joo-Hyon Kang ◽  
Seo Jin Ki ◽  
Sungmin Cha ◽  
...  
Keyword(s):  
Water Quality ◽  
Flood Control ◽  
Nitrogen Loading ◽  
Quality Data ◽  
Phosphorus Loading ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
Data Set ◽  
Study Results ◽  
Meteorological Effect

The Yeongsan (YS) Reservoir is an estuarine reservoir which provides surrounding areas with public goods, such as water supply for agricultural and industrial areas and flood control. Beneficial uses of the YS Reservoir, however, are recently threatened by enriched non-point and point source inputs. A series of multivariate statistical approaches including principal component analysis (PCA) were applied to extract significant characteristics contained in a large suite of water quality data (18 variables monthly recorded for 5 years); thereby to provide the important phenomenal information for establishing effective water resource management plans for the YS Reservoir. The PCA results identified the most important five principal components (PCs), explaining 71% of total variance of the original data set. The five PCs were interpreted as hydro-meteorological effect, nitrogen loading, phosphorus loading, primary production of phytoplankton, and fecal indicator bacteria (FIB) loading. Furthermore, hydro-meteorological effect and nitrogen loading could be characterized by a yearly periodicity whereas FIB loading showed an increasing trend with respect to time. The study results presented here might be useful to establish preliminary strategies for abating water quality degradation in the YS Reservoir.

Water quality assessment using multivariate statistical techniques in Río Tercero Reservoir, Argentina

2014 ◽  
Vol 46 (3) ◽  
pp. 377-388 ◽  
Author(s):  
Matias Bonansea ◽  
Claudia Ledesma ◽  
Claudia Rodriguez ◽  
Lucio Pinotti
Keyword(s):  
Water Quality ◽  
Quality Data ◽  
Spatial And Temporal Variation ◽  
Multidimensional Data ◽  
Statistical Techniques ◽  
Data Sets ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
Data Set

Water quality monitoring programs generate complex multidimensional data sets. In this study, multivariate statistical techniques were employed as an effective tool for the analysis and interpretation of these water quality data sets. Principal component analysis (PCA) and cluster analysis (CA) were applied to evaluate spatial and temporal variation of water quality in Río Tercero Reservoir (Argentina). Six sampling sites were surveyed each climatic season for 21 parameters during 2003–2010. The results revealed that PCA showed the existence of four significant principal components (PCs) which account for 96.7% of the total variance of the data set. The first PC was assigned to mineralization whereas the other PCs were built from variables indicative of pollution. Hierarchical CA grouped the six monitoring sites into three clusters and classified the different climatic seasons into two clusters based on similarities in water quality characteristics.

scholarly journals Pioneering water quality data on the Lake Victoria watershed: effects on human health

2015 ◽  
Vol 13 (3) ◽  
pp. 920-930 ◽  
Author(s):  
Tamie J. Jovanelly ◽  
Julie Johnson-Pynn ◽  
James Okot-Okumu ◽  
Richard Nyenje ◽  
Emily Namaganda
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Lake Victoria ◽  
Fecal Coliforms ◽  
World Health ◽  
Quality Data ◽  
Water Quality Data ◽  
Data Set ◽  
Forest Reserves ◽  
Elevated Exposure

Four forest reserves within 50 km of Kampala in Uganda act as a critical buffer to the Lake Victoria watershed and habitat for local populations. Over a 9-month period we capture a pioneering water quality data set that illustrates ecosystem health through the implementation of a water quality index (WQI). The WQI was calculated using field and laboratory data that reflect measured physical and chemical parameters (pH, dissolved oxygen, biological oxygen on demand, nitrates, phosphates, fecal coliform, and temperature turbidity). Overall, the WQI for the four forest reserves reflect poor to medium water quality. Results compared with US Environmental Protection Agency and World Health Organization drinking water standards indicate varying levels of contamination at most sites and all designated drinking water sources, with signatures of elevated nitrates, phosphates, and/or fecal coliforms. As critical health problems are known to arise with elevated exposure to contaminants in drinking water, this data set can be used to communicate necessary improvements within the watershed.

A GIS-Based Spatial-Multivariate Statistical Analysis of Water Quality Data in the Porsuk River, Turkey

2009 ◽  
Vol 44 (3) ◽  
pp. 279-293 ◽  
Author(s):  
Ozan Arslan
Keyword(s):  
Water Quality ◽  
Statistical Analysis ◽  
Quality Assessment ◽  
Statistical Methods ◽  
Multivariate Statistical Analysis ◽  
Water Quality Assessment ◽  
Quality Data ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
Porsuk River

