scholarly journals ESTATÍSTICA MULTIVARIADA APLICADA À ANÁLISE DE QUALIDADE DA ÁGUA EM DIFERENTES AMBIENTES DE MICROBACIAS HIDROGRÁFICAS

Nativa ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 185
Author(s):  
Milena Scaramussa Pastro ◽  
Roberto Avelino Cecílio ◽  
Sidney Sara Zanetti ◽  
Francielle Rodrigues de Oliveira ◽  
Fabrina Teixeira Ferraz
Keyword(s):  
Water Quality ◽  
Cluster Analysis ◽  
Oxygen Demand ◽  
Principal Component ◽  
River Basin Management ◽  
Multivariate Statistical ◽  
Total Data ◽  
Calcium Magnesium ◽  
The Difference ◽  
Main Components

Estudos sobre o comportamento da qualidade de água são importantes, desse modo, o objetivo deste trabalho foi agrupar as águas estudadas quanto à similaridade e selecionar as características físico-químicas para explicar a variabilidade da qualidade das águas em quatro microbacias. Para tanto, foram selecionadas quatro microbacias com diferentes usos do solo: pastagem, regeneração florestal, floresta e cafezal; sob diferentes ambientes: ambientes lêntico e lótico, nascentes e águas subterrâneas. As coletas ocorreram entre fevereiro de 2014 e dezembro de 2014, sendo analisados: coliformes totais e termotolerantes; oxigênio dissolvido (OD); nitrogênio total (Nt); PO43-; turbidez; temperatura; pH; Demanda Bioquímica de Oxigênio (DBO); condutividade elétrica (CE); sólidos totais (ST); sólidos dissolvidos (SD); sólidos suspensos (SS); e os metais cálcio, magnésio e ferro. Utilizou-se técnicas de análise estatística multivariada, por meio da análise de agrupamento (AA) e análise de componentes principais (ACP). Na AA, foram formados quatro grupos distintos no período chuvoso e três no período seco. A diferença entre os ambientes foi o principal fator de influência na segregação dos grupos. A partir da ACP foram selecionadas 4 componentes principais que explicaram 73,09% da variância total dos dados. As variáveis selecionadas foram CE, turbidez, magnésio, ferro, SD, Nt, DBO, pH e coliformes termotolerantes.Palavras-chave: recursos hídricos; análise de agrupamento; componentes principais; manejo de bacias hidrográficas. MULTIVARIATE STATISTICS APPLIED TO WATER QUALITY IN DIFFERENT HYDROGRAPHIC MICROBASE ENVIRONMENTS ABSTRACT: Studies on the water quality behavior are important, so the objective of this work was to group the studied waters regarding the similarity and to select the physical-chemical characteristics to explain the variability of water quality in four micro-basins. Four micro-basins with different soil uses were selected: pasture, forest regeneration, forest and coffee; under different environments: lentic and lotic environments, springs and groundwater. The collections occurred between February 2014 and December 2014, being analyzed: total coliforms and thermotolerant; dissolved oxygen (OD); total nitrogen (Nt); PO43-; turbidity; temperature; pH; Biochemical Oxygen Demand (BOD); electrical conductivity (EC); total solids (TS); dissolved solids (SD); suspended solids (SS); and the metals calcium, magnesium and iron. Multivariate statistical analysis techniques were used, through cluster analysis (AA) and principal component analysis (PCA). In AA, four distinct groups were formed in the rainy season and three in the dry season. The difference between the environments was the main factor of influence in the segregation of the groups. From the PCA, 4 main components were selected, which explained 73.09% of the total data variance. The selected variables were CE, turbidity, magnesium, iron, SD, Nt, BOD, pH and thermotolerant coliforms.Keywords: water resources; cluster analysis; principal components; river basin management.

