High-resolution urban air quality monitoring using sentinel satellite images and low-cost ground-based sensor networks

• Published in: E3S web of conferences Vol. 171; p. 2002
• Main Authors: Gitahi, Joseph, Hahn, Michael
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Les Ulis EDP Sciences 01.01.2020
• Subjects: Aerosol Robotic Network; Aerosols; Air monitoring; Air quality; Algorithms; Correlation coefficient; Correlation coefficients; Dependent variables; Image quality; Independent variables; Lookup tables; Meteorological data; Optical analysis; Optical thickness; Particulate emissions; Particulate matter; Radiative transfer; Remote monitoring; Remote sensing; Satellite imagery; Satellites; Spatial discrimination; Spatial resolution; Urban environments
• ISSN: 2267-1242; 2555-0403; 2267-1242
• DOI: 10.1051/e3sconf/202017102002

Satellite remote sensing aerosol monitoring products are readily available but limited to regional and global scales due to low spatial resolutions making them unsuitable for city-level monitoring. Freely available satellite images such as Sentinel -2 at relatively high spatial (10m) and temporal (5 days) resolutions offer the chance to map aerosol distribution at local scales. In the first stage of this study, we retrieve Aerosol Optical Depth (AOD) from Sentinel -2 imagery for the Munich region and assess the accuracy against ground AOD measurements obtained from two Aerosol Robotic Network (AERONET) stations. Sen2Cor, iCOR and MAJA algorithms which retrieve AOD using Look-up-Tables (LUT) pre-calculated using radiative transfer (RT) equations and SARA algorithm that applies RT equations directly to satellite images were used in the study. Sen2Cor, iCOR and MAJA retrieved AOD at 550nm show strong consistency with AERONET measurements with average correlation coefficients of 0.91, 0.89 and 0.73 respectively. However, MAJA algorithm gives better and detailed variations of AOD at 10m spatial resolution which is suitable for identifying varying aerosol conditions over urban environments at a local scale. In the second stage, we performed multiple linear regression to estimate surface Particulate Matter (PM 2.5 ) concentrations using the satellite retrieved AOD and meteorological data as independent variables and ground-measured PM 2.5 data as the dependent variable. The predicted PM2.5 concentrations exhibited agreement with ground measurements, with an overall coefficient (R 2 ) of 0.59.