ESEL Paper Review _ 20101012 by Aamir Alaud-din
1. Title and Authors

Title: Interpretation of seasonal water quality variation in the Yeongsan Reservoir, Korea using multivariate statistical analyses

Authors: Kyung Hwa Choa, Yongeun Parkb, Joo-Hyon Kangc, Seo Jin Kid, Sungmin Chae, Seung Won Leef and Joon Ha Kimg

Institute:
a School of Environmental Science and Engineering, Gwangju Institute of Science and Technology, 261 Cheomdan Gwagiro (Oryong-dong) Buk-gu, Gwangju 500-712, Republic of Korea
b School of Environmental Science and Engineering, Gwangju Institute of Science and Technology, 261 Cheomdan Gwagiro (Oryong-dong) Buk-gu, Gwangju 500-712, Republic of Korea
c School of Environmental Science and Engineering, Gwangju Institute of Science and Technology, 261 Cheomdan Gwagiro (Oryong-dong) Buk-gu, Gwangju 500-712, Republic of Korea
d School of Environmental Science and Engineering, Gwangju Institute of Science and Technology, 261 Cheomdan Gwagiro (Oryong-dong) Buk-gu, Gwangju 500-712, Republic of Korea
e School of Environmental Science and Engineering, Gwangju Institute of Science and Technology, 261 Cheomdan Gwagiro (Oryong-dong) Buk-gu, Gwangju 500-712, Republic of Korea
f School of Environmental Science and Engineering, Gwangju Institute of Science and Technology, 261 Cheomdan Gwagiro (Oryong-dong) Buk-gu, Gwangju 500-712, Republic of Korea
g School of Environmental Science and Engineering, Gwangju Institute of Science and Technology, 261 Cheomdan Gwagiro (Oryong-dong) Buk-gu, Gwangju 500-712, Republic of Korea

2. Summary of Paper
Yeongsan (YS) reservoir is used for irrigation of crops and flood control during monsoon season. To study the seasonal variation in water quality, principal component analysis (PCA) gave 18 parameters. Five years data for these parameters was received form National Institute of Environmental Research (NIER) for three different stations at YS. Statistical study of these parameters helped in finding the important parameters and their variation with respect to time was studies statistically.

Each parameter had 60 values. Using these values average value, standard deviation was found for each parameter. A list of PCAs is given in Table1. 


Table 1. Principal Components Analysis of Water from YS

S No. Principal Component Abbreviation
1. Water Temperature -
2. pH pH
3. Dissolved Oxygen DO
4. Biochemical Oxygen Demand BOD
5. Chemical Oxygen Demand COD
6. Suspended Solids SS
7. Total Coliform TC
8. Total Nitrogen TN
9. Total Phosphorous TP
10. Secchi Disk Depth SD
11. Chlorophyll-a -
12. Electrical Conductivity EC
13. Nitrate-nitogen NO3-N
14. Ammonia-nitrogen NH4-N
15. Fecal Indicator Bacteria FIB
16. Phosphate-phosphorous PO4-P
17. Dissolved Total Nitrogen DTN
18. Dissolved Total Phosphate DTP

This statistical study showed that five principal components (PCs) out of 18 PCAs were important as they counted 74% of the total variance. The list of PCs on the basis of their %age of total variance is given in Table 2.

Table 2. Selected Principal Components (PCs)

S No. Selected PCs Components of PC %age of Total Variance
1. PC1 Temperature, DO, SD, EC 17.8
2. PC2 COD, SS, TN, NO3-N, NH4-N, DTN 17.3
3. PC3 TP, PO4-P,DTP 14.4
4. PC4 pH, BOD, Chlorophyll-a 13.4
5. PC5 log(TC), log(FIB) 11.1
TOTAL 74.0

A study of temporal variation of PC score of each PC and auto-covariance as a function of time lag revealed that only PC1 and PC2 showed periodicity. Kendall test was performed for p values less than 0.05. Trend indicator values of PC2 were found negative whereas those of PC5 were found positive with p-values < 0.05.

3. Contribution to ESEL
This statistical study can be helpful in grouping of parameters in case of a complex system. It will make the system simpler to study and get good results.

By: Aamir Alaud-din
aamiralauddin@gist.ac.kr