• Journal of Korean Society of Rural Planning
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• v.28 no.4
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• pp.11-18
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• 2022

Recently, the relevance of the Air Quality Index considering major factors related to the air environment and the local economy and community was analyzed in abraod. In Korea, a comprehensive air-quality index has been proposed. In this study, the comprehensive air-quality index and the index that can integrate Gross Domestic Product per capita were summarized as regional attractiveness. As a result of the analysis, Ulsan, Chungnam, Seoul, Chungbuk, and Jeonnam had the highest Gross domestic product per capita, and Jeju, Gyeongnam, and Gyeongbuk had the best Comprehensive air-quality index, and Ulsan had the highest attractiveness. As a result of the correlation analysis, it was found that there was no correlation between the two variables, Gross domestic product per capita and Comprehensive air-quality index, because various factors such as topographical characteristics, hazardous substances, and local government's efforts were not taken into account. As a result of sensitivity analysis, Ulsan had the highest sensitivity and variance for Gross domestic product per capita and Comprehensive air-quality index. As for the relative ratio of attractiveness between regions, Ulsan's attractiveness was 2.95 times that of Daegu's, indicating a large difference between regions.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2010.04a
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• pp.329-330
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• 2010

• Journal of Environmental Science International
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• v.20 no.1
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• pp.81-91
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• 2011

Air quality monitoring networks are very important facilities to manage urban air pollution control and to set up an environmental policy. Since air quality monitoring network of Daegu was allocated from 1980s to mid-90s, there is need to reevaluate it and relocated its site. This study was evaluated the position of Daegu air quality monitoring station by unit environmental sensitivity index, grid emission rate, CAI (Comprehensive Air-quality Index) point. The investigation domain covered an area of 16 $\times$ 24 km centered at the metropolitan area of Daegu with grid spacing of 2 km. The location of alternative air quality monitoring networks was selected through optimization and quintiles analysis of total score. The result showed that all things considered, new air quality monitoring network need to install grid numbers 10, 28, 36, 37, 46. We also recommand three scenarios of alternative air quality monitoring network when considering unit environmental sensitivity index, emission rate and CAI point.

• Environmental Engineering Research
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• v.20 no.3
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• pp.260-267
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• 2015

The $PM_{10}$, $SO_2$ and $NO_2$ mass concentrations were obtained over five years from monitoring stations across Nanchong, a southwest city in China. Changes in urban air quality over time, as well as the factors influencing that change, were evaluated based on air pollutant concentrations, the Air Pollution Index (API), and the Comprehensive Pollution Index (P). The results showed that the total annual mean $PM_{10}$, $SO_2$ and $NO_2$ concentrations over the five years studied were $61.1{\pm}1.1$, $45.0{\pm}3.9$ and $34.9{\pm}4.9{\mu}g{\cdot}m^{-3}$, respectively. The annual mean concentrations displayed a generally decreasing trend; lower than the annual mean second-level air quality limit. Meanwhile, the annual mean API values were in a small range of 52-53, the air quality levels were grade II, and P values were 1.06-1.21 less than the slight level ($P{\leq}1.31$). Total monthly mean $PM_{10}$, $SO_2$, $NO_2$ concentrations, and API and P values were consistently higher in winter and spring than during autumn and summer. The results of a correlation analysis showed that temperature and pressure were the major meteorological factors influencing pollution levels. Pollution sources included industrial coal and straw burning, automobiles exhaust and road dust, fireworks, and dust storms.

• Journal of Environmental Impact Assessment
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• v.30 no.6
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• pp.380-392
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• 2021

An indoor air quality index for school classrooms based on the Hazard Quotient (HQ) was developed in this study. The current index (IAQI-S) covered PM₁₀, PM_2.5, and CO₂, and breakpoints of IAQI-S were calculated using each HQ value. IAQI-S was applied to the real-time data obtained from 123 classrooms of 46 schools during November 2017 to December 2020. As a result of the comprehensive assessment, 93.1% of PM₁₀ and 94% of PM_2.5 belonged to 'good' and 'moderate', respectively. The IAQI-S based on an 8-hour predicted moving average was compared with Comprehensive Air-quality Index (CAI) and with the IAQI of a foreign reference. The IAQI-S includes CO₂ and consists of more stringent levels comparing to outdoor index (CAI).

