Scenario-based statistical analysis for PM2.5 concentration: A case study of Seoul, South Korea

Abstract

Rapid urban expansion and active economic development significantly contribute to increased levels of PM2.5, which degrades the urban environment. This study aims to establish a foundation for practical policy guidance on the impact of urban factors on PM2.5 concentrations in Seoul, South Korea. To achieve this objective, we utilized various urban factors, including socio-demographic data, three-dimensional urban geometry, land use, and traffic volume, in conjunction with PM2.5 concentrations estimated by satellite images. According to our statistical models, all urban development-related factors, except for Forest and Open Space (FOS), showed a statistically significant increase in PM2.5 concentration. However, FOS demonstrated an effective mitigation capability, with a 1% increase in FOS corresponding to an approximate 5% decrease in PM2.5 levels. In addition, five scenario analyses based on statistical results were conducted to evaluate the potential reduction of PM2.5 and associated spatial implications resulting from the removal of batching plants, decreased traffic volume, and the substitution of transportation network with green spaces in Seoul. They highlighted the importance of reducing traffic and increasing green spaces in order to lower PM2.5 levels. The findings suggest that a multifaceted strategy is necessary to develop efficient policies for mitigating PM2.5 for a sustainable environment.

Keywords

• Particulate matter 2.5
• Spatial Regression
• Scenario analysis