Jñānābha, Vol. 50 (2) (2020), (30-37)
STATISTICAL AND TIME SERIES ANALYSIS OF BLACK CARBON IN THE MAJOR COAL MINES OF INDIA
By
Sidhu Jitendra Singh Makkhan
Department of Mathematics, School of Chemical Engineering and Physical Sciences,
Lovely Professional University, Punjab, India-144411.
Department of Mathematics, Sri Guru Angad Dev College,
Khadoor Sahib, Tarn Taran, Punjab, India-143117.
Email: sidhujatinder78@gmail.com
Kulwinder Singh Parmar
Department of Mathematics, I. K.Gujral Punjab Technical University,
Jalandhar, Punjab, India-144603.
Email: kulmaths@gmail.com
Sachin Kaushal
Department of Mathematics, School of Chemical Engineering and Physical Sciences,
Lovely Professional University, Punjab, India-144411.
Email: sachin.22206@lpu.co.in
Kirti Soni
CSIR-National Physical Laboratory, New Delhi, India-110012.
Email: 2006.kirti@gmail.com
(Received: October 03, 2019; Revised: August 07, 2020)
DOI: https://doi.org/10.58250/jnanabha.2020.50203
Time series analysis has been widely used by the researchers in the field of mathematical forecasting; it has been mainly used to obtain the forecast of time series dealing with pollutants, groundwater level, and stock exchange so as to study their future behavior of such time series. The present research work deals with the black carbon concentrations in three major coal mines of India namely, Bokaro, Jharia and Raniganj. In this study, a time series data last 38 years (from 1980 to 2018) obtained from a reliable source (NASA) have been considered by statistical analysis tools like mean, median, mode, standard deviation, skewness, kurtosis, coefficient of variation and time series (ARIMA (Autoregressive Integrated Moving Average)) model at 95% confidence limits have been applied. The validation of the model is tested using R-square, stationary R-square, root mean square error (RMSE), normalized Bayesian information criterion (BIC). It is observed that the model fitted very well, based on these past observations, ARIMA model is applied to obtain the prediction of the amount of black carbon emission for next 7 years 5 months (from Jun 2018 to Oct 2025). These results will help to develop new policies and preventive measures in future by the government agencies, NGOs in these areas and take a note of the seriousness and impact of such huge concentration of black carbon emission in these areas.
2010 Mathematics Subject Classifications: 93A30, 97M10.
Keywords and phrases: ARIMA, Black Carbon, RMSE, Mathematical Modeling.