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2008.10a
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• pp.371-372
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• 2008

• Journal of Korean Society of Rural Planning
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• v.29 no.3
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• pp.91-97
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• 2023

The purpose of this research is to apply the regional development attractiveness of the national level determined in the previous study to the city and county level of Chungcheongnam-do. We verified results with the population change of the floating population data. In order to measure regional development attractiveness in 2020, Chungcheongnam-do's integrated air environment index and per capita gross regional product were gathered. Population movement data over the past five years have been used to analyze population changes in the floating population data. Regional development attractiveness depended on the data of GDP per capita, which had a large difference between the maximum and minimum values. The rate of increase or decrease in population change by city and county in Chungcheongnam-do over the past five years has changed significantly since 2021 and characteristics of each group were grouped into four groups. Based on the environment and economic feasibility of the region, it can be the starting point for a new analysis of Korea's regional development projects and the selection of target sites. Policy suggestions can also be made in spatial plans such as short-term comprehensive plans, long-term comprehensive plans, and development plans. It can be a limit of this research that regional development attractiveness was determined by the relatively large per capita gross domestic product. It is necessary to further develop regional development attractiveness by closely investigating the characteristics of the region, social problems, and emissions of environmentally harmful substances.

• Journal of Korean Society for Atmospheric Environment
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• v.26 no.6
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• pp.666-682
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• 2010

The forecasting system for Today's and Tomorrow's PM10 was developed based on the statistical model and the forecasting was performed at 9 AM to predict Today's 24 hour average PM10 concentration and at 5 PM to predict Tomorrow's 24 hour average PM10. The Today's forecasting model was operated based on measured air quality and meteorological data while Tomorrow's model was run by monitored data as well as the meteorological data calculated from the weather forecasting model such as MM5 (Mesoscale Meteorological Model version 5). The observed air quality data at ambient air quality monitoring stations as well as measured and forecasted meteorological data were reviewed to find the relationship with target PM10 concentrations by the regression analysis. The PM concentration, wind speed, precipitation rate, mixing height and dew-point deficit temperature were major variables to determine the level of PM10 and the wind direction at 500 hpa height was also a good indicator to identify the influence of long-range transport from other countries. The neural network, regression model, and decision tree method were used as the forecasting models to predict the class of a comprehensive air quality index and the final forecasting index was determined by the most frequent index among the three model's predicted indexes. The accuracy, false alarm rate, and probability of detection in Tomorrow's model were 72.4%, 0.0%, and 42.9% while those in Today's model were 80.8%, 12.5%, and 77.8%, respectively. The statistical model had the limitation to predict the rapid changing PM10 concentration by long-range transport from the outside of Korea and in this case the chemical transport model would be an alternative method.

• Journal of Korean Society for Atmospheric Environment
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• v.27 no.1
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• pp.30-40
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• 2011

Numerical simulations were carried out to investigate the impact of SST spatial distribution on the result of air quality modeling. Eulerian photochemical dispersion model CAMx (Comprehensive Air quality Model with eXtensions, version 4.50) was applied in this study and meteorological fields were prepared by RAMS (Regional Atmospheric Modeling System). Three different meteorological fields, due to different SST spatial distributions were used for air quality modeling to assess the sensitivity of CAMx modeling to the different meteorological input data. The horizontal distributions of surface ozone concentrations were analyzed and compared. In each case, the simulated ozone concentrations were different due to the discrepancies of horizontal SST distributions. The discrepancies of land-sea breeze velocity caused the difference of daytime and nighttime ozone concentrations. The result of statistic analysis also showed differences for each case. Case NG, which used meteorological fields with high resolution SST data was most successfully estimated correlation coefficient, root mean squared error and index of agreement value for ground level ozone concentration. The prediction accuracy was also improved clearly for case NG. In conclusion, the results suggest that SST spatial distribution plays an important role in the results of air quality modeling on high ozone episode at coastal region.

• Journal of Environmental Impact Assessment
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• v.2 no.1
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• pp.31-38
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• 1993

As the environmental concerns have shifted gradually from the environmental pollutions to the Quality of Life (QOL) enhancement, it has become increasingly important to implement comprehensive assessment of environmental quality for public information as well as decision making. This study focuses on development of PEI(Public Environment Index) which has purposes to improve regional environment and analyzes the status of environment by public as one kind of environmental indices. Factors of PEI calculations are air, water, noise, green space and landscape. Factors are composed of 20 items including soiling and odor. Major contents are calculation of PEI, correlation analysis of factors and items of PEI, regression analysis of PEI and objective environmental indicators prepared as a dong unit, and PEI presentation using GIS. Also, for applying PEI effectively, environmental information as a dong unit is to be collected and managed periodically.