Spatial and temporal patterns of urban air pollution in tehran with a focus on PM_2.5 and associated pollutants

Understanding the spatial and temporal dynamics of air pollutants is crucial for effective urban air pollution management. This study focuses on the temporal dynamics of air quality monitoring stations (AQMSs) and the association among air pollutants, particularly PM_2.5, in Tehran, Iran. Using time series clustering and the Copula model, we analyzed data from 2019 to 2022. We found that the levels and dynamics of O₃ and SO₂ were similar across most AQMSs and unrelated to geographical positions. CO levels and dynamics were consistent among urban and border AQMSs, with higher concentrations in urban stations. NO₂ levels and dynamics varied significantly among northern AQMSs with no relationship geographical positions. PM₁₀ levels and dynamics had a relationship with geographical positions, with western clusters having the highest and northern clusters the lowest concentrations. The dynamics of PM_2.5 showed significant relationship among AQMSs in the eastern, southern, and western regions, but not in the north. We also observed that PM₁₀ and O₃ levels were higher in warm seasons, whereas CO, SO₂, NO₂, and PM_2.5 levels were higher in cold seasons. Most pollutants, except O₃, peaked during traffic hours. Notably, the significant increase in PM_2.5 since spring 2021 was primarily due to PM₁₀. Policymakers should focus on these spatial and temporal variations to improve urban air quality and public health outcomes through targeted interventions.