EVALUATION AND MODELLING OF GROUND WATER QUALITY DATA OF ALLAHABAD CITY BY ENVIRONMETRIC METHODS

2016 ◽  
Vol 2 (4) ◽  
pp. 211
Author(s):  
Girdhari Lal Chaurasia ◽  
Mahesh Kumar Gupta ◽  
Praveen Kumar Tandon
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Sampling Location ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Positive Loading

Water is an essential resource for all the organisms, plants and animals including the human beings. It is the backbone for agricultural and industrial sectors and all the small business units. Increase in human population and economic activities have tremendously increased the demand for large-scale suppliers of fresh water for various competing end users.The quality evaluation of water is represented in terms of physical, chemical and Biological parameters. A particular problem in the case of water quality monitoring is the complexity associated with analyzing the large number of measured variables. The data sets contain rich information about the behavior of the water resources. Multivariate statistical approaches allow deriving hidden information from the data sets about the possible influences of the environment on water quality. Classification, modeling and interpretation of monitored data are the most important steps in the assessment of water quality. The application of different multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA) help to identify important components or factors accounting for most of the variances of a system. In the present study water samples were analyzed for various physicochemical analyses by different methods following the standards of APHA, BIS and WHO and were subjected to further statistical analysis viz. the cluster analysis to understand the similarity and differences among the various sampling stations.  Three clusters were found. Cluster 1 was marked with 3 sampling locations 1, 3 & 5; Cluster-2 was marked with sampling location-2 and cluster-3 was marked with sampling location-4. Principal component analysis/factor analysis is a pattern reorganization technique which is used to assess the correlation between the observations in terms of different factors which are not observable. Observations correlated either positively or negatively, are likely to be affected by the same factors while the observations which are not correlated are influenced by different factors. In our study three factors explained 99.827% of variances. F1 marked  51.619% of total variances, high positive strong loading with TSS, TS, Temp, TDS, phosphate and moderate with electrical conductivity with loading values of 0.986, 0.970, 0.792, 0.744, 0.695,  0.701, respectively. Factor 2 marked 27.236% of the total variance with moderate positive loading with total alkalinity & temp. with loading values 0.723 & 0.606 respectively. It also explained the moderate negative loading with conductivity, TDS, and chloride with loading values -0.698, -0.690, -0.582. Factor F 3 marked 20.972 % of the variances with positive loading with PH, chloride, and phosphate with strong loading of pH 0.872 and moderate positive loading with chloride and phosphate with loading values 0.721, and 0.569 respectively. 

scholarly journals Water Quality Assessment of Porsuk River, Turkey

2010 ◽  
Vol 7 (2) ◽  
pp. 593-599 ◽  
Author(s):  
Suheyla Yerel
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Surface Water ◽  
Surface Water Quality ◽  
Principal Component ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Porsuk River

The surface water quality of Porsuk River in Turkey was evaluated by using the multivariate statistical techniques including principal component analysis, factor analysis and cluster analysis. When principal component analysis and factor analysis as applied to the surface water quality data obtain from the eleven different observation stations, three factors were determined, which were responsible from the 66.88% of total variance of the surface water quality in Porsuk River. Cluster analysis grouped eleven observation stations into two clusters under the similarity of surface water quality parameters. Based on the locations of the observation stations and variable concentrations at these stations, it was concluded that urban, industrial and agricultural discharge strongly affected east part of the region. Finally, this study shows that the usefulness of multivariate statistical techniques for analysis and interpretation of datasets and determination pollution factors for river water quality management.

scholarly journals Multivariate Statistical Application for the Assessment of Surface Water Quality in Lower Zab River, Kirkuk

2020 ◽  
Vol 16 ◽  
Keyword(s):  
Water Quality ◽  
Winter Season ◽  
Oxygen Demand ◽  
Principal Component ◽  
Total Variance ◽  
Data Matrix ◽  
Total Hardness ◽  
High Concentration ◽  
Multivariate Statistical ◽  
High Positive Correlation

