Tehran University of Medical Sciences Iranian Journal of Public Health 2251-6085 55 3 2026 10 04 42590 42590 Kazem Naddafi Department of Environmental Health Engineering, Tehran University of Medical Sciences, Tehran, Iran Samaneh Dehghani Department of Environmental Health Engineering, Tehran University of Medical Sciences, Tehran, Iran Mohebat Vali Department of Epidemiology, Faculty of Health, Shiraz University of Medical Sciences, Shiraz, Iran Abbas Shahsavani 4. Department of Environmental Health Engineering, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran Mohammad Sadegh Hassanvand Department of Environmental Health, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran 2026 05 12 2026 06 15 Background: Ambient air pollution has been recognized as a major environmental risk factor for lung cancer mortality. However, the nature of these associations, particularly their potential nonlinearity and spatial variation, remains insufficiently understood in highly polluted urban settings. Methods: This ecological time-series study examined the association between ambient air pollution and lung cancer mortality across the 22 municipal districts of Tehran city, Iran, from 2017 to 2022. Annual district-level data on air pollutants, meteorological variables, and economic indicators were analyzed. Generalized additive models (GAMs) were applied to capture potential nonlinear associations, while quasi-Poisson regression was used as a comparative approach. Temporal trends were assessed using Joinpoint regression, and both concurrent (Lag 0) and one-year lagged (Lag 1) exposures were evaluated. Results: A total of 4,682 lung cancer–related deaths were analyzed during the study period. GAM analysis identified PM₂.₅ as the only environmental variable significantly associated with mortality, with evidence of a nonlinear exposure–response relationship. Other pollutants and meteorological factors did not retain independent associations after adjustment. Quasi-Poisson regression suggested additional associations; however, these were not supported in the nonlinear framework. Temporal analysis indicated a general decline in mortality, although statistically significant reductions were observed only in a limited number of districts. A strong regional effect was observed, indicating substantial intra-urban heterogeneity. In lagged models, most associations were attenuated, and PM₂.₅ was no longer statistically significant. Conclusion: PM₂.₅ emerged as the most robust environmental predictor of lung cancer mortality in Tehran, with effects that vary across exposure levels and regions. The findings highlight the importance of accounting for nonlinear relationships and spatial heterogeneity in environmental epidemiology. Targeted strategies to reduce PM₂.₅ exposure may play a key role in lowering lung cancer mortality and addressing health inequalities in urban populations. https://ijph.tums.ac.ir/index.php/ijph/article/view/42590