TIME SERIES ANALYSIS AND SPATIAL VARIATION OF PM10, SO2, AND CO CONCENTRATION IN THE AMBIENT AIR OF DKI JAKARTA

Air quality monitoring in DKI Jakarta is a matter that must be done to maintain air quality in DKI Jakarta. Some air pollutants that can damage air quality include PM10, SO2, and CO. But the results of monitoring are still difficult to interpret because the data is still rough so it is necessary to...

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Bibliographic Details
Main Author: Fitra Perdana, Praba
Format: Theses
Language:Indonesia
Subjects:
Online Access:https://digilib.itb.ac.id/gdl/view/36809
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Institution: Institut Teknologi Bandung
Language: Indonesia
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Summary:Air quality monitoring in DKI Jakarta is a matter that must be done to maintain air quality in DKI Jakarta. Some air pollutants that can damage air quality include PM10, SO2, and CO. But the results of monitoring are still difficult to interpret because the data is still rough so it is necessary to analyze it. In DKI Jakarta there are automatic air quality monitoring stations so that certain pollutant concentrations are obtained every half hour. Time series analysis and spatial variation were carried out on the data of the three pollutants in DKI Jakarta. The main objective of this research is to obtain information about air quality conditions in DKI Jakarta. The main analysis carried out in this study were identification of trend patterns, identification of seasonal influences, comparison between locations, and forecasting. The method used to detect the trend pattern is Mann-Kendall test, identification of the effect of the seasonal is with t-test, comparison of locations are with ANOVA and Tukey, and forecasting is with ARIMA. PM10 mostly showed decreasing trends, while SO2 mostly showed increasing trends, and CO showed inconsistent trends. For PM10 and SO2 the concentration is greater in the dry season. In the comparison analysis between locations for PM10 there was no significant difference between DKI1, DKI2, and DKI5. In the forecasting analysis it was found that the applied ARIMA model was not good enough, both in terms of errors obtained and from the forecast plots obtained. ARIMA model and forecast results can be better with more data.