Improving environmental sustainability by characterizing spatial and temporal concentrations of ozone

Statistical methods have been widely used to predict pollutant concentrations. However, few efforts have been made to examine spatial and temporal characteristics of ozone in Korea. Ozone monitoring stations are often geographically grouped, and the ozone concentrations are separately predicted for each group. Although geographic information is useful in grouping the monitoring stations, the accuracy of prediction can be improved if the temporal patterns of pollutant concentrations is incorporated into the grouping process. The goal of this research is to cluster the monitoring stations according to the temporal patterns of pollutant concentrations using a k-means clustering algorithm. In addition, this study characterizes the meteorology and various pollutant concentrations linked to high ozone concentrations ( > 0.08 ppm, 1-h average concentration) based on a decision tree algorithm. The data used include hourly meteorology (temperature, relative humidity, solar insolation, and wind speed) and pollutant concentrations (O₃, CO, NO_x, SO₂, and PM₁₀) monitored at 25 stations in Seoul, Korea between 2005 and 2010. Results demonstrated that 25 stations were grouped into four clusters, and PM10, temperature, and relative humidity were the most important factors that characterize high ozone concentrations. This method can be extended to the characterization of other pollutant concentrations in other regions.