Spatiotemporal variation analysis of water quality in the river is very vital for water resources protection and sustainable consumption. In this study, Multivariate statistical methods, i.e., Principal Component Analysis (PCA), Canonical Correlation Analysis (CCA) and Multi Recreation Analysis (MRA) were used to interpret of data matrix in water quality over a period of five years (2015–2019) in the Lower Zab River. The results from PCA matrix showed high positive correlation between total hardness and sulphate (SO4) at 95% confidence level (0.934) indicating that non-carbonate hardness is a measure in the river. By using Varimax rotation and by extracting the eigenvalues greater than 1 from the correlation matrix, number of significant principal components (PCs) were extracted. Five and four latent factors respectively in Lower Zab station 3 (LZ3) and Lower Zab station 2 (LZ2) were identified as responsible for the data structure, explaining 86.8% of total variance in winter season in LZ3 station and it is strong positive related to the SO4, nitrate (NO3), chloride (CL) and pH. All these variables are related to the weathering of minerals component of the river. 87.5% of total variance for winter season in LZ2 station, which is strongly positive, related to the NO3, pH, sodium (NA) and magnesium (MG) related to the weathering of minerals component. The strong correlations between PC3 and five-day biochemical oxygen demand (BOD5) as a result of CCA in LZ3 station during summer season, indicates that the high concentration of calcium (CA) and dissolved oxygen (DO) in water cause the low concentration of BOD5. Among 72 multiple regression model run, only eight dependent variables had statistically significant relationships with independent variables. These results provide may useful information for water quality in the Lower Zab River, which can mainly affected by weathering of minerals component of the river, soil structure and run-off.

scholarly journals Chemical Fingerprinting of Cryptic Species and Genetic Lineages of Aneura pinguis (L.) Dumort. (Marchantiophyta, Metzgeriidae)

Molecules ◽  
2021 ◽  
Vol 26 (4) ◽  
pp. 1180
Author(s):  
Rafał Wawrzyniak ◽  
Wiesław Wasiak ◽  
Beata Jasiewicz ◽  
Alina Bączkiewicz ◽  
Katarzyna Buczkowska
Keyword(s):  
Cluster Analysis ◽  
Cryptic Species ◽  
Plant Material ◽  
Chemical Constituents ◽  
Principal Component ◽  
Molecular Data ◽  
Rapid Identification ◽  
Multivariate Statistical ◽  
Genetic Lineages ◽  
Marker Compounds

Aneura pinguis (L.) Dumort. is a representative of the simple thalloid liverworts, one of the three main types of liverwort gametophytes. According to classical taxonomy, A. pinguis represents one morphologically variable species; however, genetic data reveal that this species is a complex consisting of 10 cryptic species (named by letters from A to J), of which four are further subdivided into two or three evolutionary lineages. The objective of this work was to develop an efficient method for the characterisation of plant material using marker compounds. The volatile chemical constituents of cryptic species within the liverwort A. pinguis were analysed by GC-MS. The compounds were isolated from plant material using the HS-SPME technique. Of the 66 compounds examined, 40 were identified. Of these 40 compounds, nine were selected for use as marker compounds of individual cryptic species of A. pinguis. A guide was then developed that clarified how these markers could be used for the rapid identification of the genetic lineages of A. pinguis. Multivariate statistical analyses (principal component and cluster analysis) revealed that the chemical compounds in A. pinguis made it possible to distinguish individual cryptic species (including genetic lineages), with the exception of cryptic species G and H. The classification of samples based on the volatile compounds by cluster analysis reflected phylogenetic relationships between cryptic species and genetic lineages of A. pinguis revealed based on molecular data.

scholarly journals STATISTICAL MULTIVARIATE ANALYSIS APPLIED TO ENVIRONMENTAL CHARACTERIZATION OF SOIL IN SEMIARID REGION1

2019 ◽  
Vol 32 (1) ◽  
pp. 200-210
Author(s):  
Antônio Italcy de Oliveira Júnior ◽  
Luiz Alberto Ribeiro Mendonça ◽  
Sávio de Brito Fontenele ◽  
Adriana Oliveira Araújo ◽  
Maria Gorethe de Sousa Lima Brito
Keyword(s):  
Organic Matter Content ◽  
Principal Component ◽  
Matter Content ◽  
Multivariate Statistical ◽  
Statistical Tool ◽  
Environmental Characterization ◽  
Hierarchical Grouping ◽  
Two Factors ◽  
Total Data

