Objective:
The objective of this research is to conduct spatio-temporal zoning and analysis of air pollution in Tehran city by integrating satellite data and Geographic Information System (GIS), with the aim of identifying spatial distribution patterns of pollutants, determining critical areas, and examining statistical relationships between environmental indices and major air pollutants during the period 2019 to 2023. The primary focus of the research is on understanding the differences in the patterns of primary pollutants and the secondary pollutant ozone at the scale of Tehran's 22 districts.

Research Method:
This study is applied in nature with a descriptive-analytical approach. The data used include Landsat satellite images for extracting environmental indices such as Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Albedo, and the VBSWIR spectral component, as well as Sentinel-5P satellite data for extracting the column concentrations of gaseous pollutants CO₂, NO₂, SO₂, and O₃. After preprocessing steps, maps of environmental indices and pollutants were generated, and their spatial averages were calculated at the level of the 22 districts. Spatial zoning was performed solely to display spatial patterns, and the analysis of relationships between variables was conducted using Pearson’s correlation coefficient. All processing and analysis steps were carried out in ENVI, ArcGIS Pro, and Google Earth Engine software environments.

Findings:
The results indicate that air pollution in Tehran city has a heterogeneous yet continuous spatial pattern. Primary pollutants CO₂ and NO₂ are mainly concentrated in the central, northern, and eastern regions, while the highest levels of SO₂ are found in the southern and southeastern parts of Tehran. In contrast, the secondary pollutant O₃ exhibits a different spatial pattern, with higher levels primarily observed in the southern and central areas of the city. The results of Pearson’s correlation analysis reveal significant statistical relationships between environmental indices and pollutants. Specifically, LST shows an inverse relationship with primary pollutants and a strong direct relationship with O₃, while NDVI is generally associated with a reduction in pollutant concentrations.

Conclusion:
Based on the findings, air pollution in Tehran is a multifactorial phenomenon resulting from the interaction of urban physical structure, environmental conditions, and human activities. The differences in the spatial patterns of primary and secondary pollutants indicate that pollution control policies should be region-specific and, in addition to reducing emission sources, should also consider the environmental processes influencing the formation of secondary pollutants.