Development of Land Use Regression Models for Air Pollution Prediction and Analysis in South Korea

Since urbanization in the Republic of Korea is on its growth, air pollution due to industrialization is also increasing, enhancing diverse risks and exposing citizens to air pollution-related health problems. Specially, anthropogenic factors as land use change, vehicles, etc., along with geographic and climatic factors, affect seriously to the air pollution rate and its dispersion. Understanding the pollutant emission and dispersion is important for further health risk assessment and policy making.

Land use regression (LUR) model is being a widely used methodology for predicting air pollutant rate and dispersion in order to regulate air pollution epidemiology. LUR is based on Geographic Information System (GIS) and Statistical software as SPSS to manage input data and define the relationship between air pollutant concentration and the characteristics of its surrounding area.

In this study, we used monitoring sites including urban sites, traffic pollution sites and suburban sites to get the air pollutant concentration to be applied in the LUR modelling, as well as traffic variables as road length, land use variables as industrial area or open space, population variables, climate variables and other variables as altitude and distance to coast. We used SPSS as the main statistical package for statistical analysis and LUR model development, from which we got the corresponding regression model for non-monitoring sites pollutant concentration prediction and a more site-specific analysis.

Predicting air pollutant concentration and dispersion regarding land use and social performances is essential for a more precise understanding of the relationship between air pollution and human health. This study could contribute to urbanization management planning and air pollution epidemiology related decision making.

This research was supported by the Korea Environment Industry and Technology Institute (KEITI) grant (No. 2018000210006), funded by the Ministry of the Environment (MOE).

Keywords: Land use regression, air pollution, Geographic Information System, air pollution epidemiology, urbanization management