ABSTRACT Soil is a dynamic and complex system that requires a considerable number of samples for analysis and research purposes. Using multivariate statistical methods, favorable conditions can be created by analyzing the samples, i.e., structural reduction and simplification of the data. The objective of this study was to use multivariate statistical analysis, including factorial analysis (FA) and hierarchical groupings, for the environmental characterization of soils in semiarid regions, considering anthropic (land use and occupation) and topographic aspects (altitude, moisture, granulometry, PR, and organic-matter content). As a case study, the São José Hydrographic Microbasin, which is located in the Cariri region of Ceará, was considered. An FA was performed using the principal component method, with normalized varimax rotation. In hierarchical grouping analysis, the “farthest neighbor” method was used as the hierarchical criterion for grouping, with the measure of dissimilarity given by the “square Euclidean distance.” The FA indicated that two factors explain 75.76% of the total data variance. In the analysis of hierarchical groupings, the samples were agglomerated in three groups with similar characteristics: one with samples collected in an area of the preserved forest and two with samples collected in areas with more anthropized soils. This indicates that the statistical tool used showed sensitivity to distinguish the most conserved soils and soils with different levels of anthropization.

scholarly journals Long-Term River Water Quality Trends and Pollution Source Apportionment in Taiwan

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1394 ◽  
Author(s):  
Marsha Putri ◽  
Chao-Hsun Lou ◽  
Mat Syai’in ◽  
Shang-Hsin Ou ◽  
Yu-Chun Wang
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
Heavy Metal ◽  
Cluster Analysis ◽  
River Water ◽  
Principal Component ◽  
Pollution Level ◽  
Metal Levels ◽  
Metal Monitoring

The application of multivariate statistical techniques including cluster analysis and principal component analysis-multiple linear regression (PCA-MLR) was successfully used to classify the river pollution level in Taiwan and identify possible pollution sources. Water quality and heavy metal monitoring data from the Taiwan Environmental Protection Administration (EPA) was evaluated for 14 major rivers in four regions of Taiwan with the Erren River classified as the most polluted river in the country. Biochemical oxygen demand (6.1 ± 2.38), ammonia (3.48 ± 3.23), and total phosphate (0.65 ± 0.38) mg/L concentration in this river was the highest of the 14 rivers evaluated. In addition, heavy metal levels in the following rivers exceeded the Taiwan EPA standard limit (lead: 0.01, copper: 0.03, and manganese: 0.03) mg/L concentration: lead-in the Dongshan (0.02 ± 0.09), Jhuoshuei (0.03 ± 0.03), and Xinhuwei Rivers (0.02 ± 0.02) mg/L; copper: in the Dahan (0.036 ± 0.097), Laojie (0.06 ± 1.77), and Erren Rivers are (0.05 ± 0.158) mg/L; manganese: in all rivers. A total 72% of the water pollution in the Erren River was estimated to originate from industrial sources, 16% from domestic black water, and 12% from natural sources and runoff from other tributaries. Our research demonstrated that applying PCA-MLR and cluster analysis on long-term monitoring water quality would provide integrated information for river water pollution management and future policy making.

scholarly journals SPATIAL EVALUATION OF THE WATER QUALITY FROM THE VELHAS RIVER CHANNEL, IN THE STATE OF MINAS GERAIS

2018 ◽  
Vol 15 (30) ◽  
pp. 75-86
Author(s):  
C. C. PINTO ◽  
K. B. ALMEIDA ◽  
S. C. OLIVEIRA
Keyword(s):  
Water Quality ◽  
Principal Component ◽  
Industrial Effluents ◽  
Metropolitan Region ◽  
Multivariate Statistical ◽  
Pollution Levels ◽  
Group 3 ◽  
Group 2 ◽  
Belo Horizonte ◽  
Monitoring Stations

This study presents an evaluation of the water quality variability of 19 monitoring stations located in the channel of the Velhas river, using multivariate statistical techniques - Cluster Analysis (CA) and Principal Component Analysis/Factor Analysis (PCA/FA). Sixteen physical-chemical parameters were evaluated between January 2009 and June 2016, totalizing 27,232 valid observations. The CA grouped the nineteen monitoring stations into three groups based on the pollution levels. The PCA/FA resulted in six latent factors for group 1, four for group 2 and five for group 3, accounting for 71.44%, 65.32% and 61.69% of the total variance in the respective water quality. The factors indicated that the parameters responsible for the variations in water quality are mainly related to the release of sanitary sewage and industrial effluents and also to agriculture and livestock activities. These results reflect different water quality conditions of the Velhas River in its extension but, in fact, it is verified a greater variability of the water in the Metropolitan Region of Belo Horizonte and its downstream, justified by the different loads of pollutants received in this region, mainly the releases of domestic sewage and industrial effluents.

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