Open AccessData analysis on sea water quality data in Jakarta Bay using Principal Components Analysis (PCA) method during transitional monsoon 2012
Author(s) -
Atria Martina,
Ivonne M. Radjawane
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/339/1/012023
Subject(s) - principal component analysis , turbidity , salinity , water quality , bay , biochemical oxygen demand , environmental science , chemical oxygen demand , total suspended solids , hydrology (agriculture) , statistics , environmental engineering , mathematics , ecology , wastewater , oceanography , geology , geotechnical engineering , biology
To get a conclusion from a data matrix consisting of 3 individuals and 2 variables is relatively easy. However, it is very difficult to understand the large amount of data. Therefore, it requires data analysis methods for an easier representation. Based on sea water quality data in Jakarta Bay from BPLHD DKI Jakarta (Jakarta Environmental Management Board), there are 24 biological, physical, and chemical parameters in 23 stations. Based on the quality and quantitative of data, we use only one set data on October 2012 as representative of the Second Transition monsoon. Analysing was conducted for 10 parameters namely turbidity, total suspended solid (TSS), temperature, pH, salinity, dissolved oxygen (DO), biological oxygen demand (BOD), methylene blue active substances, phenol, and zinc (Zn) at 23 stations. Consequently, in this paper, we get a conclusion from the data using principal component analysis (PCA) method for its application in data analysis. The method of PCA is used to analyse the data matrix from a similarity point of view between stations and correlation between parameters. The result of PCA is four principal components i.e. PC 1 (27.73% of the variance) is mainly related to TSS, temperature, salinity, and DO. PC 2 (16.33% of the variance) is mainly related to BOD. PC 3 (12.39% of variance) is mainly related to MBAS, phenol, and zinc. PC 4 explains 11.09% of variances related mainly to turbidity.