Risk of cardiovascular and respiratory diseases attributed to satellite-based PM_2.5 over 2017–2022 in Sanandaj, an area of Iran

The risk of cardiovascular and respiratory diseases attributed to satellite-based PM_2.5 has been less investigated. In this study, the attributable risk was estimated in an area of Iran. The predicted air PM_2.5 using satellite data and a two-stage regression model was used as the predictor of the diseases. The dose-response linkage between the bias-corrected predictor employing a strong statistical approach and the outcomes was evaluated using the distributed lag nonlinear model. We considered two distinct scenarios of PM_2.5 for the risk estimation. Alongside the risk, the attributable risk and number were estimated for different levels of PM_2.5 by age and gender categories. The cumulative influence of PM_2.5 particles on respiratory illnesses was statistically significant at 13–16 µg/m3 relative to the reference value (median), mostly apparent in the middle delays. The cumulative relative risk of 90th and 95th percentiles were 2.03 (CI 95%: 1.28, 3.19) and 2.25 (CI 95%: 1.28, 3.96), respectively. Nearly 600 cases of the diseases were attributable to the non-optimum values of the pollutant during 2017–2022, of which more than 400 cases were attributed to high values range. The predictor’s influence on cardiovascular illnesses was along with uncertainty, indicating that additional research into their relationship is needed. The bias-corrected PM_2.5 played an essential role in the prediction of respiratory illnesses, and it may likely be employed as a trigger for a preventative strategy.