How does air quality reflect the land cover changes: remote sensing approach using Sentinel data

Significant environmental challenges, such as urban and industrial expansion, alongside vegetation preservation, directly influence the concentrations of critical air pollutants and greenhouse gases in cities and their surroundings. The urban development and expansion process is aptly captured by classifying land use and land cover (LULC). We aimed to analyze LULC changes in an Andean area, Ecuador, and to reveal the relations of LULC classes with three air pollutants ozone ( $$ ), nitrogen dioxide ( $$ ), and sulfur dioxide ( $$ ), using remote sensing datasets (Sentinel-5P - Sentinel 1 - Sentinel-2) across different periods. Results showed that $$ is not a reliable indicator for assessing its behavior based on LULC classes, as it was difficult to distinguish between different land cover types using this pollutant. For $$ , the analysis showed a moderate distinction among LULC classes, suggesting some variability in its distribution across different land cover classes. On the other hand, $$ analysis shows that all land cover classes are statistically distinguishable, demonstrating that urban, shrubland, green areas, and forest classes influenced ozone distribution. These findings emphasize the importance of accurate land cover classification in understanding air pollutants' spatial distribution and dynamics. This analysis is crucial for understanding the impacts of land use and land cover changes on urban health and well-being and the effects of rapid urban expansion.