Spatial-temporal variation assessment of the water quality in the Baiyangdian Lake of North China for the period of 2006 to 2016

2020 ◽  
Author(s):  
Han Quan ◽  
Sun Wenchao ◽  
Li Zhanjie
Keyword(s):  
Water Quality ◽  
Water Resource Management ◽  
North China ◽  
Anthropogenic Activities ◽  
Principal Component ◽  
Quality Data ◽  
Spatial Clusters ◽  
Water Quality Data ◽  
Baiyangdian Lake ◽  
Monitoring Stations

<p>Baiyangdian Lake is the largest freshwater lake in the North China Plain. In order to examine the driving mechanisms of changes on the lake’s water quality, an improved Water Quality Index (WQI) method and multivariate statistical techniques were applied to analyze water quality in this study. Water quality data from six monitoring stations for the period of 2006 to 2016 were used. The calculation of the annual WQI indicated an improvement in lake’s water quality over the past decade. Cluster analysis classified 12 months and the six monitoring stations into two clusters (dry-wet period and western-eastern part), respectively. Discriminant analysis provided fewer effective variable with only two parameters and six parameters to afford 96.0% and 93.8% correct assignations in the temporal and spatial clusters. Principal component analysis and factor analysis detected similar varifactors in the two temporal clusters, interpreting more variance related human activities in the water quality variation than the ones representing natural factors. The different varifactors related to pollution source were evaluated in the two spatial clusters. The result indicated water quality in the two regions are influenced by different types of anthropogenic activities. Our findings provide valuable information for decision-making related to pollution control, ecosystem restoration, and water resource management in Baiyangdian Lake, as well as other large, shallow lakes in high-intensity hu+man activities regions.</p>

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 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.

scholarly journals MULTIVARIATE STATISTICS APPLIED TO IRRIGATION WATER QUALITY DATA OF A WATERSHED IN THE SEMIARID REGION OF BRAZIL1

2021 ◽  
Vol 34 (3) ◽  
pp. 650-658
Author(s):  
RAIMUNDO FERNANDES DE OLIVEIRA JÚNIOR ◽  
LUIS CÉSAR DE AQUINO LEMOS FILHO ◽  
RAFAEL OLIVEIRA BATISTA ◽  
LARISSA LUANA NICODEMOS FERREIRA ◽  
LUCAS RAMOS DA COSTA ◽  
...  
Keyword(s):  
Water Quality ◽  
Multivariate Statistics ◽  
Water Resource Management ◽  
Management Strategies ◽  
Principal Component ◽  
Quality Data ◽  
Semiarid Region ◽  
Water Quality Data ◽  
Quality Of Waters ◽  
Medium Risk

ABSTRACT Water scarcity is one of the main problems in the Semiarid region of Brazil, which can be mitigated by water resource management strategies. The objective of this work was to classify waters of a watershed in the Semiarid region of Brazil and select the water attributes that most affect the quality of waters used for irrigation (QWI), using multivariate statistics. The study area was the Riacho da Bica watershed, which is between the municipalities of Portalegre and Viçosa, Rio Grande do Norte, Brazil. The QWI was determined using water samples from 15 collections carried out from 2016 to 2018, in five specific points of the watershed, starting in the spring and following the water course. The water attributes evaluated were: electrical conductivity (EC), potential hydrogen (pH), and sodium (Na+), potassium (K+), magnesium (Mg2+), calcium (Ca2+), carbonate (CO32-), chloride (Cl-), and bicarbonate (HCO3-) contents. The water quality data were subjected to multivariate statistics through factorial analysis (FA) and principal component analysis (PCA). The application of multivariate statistics through FA-PCA generated four principal components. The attributes that most explained the QWI variation were potassium, calcium, and pH for Factor 01, and sodium and RAS for Factor 02. The watershed waters were classified as low risk of salinity and medium risk of sodicity (C1S2) for irrigation purposes.

