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

2015 ◽  
Vol 77 (1) ◽  
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.

scholarly journals Control limit detection for source apportionment in Perlis River Basin, Malaysia

2017 ◽  
Vol 13 (3) ◽  
Author(s):  
Mohd Saiful Samsudin ◽  
Saiful Iskandar Khalit ◽  
Azman Azid ◽  
Hafizan Juahir ◽  
Ahmad Shakir Mohd Saudi ◽  
...  
Keyword(s):  
River Basin ◽  
Control Process ◽  
Principal Component ◽  
River Pollution ◽  
Quality Data ◽  
Water Quality Data ◽  
Data Set ◽  
Hierarchical Agglomerative Cluster ◽  
Limit Detection ◽  
Hierarchical Agglomerative Cluster Analysis

This study presents the application of selected environmetric in the Perlis River Basin. The results show PCA extracted nine principal components (PCs) with eigenvalues greater than one, which equates to about 77.15% of the total variance in the water-quality data set. The absolute principal component scores (APCS)-MLR model discovered BOD and COD as the main parameters, which indicates the measure of the agricultural pollution in the Perlis River Basin, the hierarchical agglomerative cluster analysis (HACA) shows 11 monitoring stations assembled into two clusters in accordance with similarities in the concentration of BOD and COD, which are grouped in P4. The X ̅ control chart shows that the mean concentration of BOD and COD in P4 is in the control process. The capability ratio (Cp) was applied to measure the risk of the concentration in terms of the river pollution in a subsequent period of time using the limit NWQS.

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

2020 ◽  
Vol 17 (14) ◽  
pp. 5054 ◽  
Author(s):  
Rui Shi ◽  
Jixin Zhao ◽  
Wei Shi ◽  
Shuai Song ◽  
Chenchen Wang
Keyword(s):  
Water Quality ◽  
Inner Mongolia ◽  
Principal Component ◽  
Middle Part ◽  
Quality Data ◽  
Quality Factors ◽  
Water Quality Data ◽  
Irrigation Area ◽  
Irrigation Period ◽  
Hetao Irrigation Area

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.

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

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. 

scholarly journals Multivariate Analysis of Water Quality Measurements on the Danube River

Water ◽  
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.

Ranking methods to set restoration and remediation priorities on a watershed scale

2008 ◽  
Vol 58 (10) ◽  
pp. 2025-2030 ◽  
Author(s):  
William T. Stringfellow
Keyword(s):  
Water Quality ◽  
Implementation Process ◽  
Scientific Data ◽  
Quality Data ◽  
Quadrant Analysis ◽  
Ranking Methods ◽  
Water Quality Data ◽  
Management Actions ◽  
Sources Of Pollution ◽  
The Relationship

The establishment of a total maximum daily load (TMDL) is part of management process that results in the institution of watershed-based controls of otherwise unregulated sources of pollution. In California (USA), the implementation of a TMDL is driven forward in a process where watershed stakeholders are expected to cooperate on actions needed to improve ecosystem health. In the TMDL process, methods are needed for synthesizing complex scientific data into actionable management information. Where pollutant load analysis may be misleading or perceived as unfair, non-parametric statistical methods can be applied to flow and water quality data to guide the selection of drainages for remediation. The calculation of normalized rank means (NRMs) for flow and water quality can be used to set priorities for the implementation of TMDL management actions. Drainages can be classified into one of four categories (quadrants) based on the relationship between flow and water quality NRMs. Drainages can be included or excluded from management action based on their quadrant classification. Although there are many possible alternative approaches, this “quadrant analysis” is suggested as a scientifically rigorous methods for identifying priority watersheds in the often contentious, stakeholder driven TMDL implementation process.

scholarly journals Phytoplankton in relation to water quality of Tanguar Haor ecosystem, Bangladesh: I. Rauar station

2019 ◽  
Vol 28 (2) ◽  
pp. 131-138
Author(s):  
Mohammad Azmal Hossain Bhuiyan ◽  
SAM Shariar Islam ◽  
Abu Kowser ◽  
Md Rasikul Islam ◽  
Shahina Akter Kakoly ◽  
...  
Keyword(s):  
Water Quality ◽  
Air Temperature ◽  
Environmental Variables ◽  
Secchi Depth ◽  
Soluble Reactive Phosphorus ◽  
Organic Pollution ◽  
Quality Data ◽  
Water Quality Data ◽  
Chl A ◽  
Reactive Phosphorus

