Analysis of Air Quality during the COVID-19 Pandemic Using Remote Sensing Data in DKI Jakarta

Abstract

The distribution of air pollution in an area can be determined by using remote sensing data. This study aims to (1) identify and inventory the parameters determining the level of air quality; (2) implementing a number of algorithms to measure air quality using remote sensing data; and (3) evaluate the results of air quality calculations from remote sensing data with in-situ data in the field. The map-making process is carried out by processing Landsat 8 OLI/TIRS image data for 1 year starting from January to December 2020 in DKI Jakarta and by applying a number of algorithms. The algorithms used in determining air quality include: the algorithm developed by Othman to measure the PM10 concentration, the Somvashi algorithm to measure the CO concentration, the SO2 concentration determined by applying the Hasan algorithm, and the algorithm developed by Alserory to calculate the NOx concentration in the air. Air quality parameters that can be known by using Landsat 8 OLI/TIRS satellite imagery are PM10, CO, SO2, and NOx. These results are then correlated with the SPKU belonging to the DKI Jakarta Environment and Forestry Service. The PM10 parameter has a correlation of -0,13, 0,26, -0,72, -0,25 and -0,18. The next parameter is SO2 with correlation values -0.02, 0,3, -0,6, -0,4 and -0,3. As for the CO parameter, it cannot do the correlation test because the value of the satellite image processing results is 1. -0,39, -0,34, -0,31, -0,45, and -0,53 are the correlation values for the NOx parameter. Based on these results, it is necessary to develop a new algorithm and make adjustments to certain areas to analyze air quality, so that it can produce reliable and accountable information on air quality.