Abstract: It is important to have a clear understanding of the status of air pollution and to provide forecasts and insights related to air quality to both the public and environmental researchers. Previous studies have shown that even a short-term exposure to high concentrations of atmospheric fine particulate matter can be hazardous to people's health. In this study, we develop a spatio-temporal model with space-time interaction for air pollution data (PM_2.5 ). Along with the spatial and temporal components, the proposed model uses a parametric space-time interaction component in the mean structure, as well as a random-effects component specified in the form of zero-mean spatio-temporal processes. To apply the model, we analyze air pollution data (PM_2.5) from 66 monitoring stations across Taiwan.

Key words and phrases: Dynamical dependence, fine particulate matter, Lagrange multiplier test, spatial dependence.