The water quality at Rauar station of Tanguar Haor, Sunamganj was assessed studying phytoplankton and associated environmental variables. The environmental variables were monitored over a period of one year, collecting samples at two months interval between March, 2017 and March, 2018. Air temperature, rainfall, and humidity ranged from 22.6 - 32.1°C, 48 - 76% and 8 - 930 mm, respectively. Air temperature showed a direct relationship with water temperature which varied from 22.4 - 31.0°C during the study period. The water transparency remained relatively constant throughout the year having a mean Secchi depth (Zs) value of 2.48 m. Total dissolved solids (TDS), conductivity, and pH of the water ranged from 51 - 85 mg/l, 60 - 110 μS/cm, and 7.2 - 9.7, respectively. In December, because of a temperature fall, the dissolved oxygen (DO) concentration of the water reached its maximum value of 6.09 mg/l. In the rest of the period, the concentration remained between 2.44 and 4.80 mg/l. The value of alkalinity ranged from 0.43 - 1.35 meq/l. Among the nutrients, soluble reactive phosphorus (SRP), soluble reactive silicate (SRS), and NO3-N ranged from 5.43 - 36.43 μg/l, 4 - 14.58 mg/l, and 0.06 - 0.31 mg/l, respectively. The concentration of NH4+ ranged from 238 - 1230 μg/l. The highest concentrations (905 and 1230 μg/l) occurred between September and December, 2017. This might be attributed to the higher density of migratory birds during that period. The phytoplanktonic biomass expressed as chlorophyll-a (Chl-a) ranged from 1.35 - 8.45 μg/l while its degraded product phaeophytin concentration ranged from 0.08 - 3.5 μg/l. The standing crop of phytoplankton ranged from 397 - 2480 × 103 individuals/l of haor water exhibiting its maximum abundance in September. This parameter showed a highly significant positive correlation with SRP. From the correlation analysis, the degradation of chl-a to phaeophytin was found to be temperature dependent. Considering the different physicochemical and biological water quality data, it could be said that the Tanguar Haor is still free from organic pollution. However, the range of soluble reactive phosphorus data (5.43 - 36.43 μg/l) show that the Haor has been passing a meso-eutrophic state. Dhaka Univ. J. Biol. Sci. 28(2): 131-138, 2019 (July)

scholarly journals Water Quality Assessment of 14 Reservoirs in Geum River Basin Using Multivariate Statistical Analysis

2021 ◽  
Vol 43 (3) ◽  
pp. 171-186
Author(s):  
Jin Ho Kim ◽  
Jin Chul Joo ◽  
Chae Min Ahn ◽  
Dae Ho Hwang
Keyword(s):  
Water Quality ◽  
Factor Analysis ◽  
Correlation Analysis ◽  
Principal Components ◽  
Organic Pollution ◽  
Quality Characteristics ◽  
Quality Data ◽  
Water Quality Data ◽  
River Watershed ◽  
Internal Production

Objectives : 14 reservoirs in the Geum river watershed were clustered and classified using the results of factor analysis based on water quality characteristics. Also, correlation analysis between pollutants (land system, living system, livestock system) and water quality characteristics was performed to elucidate the effect of pollutants on water quality.Methods : Cluster analysis (CA), principal component analysis (PCA), and factor analysis (FA) using water quality data of 14 reservoirs in the Geum river watershed during the last 5 years (2014-2018) were performed to derive the principal components. Then, correlation analysis between principal components and pollutants was performed to verify the feasibility of clustering.Results and Discussion : From the factor analysis (FA) using water quality data of 14 reservoirs in the Geum river watershed, three to six principal components (PCs) were extracted and extracted PCs explained approximately 74% of overall variations in water quality. As a result of clustering reservoirs based on the extracted PCs, the reservoirs clustered by nitrogen and seasonal PCs were Ganwol, Geumgang, and Sapgyo, the reservoirs clustered by organic pollution and internal production PCs were Tapjung, Dae, Seokmun, and Yongdam, the reservoirs clustered by organic pollution, internal production, and phosphorus are Bunam, Yedang, and Cheongcheon, and finally the remaining Boryeong, Daecheong, Chopyeong, and Songak were clustered as other factors. From the correlation analysis between principal components and pollutants, significant correlation between the land, living, and livestock pollutants and water quality characteristics was found in Ganwol, Topjeong, Daeho, Bunam, and Daecheong. These reservoirs are considered to require continuous and careful management of specific (land, living, livestock) pollutants. In terms of water quality and pollutant management, the Ganwol, Sapgyo, and Seokmunho are considered to implement intensive measures to improve water quality and to reduce the input of various pollutants.Conclusions : Although the water quality of the reservoir is a result of complex interactions such as influent water factors, morphological and hydrological factors, internal production factors, and various pollutants, optimized watershed and water quality management measures can be implemented through multivariate statistical analysis.

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.

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