Urban air pollution is a multifaceted and dynamic mixture of Land Surface Temperature (LST), gaseous pollutants, and particulate matter with daily and seasonal changes due to anthropogenic activities, land-cover transformation, and climatic conditions. Today, the relationship between urban biophysical and thermal conditions and their relationship with land-cover is well-known. However, the absence of a dense network of land-based meteorological stations was an obstacle to the study of LST in comparison to the Major Air Pollutant (MAP).
The current research proposes an integration of LST derived by Sentinel-3 SLSTR, to investigate LST relationships to the MAP derived by Sentinel-5 Precursor, and air pollution monitoring system station of Iran in the case of Tehran province. The method of research is designed in a time-series manner with the use of a Python application programming interface, geographical information system, and remote sensing.
Based on the mean concentration of the Particular Matter (PM), Sulfur dioxide (SO₂), and Nitrogen dioxide (NO₂) are mainly in the Tehran metropolis and the core of the urban area. A negative correlation was noted between the PM₂₅, SO₂, NO₂, and altitude, additionally, increasing the altitude negatively affects the concentration of the LST, Carbon monoxide (CO), and Ozone (O₃) values in Tehran province. Unlike, CO and O₃ have positive correlations with LST, which stand for the mutual impacts of the LST, CO, and O₃ values in Tehran province.