Abstract The study offers a GIS-based multivariate statistical analysis strategy to assess river water quality. Multivariate statistical methods and Geographic Information System (GIS) technology have effectively been used for water quality management. Recognizing the fact that the use of standard statistical methods can be restrictive due to the complexity of water quality datasets, geospatial statistical methods have been recommended for the water quality assessment. The objective of the study was to explore the potential capabilities of GIS-based joint multivariate statistical analysis for water quality assessment of Porsuk River in Turkey. A well-known multivariate statistical technique, principal component analysis (PCA), is incorporated into a geographic database for interpretation of water quality data. To characterize spatial variability of water quality data, spatial PCA was performed on the basis of spatial autocorrelation. Application of the joint spatio-multivariate statistical analysis for interpretation of the water quality database offered a better understanding of the hydrochemistry in the study region.

scholarly journals Long-Term Water Quality Patterns of a Flow Regulated Tropical Lowland River

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 37 ◽  
Author(s):  
Lizaan de Necker ◽  
Tinyiko Neswiswi ◽  
Richard Greenfield ◽  
Johan van Vuren ◽  
Luc Brendonck ◽  
...  
Keyword(s):  
Water Quality ◽  
Base Flow ◽  
Quality Data ◽  
Severe Drought ◽  
Anthropogenic Pressure ◽  
Nutrient Inputs ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
Assessment Index

Floodplain ecosystems in Africa are under threat due to direct anthropogenic pressure and climate change. The lower Phongolo River and associated floodplain is South Africa’s largest inland floodplain ecosystem and has been regulated by the Pongolapoort Dam since the 1970s. The last controlled flood release from the dam occurred in December 2014, after which a severe drought occurred and only a base flow was released. The central aims of this study were to determine the historic and present water quality state of the middle and lower Phongolo River and assess the possible effects of the most recent drought may have had. Historic water quality data (1970s to present) were obtained from monitoring stations within the Phongolo River catchment to assess the long-term water quality patterns. Using multivariate statistical analyses as well as the Physicochemical Driver Assessment Index (PAI), a water quality index developed for South African riverine ecosystems, various in situ and chemical water variables were analysed. Key findings included that the water quality of the middle and lower Phongolo River has degraded since the 1970s, due to increased salinity and nutrient inputs from surrounding irrigation schemes. The Pongolapoort Dam appears to be trapping nutrient-rich sediments leading to nutrient-depleted water entering the lower Phongolo River. The nutrient levels increase again as the river flows through the downstream floodplain through input from nutrient rich soils and fertilizers. The drought did not have any significant effect on water quality as the PAI remained similar to pre-drought conditions.

scholarly journals Investigation of the Soan River Water Quality Using Multivariate Statistical Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Zakaullah ◽  
Naeem Ejaz
Keyword(s):  
Water Quality ◽  
River Water ◽  
Water Quality Management ◽  
Oxygen Demand ◽  
River Water Quality ◽  
Quality Data ◽  
Spatiotemporal Pattern ◽  
Multivariate Statistical ◽  
Water Quality Data

Evaluating the quality of river water is a critical process due to pollution and variations of natural or anthropogenic origin. For the Soan River (Pakistan), seven sampling sites were selected in the urban area of Rawalpindi/Islamabad, and 18 major chemical parameters were examined over two seasons, i.e., premonsoon and postmonsoon 2019. Multivariate statistical approaches such as the Spearman correlation coefficient, cluster analysis (CA), and principal component analysis (PCA) were used to evaluate the water quality of the Soan River based on temporal and spatial patterns. Analytical results obtained by PCA show that 92.46% of the total variation in the premonsoon season and 93.11% in the postmonsoon season were observed by only two loading factors in both seasons. The PCA and CA made it possible to extract and recognize the origins of the factors responsible for water quality variations during the year 2019. The sampling stations were grouped into specific clusters on the basis of the spatiotemporal pattern of water quality data. The parameters dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), turbidity, and total suspended solids (TSS) are among the prominent contributing variations in water quality, indicating that the water quality of the Soan River deteriorates gradually as it passes through the urban areas, receiving domestic and industrial wastewater from the outfalls. This study indicates that the adopted methodology can be utilized effectively for effective river water quality management.

The role of citizen science as a tool of public information in water quality management in the Brantas catchment, Indonesia

2021 ◽  
Author(s):  
Reza Pramana ◽  
Schuyler Houser ◽  
Daru Rini ◽  
Maurits Ertsen
Keyword(s):  
Water Quality ◽  
Quality Management ◽  
Citizen Science ◽  
Water Resource ◽  
Water Resource Management ◽  
Water Quality Management ◽  
Data Availability ◽  
Quality Data ◽  
Water Quality Data ◽  
Data Set

