A demographic optimization proximity model for air pollution exposure assessment

Air pollution poses a significant threat to human health. Effective and efficient assessing individual exposure intensity will provide a crucial reference to mitigate potential air pollution risk. Previous proximity models in scenario simulation method neglect the variations among individual absorption efficiency. In this study, we proposed a novel demographic optimization proximity model (DOPM) to quantified sulfur dioxide (SO₂) exposure risk under eight groups’ respiratory rates. In assessing exposure risk in Wuhan, one of the megacities in central China, we compared the capacity of DOPM and a classic proximity model on simulating exposure intensity, utilizing the near-Gaussian bi-square diffusion function and the filter optimal bandwidth. Subsequently, we utilized ordinary kriging (OK) interpolation to visualize the SO₂ exposure risk map based on the simulation results of DOPM. We found that 9.5 km was the optimal bandwidth when near-Gaussian bi-square diffusion function utilized in proximity model. We also observed DOPM has stronger ability to simulate individual exposure intensity with a <i>R</i>-square of 0.44 when distinguished respiratory rate among receptors. These insights will aid public health researchers in assessing exposure risk across absorption efficiency and help local government officials devise more effective air pollution control strategies.