Improving Environmental Sustainability by Characterizing Spatial and Temporal Concentrations of Ozone

Kyu Jong Lee, Hyungu Kahng, Seoung Bum Kim and Sun Kyoung Park
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Kyu Jong Lee: School of Industrial Management Engineering, Korea University, Seoul 02841, Korea
Hyungu Kahng: School of Industrial Management Engineering, Korea University, Seoul 02841, Korea
Seoung Bum Kim: School of Industrial Management Engineering, Korea University, Seoul 02841, Korea
Sun Kyoung Park: School of ICT-Integrated studies, Pyeongtaek University, Pyeongtaek 17869, Korea

Sustainability, 2018, vol. 10, issue 12, 1-11

Abstract: 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 (O 3 , CO, NO x , SO 2 , and PM 10 ) monitored at 25 stations in Seoul, Korea between 2005 and 2010. Results demonstrated that 25 stations were grouped into four clusters, and PM 10 , 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.

Keywords: ozone; k-means clustering; decision tree algorithm; PM10; temperature; relative humidity (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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