scholarly journals Groundwater quality assessment using principal component analysis and hierarchical cluster analysis in Alaçam, Turkey

2021 ◽  
Author(s):  
Mehmet Taşan ◽  
Yusuf Demir ◽  
Sevda Taşan
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Groundwater Quality ◽  
Seawater Intrusion ◽  
Hierarchical Cluster Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Component Analysis ◽  
Quality Parameters ◽  
Multivariate Statistical

Abstract This study assessed groundwater quality in Alaçam, where irrigations are performed solely with groundwaters and samples were taken from 35 groundwater wells at pre and post irrigation seasons in 2014. Samples were analyzed for 18 water quality parameters. SAR, RSC and %Na values were calculated to examine the suitability of groundwater for irrigation. Hierarchical cluster analysis and principal component analysis were used to assess the groundwater quality parameters. The average EC value of groundwater in the pre-irrigation period was 1.21 dS/m and 1.30 dS/m after irrigation in the study area. It was determined that there were problems in two wells pre-irrigation and one well post-irrigation in terms of RSC, while there was no problem in the wells in terms of SAR. Piper diagram and cluster analysis showed that most groundwaters had CaHCO3 type water characteristics and only 3% was NaCl- as the predominant type. Seawater intrusion was identified as the primary factor influencing groundwater quality. Multivariate statistical analyses to evaluate polluting sources revealed that groundwater quality is affected by seawater intrusion, ion exchange, mineral dissolution and anthropogenic factors. The use of multivariate statistical methods and geographic information systems to manage water resources will be beneficial for both planners and decision-makers.

scholarly journals Analysis of Geomagnetic Ap Index on Worldwide Earthquake Occurrence using the Principal Component Analysis and Hierarchical Cluster Analysis

2021 ◽  
Vol 50 (4) ◽  
pp. 1157-1164
Author(s):  
Nur Hidayah Ismail ◽  
Nazhatulshima Ahmad ◽  
Nur Anisah Mohamed ◽  
Mohammad Redzuan Tahar
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Component Analysis ◽  
Research Centre ◽  
Before And After ◽  
Solar Events ◽  
Ap Index

Geoeffective solar events, especially the coronal mass ejection (CME) and the high-speed solar wind (HSSW) will induce geomagnetic storm upon its arrival to Earth. The solar events could trigger an earthquake occurred during the arrival. In this study, the focus is on the proxy of the geoeffective solar events, which is the geomagnetic Ap index and the data of shallow worldwide earthquakes. The main objective was to investigate the impact of geomagnetic storms on the occurrences of earthquakes from 1994 to 2017 from a statistical perspective. The geomagnetic Ap index data was obtained from the Helmholtz-Centre Postdam - GFZ German Research Centre for Geosciences and the shallow worldwide earthquake data were from the United States Geological Survey (USGS) earthquake catalogue. The Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were used to analyse the data. Two groups were obtained from the PCA biplot: Group 1 - before the event (Day-4 to Day-1) and Group 2 - after the event group (Day 0 to Day+4). A two-cluster solution was obtained from the HCA, which shows that days before and after geostorm are divided into two main clusters. The statistical results show that earthquakes activity might have different behaviour before and after the geostorm occurred. In conclusion, the results emphasize that there are differences between days before and after the geostorm occurrence, hence, the solar influence upon earthquake occurrences cannot be neglected entirely.

scholarly journals Quantification of Compatibility Between Polymeric Excipients and Atenolol Using Principal Component Analysis and Hierarchical Cluster Analysis

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Barbara Rojek ◽  
Maria Gazda ◽  
Marek Wesolowski
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Active Pharmaceutical Ingredient ◽  
Component Analysis ◽  
Scanning Calorimetry ◽  
Solid Dosage ◽  
Important Challenge

Abstract An important challenge to overcome in the solid dosage forms technology is the selection of the most biopharmaceutically efficient polymeric excipients. The excipients can be selected, among others, by compatibility studies since incompatibilities between ingredients of the drug formulations adversely affect their bioavailability, stability, efficacy, and safety. Therefore, new, fast, and reliable methods for detecting incompatibility are constantly being sought. Hence, the purpose of this work was to assess the usefulness of a heating, cooling, and reheating differential scanning calorimetry (DSC) program for detecting potential incompatibilities between atenolol, an active pharmaceutical ingredient (API), and polymeric excipients. Hot-stage microscopy (HSM), Fourier transform infrared (FTIR) spectroscopy, and powder X-ray diffraction (PXRD) were used as supporting techniques. Additionally, principal component analysis (PCA) and hierarchical cluster analysis (HCA) served as tools to support the interpretation of the data acquired from the DSC curves and FTIR spectra. As the alterations in the shape of the DSC peak of atenolol which are indicative of incompatibility are visible only on the cooling and reheating curves of the mixtures, the DSC heating–cooling–reheating program was found to be very useful for identifying potential incompatibilities in the binary mixtures of atenolol and polymeric excipients. The melting and recrystallization of atenolol alone and in its mixtures were also confirmed by HSM, while FTIR displayed changes in the spectra of mixtures due to incompatibility. These studies revealed that atenolol is incompatible with hydroxyethylcellulose, hypromellose, and methylcellulose. PXRD measurements at room temperature revealed that the crystallinity of atenolol did not change in these mixtures. However, its crystallinity was reduced in the mixtures previously heated up to 155 °C and then cooled to 25 °C. Graphical abstract

Sign in / Sign up

Export Citation Format

Share Document