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

2018 ◽  
Author(s):  
Martina Botter ◽  
Paolo Burlando ◽  
Simone Fatichi
Keyword(s):  
Water Quality ◽  
Anthropogenic Impacts ◽  
Stream Water ◽  
Anthropogenic Activities ◽  
Quality Data ◽  
Anthropogenic Factors ◽  
Water Quality Data ◽  
Natural And Anthropogenic Factors

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 eleven Swiss catchments with the objective to discern the influence of catchment characteristics and anthropogenic 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 different solute behaviors. Although the influence of catchment geology, morphology and size is sometime visible on in-stream solute concentrations, anthropogenic impacts are much more evident. Solute variability is generally smaller than discharge variability. The majority of solutes shows dilution with increasing discharge, especially geogenic species, while sediment-related solutes (e.g. Total Phosphorous and Organic Carbon species) show higher concentrations with increasing discharge. Both natural and anthropogenic factors impact 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 catchments that exhibit different natural, climatic and anthropogenic features.

scholarly journals Investigation of water quality from Damanga estuary through the water quality indexing Gujarat

2021 ◽  
Vol 9 (2) ◽  
Author(s):  
Shefaliben Sureshbhai Patel ◽  
Susmita Sahoo
Keyword(s):  
Heavy Metals ◽  
Water Quality ◽  
Anthropogenic Activities ◽  
River Estuary ◽  
Poor Quality ◽  
Quality Parameters ◽  
Quality Data ◽  
Water Quality Data ◽  
Investigation Area

The seasonal investigation about the water quality from Damanganga river estuary on two habitats downstream and upstream was carried out from January to December 2019 containing three major seasons: winter, summer and monsoon. For this monitoring activity total 29 parameters (24 physico-chemical parameters and 5 heavy metals) were analyzed. Multivariate analyses suggested inter dependency among these studied parameters. Water Quality Index is computed based on the major fluctuated and affected parameters. The calculated values of WQI for all three seasons ranged from 122.84 to 173.82 which suggested poor water quality of the water body. WQI values of the investigation area proposed that the estuarine water quality is deteriorated due to high value of presented heavy metals (Aluminum, Iron, Manganese, Boron and Zinc), Chloride, Ammonium and Sulfate in water sample. In this case, the downstream station is having accessional pollutant contaminations while the upstream station is having diminutive pollutant contaminants. Temporally, the dominant frailty found during the winter followed by summer and monsoon. This study field exhibited poor quality of water; the reason behind this might be the impressive surrounding industrial zone as well as other anthropogenic activities. There is quite normal probability distribution expressed by the represented water quality data at the both habitats. The Bray-Curtis cluster analysis shows different percentage similarity level between the water quality parameters.  

scholarly journals Assessment of Water Quality of Okomu Wetland Using Water Quality Index

2020 ◽  
Vol 4 (2) ◽  
pp. 450-457
Author(s):  
S. I. Ehiorobo ◽  
A. E. Ogbeibu
Keyword(s):  
Water Quality ◽  
Water Body ◽  
Water Quality Index ◽  
Quality Index ◽  
Anthropogenic Activities ◽  
Physicochemical Characteristics ◽  
Poor Quality ◽  
Quality Data ◽  
Water Quality Data

The water quality of the Okomu Wetland was evaluated using the Water Quality Index (WQI) technique which provides a number that expresses overall water quality of a water body or water sample at a particular time. Sampling of physicochemical parameters spanned two years covering the wet and dry seasons and the water quality data were obtained from 10 sampling locations; Ponds 36, 52, 54, 61, 64, 90, 94, Arhakhuan Stream, Okomu River (Agekpukpu) and Okomu River (Iron bridge) all within the Okomu National Park. Parameters such as Total Dissolved Solids (TDS), Turbidity, pH, Electrical conductivity (EC), Chlorine (Cl), Nitrate (NO3), Sulphate (SO4), Sodium (Na), Magnesium (Mg), (Iron) Fe, Chromium (Cr), Zinc (Zn), Copper (Cu), Manganese (Mn), Lead (Pb), and Nikel (Ni) were used to compute WQI and the values obtained for the wetland ranged between 34.36 and 167.28. The Index shows that pond 36, 52 and 54 are unfit for drinking with values between 103.86 and 167.28; ponds 61 and 64 are of the very poor quality category with WQI values of 95.19 and 92.44 respectively, Pond 90, pond 94, Arhakhuan Stream and Okomu River (Agekpukpu) are of poor quality and WQI values between and 53.58 and 73.15. Whereas, the Okomu River (Iron bridge) is within the good water quality (34.36) category. The Okomu River by Iron bridge is of good quality rating while other sampled points were of poor, very poor or unfit for drinking though these water bodies are mostly free from anthropogenic activities because of the conservative status of the study area. A major source of pollution within the wetland is surface runoff. The water quality of the wetland may not be suitable for man’s consumption especially pond water which are majorly impacted by runoff, yet very important for the survival and sustenance of the forest animals and plants. The water quality index (WQI) interprets physicochemical characteristics of water by providing a value which expresses the overall water quality and thus, reveals possible pollution problems of a water body. It turns complex water quality data into information that is easily understandable and usable by scientists, researchers and the general public.

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.

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.

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