MODELING PM.0, PM2.5 AND PM10 EXPOSURE IN THE DKI JAKARTA AREA USING SPATIAL INTERPOLATION METHOD - Yusuf Suyuti Purboyo, Sidqy
Air pollution is one of the environmental issues that can affect human health. Particulate is one types of air pollution which based on its diameter can be divided into PM1.0, PM2.5 and PM10, each of which has its own composition, characteristic and effects on health, so it is important to know the particulate concentration of each location. One way to determine particulate concentration is to perform spatial interpolation from existing air quality monitoring stations. In the study which is part of the UDARA research, an evaluation of the spatial interpolation method used to determine the the best spatial model of PM1.0, PM2.5 and PM10 in the DKI Jakarta Province was conducted. Particulates were measured using the low-cost Alphasense OPC-N2 at 26 stations. The choices if the spatial interpolation models used to determine particulate concentrations are Radial Base Functions (RBF), Inverse Distance Weighted (IDW), and Ordinary Kriging (OK). Model accuracy is evaluated by using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results show that the MAE obtained in the IDW method is below the other methods (19.3 µg/m3 for PM1.0, 24.59 µg/m3 for PM2.5, and 26 µg/m3 for PM10) so it can be stated that the best interpolation method used is IDW with a note that MAE values are relatively large such that at PM1.0 and PM2.5 it might affect qualitative assessments based on the particulate concentration. The results of the analysis using the best particulate concentration spatial model show that there is a strong and significant positive correlation between population density and particulate concentration.