PM2.5 is a major air pollutant which has a high probability to cause many serious cardiopulmonary diseases, such as asthma, lung cancer, trachea cancer, bronchus cancer, etc. Up to 2014, a World Health Organization (WHO) air quality model confirmed that 92% of the population in the world lived in areas where air quality levels exceeded WHO limits (i.e., 10 µg/m3). This indicates that PM2.5 is still one of the most serious world-wide problems, and monitoring PM2.5 concentrations is extremely necessary. In this paper, we proposed a easy and flexible spatial-temporal Gaussian mixture model to analyze annual average PM2.5 concentrations. Because of the bimodal distribution of PM2.5 concentrations, we decided for a two- component Gaussian mixture model with county-year-level spatial-temporal random effects. A Markov Chain Monte Carlo (MCMC) algorithm is used to estimating model parameters.