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Analyses of factors that affect PM10 level of Seoul focusing on meteorological factors and long range transferred carbon monooxide  

Park, A.K. (Departments of Biomedical Sciences, Seoul National University College of Medicine)
Heo, J.B. (Civil and Environmental Engineering Department, University of Wisconsin-Madison)
Kim, H. (Department of Epidemiology and Biostatistics, School of Public Health, Seoul National University)
Publication Information
Particle and aerosol research / v.7, no.2, 2011 , pp. 59-68 More about this Journal
Abstract
The objective of the study was to investigate the main factors that contribute the variation of $PM_{10}$ concentration of Seoul and to quantify their effects using generalized additive model (GAM). The analysis was performed with 3 year air pollution data (2004~2006) measured at 27 urban sites and 7 roadside sites in Seoul, a background site in Gangwha and a rural site in Pocheon. The diurnal variation of urban $PM_{10}$ concentrations of Seoul showed a typical bimodal pattern with the same peak times as that of roadside, and the maximum difference of $PM_{10}$ level between urban and roadside was about $14{\mu}g/m^{3}$ at 10 in the morning. The wind direction was found to be a major factor that affects $PM_{10}$ level in all investigated areas. The overall $PM_{10}$ level was reduced when air came from east, but background $PM_{10}$ level in Gangwha was rather higher than the urban $PM_{10}$ level in Seoul, indicating that the $PM_{10}$ level in Gangwha is considerably influenced by that in Seoul metropolitan area. When hourly variations of $PM_{10}$ were analyzed using GAM, wind direction and speed explained about 34% of the variance in the model where the variables were added as a 2-dimensional smoothing function. In addition, other variables, such as diurnal variation, difference of concentrations between roadside and urban area, precipitation, month, and the regression slope of a plot of carbon monooxide versus $PM_{10}$, were found to be major explanatory variables, explaining about 64% of total variance of hourly variations of $PM_{10}$ in Seoul.
Keywords
$PM_{10}$; Meteorological condition; Generalized additive model;
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