IJEP 41(11): 1245-1251 : Vol. 41 Issue. 11 (November 2021)
1. Amity University, Patna – 801503, Bihar, India
2. GLA University, Mathura – 281 406, U.P., India
3. BIT Lalpur, Ranchi – 834 001, Jharkhand, India
In modern era, data science has emerged as an efficient tool for generating forecasting models. With the help of it, prediction has become easier than ever before. Making a prediction is necessary to build strategies accordingly. Therefore, it is now being implemented in every field including environmental studies. Assessment of the long-term concentration trends of air pollutants can be one of its fields of application. The present study can be viewed as a small step towards this. The study deals with generating a time-series model to forecast the future level of concentration of air pollutants in an industrial city Jamshedpur of eastern part of India which is a prominent site for mining and industries. Four monitoring stations were selected to monitor the air quality of the entire city during the year 2007-2017. The selection of the study period is based on the availability of data. Auto regressive integrated moving average and the SAS coding were employed in the prediction. Results showed that the model performed more than satisfactory in predicting the concentration levels of various pollutants. The finding of this study will help to bring awareness about the situation of pollution and the trend in the area which are at the least priority for the government bodies and will also help in its rational management. The findings also depict the efficiency of data science and the requirement for further research in this area. The method utilised is not area specific and can be applied to other regions.
Data science, forecasting, Python, ARIMA, air pollution
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