Predictability and Wavelet Analysis of Air Pollutants for Commercial and Industrial Regions in Delhi
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This paper deals with the statistical and wavelet analysis of air pollutants SO2 (sulphur dioxide), CO (carbon monoxide) and PM10 (coarse particulate matter) monitored at two regions of New Delhi – Shadipur, the residential cum industrial area and Indira Gandhi International Airport (IGIA), the primary civilian aviation hub. Pearson product-moment correlation coefficient is a measure of the strength and direction of the linear relationship between two variables. It is observed that CO is highly correlated with SO2 and PM10 follows Brownian motion with SO2. Descriptive statistics are ways of summarising large sets of quantitative (numerical) information. It is observed that CO and PM10 have higher values of mean, median, mode at IGIA than Shadipur but SO2 has higher values at Shadipur than IGIA. Also, CO and PM10 have lower values of kurtosis, skewness at IGIA than Shadipur but SO2 has lower values at Shadipur than IGIA. All parameters have low value of range and standard deviation, at IGIA as compared to Shadipur. As the values of standard deviation for air pollutants PM10, CO and SO2 are high thus pollutant concentrations are spread out from the mean line.
Daubechies wavelet at level 5 (Db5) aims the finer scale approximation and decomposition of each pollutant using in parts namely as s, ai and di, where ‘s’ represents signal or raw data; low-frequency part ‘ai’ gives an approximate of signal at level i; high-frequency parts di contain the detail of ‘s’ at different level. The maximum value of PM10, CO, SO2 at IGI Airport is 1000, 40, 100 and at Shadipur 20000, 30 and 300, respectively. It is concluded that there are significant variations in the range of air pollutant concentration of Shadipur and aviation hub.
Air pollutant, Wavelet analysis, Pearson's correlation, Fractal dimension, Predictability analysis.