Prediction and comparison of seasonal NO2 distribution due to climate change at Korean capital area
Abstract
Due to heavy urbanization and use of transportation, air pollution have been steadily increasing and raising as a serious problem, thus the management and reduction of it is important since it relates directly with human health. Seoul Capital Area, contains approximately 50% of the total national inhabitants, having high emission rate of air pollutants. However, the number of monitoring sites for NO2 are relatively small in amount to represent the whole pollution rate. To overcome the limited situation, Land Use Regression (LUR) models are widely used in many studies, consisting on linear regression analysis for associations between measured pollutants concentrations and several predictor variables. In this paper, we aim to predict NO2 concentration at unmonitored locations through Land Use Regression models, reflecting the surrounding land use and climate variables. We made four regression models for each season and predicted present and future pollution applying climate change scenarios within the actual land use condition. Once the comparison was developed, we extracted highly polluted areas in order to define expected health exposure of people nearby. Among the variables used, road length nearby monitoring sites, showed the highest correlation with NO2 concentration, followed by climate variables as temperature and precipitation. From the resulting present seasonal maps, we noticed that the concentration expansion on winter and spring were much higher than that on fall and summer, the last one being the less polluted. When comparing the present seasonal results with the future predicted maps, highest rate concentrations on fall season showed the greatest pollution change, followed by winter. Finally, we extracted hazardous areas with highest present pollution rate and future pollution change to analyze and detect the health risk exposition. Regarding the limitation of not considering the land use change scenario for future prediction, our study may contribute for a more detailed local climate change adaptation and health decision-makings. This work was supported by the Korea Environment Industry and Technology Institute (KEITI) through The Decision Support System Development Project for Environmental Impact Assessment, funded by the Korea Ministry of Environment (MOE) (No. 2020002990009)
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2021
- Bibcode:
- 2021AGUFMGH15A0608L