Furthering a Partnership: Air quality modeling and improving public health

This peer-reviewed review article examines how air quality modelling can strengthen public health research, exposure assessment, and air quality management. It describes the complementary roles of photochemical, dispersion, and hybrid air quality models in estimating population exposure to pollutants such as fine particulate matter (PM₂.₅), ozone (O₃), hazardous air pollutants, and wildfire smoke. The paper discusses how models can be integrated with ground-based monitoring and satellite observations to improve spatial and temporal estimates of air pollution exposure, particularly where monitoring networks are sparse.

The article also explores the application of air quality models in epidemiological studies, health impact assessments, and regulatory analyses, highlighting their value in evaluating emission reduction scenarios and supporting evidence-based policy decisions. It identifies current challenges, including model uncertainty, exposure misclassification, and the need for closer collaboration between atmospheric scientists and public health researchers. The resource is intended for researchers, air quality practitioners, and policymakers seeking to apply modelling tools to better understand and reduce the health impacts of air pollution.