<p>Water quality in the rivers and tributaries of the Brantas catchment (about 12.000 km<sup>2</sup>) is deteriorating due to various reasons, including rapid economic development, insufficient domestic water treatment and waste management, and industrial pollution. Various parameters measured by agencies involved in water resource development and management and environmental management consistently demonstrate exceedance of the local water quality standards. Between the different agencies, water quality data are available – intermittently from 2009 until 2019 at 104 locations, but generally on a monthly basis. Still, opportunities to improve data availability are apparent, both to increase the amount and representability of the data sets. The opportunity to expand available data via citizen science is simultaneously an opportunity to provide education on water stewardship and empower citizens to participate in water quality management. We plan to involve people from eight communities living close to the river and researchers from two local universities in a citizen-science campaign. The community members would sample weekly at 10 locations, from upstream to downstream of the catchment. We will use probes and test strips to measure the temperature, electrical conductivity, pH, nitrate, phosphate, ammonia, iron, and dissolved oxygen. The results will potentially be combined with the data from government agencies to construct an integrated water quality data set to improve decision making and the quality of community engagement in water resource management.</p>

Advanced monitoring of water systems using in situ measurement stations: data validation and fault detection

2013 ◽  
Vol 68 (5) ◽  
pp. 1022-1030 ◽  
Author(s):  
Janelcy Alferes ◽  
Sovanna Tik ◽  
John Copp ◽  
Peter A. Vanrolleghem
Keyword(s):  
Water Quality ◽  
Treatment Plant ◽  
Principal Component ◽  
Quality Data ◽  
Water Quality Data ◽  
Data Set ◽  
Data Quality Assessment ◽  
Multiple Sensor ◽  
Pertinent Information

In situ continuous monitoring at high frequency is used to collect water quality information about water bodies. However, it is crucial that the collected data be evaluated and validated for the appropriate interpretation of the data so as to ensure that the monitoring programme is effective. Software tools for data quality assessment with a practical orientation are proposed. As water quality data often contain redundant information, multivariate methods can be used to detect correlations, pertinent information among variables and to identify multiple sensor faults. While principal component analysis can be used to reduce the dimensionality of the original variable data set, monitoring of some statistical metrics and their violation of confidence limits can be used to detect faulty or abnormal data and can help the user apply corrective action(s). The developed algorithms are illustrated with automated monitoring systems installed in an urban river and at the inlet of a wastewater treatment plant.

WATER QUALITY SIMULATION OF TE-CHI RESERVOIR USING TWO-DIMENSIONAL MODELS

1994 ◽  
Vol 30 (2) ◽  
pp. 63-72 ◽  
Author(s):  
Jan-Tai Kuo ◽  
Jiann-Homg Wu ◽  
Wen-sen Chu
Keyword(s):  
Water Quality ◽  
Organic Phosphorus ◽  
Inorganic Phosphorus ◽  
Ammonia Nitrogen ◽  
Quality Data ◽  
Phosphorus Loading ◽  
Limiting Factor ◽  
Two Dimensional ◽  
Quality Model ◽  
Water Quality Data

The application of a two-dimensional laterally averaged hydrodynamics model (LARM2) and a water quality model (WASP3) for the study of eutrophication problem in Te-Chi Reservoir in Taiwan is presented. The models were first calibrated and validated with field temperature and water quality data. The combined models were then used to characterize the temperature distribution, seasonal overturning phenomena, and the variations of chlorophyll-a, organic nitrogen, ammonia nitrogen, nitrate nitrogen, organic phosphorus, inorganic phosphorus, and dissolved oxygen in the reservoir. It was shown that the limiting factor for eutrophication in Te-Chi Reservoir is phosphorus, and that better control of phosphorus loading into the reservoir is the crucial step toward improving the water quality of Te-Chi Reservoir.

scholarly journals SPATIAL AND TEMPORAL ASSESSMENT OF WATER QUALITY DATA USING MULTIVARIATE STATISTICAL TECHNIQUES

2016 ◽  
Vol 38 (2) ◽  
pp. 577
Author(s):  
Nícolas Reinaldo Finkler ◽  
Taison Anderson Bortolin ◽  
Jardel Cocconi ◽  
Ludmilson Abritta Mendes ◽  
Vania Elisabete Schneider
Keyword(s):  
Water Quality ◽  
Anthropogenic Activities ◽  
Principal Component ◽  
Quality Data ◽  
Spatial And Temporal Variation ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
And Cluster Analysis ◽  
Quality Variation ◽  
Human Density

The natural factors and anthropogenic activities that contribute to spatial and temporal variation in superficial waters in Caxias do Sul’s urban hydrographic basins were determined applying multivariate analysis of data. The techniques used in this study were Principal Component Analysis and Cluster Analysis. The monitoring was executed in 12 sampling stations, during January, 2009 to January, 2010 with monthly periodicity in total of 13 campaigns. Between chemical, biological and physical, 20 parameters were analyzed. The results state that with the use of ACP, a data variance of 70.94% was observed. Therefore, it testifies that major pollutants that contribute to a water quality variation in the county are classified as domestic and industrial pollutants, mainly from galvanic industry. Moreover, two clusters were found which differentiated regarding their location and distance from areas with a high human density, corroborating on identifying of impact due to human activities in urban rivers.

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