Health effect assessment of PM2.5 pollution due to vehicular traffic (case study: Isfahan)

Introduction: Air pollution is the most challenging issue in megacities worldwide. Isfahan is the third most populated city in Iran, and its pollution is a leading cause of many diseases and deaths, and economic problems every year. Root cause analysis of air pollution can help the government and legislators make laws for reducing air pollution. As critical factors play an important role in fine particulate matter (PM2.5) emission, traffic parameters should be essential in air pollution modeling. Methods: The generalized additive model (GAM), as a flexible statistical model, is implemented to demonstrate non-linear and linear relationships between response and predictive variables by nonparametric smooth functions. Traffic counts and meteorological and spatial variables were considered independent variables in this research. Regarding evaluating the health outcomes of air pollution due to traffic, the AirQ+ model is implemented. Results: This research was applied for Isfahan in the two years from 2018 to 2020 based on experimental data. The results indicated that the models could explain 54%-69% of PM2.5 concentrations in different areas. In Isfahan's southern and central regions, the effect of traffic on PM2.5 pollution was more. Low values of temperature also affected concentration pollution. Moreover, the model was estimated seasonally for further investigation. Also, annual premature mortality attributed to PM2.5 due to traffic is 150 and 157 cases over two years. Conclusions: The results show the health impacts of air pollution due to vehicular PM2.5 emissions are remarkable.