Comprehensive Assessment of Water Quality and Pollution Source Apportionment in Wuliangsuhai Lake, Inner Mongolia, China

Water quality is a key indicator of human health. Wuliangsuhai Lake plays an important role in maintaining the ecological balance of the region, protecting the local species diversity and maintaining agricultural development. However, it is also facing a greater risk of water quality deterioration. The 24 water quality factors that this study focused on were analyzed in water samples collected during the irrigation period and non-irrigation period from 19 different sites in Wuliangsuhai Lake, Inner Mongolia, China. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were conducted to evaluate complex water quality data and to explore the sources of pollution. The results showed that, during the irrigation period, sites in the middle part of the lake (clusters 1 and 3) had higher pollution levels due to receiving most of the agricultural and some industrial wastewater from the Hetao irrigation area. During the non-irrigation period, the distribution of the comprehensive pollution index was the opposite of that seen during the irrigation period, and the degree of pollutant index was reduced significantly. Thus, run-off from the Hetao irrigation area is likely to be the main source of pollution.

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Online Analytics for Shrimp Farm Management to Control Water Quality Parameters and Growth Performance

Sustainability ◽

10.3390/su13115839 ◽

2021 ◽

Vol 13 (11) ◽

pp. 5839

Author(s):

Siriwan Kajornkasirat ◽

Jareeporn Ruangsri ◽

Charuwan Sumat ◽

Pete Intaramontri

Keyword(s):

Water Quality ◽

Growth Performance ◽

Mobile Application ◽

Database Management System ◽

Quality Data ◽

Analysis Tool ◽

Quality Factors ◽

Google Maps ◽

Water Quality Data ◽

Shrimp Farm

An online analytic service system was designed as a web and a mobile application for shrimp farmers and shrimp farm managers to manage the growth performance of shrimp. The MySQL database management system was used to manage the shrimp data. The Apache Web Server was used for contacting the shrimp database, and the web content displays were implemented with PHP script, JavaScript, and HTML5. Additionally, the program was linked with Google Charts to display data in various graphs, such as bar graphs and scatter diagrams, and Google Maps API was used to display water quality factors that are related to shrimp growth as spatial data. To test the system, field survey data from a shrimp farm in southern Thailand were used. Growth performance of shrimp and water quality data were collected from 13 earthen ponds in southern peninsular Thailand, located in the Surat Thani, Krabi, Phuket, and Satun provinces. The results show that the system allowed administrators to manage shrimp and farm data from the field sites. Both mobile and web applications were accessed by the users to manage the water quality factors and shrimp data. The system also provided the data analysis tool required to select a parameter from a list box and shows the association between water quality factors and shrimp data with a scatter diagram. Furthermore, the system generated a report of shrimp growth for the different farms with a line graph overlay on Google Maps™ in the data entry suite via mobile application. Online analytics for the growth performance of shrimp as provided by this system could be useful as decision support tools for effective shrimp farming.

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

The Scientific World JOURNAL ◽

10.1155/2020/8897029 ◽

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.

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Evaluation of water pollution and source identification in Merbok River Kedah, Northwest Malaysia

Malaysian Journal of Fundamental and Applied Sciences ◽

10.11113/mjfas.v16n4.1735 ◽

2020 ◽

Vol 16 (4) ◽

pp. 458-463

Author(s):

Ateshan Msahir Haidr ◽

Misnan Rosmilah ◽

Sinang Som Cit ◽

Koki Baba Isa

Keyword(s):

Water Quality ◽

Principal Component Analysis ◽

Anthropogenic Activities ◽

Human Health Risk ◽

Principal Component ◽

Component Analysis ◽

Quality Data ◽

Toxic Heavy Metals ◽

Water Quality Data ◽

Fish And Shellfish

This study investigates the temporal water quality variations and pollution sources identification in Merbok River using principal component analysis. The variables analyzed include As, Cd, Pb, Fe, Cr, Mn, Zn, Ni, Ca, Mg, Na, K, NH4, F, Cl, Br, NO2, NO3, SO4, PO4, pH, BOD, DO, COD, turbidity, and salinity. These variables were analyzed using inductively coupled plasma mass spectrometry, ion chromatography, and YSI multiprobe. Principal component analysis (PCA) was utilized to evaluate the variations of the most significant water quality parameters and identify the probable source of the pollutants. From the results of PCA, 86% of the total variations were observed in the water quality data with strong dominance of toxic heavy metals (As, Pb, and Cr), parameters associated with industrial discharge, domestic inputs, overland runoff (NH4, pH, BOD, DO, COD), agrochemicals (NO2, NO3, SO4, PO4), and weathering of basement rocks (Ca, Mg, Cl, F, K, and Na). Most of these parameters were present in concentrations exceeded the reference standards limits used in this study, indicating pollution of the river water. Together with the presence of microbial contamination, the results suggest potential human health risk due to water uses, fish and shellfish consumption. Moreover, the results revealed that anthropogenic activities and weathering were the main sources of pollutants in Merbok River.

