scholarly journals Principal component analysis of surface water quality data of the River Drava in eastern Croatia (24 year survey)

2012 ◽  
Vol 14 (4) ◽  
pp. 1051-1060 ◽  
Author(s):  
Vlatka Gvozdić ◽  
Josip Brana ◽  
Nela Malatesti ◽  
Danijela Roland
Keyword(s):  
Principal Component Analysis ◽  
Heterotrophic Bacteria ◽  
Surface Water Quality ◽  
Principal Component ◽  
Component Analysis ◽  
Quality Data ◽  
Pollution Level ◽  
Complex Data ◽  
Contributing Factors ◽  
Water Quality Data

The River Drava is one of the major, inexhaustible water sources not only for Croatia, but also for the other European countries it flows through. This study is based on the observations of 15 water variables at three sampling stations in the lower River Drava over a 24 year period. Although the obtained results revealed an improvement of most of the parameters, the values of some of them (i.e. NH4-N, NO3-N, BOD5, total coliform and heterotrophic bacteria) are still above the approved limits for water Class II. The results of principal component analysis (PCA) confirmed an existence of three clearly separated zones. The first zone corresponds to a rural upstream part of River Drava, which is characterised with low level pollution. The influences of untreated domestic waters become more noticeable in the second more densely populated suburban zone (II) located upstream of the city of Osijek. According to the results of the PCA, untreated wastewaters from Osijek are becoming contributing factors to the high pollution level of the river in the third (III) suburban zone. This study shows the usefulness of the PCA method for analysis and interpretation of complex data sets as well as for determination of pollution sources.

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.

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

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).

scholarly journals Water Quality Modelling of River Mahanadi using Principal Component Analysis (PCA) and Multiple Linear Regression (MLR)

2021 ◽  
Vol 10 (1) ◽  
pp. 83-98
Author(s):  
Chandra Sekhar Matli ◽  
Nivedita
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Water Quality Management ◽  
Surface Water Quality ◽  
Principal Component ◽  
Quality Data ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
Sampling Locations ◽  
Statistical Approaches

Surface water quality is one of the critical environmental concerns of the globe and water quality management is top priority worldwide. In India, River water quality has considerably deteriorated over the years and there is an urgent need for improving the surface water quality. The present study aims at use of multivariate statistical approaches for interpretation of water quality data of Mahanadi River in India. Monthly water quality data pertaining to 16 parameters collected from 12 sampling locations on the river by Central Water Commission (CWC) and Central Pollution Control Board (CPCB) is used for the study. Cluster analysis (CA), is used to group the sampling locations on the river into homogeneous clusters with similar behaviour. Principal component analysis (PCA) is quite effective in identifying the critical parameters for describing the water quality of the river in dry and monsoon seasons. PCA and Factor Analysis (FA) was effective in explaining 69 and 66% of the total cumulative variance in the water quality if dry and wet seasons respectively. Industrial and domestic wastewaters, soil erosion and weathering, soil leaching organic pollution and natural pollution were identified as critical sources contribution to pollution of river water. However, the quantitative contributions were variable based on the season. Results of multiple linear regression (MLR) are effective in explaining the factor loadings and source contributions for most water quality parameters. The study results indicate suitability of multivariate statistical approaches to design and plan sampling and sampling programs for controlling water quality management programs in river basins.

scholarly journals A Systematic Review of Principal Component Analysis–Derived Dietary Patterns in Japanese Adults: Are Major Dietary Patterns Reproducible Within a Country?

2019 ◽  
Vol 10 (2) ◽  
pp. 237-249 ◽  
Author(s):  
Kentaro Murakami ◽  
Nana Shinozaki ◽  
Aya Fujiwara ◽  
Xiaoyi Yuan ◽  
Ayumi Hashimoto ◽  
...  
Keyword(s):  
Systematic Review ◽  
Principal Component Analysis ◽  
Dietary Patterns ◽  
Principal Component ◽  
Component Analysis ◽  
Nutritional Epidemiology ◽  
Quality Data ◽  
Japanese Adults ◽  
Dietary Record ◽  
Different Populations

