• 한국입자에어로졸학회지
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• 제8권1호
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• pp.17-28
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• 2012

Air pollution levels of gases and aerosol particles inside the Jeongneung and Hongjimun tunnels of the Naebu express way in Seoul were investigated through on-road measurement using a mobile emission laboratory (MEL) on February 8, 2011. The concentrations of $NO_x$, $CO_2$, number concentration of particles ranging 21-560 nm, and surface area of particles deposited on a human lung almost linearly increased with increasing distance from the tunnel entrance, and decreased rapidly before the tunnel exit. This trend was observed regardless of tunnel length and driving directions, which thought to be caused by semi-transverse ventilation facilities of the tunnels. The concentration increments per 1-m distance for $NO_x$, $CO_2$, deposited particle surface area, and number of particles ranging 21-560 nm were 0.61~0.80 ppb, 0.16~0.21 ppm, $0.20{\sim}0.29{\mu}m^2/cm^3$, and 117~192 particles/$cm^3$, respectively. Average pollution levels inside the two tunnels for $CO_2$, deposited particle surface area, and number of particles >5.6 nm ranged 681~748 ppm, $246{\sim}381{\mu}m^2/cm^3$, and $2.4{\sim}6.7{\times}10^5$ particles/$cm^3$, respectively. In case of $NO_x$, the maximum concentration exceeded 1 ppm. These pollution levels inside the tunnels are much higher than those at urban background sites. This result can be utilized as basic data to evaluate the effectiveness of present ventilation system for reducing the pollution level caused by vehicles inside the tunnels.

• 한국환경과학회지
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• 제25권8호
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• pp.1065-1076
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• 2016

This study analyzes the $PM_{10}$ characteristics (particulate matter with aerodynamic diameter less than $10{\mu}m$), concentration, and emissions in eight large South Korean cities (Seoul, Incheon, Daejeon, Daegu, Gwangju, Ulsan, Busan, Jeju). The annual median of $PM_{10}$ concentration showed a decline of $0.02{\sim}1.97{\mu}g/m^3$ in the regions, except for Incheon, which recorded an annual $0.02{\mu}g/m^3$ increase. The monthly distribution levels were high in spring, winter, fall, and the summer, but were lower in summer for all regions except for Ulsan. These differences are thought to be due to the dust in spring and the cleaning effect of precipitation in summer. The variation in concentrations during the day (diurnal variation) showed that $PM_{10}$ levels were very high during the rush hour and that this was most extreme in the cities (10.00 and 18.00-21.00). The total annual $PM_{10}$ emissions analysis suggested that there had been a general decrease, except for Jeju. On-road mobile (OM) sources, which contributed a large proportion of the particulates in most regions, decreased, but fugitive dust (FD) sources increased in the remaining regions, except for Daegu. The correlation analysis between $PM_{10}$ concentrations and emissions showed that FD could be used as a valid, positive predictor of $PM_{10}$ emissions in Seoul (74.5% (p<0.05)), Dajeon (47.2% (p<0.05)), and Busan (59.1% (p<0.01)). Furthermore, industrial combustion (IC) was also a significant predictor in Incheon (61.7% (p<0.01)), and on-road mobile (OC) sources were a valid predictor in Daegu (24.8% (p<0.05)).

• 한국대기환경학회지
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• 제21권4호
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• pp.401-411
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• 2005

