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http://dx.doi.org/10.14249/eia.2019.28.6.568

An Analysis of the Correlation between Seoul's Monthly Particulate Matter Concentrations and Surrounding Land Cover Categories  

Choi, Tae-Young (National Institute of Ecology)
Kang, Da-In (National Institute of Ecology)
Cha, Jae-Gyu (National Institute of Ecology)
Publication Information
Journal of Environmental Impact Assessment / v.28, no.6, 2019 , pp. 568-579 More about this Journal
Abstract
The present study aims to identify the effect of land cover categories on particulate matter (PM) concentrations by analyzing the correlation between monthly PM concentrations in Seoul's air quality monitoring network and the percentages of land cover categories by buffers around air quality monitoring stations. According to a monthly correlation analysis between land cover categories and PM concentrations, in the buffer 3km, PM10 showed a better correlation than PM2.5, there was a clear negative correlation with the forest area, the grassland and the urbanized area had some positive correlation with PM10, and the barren land and the urbanized area had some positive correlation with PM2.5. According to a monthly correlation analysis of dominant land cover sub-categories and sub-sub-categories within the buffer 3km, PM10 showed a clear negative correlation with the broad-leaved forest, and some positive correlation with the road was dominant. PM2.5 showed partly negative correlation with the broad-leaved forest and partly positive correlation with the commercial area. There was a very low or no correlation with other grassland and bare land subcategories. A monthly stepwise regression analysis on noticeable land cover sub-categories and sub-sub-categories with positive or negative correlations revealed that an increasing percentage of the broad-leaved forest had a clear effect on reducing PM10 concentrations, and the road was excluded from the selected variables. Although an increasing percentage of the commercial area had some effect on increasing monthly PM2.5 concentrations and an increasing percentage of the broad-leaved forest had an effect on decreasing the PM2.5 concentrations, their effect size was smaller than that on PM10. The forest area around the city center had the largest and clearest effect on reducing PM concentrations. The urbanized area's sub-categories and sub-sub-categories were also confirmed to have some effect on increasing PM concentrations.
Keywords
$PM_{10}$; $PM_{2.5}$; Correlation analysis; Regression analysis;
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