High-resolution urban air quality monitoring using sentinel satellite images and low-cost ground-based sensor networks
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...
| Published in: | E3S Web of Conferences |
|---|---|
| Main Authors: | , |
| Format: | Article in Journal/Newspaper |
| Language: | English French |
| Published: |
EDP Sciences
2020
|
| Subjects: | |
| Online Access: | https://doi.org/10.1051/e3sconf/202017102002 https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/31/e3sconf_eepgtech2019_02002.pdf https://doaj.org/article/7f9efd52916f48b78695b315adf92e68 |
| Summary: | 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 (PM2.5) concentrations using the satellite retrieved AOD and meteorological data as independent variables and ground-measured PM2.5 data as the dependent variable. The predicted PM2.5 concentrations exhibited agreement with ground measurements, with an overall coefficient (R2) of 0.59. |
|---|