ABSTRACT Principal component analysis (PCA) has been widely used in nutritional epidemiology to derive dietary patterns. However, although PCA-derived dietary patterns are population-dependent, their reproducibility in different populations is largely unexplored. We aimed to investigate whether major dietary patterns are consistently identified among different populations within a country and, if so, how similar these dietary patterns are. We conducted a systematic review of PCA-derived dietary patterns in Japanese adults using PubMed and Web of Science for English articles and Ichushi-Web and CiNii databases for Japanese articles. We assessed the reproducibility of major dietary patterns using congruence coefficients (CCs), with values ≥0.80 considered to represent fair similarity. From 65 articles (80 studies) included in this review, 285 different dietary patterns were identified. Based on the names of these patterns, major dietary patterns were Western (n = 34), Japanese (n = 12), traditional (n = 10), traditional Japanese (n = 9), healthy (n = 18), and prudent (n = 9) patterns. When assessment was limited to high-quality data (i.e., studies based on a sample size ≥200 and use of a validated dietary assessment questionnaire or multiple-day dietary record), the median CC was low for Western (0.44), traditional (0.59), and traditional Japanese (0.31) patterns. Conversely, the median CC was 0.89 for healthy, 0.86 for prudent, and 0.80 for Japanese patterns; and the proportion of pairs with a CC ≥0.80 was 87.3%, 64.3%, and 50.0%, respectively. Characteristics shared among these 3 dietary patterns included higher intakes of mushrooms, seaweeds, vegetables, potatoes, fruits, pulses, and pickles. In conclusion, this systematic review showed that some of the major dietary patterns are relatively reproducible in different populations within a country, whereas others are not. This highlights the importance of careful interpretation of PCA-derived dietary patterns. Our findings in Japan should be confirmed in different countries and globally. This study was registered at https://www.crd.york.ac.uk/prospero/ as CRD42018087669.

scholarly journals Simplified representation of atmospheric aerosol size distributions using absolute principal component analysis

2007 ◽  
Vol 7 (3) ◽  
pp. 875-886 ◽  
Author(s):  
T. W. Chan ◽  
M. Mozurkewich
Keyword(s):  
Principal Component Analysis ◽  
Size Distribution ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
New Method ◽  
Aerosol Size Distribution ◽  
Distribution Data ◽  
Complex Data ◽  
Data Set

Abstract. Principal component analysis provides a fast and robust method to reduce the data dimensionality of an aerosol size distribution data set. Here we describe a methodology for applying principal component analysis to aerosol size distribution measurements. We illustrate the method by applying it to data obtained during five field studies. Most variations in the sub-micrometer aerosol size distribution over periods of weeks can be described using 5 components. Using 6 to 8 components preserves virtually all the information in the original data. A key aspect of our approach is the introduction of a new method to weight the data; this preserves the orthogonality of the components while taking the measurement uncertainties into account. We also describe a new method for identifying the approximate number of aerosol components needed to represent the measurement quantitatively. Applying Varimax rotation to the resultant components decomposes a distribution into independent monomodal distributions. Normalizing the components provides physical meaning to the component scores. The method is relatively simple, computationally fast, and numerically robust. The resulting data simplification provides an efficient method of representing complex data sets and should greatly assist in the analysis of size distribution data.

scholarly journals A Robust Procedure for Damage Detection from Strain Measurements Based on Principal Component Analysis

2013 ◽  
Vol 558 ◽  
pp. 128-138 ◽  
Author(s):  
Alfredo Guemes ◽  
J. Sierra-Pérez ◽  
J. Rodellar ◽  
L. Mujica
Keyword(s):  
Principal Component Analysis ◽  
Multivariable Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Complex Data ◽  
Data Set ◽  
Strain Measurements ◽  
Before And After ◽  
Small Cracks ◽  
Pca Algorithm

FBGs are excellent strain sensors, because of its low size and multiplexing capability. Tens to hundred of sensors may be embedded into a structure, as it has already been demonstrated. Nevertheless, they only afford strain measurements at local points, so unless the damage affects the strain readings in a distinguishable manner, damage will go undetected. This paper show the experimental results obtained on the wing of a UAV, instrumented with 32 FBGs, before and after small damages were introduced. The PCA algorithm was able to distinguish the damage cases, even for small cracks. Principal Component Analysis (PCA) is a technique of multivariable analysis to reduce a complex data set to a lower dimension and reveal some hidden patterns that underlie.

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