Seemingly unrelated regression modeling of urban air quality by direct Monte Carlo algorithm

Urban air quality is subject to the increasing pressure of urbanization, and analysis of potential impact of air pollution is one of the most important problems for urban air quality management. This paper established a seemingly unrelated regression (SUR) model and adopt a direct Monte Carlo (DMC) algorithm to analyze the air quality data on three pollutants and four external driving factors from urban district of Xiamen City, China. The results obtained by the DMC approach are compared with those yielded by Bayesian hierarchy model (BHM) developed in literature for analysis of this air quality data. It concludes that the DMC approach is worthwhile and applicable, which is better than the BHM method.