Advances in Science, Technology and Engineering Systems, vol: 6,1 (2021)
Trend analysis of NOX and SO2 emissions in Indonesia from the period of 1990 -2015 using data analysis tool
Sunarno S., Purwanto P., Suryono S.
Abstract
NOX and SO2 gas pollution have a direct impact on health problems and environmental damage. Therefore, to map the emission patterns and predict the resulting impacts, complete data and information on emissions of the two pollutants are needed. In Indonesia, data on NOX and SO2 emissions that are recorded over a long period of time (for example 5 decades) are very difficult to obtain. Meanwhile, REASv3.1 is a global emission inventory that provides complete data on air emissions in Asia during 1950 – 2015. Therefore, this study aimed to analyze NOX and SO2 emission trends, forecast data for 2016 – 2020, and compare the accuracy of calculations from the method used. The processing of both emission data used two methods, namely trend analysis based on exponential and polynomial approaches, and smoothing methods based on Double Moving Average (DMA) and Double Exponential Smoothing (DES). Furthermore, validation of the accuracy from both methods used the value of Mean Absolute Deviation (MAD), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results showed that for the smoothing method, DMA was more accurate than DES. Meanwhile, the indicators are MAD, RMSE, and MAPE values, which are smaller and at a very good category. For forecasting results for 2016 – 2020, it was shown that the total emissions of both NOX and SO2 showed an increase, but with different gains. Furthermore, the total NOX emission gain was two times greater than the total of SO2. The road transportation and power plant sectors in NOX emissions showed an increasing trend, with an emission gain ratio of 3:20. Meanwhile, for SO2, the power plant sector experienced a significant increase, while the industrial sector actually showed a downward trend. © 2021 ASTES Publishers. All rights reserved.
Keyword: Air Emission; Global Emission Inventory; Smoothing Methods; Trend Analysis