Open Access Open Access  Restricted Access Subscription or Fee Access

Characterization by Statistical Analysis of Chemical Facies of Rainwater during Cloud Seeding Operation: A Case Study

IV Muralikrishna, Valli Manickam

Abstract


Atmospheric aerosol particles play a major role in the chemistry of the rainwater. Rain water samples were collected during the cloud seeding operations carried out in Andhra Pradesh from 2005-2007. These samples were analyzed for the various chemical components. The present paper involves the use of multivariate statistical analysis in the interpretation of the ionic components of rainwater in the Kurnool, Anantapur, Chittoor, and Nellore districts of Rayalaseema region in Andhra Pradesh. Statistical and mathematical methods for treating and interpreting rainwater chemical compositions have been applied to understand the ionic compositions of precipitation. Attempts have been made to correlate the seeding material used in the operations to the chemical constituents found in the collected rainwater samples. pH values varied in the range of 5.0 to 10.0 with the mean value around 7.0. The standard deviation values were greater than 0.5 for all the measured parameters except for total dissolved solids and sulphate ions. Principal component analysis and factor analysis were applied to certain chemical components to identify the similarities. The statistical tests were carried out using SYSTAT 7.0 Software. These statistical tests have indicated the presence of certain chemicals used in the operations in rain water samples. However all these chemicals were found to be within the Indian Standards (IS) prescribed limits for irrigation and drinking water purposes. The high deviations in the values of skewness and kurtosis for Nellore district shows that the seeding operations are more effective in this district when compared to the others. A positive correlation has been found between the occurrence of ions and the variability, which can be attributed to cloud seeding.

Keywords


statistical analysis, skewness, kurtosis, and principal component analysis, factor analysis, cloud seeding.

Full Text:

PDF


Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.