scholarly journals Principal component analysis of rainwater composition at BAPMoN stations in India

MAUSAM ◽  
2022 ◽  
Vol 44 (2) ◽  
pp. 179-184
Author(s):  
B. MUKHOPADHYAY ◽  
S.S. SINGH ◽  
S. V. DATAR

Data from Indian BAPMoN stations were analyzed using the Principal Component Analysis (PCA) by examining broadly the temporal and spatial distribution characteristics of the ions, from mineral and gaseous sources, observed in rainwater samples collected over the Indian BAPMoN stations over along period (1976-87), The results show that the pH of rainwater can be generally explained In terms of the concentration of SO.-2 , NO3 -1, CI-l, Ca+2 and Na+1 ion~, However, other mechanisms could determine the overall nature of the Interactions, These mechanisms have become more clear by performing principal component analysis.

2000 ◽  
Vol 21 ◽  
Author(s):  
Luo Wenqiang ◽  
Zhang Zhuoyuan ◽  
Huang Runqiu

Morshita Spread Index Iδ was applied for the study of temporal and spatial distribution characteristics of landslides in the Shanxi and Gansu provinces of China. For this purpose, the landslides larger than 105 m3 in volume were considered. In the study area, the spatial distribution of Morishita Spread Index Iδ (l) isgreater than 1 and decreases with increasing mesh scale. Such a trend indicates cluster distribution of landslides. On the other hand, the temporal distribution of Morishita Spread Index Iδ (t) for the above landslides showed a maximum and a minimum, corresponding to the years with high frequency of landslide occurrence.


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