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Multivariate Analysis of Water Quality Measurements on the Danube River

Water ◽

10.3390/w13243634 ◽

2021 ◽

Vol 13 (24) ◽

pp. 3634

Author(s):

Zoltan Horvat ◽

Mirjana Horvat ◽

Kristian Pastor ◽

Vojislava Bursić ◽

Nikola Puvača

Keyword(s):

Water Quality ◽

Principal Component Analysis ◽

Multivariate Analysis ◽

Cross Sections ◽

Principal Component ◽

Component Analysis ◽

Danube River ◽

Quality Data ◽

Water Quality Data ◽

The Danube River

This study investigates the potential of using principal component analysis and other multivariate analysis techniques to evaluate water quality data gathered from natural watercourses. With this goal in mind, a comprehensive water quality data set was used for the analysis, gathered on a reach of the Danube River in 2011. The considered measurements included physical, chemical, and biological parameters. The data were collected within seven data ranges (cross-sections) of the Danube River. Each cross-section had five verticals, each of which had five sampling points distributed over the water column. The gathered water quality data was then subjected to several multivariate analysis techniques. However, the most attention was attributed to the principal component analysis since it can provide an insight into possible grouping tendencies within verticals, cross-sections, or the entire considered reach. It has been concluded that there is no stratification in any of the analyzed water columns. However, there was an unambiguous clustering of sampling points with respect to their cross-sections. Even though one can attribute these phenomena to the unsteady flow in rivers, additional considerations suggest that the position of a cross-section can have a significant impact on the measured water quality parameters. Furthermore, the presented results indicate that these measurements, combined with several multivariate analysis methods, especially the principal component analysis, may be a promising approach for investigating the water quality tendencies of alluvial rivers.

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SPATIAL CHARACTERIZATION AND IDENTIFICATION SOURCES OF POLLUTION USING MULTIVARIATE ANALYSIS AT TERENGGANU RIVER BASIN, MALAYSIA

Jurnal Teknologi ◽

10.11113/jt.v77.4054 ◽

2015 ◽

Vol 77 (1) ◽

Cited By ~ 3

Author(s):

Ahmad Firdaus Kamaruddin ◽

Mohd Ekhwan Toriman ◽

Hafizan Juahir ◽

Sharifuddin Md Zain ◽

Mohd Nordin Abdul Rahman ◽

...

Keyword(s):

Water Quality ◽

Principal Component ◽

Organic Pollution ◽

Agricultural Runoff ◽

Pollution Sources ◽

Anthropogenic Sources ◽

Quality Data ◽

Water Quality Data ◽

Hierarchical Agglomerative Cluster ◽

Sources Of Pollution

The spatial water quality data (281x22) obtained from 12 sampling stations located along the Terengganu River and its main tributaries were evaluated with environmetric methods. Principal component analysis was used to investigate the origin of each variable due to land use and human activities based on the three clustered regions obtained from the hierarchical agglomerative cluster analysis. Six principal components (PCs) were obtained, where six varimax factor (VF) of values more than 0.70 that considered strong loading are discussed. The possible pollution sources identified are of anthropogenic sources, mainly municipal waste, surface runoff, agricultural runoff, organic pollution and urban storm runoff. As a conclusion, the application of environmetric methods could reveal important information on the spatial variability of a large and complex river water quality data in order to control pollution sources.

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

Water Science & Technology ◽

10.2166/wst.2013.302 ◽

2013 ◽

Vol 68 (5) ◽

pp. 1022-1030 ◽

Cited By ~ 16

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.

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Evaluation of source water quality for selection of drinking water purification system

Water Science & Technology Water Supply ◽

10.2166/ws.2008.071 ◽

2008 ◽

Vol 8 (3) ◽

pp. 271-278 ◽

Cited By ~ 1

Author(s):

N. Hayashi ◽

H. Yokota ◽

H. Furumai ◽

M. Fujiwara

Keyword(s):

Water Quality ◽

Drinking Water ◽

Water Purification ◽

Principal Component ◽

Water Utilities ◽

Quality Data ◽

Source Water ◽

Water Quality Data ◽

Purification System

When renewing water purification facilities, it is important to select a suitable purification system that can accommodate the quality of the respective source water. The Japan Water Research Center has been collecting a large amount of water quality data from drinking-water utilities across Japan, categorising and analysing these data, and evaluating the suitability of water purification processes. Multivariate analyses such as hierarchical cluster analysis and principal component analysis were performed to investigate the relationships between the quality of source water used for water supply and various factors that affect the purification process. Based on these results, water sources throughout Japan were clearly categorised into four groups, and suitable water purification systems were identified for the different water quality groups. The results can serve as an important reference for water utilities during future facility renewal projects.

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Analysis of Water Quality for Marinculture in Moro, Karimun, Riau Islands with Principal Component Analysis

Journal of Aquaculture and Fish Health ◽

10.20473/jafh.v9i2.17192 ◽

2020 ◽

Vol 9 (2) ◽

pp. 143

Author(s):

Wiyoto Wiyoto ◽

Irzal Effendi

Keyword(s):

Water Quality ◽

Principal Component Analysis ◽

Principal Component ◽

Component Analysis ◽

Quality Data ◽

Multiple Linear Regressions ◽

Water Quality Data ◽

Seawater Quality ◽

Linear Regressions ◽

Surface Water Samples

Finding a good location is of important aspects in mariculture. This can be done by evaluating the water quality data. The aims of the study were to evaluate the seawater quality at Moro, Karimun, Riau Islands and to analyze its compatibility for mariculture by using principal component analysis (PCA) and multiple linear regressions. Generally, seawater qualities in the study area were in the tolerance range for mariculture. Surface water samples were collected from five different sampling points around Moro Sea. PCA results demonstrated that there were eleven variation factors which explained 95.4% of the total variance. In addition, based on PCA and multiple linear regressions, four water quality predictors for environmental quality could be identified, that is nitrite (NO2), temperature, pH and dissolved oxygen. Multiple linear regressions showed that the contribution of each parameter to the water quality was significant (R2=1, P < 0.05).

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

Ciência e Natura ◽

10.5902/2179460x18168 ◽

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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