This study was conducted to develop a method to be able to estimate the vehicular emissions according to spatial scales-Seoul province, 25 counties and hundreds of grids $(1km{\times}1km)$. First, the emissions at each spatial scale was calculated by using the road network and the travel volume and speed of each link modeled by travel demand model (TDM). Second, the emission at each spatial scale was calculated on the basis of average speeds estimated by using three kinds of averaging method. These are called the provincial, volume-delay function (VDF) and zonal method, respectively. Third, three kinds of emissions and those by TDM are compared each other at three spatial scales. In Seoul (provincial scale), three kinds of emissions are less than those by TDM, but the differences of TDM from three speed averaging methods (SAMs) are small. The relative ratios of three SAMs to TDM are $88\~90\%\;in\;CO,\;99\~100\%\;in\;NOx,\;84\~85\%$ in VOCs. At county scale, NOx among three pollutants showed the highest correlation between TDM and three SAMs and the zonal method among three SAMs was proven to be the highest correlation with TDM. NOx showed the coefficients $(R^2)$ greater than 0.9 in all three SAMs but CO and VOC showed the coefficients $(R^2)$ greater than 0.9 in only zonal method. Slopes of co..elations of all pollutants showed the values close to '1' in zonal method. In the other two SAMs, slopes of NOx showed the values close to '1', but those of CO and VOC showed the values less than 0.85. At grid scale, correlations between TDM and three SAMs were not high. CO showed $0.68\~0.77\;in\;R^2s\;and\;58\~0.68$ in slopes. NOx showed $0.90\~0.94\;in\;R^2s\;and\;0.86\~0.94$ in slopes. VOC showed $0.56\~0.70\;in\;R^2s\;and\;0.48\~0.57$ in slopes. There are not high correlations between TDM and three SAMs in grid scale. This study showed that there is the most suitable method for calculating the average travel speed at each spatial scale and it is thought that the zonal method is more suitable than the VDF or provincial method.

• 한국분무공학회지
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• 제29권2호
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• pp.53-59
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• 2024

Cars are one of the main causes of air pollution in large cities, and 34.6% of domestic air pollution emissions come from mobile sources, of which cars account for 69.6%. In particular, the importance of nitrogen oxides (NOx) and particulate matter (PM), which are major pollutants in diesel vehicles, is increasing due to their high contribution to emissions. Therefore, in this study, the problem of natural regeneration caused by low exhaust gas temperature during low speed and low load operation was solved by applying a complex regeneration DPF that is not affected by temperature conditions to large diesel vehicles with higher driving time and engine displacement than small and medium-sized vehicles. And the feasibility of application to large diesel vehicles was reviewed by measuring the emission reduction efficiency. As a result of the reduction efficiency test on the actual vehicle durability product, PM showed a reduction efficiency of 84% to 86%, and the reduction efficiency of gaseous substances showed a high reduction efficiency of over 90%. The actual vehicle applicability test was completed with three driving patterns: village bus vehicle, police car, and road-going construction equipment vehicle, and no device problems occurred until the end of the test. Both load and no-load smoke measurement results showed a smoke reduction efficiency of over 96%.

• 대한원격탐사학회지
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• 제37권6_2호
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• pp.1849-1858
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• 2021

본 연구에서는 2011년부터 2020년까지 수원시 내에서 측정한 기상과 미세먼지 농도 자료를 이용하여 월별, 연별 기상과 미세먼지 농도의 변화 경향을 분석하였고, 수원시 미세먼지 농도(PM₁₀, PM_2.5)와 기상 인자(풍속, 기온), 도시공간정보(건물부피비, 도로면적비) 간의 연관성을 조사하였다. 도시대기측정소의 도시공간정보를 산출하기 위해 국토지리정보원과 환경공간정보서비스에서 제공하는 수치지형도의 건물 고도 정보와 세분류 토지피복자료의 도로 정보를 이용하였고, 도시대기측정소를 중심으로 2 km×2 km 영역의 건물 부피와 도로면적을 계산하였다. 기상측정자료를 분석한 결과, 수원시는 주로 서풍과 서북서풍이 우세하게 나타났고, 평균풍속은 봄철에 높았다. 미세먼지 농도는 여름철(7월~9월)에 낮았고 봄철과 겨울철에 비교적 높았다. 연별 추이를 분석한 결과, 대부분의 지점에서 2020년에 미세먼지 농도가 가장 낮았다. 측정 미세먼지 농도는 풍속과 기온과 약한 음의 상관관계를 보였는데, 기온과 PM_2.5 농도의 상관성이 상대적으로 높았다. 6개 도시대기측정소의 PM₁₀, PM_2.5 평균 농도와 도시매개수의 상관성을 조사한 결과, PM₁₀ 농도는 도로 배출이나 건물에 의한 지상 풍속 감소의 영향보다는 외부로부터의 유입 영향을 더 크게 받았고, PM_2.5 농도는 국지적 배출이나 건물에 의한 풍속 감소 영향을 더 크게 받는 것으로 분석된다.