Satellite Derived pH Estimation for Acidity in Precipitation Using Water Vapor and Nitrogen Dioxide Imagery

Pollution increases day-by-day due to the population explosion, vehicular exhaust, industrialization, burning of fossil fuels which contain gases like nitrogen, sulphur, carbon and many other, leads to the formation of acidity in the atmosphere, resulting in the form of acid rains. Acid rain is a serious environmental predicament that affects human, aquatic, vegetation, animal life and historical constructions of the world. The present research, presents a methodology for estimating the happening of acid rains using satellite imagery of water vapor and Nitrogen Dioxide (NO2). Initially, the proposed methodology applies k-means clustering method followed by Haar wavelet transform in order to compute normality for water vapor and NO2 satellite imagery. Based on the value of normality, pH value is estimated and if the value lies in the range of 1 ≤ pH ≤ 5, it is detected as acid rain. The proposed methodology in the detection of acid rains is addressed for the first time using clustering and wavelet transformation techniques. The resulted outcome indicates that the amount of rainfall with the concentration of NO2 have a strong influence on the occurrence of acid rains.